宝马与Ruetz系统解决方案公司联合测试过程

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"2_Test_Process_ECU and network test.pdf - 它是关于ECU(电子控制单元)和网络测试的测试过程文档,由Thomas Kirchmeier(宝马公司)和Georg Janker(Ruetz System Solutions GmbH)共同编写。文档版本为1.0,最终定稿日期为2016年9月22日,公开于OPEN Alliance。" 在TC8-ECU和网络测试中,该文档详细阐述了汽车行业中电子控制单元和网络系统的测试流程。测试过程是确保汽车电子系统质量和可靠性的关键环节,它涵盖了从测试准备到执行,再到结果分析和反馈的整个周期。 首先,文档可能详细介绍了测试环境的建立,包括ECU的安装、配置以及网络的布线和调试。ECU测试可能涉及功能验证、性能评估、故障模拟等多种场景,以确保ECU能够正确地接收和处理来自传感器和控制器的信号,并按照预定逻辑进行响应。 网络测试则关注通信协议的合规性、数据传输的效率和稳定性。这可能包括CAN、LIN、FlexRay或以太网等不同车载网络的测试,检查它们在多ECU环境下的协同工作能力,防止数据冲突和通信错误。 在测试过程中,文档可能提到了使用的问题问卷,这是为了收集DUT(设备-under-test,即被测设备)的相关信息,如硬件配置、软件版本、预期行为等,以便更准确地制定测试计划和测试用例。 版本控制部分显示了文档的迭代历史,从0.1版的初稿合并到0.3版的反馈审查,每次更新都对测试流程进行了细化和完善。特别地,0.2版更新了ECU测试和网络测试的描述,而0.3版则根据评审反馈进行了修订,并解决了链接问题,明确了新内容将融入到官方的测试规范文档中。 最后,文档强调了所有技术成员对测试规范的正式审查和发布,以确保测试标准的严谨性和行业共识。这种协作和标准化的流程对于保持汽车行业的高质量标准至关重要。 这份文档为汽车制造商和测试机构提供了ECU与网络系统测试的全面指南,有助于提高测试效率,减少潜在问题,保障汽车电子系统的安全和可靠性。

SELECT bs.sample_id, bs.item_id, bs.report_id, bs.order_no, bs.order_id, bs.order_business_type, bs.commission_date, bs.customer_name, bs.applicant, bs.phone, bs.receive_user_name, bs.contract_no, bs.special_requirements, bs.report_org_name, bs.report_org_address, bs.sample_name, bs.standard_instrument_name, bs.complete_day, bs.sample_remark AS remark, bs.standard_instrument_id, bs.sample_no, bs.factory_number, bs.item_name, /*bs.item_quantity,*/ bs.inspection_type, bs.mandatory_flag, bs.test_quantity, bs.sample_state, bs.current_site, bs.plan_complete_date, bs.affix, bs.ranges, bs.grade, bs.factory, bs.calibrat_point, bs.apply_dept, bs.specification, bs.final_fee, bs.service_type, CASE WHEN bs.actual_complete_date IS NOT NULL THEN DATEDIFF( bs.plan_complete_date, bs.actual_complete_date ) ELSE datediff( bs.plan_complete_date, now()) END AS surplus_days, bs.report_no, bs.is_report_back, bs.back_reason AS report_back_reason, bs.is_just_certificate, bs.report_state, bs.temper, bs.humidity, bs.test_result, bs.test_date, bs.next_test_date, bs.test_cycle, bs.test_address, bs.generate_time, bs.point_report_id, bs.is_merge, bs.circulation_flag, bs.item_proposal_fee AS proposal_fee, bs.change_price_reason, bs.test_user_name, bs.group_id, bs.group_name, bs.charging_num, bs.other_fee, bs.receivable_fee, bs.affix_quantity, bs.test_org, bs.out_org_order_no, bs.out_org_sample_no, bs.business_user_name, bs.pdf_path, bs.settlement_state, bs.result_describe, bsa.attach_id FROM view_sample_info bs JOIN bus_sample_report bsr ON bs.report_id = bsr.id JOIN bus_sample sa ON bsr.sample_id = sa.id JOIN bus_sample_attr bsa ON sa.id = bsa.id 根据bs.commission_date 进行排序最近的排上面 bs.commission_date

2023-07-15 上传

select distinct a.EMPI_ID, a.PATIENT_NO, a.MR_NO, a.PAT_NAME, a.PAT_SEX, a.PAT_AGE, a.PAT_PHONE_NO, b.DIAG_RESULT, a.ADMIT_DATE, a.DISCHARGE_DEPT_NAME, a.ATTEND_DR from BASIC_INFORMATION a join PA_DIAG b on a.MZZY_SERIES_NO=b.MZZY_SERIES_NO join EXAM_DESC_RESULT_CODE c on a.MZZY_SERIES_NO=c.MZZY_SERIES_NO join DRUG_INFO d on a.MZZY_SERIES_NO=d.MZZY_SERIES_NO join EMR_CONTENT e on a.MZZY_SERIES_NO=e.MZZY_SERIES_NO JOIN TEST_INFO A17 ON a.MZZY_SERIES_NO = A17.MZZY_SERIES_NO where a.PAT_AGE>='18' and (to_char(a.ADMIT_DATE,'YYYY-MM-DD') >= '2021-01-01') AND (b.DIAG_RESULT LIKE '%鼻咽癌%' or b.DIAG_RESULT LIKE '%鼻咽恶性肿瘤%' or b.DIAG_CODE LIKE '%C11/900%') and d.DRUG_NAME not in (select DRUG_NAME FROM DRUG_INFO WHERE DRUG_NAME like '卡培他滨') and b.DIAG_RESULT NOT IN (SELECT DIAG_RESULT FROM PA_DIAG WHERE DIAG_RESULT LIKE '%HIV阳性%') and b.DIAG_RESULT NOT IN (SELECT DIAG_RESULT FROM PA_DIAG WHERE DIAG_RESULT LIKE '%充血性心力衰竭%') AND to_char(( A17.TEST_DETAIL_ITEM_NAME = '中性粒细胞' AND A17.TEST_RESULT >= 1.5 ) OR ( A17.TEST_DETAIL_ITEM_NAME = '血小板' AND A17.TEST_RESULT >= 100 ) OR ( A17.TEST_DETAIL_ITEM_NAME = '血红蛋白' AND A17.TEST_RESULT >= 9 ) OR ( A17.TEST_DETAIL_ITEM_NAME = '丙氨酸氨基转移酶' AND A17.TEST_RESULT <= 2.5 ) OR ( A17.TEST_DETAIL_ITEM_NAME = '天门冬氨酸氨基转移酶' AND A17.TEST_RESULT <= 2.5 ) OR ( A17.TEST_DETAIL_ITEM_NAME = '肌酐清除率' AND A17.TEST_RESULT > 51 ) OR ( A17.TEST_DETAIL_ITEM_NAME = '肌酐' AND A17.TEST_RESULT <=1.5 ) OR ( A17.TEST_DETAIL_ITEM_NAME = '凝血酶原时间' AND A17.TEST_RESULT <= 1.5 ))语句哪里有问题

2023-06-07 上传

修改和补充下列代码得到十折交叉验证的平均auc值和平均aoc曲线,平均分类报告以及平均混淆矩阵 min_max_scaler = MinMaxScaler() X_train1, X_test1 = x[train_id], x[test_id] y_train1, y_test1 = y[train_id], y[test_id] # apply the same scaler to both sets of data X_train1 = min_max_scaler.fit_transform(X_train1) X_test1 = min_max_scaler.transform(X_test1) X_train1 = np.array(X_train1) X_test1 = np.array(X_test1) config = get_config() tree = gcForest(config) tree.fit(X_train1, y_train1) y_pred11 = tree.predict(X_test1) y_pred1.append(y_pred11 X_train.append(X_train1) X_test.append(X_test1) y_test.append(y_test1) y_train.append(y_train1) X_train_fuzzy1, X_test_fuzzy1 = X_fuzzy[train_id], X_fuzzy[test_id] y_train_fuzzy1, y_test_fuzzy1 = y_sampled[train_id], y_sampled[test_id] X_train_fuzzy1 = min_max_scaler.fit_transform(X_train_fuzzy1) X_test_fuzzy1 = min_max_scaler.transform(X_test_fuzzy1) X_train_fuzzy1 = np.array(X_train_fuzzy1) X_test_fuzzy1 = np.array(X_test_fuzzy1) config = get_config() tree = gcForest(config) tree.fit(X_train_fuzzy1, y_train_fuzzy1) y_predd = tree.predict(X_test_fuzzy1) y_pred.append(y_predd) X_test_fuzzy.append(X_test_fuzzy1) y_test_fuzzy.append(y_test_fuzzy1)y_pred = to_categorical(np.concatenate(y_pred), num_classes=3) y_pred1 = to_categorical(np.concatenate(y_pred1), num_classes=3) y_test = to_categorical(np.concatenate(y_test), num_classes=3) y_test_fuzzy = to_categorical(np.concatenate(y_test_fuzzy), num_classes=3) print(y_pred.shape) print(y_pred1.shape) print(y_test.shape) print(y_test_fuzzy.shape) # 深度森林 report1 = classification_report(y_test, y_prprint("DF",report1) report = classification_report(y_test_fuzzy, y_pred) print("DF-F",report) mse = mean_squared_error(y_test, y_pred1) rmse = math.sqrt(mse) print('深度森林RMSE:', rmse) print('深度森林Accuracy:', accuracy_score(y_test, y_pred1)) mse = mean_squared_error(y_test_fuzzy, y_pred) rmse = math.sqrt(mse) print('F深度森林RMSE:', rmse) print('F深度森林Accuracy:', accuracy_score(y_test_fuzzy, y_pred)) mse = mean_squared_error(y_test, y_pred) rmse = math.sqrt(mse) print('F?深度森林RMSE:', rmse) print('F?深度森林Accuracy:', accuracy_score(y_test, y_pred))

2023-06-02 上传

下面的代码哪里有问题,帮我改一下from __future__ import print_function import numpy as np import tensorflow import keras from keras.models import Sequential from keras.layers import Dense,Dropout,Flatten from keras.layers import Conv2D,MaxPooling2D from keras import backend as K import tensorflow as tf import datetime import os np.random.seed(0) from sklearn.model_selection import train_test_split from PIL import Image import matplotlib.pyplot as plt from keras.datasets import mnist images = [] labels = [] (x_train,y_train),(x_test,y_test)=mnist.load_data() X = np.array(images) print (X.shape) y = np.array(list(map(int, labels))) print (y.shape) x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=0) print (x_train.shape) print (x_test.shape) print (y_train.shape) print (y_test.shape) ############################ ########## batch_size = 20 num_classes = 4 learning_rate = 0.0001 epochs = 10 img_rows,img_cols = 32 , 32 if K.image_data_format() =='channels_first': x_train =x_train.reshape(x_train.shape[0],1,img_rows,img_cols) x_test = x_test.reshape(x_test.shape[0],1,img_rows,img_cols) input_shape = (1,img_rows,img_cols) else: x_train = x_train.reshape(x_train.shape[0],img_rows,img_cols,1) x_test = x_test.reshape(x_test.shape[0],img_rows,img_cols,1) input_shape =(img_rows,img_cols,1) x_train =x_train.astype('float32') x_test = x_test.astype('float32') x_train /= 255 x_test /= 255 print('x_train shape:',x_train.shape) print(x_train.shape[0],'train samples') print(x_test.shape[0],'test samples')

2023-05-25 上传