基于朴素贝叶斯的重力感应电子秤定载荷点选择

针对重力感应电子秤荷载点选择受校准数据信号影响，定载荷点概率计算准确性低，导致电子秤定载荷点选择效果差，提出基于朴素贝叶斯的重力感应电子秤定载荷点选择方法。以电子秤平放和标定为基础校准重力加速度传感器，并缓存记录一组传感器上传的数据，校准过程中存储这组数据，依据存储数据校准传感器的上传数据。采用朴素贝叶斯分类求解校准后的数据信号，精准计算电子秤各类定载荷点概率，依据最大概率识别电子秤定载荷点类别。利用多源融合技术将载荷点类别信息互补去杂，实现电子秤定载荷点选择。实验结果表明，该方法对重力感应电子秤定载荷点选择效果佳，且具有较高的重力感应信息融合效率和分类精度，可有效提高电子秤的称量效果。

Selection of fixed load point of gravity induction electronic scale based on Naive Bayes

The load point selection of the gravity induction electronic scale is affected by the calibration data signal, and the accuracy of the probability calculation of the fixed load point is low, which results in the poor effect of the selection of the fixed load point of the electronic scale. Calibrate the accelerometer on the basis of the scale flattening and calibration, and cache and record the data uploaded by a group of sensors. During the calibration process, store the data, and calibrate the uploaded data of the sensor according to the stored data. Using naive Bayes classification to solve the calibrated data signal, accurately calculate the probability of all kinds of fixed load points of the electronic scale, and identify the fixed load points of the electronic scale according to the maximum probability. By using multi-source fusion technology, the information of load point category can be complementary to eliminate clutter, and the selection of fixed load point of electronic scale can be realized. The experimental results show that this method is effective in selecting the fixed load point of the gravity induction electronic scale, and has high efficiency of information fusion and classification accuracy of gravity induction, which can effectively improve the weighing effect of the electronic scale.