Civil aviation control system sub-safety state hawk-dove game regulation method
By applying the Eagle-Dove game model to the air traffic control system for quantitative analysis of risk disturbances and fitness, the problem of risk identification and control under sub-safe conditions was solved, achieving accurate perception of system state and transition to safe state, and reducing the probability of unsafe events.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CIVIL AVIATION UNIV OF CHINA
- Filing Date
- 2022-06-17
- Publication Date
- 2026-05-29
AI Technical Summary
Existing air traffic control systems lack sufficient anti-interference capabilities under sub-safe conditions and cannot effectively identify system vulnerabilities, leading to increased operational risks. Furthermore, traditional methods for analyzing the operational status of control systems lack the ability to regulate risk disturbances and the adaptive game process, thus failing to prevent unsafe incidents from occurring.
The Eagle-Dove game model is used to quantitatively analyze the risk disturbances and fitness of the control system. By monitoring the operation data of the control system, a game model of risk disturbances and fitness is established, the Nash equilibrium probability is calculated, the system state transition is predicted, and timely control measures are taken to avoid the system from a sub-safe state to a dangerous state.
It enables objective assessment of the sub-safe state of the control system, provides quantitative operational trend analysis, and can predict and regulate system state transitions in a timely manner, reduce the probability of unsafe events, and improve the safety and stability of the system.
Smart Images
Figure CN115630879B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of air traffic control safety, and in particular to a sub-safe state eagle-dove game control method for civil aviation control systems. This method can be applied to the operational safety of air traffic control, and can effectively identify vulnerable links in the system, move the risk control checkpoint forward, and realize the transformation of the control system from a sub-safe state to a safe state. Background Technology
[0002] The number of unsafe incidents in air traffic control is on the rise, and the control system is often in a sub-safe state. Although no accidents occur in this sub-safe state, its ability to resist interference and respond to emergencies is clearly insufficient, greatly increasing operational risks.
[0003] The sub-safe state of a control system is the result of a game between risk disturbances and fitness. This game involves the system's own fitness being able to effectively respond to risk disturbances. Based on antifragility theory, it is necessary to view the role of risk disturbances positively in order to study the game process between risk disturbances and fitness.
[0004] Existing methods for analyzing the operational status of control systems, such as the patent application "202111222762.2", which is "A method and system for reviewing and analyzing the operational status of control based on multiple data sources", conduct post-incident analysis after an unsafe event occurs. They do not take into account the role of regulation during the unsafe event. While post-incident analysis can certainly summarize experience, the losses caused by unsafe events cannot be eliminated.
[0005] Current research on regulatory security largely focuses on risk assessment in regulatory operations, paying less attention to the positive effects of the game process between sub-safety risk disturbances and fitness in the regulatory system. Timely regulation of risk disturbances and fitness during the game process can ensure the safe operation of the regulatory system with less resource consumption. Summary of the Invention
[0006] To fill the gap in research on control methods before unsafe events occur in air traffic control systems, this invention provides a sub-safe state eagle-dove game control method for civil aviation air traffic control systems. Its purpose is to provide a method for analyzing the operational trends of air traffic control systems in a sub-safe state, refine the sub-safe state risk disturbances and fitness game process, quantitatively analyze the game's outcome based on air traffic control system operational data, achieve accurate perception of the air traffic control system's operational status, and take timely measures before the air traffic control system transitions from a sub-safe state to a dangerous state, thereby preventing unsafe events and ensuring the safe and stable operation of the air traffic control system.
[0007] To achieve the above objectives, the present invention employs the following technical solution: a sub-safe state eagle-dove game control method for civil aviation control systems, the method comprising the following steps performed in sequence:
[0008] Step 1) Obtain the control system operation data from the airspace resource subsystem, and determine whether the control system is in a sub-safe state based on the occurrence of unsafe events. The control system operation data includes surveillance data, meteorological data, flow control data, and control voice data.
[0009] Step 2) If the system is in a sub-safe state, the control system is monitored in real time, the risk disturbances and fitness of the control system are dynamically identified, and an eagle-dove game model is established for the risk disturbances and fitness. If the system is in a safe state, no control is required. If the system is in a dangerous state, safety rectification measures are taken immediately.
[0010] Step 3) Input the monitored control system operation data into the Eagle-Dove game model, which will assign values to the risk disturbance strength K, fitness strength 1-K, game payoff V, and game cost C.
[0011] Step 4) Substitute the values of risk disturbance strength K, fitness strength 1-K, game payoff V, and game cost C obtained in Step 3) into the Nash equilibrium probability formula to calculate the Nash equilibrium probability P of risk disturbance and fitness adopting the hawk strategy.
[0012] Step 5) Combining the risk disturbance and fitness obtained in Step 4) with the Nash equilibrium probability P of the hawk strategy, calculate the probability of the control system transitioning to different states, and provide corresponding control methods based on the probability of the control system transitioning to different states.
[0013] The specific steps for establishing the Eagle-Dove game model based on risk perturbation and fitness, as described in step 2), are as follows:
[0014] Step 2.1) The Eagle-Dove game model treats the risk perturbation and fitness of the sub-safe state of the control system as two independent entities, each adopting different strategies to pursue the maximum benefit of the sub-safe state. If the risk perturbation is an identified perturbation, it is a dove strategy; if it is an unidentified perturbation, it is an eagle strategy. Fitness is divided into strong and weak according to the degree of its effect on the perturbation, with strong fitness being regarded as eagle strategy and weak fitness as dove strategy.
[0015] Step 2.2) Different decisions regarding risk disturbance and fitness correspond to different operating trends of the system. When both risk disturbance and fitness adopt a dove strategy, the system tends to a stable sub-safe state. When both risk disturbance and fitness adopt a hawk strategy, the system tends to an oscillating sub-safe state. When risk disturbance adopts a dove strategy and fitness adopts a hawk strategy, the system tends to a safe state. When risk disturbance adopts a hawk strategy and fitness adopts a dove strategy, the system tends to a dangerous state.
[0016] Step 2.3) Set the game payoff as V, which represents the total payoff that both sides of the game can obtain, V > 0. The game payoff V is determined according to the instability degree of the control system. The higher the instability degree of the control system, the greater the game payoff V, and vice versa. The instability degree of the control system is determined according to the number of abnormal events and unsafe events that occurred in the previous year of the control system; Set the game cost as C, which represents the cost of conflict between both sides of the game, C > 0. The game cost C is determined according to the safety management level of the control system. The higher the safety management level of the control system, the higher the game cost C, and vice versa. Among them, C ≥ V, otherwise both sides of the game can always obtain positive payoffs in the conflict, and the system operation state will exceed the sub - safe state range, which contradicts the premise that the control system is in the sub - safe state;
[0017] Step 2.4) Count the number of risk disturbances a and the number of times the controller takes response measures b within one week before the current time point. The strength of the risk disturbance The strength of the fitness Because the effect of some risk disturbances on the control system is persistent and the controller needs to take response measures multiple times, so a < b, thus 0.5 < K < 1. The former strength is greater than the latter. K and 1 - K represent the probabilities of winning in the conflict for competing for the sub - safe resource space between risk disturbances and fitness. When both sides of the game adopt the hawk strategy and conflict occurs, the payoff obtained by the risk disturbance is The payoff obtained by the fitness is When both the risk disturbance and the fitness adopt the dove strategy, the payoff obtained by the fitness is (1 - K)V, and the payoff obtained by the risk disturbance is KV; when the risk disturbance adopts the hawk strategy and the fitness adopts the dove strategy, the payoff obtained by the risk disturbance is equal to the game payoff V, and the payoff obtained by the fitness is 0; when the fitness adopts the hawk strategy and the risk disturbance adopts the dove strategy, the payoff obtained by the fitness is equal to the game payoff V, and the payoff obtained by the risk disturbance is 0.
[0018] The Nash equilibrium probability formula described in Step 4 is as follows:
[0019] The Nash equilibrium probability that the risk disturbance and the fitness adopt the hawk strategy Among them, K is the strength of the risk disturbance, (1 - K) is the strength of the fitness, C is the game cost, and V is the game payoff.
[0020] The method for calculating the probability of the control system transitioning to different states described in Step 5 is as follows:
[0021] Based on the risk disturbance and fitness obtained in step 4), the Nash equilibrium probability P of adopting the hawk strategy is obtained, and the probability of the control system transitioning to different operating states is obtained. The probability of the control system transitioning to the dangerous state is P(1-P), and the probability of transitioning to the oscillating sub-safe state is P. 2 The probability of transitioning to a stable sub-safe state is (1-P). 2 The probability of transitioning to a safe state is P(1-P).
[0022] The beneficial effects of this invention are:
[0023] This invention provides a sub-safe state control method for civil aviation control systems based on the Eagle-Dove game model. This method offers an objective assessment approach in the field of air traffic control safety. Previous methods for assessing the operational status of control systems often relied on traditional manual methods, which are highly subjective. This new method, however, utilizes real operational data from the control system to quantitatively analyze the operational trends of the control system in a sub-safe state. Compared to traditional methods, it is more objective and can more easily calculate the probability of the control system transitioning to different states. This provides intuitive data references for safety management personnel's decision-making, enabling the control system to transition from a sub-safe state to a safe state. Furthermore, this method starts before unsafe events occur, i.e., from the game process of risk disturbance and fitness, predicting the outcome of the game and implementing timely adjustments to prevent the control system from transitioning from a sub-safe state to a dangerous state, effectively reducing the probability of unsafe events. Attached Figure Description
[0024] Figure 1 This is a flowchart of the sub-safe state eagle-dove game control method for civil aviation control system;
[0025] Figure 2 This is a trend chart of the system operation during the Eagle-Dove game. Detailed Implementation
[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of this invention and are therefore merely examples, and should not be used to limit the scope of protection of this invention.
[0027] like Figure 1 As shown in the figure, this embodiment of the invention provides a sub-safe state eagle-dove game control method for civil aviation control systems.
[0028] This embodiment is based on the simulation of the air traffic control system operation data of a certain region in the second quarter of 2021. The operation data of the seven days before the unsafe incident on March 14, the operation data of the seven days after the unsafe incident, and the operation data of the seven days one month after the unsafe incident are selected and compared and analyzed to illustrate the specific implementation process of this method.
[0029] Step 1) The air traffic control system of a certain region is in a busy state all year round. Extract and analyze the operation data of the control system to determine whether it is in a sub-safe state. The operation data of the control system includes surveillance data, meteorological data, flow control data and control voice data.
[0030] Step 2) It was discovered that the control system experienced multiple unsafe incidents and even serious accident precursors each year, indicating that it was in a sub-safe state. Real-time monitoring of the control system's operational data was then conducted, and a Hawk-Dove game model was established. The specific process for establishing the Hawk-Dove game model is as follows:
[0031] Step 2.1) The Eagle-Dove game model treats the risk perturbation and fitness of the sub-safe state of the control system as two independent entities. Each entity adopts different strategies in order to maximize its interests in the sub-safe state. If the risk perturbation is an identified perturbation, it is a dove strategy; if it is an unidentified perturbation, it is an eagle strategy. Fitness is divided into strong and weak according to the degree of its effect on the perturbation. Strong fitness is regarded as eagle strategy, and weak fitness is regarded as dove strategy.
[0032] Step 2.2) as Figure 2 As shown, different decisions regarding risk disturbance and fitness correspond to different operational trends of the system. When both risk disturbance and fitness adopt a dove strategy, the system tends to a stable sub-safe state; when both risk disturbance and fitness adopt a hawk strategy, the system tends to an oscillating sub-safe state; when risk disturbance adopts a dove strategy and fitness adopts a hawk strategy, the system tends to a safe state; and when risk disturbance adopts a hawk strategy and fitness adopts a dove strategy, the system tends to a dangerous state.
[0033] Step 2.3) Let the game payoff be V, representing the total payoff that both parties can obtain, where V > 0. The game payoff V is determined by the instability of the control system; the higher the instability of the control system, the greater the game payoff V, and vice versa. The instability of the control system is determined by the number of abnormal and unsafe events that occurred in the control system in the previous year. Let the game cost be C, representing the cost of conflict between the two parties, where C > 0. The game cost C is determined by the safety management level of the control system; the higher the safety management level of the control system, the higher the game cost C, and vice versa. Where C ≥ V, otherwise both parties will always obtain positive payoffs in conflict, and the system's operating state will exceed the sub-safe state range, which contradicts the premise that the control system is in a sub-safe state.
[0034] Step 2.4) Calculate the number of risk disturbances (a) and the number of times controllers took countermeasures (b) in the week prior to the current time point, and determine the strength of the risk disturbances. The strength of adaptability Because the effects of certain risk disturbances on the control system are persistent and air traffic controllers need to take countermeasures multiple times, so a < b, thus 0.5 < K < 1, where the former strength is greater than the latter. K and 1 - K represent the probabilities of winning in the conflict over the sub - safety resource space between risk disturbances and fitness. When both sides of the game adopt the hawk strategy and a conflict breaks out, the benefit obtained from the risk disturbance is The benefit obtained from fitness is When both the risk disturbance and fitness adopt the dove strategy, the benefit obtained from fitness is (1 - K)V, and the benefit obtained from the risk disturbance is KV; when the risk disturbance adopts the hawk strategy and fitness adopts the dove strategy, the benefit obtained from the risk disturbance is equal to the game benefit V, and the benefit obtained from fitness is 0; when fitness adopts the hawk strategy and the risk disturbance adopts the dove strategy, the benefit obtained from fitness is equal to the game benefit V, and the benefit obtained from the risk disturbance is 0.
[0035] Step 3) Statistically obtain the required data from the operation data of this control system, as shown in Table 1. Before the occurrence of the unsafe event on March 14th, after the occurrence, and one month after the occurrence, the number of risk disturbance times a within one week before the three time points are 8, 3, and 4 respectively, and the number of countermeasure times b by air traffic controllers are 18, 14, and 17 respectively. According to Step 2.4), the risk disturbance strengths K can be obtained as 0.692, 0.824, and 0.810 respectively, and the fitness strengths 1 - K can be obtained as 0.308, 0.176, and 0.190 respectively. According to the number of unsafe events, the game benefits V are 8, 9, and 9 respectively, and according to the safety management level, the game costs C are 10, 25, and 25 respectively.
[0036] Step 4) Substitute the assignment results of the risk disturbance strength K, fitness strength 1 - K, game benefit V, and game cost C obtained in Step 3) into the Nash equilibrium probability calculation formula The Nash equilibrium probability P of the risk disturbance and fitness adopting the hawk strategy can be obtained, and the calculation results are shown in Table 1.
[0037] Table 1 Required data statistically obtained from the control operation data of a certain regional air traffic control bureau
[0038]
[0039] Step 5) According to the Nash equilibrium probability P of the risk disturbance and fitness adopting the hawk strategy obtained in Step 4), obtain the probabilities of the control system transitioning to different operating states. The probability of the control system transitioning to the dangerous state is P(1 - P), the probability of transitioning to the oscillating sub - safety state is P 2 , the probability of transitioning to the stable sub - safety state is (1 - P) 2 , and the probability of transitioning to the safe state is P(1 - P). The calculation results are shown in Table 2.
[0040] Table 2 Probability of the control system transitioning to different operating states
[0041]
[0042] As shown in Table 2, before the unsafe incident on March 14th, the probability of the system transitioning to a dangerous state was 0.230, significantly higher than the probabilities of 0.038 after the incident and 0.040 one month later. Immediate control measures are needed to improve the safety management level of the control system, i.e., increase the game cost of risk disturbances and adaptability, effectively reducing the probability of the control system transitioning to a dangerous state and prompting the sub-safe control system to transition to a safe state. Furthermore, the data in Table 2 also shows that the probability of the control system transitioning to a dangerous state at the two time points after the incident and one month later is relatively low. Therefore, it is sufficient to strengthen the monitoring of the system's operational status to ensure timely detection of significant changes in the control system's operational status.
Claims
1. A sub-safe state eagle-dove game control method for civil aviation control systems, characterized in that: The method includes the following steps performed in sequence: Step 1) Obtain the control system operation data from the airspace resource subsystem, and determine whether the control system is in a sub-safe state based on the occurrence of unsafe events. The control system operation data includes surveillance data, meteorological data, flow control data, and control voice data. Step 2) If the system is in a sub-safe state, the control system is monitored in real time, the risk disturbances and fitness of the control system are dynamically identified, and an eagle-dove game model is established for the risk disturbances and fitness. If the system is in a safe state, no control is required. If the system is in a dangerous state, safety rectification measures are taken immediately. Step 3) Input the monitored control system operation data into the Eagle-Dove game model, which will assign values to the risk disturbance strength K, fitness strength 1-K, game payoff V, and game cost C. Step 4) Substitute the values of risk disturbance strength K, fitness strength 1-K, game payoff V, and game cost C obtained in Step 3) into the Nash equilibrium probability formula to calculate the Nash equilibrium probability P of risk disturbance and fitness adopting the hawk strategy. Step 5) Combining the risk disturbance and fitness obtained in Step 4) with the Nash equilibrium probability P of the hawk strategy, calculate the probability of the control system transitioning to different states, and provide corresponding control methods based on the probability of the control system transitioning to different states.
2. The sub-safe state eagle-dove game control method for civil aviation control system according to claim 1, characterized in that: The specific steps for establishing the Eagle-Dove game model based on risk perturbation and fitness, as described in step 2), are as follows: Step 2.1) The Eagle-Dove game model treats the risk perturbation and fitness of the sub-safe state of the control system as two independent entities, each adopting different strategies to pursue the maximum benefit of the sub-safe state. If the risk perturbation is an identified perturbation, it is a dove strategy; if it is an unidentified perturbation, it is an eagle strategy. Fitness is divided into strong and weak according to the degree of its effect on the perturbation, with strong fitness being regarded as eagle strategy and weak fitness as dove strategy. Step 2.2) Different decisions regarding risk disturbance and fitness correspond to different operating trends of the system. When both risk disturbance and fitness adopt a dove strategy, the system tends to a stable sub-safe state. When both risk disturbance and fitness adopt a hawk strategy, the system tends to an oscillating sub-safe state. When risk disturbance adopts a dove strategy and fitness adopts a hawk strategy, the system tends to a safe state. When risk disturbance adopts a hawk strategy and fitness adopts a dove strategy, the system tends to a dangerous state. Step 2.3) Let the game payoff be V, which represents the total payoff that both parties can obtain. V>0. The game payoff V is determined by the instability of the control system. The higher the instability of the control system, the greater the game payoff V, and vice versa. The instability of the control system is determined by the number of abnormal and unsafe events that occurred in the control system in the previous year. Let the game cost be C, which represents the cost of conflict between the two parties. C>0. The game cost C is determined by the safety management level of the control system. The higher the safety management level of the control system, the higher the game cost C, and vice versa. Where C≥V, otherwise both parties can always obtain positive payoffs in the conflict, and the system operation state will exceed the sub-safe state range, which contradicts the premise that the control system is in a sub-safe state. Step 2.4) Count the number of risk disturbances a and the number of response measures b made by controllers within the previous week before the current time point, and the strength of the risk disturbances The strength of fitness Because the effect of some risk disturbances on the control system is persistent and the controller needs to take response measures multiple times, so a < b, thus 0.5 < K < 1. The strength of the former is greater than that of the latter. K and 1 - K represent the probabilities of winning in the conflict over the sub - security resource space between risk disturbances and fitness. When both sides of the game adopt the hawk strategy and a conflict breaks out, the benefit obtained by the risk disturbance is The benefit obtained by fitness is When both the risk disturbance and fitness adopt the dove strategy, the benefit obtained by fitness is (1 - K)V, and the benefit obtained by the risk disturbance is KV; When the risk disturbance adopts a hawkish strategy and the fitness adopts a doveish strategy, the payoff of the risk disturbance is equal to the game payoff V, and the payoff of the fitness is 0; when the fitness adopts a hawkish strategy and the risk disturbance adopts a doveish strategy, the payoff of the fitness is equal to the game payoff V, and the payoff of the risk disturbance is 0.
3. The sub-safe state eagle-dove game control method for civil aviation control system according to claim 1, characterized in that: The Nash equilibrium probability formula mentioned in step 4) is as follows: Nash equilibrium probability of risk disturbance and fitness adopting a hawkish strategy Where K represents risk disturbance strength, (1-K) represents fitness strength, C represents game cost, and V represents game payoff.
4. The sub-safe state eagle-dove game control method for civil aviation control system according to claim 1, characterized in that: The calculation method for determining the probability of the control system transitioning to different states, as described in step 5), is as follows: Based on the risk disturbance and fitness obtained in step 4), the Nash equilibrium probability P of adopting the hawk strategy is obtained, and the probability of the control system transitioning to different operating states is obtained. The probability of the control system transitioning to the dangerous state is P(1-P), and the probability of transitioning to the oscillating sub-safe state is P. 2 The probability of transitioning to a stable sub-safe state is (1-P). 2 The probability of transitioning to a safe state is P(1-P).