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申请号:202010145317.X 公开号:CN111235501A 主分类号:C22F1/08
摘要:【中文】本发明公开了一种超薄铜带退火不粘带工艺,超薄铜带经精轧机轧制完成后先经过松卷机,然后送往罩式退火炉进行高温退火;其中精轧机轧辊粗糙度控制在0.35‑0.2μm,精轧机轧制油粘度控制在7.5‑8.8mm2/s,松卷机收卷张力控制在1.3‑3.3KN,罩式退火炉升温速率控制在90‑120℃/h。本发明通过在原铜带生产退火工艺的基础上,对工艺参数进行进一步的优化,从而在不添加防粘剂的情况下,仍然能够实现铜带的不粘带工艺,从而无需吸入挥发到空气中的防粘剂,消除员工潜在的职业健康危害,并且降低了铜带生产成本。 【EN】The invention discloses an annealing and non-sticking process for an ultrathin copper strip, wherein the ultrathin copper strip is rolled by a finishing mill, then passes through an uncoiler and then is conveyed to a hood-type annealing furnace for high-temperature annealing; wherein the roughness of the roller of the finishing mill is controlled to be 0.35-0.2 μm, and the viscosity of the rolling oil of the finishing mill is controlled to be 7.5-8.8mm2The winding tension of the uncoiler is controlled to be 1.3-3.3KN, and the heating rate of the cover type annealing furnace is controlledThe temperature is controlled to be between 90 and 120 ℃ per hour. According to the invention, on the basis of the original copper strip production annealing process, the process parameters are further optimized, so that the non-sticking process of the copper strip can be realized under the condition of not adding an anti-sticking agent, the anti-sticking agent volatilized into the air is not required to be sucked, the potential occupational health hazards of staff are eliminated, and the production cost of the copper strip is reduced.
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申请号:201911332860.4 公开号:CN111018084A 主分类号:C02F1/72
申请人:【中文】大连理工大学【EN】DALIAN UNIVERSITY OF TECHNOLOGY 申请日:2019.12.23 公开日:2020.04.17
摘要:【中文】本发明提供了一种应用于氧氟沙星废水处理的生物炭‑针铁矿非均相芬顿氧化方法,属于环境工程废水处理领域。利用小麦秸秆为原料以限氧热解法制备生物炭材料,配制氧氟沙星模拟废水并调节pH,在好氧条件下添加生物炭、针铁矿和过氧化氢,置于磁力搅拌器上反应完全。本发明中添加微量的生物炭和针铁矿对于氧氟沙星的吸附量很少,但是微量的针铁矿和生物炭作为反应过程催化剂,能够有效地提高氧氟沙星废水的降解速率,减少金属污泥对环境的二次污染,且针铁矿和生物炭来源广泛,成本低廉,有利于在实际废水处理过程中推广应用。 【EN】The invention provides a biochar-goethite heterogeneous Fenton oxidation method applied to ofloxacin wastewater treatment, and belongs to the field of environmental engineering wastewater treatment. The method comprises the steps of preparing a biochar material by an oxygen-limited pyrolysis method by using wheat straws as a raw material, preparing ofloxacin simulated wastewater, adjusting pH, adding biochar, goethite and hydrogen peroxide under an aerobic condition, and placing the biochar, goethite and hydrogen peroxide on a magnetic stirrer for complete reaction. In the invention, trace biochar and goethite are added, the adsorption quantity of the goethite to the ofloxacin is very small, but the trace goethite and the biochar are used as catalysts in the reaction process, so that the degradation rate of the ofloxacin wastewater can be effectively improved, the secondary pollution of metal sludge to the environment is reduced, and the sources of the goethite and the biochar are wide, the cost is low, and the popularization and the application in the actual wastewater treatment process are facilitated.
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申请号:202610114710.X 公开号:CN122065391A 主分类号:G06F30/13
摘要:本发明公开了一种基于深度学习大模型的钢支撑伺服轴力智能预测与主动补偿系统、方法及平台;通过依次设置的数据生成获取处理单元、模型创建及变化趋势预测单元、轴力补偿控制处理单元和作业状态监控单元,及与系统相应的方法、平台,能够提前预测钢支撑轴力变化、主动进行补偿调节的智能系统,显著提高基坑开挖过程中的安全性、稳定性以及施工效率,填补了现有技术中的空白。即通过集成高精度传感器、深度学习模型及智能控制算法,实现对钢支撑轴力的实时预测与主动补偿。能够提前预测轴力变化趋势,并通过伺服液压系统精准调节支撑力,有效解决传统被动补偿方法的滞后性问题,显著提升基坑施工的安全性与稳定性。
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申请号:201911395898.6 公开号:CN111073712A 主分类号:C10K1/02
申请人:【中文】河北工业大学【EN】Hebei University of Technology 申请日:2019.12.30 公开日:2020.04.28
摘要:【中文】本发明专利所涉及的一种生物质热解气净化及冷凝液回收工艺,涉及以除尘、冷凝、萃取净化为特征的处理生物质热解气工艺,采用旋风分离器1清除热解气中的炭颗粒和灰分,循环水槽2中的循环水将炭颗粒和灰分带出净化系统,清除炭颗粒和灰分后的热解气依次通入一级冷凝塔3、二级冷凝塔4、三级冷凝塔5将热解气中可冷凝物质冷凝,各冷凝塔所冷凝物质分别存储于第一储罐9、第二储罐10和第三储罐11。冷凝后的热解气于萃取净化塔7中进行萃取,萃取后的热解气储存于燃气储罐8。相比于现有处理工艺,该工艺克服了热解气中焦油含量高、水分大和热值低的问题,同时也解决了冷凝液后续处理难度大的问题。 【EN】The invention relates to a biomass pyrolysis gas purification and condensate recovery process, which relates to a biomass pyrolysis gas treatment process characterized by dust removal, condensation and extraction purification, wherein a cyclone separator 1 is adopted to remove carbon particles and ash in pyrolysis gas, circulating water in a circulating water tank 2 is used for carrying the carbon particles and the ash out of a purification system, the pyrolysis gas after the carbon particles and the ash are removed is sequentially introduced into a primary condensing tower 3, a secondary condensing tower 4 and a tertiary condensing tower 5 to condense condensable substances in the pyrolysis gas, and the condensed substances in each condensing tower are respectively stored in a first storage tank 9, a second storage tank 10 and a third storage tank 11. The condensed pyrolysis gas is extracted in an extraction purification tower 7, and the extracted pyrolysis gas is stored in a fuel gas storage tank 8. Compared with the prior treatment process, the process overcomes the problems of high tar content, large moisture content and low heat value in the pyrolysis gas, and simultaneously solves the problem of high difficulty in subsequent treatment of the condensate.
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申请号:202411475829.7 公开号:CN119238516A 主分类号:B25J9/16
申请人:海南大学 申请日:2024.10.22 公开日:2025.01.03
摘要:本发明涉及人工智能视觉与机械臂应用技术领域,尤其涉及一种视觉机械臂物品定位拾取方法及系统,集成高精度的视觉检测与灵活的机械臂执行机构,实现了从目标检测到精准拾取的全自动化流程,减轻了人工操作的负担,提高了生产效率,针对传统视觉识别系统计算量大与资源消耗高的痛点,本发明进行了深度的轻量化改进,通过优化算法结构、减少模型参数量及计算复杂度,本系统相比原模型在性能上实现了成倍的提升,同时显著降低了对计算资源和硬件配置的依赖,这不仅使得系统能够在树莓派等低成本硬件平台上高效运行,还大幅降低了部署成本,提升了系统的普及率和可用性。
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申请号:201911395893.3 公开号:CN111073704A 主分类号:C10J3/20
申请人:【中文】河北工业大学【EN】Hebei University of Technology 申请日:2019.12.30 公开日:2020.04.28
摘要:【中文】本发明公开了一种具有压料装置的下吸式生物质气化炉,包括灰室、气化炉炉体和压料装置。工作时,气化炉炉体加入适量秸秆物料后,启动涡轮丝杆升降机,关闭涡轮减速机,压料盘会在涡轮丝杆升降机带动向下运动对物料进行挤压,关闭涡轮丝杆升降机,启动涡轮减速机,压料盘就会进行旋转对物料进行均匀布料,完成压料后回到原来位置,待炉内燃烧后重复上述步骤。本发明不仅可以对进入下吸式生物质气化炉的物料起到均匀布料的作用,并且还可以对原料压实增加其密实度,从而提高气化炉的处理能力。同时可对物料燃烧后的积存的灰渣压碎,使其顺利落入出灰装置中。 【EN】The invention discloses a downdraft biomass gasifier with a material pressing device. When the gasification furnace is in operation, after a proper amount of straw materials are added into a furnace body of the gasification furnace, the turbine screw rod lifter is started, the turbine speed reducer is closed, the material pressing disc can drive the downward movement at the turbine screw rod lifter to extrude the materials, the turbine screw rod lifter is closed, the turbine speed reducer is started, the material pressing disc can rotate to uniformly distribute the materials, the materials return to the original position after being pressed, and the steps are repeated after the materials are combusted in the furnace. The invention not only can play a role of uniformly distributing materials entering the downdraft biomass gasifier, but also can compact the raw materials to increase the compactness of the raw materials, thereby improving the processing capacity of the gasifier. Meanwhile, the ash and slag accumulated after the material is burnt can be crushed, so that the ash and slag can smoothly fall into the ash discharging device.
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申请号:201911132142.2 公开号:CN110852295A 主分类号:G06K9/00
摘要:【中文】一种基于多任务监督学习的视频行为识别方法,包括:步骤1)将输入视频划分成多个视频序列,每个视频序列包含若干帧图像;以及步骤2)设计3D卷积神经网络对视频序列提取时间和空间联合特征,实现显著性区域检测和视频行为分类。其中,这两个任务共享特征提取模块,显著性特征图用于指导行为识别特征的提取,使得神经网络更加关注于行为发生的区域,有利于网络学习到对运动分类敏感的特征,从而提高了行为识别的检测精度。同时,相比于单任务检测模型,显著性区域检测加快了视频行为分类任务的训练速度。在当前公布的测试数据集上,本发明提出的方法取得了领先的检测水平。 【EN】A video behavior identification method based on multitask supervised learning comprises the following steps: step 1) dividing an input video into a plurality of video sequences, wherein each video sequence comprises a plurality of frame images; and step 2) designing a 3D convolutional neural network to extract time and space combined features from the video sequence, and realizing salient region detection and video behavior classification. The two tasks share the feature extraction module, and the significant feature map is used for guiding the extraction of the behavior recognition features, so that the neural network focuses more on the region where the behavior occurs, the network learning of the features sensitive to the motion classification is facilitated, and the detection precision of the behavior recognition is improved. Meanwhile, compared with a single-task detection model, the significance region detection speeds up the training speed of the video behavior classification task. On the currently published test data set, the method provided by the invention achieves a leading detection level.
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申请号:201911366208.4 公开号:CN110882869A 主分类号:B05B12/02
摘要:【中文】一种用在混凝土施工便道上的自动喷雾装置涉及一种能够对冬季施工便道上的结冰喷射除冰雾的装置。主要是为解决目前人工清理便道出现时间间歇,导致清理过的路面又结冰的问题而设计的。它包括风能专用AGM储能铅酸蓄电器,数字温控器温控开关与风能专用AGM储能铅酸蓄电器顶部连接,风能专用AGM储能铅酸蓄电器与电流控制器通过电线连接,电流控制器通过电线与分线孔并联连接器连接;电动喷雾器与分线孔并联连接器通过电线连接,数字温控器温控开关与电流控制器通过数据线连接;湿度传感器通过电线与风能蓄电器直接连接,湿度传感器与电流控制器通过数据线连接。优点是可对混凝土施工便道结冰路面自动处理。 【EN】An automatic spraying device used on a concrete construction pavement relates to a device which can spray deicing fog on the pavement in winter construction. The cleaning device is mainly designed for solving the problem that the cleaned pavement is frozen due to the intermittent time of manual cleaning of the pavement at present. The wind energy AGM energy storage lead-acid battery comprises a wind energy special AGM energy storage lead-acid battery, wherein a digital temperature controller temperature control switch is connected with the top of the wind energy special AGM energy storage lead-acid battery; the electric sprayer is connected with the branching hole parallel connector through a wire, and the digital temperature controller temperature control switch is connected with the current controller through a data line; the humidity sensor is directly connected with the wind energy accumulator through an electric wire, and the humidity sensor is connected with the current controller through a data wire. The method has the advantage of automatically treating the frozen pavement of the concrete construction pavement.
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申请号:201911278900.1 公开号:CN110934587A 主分类号:A61B5/055
摘要:【中文】一种基于图卷积神经网络的阿尔兹海默症辅助诊断方法,包括:对脑部功能磁共振影像进行处理,得到各大脑区域的时间序列;计算各大脑区域的时间序列中任意两个时间序列之间的皮尔森相关系数,得到以每个时间序列作为节点、该皮尔森相关系数作为两节点之间连接边的权重的脑功能连接网络;将所有权重小于设定阈值的边去掉,简化脑功能连接网络,得到图结构数据;以及设计图卷积神经网络模型,并使用图结构数据训练设计的图卷积神经网络模型,最后得到验证集上表现最佳的训练结果作为辅助诊断模型,输出整个图结构对应的疾病状态。本发明方法得到了比传统方法更好的检测准确度,取得了在该疾病上领先的辅助诊断分类水平。 【EN】An aided diagnosis method for Alzheimer's disease based on a atlas neural network comprises the following steps: processing the brain functional magnetic resonance image to obtain a time sequence of each brain region; calculating a Pearson correlation coefficient between any two time sequences in the time sequences of each brain region to obtain a brain function connection network which takes each time sequence as a node and the Pearson correlation coefficient as the weight of a connection edge between the two nodes; removing all edges with the weight less than a set threshold value, and simplifying a brain function connection network to obtain graph structure data; and designing a graph convolution neural network model, training the designed graph convolution neural network model by using graph structure data, finally obtaining a training result which is best represented on a verification set and is used as an auxiliary diagnosis model, and outputting a disease state corresponding to the whole graph structure. The method of the invention obtains better detection accuracy than the traditional method and obtains the advanced auxiliary diagnosis classification level on the disease.
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申请号:202010063741.X 公开号:CN111195915A 主分类号:B25J9/16
摘要:【中文】一种基于遗传算法的机械臂绝对定位精度标定方法,包括:步骤1)利用理论DH参数建立机械臂运动学参数模型,并得到理论终端坐标;步骤2)获取机械臂终端实际坐标,以及对应关节的角度;步骤3)在理论DH参数上加上一个微小误差,并建立含有误差的机械臂终端坐标;步骤4)对所有采集到的点位计算并求和,其中,Perr为含有误差的机械臂终端坐标,Preal为机械臂终端实际坐标;步骤5)通过多阶段遗传算法求得误差参数方程极小时的微小误差以作为机构参数修正量,然后加上理论DH参数得到最优机构参数该方法采用遗传算法对机械臂绝对定位精度进行分段标定,通过在不同阶段中采用不同的交叉、变异策略使得遗传算法标定结果的精确度更高。 【EN】A calibration method for absolute positioning precision of a mechanical arm based on a genetic algorithm comprises the following steps: step 1) establishing a mechanical arm kinematics parameter model by using a theoretical DH parameter, and obtaining a theoretical terminal coordinate; step 2) acquiring actual coordinates of the mechanical arm terminal and angles of corresponding joints; step 3) adding a small error to the theoretical DH parameter, and establishing a mechanical arm terminal coordinate containing the error; step 4) calculating all collected point positionsAnd summing, wherein PerrFor the coordinates of the end of the arm containing errors, PrealThe actual coordinates of the mechanical arm terminal are obtained; step 5) solving an error parameter equation through a multi-stage genetic algorithmThe small error in a very small time is taken as the mechanism parameter correction quantity, and then the theoretical DH parameter is added to obtain the optimal mechanismThe parameter method adopts a genetic algorithm to carry out segmented calibration on the absolute positioning precision of the mechanical arm, and adopts different crossing and variation strategies in different stages to ensure that the precision of the calibration result of the genetic algorithm is higher.
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