摘要：【中文】本发明公开了一种丝绸坯布的精炼液，按质量份数计,由下列组分组成,碳酸钠3份、硅酸钠12份，脂肪酸聚氧乙烯酯15份、纯碱25份，连二亚硫酸钠8份，磷酸辛酯5份、保险粉5份，二甲基硅油4份，渗透剂3份，橙油1份，离子水150份。该丝绸坯布的精炼液方便精炼且有利于后道染色。 【EN】The invention discloses a refining liquid for silk grey cloth, which comprises, by mass, 3 parts of sodium carbonate, 12 parts of sodium silicate, 15 parts of polyoxyethylene fatty acid ester, 25 parts of soda ash, 8 parts of sodium hydrosulfite, 5 parts of octyl phosphate, 5 parts of sodium hydrosulfite, 4 parts of dimethyl silicone oil, 3 parts of a penetrating agent, 1 part of orange oil and 150 parts of ionized water. The refining liquid of the silk grey cloth is convenient to refine and is beneficial to the subsequent dyeing.

摘要：【中文】本发明公开了一种基于贝叶斯神经网络的光伏概率预测方法及系统，包括：获取待预测点的天气预报数据和光伏设备的历史出力数据；对所述天气预报数据进行降维处理，并基于降维处理后的天气预报数据和光伏设备的历史出力数据得到特征数据；将所述特征数据带入预先构建的改进型贝叶斯神经网络模型，得到待预测点的光伏出力分布。本发明获得了待预测点的光伏出力分布，且本发明提供的光伏概率预测方法和确定性预测方式相比在达到相同预测准确率时具有更小的平均区间宽度，提高了预测精度，对提升电网的安全稳定性具有重要意义。 【EN】The invention discloses a photovoltaic probability prediction method and a system based on a Bayesian neural network, which comprise the following steps: acquiring weather forecast data of a point to be predicted and historical output data of photovoltaic equipment; performing dimension reduction processing on the weather forecast data, and obtaining characteristic data based on the weather forecast data subjected to the dimension reduction processing and historical output data of the photovoltaic equipment; and substituting the characteristic data into a pre-constructed improved Bayesian neural network model to obtain the photovoltaic output distribution of the points to be predicted. The photovoltaic output distribution of the points to be predicted is obtained, and compared with a deterministic prediction mode, the photovoltaic probability prediction method provided by the invention has smaller average interval width when the same prediction accuracy is achieved, the prediction precision is improved, and the method has important significance for improving the safety and stability of a power grid.

摘要：【中文】一种水稻稻瘟病抗性基因Pi‑kf2(t)的特异性分子标记引物及应用，包括：序列表SEQ ID NO.1核苷酸序列的引物：SPikf‑F：AGTGGAAGCATATCTCTATCTCT；序列表SEQ ID NO.2核苷酸序列的引物：SPikf‑R：TCATCTCAGGTTAGCATGCG。采用本发明，能准确判断水稻育种材料中是否含有Pi‑kf2(t)基因，使用方法简单，效率高，可有效降低育种成本，同时在苗期即可对水稻育种材料是否含有稻瘟病抗性基因Pi‑kf2(t)进行准确高效的鉴定，有效解决了常规育种方法中存在的育种周期长、选择效率低、鉴定不准确和易受环境影响等问题，可明显加快育种进程，选择效率理论上达到100%的准确性，在实际应用中，检测一个含有Pi‑kf2(t)基因的F2分离群体，抗性和基因型表现完全一致，选择效率达到100%，在水稻分子标记辅助选择育种中发挥重要作用。 【EN】A specific molecular marker primer of a rice blast resistance gene Pi-kf2(t) and application thereof comprise: the primer of the nucleotide sequence of SEQ ID NO.1 of the sequence table: SPikf-F: AGTGGAAGCATATCTCTATCTCT, respectively; the primer of the nucleotide sequence of SEQ ID NO.2 of the sequence table: SPikf-R: TCATCTCAGGTTAGCATGCG are provided. The method can accurately judge whether the rice breeding material contains the Pi-kf2(t) gene, has simple use method and high efficiency, can effectively reduce the breeding cost, can accurately and efficiently identify whether the rice breeding material contains the rice blast resistance gene Pi-kf2(t) in the seedling stage, effectively solves the problems of long breeding period, low selection efficiency, inaccurate identification, easy environmental influence and the like in the conventional breeding method, can obviously accelerate the breeding process, has the selection efficiency theoretically reaching 100 percent accuracy, can detect an F2 segregation population containing the Pi-kf2(t) gene in practical application, has completely consistent resistance and genotype expression, has the selection efficiency reaching 100 percent, and plays an important role in rice molecular marker-assisted selection breeding.

摘要：【中文】本发明公开了考虑时空相关性的多维时序数据的聚合方法。本发明针对含风电、光伏发电的电力系统中多能源跨季互补年/月优化调度的问题，提出采用马尔可夫决策的方法优化出风电、光伏与负荷在不同初始状态组合下的最优动作策略。从而达到在该初始状态组合条件下所选的日场景组合在数值概率分布式上最接近原始时序数据。再采用马尔科夫蒙特卡洛方法抽样生成具有相关性的3×N的马尔可夫状态矩阵，将马尔科夫决策获得的最优策略与马尔可夫状态矩阵内的状态列向量组合相匹配，得到具有时空相关性的风电、光伏与负荷的聚合序列，得到符合目标地区风、光与负荷的典型功率场景，从而为系统优化调度上的年/月电量计划做指导。 【EN】The invention discloses a method for aggregating multidimensional time series data by considering space-time correlation. Aiming at the problem of multi-energy source season-crossing complementary year/month optimized scheduling in an electric power system containing wind power and photovoltaic power generation, the invention provides an optimal action strategy for optimizing wind power, photovoltaic and load under different initial state combinations by adopting a Markov decision method. So as to achieve that the selected day scene combination under the initial state combination condition is closest to the original time sequence data in a numerical probability distribution mode. And then, a Markov Monte Carlo method is adopted to generate a 3 XN Markov state matrix with correlation in a sampling mode, an optimal strategy obtained by Markov decision is matched with a state column vector combination in the Markov state matrix to obtain a wind power, photovoltaic and load aggregation sequence with space-time correlation, and a typical power scene conforming to wind, light and load in a target area is obtained, so that guidance is provided for a year/month electric quantity plan in system optimization scheduling.

摘要：【中文】本发明提供了一种多个风电场出力的时间序列构建方法及系统，包括：基于多个风电场的出力数据和预先构建的混合高斯隐马尔科夫模型得到多维混合高斯分布和累积状态转移概率矩阵；基于所述多维混合高斯分布和累积状态转移概率矩阵确定预设周期内每一时刻所述多个风电场的出力；由所述周期内所有时刻对应的多个风电场的出力构成所述多个风电场出力的时间序列。本发明采用混合高斯隐马尔科夫模型描述多个风电场之间的时空相关性，采用多维混合高斯分布描述多个风电场出力在不同相关性状态下的联合概率分布，通过蒙特卡洛仿真生成具有时空相关性的多风电场出力时间序列，提高了风电场出力时间序列的准确性。 【EN】The invention provides a method and a system for constructing a time sequence of output of a plurality of wind power plants, wherein the method comprises the following steps: obtaining multidimensional Gaussian mixture distribution and an accumulative state transition probability matrix based on output data of a plurality of wind power plants and a pre-constructed Gaussian mixture hidden Markov model; determining the output of the plurality of wind power plants at each moment in a preset period based on the multidimensional Gaussian mixture distribution and the cumulative state transition probability matrix; and the output of the plurality of wind power plants corresponding to all the moments in the period forms a time sequence of the output of the plurality of wind power plants. According to the method, the hybrid Gaussian hidden Markov model is adopted to describe the time-space correlation among the wind power plants, the multidimensional hybrid Gaussian distribution is adopted to describe the joint probability distribution of the output of the wind power plants in different correlation states, the Monte Carlo simulation is used for generating the multi-wind power plant output time sequence with the time-space correlation, and the accuracy of the wind power plant output time sequence is improved.

摘要：【中文】本发明涉及一种基于深度学习的多层次生物分子网络构建与优化方法，属于人工智能领域，包括步骤一：搭建软硬件环境；步骤二：多层次生物分子网络数据收集及预处理，初步建立多层次生物分子网络；步骤三：收集生物各层次生物分子特征数据并进行相应特征编码，分为训练集和测试集；步骤四：根据网络优化目标及现有特征，构建网络优化模型；步骤五：使用多层次生物分子网络数据以及处理好的各层次生物分子特征数据进行训练，求解模型中各层的参数，达到预期效果停止训练并保存各层参数；步骤六：将训练好的神经网络模型进行部署，用于多层次生物分子网络优化。本发明解决了现有生物分子网络可拓展性不强，对复杂生物系统的描述不够深入的问题。 【EN】The invention relates to a deep learning-based multi-level biomolecular network construction and optimization method, which belongs to the field of artificial intelligence and comprises the following steps: building a software and hardware environment; step two: collecting and preprocessing multi-level biomolecular network data, and initially establishing a multi-level biomolecular network; step three: collecting biological molecular characteristic data of each biological level, carrying out corresponding characteristic coding, and dividing the data into a training set and a test set; step four: constructing a network optimization model according to the network optimization target and the existing characteristics; step five: training by using multi-level biomolecular network data and processed each level biomolecular characteristic data, solving parameters of each layer in the model, stopping training and storing parameters of each layer when an expected effect is achieved; step six: and deploying the trained neural network model for multi-level biomolecular network optimization. The invention solves the problems that the existing biomolecular network has poor expansibility and cannot deeply describe a complex biological system.

摘要：【中文】本发明提供了一种分布式电源直流并网的控制方法和系统，包括：按照第一检测周期检测直流母线电压；如果直流母线电压在设定的波动范围内，执行恒功率控制模式；否则，按照第二检测周期检测直流母线电压的变化率，根据变化率，确定执行恒功率控制模式或执行恒电压控制模式；其中，第二检测周期时长小于第一检测周期。该方法和系统采用分阶段变步长检测直流母线电压的波动和变化率来控制直流母线电压，能够快速检测出直流母线电压故障，缩短直流母线电压控制的暂态过程，减小直流母线电压的跌落，提升分布式电源直流并网的稳定性。 【EN】The invention provides a control method and a system for direct current grid connection of a distributed power supply, which comprise the following steps: detecting the voltage of the direct current bus according to a first detection period; if the voltage of the direct current bus is within a set fluctuation range, executing a constant power control mode; otherwise, detecting the change rate of the direct-current bus voltage according to a second detection period, and determining to execute a constant power control mode or a constant voltage control mode according to the change rate; and the duration of the second detection period is less than that of the first detection period. The method and the system control the direct-current bus voltage by detecting the fluctuation and the change rate of the direct-current bus voltage in a staged and step-variable manner, can quickly detect the direct-current bus voltage fault, shorten the transient process of direct-current bus voltage control, reduce the drop of the direct-current bus voltage and improve the stability of the direct-current grid connection of the distributed power supply.

摘要：【中文】本发明公开了一种通过二维码追溯定位生产一线网络故障位置的系统，属于通信技术领域。一种通过二维码追溯定位生产一线网络故障位置的系统，包括二维码终端、交换机及二维码服务器；所述一种通过二维码追溯定位生产一线网络故障位置的系统的工作过程包括以下步骤，二维码终端发出故障判断指令给交换机；交换机传输数据至二维码服务器；二维码服务器判断出网络故障点，将数据传输给交换机；交换机将数据传输至二维码终端显示判断结果。本发明提供的通过二维码追溯定位生产一线网络故障位置的系统能够快速准确的判断出网络故障点，缩短流水线停车时间，提高设备产能。 【EN】The invention discloses a system for tracing and positioning a fault position of a production line network through a two-dimensional code, and belongs to the technical field of communication. A system for tracing and positioning a fault position of a production line network through a two-dimension code comprises a two-dimension code terminal, a switch and a two-dimension code server; the working process of the system for tracing and positioning the fault position of the production first-line network through the two-dimension code comprises the following steps that a two-dimension code terminal sends a fault judgment instruction to a switch; the switch transmits data to the two-dimensional code server; the two-dimensional code server judges a network fault point and transmits data to the switch; and the switch transmits the data to the two-dimensional code terminal to display the judgment result. The system for tracing and positioning the network fault position of the production line through the two-dimension code can quickly and accurately judge the network fault point, shorten the shutdown time of the production line and improve the productivity of equipment.

摘要：【中文】本发明公开了一种石墨烯掺杂C/C复合材料的制备方法，包括如下步骤：S1)石墨/碳纤维/酚醛树脂热压坯的制备：将掺杂有石墨烯的石墨烘干除湿后与酚醛树脂充分混合，再与脱胶分散的碳纤维混合，热压成型；S2)碳/碳复合材料预制体的形成：将石墨/碳纤维/酚醛树脂热压坯在氩气氛保护下的高温碳化炉中梯度升温完成碳化过程；S3)采用化学气相渗透工艺，通过高温梯度沉积得碳/碳复合材料：以气态烃类为前驱气体，将碳/碳复合材料预制体的表面暴露于高温热分解的碳氢化合物气氛中，逐渐完成热解碳的沉积过程。本发明工艺过程简单，工艺参数易控，所得材料导电导热性能良好，强度高，可作为用于湿法冶金阴极的石墨烯掺杂C/C复合材料。 【EN】The invention discloses a preparation method of a graphene-doped C/C composite material, which comprises the following steps: s1) preparation of graphite/carbon fiber/phenolic resin hot pressed compact: drying and dehumidifying graphite doped with graphene, fully mixing the graphite with phenolic resin, mixing the mixture with degummed and dispersed carbon fibers, and performing hot press molding; s2) formation of carbon/carbon composite preform: carrying out gradient temperature rise on the graphite/carbon fiber/phenolic resin hot pressed compact in a high-temperature carbonization furnace under the protection of argon atmosphere to finish the carbonization process; s3) adopting a chemical vapor infiltration process, and obtaining the carbon/carbon composite material through high-temperature gradient deposition: and exposing the surface of the carbon/carbon composite material preform to a hydrocarbon atmosphere subjected to high-temperature thermal decomposition by taking gaseous hydrocarbons as precursor gas, and gradually finishing the deposition process of pyrolytic carbon. The method has the advantages of simple process, easily-controlled process parameters, good electric and heat conducting performance of the obtained material and high strength, and can be used as a graphene-doped C/C composite material for a hydrometallurgy cathode.

摘要：【中文】本发明提供了一种多风电场出力场景的生成方法及系统，包括：基于多个风电场的出力预测误差数据和预先构建的高斯隐马尔科夫模型得到多维高斯分布和累积状态转移概率矩阵；基于所述多维高斯分布和累积状态转移概率矩阵确定预设周期内每一时刻每个风电场的预测误差；基于各风电场的点预测出力值和所述风电场的预测误差，获得所有风电场的预测出力场景。本发明能够计及不同风电场预测误差之间的相关性，基于高斯隐马尔可夫模型的多个风电场预测出力场景生成方法，对不同风电场之间进行建模，所生成的风电场预测出力场景能够计及不同风电场之间的相关性，所得到的预测出力场景更加科学、合理，准确度高。 【EN】The invention provides a method and a system for generating a multi-wind-farm output scene, which comprise the following steps: obtaining a multi-dimensional Gaussian distribution and accumulated state transition probability matrix based on output prediction error data of a plurality of wind power plants and a pre-constructed Gaussian hidden Markov model; determining a prediction error of each wind power plant at each moment in a preset period based on the multi-dimensional Gaussian distribution and the cumulative state transition probability matrix; and obtaining the predicted output scene of all the wind power plants based on the point predicted output value of each wind power plant and the prediction error of the wind power plant. The method can calculate the correlation among the prediction errors of different wind power plants, the multiple wind power plant predicted output scenes based on the Gaussian hidden Markov model are generated to model different wind power plants, the generated wind power plant predicted output scenes can calculate the correlation among different wind power plants, and the obtained predicted output scenes are more scientific and reasonable and have high accuracy.