空中交通管制员的心理负荷对航空安全和效率具有重大影响，在航空流量日益增加和空域日趋复杂的背景下，迫切需要能准确对其进行预测的模型。但现有模型仍缺乏足够的预测效力，且多为数据驱动模型，缺乏普适性和诊断性，无法揭示管制员不同策略的影响和具体的干预过程。为解决以上问题，本研究提出一个新的理论模型。该模型以判断飞行器潜在冲突的关联复杂性网络作为管制员的信息空间，提出管制员采取“仔细确认”和“预防性干预”策略的前提条件和实施过程，并预测在采取“仔细确认”策略时，管制员心理负荷将主要由在网络中进行冲突探测的难度决定；而在采用“预防性干预”策略时，管制员的心理负荷将由按照“大度优先”原则进行“解网”的步骤决定，该步骤还可能受到飞行器注意显著性特征和操作性约束的共同影响。本研究计划通过访谈、参与观察、模拟机实验、生理测量和管制席位数据挖掘的方法验证并完善该理论，将其用于对实际数据的预测。

The workload of air traffic controllers plays a vital role in aviation safety and efficiency, as air traffic volume is growing rapidly and sectors are becoming more and more complex, an accurate model of controllers’ workload is in great need. However, current models (most of them are data-driven ones) still lack predictive power, as well as general applicability and diagonosticity, failing to consider the effect of controllers’ intervention strategy and analyze their actual intervention process. To solve these problems, this study proposes a theory-driven approach in which the relational complexity network is treated as the key information space for controllers. It identifies the forms and prerequisites of two major strategies in conflict resolution (careful-examination vs. defensive intervention). We hypothesized that when adopting a careful-examination strategy, controllers’ workload is predicted by the difficulty to detect conflict within the relational complexity network; when adopting a “defensive intervention” strategy, their workload, is however, predicted by the steps to disentangle the relational complexity network following a large-node-first principle, which can also be influenced attentional salient feature of aircraft and operational constraints. The study plans to use interview, observation, simulator experiments, psycho-physiological measurement and data-mining of real ATC data to validate the model in order to use it in practice.