迈瑞BC-5000血液分析仪中文使用手册V13.0

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本资源为迈瑞BC-5000系列中文使用说明书,由深圳迈瑞生物医疗电子股份有限公司制作。该手册详细介绍了BC-5000全自动血液细胞分析仪的操作、维护和保修政策。知识产权方面,未经迈瑞公司许可,用户不得擅自复制、修改或翻译使用说明书。 主要内容包括: 1. 产品介绍:BC-5000是一款全自动血液细胞分析仪,专为临床实验室设计,能快速准确地进行血液检测,为诊断提供支持。 2. 操作与维护:强调了只有经过迈瑞公司认可的专业人员才能进行仪器的装配、扩充、重调、改进和修理,确保设备的安全性和可靠性。同时,使用过程中必须按照说明书操作,耗材更换需使用迈瑞公司原装或认可的配件。 3. 保修与维修服务:保修期以销售合同为准,耗材不包含在保修范围内。保修期自安装日期开始计算,必须在验收后30天内填写并寄回《设备保修卡》。保修期内,人为损坏、使用不当、电压异常、自然灾害、非原厂部件维修等原因导致的维修将收取费用。保修期满后仍可提供收费维修。 4. 用户限制:仪器仅限经迈瑞公司或其代理商培训的专业人员、医生或实验员操作,医院或机构需确保有一套满意的维修服务系统。 5. 注意事项:未按规定操作可能导致保修失效,如未能及时填写保修卡、提供正确设备序列号,或者使用非官方部件进行维修等,都将影响保修权益。 这份使用说明书为BC-5000的使用者提供了全面的操作指南和维修服务政策,旨在确保设备的高效运行和用户的权益保障。

帮我分析这段abap代码:FUNCTION zhs_jcy_dmt001. *"---------------------------------------------------------------------- *"*"本地接口: *" IMPORTING *" VALUE(IM_MTART) TYPE ZHS_RANGE_MTART OPTIONAL *" VALUE(IM_MATKL) TYPE ZHS_RANGE_MATKL OPTIONAL *" VALUE(IM_SENDMSG) TYPE FLAG DEFAULT SPACE *" TABLES *" ET_DATA STRUCTURE ZHS_JCY_DTM001 OPTIONAL *"---------------------------------------------------------------------- INCLUDE zgen_bc_x_fmlog_first_phase. WAIT UP TO 3 SECONDS. INCLUDE zgen_bc_x_fmlog_last_phase. EXIT. DATA: lo_jcy TYPE REF TO zcl_hans_jcy_handle, lt_dtm001 TYPE TABLE OF zhs_jcy_dtm001, ls_dtm001 TYPE zhs_jcy_dtm001, lt_dtmc01 TYPE TABLE OF zhs_jcy_dmt01, ls_dtmc01 TYPE zhs_jcy_dmt01. "实例化类 CREATE OBJECT lo_jcy EXPORTING im_hs_tcode = gc_hs_tcode_dtm001. "判断监控点是否启用 IF lo_jcy->is_active( ) EQ zcl_hans_jcy_handle=>no. RETURN. ENDIF. PERFORM frm_get_dmtc01. REFRESH:lt_dtm001. SELECT a~mtart a~matnr b~maktx a~matkl INTO CORRESPONDING FIELDS OF TABLE lt_dtm001 FROM mara AS a INNER JOIN makt AS b ON a~matnr EQ b~matnr AND b~spras EQ sy-langu WHERE mtart IN im_mtart AND matkl IN im_matkl. DATA: l_char TYPE c, l_num TYPE n, l_len TYPE i, l_cnt TYPE i. LOOP AT lt_dtm001 INTO ls_dtm001. CLEAR l_cnt. LOOP AT gt_dtmc01 INTO gs_dtmc01 WHERE mtart EQ ls_dtm001-mtart AND zmatcode NE space. CLEAR:l_char,l_len. CONDENSE: gs_dtmc01-zmatcode. l_len = strlen( gs_dtmc01-zmatcode ). DO l_len TIMES. l_char = gs_dtmc01-zmatcode+l_cnt(1). ENDDO. ENDLOOP. ENDLOOP. CHECK lt_dtm001[] IS NOT INITIAL. ls_dtm001-statm = gc_mat_statm_k. MODIFY lt_dtm001 FROM ls_dtm001 TRANSPORTING statm WHERE statm NE gc_mat_statm_k. et_data[] = lt_dtm001[]. FREE:lt_dtm001. CHECK im_sendmsg EQ 'X'. REFRESH:gt_zhs_jcy_ab_data. CLEAR: gs_return. LOOP AT et_data INTO ls_dtm001. "设置WS参数值 CLEAR gs_zhs_jcy_ab_data. * KEY值 gs_zhs_jcy_ab_data-key03 = ls_dtm001-statm. gs_zhs_jcy_ab_data-key02 = ls_dtm001-mtart. gs_zhs_jcy_ab_data-key01 = ls_dtm001-matnr. "栏位值 gs_zhs_jcy_ab_data-field01 = ls_dtm001-mtart. gs_zhs_jcy_ab_data-field02 = ls_dtm001-matnr. gs_zhs_jcy_ab_data-field03 = ls_dtm001-maktx. gs_zhs_jcy_ab_data-field04 = ls_dtm001-zmatcode. gs_zhs_jcy_ab_data-field05 = ls_dtm001-matkl. gs_zhs_jcy_ab_data-field06 = ls_dtm001-statm. APPEND gs_zhs_jcy_ab_data TO gt_zhs_jcy_ab_data. ENDLOOP. CALL METHOD lo_jcy->call_ws_monitor EXPORTING im_zhs_jcy_ab_data = gt_zhs_jcy_ab_data IMPORTING ex_return = gs_return. FREE lo_jcy. ENDFUNCTION.

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CMake Warning: Ignoring extra path from command line: "../openMVS" -- Detected version of GNU GCC: 94 (904) Compiling with C++17 CMake Error at /home/xujx/.local/lib/python3.8/site-packages/cmake/data/share/cmake-3.26/Modules/CMakeDetermineCompilerId.cmake:751 (message): Compiling the CUDA compiler identification source file "CMakeCUDACompilerId.cu" failed. Compiler: /usr/bin/nvcc Build flags: Id flags: --keep;--keep-dir;tmp -v The output was: 255 #$ _SPACE_= #$ _CUDART_=cudart #$ _HERE_=/usr/lib/nvidia-cuda-toolkit/bin #$ _THERE_=/usr/lib/nvidia-cuda-toolkit/bin #$ _TARGET_SIZE_= #$ _TARGET_DIR_= #$ _TARGET_SIZE_=64 #$ NVVMIR_LIBRARY_DIR=/usr/lib/nvidia-cuda-toolkit/libdevice #$ PATH=/usr/lib/nvidia-cuda-toolkit/bin:/usr/local/cuda-11.8/bin:/home/xujx/anaconda3/bin:/home/xujx/anaconda3/condabin:/home/xujx/.local/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin #$ LIBRARIES= -L/usr/lib/x86_64-linux-gnu/stubs -L/usr/lib/x86_64-linux-gnu #$ rm tmp/a_dlink.reg.c #$ gcc -D__CUDA_ARCH__=300 -E -x c++ -DCUDA_DOUBLE_MATH_FUNCTIONS -D__CUDACC__ -D__NVCC__ -D__CUDACC_VER_MAJOR__=10 -D__CUDACC_VER_MINOR__=1 -D__CUDACC_VER_BUILD__=243 -include "cuda_runtime.h" -m64 "CMakeCUDACompilerId.cu" > "tmp/CMakeCUDACompilerId.cpp1.ii" #$ cicc --c++14 --gnu_version=90400 --allow_managed -arch compute_30 -m64 -ftz=0 -prec_div=1 -prec_sqrt=1 -fmad=1 --include_file_name "CMakeCUDACompilerId.fatbin.c" -tused -nvvmir-library "/usr/lib/nvidia-cuda-toolkit/libdevice/libdevice.10.bc" --gen_module_id_file --module_id_file_name "tmp/CMakeCUDACompilerId.module_id" --orig_src_file_name "CMakeCUDACompilerId.cu" --gen_c_file_name "tmp/CMakeCUDACompilerId.cudafe1.c" --stub_file_name "tmp/CMakeCUDACompilerId.cudafe1.stub.c" --gen_device_file_name "tmp/CMakeCUDACompilerId.cudafe1.gpu" "tmp/CMakeCUDACompilerId.cpp1.ii" -o "tmp/CMakeCUDACompilerId.ptx" #$ ptxas -arch=sm_30 -m64 "tmp/CMakeCUDACompilerId.ptx" -o "tmp/CMakeCUDACompilerId.sm_30.cubin" ptxas fatal : Value 'sm_30' is not defined for option 'gpu-name' # --error 0xff -- Call Stack (most recent call first): /home/xujx/.local/lib/python3.8/site-packages/cmake/data/share/cmake-3.26/Modules/CMakeDetermineCompilerId.cmake:8 (CMAKE_DETERMINE_COMPILER_ID_BUILD) /home/xujx/.local/lib/python3.8/site-packages/cmake/data/share/cmake-3.26/Modules/CMakeDetermineCompilerId.cmake:53 (__determine_compiler_id_test) /home/xujx/.local/lib/python3.8/site-packages/cmake/data/share/cmake-3.26/Modules/CMakeDetermineCUDACompiler.cmake:307 (CMAKE_DETERMINE_COMPILER_ID) CMakeLists.txt:109 (ENABLE_LANGUAGE)是什么问题

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pt_x_bc_var = Variable(torch.from_numpy(x_bc_var).float(), requires_grad=False) pt_x_in_pos_one = Variable(torch.from_numpy(x_in_pos_one).float(), requires_grad=False) pt_x_in_zeros = Variable(torch.from_numpy(x_in_zeros).float(), requires_grad=False) pt_t_in_var = Variable(torch.from_numpy(t_in_var).float(), requires_grad=False) pt_u_in_zeros = Variable(torch.from_numpy(u_in_zeros).float(), requires_grad=False) # 求边界条件的损失 net_bc_right = net(torch.cat([pt_x_in_zeros, pt_t_in_var], 1)) # u(0,t)的输出 mse_u_2 = mse_cost_function(net_bc_right, pt_u_in_zeros) # e = 0-u(0,t) 公式(2) net_bc_left = net(torch.cat([pt_x_in_pos_one, pt_t_in_var], 1)) # u(1,t)的输出 mse_u_3 = mse_cost_function(net_bc_left, pt_u_in_zeros) x_0 = torch.cat([pt_x_in_zeros, pt_t_in_var], 1) x_1 = torch.cat([pt_x_in_pos_one, pt_t_in_var], 1) pt_x_0 = x_0.detach().requires_grad_(True) pt_x_1 = x_1.detach().requires_grad_(True) net_bc_right.requires_grad_(True) net_bc_left.requires_grad_(True) u_x_0 = torch.autograd.grad(net_bc_right, pt_x_0, grad_outputs=torch.ones_like(net_bc_right), create_graph=True, allow_unused=True)[0][:, 0].unsqueeze(-1) u_x_1 = torch.autograd.grad(net_bc_left, pt_x_1, grad_outputs=torch.ones_like(net_bc_left), create_graph=True, allow_unused=True)[0][:, 0].unsqueeze(-1) u_xx_0 = torch.autograd.grad(u_x_0, pt_x_0, grad_outputs=torch.ones_like(u_x_0), create_graph=True, allow_unused=True)[0][:, 0].unsqueeze(-1) u_xx_1 = torch.autograd.grad(u_x_1, pt_x_1, grad_outputs=torch.ones_like(u_x_1), create_graph=True, allow_unused=True)[0][:, 0].unsqueeze(-1)这串代码有什么问题吗?该怎么解决

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