摘要：【中文】本发明涉及一种农机部件，即一种适用颗粒肥料精量施入作业的内旋式电动排肥器。现有的排肥器通常采用外槽轮或螺旋机构，摩擦阻力大，需要通过大功率电机或地轮带动链条、皮带、齿轮等传动机构进行驱动作业，传动机构复杂，很难实现肥料的精量控制。本发明以内旋式旋肥板(10)和导肥顶(4)作为施肥机构，降低了旋动阻力，通过机控单元(5)驱动实现施肥作业，实现了电动精量施肥，省略了复杂的地轮传动机构，使施肥作业过程简洁可控，提高了肥料按需施入的准确性。 【EN】The invention relates to an agricultural machine part, in particular to an internal rotation type electric fertilizer distributor suitable for granular fertilizer precise application operation. The prior fertilizer apparatus usually adopts an outer grooved wheel or a spiral mechanism, has large friction resistance, needs a high-power motor or a land wheel to drive a chain, a belt, a gear and other transmission mechanisms for driving operation, has complex transmission mechanism, and is difficult to realize the precise control of the fertilizer. The internal rotation type fertilizer rotating plate (10) and the fertilizer guide top (4) are used as fertilizer applying mechanisms, so that the rotating resistance is reduced, the fertilizer applying operation is realized by the driving of the machine control unit (5), the electric precise fertilizer applying is realized, a complex land wheel transmission mechanism is omitted, the fertilizer applying operation process is concise and controllable, and the accuracy of applying the fertilizer as required is improved.

摘要：【中文】本公开涉及一种目标行为的协同监督方法及装置。该方法包括：由多个数据源获取用户行为的相关数据；将所述多个数据源的用户行为相关数据进行数据整合处理生成用户特征数据；将所述用户特性数据输入目标行为判定模型中生成目标行为概率，所述目标行为概率表示用户行为中具有目标行为的概率；基于所述目标行为概率生成多个行为监督表格；以及通过所述多个行为监督表格对所述用户进行协同监督。本公开涉及的目标行为的协同监督方法及装置，能够通过多个维度对用户的行为进行判断，并且在用户具有目标行为特征的时候，通过多个维度对用户进行协同监督。 【EN】The disclosure relates to a cooperative monitoring method and device for target behaviors. The method comprises the following steps: acquiring relevant data of user behaviors from a plurality of data sources; carrying out data integration processing on the user behavior related data of the multiple data sources to generate user characteristic data; inputting the user characteristic data into a target behavior judgment model to generate a target behavior probability, wherein the target behavior probability represents the probability of having a target behavior in user behaviors; generating a plurality of behavior surveillance forms based on the target behavior probabilities; and performing collaborative supervision on the user through the plurality of behavior supervision tables. The cooperative monitoring method and the cooperative monitoring device for the target behaviors can judge the behaviors of the user through multiple dimensions, and when the user has the target behavior characteristics, the cooperative monitoring is carried out on the user through the multiple dimensions.

摘要：【中文】本发明涉及一种农机施肥方法，即一种适用颗粒肥料施入作业的旋耙式电动排肥方法。现有的排肥器通常采用外槽轮或螺旋机构，摩擦阻力大，主要通过地轮驱动传动机构，带动施肥装置完成施肥作业，可实现均一施肥，但调控施肥量十分繁琐，很难实现肥料的精确控制。本发明设计的旋耙式电动排肥方法以施肥壳体(1)、档肥板(3)、左耙齿(4)、左连杆(5)、前曲连杆(8)、电驱单元(9)、后曲连杆(11)、右连杆(13)、右耙齿(14)装配构成，降低了施肥装置的内部阻力，实现了电动施肥，省略了复杂的传动机构，使施肥作业过程简洁可控，提高了肥料按需施入的准确率。 【EN】The invention relates to a fertilizing method for an agricultural machine, in particular to a rotary-rake type electric fertilizer discharging method suitable for applying granular fertilizer. The prior fertilizer apparatus usually adopts an outer grooved wheel or a screw mechanism, has large friction resistance, mainly drives a fertilizing device to complete fertilizing operation through a land wheel driving transmission mechanism, can realize uniform fertilizing, but has very complicated regulation and control of fertilizing amount, and is difficult to realize the accurate control of fertilizer. The rotary-rake electric fertilizer discharging method designed by the invention is formed by assembling a fertilizer application shell (1), a fertilizer blocking plate (3), left rake teeth (4), a left connecting rod (5), a front curved connecting rod (8), an electric driving unit (9), a rear curved connecting rod (11), a right connecting rod (13) and right rake teeth (14), reduces the internal resistance of a fertilizer application device, realizes electric fertilizer application, omits a complex transmission mechanism, ensures that the fertilizer application process is simple and controllable, and improves the accuracy of fertilizer application as required.

摘要：【中文】本发明涉及一种农机部件，即一种适用颗粒肥料精量施入作业的旋开式电动精量排肥器。现有的排肥器通常采用外槽轮或螺旋机构，摩擦阻力大，施控困难，很难实现肥料的精量施入。本发明设计的由导肥顶(3)、机电单元(4)、壳体(5)、保护罩(6)、旋开机构(7)装配构成施肥器具有结构简单、阻力小的特点，使施肥过程简洁可控，实现了电动精量施肥，提高了播种施肥作业肥料的电动精量施入。 【EN】The invention relates to an agricultural machine part, in particular to a rotary type electric precision fertilizer distributor suitable for precision application operation of granular fertilizer. The prior fertilizer apparatus usually adopts an outer grooved wheel or a spiral mechanism, has large friction resistance and difficult control, and is difficult to realize the precise fertilizer application. The fertilizer applicator formed by assembling the fertilizer guide top (3), the electromechanical unit (4), the shell (5), the protective cover (6) and the unscrewing mechanism (7) has the characteristics of simple structure and small resistance, so that the fertilizing process is simple and controllable, the electric precision fertilization is realized, and the electric precision fertilization of the fertilizer in the seeding and fertilizing operation is improved.

摘要：【中文】本发明涉及计算机领域，尤其涉及基于朴素贝叶斯算法的电力数据分类方法及系统，包括：S1：从电力公司的电力系统中获取数据并生成数据集；S2：从数据集中取数据子集，并作增量式训练；S3：对于每一个类别，计算各类别在数据子集中的频率；S4：将数据子集划分成K个子数据子集，分别对属于C_k的子数据子集进行计算，计算出其中第j个特征的概率；S5：针对待预测样本，计算其对于每个类别C_k的后验概率，概率值最大的类别即为待预测样本的预测类别；S6：在数据集中去掉当前数据子集，并判断数据集是否为空，若非空则进入步骤S2，若空则结束分类。本发明能够迅速准确的实现数据的分类，分类性能不会因为数据的不同而展现差异，具有良好的健壮性。 【EN】The invention relates to the field of computers, in particular to a naive Bayes algorithm-based power data classification method and a naive Bayes algorithm-based power data classification system, which comprise the following steps: s1: acquiring data from a power system of a power company and generating a data set; s2: taking a data subset from the data set and performing incremental training; s3: for each category, calculating the frequency of each category in the data subset; s4: dividing the data subset into K sub-data subsets, respectively belonging to C_kCalculating the sub-data subset of the sub-data subset to calculate the probability of the jth characteristic; s5: for the sample to be predicted, it is calculated for each class C_kThe class with the maximum probability value is the prediction class of the sample to be predicted; s6: removing the current data subset from the data set, determining whether the data set is empty, if not, proceeding to step S2, otherwise, leaving emptyThe classification is ended. The invention can quickly and accurately realize the classification of the data, the classification performance can not show the difference due to the difference of the data, and the invention has good robustness.