Civil aviation approach phase crew load management method and system based on dynamic task optimization

Abstract

The application discloses a kind of civil aviation approach phase crew load management method and system based on dynamic task optimization, it is related to civil aviation flight management technical field, the civil aviation approach phase crew load management method and system based on dynamic task optimization disclosed, through real-time data fusion, dynamic task state tracking, multi-dimensional influence factor quantization and sequence optimization mechanism, the adaptive scheduling and resource allocation of approach phase task are realized, the precision and real-time of crew load management are significantly improved, can dynamically optimize task execution sequence, real-time tracking task state, scientific allocation crew resource and intuitive display task priority, improve flight safety and operating efficiency.

Description

Technical Field

[0001] This application relates to the field of civil aviation flight management technology, and in particular to a method and system for crew load management during the approach phase of civil aviation based on dynamic mission optimization. Background Technology

[0002] In civil aviation, the approach phase is a critical and complex stage of flight. During this phase, the crew needs to handle multiple tasks simultaneously, including but not limited to communicating with air traffic control (ATC), monitoring flight instruments, adjusting flight attitude and speed, and preparing for landing. With the continuous increase in air traffic volume and the increasing complexity of the flight environment, the load faced by the crew during the approach phase is also increasing. Traditional crew load management methods have the following main problems: First, they use fixed task lists and preset priorities, which cannot be dynamically adjusted according to real-time changes in the flight environment; second, they lack effective mechanisms for tracking task status, making it difficult to accurately grasp the progress of each task; third, when multiple tasks compete for crew resources simultaneously, there is a lack of scientific decision-making basis to optimize the task execution order; finally, the task priority display method is simplistic, hindering the crew from quickly identifying critical tasks. Especially in the face of emergencies or urgent tasks, traditional methods may fail to adjust task priorities and execution order in a timely and effective manner, thereby increasing flight risks. These problems are particularly prominent under complex weather conditions or high-density airspace environments, affecting flight safety and operational efficiency.

[0003] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of this application is to provide a method and system for crew load management during the approach phase of civil aviation based on dynamic task optimization, which aims to improve flight safety and operational efficiency.

[0005] To achieve the above objectives, this application proposes a method for crew load management during the approach phase of civil aviation based on dynamic task optimization. The method includes: Flight data is acquired through airborne sensors, environmental data and ATC command data are acquired through external data sources, system status data is acquired through airborne systems, and crew operation data is acquired through cockpit operation logs. The acquired flight data, environmental data, ATC command data, system status data, and crew operation data are processed in real time for task identification to obtain a task list for the approach phase. The status of each task is tracked based on a state machine model to obtain the task status. The task list and task status are processed by influencing factor calculation to obtain multiple influencing factors for each task to be processed or in progress. The multiple influencing factors are then weighted and weighted averaged to obtain the real-time comprehensive urgency-importance score for each task. The real-time comprehensive urgency-importance scores of all tasks are subjected to sequence optimization processing, and the dependency constraints between tasks and the availability of crew resources are combined to obtain an optimized task execution sequence; The optimized task execution sequence is visualized through a cockpit display device to obtain a real-time task sequence view, and tasks of different priorities are distinguished by color coding and visual saliency based on the real-time comprehensive urgency-importance score.

[0006] In one embodiment, the task status includes started, in progress, completed, and timed out; the real-time task identification process includes: The task list is generated by adding new tasks through a preset standard task list and dynamic environment triggers. State tracking is performed on each task based on a state machine model to obtain the task states, including: The task status is updated using a state machine model. When a task starts, it is marked as in progress; when a task is completed, it is marked as finished; and when a predetermined time limit is exceeded, it is marked as timed out, thus generating the task status.

[0007] In one embodiment, the influencing factors include urgency factors, importance factors, coupling factors, resource demand factors, and environmental interference factors; the task list and task status are processed by influencing factor calculation to obtain multiple influencing factors for each task to be processed or in progress, including: The urgency factor is calculated by considering the remaining time margin of the task, the relevant alarm levels, and the task deadline requirements. Importance factors are calculated by weighting the impact of missions on flight safety, regulatory compliance, and operational efficiency. The coupling factor is calculated based on whether the task is a prerequisite for other tasks. The resource requirement factor is calculated by estimating crew attention duration and task complexity. The environmental disturbance factor is calculated based on the current meteorological conditions and airspace complexity.

[0008] In one embodiment, weighting and averaging the multiple influencing factors to obtain a real-time comprehensive urgency-importance score for each task includes: Analyze the range of data values ​​for multiple influencing factors and assign weights based on the range of data values; the influencing factors with a larger range of data values ​​are given higher weights. All influencing factors are processed using a weighted average calculation to obtain the real-time comprehensive urgency-importance score.

[0009] In one embodiment, performing sequence optimization processing on the real-time integrated urgency-importance scores of all tasks includes: Based on the real-time comprehensive urgency-importance score, all tasks are sorted in descending order to generate a preliminary sequence; Heuristic rules are used to handle dependency constraints between tasks, ensuring the correct logical order of dependent tasks and adjusting the initial sequence. When multiple high-priority tasks in the initial sequence compete for the same unit resources, the resource allocation is arbitrated through an intelligent negotiation mechanism to generate the final optimized task execution sequence.

[0010] In one embodiment, the dependency constraints between tasks processed based on heuristic rules include: Check the prerequisite conditions for each task; subsequent tasks can only be added to the sequence once the prerequisite task is completed. By maintaining the dependencies between tasks in the initial sequence, the correct logical order can be ensured.

[0011] In one embodiment, arbitrating resource allocation through an intelligent negotiation mechanism to generate the final optimized task execution sequence includes: Priorities are assigned based on mission type, with missions assigned to the primary pilot as first priority and missions assigned to the co-pilot as second priority. Based on the first priority and the second priority, a resource allocation suggestion scheme is generated, which includes the primary and secondary driver task allocation rules and conflict resolution rules; The sequence optimization process is completed by adjusting the order of tasks competing for the same unit resources by applying the resource allocation suggestion scheme.

[0012] In one embodiment, visualizing the optimized task execution sequence via a cockpit display device includes: Create dynamic views using the cockpit's main flight display or multi-function display; The task with the highest urgency-importance score is identified by the first color, the task with the highest score is identified by the second color, the task with the middle score is identified by the third color, and the task with the lowest score is identified by the fourth color. Highlight high-priority tasks by increasing icon size or centering them.

[0013] In one embodiment, the method further includes: Continuously monitor the changes in the flight data, environmental data, ATC command data, system status data, crew operation data, and mission status; When a change in the state of at least one of them is detected, the process returns to the step of performing real-time task identification processing on the acquired flight data, environmental data, ATC command data, system status data, and crew operation data to obtain a task list for the approach phase, and tracking the state of each task based on a state machine model to obtain the task state.

[0014] Furthermore, to achieve the above objectives, this application also proposes a civil aviation approach phase crew load management system based on dynamic task optimization. The civil aviation approach phase crew load management system based on dynamic task optimization includes: a memory, a processor, and a civil aviation approach phase crew load management program based on dynamic task optimization stored in the memory and executable on the processor. The civil aviation approach phase crew load management program based on dynamic task optimization is configured to implement the steps of the civil aviation approach phase crew load management method based on dynamic task optimization.

[0015] The proposed method and system for crew load management during the approach phase of civil aviation, based on dynamic mission optimization, achieves adaptive scheduling and resource allocation of missions during the approach phase through real-time data fusion, dynamic mission status tracking, multi-dimensional influencing factor quantification, and sequence optimization mechanisms. This significantly improves the accuracy and real-time performance of crew load management, dynamically optimizes mission execution sequences, tracks mission status in real time, scientifically allocates crew resources, and intuitively displays mission priorities, thereby enhancing flight safety and operational efficiency. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating an embodiment of the civil aviation approach phase crew load management method based on dynamic task optimization provided in this application. Figure 2 This is a schematic diagram of a structural embodiment of the civil aviation approach phase crew load management system based on dynamic task optimization, as provided in this application.

[0019] Explanation of icon numbers: 10. Memory; 20. Processor.

[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of this application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0022] It should be understood that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0023] In civil aviation, the approach phase is a critical and complex stage of flight. During this phase, the crew needs to handle multiple tasks simultaneously, including but not limited to communicating with air traffic control (ATC), monitoring flight instruments, adjusting flight attitude and speed, and preparing for landing. With the continuous increase in air traffic volume and the increasing complexity of the flight environment, the load faced by the crew during the approach phase is also increasing. Traditional crew load management methods have the following main problems: First, they use fixed task lists and preset priorities, which cannot be dynamically adjusted according to real-time changes in the flight environment; second, they lack effective mechanisms for tracking task status, making it difficult to accurately grasp the progress of each task; third, when multiple tasks compete for crew resources simultaneously, there is a lack of scientific decision-making basis to optimize the task execution order; finally, the task priority display method is simplistic, hindering the crew from quickly identifying critical tasks. Especially in the face of emergencies or urgent tasks, traditional methods may fail to adjust task priorities and execution order in a timely and effective manner, thereby increasing flight risks. These problems are particularly prominent under complex weather conditions or high-density airspace environments, affecting flight safety and operational efficiency.

[0024] Based on this, embodiments of this application provide a method for crew load management during the approach phase of civil aviation based on dynamic task optimization, referring to... Figure 1 The aforementioned method for crew load management during the approach phase of civil aviation based on dynamic task optimization includes steps S100 to S500, wherein: Step S100: Acquire flight data through airborne sensors, acquire environmental data and ATC command data through external data sources, acquire system status data through airborne systems, and acquire crew operation data through cockpit operation records; Step S200: The acquired flight data, environmental data, ATC command data, system status data and crew operation data are processed for real-time task identification to obtain a task list for the approach phase, and the status of each task is tracked based on the state machine model to obtain the task status. Step S300: Perform impact factor calculation on the task list and task status to obtain multiple impact factors for each task to be processed or in progress, and perform weight allocation and weighted average processing on the multiple impact factors to obtain the real-time comprehensive urgency-importance score of each task. Step S400: Perform sequence optimization processing on the real-time comprehensive urgency-importance scores of all tasks, and combine the dependency constraints between tasks and the availability of unit resources to obtain an optimized task execution sequence; Step S500: The optimized task execution sequence is visualized through the cockpit display device to obtain a real-time task sequence view, and tasks of different priorities are distinguished by color coding and visual saliency based on the real-time comprehensive urgency-importance score.

[0025] In this embodiment, real-time task identification processing is a process of dynamically generating a task list based on current flight conditions. For example, a task list can be generated by matching the current flight situation with a preset rule base, or the task list can be supplemented by manually inputting key task information, mainly to achieve dynamic updates of the task list. Furthermore, the state machine model can be implemented in the form of a finite state automaton, where changes in task state are triggered by events, such as updating the task state when a specific signal is received, to ensure that the task state reflects the actual situation in a timely manner. Influence factor calculation processing can be implemented in various ways, such as setting initial weights for each factor based on expert experience, or determining the base values ​​of factors through historical data analysis, mainly to quantify the urgency and importance of the task. Weight allocation and weighted average processing can adopt a fixed weight allocation scheme, such as pre-setting weight ratios according to task type, or dynamically adjusting weights through a linear regression model, to improve the adaptability of the comprehensive urgency-importance score. Sequence optimization processing can be implemented using heuristic algorithms, such as generating a preliminary sequence based on a greedy algorithm, or optimizing the sequence through simulated annealing, mainly to coordinate the dependencies and resource competition between tasks. Visualization can be achieved in various ways, such as using icon size to distinguish task priorities on cockpit displays, or highlighting high-priority tasks through flashing effects, with the aim of reducing the cognitive load on the crew.

[0026] In this embodiment, this application achieves real-time optimized management of crew load by integrating multi-source dynamic data, effectively overcoming the shortcomings of traditional fixed task lists that cannot adapt to the complex changes during the approach phase. Specifically, it acquires multi-dimensional information such as flight data through airborne sensors, comprehensively perceives the current situation based on real-time flight status, environmental conditions, air traffic control instructions, system operation, and crew operation behavior, freeing task identification from the constraints of preset rules, enabling it to respond to emergencies and dynamically generate task lists. Furthermore, it accurately tracks task states based on a state machine model, and combines influencing factor calculation and sequence optimization to ensure that high-priority tasks are executed first and their logical order conforms to operational specifications, thereby resolving resource allocation conflicts when multiple tasks are concurrent.

[0027] In this embodiment, multi-dimensional dynamic data is collected and integrated in real time through airborne sensors, external data sources, airborne systems, and cockpit operation records. This includes flight data, environmental data, ATC command data, system status data, and crew operation data. The comprehensive acquisition of this data provides fundamental support for subsequent task identification, ensuring that a task list for the approach phase can be dynamically generated based on the current flight situation. Furthermore, each task is tracked based on a state machine model, with task states including initiation, in progress, completion, and timeout, thereby achieving precise monitoring of the task execution process. Specifically, a task is marked as in progress when it is initiated, as completed when it is finished, and as timeout when it exceeds a predetermined time limit, thus ensuring the high timeliness of task status information.

[0028] In this embodiment, based on the task list and task status, multiple influencing factors are calculated to quantify the urgency and importance of each pending or ongoing task. The calculation of these influencing factors covers multiple dimensions, including remaining time margin, safety impact weight, task coupling relationships, resource requirement assessment, and the degree of environmental interference. Furthermore, by analyzing the range of data value variations for each influencing factor and assigning weights accordingly, a weighted average is used to derive a real-time comprehensive urgency-importance score for each task. This process allows priority assessment to closely align with the dynamic needs of the approach phase, significantly improving the adaptability of priority determination.

[0029] In this embodiment, the real-time comprehensive urgency-importance score of all tasks is used for sequence optimization. Specifically, tasks are first sorted in descending order based on their scores to generate a preliminary sequence, which is then adjusted based on the dependencies between tasks and the availability of crew resources. For example, heuristic rules are used to check the conditions of preceding tasks, ensuring that subsequent tasks can only be added to the sequence after the preceding tasks are completed, thus guaranteeing the correctness of the logical order. Furthermore, when multiple high-priority tasks compete for the same crew resources, an intelligent negotiation mechanism arbitrates resource allocation, ultimately generating an optimized task execution sequence.

[0030] In this embodiment, the optimized task execution sequence is visualized through the cockpit display device. Based on a real-time comprehensive urgency-importance score, color coding and visual saliency design are used to distinguish tasks of different priorities. For example, the first color indicates the highest priority task, the second color indicates high priority tasks, the third color indicates medium priority tasks, and the fourth color indicates low priority tasks. Simultaneously, by increasing the icon size or adjusting the center position to highlight high priority tasks, the cognitive load on the crew is significantly reduced, and decision-making response is accelerated. Thus, precise control of crew load is achieved in a dynamic environment, effectively solving the problem that traditional methods cannot dynamically adapt to changes in the flight environment and adjust task priorities and execution sequences in real time.

[0031] In one feasible implementation, the task status includes start, in progress, completed, and timeout; the real-time task identification processing includes: adding new tasks through a preset standard task list and dynamic environment triggering to generate the task list; tracking the status of each task based on a state machine model to obtain the task status includes: updating the task status through the state machine model, marking it as in progress when the task starts, marking it as completed when the task is completed, and marking it as timeout when a predetermined time limit is exceeded, to generate the task status.

[0032] In this embodiment, mission status refers to the identifier of different execution stages of a mission during the approach phase. It can be implemented using four distinct states: Initiation, In Progress, Completed, and Timeout. Specifically, the Initiation state indicates that the mission has been triggered but has not yet started execution; the In Progress state indicates that the mission is being executed; the Completed state indicates that the mission has successfully ended; and the Timeout state indicates that the mission failed to complete within the predetermined time limit. Its purpose is to cover all stages of a mission from start to finish and effectively handle abnormal situations such as timeouts, thereby reducing the risk of mission omissions or delays due to unclear statuses. In practical applications, a pre-defined standard mission list refers to a predefined set of basic mission frameworks, which can be implemented using a standardized mission list in the flight operations manual. Its purpose is to ensure the stability of mission management. Dynamic environment triggering refers to dynamically adding new missions based on real-time changes in conditions during flight (such as ATC commands or system alarms). This can be implemented through sensor data monitoring or external input signal detection, aiming to enhance the flexibility of the mission list and enable it to adapt to unexpected needs during the approach phase. Furthermore, the state machine model is a structured state transition mechanism that can be implemented through event-driven state transition diagrams or rule tables, with the aim of ensuring the reliability and consistency of task state updates.

[0033] In this embodiment, the above-described scheme solves the ambiguity problem in task identification and status tracking by defining clear task states and introducing a state machine model, ensuring more accurate and timely task management. In real-time task identification and processing, a pre-set standard task list provides a stable basic task framework, while dynamic environment triggering allows new tasks to be added based on real-time changes during flight, making the task list both comprehensive and flexible. The task status tracking mechanism based on the state machine model ensures the accuracy and real-time nature of task status updates by driving state transitions through specific events. For example, when a task is initiated, the state machine model immediately marks it as in progress to prevent task delays from being tracked; when a task is completed, the state machine model accurately records its completion state; when a task exceeds a predetermined time limit, the state machine model promptly marks it as timed out to quickly handle delayed tasks. This precise state transition logic not only enhances the reliability of status tracking but also reduces the uncertainty of crew load management. Furthermore, the above scheme, combined with steps such as acquiring flight data through airborne sensors, acquiring environmental data and ATC command data through external data sources, further enhances the dynamic adaptability of task status tracking, thereby achieving more efficient task management and priority adjustment in complex flight environments.

[0034] In one feasible implementation, the influencing factors include urgency factors, importance factors, coupling factors, resource demand factors, and environmental interference factors. The task list and task status are processed to calculate the influencing factors to obtain multiple influencing factors for each pending or ongoing task, including: calculating the urgency factor based on the remaining time margin, relevant alarm levels, and task deadlines; calculating the importance factor based on the weight of the task's impact on flight safety, regulatory compliance, and operational efficiency; calculating the coupling factor based on whether the task is a prerequisite for other tasks; calculating the resource demand factor based on the estimated crew attention duration and task complexity; and calculating the environmental interference factor based on current weather conditions and airspace complexity.

[0035] In this embodiment, the urgency factor refers to a quantitative indicator reflecting the urgency of the task. It can be comprehensively evaluated using parameters such as the remaining time margin, relevant alarm levels, and task deadlines. The remaining time margin can be implemented using a countdown mechanism, the relevant alarm levels can be reflected through a tiered alarm system, and the task deadlines can be dynamically adjusted based on preset time thresholds. The purpose is to ensure that the time pressure of the task can be accurately quantified. The importance factor refers to a key indicator measuring the core value of the task. It can be calculated by weighting the impact of the task on flight safety, regulatory compliance, and operational efficiency. For example, the impact weight on flight safety can be determined through a risk assessment model, regulatory compliance can be achieved through a compliance check algorithm, and operational efficiency can be obtained through statistical analysis of task completion efficiency. The purpose is to highlight the priority of the task in the overall flight process.

[0036] In this embodiment, the coupling factor is a quantitative indicator describing the dependency relationship between tasks. It can be implemented by logically determining whether a task is a prerequisite for other tasks. For example, using a task dependency graph or task association matrix, the sequential relationship between tasks can be clearly identified, aiming to avoid operational errors caused by incorrect task logical order. In practical applications, the resource requirement factor is a quantitative indicator reflecting the task's requirements for crew attention and operational complexity. It can be comprehensively evaluated by estimating crew attention duration and task complexity. For example, crew attention duration can be estimated using an attention allocation model, while task complexity can be achieved through task decomposition and difficulty scoring mechanisms, aiming to optimize the rational allocation of crew resources. The environmental interference factor is an indicator that quantifies the degree of interference of the external environment on task execution. It can be calculated using current meteorological conditions and airspace complexity. For example, meteorological conditions can be obtained through a real-time meteorological data interface, while airspace complexity can be analyzed through an airspace traffic monitoring system, aiming to dynamically integrate the impact of external environmental factors on task priority.

[0037] In this embodiment, the above scheme solves the problems of incomplete and inaccurate impact factor calculation by defining specific impact factor types and their calculation methods, thereby improving the accuracy and dynamic adaptability of priority assessment. Five impact factors cover different dimensions of the task, including time pressure, safety compliance, task dependency, resource consumption, and environmental factors, ensuring comprehensive calculation. Impact factor calculations are performed on the task list and task status, utilizing task information and status change data to make the calculations real-time and dynamic, avoiding the shortcomings of static assessment. Calculations are performed for tasks in specific states, ensuring the relevance and real-time nature of the assessment. Furthermore, urgency factors are calculated using remaining time margin, relevant alarm levels, and task deadlines to quantify the urgency of the task; importance factors are calculated using the impact weights of the task on flight safety, regulatory compliance, and operational efficiency to quantify the core value of the task; coupling factors are calculated using whether the task is a prerequisite for other tasks to quantify the dependencies between tasks; resource demand factors are calculated using estimated crew attention duration and task complexity to quantify resource consumption; and environmental interference factors are calculated using current weather conditions and airspace complexity to quantify external interference. These steps together constitute a complete priority assessment system, which significantly improves the effectiveness of unit load management.

[0038] Based on this, the above scheme forms an organic whole with the task list generation, task status tracking, and subsequent weight allocation and weighted average processing during the approach phase. By introducing multiple influencing factors and combining them with real-time dynamic data, not only is the accuracy of task priority assessment improved, but the system's adaptability to complex flight environments is also enhanced, thereby effectively reducing flight risks.

[0039] In one feasible implementation, weighting and weighted averaging multiple influencing factors to obtain a real-time comprehensive urgency-importance score for each task includes: analyzing the range of data value changes of multiple influencing factors and assigning weights according to the range of data value changes, with influencing factors having a larger range of data value changes being given higher weights; and calculating and averaging all influencing factors to obtain the real-time comprehensive urgency-importance score.

[0040] In this embodiment, the range of data value variation refers to the difference between the maximum and minimum values ​​that a certain influencing factor may take in different tasks or time periods. This can be achieved using statistical analysis methods, such as calculating the standard deviation or range by collecting historical data, to reflect the volatility and dynamic characteristics of the influencing factor. Weight allocation refers to determining the relative importance of each influencing factor in the comprehensive calculation based on the range of data value variation. This can be achieved by converting the range of variation into weight values ​​using linear mapping functions or nonlinear functions, aiming to ensure that factors with larger fluctuations have a more significant impact on the final score.

[0041] In this embodiment, the above-mentioned scheme solves the problem that fixed weights cannot adapt to changes in the importance of different influencing factors by analyzing the range of data value changes of influencing factors and dynamically allocating weights. First, the system continuously monitors the actual data value of each influencing factor and calculates its range of change. Then, based on the size of the range of change, the system automatically adjusts the weight of each influencing factor; the larger the range of change, the higher the weight. On this basis, all influencing factors are processed by weighted average calculation to generate a real-time comprehensive urgency-importance score. This process ensures that the score can comprehensively reflect the urgency and importance of the task, thus providing a reliable basis for task priority ranking. In addition, this scheme, combined with the aforementioned influencing factor calculation and processing, further improves the dynamic adaptability and decision-making efficiency of unit load management. Thus, this scheme realizes dynamic weight allocation and comprehensive score calculation, optimizes the rationality of task priority, and effectively improves the unit's load management capability during the approach phase.

[0042] In one feasible implementation, the sequence optimization process for the real-time comprehensive urgency-importance scores of all tasks includes: sorting all tasks in descending order based on the real-time comprehensive urgency-importance scores to generate a preliminary sequence; processing the dependency constraints between tasks based on heuristic rules to ensure the correct logical order of dependent tasks and adjusting the preliminary sequence; when multiple high-priority tasks in the preliminary sequence compete for the same unit resources, arbitrating resource allocation through an intelligent negotiation mechanism to generate the final optimized task execution sequence.

[0043] In this embodiment, the real-time comprehensive urgency-importance score refers to a numerical value calculated based on the urgency and importance of a task. This score can be achieved using various algorithms to quantitatively evaluate the urgency and importance of tasks. Heuristic rules can be understood as a set of rules derived from experience or logic, which can determine the dependencies between tasks and adjust their order based on preset logical conditions. The intelligent negotiation mechanism is a decision-making method for resolving resource conflicts, which can be implemented using priority-based resource allocation strategies or multi-objective optimization algorithms. The purpose of introducing these features is to improve the rationality and feasibility of task execution sequences from three aspects: task sequencing, dependency handling, and resource conflict resolution.

[0044] In this embodiment, the technical solution first sorts all tasks in descending order based on real-time comprehensive urgency-importance scores, ensuring that high-priority tasks are given priority consideration, thus providing a basic framework for subsequent optimization. Based on this, heuristic rules are used to handle dependency constraints between tasks, such as checking whether prerequisite tasks have been completed and adjusting the initial sequence accordingly, thereby avoiding task execution errors caused by chaotic dependency relationships. Furthermore, when multiple high-priority tasks compete for the same crew resources, a smart negotiation mechanism is used to arbitrate resource allocation, for example, allocating priorities based on task type and generating resource allocation suggestions based on the responsibilities of the pilot and co-pilot, thus achieving fair and efficient arbitration under resource constraints. These steps work together to effectively solve the problems of neglected dependency constraints and resource conflicts that may exist in the task execution sequence. In addition, this solution, combined with the aforementioned steps of obtaining the task list, tracking task status, and calculating influencing factors, can achieve more efficient task management and resource allocation in dynamic flight environments, thereby significantly improving crew load management efficiency and flight safety. Thus, the above technical solutions not only improve the rationality of the mission execution sequence, but also enhance the system's adaptability in complex flight environments, providing reliable technical support for crew load management during the approach phase.

[0045] In one feasible implementation, handling the dependency constraints between tasks based on heuristic rules includes: checking the prerequisite conditions of each task, and only adding subsequent tasks to the sequence when the prerequisite task is completed; and ensuring the correct logical order by maintaining the pre- and post-delay dependencies between tasks in the initial sequence.

[0046] In this embodiment, prerequisite tasks refer to certain specific tasks that must be completed before the task execution process can proceed. In practical applications, a task dependency table can be established to check prerequisite tasks, ensuring that the preconditions for task execution are met and avoiding execution conflicts caused by logical breaks. The preliminary sequence can be understood as the task ranking result generated based on real-time comprehensive urgency-importance scores. This sequence maintains the dependencies between tasks, which can be achieved by setting dependency flags or dependency lists. The purpose is to dynamically adjust and maintain the logical connections between tasks, thus maintaining the coherence of the overall execution process.

[0047] In this embodiment, the scheme first checks the completion status of each task's prerequisite tasks to ensure that only tasks meeting the prerequisite conditions can enter the execution sequence. Based on this, the system continuously maintains the dependencies between tasks in the initial sequence, and automatically adjusts the order when it detects that the task order might violate the dependency logic. For example, if a high-priority task needs to be executed earlier but its prerequisite tasks are not completed, the system will temporarily keep it outside the sequence until the prerequisite tasks are completed. This mechanism, combined with task status tracking, forms a complete task management closed loop, which not only ensures the logical correctness of task execution but also improves the safety and reliability of flight operations, effectively solves the problem of incorrect task execution order, thereby significantly reducing flight risks and improving the accuracy and adaptability of crew load management.

[0048] In one feasible implementation, arbitrating resource allocation through an intelligent negotiation mechanism to generate the final optimized task execution sequence includes: assigning priorities by task type, marking tasks of the primary pilot's responsibility as first priority and tasks of the co-pilot's responsibility as second priority; generating a resource allocation proposal based on the first and second priorities, the proposal including primary and co-pilot task allocation rules and conflict resolution rules; and adjusting the order of tasks competing for the same crew resources by applying the resource allocation proposal to complete the sequence optimization process.

[0049] In this embodiment, the intelligent negotiation mechanism refers to a resource allocation method based on preset rules and dynamic adjustment strategies, which can be implemented by defining a clear priority framework and conflict resolution methods. Task type priority allocation can be understood as a process of classifying and labeling tasks according to their assigned responsibilities. For example, tasks involving core flight safety operations are assigned to the primary pilot's responsibilities and given first priority, while auxiliary or support tasks are assigned to the co-pilot's responsibilities and given second priority. The purpose is to ensure that task allocation complies with aviation operation standards and avoids resource conflicts caused by overlapping responsibilities.

[0050] In this embodiment, the resource allocation recommendation scheme refers to a structured framework that includes task allocation logic and conflict resolution principles. It can be implemented by formulating primary and secondary pilot task allocation rules and conflict resolution rules. For example, when multiple tasks compete for the same crew resources, the task execution order can be quickly determined based on priority rules, and resource allocation can be dynamically adjusted in conjunction with conflict resolution rules. Furthermore, the process of adjusting the task order using the resource allocation recommendation scheme can employ real-time calculation and feedback mechanisms. The purpose is to ensure that resource competition issues are resolved promptly, thereby improving the coordination and overall efficiency of task execution.

[0051] In this embodiment, the above-mentioned technical solution constructs a systematic resource allocation process centered on prioritizing task types. First, prioritizing tasks based on the responsibilities of the lead pilot and first officer ensures clear boundaries between duties, conforming to aviation operational standards and effectively reducing operational risks arising from confusing responsibilities. Second, the process of generating resource allocation recommendations incorporates a rule-based mechanism. By predefining task allocation logic and conflict resolution principles, the arbitration process gains objective grounds, significantly reducing the possibility of subjective intervention. Finally, during task sequence optimization, dynamically implementing resource allocation recommendations ensures priority for high-priority tasks while also accommodating the reasonable arrangement of low-priority tasks, thereby improving the overall smoothness and efficiency of task execution.

[0052] Building upon this foundation, the aforementioned technical solution forms a close technical link with the steps of acquiring mission status, calculating influencing factors, and performing sequence optimization. By combining real-time integrated urgency-importance scores and resource allocation recommendations, it is possible not only to dynamically adjust the mission execution sequence but also to quickly respond to unexpected mission demands in complex flight environments, thereby further enhancing the adaptability and reliability of crew load management. This technical solution effectively solves the problem of high-priority missions competing for the same crew resources, significantly improving the safety and efficiency of crew operations during the approach phase.

[0053] In one feasible implementation, visualizing the optimized task execution sequence through a cockpit display device includes: creating a dynamic view through the cockpit's main flight display or multi-function display; using a first color to identify the task with the highest urgency-importance score in real time, using a second color to identify tasks with high scores, using a third color to identify tasks with medium scores, and using a fourth color to identify tasks with low scores; and highlighting high-priority tasks by increasing the icon size or centering the position.

[0054] In this embodiment, the dynamic view refers to a graphical interface that can update in real time and reflect the current mission status and priority. It can be implemented using the existing main flight display or multi-function display in the cockpit, aiming to ensure that the crew can directly obtain mission information without switching operating interfaces. The first, second, third, and fourth colors are used to identify missions of different priorities. These colors can be selected based on actual needs, using high-contrast color combinations such as red, orange, yellow, and green to enhance visual differentiation. Furthermore, increasing the icon size or center position is a technique to attract attention by adjusting the salience of visual elements, aiming to quickly guide the crew to focus on high-priority missions, thereby reducing decision-making delays.

[0055] In this embodiment, the above-mentioned technical solution achieves an intuitive presentation of mission priorities through a dynamic view on the cockpit display device. First, the dynamic view created based on the main flight display or multi-function display can be seamlessly integrated into the crew's operating environment, ensuring that mission information and flight instrument data are updated synchronously. Second, by using color coding to visually map mission priorities, the crew can quickly identify the highest priority mission, reducing cognitive load. Simultaneously, by increasing the icon size or placing it in the center, the visual salience of high-priority missions is further enhanced, ensuring that the crew can quickly focus on critical missions under high-pressure conditions. This multi-layered visual optimization design not only improves the readability of the mission sequence but also effectively reduces the risk of misjudgment, providing reliable auxiliary support for the crew during complex approach phases. This enables the crew to quickly identify mission priorities in a dynamically changing flight environment, thereby optimizing decision-making efficiency and reducing operational risks.

[0056] In one feasible implementation, the method further includes: continuously monitoring the state changes of the flight data, the environmental data, the ATC command data, the system status data, the crew operation data, and the mission status; when a state change of at least one of them is detected, returning to the step of performing real-time mission identification processing on the acquired flight data, environmental data, ATC command data, system status data, and crew operation data to obtain a mission list for the approach phase, and performing state tracking on each mission based on a state machine model to obtain the mission status.

[0057] In this embodiment, continuous monitoring refers to the uninterrupted collection and analysis of changes in relevant data streams through sensors or data interfaces. This can be achieved using polling, event-driven, or hybrid mechanisms, with the aim of ensuring that the system can promptly capture any critical changes that may affect the task sequence. State changes can be understood as data values ​​exceeding preset thresholds, data type conversions, or task states switching from one mode to another. These changes can be detected using comparison algorithms, state transition graphs, or rule engines, with the aim of providing accurate triggering conditions to initiate subsequent processes.

[0058] In this embodiment, the scheme achieves comprehensive perception of flight data, environmental data, ATC command data, system status data, crew operation data, and mission status through a dynamic monitoring mechanism, thus laying the foundation for the system's adaptive capabilities. In actual operation, the monitoring module analyzes changes in each data source in real time. Once a change in key data or status is detected, the system automatically triggers a backtracking mechanism to re-execute the mission identification and status tracking steps. This not only ensures that the mission list and status are always generated based on the latest information but also enhances the overall scheme's flexibility and response speed through a feedback loop. Furthermore, this mechanism is closely integrated with the aforementioned mission identification, status tracking, and sequence optimization steps, forming a closed-loop control system. This effectively solves the optimization lag problem that may occur in dynamic flight environments, improves the efficiency and reliability of crew load management, and enables the system to maintain the real-time performance and accuracy of mission sequence optimization under complex and changing flight conditions, significantly reducing flight risks caused by outdated data or response delays.

[0059] In the embodiments of this application, the civil aviation approach phase crew load management method based on dynamic task optimization achieves adaptive scheduling and resource allocation of approach phase tasks through real-time data fusion, dynamic task status tracking, multi-dimensional influencing factor quantification, and sequence optimization mechanism. This significantly improves the accuracy and real-time performance of crew load management, and can dynamically optimize task execution sequences, track task status in real time, scientifically allocate crew resources, and intuitively display task priorities, thereby improving flight safety and operational efficiency.

[0060] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the civil aviation approach phase crew load management method based on dynamic task optimization. Any simple modifications based on this technical concept are within the scope of protection of this application.

[0061] This application also provides a civil aviation approach phase crew load management system based on dynamic task optimization, referenced... Figure 2 The civil aviation approach phase crew load management system based on dynamic task optimization includes: a memory 10, a processor 20, and a civil aviation approach phase crew load management program based on dynamic task optimization stored on the memory 10 and executable on the processor 20. The civil aviation approach phase crew load management program based on dynamic task optimization is configured to implement the steps of the civil aviation approach phase crew load management method based on dynamic task optimization.

[0062] The civil aviation approach phase crew load management system based on dynamic mission optimization provided in this application, employing the civil aviation approach phase crew load management method based on dynamic mission optimization in the above embodiments, can improve flight safety and operational efficiency. Compared with the prior art, the beneficial effects of the civil aviation approach phase crew load management system based on dynamic mission optimization provided in this application are the same as the beneficial effects of the civil aviation approach phase crew load management method based on dynamic mission optimization provided in the above embodiments, and other technical features in the civil aviation approach phase crew load management system based on dynamic mission optimization are the same as the features disclosed in the methods of the above embodiments, and will not be repeated here.

[0063] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0064] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. All equivalent structural transformations made under the technical concept of this application using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included within the scope of patent protection of this application.

Claims

1. A method for dynamic task optimization based civil aviation approach phase crew workload management, characterized in that, The method includes: Flight data is acquired through airborne sensors, environmental data and ATC command data are acquired through external data sources, system status data is acquired through airborne systems, and crew operation data is acquired through cockpit operation logs. The acquired flight data, environmental data, ATC command data, system status data, and crew operation data are processed in real time for task identification to obtain a task list for the approach phase. The status of each task is tracked based on a state machine model to obtain the task status. The task list and task status are processed by influencing factor calculation to obtain multiple influencing factors for each task to be processed or in progress. The multiple influencing factors are then weighted and weighted averaged to obtain the real-time comprehensive urgency-importance score for each task. The real-time comprehensive urgency-importance scores of all tasks are subjected to sequence optimization processing, and the dependency constraints between tasks and the availability of crew resources are combined to obtain an optimized task execution sequence; The optimized task execution sequence is visualized through a cockpit display device to obtain a real-time task sequence view, and tasks of different priorities are distinguished by color coding and visual saliency based on the real-time comprehensive urgency-importance score.

2. The method for crew load management during the approach phase of civil aviation based on dynamic task optimization as described in claim 1, characterized in that, The task status includes started, in progress, completed, and timed out; The real-time task identification and processing includes: The task list is generated by adding new tasks through a preset standard task list and dynamic environment triggers. State tracking is performed on each task based on a state machine model to obtain the task states, including: The task status is updated using a state machine model. When a task starts, it is marked as in progress; when a task is completed, it is marked as finished; and when a predetermined time limit is exceeded, it is marked as timed out, thus generating the task status.

3. The method for crew load management during the approach phase of civil aviation based on dynamic task optimization as described in claim 1, characterized in that, The influencing factors include urgency factors, importance factors, coupling factors, resource demand factors, and environmental interference factors; the task list and task status are processed to calculate the influencing factors to obtain multiple influencing factors for each task to be processed or in progress, including: The urgency factor is calculated by considering the remaining time margin of the task, the relevant alarm levels, and the task deadline requirements. Importance factors are calculated by weighting the impact of missions on flight safety, regulatory compliance, and operational efficiency. The coupling factor is calculated based on whether the task is a prerequisite for other tasks. The resource requirement factor is calculated by estimating crew attention duration and task complexity. The environmental disturbance factor is calculated based on the current meteorological conditions and airspace complexity.

4. The method for crew load management during the approach phase of civil aviation based on dynamic task optimization as described in claim 3, characterized in that, The multiple influencing factors are weighted and weighted averaged to obtain a real-time comprehensive urgency-importance score for each task, including: Analyze the range of data values ​​for multiple influencing factors and assign weights based on the range of data values; the influencing factors with a larger range of data values ​​are given higher weights. All influencing factors are processed using a weighted average calculation to obtain the real-time comprehensive urgency-importance score.

5. The method for crew load management during the approach phase of civil aviation based on dynamic task optimization as described in claim 1, characterized in that, The sequence optimization process for all tasks' real-time integrated urgency-importance scores includes: Based on the real-time comprehensive urgency-importance score, all tasks are sorted in descending order to generate a preliminary sequence; Heuristic rules are used to handle dependency constraints between tasks, ensuring the correct logical order of dependent tasks and adjusting the initial sequence. When multiple high-priority tasks in the initial sequence compete for the same unit resources, the resource allocation is arbitrated through an intelligent negotiation mechanism to generate the final optimized task execution sequence.

6. The method for crew load management during the approach phase of civil aviation based on dynamic task optimization as described in claim 5, characterized in that, Heuristic rule-based processing of dependency constraints between tasks includes: Check the prerequisite conditions for each task; subsequent tasks can only be added to the sequence once the prerequisite task is completed. By maintaining the dependencies between tasks in the initial sequence, the correct logical order can be ensured.

7. The method for crew load management during the approach phase of civil aviation based on dynamic task optimization as described in claim 5, characterized in that, The resource allocation is arbitrated through an intelligent negotiation mechanism to generate the final optimized task execution sequence, including: Priorities are assigned based on mission type, with missions assigned to the primary pilot as first priority and missions assigned to the co-pilot as second priority. Based on the first priority and the second priority, a resource allocation suggestion scheme is generated, which includes the primary and secondary driver task allocation rules and conflict resolution rules; The sequence optimization process is completed by adjusting the order of tasks competing for the same unit resources by applying the resource allocation suggestion scheme.

8. The method for crew load management during the approach phase of civil aviation based on dynamic task optimization as described in claim 1, characterized in that, Visualizing the optimized task execution sequence through a cockpit display device includes: Create dynamic views using the cockpit's main flight display or multi-function display; The task with the highest urgency-importance score is identified by the first color, the task with the highest score is identified by the second color, the task with the middle score is identified by the third color, and the task with the lowest score is identified by the fourth color. Highlight high-priority tasks by increasing icon size or centering them.

9. The method for crew load management during the approach phase of civil aviation based on dynamic task optimization as described in claim 1, characterized in that, The method further includes: Continuously monitor the changes in the flight data, environmental data, ATC command data, system status data, crew operation data, and mission status; When a change in the state of at least one of them is detected, the process returns to the step of performing real-time task identification processing on the acquired flight data, environmental data, ATC command data, system status data, and crew operation data to obtain a task list for the approach phase, and tracking the state of each task based on a state machine model to obtain the task state.

10. A crew load management system for civil aviation approach phase based on dynamic task optimization, characterized in that, The civil aviation approach phase crew load management system based on dynamic task optimization includes: a memory, a processor, and a civil aviation approach phase crew load management program based on dynamic task optimization stored in the memory and executable on the processor. The civil aviation approach phase crew load management program based on dynamic task optimization is configured to implement the steps of the civil aviation approach phase crew load management method based on dynamic task optimization as described in any one of claims 1 to 9.

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