Airplane cockpit man-machine function distribution method based on queue scheduling algorithm

Abstract

The invention discloses an aircraft cockpit man-machine function distribution method based on a queue scheduling algorithm. The method comprises the following steps: analyzing a flight crew operation process in a typical flight mission stage; pilot information flow requirements at all moments are extracted, and a pilot information flow time sequence diagram is constructed; calculating the information flow load intensity of each information flow, analyzing the information flow transmission direction and the coupling relationship, and constructing a man-machine coupling information interaction network model; comparing the information flow load intensity at each moment with an information flow load intensity threshold value of the queue, and identifying an information flow load overload moment by utilizing an improved WRR queue scheduling algorithm; and taking the difference value between the information flow load intensity threshold value and the information flow load intensity as a scheduling margin to trigger man-machine function distribution, adjusting the automation level of a man-machine system of an aircraft cockpit, and evaluating a distribution result by calculating related performance indexes of a man-machine coupling information interaction network. According to the invention, the pilot information flow load in the man-machine interaction process of the aircraft cockpit can be balanced, and the flight safety and the system resource utilization rate are improved.

Description

Technical Field

[0001] The invention belongs to the field of human-computer interaction, relates to human-computer function allocation in an aircraft cockpit, and specifically relates to a human-computer function allocation method in an aircraft cockpit based on a queue scheduling algorithm. Background Art

[0002] Human-machine function allocation refers to the process of allocating system functions to humans and machines for execution respectively, based on the different characteristics of humans and machines and the different requirements of tasks, after a trade-off analysis of human and machine characteristics. Human-machine function allocation is mainly divided into human-machine capability comparison allocation, quantitative evaluation modeling allocation and adaptive human-machine function allocation. Human-machine capability comparison allocation is to compare the advantages of "human" and "machine" to determine the allocation of tasks; quantitative evaluation modeling allocation is based on human-machine capability comparison allocation, and is carried out by combining quantitative evaluation with mathematical modeling through experimental research to allocate functions; in order to adapt to the increasingly complex systems with a high degree of intelligence in modern times, adaptive human-machine function allocation dynamically grants different permissions to the intelligent system on the basis of the first two, thereby maximizing the comprehensive performance of the entire system. Due to the particularity of aircraft flight missions and system environments, existing allocation methods are difficult to achieve human-machine function allocation in aircraft cockpits due to their complex allocation process and difficulty in integrating measurement equipment. Summary of the invention

[0003] Purpose of the invention: In order to overcome the deficiencies in the prior art, a method for allocating human-machine functions in an aircraft cockpit based on a queue scheduling algorithm is provided, thereby balancing the information flow load of pilots during human-machine interaction in the aircraft cockpit, and improving flight safety and system resource utilization.

[0004] Technical solution: To achieve the above purpose, the present invention provides a method for allocating human-machine functions in an aircraft cockpit based on a queue scheduling algorithm, comprising the following steps:

[0005] S1: Analyze the flight crew operation process in typical flight mission phases;

[0006] S2: Extract the pilot information flow requirements at each moment and construct the pilot information flow timing diagram;

[0007] S3: Based on the pilot information flow timing diagram, calculate the information flow load intensity of each information flow, analyze the information flow transmission direction and coupling relationship, and build a human-machine coupling information interaction network model based on complex network theory;

[0008] S4: Compare the information flow load intensity at each moment with the information flow load intensity threshold of the queue, and use the improved WRR queue scheduling algorithm to identify the information flow load overload moment;

[0009] S5: The difference between the information flow load intensity threshold and the information flow load intensity is used as the scheduling margin to trigger the human-machine function allocation, adjust the automation level of the aircraft cockpit human-machine system, and evaluate the allocation result by calculating the relevant performance indicators of the human-machine coupling information interaction network.

[0010] Furthermore, the analysis of the flight crew operation process in the typical flight mission phase in step S1 includes:

[0011] A1: Select typical flight mission phases and divide the flight mission into sub-procedures;

[0012] A2: Disassemble the operation tasks or actions under each subroutine.

[0013] Furthermore, the step S2 is specifically as follows:

[0014] B1: Decompose the operation tasks or operation actions into discrete operations and continuous operations;

[0015] B2: Connect the information flows with arrowed lines to construct a pilot information flow timing diagram. The arrows between the information flows indicate the time sequence, and the start and end times of continuous operations are marked with specific symbols.

[0016] Furthermore, the method for constructing the human-machine coupling information interaction network model in step S3 includes:

[0017] C1: Determine the nodes of the human-machine coupling information interaction network, which come from the various information flows shown in the pilot information flow sequence diagram and the pilot himself;

[0018] C2: Analyze the information flow transmission direction and coupling relationship, and determine the directed and undirected edges between nodes;

[0019] C3: Calculate the information flow load intensity S including the flight mission program segment weight, the level of automation of the aircraft cockpit human-machine system and the amount of information on the human-machine interface i,j ;

[0020] C4: Based on information flow load intensity S i,j The information flow coupling degree is calculated, network edges are weighted, and a human-machine system risk propagation network model G(N,V) is constructed, where N is the network node set and V is the network edge set.

[0021] Furthermore, in step C3, the information flow load intensity S i,j The calculation formula is as follows:

[0022] S i,j =ω j ·L i I

[0023] In the formula, ω jrepresents the weight factor of the jth flight mission subroutine, L i It represents the level of automation of the aircraft cockpit human-machine system when the pilot executes the i-th information flow, and I represents the amount of information carried by the information flow when it circulates the human-machine interface;

[0024] ω j It is also determined by the complexity of flight mission operations and the complexity of pilot resource requirements. The calculation formula is as follows:

[0025]

[0026] In the formula, α and β are the weight coefficients of operation complexity and resource demand complexity respectively, OC represents operation complexity, and RDC represents resource demand complexity.

[0027] Furthermore, in step C3, in order to characterize the automation level L i The negative correlation with the information flow load intensity will increase the automation level L i It is expressed as:

[0028]

[0029] Where: j represents the actual automation level of the aircraft cockpit human-machine system, and max(L) represents the highest evaluation level of the automation level.

[0030] The calculation of I is divided into display devices, continuous operation devices and discrete operation devices. The amount of information carried by the display device is related to the type of information appearing on the display page. The amount of information carried by the continuous operation device is related to the display device of its output signal. The amount of information carried by the discrete operation device is 1 bit each time the pilot executes it.

[0031] Furthermore, the specific steps of identifying the pilot information flow overload moment in step S4 are as follows:

[0032] D1: Based on the mapping relationship between the aircraft cockpit human-machine system and the computer operating system, the queue priority and the pilot information flow load intensity threshold are set to improve the WRR algorithm;

[0033] D2: Count the pilot information flow load intensity at each moment and input it into the improved WRR algorithm for information flow scheduling;

[0034] D3: Filter out the information flow load overload moments and count the information flow load strength margin, that is, the difference between the pilot information flow load strength threshold and the load strength of each information flow.

[0035] Furthermore, the step S5 is specifically as follows:

[0037] E1: Based on the information flow load strength margin counted in step D3, analyzing the cause of the information flow load overload, so as to adjust the aircraft cockpit human-machine system automation level at the moment of information flow load overload;

[0038] E2: Recalculate the information flow load intensity and information flow coupling degree after the human-machine function allocation, and establish the human-machine coupling information interaction network after the human-machine function allocation;

[0039] E3: Count the performance indicators of the human-machine coupling information interaction network before and after the human-machine function allocation and conduct comparative analysis. The performance indicators include closeness centrality, betweenness centrality, information flow load intensity, etc. to measure node performance, as well as network density, network robustness and information flow coupling to measure the overall performance of the network;

[0040] E4: Analyze the rationality of human-machine function allocation based on the comparison results of the human-machine coupling information interaction network performance indicators before and after human-machine function allocation.

[0041] The essence of information flow is the resource exchange formed by the information and control being transmitted between carriers such as the human-machine interface, the pilot, and the airborne system in a flowing manner, so the information flow is a cyclic transmission process. The present invention mainly focuses on the resource exchange between the pilot and the human-machine interface when the information flow flows through the pilot, that is, the process of the pilot obtaining information from the display component and exerting control on the control component, quantifies the load intensity generated by this information flow on the pilot, explores the information flow load overload situation when the pilot performs the flight mission, and makes reasonable human-machine function allocation to balance the pilot's information flow load.

[0042] Beneficial effects: Compared with the prior art, the present invention solves the problem that the existing human-machine function allocation method has poor versatility for the complex human-machine system in the aircraft cockpit. Due to the particularity of the flight mission and the system environment, the human-machine function allocation method based on global considerations will be difficult to implement or even impossible to implement, and it is difficult to truly improve resource utilization. Based on the obvious task-driven characteristics of the information process of the complex human-machine system in the cockpit, the present invention focuses the human-machine function allocation on the information flow process between the pilot and the airborne system. On the basis of quantifying the information flow load intensity, considering the coupling effect of the flight mission process and the pilot's cognitive process, the improved WRR algorithm is used to identify the moment when the pilot's information flow load is overloaded. The scheduling result is used as the trigger mechanism for the human-machine function allocation of the complex human-machine system in the cockpit, and the automation level of the human-machine system is adjusted according to the overload moment to achieve efficient and concise dynamic human-machine function allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a mapping diagram between the cockpit human-machine system and the computer operating system;

[0044] Figure 2It is a timing diagram of pilot information flow in a specific embodiment;

[0045] Figure 3 It is a human-machine coupling information interaction network diagram in a specific embodiment;

[0046] Figure 4 It is a comparison diagram of information flow load intensity before and after human-machine function allocation in a specific embodiment;

[0047] Figure 5 It is a comparison diagram of the information flow coupling degree before and after the human-machine function allocation in a specific embodiment. DETAILED DESCRIPTION

[0048] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent forms of modifications to the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0049] The present invention provides a method for allocating human-machine functions in an aircraft cockpit based on a queue scheduling algorithm, comprising the following steps:

[0050] S1: Analyze the flight crew operation process in typical flight mission phases, including:

[0051] A1: Select typical flight mission phases and divide the flight mission into sub-procedures;

[0052] A2: Disassemble the operation tasks or actions under each subroutine.

[0053] S2: Extract the pilot information flow requirements at each moment and construct the pilot information flow timing diagram, including:

[0054] B1: Decompose the operation tasks or operation actions into discrete operations and continuous operations;

[0055] B2: Connect the information flows with arrowed lines to construct a pilot information flow timing diagram. The arrows between the information flows indicate the time sequence, and the start and end times of continuous operations are marked with specific symbols.

[0056] S3: Based on the pilot information flow timing diagram, calculate the information flow load intensity of each information flow, analyze the information flow transmission direction and coupling relationship, and build a human-machine coupling information interaction network model based on complex network theory;

[0057] The method for constructing the human-computer coupling information interaction network model includes:

[0058] C1: Determine the nodes of the human-machine coupling information interaction network, which come from the various information flows shown in the pilot information flow sequence diagram and the pilot himself;

[0059] C2: Analyze the information flow transmission direction and coupling relationship, and determine the directed and undirected edges between nodes;

[0060] C3: Calculate the information flow load intensity S including the flight mission program segment weight, the level of automation of the aircraft cockpit human-machine system and the amount of information on the human-machine interface i,j ;

[0061] Information flow load intensity S i,j The calculation formula is as follows:

[0062] S i,j =ω j ·L i I

[0063] In the formula, ω j represents the weight factor of the jth flight mission subroutine, L i It represents the level of automation of the aircraft cockpit human-machine system when the pilot executes the i-th information flow, and I represents the amount of information carried by the information flow when it circulates the human-machine interface;

[0064] ω j It is also determined by the complexity of flight mission operations and the complexity of pilot resource requirements. The calculation formula is as follows:

[0065]

[0066] In the formula, α and β are the weight coefficients of operation complexity and resource demand complexity respectively, OC represents operation complexity, and RDC represents resource demand complexity.

[0067] L represents the different “human” and “machine” task responsibilities, which are divided into 7 levels, as shown in Table 1

[0068] Table 1 Classification of aircraft cockpit automation levels

[0069]

[0070] To characterize the automation level L i The negative correlation with the information flow load intensity will increase the automation level L i It is expressed as:

[0071]

[0072] Where: j represents the actual automation level of the aircraft cockpit human-machine system, and max(L) represents the highest evaluation level of the automation level;

[0073] The calculation of I is divided into display devices, continuous operation devices and discrete operation devices. The amount of information carried by the display device is related to the type of information appearing on the display page. The amount of information carried by the continuous operation device is related to the display device of its output signal. The amount of information carried by the discrete operation device is 1 bit each time the pilot executes it.

[0074] C4: Based on information flow load intensity S i,j The information flow coupling degree is calculated, network edges are weighted, and a human-machine system risk propagation network model G(N,V) is constructed, where N is the network node set and V is the network edge set.

[0075] S4: Compare the traffic load intensity at each moment with the traffic load intensity threshold of the queue, and use the improved WRR queue scheduling algorithm to identify the traffic overload moment, including:

[0076] D1: Based on the mapping relationship between the aircraft cockpit human-machine system and the computer operating system, the queue priority and the pilot information flow load intensity threshold are set to improve the WRR algorithm;

[0077] D2: Count the pilot information flow load intensity at each moment and input it into the improved WRR algorithm for information flow scheduling;

[0078] The WRR algorithm assigns a weight value to each queue, which represents the resource allocation ratio that the queue can obtain relative to other queues. In each polling cycle, the algorithm schedules the tasks in each queue in turn according to the weight value. The queue with a higher weight value receives more service times. At the same time, the algorithm maintains a counter to record the remaining service times of each queue in the current polling cycle. Initially, the counter value is equal to the weight value of the queue. In each polling, the queue whose counter is not zero is selected to execute the task, and the counter is decremented by 1 after execution. When the counters of all queues are reset to zero, the polling cycle ends, all counters are reset to the initial weight value, and a new polling cycle begins.

[0079] The improved WRR algorithm makes full use of the multi-core characteristics of the pilot in multi-tasking processing, and expands the serial scheduling mechanism in the traditional WRR algorithm, so that the information flows in all non-empty queues can be executed simultaneously in each polling, thereby adapting to the parallel processing characteristics of the pilot. However, due to the limited information flow load that the pilot can handle, the algorithm limits the information flow load intensity of each queue. Specifically, based on the weight value of each queue, the information flow load intensity threshold that the pilot can bear is proportionally distributed to each non-empty queue to ensure that information flows with higher urgency obtain more processing resources. Unlike the processes in the operating system that can enter the background to wait for scheduling, the flight mission execution process has a strict sequence, and the information flow that has not been completed cannot be queued again. It is necessary to count the difference between the pilot's information flow load threshold and the information flow load intensity. The present invention names this difference as the scheduling margin.

[0080] D3: Filter out the information flow load overload moments and count the information flow load strength margin, that is, the difference between the pilot information flow load strength threshold and the load strength of each information flow;

[0081] In the present invention, the basis for judging the moment when the pilot information flow load intensity is overloaded is the positivity of the difference between the pilot information flow load intensity threshold and the information flow load intensity of the processed information. When the difference is a negative number, it is determined that the pilot information flow load is overloaded, and when the difference is a positive number, it is determined that the information flow load is not overloaded.

[0082] S5: Use the difference between the information flow load intensity threshold and the information flow load intensity as the scheduling margin to trigger the human-machine function allocation, adjust the automation level of the aircraft cockpit human-machine system, and evaluate the allocation result by calculating the relevant performance indicators of the human-machine coupling information interaction network. Specifically include:

[0083] E1: Based on the information flow load strength margin counted in step D3, analyzing the cause of the information flow load overload, so as to adjust the aircraft cockpit human-machine system automation level at the moment of information flow load overload;

[0084] The present invention is a qualitative judgment of the degree of overload based on the size of the scheduling margin and the in-depth analysis of the reasons behind it. For example, in the case analysis of the take-off mission, the pilots at time 52 and time 53 are overloaded when processing the information flow of "verifying the height and speed ②". However, the scheduling margin of the same information flow in the two schedulings, that is, the difference between the pilot's information flow load intensity threshold and the "verifying the height and speed ②" information flow load intensity, is different. Further analysis shows that there is a difference in the amount of information that the pilots need to process simultaneously at time 52 and time 53. At time 53, on the basis of time 52, it is also necessary to perform the operation of "putting the flap handle in the UP position", which will increase the pilot's information flow load, making it even more impossible to complete the "verifying the height and speed ②" that cannot be fully executed. Therefore, whether from the results of the scheduling margin display or the analysis of the internal factors, the degree of information flow load generated by the "verifying the height and speed ②" at time 53 relative to time 52 is deepened.

[0085] E2: Recalculate the information flow load intensity and information flow coupling degree after the human-machine function allocation, and establish the human-machine coupling information interaction network after the human-machine function allocation;

[0086] E3: Count the performance indicators of the human-machine coupling information interaction network before and after the human-machine function allocation and conduct comparative analysis. The performance indicators include closeness centrality, betweenness centrality, information flow load intensity, etc. to measure node performance, as well as network density, network robustness and information flow coupling to measure the overall performance of the network;

[0087] E4: Analyze the rationality of human-machine function allocation based on the comparison results of the human-machine coupling information interaction network performance indicators before and after human-machine function allocation.

[0088] In order to verify the effectiveness of the method of the present invention, the method of the present invention is applied in an example in this embodiment, taking the flight mission of the Boeing 737 model during the take-off phase as an example, and comprising the following steps:

[0089] Step (1) The specific steps of analyzing the flight crew operation process under a typical flight mission phase are as follows:

[0090] Step (1.1) Select a typical flight mission phase and divide the flight mission into subroutines;

[0091] Step (1.2) decomposes the operation tasks or operation actions under each subroutine. The decomposition of the take-off phase actions is shown in Table 2.

[0092] Table 2 Task division during takeoff phase

[0093]

[0094]

[0095] Step (2) extracts the pilot information flow requirements at each moment and constructs the pilot information flow timing diagram. The specific steps are as follows:

[0096] Step (2.1) decomposes the operation task or operation action into discrete operations and continuous operations, such as Figure 1 The blue blocks shown are discrete operations, and the orange blocks are continuous operations;

[0097] Step (2.2) connects the information flows with arrowed lines to construct a pilot information flow timing diagram. The arrows between the information flows indicate the time sequence, and the start and end times of continuous operations are marked with specific identifiers, such as Figure 1 shown.

[0098] Step (3) calculates the information flow load intensity of each information flow, analyzes the information flow transmission direction and coupling relationship, and constructs a human-computer coupling information interaction network model based on complex network theory. The specific steps are as follows:

[0099] Step (3.1) determines the nodes of the human-machine coupling information interaction network. The nodes come from the various information flows shown in the pilot information flow sequence diagram and the pilot itself, that is, Figure 1 The small blocks listed under each subroutine;

[0100] Step (3.2) analyzes the information flow transmission direction and coupling relationship, and determines the directed and undirected edges between nodes. Taking the “sliding program” as an example, the connection method between nodes is shown in Table 3. The target node in the table is Figure 1 The corresponding information flow under "Taxi Procedure" in

[0101] Table 3. Analysis of some node associations

[0102] Source Node Target Node Connection pilot 4-a promising pilot 4-b promising pilot 4-c promising pilot 4-d promising 4-e pilot promising pilot 4-f promising pilot 4-g promising pilot 4-h promising pilot 4-i promising pilot 4-j promising pilot 4-k promising 4-c 4-d Undirected 4-c 4-e Undirected

[0103] Step (3.3) calculates the information flow load intensity S including the flight mission program segment weight, the aircraft cockpit human-machine system automation level and the human-machine interface information volume i,j ,Taking the “gliding program” as an example, the calculation results of information flow load intensity are shown in Table 4;

[0104] Table 4 Information flow load intensity of “Taxiing procedure”

[0105] Information Flow Information flow load intensity Taxi lights on 6.5911 Parking brake released 6.5911 Gently increase thrust 17.0378 Flap handle in position 5 6.5911 Verify that the handle position and indicator indications are consistent 13.1823 Inboard landing lights on, fixed landing lights on 6.5911 Start switch continuous position 6.5911 Strobe light on 6.5911 Auto throttle arming 6.5911 Answering machine on 6.5911 Weather radar on 6.5911

[0106] Step (3.4) calculates the coupling degree based on the information flow load intensity, assigns weights to the network edges, and constructs the human-machine system risk propagation network model G(N,V), where N is the network node set and V is the network edge set, such as Figure 2 shown.

[0107] Step (4) compares the information flow load intensity at each moment with the information flow load intensity threshold of the queue, and uses the improved WRR queue scheduling algorithm to identify the information flow load overload moment. The specific steps are as follows:

[0108] Step (4.1) sets the queue priority and pilot information flow load intensity threshold for the improved WRR algorithm;

[0109] In this embodiment, a three-level priority queue is set to improve the WRR algorithm, with weight values ​​of 3, 2, and 1 respectively, and the pilot information flow load intensity threshold is set to 73.4348.

[0110] Step (4.2) counts the pilot information flow load intensity at each moment and inputs it into the improved WRR algorithm for information flow scheduling. Taking the "taxi procedure" as an example, the information flow load intensity at each moment is shown in Table 5;

[0111] Table 5 Information flow load intensity at each moment of the “Taxiing Procedure”

[0112]

[0113] Step (4.3) selects the information flow load overload moment and counts the information flow load strength margin. The scheduling results of the information flow load overload moment are shown in Table 6.

[0114] Table 6 Information flow scheduling results at overload time

[0115]

[0116] Step (5) uses the scheduling result to trigger the human-machine function allocation, adjusts the automation level of the aircraft cockpit human-machine system, and evaluates the allocation result by calculating the relevant performance indicators of the human-machine coupling information interaction network. The specific steps are as follows:

[0117] Step (5.1) is based on the information flow load strength margin counted in step (4), analyzing the cause of the information flow load overload, so as to adjust the aircraft cockpit human-machine system automation level at the moment of information flow load overload, as shown in Table 7;

[0118] Table 7 Human-machine function allocation scheme at takeoff mission overload moment

[0119]

[0120] Step (5.2) recalculates the information flow load intensity and information flow coupling degree after the human-machine function allocation, and establishes a human-machine coupling information interaction network after the human-machine function allocation;

[0121] Step (5.3) counts the performance indicators of the human-machine coupling information interaction network before and after the human-machine function allocation and conducts comparative analysis. The performance indicators include the closeness centrality, betweenness centrality, information flow load intensity, etc., which measure the performance of the node, as well as the network density, network robustness, and information flow coupling degree, which measure the overall performance of the network, as shown in Table 8. Figure 3 , Figure 4 As shown;

[0122] Table 8 Comparison of network performance before and after human-machine function allocation

[0123] Performance Indicators Before human-machine function allocation After human-machine function allocation Closeness Centrality 0.0009246 0.004461 Betweenness Centrality 0.2792 0.2805 Network density 0.02046 0.02046 Network robustness 0.002560 0.01085

[0124] Step (5.4) analyzes the rationality of human-machine function allocation based on the comparison results.

[0125] It can be seen from the information flow scheduling results that during the takeoff mission, the pilot information flow load intensity overload situation identified by the improved WRR algorithm mainly appears in the "takeoff procedure". Because during the "takeoff procedure", the pilot has to handle multiple information flows at the same time at certain moments, and it is basically continuous and uninterrupted operation and system monitoring work that requires high concentration of attention. For example, in order to maintain a positive ascent rate, the pilot needs to continuously control the joystick while monitoring the PFD at all times. The information flow scheduling results of the above embodiment are consistent with the actual situation, which proves the effectiveness of using the improved WRR algorithm to identify the information flow load overload moments when the pilot performs the flight mission.

[0126] The human-machine function allocation method proposed in the present invention increases the closeness centrality of the pilot node by 4.82 times, the betweenness centrality by 0.47%, and the network robustness by 4.24 times, effectively reducing the information flow load intensity and information flow coupling degree of the pilot at the critical moment of the "take-off mission" process. The above results show that the human-machine function allocation method proposed in the present invention can effectively realize the human-machine function allocation in the aircraft cockpit, fully proves the centrality of the pilot node in the human-machine coupling information interaction network, and further illustrates that reducing the information flow load intensity of the pilot node can improve the anti-interference performance of the human-machine coupling information interaction network, that is, improve the safety during the flight process.

Claims

1. A method for allocating human-machine functions in an aircraft cockpit based on a queue scheduling algorithm, characterized in that: The steps include: S1: Analyze the flight crew operation process in typical flight mission phases; S2: Extract the pilot information flow requirements at each moment and construct the pilot information flow timing diagram; S3: Based on the pilot information flow timing diagram, calculate the information flow load intensity of each information flow, analyze the information flow transmission direction and coupling relationship, and build a human-machine coupling information interaction network model based on complex network theory; S4: Compare the information flow load intensity at each moment with the information flow load intensity threshold of the queue, and use the improved WRR queue scheduling algorithm to identify the information flow load overload moment; S5: The difference between the information flow load intensity threshold and the information flow load intensity is used as the scheduling margin to trigger the human-machine function allocation, adjust the automation level of the aircraft cockpit human-machine system, and evaluate the allocation result by calculating the relevant performance indicators of the human-machine coupling information interaction network.

2. The method for allocating human-machine functions in an aircraft cockpit based on a queue scheduling algorithm according to claim 1, characterized in that: The analysis of the flight crew operation process in the typical flight mission phase in step S1 includes: A1: Select typical flight mission phases and divide the flight mission into sub-procedures; A2: Disassemble the operation tasks or actions under each subroutine.

3. The method for allocating human-machine functions in an aircraft cockpit based on a queue scheduling algorithm according to claim 2, characterized in that: The step S2 is specifically as follows: B1: Decompose the operation tasks or operation actions into discrete operations and continuous operations; B2: Connect the information flows with arrowed lines to construct a pilot information flow timing diagram. The arrows between the information flows indicate the time sequence, and the start and end times of continuous operations are marked with specific symbols.

4. The method for allocating human-machine functions in an aircraft cockpit based on a queue scheduling algorithm according to claim 1, characterized in that: The method for constructing the human-machine coupling information interaction network model in step S3 includes: C1: Determine the nodes of the human-machine coupling information interaction network, which come from the various information flows shown in the pilot information flow sequence diagram and the pilot himself; C2: Analyze the information flow transmission direction and coupling relationship, and determine the directed and undirected edges between nodes; C3: Calculate the information flow load intensity S including the flight mission program segment weight, the level of automation of the aircraft cockpit human-machine system and the amount of information on the human-machine interface i,j ; C4: Based on information flow load intensity S i,j The information flow coupling degree is calculated, network edges are weighted, and a human-machine system risk propagation network model G(N,V) is constructed, where N is the network node set and V is the network edge set.

5. The method for allocating human-machine functions in an aircraft cockpit based on a queue scheduling algorithm according to claim 1, characterized in that: The information flow load intensity S in step C3 i,j The calculation formula is as follows: S i,j =ω j ·L i ·I In the formula, ω j represents the weight factor of the jth flight mission subroutine, L i It represents the level of automation of the aircraft cockpit human-machine system when the pilot executes the i-th information flow, and I represents the amount of information carried by the information flow when it circulates the human-machine interface; ω j It is also determined by the complexity of flight mission operations and the complexity of pilot resource requirements. The calculation formula is as follows: In the formula, α and β are the weight coefficients of operation complexity and resource demand complexity respectively, OC represents operation complexity, and RDC represents resource demand complexity.

6. The method for allocating human-machine functions in an aircraft cockpit based on a queue scheduling algorithm according to claim 5, characterized in that: In step C3, in order to characterize the automation level L i Negative correlation with information flow load intensity, the automation level L i It is expressed as: Where: j represents the actual automation level of the aircraft cockpit human-machine system, and max(L) represents the highest evaluation level of the automation level.

7. The method for allocating human-machine functions in an aircraft cockpit based on a queue scheduling algorithm according to claim 1, characterized in that: The specific steps of identifying the pilot information flow overload moment in step S4 are as follows: D1: Based on the mapping relationship between the aircraft cockpit human-machine system and the computer operating system, the queue priority and the pilot information flow load intensity threshold are set to improve the WRR algorithm; D2: Count the pilot information flow load intensity at each moment and input it into the improved WRR algorithm for information flow scheduling; D3: Filter out the information flow load overload moments and count the information flow load strength margin, that is, the difference between the pilot's information flow load strength threshold and the load strength of each information flow.

8. The method for allocating human-machine functions in an aircraft cockpit based on a queue scheduling algorithm according to claim 7, characterized in that: The step S5 is specifically as follows: E1: Based on the information flow load strength margin counted in step D3, analyzing the cause of the information flow load overload, so as to adjust the aircraft cockpit human-machine system automation level at the moment of information flow load overload; E2: Recalculate the information flow load intensity and information flow coupling degree after the human-machine function allocation, and establish the human-machine coupling information interaction network after the human-machine function allocation; E3: Count the performance indicators of the human-machine coupling information interaction network before and after human-machine function allocation and conduct comparative analysis; E4: Analyze the rationality of human-machine function allocation based on the comparison results of the human-machine coupling information interaction network performance indicators before and after human-machine function allocation.

Images