Multi-dimensional core competency evaluation system for air traffic controller

Abstract

The invention discloses an air traffic controller multi-dimensional core competency evaluation system, and the system comprises a model management module which is used for loading and operating a stored competency evaluation model which is suitable for the multi-dimensional capability evaluation of an air traffic controller; the data processing module is used for acquiring and storing various data for competency evaluation calculation; the weight calculation module is used for calculating a subjective weight and an objective weight of each competency evaluation index based on the comparison data set and the objective performance scoring data set respectively; respectively calculating the combined weight of each competency evaluation index by taking the minimization of the deviation between the weight vectors as a target; the evaluation result generation module is used for calculating to obtain a quantitative evaluation result of the competency of the target controller; and the result output module is used for outputting the target controller information of which the quantitative evaluation result meets the competency requirement of the target task. The method has the advantages of being scientific and comprehensive in evaluation system, objective and quantitative in evaluation result, high in dynamic adaptability, easy to deploy and expand and the like.

Description

Technical Field

[0001] The present invention relates to the field of data processing methods and systems for management purposes, and in particular to a multi-dimensional core competency evaluation system for air traffic controllers. Background Art

[0002] Air traffic control (ATC) is a critical component of ensuring aviation safety, maintaining air traffic order, and improving operational efficiency. As the core of the ATC system, the competence of air traffic controllers (ATCs) is directly related to aviation safety and operational efficiency. Therefore, scientific, objective, and comprehensive competency assessments of ATCs are crucial, whether in initial training, qualification acquisition, on-the-job assessments, or selection and promotion. The International Civil Aviation Organization (ICAO), in its Doc 10056, "Manual on Competency-Based Training and Assessment for Air Traffic Controllers," advocates for a competency-based training and assessment approach, emphasizing the assessment of controllers' comprehensive capabilities demonstrated in real or simulated work scenarios.

[0003] However, the existing controller competency evaluation methods and systems still have some deficiencies in practice: 1. The evaluation dimensions are not comprehensive and systematic enough: Some existing evaluation methods may focus on the assessment of a single skill or knowledge point, making it difficult to fully cover the comprehensive ability requirements of air traffic controllers in complex operating environments. They lack systematic consideration of various ability elements and their internal connections, making it difficult to fully meet the requirements of ICAO CBTA.

[0004] 2. The evaluation process is highly subjective and lacks quantitative evidence: Traditional evaluation methods rely heavily on the personal experience and subjective judgment of evaluators (e.g., instructors and examiners). Scoring standards may not be standardized, resulting in inconsistent and poorly comparable evaluation results. Furthermore, there is a lack of effective quantitative methods to translate various evaluation indicators into measurable results, making it difficult to conduct accurate ability analysis and tracking.

[0005] 3. A single method for determining weights makes it difficult to balance subjective and objective factors: The importance of each indicator in the evaluation index system varies, and the setting of their weights directly affects the final evaluation results. Existing methods may rely solely on expert experience to set subjective weights (such as using the Analytic Hierarchy Process (AHP)) or rely solely on historical data to calculate objective weights (such as using the entropy weight method). This makes it difficult to effectively combine the guidance of expert experience with the authenticity of objective data, and may result in unscientific and unreasonable weight allocation.

[0006] 4. The evaluation system lacks adaptability and is difficult to adjust dynamically: The ATC operating environment, regulations, standards, and management policies are constantly changing. Existing evaluation systems are often static, making it difficult to dynamically adjust the weighting of evaluation indicators based on the latest industry standards, policy guidelines, or accumulated operational data. This can cause evaluation results to lag behind actual needs.

[0007] 5. Evaluation efficiency and data utilization need to be improved: Traditional evaluation processes can involve extensive manual recording, statistics, and analysis, resulting in low efficiency. Furthermore, accumulated evaluation data is not fully mined and utilized, making it difficult to form in-depth capability profiles and trend analysis of individuals or groups, and providing limited support for targeted training and management decision-making.

[0008] Therefore, there is an urgent need to develop a more scientific, objective, comprehensive and efficient multi-dimensional competency evaluation method for air traffic controllers to overcome the shortcomings of existing technologies. Summary of the Invention

[0009] In order to solve the problems existing in the above-mentioned prior art, the present invention provides the following technical solutions: The multi-dimensional core competency assessment system for air traffic controllers is deployed on a cloud platform and includes: A model management module, configured to load and run a stored competency evaluation model applicable to multi-dimensional competency assessment of air traffic controllers, wherein the competency evaluation model includes a competency evaluation indicator system determined based on industry requirements; a data processing module configured to acquire and store various data used for competency evaluation calculations via a real-time data communication link established with the terminal, the data including at least: a data set of comparisons of the relative importance of competency evaluation indicators by several experts, and a data set of objective performance scores of several air traffic controllers on various competency evaluation indicators; a weight calculation module, connected to the data processing module and the model management module, respectively, for calculating the subjective weight and the objective weight of each competency evaluation indicator based on the comparison data set and the objective performance score data set; and calculating the combined weight of each competency evaluation indicator based on the subjective weight and the objective weight, with the goal of minimizing the deviation between weight vectors; an evaluation result generating module, connected to the data processing module and the weight calculating module respectively, for calculating a quantitative evaluation result of the competence of the target air traffic controller based on the objective performance score data of the target air traffic controller and the combined weight; The result output module is connected to the evaluation result generation module and the terminal respectively, and is used to output the target controller information of whether the quantitative evaluation result meets the target task competency requirement.

[0010] Preferably, the competency evaluation index system includes a target layer, a criterion layer and an index layer corresponding to each other in sequence; The target layer is used to establish the macro-capability areas that controllers need to achieve in order to achieve comprehensive competence; The criterion layer is used to decompose the target layer into key capability dimensions for evaluation; The indicator layer is used to decompose the criterion layer into observable or measurable behavioral performance or work results; the indicator layer is the scoring basis of the objective performance scoring dataset.

[0011] Preferably, the method for the data processing module to obtain the comparison data set includes: It is used to obtain at least one expert through a real-time data communication link established with the terminal, compare the evaluation indicators at each level in the competency evaluation indicator system, and provide the comparison results according to a preset judgment scale.

[0012] Preferably, the method by which the weight calculation module calculates the subjective weight includes: A judgment matrix for each evaluation indicator in each level is constructed based on the comparison data set, and a normalized eigenvector corresponding to the maximum eigenroot of the judgment matrix is ​​calculated as the subjective weight of each evaluation indicator.

[0013] Preferably, the method by which the weight calculation module calculates the objective weight includes: The information entropy of each evaluation indicator of several controllers in the objective performance scoring data set is calculated, the difference coefficient of each evaluation indicator is calculated based on the information entropy, and the objective weight of each evaluation indicator is obtained after normalization.

[0014] Preferably, the method for calculating the combination weight by the weight calculation module includes: An objective function is constructed, wherein the objective function aims to minimize the comprehensive deviations between the combined weight vector and the subjective weight vector and the objective weight vector, respectively, and the combined weight is obtained by solving the objective function.

[0015] Preferably, the target controller information output by the result output module at least includes the controller's identifier and its corresponding quantitative evaluation result.

[0016] Beneficial effects 1. Scientific and Comprehensive Evaluation Framework: The system incorporates a multi-tiered competency evaluation indicator system based on industry requirements (such as ICAO Doc 10056), managed and applied by the Model Management Module. This structured evaluation framework ensures a systematic and comprehensive evaluation process, more accurately reflecting controllers' comprehensive competencies in complex work scenarios.

[0017] 2. Objective and quantified evaluation results: Through the collaborative work of its weight calculation module and combined weight calculation module, the system combines subjective weights based on expert experience with objective weights based on actual performance data. It then generates combined weights using a strategy that minimizes the deviation of weight vectors. This built-in weight fusion mechanism effectively balances subjective judgment and objective facts, reduces the one-sidedness of single-weight methods, and improves the scientific nature of weight allocation. This ensures that the quantitative evaluation results output by the evaluation result generation module are highly objective and credible, facilitating comparison and analysis.

[0018] 3. Strong Dynamic Adaptability: Leveraging its cloud-based deployment architecture, the system's weight calculation modules and combined weight calculation modules possess robust data processing and computational capabilities. This enables the system to automatically recalculate objective and even combined weights based on new data received by the data processing modules (such as updates to expert comparison data and accumulated data on air traffic controller performance ratings). As a result, the system can dynamically adapt to industry standards, policy changes, and accumulated data, maintaining the timeliness and effectiveness of its evaluation criteria.

[0019] 4. High evaluation efficiency and data utilization: The system significantly improves evaluation efficiency through its modular design and automated operational processes on a cloud platform (data collection, calculation, evaluation, and output are collaboratively performed by a data processing module, weight calculation modules, evaluation result generation module, and result output module). Furthermore, the system stores and analyzes quantitative evaluation results and related data, selectively outputting them through the result output module (e.g., outputting information on controllers that meet requirements). Its inherent data processing and output capabilities facilitate the in-depth mining and utilization of evaluation data, providing powerful automated data support for personalized training, precise selection, and management decision-making for controllers.

[0020] 5. Easy to deploy and expand: It is implemented on a cloud platform, which facilitates the deployment, maintenance and upgrade of the system. Users can easily access and use it through terminal devices. It has good scalability and can adapt to evaluation scenarios of different scales and needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a schematic diagram of the structure of a multi-dimensional core competency evaluation system for air traffic controllers provided in a preferred embodiment of the present invention; Figure 2 This is a schematic diagram of the competency evaluation indicator system architecture provided in another preferred embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention is further described below with reference to the accompanying drawings. In the description of the present invention, it should be understood that the terms "upper", "lower", "front", "back", "left", "right", "top", "bottom", "inner", "outer", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings and are only for the convenience of describing the present invention and simplifying the description. They do not indicate or imply that the devices or components referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, they should not be understood as limiting the present invention.

[0023] The present invention provides a multi-dimensional core competency evaluation system for air traffic controllers, which can be deployed and run in a system architecture that combines a typical cloud computing environment with a user terminal. The cloud platform in this architecture generally refers to a server cluster or virtualized resource pool with strong computing and storage capabilities that is deployed in a data center or uses public cloud / private cloud / hybrid cloud services. In the present invention, the cloud platform is the carrier and executor of the core logic of this system. It is responsible for deploying and running the core applications of this system, storing key data such as competency evaluation models, historical evaluation data, user data, etc., and performing complex computing tasks, such as calculating subjective weights, objective weights and final combined weights based on the received data, and finally generating a quantitative evaluation result for the controller. The use of the cloud platform enables this system to have good scalability, reliability and powerful data processing capabilities, and can support the needs of multi-user concurrent access and large-scale data calculation.

[0024] Corresponding to the cloud platform is the user terminal. The user terminal is the interface through which users interact with the system and can be a personal computer (PC), laptop, tablet, smartphone, or other device with network connection and data display capabilities. Different user roles (for example, evaluation experts, instructors / examiners, system administrators, trainee controllers, etc.) access the system deployed on the cloud platform through their respective terminal devices. Specifically, expert users can input comparative data on the relative importance of evaluation indicators (i.e., the comparative data set described in the claims) into the system through the terminal. Instructors or examiners can input objective performance scores of controllers in specific tasks or simulation assessments (i.e., the objective performance score data set described in the claims) into the system through the terminal. System administrators can perform system maintenance and user management through the terminal, and the evaluation results generated by the system can also be presented to relevant users through the terminal.

[0025] The cloud platform and each user terminal are connected via a network, which can be the Internet, a local area network (LAN), or other types of communications networks. This communication connection, built on these networks and ensuring timely and reliable data transmission, ensures that terminal input data is promptly received and processed by the cloud portion of the system, and that the results generated by the system are effectively fed back to the user terminal. This deployment method, in which the cloud platform is separated from the terminal and interconnected via a network, enables efficient and flexible operation of the system.

[0026] The model management module is configured to load and run a competency evaluation model stored on a cloud platform, which is suitable for evaluating the multi-dimensional capabilities of air traffic controllers; the competency evaluation model includes a competency evaluation indicator system determined based on industry requirements.

[0027] The model management module will load and activate the pre-configured and stored competency evaluation model by executing the specific application logic deployed on the cloud platform server. This model is a structured framework for describing, organizing and measuring the competencies required of air traffic controllers. It defines which competency dimensions need to be evaluated, the relationship between these dimensions, and how to measure these competencies. It includes an evaluation index system, a hierarchical relationship between indicators, and possible evaluation rules or algorithm frameworks. It converts the abstract concept of competency into a concrete structure that is operational and evaluable. It is the basic framework and calculation basis for the subsequent data collection, weight calculation, comprehensive evaluation and other operations of this system. This model is usually persistently stored in the database or file system of the cloud platform so that the model management module can call and run it at any time. It should be understood that the specific programming method for realizing the functions of this module is well known to those skilled in the art. Its specific programming process is not the focus of the present invention and will not be repeated here.

[0028] Industry requirements refer to the standards, regulations, guidelines, and best practices for air traffic control (ATC) qualifications, competencies, training, and assessment established by authoritative organizations or generally recognized by the industry. These requirements form the basis for evaluating air traffic controller competency within this system. Specifically, these requirements may include relevant documents published by the International Civil Aviation Organization (ICAO), such as Doc 10056, "Manual on Competency-Based Training and Assessment for Air Traffic Controllers," as well as laws, regulations, and advisory circulars issued by national or regional civil aviation authorities (such as China's CAAC, the US's FAA, and Europe's EASA) regarding air traffic controller licensing, training syllabi, operations manuals, and assessment standards. For example, the Civil Aviation Administration of China's "Guidelines for Competency-Based Training and Assessment (CBTA) Course Design" is available.

[0029] The competency evaluation index system is a set of interrelated evaluation indicators that are formed by breaking down the overall competency objectives layer by layer according to a certain logical structure, in order to systematically and specifically evaluate the comprehensive competency of air traffic controllers. It is usually presented as a multi-level tree structure.

[0030] In order to ensure the systematic, logical and operable nature of the evaluation, in some preferred embodiments, the competency evaluation index system used in this system is designed as a top-down, layer-by-layer hierarchical structure, such as Figure 2 As shown, it specifically includes the corresponding target layer, criterion layer and indicator layer.

[0031] The objective layer is used to define the key macro-competency areas required for a controller's overall competency. Its core function is to establish a number of independent or interrelated key competency dimensions or evaluation criteria, representing the main aspects of evaluating controller competency. For example, based on industry requirements such as ICAO Doc 10056, the criteria layer may include the following seven key competency domains: situational awareness, traffic and capacity management, separation and conflict resolution, communication, coordination, management of unusual situations, and problem solving and decision making.

[0032] The criterion layer systematically decomposes the target layer into a series of key capability dimensions that can be targeted for evaluation. For example, "situational awareness" is further decomposed into secondary elements such as "monitoring", "operational information integration and analysis", "risk identification", "use of tools", and "use of procedures".

[0033] The indicator layer further decomposes the criterion layer into a series of specific, directly observable or measurable behaviors or work results; it serves as the scoring basis for the objective performance score dataset. These indicators are the concrete manifestations of competency in actual work or simulated assessment scenarios, and they are the smallest units that evaluators can directly observe and score. Emphasizing observability or measurability is crucial, as only in this way can the objectivity and reliability of the evaluation be guaranteed. Therefore, the indicator layer constitutes the direct basis for collecting objective performance score data in the present invention's method. Instructors or examiners assess the controller's performance against each specific behavior or result listed in the indicator layer (e.g., using a five-point scale of 1-5), thereby forming the objective performance score dataset. For example, the aforementioned "monitoring" component includes "monitoring aircraft abnormal conditions," and the "operational information integration and analysis" components include "understanding flight dynamics" and "mastering all available information sources."

[0034] The data processing module is configured to receive and store various data used for competency evaluation calculations through a real-time data communication link established with the terminal, the data including at least: a comparison data set of the relative importance of competency evaluation indicators by several experts, and a data set of objective performance scores of several controllers on various competency evaluation indicators.

[0035] The data processing module utilizes a pre-established, stable, and secure real-time data communication link to actively or passively receive data from various user terminals. It then stores this data in the cloud platform's storage system (e.g., a database or distributed file system) for subsequent processing by other modules within the system. This real-time data communication link bridges information exchange between the cloud platform and the terminals, leveraging the internet or dedicated network technologies to ensure timely, accurate, and secure data transmission.

[0036] The data processing module receives various types of data, but for implementing the competency evaluation calculation of the present invention, it includes at least the following two core data sets: The first type is a comparative dataset. This dataset is derived from a number of recognized industry experts (such as experienced senior air traffic controllers, senior instructors, or air traffic management engineering experts). Using their respective operating terminals, these experts conduct pairwise comparative judgments based on pre-set rules and scales (such as the Saaty 1-9 scale) on the relative importance of indicators within the same hierarchy within the competency evaluation index system, or of lower-level indicators relative to higher-level criteria. These judgments are recorded in a structured manner to form a comparative dataset. For example, within the criterion of "situational awareness," the experts are required to determine whether the "monitoring" element or the "risk identification" element is more important, and to what extent. By collecting the comparative judgments of multiple experts, the subsequent calculation of subjective weights can incorporate collective wisdom, making them more representative and robust.

[0037] The second type is an objective performance rating dataset. This dataset originates from evaluators responsible for assessing air traffic controllers' actual performance (such as instructors, simulator examiners, or supervisors of on-the-job assessments). These evaluators observe the performance of several air traffic controllers (whether trainees or in-service personnel) in real-world scenarios, simulator training, or assessments. They then assign objective performance ratings item by item, comparing them to the indicator layers of the competency assessment system (i.e., observable and measurable behaviors or work outcomes). Scoring typically uses predefined criteria, such as the aforementioned five-point scale of 1 to 5 (1 representing unsafe consequences, 5 representing high-quality service and a significant improvement in safety margins). Each rating record is typically associated with a specific air traffic controller, a specific evaluation indicator, and the corresponding score. This dataset aggregates actual performance data on air traffic controllers across specific competency indicators.

[0038] The weight calculation module is configured to calculate the subjective weight and objective weight of each competency evaluation indicator based on the comparison data set and the objective performance score data set respectively; based on the subjective weight and the objective weight, with the goal of minimizing the deviation between weight vectors, calculate the combined weight of each competency evaluation indicator respectively.

[0039] The core task of this system's weight calculation module is to determine the importance of each evaluation indicator in the competency evaluation index system, that is, to calculate the weight. To achieve a more comprehensive and balanced evaluation, this invention innovatively adopts two weight calculation methods based on different perspectives and different data sources. The system performs these calculations in parallel or serially, resulting in two different but complementary weights: First, the weight calculation module calculates subjective weights based on the comparison dataset provided by the data processing module. This aims to quantify the consensus judgments of industry experts on the relative importance of each indicator in the evaluation index system. This module applies a specific algorithm to process the pairwise comparison data provided by these experts. In a typical preferred embodiment, the weight calculation module implements the subjective weight calculation method by constructing a judgment matrix for each evaluation indicator at each level based on the comparison dataset (if multiple experts make their judgments, data aggregation may be performed first, for example, by integrating the judgment matrix using the geometric mean method). The subjective weights are then calculated based on the normalized eigenvector corresponding to the maximum eigenroot of the judgment matrix as the subjective weight for each evaluation indicator. This normalized eigenvector represents the initial subjective weight for each evaluation indicator at that level. To ensure the consistency and logic of the expert judgments, the weight calculation module typically also performs a consistency check, such as calculating the consistency ratio (CR), to ensure the credibility of the judgment matrix. By repeating this process for all levels in the index system requiring weight determination, the system ultimately obtains a complete subjective weight system that reflects expert experience and prior knowledge.

[0040] Secondly, the weight calculation module calculates objective weights based on the objective performance score dataset provided by the data processing module. Unlike subjective weights, objective weight calculation is based on the objective performance score dataset. The purpose of objective weights is to explore the actual contribution or information content of each evaluation indicator in distinguishing air traffic controllers' performance levels based on actual observed air traffic controller performance data. The basic concept is: if all air traffic controllers have very similar scores on a certain indicator (whether high or low), then this indicator contributes less to distinguishing performance differences and should be assigned a lower objective weight. Conversely, if the scores on a certain indicator are widely distributed and vary significantly, it indicates that this indicator can effectively distinguish air traffic controllers of different levels and should be assigned a higher objective weight. In a preferred embodiment, the objective weight calculation method includes calculating the information entropy of each evaluation indicator for several air traffic controllers in the objective performance score dataset, calculating the variability coefficient for each evaluation indicator based on the information entropy, and obtaining the objective weight for each evaluation indicator after normalization. Specifically, the objective performance score dataset can be normalized (e.g., range normalization) to eliminate dimensionality effects. Lower information entropy indicates greater variability in the scoring data for that indicator and greater information content.

[0041] Finally, the weight calculation module calculates the combined weights for each competency evaluation indicator based on the subjective and objective weights, with the goal of minimizing the dispersion between the weight vectors. This combined weight design aims to overcome the potential one-sidedness of single-weight approaches and to develop a more scientific and balanced weight allocation scheme that simultaneously considers expert prior knowledge and objective data information. To achieve this goal, the present invention employs an optimization-based strategy: minimizing the combined dispersion between different weight vectors. Specifically, this involves constructing an objective function designed to minimize the combined dispersion between the combined weight vector and both the subjective and objective weight vectors, and solving the objective function to obtain the combined weights. A "weight vector" here refers to a vector composed sequentially of the same type of weights (subjective weights, objective weights, or a combination of weights to be determined) for all evaluation indicators. Minimizing dispersion means finding a set of combined weights that minimizes the sum (or some weighted combination) of the distances or differences between the combined weight vector and the subjective and objective weight vectors. This embodies the idea of ​​seeking consensus or the best compromise, ensuring that the final combined weights do not deviate excessively from expert judgment, nor are they completely dominated by data fluctuations, but rather reach the optimal balance between the two. It should be understood that in the process of solving the objective function, constraints should be introduced, for example, the sum of all combined weights must be 1, and each combined weight must be non-negative. The specific solution process can be solving a system of linear equations or executing other optimization algorithms. This part is not the focus of the present invention and will not be described in detail.

[0042] The evaluation result generating module is configured to calculate the quantitative evaluation result of the competence of the target controller based on the objective performance score data of the target controller and the combined weight.

[0043] The evaluation result generation module is the core component for the final quantitative assessment of the competencies of a specific target air traffic controller. It not only calculates a single overall score representing the target controller's overall competency but also, based on the hierarchical structure of the competency evaluation index system, calculates sub-scores for each criterion level (such as "situational awareness" and "communication and coordination"). This multi-dimensional quantitative result provides a more detailed picture of the target air traffic controller's competency, clearly identifying their strengths and weaknesses. This result is no longer a vague subjective judgment, but a specific numerical value or set of values ​​that objectively and accurately reflects the target controller's overall competency level based on actual performance data and scientifically weighted adjustments. This quantitative result forms the basis for subsequent competency comparison, ranking, diagnosis, and decision support.

[0044] The result output module is configured to output target controller information indicating whether the quantitative evaluation result meets the target task competency requirement.

[0045] The core functions of the result output module lie in result screening and information presentation. After the evaluation result generation module calculates the specific quantitative evaluation results for the target air traffic controller, the result output module does not simply list all the results, but instead conducts judgment and screening based on preset conditions. Specifically, the result output module accesses the pre-set or real-time configured target task competency requirements. This "requirement" can be a key benchmark or threshold that clearly defines the minimum competency standards that must be met to successfully perform a specific air traffic control task, position, or achieve a certain qualification level. This standard may be expressed as a minimum threshold for a comprehensive overall score or a more complex competency profile requirement, such as a specific score requirement in several key competency dimensions (such as safety awareness and emergency response). This requirement is typically set by management departments, training institutions, or based on industry regulations and stored on a cloud platform.

[0046] If a controller's evaluation results meet or exceed the requirement, the controller is deemed to have met the competency standard for the target task. For all controllers who meet the requirement, the result output module extracts and organizes their relevant target controller information. This information should at least include an identifier that uniquely identifies the controller (such as name, employee number, or trainee ID), but it often also includes a quantitative evaluation score, evaluation timestamp, or other relevant contextual information.

[0047] The result output module outputs the information of these screened target controllers who meet the competency requirements of the target tasks. The output can be in various forms, for example, generating a list or report of qualified personnel on the terminal interface of the authorized user, sending notifications or data interfaces to relevant managers or systems (such as human resources systems, training management systems), or presenting them in other ways that facilitate decision support. The significance of the result output module lies in that it converts the original evaluation data into information with direct application value, which can effectively help decision makers identify qualified controllers who meet the requirements of specific positions or tasks, thereby providing objective and reliable data support for management activities such as personnel selection, job allocation, training effect evaluation, and qualification certification, significantly improving the application efficiency of the evaluation results.

[0048] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the foregoing embodiments. The foregoing embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. The multi-dimensional core competency evaluation system for air traffic controllers is characterized by: The system is deployed on a cloud platform and includes: A model management module, configured to load and run a stored competency evaluation model applicable to multi-dimensional competency assessment of air traffic controllers, wherein the competency evaluation model includes a competency evaluation indicator system determined based on industry requirements; a data processing module configured to acquire and store various data used for competency evaluation calculations via a real-time data communication link established with the terminal, the data including at least: a data set of comparisons of the relative importance of competency evaluation indicators by several experts, and a data set of objective performance scores of several air traffic controllers on various competency evaluation indicators; a weight calculation module, connected to the data processing module and the model management module, respectively, for calculating the subjective weight and the objective weight of each competency evaluation indicator based on the comparison data set and the objective performance score data set; and calculating the combined weight of each competency evaluation indicator based on the subjective weight and the objective weight, with the goal of minimizing the deviation between weight vectors; an evaluation result generating module, connected to the data processing module and the weight calculating module respectively, for calculating a quantitative evaluation result of the competence of the target air traffic controller based on the objective performance score data of the target air traffic controller and the combined weight; The result output module is connected to the evaluation result generation module and the terminal respectively, and is used to output the target controller information of whether the quantitative evaluation result meets the target task competency requirement.

2. The multi-dimensional core competency assessment system for air traffic controllers according to claim 1, characterized in that: The competency evaluation index system includes a target layer, a criterion layer and an index layer corresponding to each other in sequence; The target layer is used to establish the macro-capability areas that controllers need to achieve in order to achieve comprehensive competence; The criterion layer is used to decompose the target layer into key capability dimensions for evaluation; The indicator layer is used to decompose the criterion layer into observable or measurable behavioral performance or work results; the indicator layer is the scoring basis of the objective performance scoring dataset.

3. The multi-dimensional competency evaluation method for air traffic controllers according to claim 1, characterized in that: The method for the data processing module to obtain the comparison data set includes: It is used to obtain at least one expert through a real-time data communication link established with the terminal, compare the evaluation indicators at each level in the competency evaluation indicator system, and provide the comparison results according to a preset judgment scale.

4. The multi-dimensional core competency evaluation system for air traffic controllers according to claim 3, characterized in that: The method for the weight calculation module to calculate the subjective weight includes: A judgment matrix for each evaluation indicator in each level is constructed based on the comparison data set, and a normalized eigenvector corresponding to the maximum eigenroot of the judgment matrix is ​​calculated as the subjective weight of each evaluation indicator.

5. The multi-dimensional core competency evaluation system for air traffic controllers according to claim 1, characterized in that: The method for the weight calculation module to calculate the objective weight includes: The information entropy of each evaluation indicator of several controllers in the objective performance scoring data set is calculated, the difference coefficient of each evaluation indicator is calculated based on the information entropy, and the objective weight of each evaluation indicator is obtained after normalization.

6. The multi-dimensional core competency evaluation system for air traffic controllers according to claim 1, characterized in that: The method for calculating the combination weight by the weight calculation module includes: An objective function is constructed, wherein the objective function aims to minimize the comprehensive deviations between the combined weight vector and the subjective weight vector and the objective weight vector, respectively, and the combined weight is obtained by solving the objective function.

7. The multi-dimensional core competency evaluation system for air traffic controllers according to claim 1, characterized in that: The target controller information output by the result output module at least includes the controller's identifier and its corresponding quantitative evaluation result.

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