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基于多傳感器數(shù)據(jù)融合的旋耕耕深檢測(cè)系統(tǒng)研究
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國(guó)家重點(diǎn)研發(fā)計(jì)劃項(xiàng)目(2021YFD2000601)


Rotary Tillage Depth Detection Based on Multi-sensor Data Fusion
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    摘要:

    旋耕作業(yè)時(shí)通常用耕深衡量作業(yè)質(zhì)量,適合的耕深能打破土壤板結(jié)、改善土壤結(jié)構(gòu),促進(jìn)作物根系生長(zhǎng),。目前耕深檢測(cè)研究場(chǎng)景主要為旱田,,檢測(cè)方法主要為傾角傳感器間接檢測(cè),而水田土質(zhì)松軟,,作業(yè)過(guò)程中會(huì)出現(xiàn)機(jī)具下陷和姿態(tài)變化的問(wèn)題,。為此,本文設(shè)計(jì)了一種適用于不同土壤環(huán)境的耕深檢測(cè)系統(tǒng),。為檢測(cè)地形起伏及機(jī)具下陷,,設(shè)計(jì)一種地面仿形機(jī)構(gòu),通過(guò)耦合仿真探究其設(shè)計(jì)參數(shù),,驗(yàn)證可行性,;為提高單傳感器間接檢測(cè)穩(wěn)定性,使用三點(diǎn)懸掛傾角檢測(cè)和北斗衛(wèi)星系統(tǒng)(Beidou system, BDS)相對(duì)高程數(shù)據(jù)融合的檢測(cè)方法,,并建立耕深檢測(cè)模型(Tillage detection model, TDM),。提出自適應(yīng)迭代擴(kuò)展卡爾曼算法(Adaptive iterative extended Kalman filter, AIEKF)對(duì)傳感器獲取數(shù)據(jù)先濾波再融合,獲得更穩(wěn)定,、準(zhǔn)確的耕深,。在參考傳統(tǒng)耕深測(cè)量方法的基礎(chǔ)上,提出了RTK-BDS高程差值的測(cè)量方法獲取耕深真值,,并和傳統(tǒng)方法進(jìn)行了對(duì)比,。仿形機(jī)構(gòu)耦合仿真試驗(yàn)結(jié)果表明,所設(shè)計(jì)的仿形機(jī)構(gòu)檢測(cè)絕對(duì)誤差小于0.5 cm,,仿形后土槽高程最大形變量為2.89 mm,。田間試驗(yàn)結(jié)果表明,TDM檢測(cè)模型能有效反映耕深變化,,AIEKF處理后數(shù)據(jù)較KF處理后數(shù)據(jù)信噪比平均提升1.41 dB,融合得到的耕深較兩種單一傳感器檢測(cè)的耕深MAPE平均分別提高2.30%和2.07%,,融合后平均MAPE為3.95%,平均RMSE為1.08 cm。提出的RTK-BDS差值真值檢測(cè)法和傳統(tǒng)檢測(cè)法最大絕對(duì)誤差為2.45 cm,,能較好完成耕深真值檢測(cè),。

    Abstract:

    Tillage depth is usually used to measure the quality of rotary tillage. Suitable tillage depth can break soil compaction, improve soil structure and promote crop root growth. At present, the research scene of ploughing depth detection is mainly dry field, and the detection method is mainly indirect detection by inclination sensor. However, the soil of paddy field is soft, and the problems of sinking and attitude change will occur in the operation process. Therefore, a tillage depth detection system suitable for different soil environments was designed. In order to detect terrain relief and tool sag, a ground copying mechanism was designed, and its design parameters were explored through coupling simulation to verify its feasibility. In order to improve the indirect detection stability of single sensor, the detection method of three-point suspension inclination detection and relative elevation data fusion of Beidou system (BDS) were used, and a tillage detection model (TDM) was established. Adaptive iterative extended Kalman filter (AIEKF) was proposed to filter and then fuse data acquired by sensors to obtain more stable and accurate depth. On the basis of referring to the traditional tillage depth measurement method, the RTK-BDS elevation difference measurement method was proposed to obtain the true tillage depth value, and compared with the traditional method. The coupling simulation results showed that the absolute error was less than 0.5 cm and the maximum shape variable of soil trough elevation was 2.89 mm. Field experiment results showed that TDM detection model can effectively reflect the change of ploughing depth. The signal to noise ratio of AIEKF processing data was increased by 1.41 dB on average compared with that of KF processing data, and the ploughing depth data obtained by fusion was increased by 2.30% and 2.07% on average compared with the ploughing depth MAPE detected by two single sensors, respectively. After fusion, the average MAPE was 3.95% and the average RMSE was 1.08 cm. The maximum absolute error of RTK-BDS difference truth detection method and traditional detection method was 2.45 cm, which can complete the truth detection of ploughing depth well.

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馬若飛,偉利國(guó),趙博,周利明,劉陽(yáng)春,邢高勇.基于多傳感器數(shù)據(jù)融合的旋耕耕深檢測(cè)系統(tǒng)研究[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2024,55(9):52-64. MA Ruofei, WEI Liguo, ZHAO Bo, ZHOU Liming, LIU Yangchun, XING Gaoyong. Rotary Tillage Depth Detection Based on Multi-sensor Data Fusion[J]. Transactions of the Chinese Society for Agricultural Machinery,2024,55(9):52-64.

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  • 收稿日期:2023-12-07
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  • 在線發(fā)布日期: 2024-09-10
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