Recently, sports training sessions have been generated automatically according to the TRIMP load quantifier that can be calculated easily using data obtained from mobile devices worn by an athlete during the session. This paper focuses on generating a sport training session in cycling, and bases on data obtained from power-meters that, nowadays, present unavoidable tools for cyclists. In line with this, the TSS load quantifier, based on power-meter data, was applied, while the training plan was constructed from a topology of already realized training sessions represented as a topological graph, where the edges in the graph are equipped with the real length, absolute ascent and average power needed for overcoming the path between incident nodes. The problem is defined as an optimization, where the optimal path between two user selected nodes is searched for, and solved with an Evolutionary Algorithm using variable length representation of individuals, an evaluation function inspired by the TSS quantifier, while the variation operators must be adjusted to work with the representation. The results, performed on an archive of sports training sessions by an amateur cyclist showed the suitability of the method also in practice.

最近，已经根据TRIMP负荷量词自动生成了运动训练课程，可以使用从运动员在运动过程中佩戴的移动设备获得的数据轻松地计算出该运动量。本文着重于在自行车运动中产生运动训练课程，并基于从功率计获得的数据，这些数据如今为骑自行车的人提供了不可避免的工具。与此相符，应用了基于功率计数据的TSS负载量化器，而训练计划是从已经实现的训练课程的拓扑结构（表示为拓扑图）构建的，其中图中的边缘配备了克服入射节点之间路径所需的实际长度，绝对上升和平均功率。问题定义为优化，在其中搜索两个用户选择的节点之间的最佳路径，并通过使用进化算法（使用可变长度的个体表示法）进行求解，该算法是受TSS量词启发的评估函数，同时必须调整变异算子以使用该表示法。业余骑车人在运动训练课的存档中执行的结果表明，该方法在实践中也适用。