摘要：【中文】本发明公开了一种任务分配方法及装置，其中，该方法包括：获取多个待分配任务的任务信息和多个任务执行模块的信息；根据多个待分配任务的任务信息和多个任务执行模块的信息，分别确定每个任务执行模块与每个待分配任务之间的匹配度；将每个待分配任务分为多个子任务，将全部子任务按照子任务的预计完成时刻划分为多个子任务集；根据匹配度，分别对每个子任务集构建二分图模型；对二分图模型进行优化求解，得到任务分配的最优解；根据任务分配的最优解，将多个待分配任务分配给多个任务执行模块。本发明可以实现待分配任务的优化分配，提高任务分配的效率。 【EN】The invention discloses a task allocation method and a device, wherein the method comprises the following steps: task information of a plurality of tasks to be distributed and information of a plurality of task execution modules are obtained; respectively determining the matching degree between each task execution module and each task to be distributed according to the task information of the tasks to be distributed and the information of the task execution modules; dividing each task to be distributed into a plurality of subtasks, and dividing all subtasks into a plurality of subtask sets according to the predicted completion time of the subtasks; respectively constructing a bipartite graph model for each subtask set according to the matching degree; carrying out optimization solution on the bipartite graph model to obtain an optimal solution of task allocation; and distributing the plurality of tasks to be distributed to the plurality of task execution modules according to the optimal solution of task distribution. The invention can realize the optimized distribution of the tasks to be distributed and improve the efficiency of task distribution.

摘要：【中文】本发明公开了一种基于云边融合架构的小水电发电量预测系统及其实现方法，从外部系统采集所测区域的实时气象数据和小水电发电数据，通过VPN通道直接上送云计算服务器，通过互联网实现系统的人机界面；云计算服务器将气象数据和小水电发电数据传给边缘服务器储存，构建出小水电发电量预测模型，并利用该模型进行小水电发电量预测，然后将预测结果回传云计算服务器，完成了数据分析、挖掘、预测等系统核心分析功能，最终将预测、分析结果通过互联网传输到终端设备上展示。本发明是云计算及边缘计算技术的有机结合应用的典型案例，在提高系统可靠性的同时，有效的解决了电网敏感数据储存、传输、展示的问题。 【EN】The invention discloses a system for predicting the power generation capacity of a small hydropower station based on a cloud-edge fusion framework and an implementation method thereof.A real-time meteorological data and small hydropower station power generation data of a measured area are acquired from an external system, and are directly uploaded to a cloud computing server through a VPN (virtual private network) channel, and a human-computer interface of the system is implemented through the Internet; the cloud computing server transmits the meteorological data and the small hydropower generation data to the edge server for storage, a small hydropower generation prediction model is constructed, the model is used for small hydropower generation prediction, then the prediction result is transmitted back to the cloud computing server, the core analysis functions of the system such as data analysis, mining and prediction are completed, and the prediction and analysis results are transmitted to the terminal equipment through the internet for display. The invention is a typical case of organically combining cloud computing and edge computing technologies, and effectively solves the problems of storage, transmission and display of sensitive data of a power grid while improving the reliability of a system.

摘要：【中文】本发明公开了一种基于大数据驱动的小水电群发电量预测方法，针对小水电群点多面广、无序管理的现状，将其按照区域划分；获取各区域的历史气象数据和发电量数据，采用数据挖掘技术对坏数据进行预处理，然后再做归一化处理；利用两层长短期记忆神经网络和一层全连接神经网络，对历史气象数据和发电量数据进行训练，并以测试数据的发电量真实值与训练模型发电量预测值的均方根误差最小为优化目标进行优化计算，最终得到小水电群的发电量预测模型。本发明的方法可对小水电群发电量进行精准预测，实现了小水电站的综合有序管理，保证了电网的安全稳定运行。 【EN】The invention discloses a method for predicting the power generation quantity of a small hydropower station group based on big data driving, which is divided into areas according to the current situation of multi-surface, wide and unordered management of the small hydropower station group; acquiring historical meteorological data and generating capacity data of each area, preprocessing bad data by adopting a data mining technology, and then performing normalization processing; and training historical meteorological data and generated energy data by using two layers of long-short term memory neural networks and one layer of fully-connected neural network, and performing optimization calculation by taking the minimum root mean square error between the true generated energy value of the test data and the predicted generated energy value of the training model as an optimization target to finally obtain a generated energy prediction model of the small hydropower station group. The method can accurately predict the power generation quantity of the small hydropower station, realizes the comprehensive and ordered management of the small hydropower stations, and ensures the safe and stable operation of the power grid.

摘要：【中文】一种高温合金的锻造和热处理方法及其产品，属于合金锻造技术领域。合金组分按重量百分比计为：Mo：21‑24，Cr：7‑10，Wu：5‑8，Fe：≤2，Mn：≤0.8，Al：≤0.5，C：≤0.03，其余为Ni。优点在于，该方法制造的产品使用温度可以达到760℃，且在760℃下使用时，低膨胀，强度高，650℃高温拉伸状态下：抗拉强度(MPa)≥896，屈服强度(MPa)≥552，延伸率(％)≥13，断面收缩率(％)≥16，有效降低了合金在锻造过程中的开裂风险。 【EN】A forging and heat treatment method of high-temperature alloy and a product thereof belong to the technical field of alloy forging. The alloy comprises the following components in percentage by weight: mo: 21-24, Cr: 7-10, Wu: 5-8, Fe: 2, Mn: less than or equal to 0.8, Al: less than or equal to 0.5, C: less than or equal to 0.03, and the balance being Ni. The method has the advantages that the product manufactured by the method can reach 760 ℃, and when the product is used at 760 ℃, the product has low expansion and high strength, and when the product is used at 650 ℃, the product is stretched at the high temperature: the tensile strength (MPa) is not less than 896, the yield strength (MPa) is not less than 552, the elongation (%) is not less than 13, and the reduction of area (%) is not less than 16, thereby effectively reducing the cracking risk of the alloy in the forging process.