Aircraft cockpit man-machine function allocation method based on queue scheduling algorithm

Abstract

The application discloses a kind of based on queue scheduling algorithm's aircraft cockpit man-machine function distribution method, comprising: analysis typical flight mission phase under the flight crew operation process;Extract pilot information flow demand at each time, construct pilot information flow timing diagram;The information flow load intensity of each information flow is calculated, the information flow transmission direction and coupling relationship are analyzed, and a man-machine coupling information interaction network model is constructed;Compare the information flow load intensity at each time with the information flow load intensity threshold value of queue, and use improved WRR queue scheduling algorithm to identify information flow load overload time;The difference between information flow load intensity threshold value and information flow load intensity is used as scheduling margin to trigger man-machine function distribution, adjust the automation level of aircraft cockpit man-machine system, and the distribution result is evaluated by calculating the related performance index of man-machine coupling information interaction network.The application can balance pilot information flow load in the process of aircraft cockpit man-machine interaction, improve flight safety and system resource utilization.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of human-computer interaction, and relates to aircraft cockpit human-computer function distribution, in particular to an aircraft cockpit human-computer function distribution method based on a queue scheduling algorithm. BACKGROUND

[0002] Human-computer function distribution refers to a process of distributing functions of a system to a human and a machine for execution respectively according to different characteristics of the human and the machine and different requirements of a task after analyzing and weighing characteristics of the human and the machine. Human-computer function distribution is mainly divided into human-computer capability comparison distribution, quantitative evaluation modeling distribution and adaptive human-computer function distribution. Human-computer capability comparison distribution is to compare advantages of a human and a machine to determine distribution of a task. Quantitative evaluation modeling distribution is to distribute functions on the basis of human-computer capability comparison distribution through a combination of quantitative evaluation and mathematical modeling in an experimental study. In order to adapt to a complex system with a higher and higher degree of intelligence, adaptive human-computer function distribution dynamically gives different permissions to an intelligent system on the basis of the former two to maximize comprehensive performance of the whole system. Due to particularity of a flight task and a system environment of an aircraft, existing distribution methods are difficult to realize human-computer function distribution of an aircraft cockpit because of a complex distribution process and difficulty in integration of measuring equipment. SUMMARY

[0003] The application aims at overcoming the deficiencies in the prior art and providing an aircraft cockpit human-computer function distribution method based on a queue scheduling algorithm to balance information flow load of a pilot in a human-computer interaction process of an aircraft cockpit and improve flight safety and system resource utilization.

[0004] The technical scheme is characterized in that the aircraft cockpit human-computer function distribution method based on the queue scheduling algorithm comprises the following steps:

[0005] S1: analyzing a flight crew operation process under a typical flight task stage;

[0006] S2: extracting information flow requirements of the pilot at each moment to construct a pilot information flow time sequence diagram;

[0007] S3: calculating information flow load intensity of each information flow based on the pilot information flow time sequence diagram, analyzing information flow transmission direction and coupling relationship and constructing a human-machine coupling information interaction network model based on a complex network theory;

[0008] S4: comparing information flow load intensity at each moment with an information flow load intensity threshold value of a queue and identifying an information flow load overload moment by using an improved WRR queue scheduling algorithm;

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

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

[0011] A1: Select a typical flight mission phase and divide the flight mission subprogram;

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

[0013] Further, step S2 is specifically:

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

[0015] B2: Connect the information flows with arrowed line segments to construct the pilot information flow timing diagram, and the arrow direction between the information flows represents the chronological order, and the continuous operations are marked with specific identifiers to indicate the start and end time.

[0016] Further, 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 each information flow shown in the pilot information flow timing diagram and the pilot himself;

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

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

[0020] C4: Calculate the information flow coupling degree based on the information flow load intensity S i,j , assign weights to the network edges, and construct 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.

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

[0022] S i,j =ω j ·L i ·I

[0023] In the formula, ω jL represents the weight factor of the jth flight mission subprogram i L represents the weight factor of the jth flight mission subprogram

[0024] ω j Meanwhile, it is determined by the flight mission operation complexity and the pilot resource demand complexity, and the calculation formula is as follows:

[0025]

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

[0027] Further, in order to represent the automation level L i The automation level L i is represented as:

[0028]

[0029] In the formula, 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 information amount carried by the display device is related to the information type of the display page. The information amount of the continuous operation device is related to the display device of its output signal. The information amount of the discrete operation device is 1 bit for each execution of the pilot.

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

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

[0033] D2: Statistics of the pilot information flow load intensity at each moment, input the improved WRR algorithm for information flow scheduling;

[0034] D3: Screen out the information flow load overload moment, and count the information flow load intensity margin, that is, the difference between the pilot information flow load intensity threshold value and each information flow load intensity.

[0035] Further, the step S5 is specifically:

[0036] E1: Based on the information flow load intensity margin calculated in step D3, analyze the information flow load overload reason to adjust the cockpit human-machine system automation level at the information flow load overload time;

[0037] E2: Recalculate the information flow load intensity and information flow coupling degree after human-machine function allocation, and establish the human-machine coupling information interaction network after human-machine function allocation;

[0038] E3: Statistic and comparative analysis of human-machine coupling information interaction network performance index before and after human-machine function allocation, performance index includes closeness centrality, betweenness centrality, information flow load intensity and network density, network robustness and information flow coupling degree;

[0039] E4: According to the comparative result of human-machine coupling information interaction network performance index before and after human-machine function allocation, analyze the rationality of human-machine function allocation.

[0040] The essence of information flow is the exchange of resources formed by the transmission of information and control between human-machine interface, pilot and airborne system through flow, so the information flow is a circulating transmission process. The present application mainly aims at the resource exchange of information flow when flowing through the pilot and human-machine interface, that is, the process of the pilot obtaining information from the display component and exerting control on the control component, quantifies the load intensity of the information flow on the pilot, explores the information flow load overload condition of the pilot when performing flight task, and balances the information flow load of the pilot by reasonable human-machine function allocation.

[0041] Beneficial effects: compared with the prior art, the present application solves the problem that the existing human-machine function allocation method has poor universality for the cockpit complex human-machine system. Due to the particularity of flight task and system environment, the human-machine function allocation method based on global consideration will have difficulty in implementation, even cannot be implemented, and it is difficult to truly improve the resource utilization rate. Based on the obvious task-driven characteristics of the informationization process of cockpit complex human-machine system, the present application focuses on the information flow process of pilot and airborne system. On the basis of quantifying the information flow load intensity, considering the coupling effect of flight task process and pilot cognitive process, the improved WRR algorithm is used to identify the information flow load overload time of pilot. The scheduling result is used as the human-machine function allocation trigger mechanism of cockpit complex human-machine system, the automation level of human-machine system is adjusted at the overload time, and efficient and simple dynamic human-machine function allocation is realized. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 The mapping relationship diagram of cockpit human-machine system and computer operating system;

[0043] Figure 2For the pilot information flow timing diagram in the specific embodiment;

[0044] Figure 3 For the man-machine coupling information interaction network diagram in the specific embodiment;

[0045] Figure 4 For the man-machine function allocation before and after information flow load intensity comparison chart in the specific embodiment;

[0046] Figure 5 For the man-machine function allocation before and after information flow coupling degree comparison chart in the specific embodiment. DETAILED DESCRIPTION

[0047] The present application will be further illustrated below in conjunction with the drawings and specific embodiments, and it should be understood that these embodiments are only used to illustrate the present application and not to limit the scope of the present application, and after reading the present application, various equivalent modifications of the present application by those skilled in the art fall within the scope defined by the appended claims.

[0048] The present application provides a kind of based on queue scheduling algorithm's aircraft cockpit man-machine function allocation method, comprising the following steps:

[0049] S1: analysis typical flight mission phase under the flight crew operation process, including:

[0050] A1: select typical flight mission phase, divide flight mission subprogram;

[0051] A2: disassemble the operation task or operation action under each subprogram.

[0052] S2: extract pilot information flow demand at each time, build pilot information flow timing diagram, including:

[0053] B1: the operation task or operation action is decomposed into discrete operation and continuous operation;

[0054] B2: information flow is connected with arrowed line segment to build pilot information flow timing diagram, and the arrow between information flow indicates the order of time, and continuous operation is marked with specific mark to mark its start and end time.

[0055] S3: based on pilot information flow timing diagram, the information flow load intensity of each information flow is calculated, the information flow transmission direction and coupling relationship are analyzed, and man-machine coupling information interaction network model is built based on complex network theory;

[0056] The construction method of man-machine coupling information interaction network model includes:

[0057] C1: determine the nodes of man-machine coupling information interaction network, which come from each information flow shown in pilot information flow timing diagram and the pilot itself;

[0058] C2: Analyze the information flow direction and coupling relationship, determine the directed edges and undirected edges between nodes;

[0059] C3: Calculate the information flow load intensity S including flight mission segment weight, cockpit automation level and information amount of human-machine interface i,j ;

[0060] The formula of information flow load intensity S i,j is as follows:

[0061] S i,j = j ·L i ·I

[0062] In the formula, ω j represents the weight factor of the jth flight mission subprogram, L i represents the cockpit automation level when the pilot executes the ith information flow, and I represents the information amount carried by the human-machine interface when the information flow flows through the human-machine interface.

[0063] ω j is determined by the flight mission operation complexity and pilot resource demand complexity, and the formula is as follows:

[0064]

[0065] In the formula, α and β are weight coefficients of operation complexity and resource demand complexity respectively, OC represents operation complexity, and RDC represents resource demand complexity.

[0066] L represents the task assumption of different "people" and "machines", and is divided into 7 levels as shown in Table 1

[0067] Table 1: Cockpit automation level division

[0068]

[0069] In order to represent the negative correlation between automation level L i and information flow load intensity, the automation level L i is represented as:

[0070]

[0071] In the formula, j represents the actual automation level of the cockpit human-machine system, and max(L) represents the highest evaluation level of the automation level.

[0072] The calculation of I is divided into display devices, continuous operation devices and discrete operation devices, the information amount of display devices is related to the information types of display pages, the information amount of continuous operation devices is related to the display devices of output signals, and the information amount of discrete operation devices is 1 bit for each execution of pilots.

[0073] C4: Based on the information flow load intensity S i,j The information flow coupling degree is calculated, the network edges are weighted, the human-machine system risk propagation network model G(N, V) is constructed, wherein N is a network node set, and V is a network edge set.

[0074] S4: Comparing the information flow load intensity at each moment with the information flow load intensity threshold value of the queue, the improved WRR queue scheduling algorithm is used to identify the information flow load overload moment, including:

[0075] D1: Based on the mapping relationship between the aircraft cockpit human-machine system and the computer operating system, the queue priority of the improved WRR algorithm and the pilot information flow load intensity threshold value are set;

[0076] D2: The information flow load intensity of pilots at each moment is counted and input into the improved WRR algorithm for information flow scheduling;

[0077] The WRR algorithm allocates a weight value to each queue, which represents the proportion of resource allocation that the queue can obtain relative to other queues. In each polling cycle, the algorithm schedules the tasks in each queue according to the weight value, and the queue with a higher weight value obtains 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. At each polling time, the queue with a non-zero counter is selected to execute the task, and the counter is reduced by 1 after execution. When the counters of all queues are zero, the polling cycle ends, and all counters are reset to the initial weight value, and a new polling cycle begins.

[0078] The improved WRR algorithm fully utilizes the multi-core characteristics of the pilot in multi-task processing, extends the serial scheduling mechanism in the traditional WRR algorithm, so that all the information flows in the non-empty queue can be executed at the same time each time polling, thereby adapting to the parallel processing characteristics of the pilot. However, since the information flow load that the pilot can handle is limited, 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 allocated to each non-empty queue, ensuring that the information flow with higher urgency obtains more processing resources. Unlike the processes in the operating system which can enter the background and wait for scheduling, the flight task execution process has a strict sequence, and the information flow that has not been executed cannot be re-queued, so the difference between the information flow load threshold of the pilot and the load intensity of the information flow needs to be counted, and the difference is named as the scheduling margin in the application.

[0079] D3: screening out the information flow load overload moment, and counting the information flow load intensity margin, i.e. the difference between the information flow load intensity threshold of the pilot and the load intensity of each information flow;

[0080] The judgment basis of the information flow load intensity overload moment of the pilot in the application is the positive and negative of the difference between the information flow load intensity threshold of the pilot and the load intensity of the processed information. When the difference is negative, it is determined that the information flow load of the pilot is overloaded, and when the difference is positive, it is determined that the information flow load is not overloaded.

[0081] S5: taking the difference between the information flow load intensity threshold and the information flow load intensity as the scheduling margin to trigger the human function allocation, adjusting the automation level of the aircraft cockpit human-machine system, and evaluating the allocation result by calculating the performance index of the human-machine coupling information interaction network. Specifically, it includes:

[0082] E1: based on the information flow load intensity margin counted in step D3, analyzing the information flow load overload reason to adjust the automation level of the aircraft cockpit human-machine system at the information flow load overload moment;

[0083] The present application is a qualitative judgment of the overload degree according to the size of the scheduling margin and the in-depth analysis of the reason behind it. For example, in the case analysis of the take-off task, the pilots are overloaded when processing the information flow of "verify height and speed ②" at time 52 and time 53, however, the scheduling margin of the same information flow in the two scheduling, that is, the difference between the pilot information flow load intensity threshold and the "verify height and speed ②" information flow load intensity is not the same. Further analysis shows that there is a difference in the amount of information that the pilots need to process at time 52 and time 53. At time 53, the pilot also needs to perform the operation of "put the flap handle to the UP position" based on time 52, which will increase the information flow load of the pilot, making it impossible to complete the "verify height and speed ②" which is already unable to be fully executed. Therefore, whether from the result of the scheduling margin or from the analysis of the internal cause, the information flow load degree of "verify height and speed ②" on the pilot at time 53 relative to time 52 is deepened.

[0084] 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;

[0085] E3: Statistically compare the performance indicators of the human-machine coupling information interaction network before and after the human-machine function allocation, including the closeness centrality, betweenness centrality, information flow load intensity, network density, network robustness and information flow coupling degree, etc.

[0086] E4: Analyze the rationality of the human-machine function allocation according to the comparison results of the performance indicators of the human-machine coupling information interaction network before and after the human-machine function allocation.

[0087] In order to verify the effectiveness of the method of the present application, the method of the present application is applied in this embodiment as an example, taking the take-off task of Boeing 737 model as an example, including the following steps:

[0088] Step (1) The specific steps of analyzing the flight crew operation process under the typical flight task stage are as follows:

[0089] Step (1.1) Select a typical flight task stage, and divide the flight task subprogram;

[0090] Step (1.2) Disassemble the operation task or operation action under each subprogram, and the take-off stage action disassembly is shown in Table 2.

[0091] Table 2 Take-off stage task division

[0092]

[0093]

[0094] Step (2) extracts the pilot information flow demand at each time, and constructs a pilot information flow timing diagram. The specific steps are as follows:

[0095] Step (2.1) decomposes the operation task or operation action into discrete operation and continuous operation, as shown in the figure. The blue square is the discrete operation, and the orange square is the continuous operation. Figure 1

[0096] Step (2.2) connects the information flow with an arrow to construct the pilot information flow timing diagram. The arrow between the information flows indicates the time sequence, and the continuous operation is marked with a specific identifier to indicate the start and end time, as shown in the figure. Figure 1

[0097] 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-machine coupling information interaction network model based on complex network theory. The specific steps are as follows:

[0098] Step (3.1) determines the nodes of the human-machine coupling information interaction network, which come from each information flow shown in the pilot information flow timing diagram and the pilot himself, i.e. Figure 1 The small squares listed under each subprogram in the figure.

[0099] Step (3.2) analyzes the information flow transmission direction and coupling relationship, determines the directed edges and undirected edges between nodes, and takes the "taxi program" as an example. The connection mode between nodes is shown in Table 3. The target node in the table is Figure 1 The corresponding information flow under the "taxi program" in the figure.

[0100] Table 3 Partial node association analysis

[0101] Source node Target node Connection mode Pilot 4-a Directed Pilot 4-b Directed Pilot 4-c Directed Pilot 4-d Directed 4-e Pilot Directed Pilot 4-f Directed Pilot 4-g Directed Pilot 4-h Directed Pilot 4-i Directed Pilot 4-j Directed Pilot 4-k Directed 4-c 4-d Pilot 4-c 4-e Directed

[0102] Step (3.3) calculates the information flow load intensity S i,j , including the weight of the flight task program segment, the automation level of the aircraft cockpit human-machine system, and the amount of human-machine interface information. Taking the "taxi program" as an example, the information flow load intensity calculation results are shown in Table 4.

[0103] Table 4 Information flow load intensity of "taxi program"

[0104] Pilot Directed Pilot 6.5911 Directed 6.5911 Pilot 17.0378 Directed 6.5911 Pilot 13.1823 Directed 6.5911 Pilot 6.5911 Directed 6.5911 Pilot 6.5911 Directed 6.5911 Pilot 6.5911

[0105] Step (3.4) calculates the coupling degree based on the information flow load intensity, assigns weights to the network edges, and constructs a human-machine system risk propagation network model G(N, V), where N is the network node set and V is the network edge set, as shown in the figure. Directed

[0106] ​​​Step (4) compares the information flow load intensity at each time with the information flow load intensity threshold of the queue, and identifies the information flow load overload time using the improved WRR queue scheduling algorithm. The specific steps are as follows:

[0107] Step (4.1) sets the queue priority for the improved WRR algorithm and the pilot information flow load intensity threshold;

[0108] This embodiment sets a three-level priority queue for the improved WRR algorithm, with weight values of 3, 2, and 1, and sets the pilot information flow load intensity threshold to 73.4348.

[0109] Step (4.2) statistics the pilot information flow load intensity at each time, and inputs it into the improved WRR algorithm for information flow scheduling. Taking the "taxi program" as an example, the information flow load intensity at each time is shown in Table 5.

[0110] Table 5 Information flow load intensity at each time of "taxi program"

[0111]

[0112] Step (4.3) screens out the information flow load overload time and statistics the information flow load intensity margin. The scheduling result of the information flow load overload time is shown in Table 6.

[0113] Table 6 Information flow scheduling result at overload time

[0114]

[0115] Step (5) triggers the human-machine function allocation using the scheduling result, adjusts the automation level of the aircraft cockpit human-machine system, and evaluates the allocation result by calculating the related performance indicators of the human-machine coupling information interaction network. The specific steps are as follows:

[0116] Step (5.1) analyzes the information flow load overload reason based on the information flow load intensity margin statistics in step (4), and adjusts the automation level of the aircraft cockpit human-machine system at the information flow load overload time, as shown in Table 7.

[0117] Table 7 Human-machine function allocation scheme at take-off task overload time

[0118]

[0119] Step (5.2) recalculates the information flow load intensity and information flow coupling degree after the human-machine function allocation, and establishes the human-machine coupling information interaction network after the human-machine function allocation.

[0120] Step (5.3) compares and analyzes the network performance indexes of human-machine coupling information interaction before and after the human-machine function allocation, including the closeness centrality, betweenness centrality and information flow load intensity of the node performance indexes, and the network density, network robustness and information flow coupling degree of the network overall performance indexes, as shown in Table 8, Pilot 、 Directed

[0121] Table 8 Comparison of network performance before and after human-machine function allocation

[0122] Pilot Directed Pilot Directed 0.0009246 0.004461 Pilot 0.2792 0.2805 Directed 0.02046 0.02046 Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot Directed Pilot 0.002560 0.01085

[0123] Step (5.4) analyzes the rationality of the human-machine function allocation according to the comparison result.

[0124] From the information flow scheduling result, it can be seen that, in the take-off task process, the information flow load intensity overload of the pilot identified by the improved WRR algorithm is concentrated in the “take-off procedure”. Because in the “take-off procedure”, the pilot needs to process multiple information flows at the same time at some time, and the basic operation is continuous and uninterrupted, and the system monitoring work needs high concentration of attention, for example, the pilot needs to continuously control the joystick while monitoring the PFD at any time in order to maintain the positive climb rate. The information flow scheduling result of the above embodiment is consistent with the actual situation, which proves the effectiveness of the improved WRR algorithm in identifying the information flow load overload time of the pilot in the process of performing the flight task.

[0125] The human-machine function allocation method proposed in the application increases the closeness centrality of the pilot node by 4.82 times, increases the betweenness centrality by 0.47%, and increases 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 in the process of “take-off task”. The above results show that the human-machine function allocation method proposed in the application 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 shows 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 in 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, Includes the following steps: S1: Analyze the flight crew operation procedures during typical flight mission phases; S2: Extract the information flow requirements of pilots at each moment and construct a time sequence diagram of pilot information flow; S3: Based on the pilot information flow time sequence diagram, calculate the information flow load intensity of each information flow, analyze the information flow transmission direction and coupling relationship, and construct a human-machine coupled information interaction network model based on complex network theory. S4: Compare the information flow load intensity with the information flow load intensity threshold at each time point, and use the improved WRR queue scheduling algorithm to identify the time when the information flow load is overloaded; 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 allocation of human-machine functions, 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 coupled 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 procedures under typical flight mission phases in step S1 includes: A1: Select a typical flight mission phase and divide it into flight mission subroutines; A2: Break down 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, Step S2 specifically involves: B1: Decompose operational tasks or actions into discrete operations and continuous operations; B2: Connect the information flows with arrowed line segments to construct a pilot information flow sequence diagram. The arrows between the information flows indicate the chronological order, and the start and end times of consecutive operations are marked with specific identifiers.

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 coupled information interaction network model in step S3 includes: C1: Identify the nodes of the human-machine coupled information interaction network. The nodes come from the various information flows shown in the pilot information flow sequence diagram and the pilot himself. C2: Analyze the direction of information flow and coupling relationships to determine the directed and undirected connections between nodes; C3: Calculate the information flow load intensity S, including flight mission procedure segment weights, aircraft cockpit human-machine system automation level, and human-machine interface information volume. i,j ; C4: Based on information flow load intensity S i,j Calculate the information flow coupling degree, assign weights to network edges, and construct a human-machine system risk propagation network model G(N,V), where N is the set of network nodes and V is the set of network edges.

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 L represents the weight factor of the j-th flight mission subroutine. i This indicates 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 when the information flow passes through the human-machine interface; ω j It is determined by both the operational complexity of the flight mission and the complexity of pilot resource requirements, and the calculation formula is as follows: In the formula, α and β are the weighting coefficients of operational complexity and resource requirement complexity, respectively, OC represents operational complexity, and RDC represents resource requirement 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 The negative correlation with information flow load intensity will affect the automation level L. i Represented as: In the formula: j represents the actual level of automation of the aircraft cockpit human-machine system, and max(L) represents the highest evaluation level of automation.

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 for 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, set queue priority and pilot information flow load intensity threshold to improve the WRR algorithm; D2: Statistically analyze the pilot information flow load intensity at each time point and input it into the improved WRR algorithm for information flow scheduling; D3: Filter out the times when the information flow load is overloaded, and calculate 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.

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, Step S5 specifically involves: E1: Based on the information flow load strength margin statistically analyzed in step D3, analyze the reasons for information flow load overload in order to adjust the automation level of the aircraft cockpit human-machine system at the time 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 a human-machine coupling information interaction network after the human-machine function allocation; E3: Statistically analyze and compare the performance indicators of the human-machine coupled information interaction network before and after the allocation of human-machine functions; E4: Analyze the rationality of human-machine function allocation based on the comparison results of human-machine coupling information interaction network performance indicators before and after human-machine function allocation.

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