A controller cbta evaluation system and method based on a multi-dimensional competency model

Abstract

The application provides a controller CBTA evaluation system and method based on a multi-dimensional competency model. A first air traffic control task is input by an input module. The first air traffic control task includes multiple task stages and multiple sets of task parameters. Each task stage corresponds to a set of task parameters. A loading module loads a multi-dimensional competency model. The multi-dimensional competency model includes multiple competency models. A data acquisition module is configured to obtain multiple sets of historical assessment data corresponding to multiple air traffic controllers according to the multiple task stages and the multiple sets of task parameters. An evaluation module determines multiple evaluation results according to the multiple task stages, the multiple sets of task parameters, the multi-dimensional competency model, and the multiple sets of historical assessment data. An output module selects a target evaluation result according to the multiple evaluation results and outputs a target air traffic controller corresponding to the target evaluation result. The corresponding controller can be dynamically matched based on the task itself, so that the controller CBTA evaluation is more intelligent.

Description

Technical Field

[0001] This application relates to the field of data processing technology for management purposes, specifically to a controller CBTA evaluation system and method based on a multidimensional competency model. Background Technology

[0002] Controller CBTA refers to Competency-Based Training and Assessment. In practice, CBTA divides pilot competence into technical and non-technical skills, which specifically cover nine dimensions: knowledge application, route management (manual and automated), communication, problem solving and decision-making, procedure execution, situational awareness, workload management, and teamwork.

[0003] Air traffic controller CBTA (Conditional Business Assessment) primarily focuses on enhancing the core competencies required for controllers in practical work, such as technical, procedural, and managerial skills, as well as non-technical skills like information gathering, analysis, integration, decision-making, and coping with psychological factors. Currently, CBTA evaluation for air traffic controllers is rather mechanical, especially for special missions. Air traffic controller assessments are simply based on CBTA results without considering the specific characteristics of the mission itself. This results in a lack of flexibility in CBTA evaluation and hinders the intelligent and flexible allocation of controllers. The question of how to dynamically match appropriate controllers based on the mission's specific circumstances, thereby making CBTA evaluation more intelligent, urgently needs to be addressed. Summary of the Invention

[0004] This application provides a controller CBTA evaluation system and method based on a multidimensional competency model. It can dynamically match the appropriate controllers based on the task itself, making the controller CBTA evaluation more intelligent. It can also recommend the appropriate controllers based on the task, further enhancing the value of the controller CBTA evaluation system.

[0005] In a first aspect, embodiments of this application provide a controller CBTA evaluation system based on a multidimensional competency model. The controller CBTA evaluation system based on the multidimensional competency model includes: an input module, a loading module, a data acquisition module, an evaluation module, and an output module, wherein... The input module is used to input a first air traffic control task, which includes multiple task stages and multiple sets of task parameters. Each task stage corresponds to a set of task parameters, and each task parameter includes: task scenario, task content, task importance, and task difficulty. The loading module is used to load the multidimensional competency model, which includes multiple competency models, with each dimension corresponding to one competency model. The data acquisition module is used to obtain multiple sets of historical assessment data corresponding to multiple air controllers based on the multiple task stages and the multiple sets of task parameters. Each set of historical assessment data corresponds to one air controller, and each set of historical assessment data includes multiple historical assessment datasets. Each historical assessment dataset corresponds to one task stage. The evaluation module is used to determine multiple evaluation results based on the multiple task stages, the multiple sets of task parameters, the multidimensional competency model, and the multiple sets of historical assessment data. The output module is used to select a target evaluation result based on the multiple evaluation results and output the target air controller corresponding to the target evaluation result.

[0006] Secondly, embodiments of this application provide a controller CBTA evaluation method based on a multidimensional competency model. The controller CBTA evaluation method based on the multidimensional competency model, applied to the controller CBTA evaluation system based on the multidimensional competency model, includes: an input module, a loading module, a data acquisition module, an evaluation module, and an output module. The controller CBTA evaluation method based on the multidimensional competency model includes: The first air traffic control task is input through the input module. The first air traffic control task includes multiple task stages and multiple sets of task parameters. Each task stage corresponds to a set of task parameters. Each task parameter includes: task scenario, task content, task importance, and task difficulty. The multidimensional competency model is loaded through the loading module. The multidimensional competency model includes multiple competency models, with each dimension corresponding to one competency model. The data acquisition module obtains multiple sets of historical assessment data corresponding to multiple air controllers based on the multiple task stages and the multiple sets of task parameters. Each set of historical assessment data corresponds to one air controller, and each set of historical assessment data includes multiple historical assessment datasets. Each historical assessment dataset corresponds to one task stage. The evaluation module determines multiple evaluation results based on the multiple task stages, the multiple sets of task parameters, the multidimensional competency model, and the multiple sets of historical assessment data. The output module selects the target evaluation result based on the multiple evaluation results and outputs the target air traffic controller corresponding to the target evaluation result.

[0007] The following are the specific beneficial effects of using the embodiments of this application: This application provides a controller CBTA evaluation system and method based on a multidimensional competency model. The controller CBTA evaluation system includes an input module, a loading module, a data acquisition module, an evaluation module, and an output module. First, the input module inputs a first air traffic control task, which includes multiple task stages and multiple sets of task parameters. Each task stage corresponds to a set of task parameters, and each task parameter includes: task scenario, task content, task importance, and task difficulty. The loading module loads a multidimensional competency model, which includes multiple competency models, with each dimension corresponding to one competency model. Then, the data acquisition module obtains multiple sets of historical assessment data corresponding to multiple air controllers based on the multiple task stages and multiple sets of task parameters. Each set of historical assessment data corresponds to one air controller and includes multiple historical assessment datasets. Each historical assessment dataset corresponds to one task stage and can be based on the task parameters. Each task phase is configured with corresponding historical assessment data to accurately evaluate competence in the corresponding dimensions of each task phase. Then, the evaluation module determines multiple evaluation results based on multiple task phases, multiple sets of task parameters, multi-dimensional competence models, and multiple sets of historical assessment data. It can adapt the corresponding assessment data to the corresponding dimensions of each task phase to facilitate accurate evaluation by phase and dimension, obtaining evaluation results for each phase. Then, the evaluation results of each phase are aggregated to obtain the evaluation result for each air traffic controller. That is, the task is phased and scenario-based, and the task is subdivided into phases so that each task phase can be adapted to the corresponding competence evaluation. Finally, the output module selects the target evaluation result based on multiple evaluation results and outputs the target air traffic controller corresponding to the target evaluation result. In this way, the appropriate controller can be dynamically matched based on the situation of the task itself, making the controller CBTA evaluation more intelligent. It can also recommend the corresponding controller based on the task, further enhancing the value of the controller CBTA evaluation system. Attached Figure Description

[0008] 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, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a schematic diagram of the structure of a controller CBTA evaluation system based on a multidimensional competency model provided in an embodiment of this application; Figure 2 This is a schematic diagram of the functional execution flow of a controller CBTA evaluation system based on a multidimensional competency model provided in an embodiment of this application; Figure 3 This is a flowchart illustrating a controller CBTA evaluation method based on a multidimensional competency model provided in an embodiment of this application. Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0010] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0011] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0012] It should be understood that the term "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this document indicates that the preceding and following related objects are in an "or" relationship. In the embodiments of this application, "multiple" refers to two or more.

[0013] In the embodiments of this application, "at least one item" or its similar expression refers to any combination of these items, including any combination of a single item or a plurality of items. "One or more" means one or more, while "multiple" means two or more. For example, "at least one item" of a, b, or c can represent the following seven cases: a, b, c; a and b; a and c; b and c; a, b, and c. Each of a, b, and c can be an element or a set containing one or more elements.

[0014] In this application, the term "connection" refers to various connection methods, such as direct connection or indirect connection, to achieve communication between devices. This application does not impose any limitations on this.

[0015] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0016] In this application embodiment, the multidimensional competency model can be understood as a system framework for assessing the key competencies required by an individual or organization in a specific field. This multidimensional competency model can comprehensively measure competency through multiple dimensions, helping to conduct personnel selection, performance appraisal, and training system design.

[0017] Please see Figure 1 , Figure 1 This is a schematic diagram of the structure of a controller CBTA evaluation system based on a multidimensional competency model provided in this application embodiment. The controller CBTA evaluation system based on a multidimensional competency model includes: an input module, a loading module, a data acquisition module, an evaluation module, and an output module. The input module is used to input a first air traffic control task, which includes multiple task stages and multiple sets of task parameters. Each task stage corresponds to a set of task parameters, and each task parameter includes: task scenario, task content, task importance, and task difficulty. The loading module is used to load the multidimensional competency model, which includes multiple competency models, with each dimension corresponding to one competency model. The data acquisition module is used to obtain multiple sets of historical assessment data corresponding to multiple air controllers based on the multiple task stages and the multiple sets of task parameters. Each set of historical assessment data corresponds to one air controller, and each set of historical assessment data includes multiple historical assessment datasets. Each historical assessment dataset corresponds to one task stage. The evaluation module is used to determine multiple evaluation results based on the multiple task stages, the multiple sets of task parameters, the multidimensional competency model, and the multiple sets of historical assessment data. The output module is used to select a target evaluation result based on the multiple evaluation results and output the target air controller corresponding to the target evaluation result.

[0018] The controller CBTA evaluation system based on the multidimensional competency model may include: an input module, a loading module, a data acquisition module, an evaluation module, and an output module. Each of the input module, loading module, data acquisition module, evaluation module, and output module may include a hardware module and / or a software module. The input module, loading module, data acquisition module, evaluation module, and output module may be communicatively connected and / or electrically connected.

[0019] The multidimensional competency model can include multiple competency models, with each dimension corresponding to a competency model. For example, the communication dimension corresponds to a competency model, and situational awareness corresponds to a competency model.

[0020] The input module can be used to implement input functions. For example, the input module can include a touch screen, keyboard, stylus, microphone, etc. Specifically, the input module can be used to input the first air traffic control task, which includes multiple task stages and multiple sets of task parameters. Each task stage corresponds to a set of task parameters, and each task parameter includes: task scenario, task content, task importance, and task difficulty. The first air traffic control task can be preset or set by the system default, or it can be set by the user based on the actual situation. For example, the user can generate the corresponding first air traffic control task based on voice description.

[0021] The mission scenario can be understood as the specific operational environment and process in which the aircraft is commanded and coordinated by the ground or air control center at each stage from takeoff to landing to ensure flight safety and improve airspace utilization efficiency. Examples include tower control scenarios, approach control scenarios, area control scenarios, surface control scenarios, flight plan formulation scenarios, real-time monitoring of flight status scenarios, and handling of emergencies.

[0022] The task content can be understood as what needs to be done in each task stage, how to do it, the task purpose, the task objective, etc.

[0023] Task importance can be understood as the degree of importance of a task, such as the level of importance of a task.

[0024] The difficulty of a task can be understood as the degree of difficulty of that task.

[0025] In this embodiment, tasks can be phased and scenario-based, and tasks can be subdivided into stages so that each task stage can be adapted to the corresponding competency assessment.

[0026] The loading module can be used to load multidimensional competency models. In specific implementation, the corresponding multidimensional competency model can be loaded based on the characteristics of the task.

[0027] Multiple air traffic controllers can be pre-set or defaulted to by the system, or they can be obtained based on the requirements parameters of the first air traffic control mission. The requirements parameters may include at least one of the following: air traffic controller experience requirements (operational experience, communication experience, number of missions performed, mission evaluation, etc.), mission scenario of the first air traffic control mission, mission content of the first air traffic control mission, mission importance of the first air traffic control mission, mission difficulty of the first air traffic control mission, etc., which are not limited here.

[0028] The data acquisition module can obtain multiple sets of historical assessment data for multiple air controllers based on multiple task stages and multiple sets of task parameters. Each set of historical assessment data corresponds to one air controller, and each set includes multiple historical assessment datasets. Each historical assessment dataset corresponds to one task stage, meaning that the historical assessment dataset for each task stage meets the task parameter requirements, such as task scenario requirements, task content requirements, task importance requirements, task difficulty requirements, etc. This allows for the configuration of corresponding historical assessment data for each task stage based on task parameters, enabling accurate evaluation of competence in corresponding dimensions for each task stage. For any given air controller, there may be overlap between historical assessment datasets from different task stages; alternatively, there may be no overlap. The data acquisition module may include a processor.

[0029] Each set of historical assessment data can meet certain conditions, which can be preset or defaulted to by the system. For example, these conditions can be time range conditions (such as a specified time period), geographical range conditions (such as a specified location range), and so on.

[0030] Each historical assessment dataset may include relevant assessment data for at least one dimension. The relevant assessment data may include at least one of the following: questionnaires, expert evaluations, test data, relevant videos, relevant audio recordings, relevant simulation operation records, etc., without limitation.

[0031] The evaluation module can determine multiple evaluation results based on multiple task stages, multiple sets of task parameters, multi-dimensional competency models, and multiple sets of historical assessment data. This means it can adapt the corresponding assessment data to the relevant dimensions of each task stage, enabling precise evaluation by stage and dimension, obtaining evaluation results for each stage, and then aggregating the evaluation results from all stages to obtain the evaluation result for each air traffic controller. The evaluation module may include a processor.

[0032] The output module can select a target evaluation result from multiple evaluation results and output the target air traffic controller corresponding to the target evaluation result. Alternatively, it can output the evaluation result of that target air traffic controller, or the evaluation result of each stage of the first air traffic control task corresponding to that target air traffic controller. The output module may include a display screen, a communication module, a projection device, etc.

[0033] The number of target air controllers can be one or more. A threshold can be set for each mission phase, or a threshold can be set for the entire mission. For example, for each mission phase, an evaluation result greater than the corresponding threshold can be determined first, and then an air controller with that evaluation result can be recommended. In this way, corresponding air controllers can be configured in stages, ensuring orderly cooperation among multiple air controllers and fully guaranteeing mission execution efficiency.

[0034] The first air traffic control mission may include simulated missions or actual missions, without limitation. The first air traffic control mission may include complex missions, special missions, or multi-phase missions.

[0035] In this embodiment, firstly, the input module inputs a first air traffic control task, which includes multiple task stages and multiple sets of task parameters. Each task stage corresponds to a set of task parameters, and each task parameter includes: task scenario, task content, task importance, and task difficulty. Then, the loading module loads a multi-dimensional competency model, which includes multiple competency models, with each dimension corresponding to one competency model. Next, the data acquisition module obtains multiple sets of historical assessment data corresponding to multiple air controllers based on the multiple task stages and multiple sets of task parameters. Each set of historical assessment data corresponds to one air controller, and each set of historical assessment data includes multiple historical assessment datasets. Each historical assessment dataset corresponds to one task stage. Corresponding historical assessment data can be configured for each task stage based on the task parameters to refine the competency of each task stage in corresponding dimensions. Next, the evaluation module determines multiple evaluation results based on multiple task stages, multiple sets of task parameters, a multi-dimensional competency model, and multiple sets of historical assessment data. It can adapt the corresponding assessment data to the corresponding dimensions of each task stage, so as to conduct accurate evaluation by stage and dimension and obtain the evaluation results of each stage. Then, the evaluation results of each stage are aggregated to obtain the evaluation results of each air traffic controller. That is, the task is staged and scenario-based, and the task is subdivided into stages so that each task stage can be adapted to the corresponding competency evaluation. Finally, the output module selects the target evaluation result based on multiple evaluation results and outputs the target air traffic controller corresponding to the target evaluation result. In this way, the appropriate controller can be dynamically matched based on the situation of the task itself, making the controller CBTA evaluation more intelligent. It can also recommend the corresponding controller based on the task, further enhancing the value of the controller CBTA evaluation system.

[0036] Optionally, the evaluation module determines multiple evaluation results based on the multiple task stages, the multiple sets of task parameters, the multidimensional competency model, and the multiple sets of historical assessment data. Specifically, the evaluation module is used for: Based on the multiple task stages and the multiple sets of task parameters, determine the dimension identifiers that need to be assessed for each task stage, and obtain multiple sets of dimension identifiers; Multiple weight sets are determined based on the multiple dimension identifier sets and the multiple sets of task parameters. Each task stage corresponds to a weight set, and each weight set includes at least one weight. Each weight corresponds to a dimension identifier. Based on the multiple dimensional identifier sets and a set of historical assessment data corresponding to the first air controller, the relevant assessment data for each dimension in each task phase is determined, resulting in multiple relevant assessment data and multiple index identifiers. Each relevant assessment data corresponds to an index identifier, which consists of a task phase identifier and a dimension identifier. The first air controller is any one of the multiple air controllers. Based on the multidimensional competency model, the multiple relevant assessment data, and the multiple index identifiers, multiple initial evaluation result sets are determined, with one initial evaluation result set corresponding to each task stage; Multiple stage evaluation results are determined based on the multiple weight sets and the multiple initial evaluation result sets, with each task stage corresponding to a stage evaluation result; The evaluation result of the first air controller is determined based on the evaluation results of the multiple stages.

[0037] Specifically, since the characteristics of each task stage are different, the dimensional identifiers to be assessed in each task stage can be determined based on the task parameters of each stage, resulting in multiple sets of dimensional identifiers. For example, a pre-stored mapping relationship between preset task parameters and the dimensional identifiers to be assessed can be used to determine the corresponding dimensional identifiers to be assessed for the task parameters of each task stage, resulting in multiple sets of dimensional identifiers. Dimensional identifiers are used to identify competency dimensions, and different dimensional identifiers correspond to different competency models.

[0038] The set of multiple dimension identifiers may include at least one dimension identifier, and each dimension identifier corresponds to a competency model.

[0039] Next, multiple weight sets can be determined based on multiple dimensional identifier sets and multiple sets of task parameters. That is, each task stage corresponds to one weight set, each weight set includes at least one weight, and each weight corresponds to one dimensional identifier. In other words, the number of weights corresponds to the number of dimensional identifiers in each task stage. The sum of all weights in each weight set is 1.

[0040] Next, taking the first air controller as an example, which can be any one of multiple air controllers, the relevant assessment data for each dimension in each task stage can be determined based on multiple dimensional identifier sets and a set of historical assessment data corresponding to the first air controller. This results in multiple relevant assessment data and multiple index identifiers. Each relevant assessment data corresponds to an index identifier, which consists of a task stage identifier and a dimension identifier. In other words, the assessment data can not only be divided into stages, but also classified accordingly based on the dimension identifiers of the competency model, so as to improve the accuracy and efficiency of the corresponding competency model evaluation.

[0041] The task phase identifier can be used to identify the task phase, and different task phases correspond to different task phase identifiers.

[0042] Then, multiple initial evaluation result sets can be determined based on the multidimensional competency model, multiple relevant assessment data, and multiple index identifiers. Each task stage corresponds to one initial evaluation result set. That is, the corresponding competency model can be scheduled for evaluation in stages and dimensions based on index identifiers to obtain the evaluation result of each dimension in each task stage. Then, multiple stage evaluation results can be determined based on multiple weight sets and multiple initial evaluation result sets. Each task stage corresponds to one stage evaluation result. That is, the evaluation results of each stage are integrated. Specifically, the weight set of each task stage can be weighted with the initial evaluation result set to obtain multiple stage evaluation results. Finally, the evaluation result of the first air controller can be determined based on the multiple stage evaluation results. For example, the evaluation results of multiple stages can be used as the evaluation result of the first air controller, or the evaluation results of multiple stages can be further integrated to obtain the evaluation result of the first air controller.

[0043] This example demonstrates that the embodiments of this application can adapt corresponding assessment data to the corresponding dimensions of each task stage, so as to conduct accurate evaluations in stages and dimensions, obtain evaluation results for each stage, and then aggregate the evaluation results of each stage to obtain the evaluation results for each air traffic controller. In other words, the task is staged and scenario-based, and the task is subdivided in stages so that each task stage can be adapted to the corresponding competency evaluation. This helps to dynamically match the corresponding controllers based on the task itself, making the controller CBTA evaluation more intelligent. It can also recommend the corresponding controllers based on the task, further enhancing the value of the controller CBTA evaluation system.

[0044] Optionally, in determining multiple initial evaluation result sets based on the multidimensional competency model, the multiple relevant assessment data, and the multiple index identifiers, the evaluation module is specifically used for: Based on the multiple index identifiers, configure the corresponding competency model for each of the multiple task stages to obtain the corresponding competency model for each task stage. The initial evaluation result set corresponding to each task stage is determined by using the corresponding competency model for each task stage, the multiple relevant assessment data, and the multiple index identifiers. The plurality of initial evaluation result sets are determined based on the initial evaluation result set corresponding to each task stage.

[0045] Specifically, since the index identifiers include dimension identifiers, a corresponding competency model can be configured for each task stage in multiple task stages based on multiple index identifiers, resulting in a competency model for each task stage. Then, using the competency model for each task stage, multiple relevant assessment data, and multiple index identifiers, the initial evaluation result set for each task stage can be determined. That is, each dimension identifier in each task stage corresponds to one evaluation result. By combining the initial evaluation result sets for each task stage, multiple initial evaluation result sets can be obtained, and each task stage can correspond to one initial evaluation result set. In this way, the corresponding assessment data can be adapted to the corresponding dimensions of each task stage, so as to conduct accurate evaluation by stage and dimension and obtain the evaluation results for each stage.

[0046] Optionally, in determining the evaluation result of the first air controller based on the evaluation results of the multiple stages, the evaluation module is specifically used for: Multiple weights are determined based on the multiple task stages and the multiple sets of task parameters; The evaluation result of the first air controller is determined based on the multiple weights and the multiple stage evaluation results.

[0047] In specific implementation, evaluation results from multiple stages can be integrated to obtain the evaluation result of the first air traffic controller. Specifically, multiple weights can be determined based on multiple task stages and multiple sets of task parameters. For example, initial weights can be determined based on the task importance of each task stage, thus obtaining multiple initial weights. Then, multiple weights can be determined based on these initial weights. Specifically, a preset mapping relationship between task importance and weights can be stored in advance. Based on this mapping relationship, the weight of each task stage in multiple task stages can be determined, resulting in multiple initial weights. The sum of these initial weights is then determined, resulting in a weight sum. The ratio between each initial weight and the weight sum is then determined, resulting in multiple weights. Finally, a weighted calculation can be performed based on the multiple weights and the evaluation results from multiple stages to obtain the evaluation result of the first air traffic controller. In this way, the evaluation results of each task stage can be integrated based on the characteristics of each task stage to facilitate the observation of the controller's overall competence.

[0048] Optionally, in determining multiple weights based on the multiple task stages and the multiple sets of task parameters, the evaluation module is specifically used for: Based on the multiple sets of task parameters, an importance evaluation value is determined, resulting in multiple importance evaluation values; Estimate the duration of each task stage in the multiple task stages to obtain multiple durations; Multiple optimization parameters are determined based on the multiple durations; The multiple weights are determined based on the multiple importance evaluation values ​​and the multiple optimization parameters.

[0049] Specifically, for example, a mapping relationship between the corresponding task parameter and the evaluation value can be pre-set for at least one task parameter in multiple sets of task parameters. Then, the corresponding evaluation value can be determined based on the mapping relationship. By integrating the evaluation values, multiple importance evaluation values ​​can be obtained. Each task stage corresponds to one importance evaluation value. Taking task importance as an example, the preset mapping relationship between task importance and evaluation value can be pre-stored. Based on the mapping relationship, the evaluation value corresponding to the task importance in each task stage can be determined, that is, multiple importance evaluation values ​​can be obtained. Of course, the duration corresponding to each task stage in multiple task stages can also be estimated to obtain multiple durations. Of course, the duration of each task stage can be pre-set or the system default.

[0050] Next, a pre-stored mapping relationship between preset durations and optimization parameters can be established. Based on this mapping relationship, multiple optimization parameters corresponding to multiple durations can be determined. The value range of the optimization parameters can be preset or set by system default. The optimization parameters can dynamically perturb the importance evaluation value. For example, the value range of the optimization parameters can be -0.04 to 0.04. Multiple importance evaluation values ​​can be optimized based on multiple optimization parameters to obtain multiple target importance evaluation values. For example, based on the optimization parameters, the corresponding importance evaluation value is perturbed, and the target importance evaluation value = (1 + optimization parameter) × importance evaluation value. Finally, multiple targets can be used to... The importance evaluation value is determined by multiple weights. First, the sum of the importance evaluation values ​​of multiple targets can be determined. Then, the ratio between the importance evaluation value of each target and the sum can be determined to obtain multiple weights. This is equivalent to normalizing the importance evaluation values ​​of multiple targets so that the sum of multiple weights is 1. In this way, based on the characteristics of each task stage, the corresponding importance can be initially determined. Then, by dynamically perturbing the duration of the task stage, the evaluation results of each task stage can be integrated to make the comprehensive evaluation more accurate, objective, and scientific, so as to more intelligently observe the controller's comprehensive competence.

[0051] Optionally, in obtaining multiple sets of historical assessment data corresponding to multiple air controllers based on the multiple task stages and the multiple sets of task parameters, the data acquisition unit is specifically used for: Based on the multiple task phases and the multiple sets of task parameters, the historical assessment dataset of the second air controller in each task phase is obtained, where the second air controller is any one of the multiple air controllers. Based on the historical assessment dataset of the second air controller in each mission phase, determine a set of historical assessment data corresponding to the second air controller.

[0052] The second air controller can be any one of multiple air controllers. The first air controller and the second air controller can be the same or different, and there is no restriction on this.

[0053] Specifically, the historical assessment datasets of the second air controller in each task stage can be obtained based on multiple task phases and multiple sets of task parameters. By integrating the historical assessment datasets of the second air controller in each task stage, a set of historical assessment data corresponding to the second air controller can be obtained. In this way, the corresponding assessment data can be obtained based on the task characteristics of each task stage, so as to accurately achieve competency evaluation in stages.

[0054] Optionally, in obtaining the historical assessment dataset of the second air controller in each mission phase based on the multiple mission phases and the multiple sets of mission parameters, the data acquisition unit is specifically used for: Obtain the first task stage and the first set of task parameters corresponding to the first task stage; the first task stage is any one of the plurality of task stages. Based on the first set of task parameters, retrieve the historical assessment dataset corresponding to the second air controller and the first task phase from the preset historical assessment database.

[0055] The preset historical assessment database can be set in advance or set by system default. The preset historical assessment database can include assessment data of different air traffic controllers. Each assessment data can correspond to task scenario tags, task content tags, task importance tags, task difficulty tags, etc.

[0056] Taking the first task phase as an example, which can be any one of multiple task phases, the first task phase and the first set of task parameters corresponding to it can be obtained. Then, based on the first set of task parameters, the historical assessment dataset corresponding to the first air controller and the first task phase can be obtained from the preset historical assessment database. For example, multiple sets of task parameters can be compared with the corresponding task tags in the preset historical assessment database, and then the preset historical assessment database corresponding to the successfully matched task can be obtained. Alternatively, only one or more task parameters can be compared. The corresponding assessment data can be obtained based on the task characteristics of each task phase, so as to accurately achieve competency evaluation in stages.

[0057] Optionally, in terms of loading the multidimensional competency model, the loading module is specifically used for: Based on the multiple sets of task parameters, the dimension identifiers that need to be assessed for the first air traffic control task are determined, resulting in multiple dimension identifiers. The multidimensional competency model is loaded based on the multiple dimensional identifiers.

[0058] Specifically, different task parameters characterize task requirements, that is, which dimensions of competence need to be assessed in the task. For example, the mapping relationship between preset task parameters and dimension identifiers can be stored in advance. Based on this mapping relationship, the corresponding dimension identifiers can be determined. That is, the dimension identifiers that need to be assessed in the first air traffic control task can be determined based on multiple sets of task parameters, resulting in multiple dimension identifiers. The multidimensional competency model is then loaded based on the multiple dimension identifiers. In this way, the multidimensional competency model can be dynamically loaded based on the task situation, which can ensure the loading efficiency of the multidimensional competency model.

[0059] For example, Figure 2 As shown, in the specific implementation, the process can begin with task input, where users can input the corresponding task; then model loading, where a model adapted to the task characteristics is loaded; next, data collection, where adaptive data collection is performed based on the task characteristics; then competency evaluation, where competency is evaluated in stages and dimensions; and finally, air controller recommendation, which recommends air controllers that are deeply adapted to the task.

[0060] Optionally, in selecting the target evaluation result based on the multiple evaluation results, the output module is specifically used for: Obtain the first environmental parameters corresponding to the first air traffic control mission; Based on the first environmental parameters, the multiple mission phases, and the multiple sets of mission parameters, at least one reference air traffic control mission is determined to correspond to the first air traffic control mission. Obtain the threshold corresponding to the at least one reference air traffic control task to obtain at least one threshold. The target threshold is determined based on the at least one threshold. The target evaluation result is determined based on the target threshold and the multiple evaluation results.

[0061] The first environmental parameter may include at least one of the following: weather (such as sunny, cloudy, visibility, rainfall, snowfall, etc.), location, temperature, humidity, atmospheric pressure, etc., without limitation.

[0062] Specifically, the first environmental parameters corresponding to the first air traffic control mission can be obtained. Then, big data can be used to match the corresponding historical air traffic control missions. Specifically, at least one reference air traffic control mission corresponding to the first air traffic control mission can be determined based on the first environmental parameters, multiple mission stages, and multiple sets of mission parameters. That is, based on big data and mission conditions, similar historical missions are dynamically adapted to obtain at least one reference air traffic control mission. Then, the threshold corresponding to the at least one reference air traffic control mission is obtained, resulting in at least one threshold. Each reference air traffic control mission corresponds to one threshold. Next, a target threshold can be determined based on the at least one threshold. For example, the average value of the at least one threshold can be determined and used as the target threshold. Alternatively, the maximum or minimum value among the at least one threshold can be used as the target threshold. Finally, a target evaluation result can be determined based on the target threshold and multiple evaluation results. For example, an evaluation result greater than the target threshold can be selected as the target evaluation result. In this way, the threshold can be dynamically configured based on historical mission conditions, making the recommendation of controllers based on missions more scientific.

[0063] Please see Figure 3 , Figure 3 This is a flowchart illustrating a controller CBTA evaluation method based on a multidimensional competency model, provided in an embodiment of this application. The controller CBTA evaluation method based on a multidimensional competency model, applied to the controller CBTA evaluation system based on the multidimensional competency model, includes: an input module, a loading module, a data acquisition module, an evaluation module, and an output module. This controller CBTA evaluation method based on a multidimensional competency model includes: S301, Input the first air traffic control task through the input module. The first air traffic control task includes multiple task stages and multiple sets of task parameters. Each task stage corresponds to a set of task parameters. Each task parameter includes: task scenario, task content, task importance, and task difficulty. S302, the multidimensional competency model is loaded through the loading module. The multidimensional competency model includes multiple competency models, with each dimension corresponding to one competency model. S303, the data acquisition module obtains multiple sets of historical assessment data corresponding to multiple air controllers based on the multiple task stages and the multiple sets of task parameters. Each set of historical assessment data corresponds to one air controller. Each set of historical assessment data includes multiple historical assessment datasets. Each historical assessment dataset corresponds to one task stage. S304, the evaluation module determines multiple evaluation results based on the multiple task stages, the multiple sets of task parameters, the multidimensional competency model, and the multiple sets of historical assessment data; S305, the output module selects the target evaluation result based on the multiple evaluation results and outputs the target air traffic controller corresponding to the target evaluation result.

[0064] The relevant descriptions of each of the above steps can be found in the corresponding descriptions in the above embodiments, and will not be repeated here.

[0065] Please see Figure 4 , Figure 4 This is a schematic diagram of the electronic device provided in this application. The electronic device may include: a processor 410, a communications interface 420, a memory 430, and a communication bus 440. The processor 410, communications interface 420, and memory 430 communicate with each other via the communication bus 440. A controller CBTA evaluation system based on a multidimensional competency model is applied to the electronic device. The controller CBTA evaluation system based on the multidimensional competency model includes: an input module, a loading module, a data acquisition module, an evaluation module, and an output module. The processor 410 can call logical instructions in the memory 430 to execute the aforementioned controller CBTA evaluation method based on the multidimensional competency model. Furthermore, the logical instructions in the memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0066] The electronic device may include any computer device with computing or data processing capabilities, such as servers, terminal devices, wearable devices, etc., without limitation.

[0067] This application also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the controller CBTA evaluation method based on the multidimensional competency model provided in the above embodiments.

[0068] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer device to perform some or all of the steps of any of the methods described in the above method embodiments. The computer program product may be a software installation package, and the computer device includes an electronic device.

[0069] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0070] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0071] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0072] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0073] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0074] If the aforementioned integrated units are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0075] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage device, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0076] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A controller CBTA evaluation system based on a multidimensional competency model, characterized in that, The controller CBTA evaluation system based on a multidimensional competency model includes: an input module, a loading module, a data acquisition module, an evaluation module, and an output module. The input module is used to input a first air traffic control task, which includes multiple task stages and multiple sets of task parameters. Each task stage corresponds to a set of task parameters, and each task parameter includes: task scenario, task content, task importance, and task difficulty. The loading module is used to load the multidimensional competency model, which includes multiple competency models, with each dimension corresponding to one competency model. The data acquisition module is used to obtain multiple sets of historical assessment data corresponding to multiple air controllers based on the multiple task stages and the multiple sets of task parameters. Each set of historical assessment data corresponds to one air controller, and each set of historical assessment data includes multiple historical assessment datasets. Each historical assessment dataset corresponds to one task stage. The evaluation module is used to determine multiple evaluation results based on the multiple task stages, the multiple sets of task parameters, the multidimensional competency model, and the multiple sets of historical assessment data. The output module is used to select a target evaluation result based on the multiple evaluation results and output the target air controller corresponding to the target evaluation result.

2. The controller CBTA evaluation system based on a multidimensional competency model as described in claim 1, characterized in that, The evaluation module determines multiple evaluation results based on the multiple task stages, the multiple sets of task parameters, the multidimensional competency model, and the multiple sets of historical assessment data. Specifically, the evaluation module is used for: Based on the multiple task stages and the multiple sets of task parameters, determine the dimension identifiers that need to be assessed for each task stage, and obtain multiple sets of dimension identifiers; Multiple weight sets are determined based on the multiple dimension identifier sets and the multiple sets of task parameters. Each task stage corresponds to a weight set, and each weight set includes at least one weight. Each weight corresponds to a dimension identifier. Based on the multiple dimensional identifier sets and a set of historical assessment data corresponding to the first air controller, the relevant assessment data for each dimension in each task phase is determined, resulting in multiple relevant assessment data and multiple index identifiers. Each relevant assessment data corresponds to an index identifier, which consists of a task phase identifier and a dimension identifier. The first air controller is any one of the multiple air controllers. Based on the multidimensional competency model, the multiple relevant assessment data, and the multiple index identifiers, multiple initial evaluation result sets are determined, with one initial evaluation result set corresponding to each task stage; Multiple stage evaluation results are determined based on the multiple weight sets and the multiple initial evaluation result sets, with each task stage corresponding to a stage evaluation result; The evaluation result of the first air controller is determined based on the evaluation results of the multiple stages.

3. The controller CBTA evaluation system based on a multidimensional competency model as described in claim 2, characterized in that, In determining multiple initial evaluation result sets based on the multidimensional competency model, the multiple relevant assessment data, and the multiple index identifiers, the evaluation module is specifically used for: Based on the multiple index identifiers, configure the corresponding competency model for each of the multiple task stages to obtain the corresponding competency model for each task stage. The initial evaluation result set corresponding to each task stage is determined by using the corresponding competency model for each task stage, the multiple relevant assessment data, and the multiple index identifiers. The plurality of initial evaluation result sets are determined based on the initial evaluation result set corresponding to each task stage.

4. The controller CBTA evaluation system based on a multidimensional competency model as described in claim 2 or 3, characterized in that, In determining the evaluation result of the first air controller based on the evaluation results of the multiple stages, the evaluation module is specifically used for: Multiple weights are determined based on the multiple task stages and the multiple sets of task parameters; The evaluation result of the first air controller is determined based on the multiple weights and the multiple stage evaluation results.

5. The controller CBTA evaluation system based on a multidimensional competency model as described in claim 4, characterized in that, In determining multiple weights based on the multiple task stages and the multiple sets of task parameters, the evaluation module is specifically used for: Based on the multiple sets of task parameters, an importance evaluation value is determined, resulting in multiple importance evaluation values; Estimate the duration of each task stage in the multiple task stages to obtain multiple durations; Multiple optimization parameters are determined based on the multiple durations; The multiple weights are determined based on the multiple importance evaluation values ​​and the multiple optimization parameters.

6. The controller CBTA evaluation system based on a multidimensional competency model as described in any one of claims 1-3, characterized in that, In the process of obtaining multiple sets of historical assessment data corresponding to multiple air controllers based on the multiple task stages and the multiple sets of task parameters, the data acquisition unit is specifically used for: Based on the multiple task phases and the multiple sets of task parameters, the historical assessment dataset of the second air controller in each task phase is obtained, where the second air controller is any one of the multiple air controllers. Based on the historical assessment dataset of the second air controller in each mission phase, determine a set of historical assessment data corresponding to the second air controller.

7. The controller CBTA evaluation system based on a multidimensional competency model as described in claim 6, characterized in that, In obtaining the historical assessment dataset of the second air controller in each mission phase based on the multiple mission phases and the multiple sets of mission parameters, the data acquisition unit is specifically used for: Obtain the first task stage and the first set of task parameters corresponding to the first task stage; the first task stage is any one of the plurality of task stages. Based on the first set of task parameters, retrieve the historical assessment dataset corresponding to the second air controller and the first task phase from the preset historical assessment database.

8. The controller CBTA evaluation system based on a multidimensional competency model as described in any one of claims 1-3, characterized in that, Regarding the loading of the multidimensional competency model, the loading module is specifically used for: Based on the multiple sets of task parameters, the dimension identifiers that need to be assessed for the first air traffic control task are determined, resulting in multiple dimension identifiers. The multidimensional competency model is loaded based on the multiple dimensional identifiers.

9. The controller CBTA evaluation system based on a multidimensional competency model as described in any one of claims 1-3, characterized in that, In selecting the target evaluation result based on the multiple evaluation results, the output module is specifically used for: Obtain the first environmental parameters corresponding to the first air traffic control mission; Based on the first environmental parameters, the multiple mission phases, and the multiple sets of mission parameters, at least one reference air traffic control mission is determined to correspond to the first air traffic control mission. Obtain the threshold corresponding to the at least one reference air traffic control task to obtain at least one threshold. The target threshold is determined based on the at least one threshold. The target evaluation result is determined based on the target threshold and the multiple evaluation results.

10. A controller CBTA evaluation method based on a multidimensional competency model, characterized in that, The controller CBTA evaluation method based on the multidimensional competency model, applied to the controller CBTA evaluation system based on the multidimensional competency model, includes: an input module, a loading module, a data acquisition module, an evaluation module, and an output module. The controller CBTA evaluation method based on the multidimensional competency model includes: The first air traffic control task is input through the input module. The first air traffic control task includes multiple task stages and multiple sets of task parameters. Each task stage corresponds to a set of task parameters. Each task parameter includes: task scenario, task content, task importance, and task difficulty. The multidimensional competency model is loaded through the loading module. The multidimensional competency model includes multiple competency models, with each dimension corresponding to one competency model. The data acquisition module obtains multiple sets of historical assessment data corresponding to multiple air controllers based on the multiple task stages and the multiple sets of task parameters. Each set of historical assessment data corresponds to one air controller, and each set of historical assessment data includes multiple historical assessment datasets. Each historical assessment dataset corresponds to one task stage. The evaluation module determines multiple evaluation results based on the multiple task stages, the multiple sets of task parameters, the multidimensional competency model, and the multiple sets of historical assessment data. The output module selects the target evaluation result based on the multiple evaluation results and outputs the target air traffic controller corresponding to the target evaluation result.

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