【摘要】 结合某实际调水工程，建立了基于水力学原理的机理模型并率定了水力参数，根据系统布置参数、上游水库水位、沿线管道实测压力和流量、调流阀实测流量及开度等数据，建立了BP神经网络数据模型，并采用GA算法优化初始权值、偏置以提高数据模型精度；对比分析了两种模型计算得到的调流阀目标开度数据，并给出了两种模型的适用条件。结果表明：输入数据与训练数据的余弦相似度较高(0.95以上)时，数据模型的平均误差为0.58%,较机理模型降低了0.11个百分点，最大误差降低了0.46个百分点；然而余弦相似度小于0.95时，数据模型的最大误差可达9.02%,机理模型平均误差不超过1.13%,数据模型依赖于数据，在高余弦相似度条件下的计算速度和精度较高。更多还原

【Abstract】 For an actual water diversion project, a mechanistic model based on hydraulic principles was established, and hydraulic parameters were calibrated. Based on data such as system layout parameters, upstream reservoir’s water level, measured pressure and flow rate along the pipeline, and measured flow rate and opening of the regulating valve, a BP neural network data model was established, and GA was used to optimize the initial weights and biases to improve the accuracy of the data model. The target opening data of the regulating valve calculated by the two models were comparatively analyzed, and the applicable conditions of the two models were proposed. The results indicate that when the cosine similarity between the input data and the training data is high(≥ 0.95), the average error of the data model is 0.58%, representing a reduction of 0.11 percentage points compared with that of the mechanistic model, and the maximum error is reduced by 0.46 percentage points; however, when the cosine similarity is less than 0.95, the maximum error of the data model can reach 9.02%, while the average error of the mechanistic model does not exceed 1.13%. The data model depends on data and has high calculation speed and accuracy under conditions of high cosine similarity.更多还原