Matrix软件操作说明
IP Matrix VJD-8000 软件手册说明书
4.5
Basic keyboard settings
5
Operation
5.1
Keyboard basics
5.2
Keyboard print template
5.3
Keyboard layout
6
Troubleshooting
Table of contents | en 3
4 4 4 4 5 5 5 6 7 8 8 8 9 12 14 15 15 18 19 20
Requirements
For configuration purposes:
Hardware
Personal Computer
CPU
Dual Core, 3.0 GHz or faster
RAM
Minimum 2 GB
Operating system
Windows Server 2008 R2 (64 bit) Windows 7 SP1 (64 bit) Windows 8.1 (64 bit) Windows 10 (64 bit)
Bosch Security Systems B.V.
Software manual
2019.04 | V2 | DOC
4 en | Introduction
IP Matrix
MATRIX-E使用手册
4.1 简介 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 4.2 前视图 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 4.3 侧视图 – 右侧 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 4.4 侧视图 – 左侧 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.5 后视图 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
6 维护 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .31
python中matrix用法
python中matrix用法在Python中，我们可以使用多种库来处理矩阵，包括NumPy、SciPy和SymPy等。
其中，NumPy是最常用的库之一，它提供了丰富的矩阵操作函数和方法。
首先，我们需要安装NumPy库，可以使用以下命令来安装：python.pip install numpy.接下来，我们可以使用NumPy来创建矩阵，进行矩阵运算和其他操作。
下面是一些常见的矩阵操作用法：1. 创建矩阵。
python.import numpy as np.# 创建一个2x3的矩阵。
matrix = np.array([[1, 2, 3], [4, 5, 6]])。
print(matrix)。
2. 矩阵运算。
python.# 矩阵加法。
matrix1 = np.array([[1, 2], [3, 4]])。
matrix2 = np.array([[5, 6], [7, 8]])。
result = matrix1 + matrix2。
print(result)。
# 矩阵乘法。
result = np.dot(matrix1, matrix2)。
print(result)。
3. 矩阵转置。
python.# 矩阵转置。
matrix = np.array([[1, 2], [3, 4]])。
result = matrix.T.print(result)。
4. 矩阵求逆。
python.# 矩阵求逆。
matrix = np.array([[1, 2], [3, 4]])。
result = np.linalg.inv(matrix)。
print(result)。
除了上述操作外，NumPy还提供了很多其他矩阵操作的函数和方法，如求特征值、特征向量、行列式等。
通过使用NumPy库，我们可以方便地进行矩阵运算和操作，为数据分析和科学计算提供了很大的便利。
总之，Python中的NumPy库为我们提供了丰富的矩阵操作功能，使得我们可以方便地进行矩阵的创建、运算和其他操作，为数据分析和科学计算提供了很大的便利。
蓝眼 Matrix 矩阵控制软件使用手册说明书
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矩阵控制软件使用手册Version 1.5.11.52015/11/11使用手册本手册适用于以下产品蓝眼Matrix矩阵控制软件感谢您使用蓝眼科技的产品。
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LABEL MATRI7.0使用说明
LABEL MATRIX 7.0使用说明一、打开软件。
二、新建标签进入软件后，按NEXT，接着按FINISH就可以进入编辑状态。
三、界面介绍1、常用工具栏介绍新建 打开保存 打印 快速打印 打印预览选择打印机 放大 缩小 数据库列表2、编辑工具栏介绍（左侧）四、设置标签1.设置界面介绍在空白处双击鼠标左键，出现页面属性对话框。
图1图2图3图4图1：设置标签页面大小图2：设置边距图3：设置标签排数图4：设置数据库连接。
2.页面设置（Page size Marginit）如图1所示，WIDTH（宽度）HEIGHT(高度)，宽度最大10.4,高度以实际情况定，但不要超过实际一张标签的大小，否则会跳张即打印一张出来一张空白。
连续纸除外。
设置完标签大小后设置标签边距，即标签左右两边标签纸与底纸的距离。
左右根据实际情况设置，一般以0.2 0.3居多，上下设置为0.3.多排设置(Multiple)ACROSS列数WIDTH、HEIGHT一个小标签的宽度和高度4.连接数据库(Database)ADD添加数据库，NEXT （下一步），FILE（打开数据库文件），自动扫描数据库后，选择工作表（EXCEL 文件里Sheet1,2,3），NEXT(下一步)，FINISH（完成）。
五、编辑内容1.文本点击左边abc按钮，然后在DATA选项卡的TEXT后面输入相应文本内容。
如果是连接数据库，则ORIGIN需要选择为DA TABASE，会在下面出现表格字段，选择相应字段。
Database数据库、文本输入选择数据库输入完成后点击数据库按钮查看数据表Constant 固定值、Counter 增量如果需要使用增量，则将Origin选择为Counter。
START BY是增量的起始值，STEP 是增量大小。
如图所示，即打印出来的增量为001 003 005 007.。
2.条码点击左边的一维条码、二维条码按钮，然后在DA TA选项卡的TEXT后面输入相应文本。
Matrix软件包说明书
2nd Introduction to the Matrix packageMartin Maechler and Douglas BatesR Core Development Team******************.ethz.ch,*******************September2006(typeset on August11,2023)AbstractLinear algebra is at the core of many areas of statistical computing and from its inception the S lan-guage has supported numerical linear algebra via a matrix data type and several functions and operators,such as%*%,qr,chol,and solve.However,these data types and functions do not provide direct accessto all of the facilities for efficient manipulation of dense matrices,as provided by the Lapack subroutines,and they do not provide for manipulation of sparse matrices.The Matrix package provides a set of S4classes for dense and sparse matrices that extend the basic matrix data type.Methods for a wide variety of functions and operators applied to objects from theseclasses provide efficient access to BLAS(Basic Linear Algebra Subroutines),Lapack(dense matrix),CHOLMOD including AMD and COLAMD and Csparse(sparse matrix)routines.One notable char-acteristic of the package is that whenever a matrix is factored,the factorization is stored as part of theoriginal matrix so that further operations on the matrix can reuse this factorization.1IntroductionThe most automatic way to use the Matrix package is via the Matrix()function which is very similar to the standard R function matrix(),>library(Matrix)>M<-Matrix(10+1:28,4,7)>M4x7Matrix of class"dgeMatrix"[,1][,2][,3][,4][,5][,6][,7][1,]11151923273135[2,]12162024283236[3,]13172125293337[4,]14182226303438>tM<-t(M)Such a matrix can be appended to(using cbind()or rbind())or indexed,>(M2<-cbind(-1,M))4x8Matrix of class"dgeMatrix"[,1][,2][,3][,4][,5][,6][,7][,8][1,]-111151923273135[2,]-112162024283236[3,]-113172125293337[4,]-1141822263034381>M[2,1][1]12>M[4,][1]14182226303438where the last two statements show customary matrix indexing,returning a simple numeric vector each1. We assign0to some columns and rows to“sparsify”it,and some NA s(typically“missing values”in data analysis)in order to demonstrate how they are dealt with;note how we can“subassign”as usual,for classical R matrices(i.e.,single entries or whole slices at once),>M2[,c(2,4:6)]<-0>M2[2,]<-0>M2<-rbind(0,M2,0)>M2[1:2,2]<-M2[3,4:5]<-NAand then coerce it to a sparse matrix,>sM<-as(M2,"sparseMatrix")>10*sM6x8sparse Matrix of class"dgCMatrix"[1,].NA......[2,]-10NA150 (310350)[3,]...NA NA...[4,]-10.170 (330370)[5,]-10.180 (340380)[6,]........>identical(sM*2,sM+sM)[1]TRUE>is(sM/10+M2%/%2,"sparseMatrix")[1]TRUEwhere the last three calls show that multiplication by a scalar keeps sparcity,as does other arithmetic, but addition to a“dense”object does not,as you might have expected after some thought about“sensible”behavior:>sM+106x8Matrix of class"dgeMatrix"[,1][,2][,3][,4][,5][,6][,7][,8][1,]10NA101010101010[2,]9NA251010104145[3,]101010NA NA101010[4,]910271010104347[5,]910281010104448[6,]10101010101010101because there’s an additional default argument to indexing,drop=TRUE.If you add“,drop=FALSE”you will get submatrices instead of simple vectors.2Operations on our classed matrices include(componentwise)arithmetic(+,−,∗,/,etc)as partly seen above,comparison(>,≤,etc),e.g.,>Mg2<-(sM>2)>Mg26x8sparse Matrix of class"lgCMatrix"[1,].N......[2,]:N|...||[3,]...N N...[4,]:.|...||[5,]:.|...||[6,]........returning a logical sparse matrix.When interested in the internal str ucture,str()comes handy,and we have been using it ourselves more regulary than print()ing(or show()ing as it happens)our matrices; alternatively,summary()gives output similar to Matlab’s printing of sparse matrices.>str(Mg2)Formal class'lgCMatrix'[package"Matrix"]with6slots..@i:int[1:16]1340113422.....@p:int[1:9]0358910101316..@Dim:int[1:2]68..@Dimnames:List of2....$:NULL....$:NULL..@x:logi[1:16]FALSE FALSE FALSE NA NA TRUE.....@factors:list()>summary(Mg2)6x8sparse Matrix of class"lgCMatrix",with16entriesi j x121FALSE241FALSE351FALSE412NA522NA623TRUE743TRUE853TRUE934NA1035NA1127TRUE1247TRUE1357TRUE1428TRUE1548TRUE1658TRUEAs you see from both of these,Mg2contains“extra zero”(here FALSE)entries;such sparse matrices may be created for different reasons,and you can use drop0()to remove(“drop”)these extra zeros.This should never matter for functionality,and does not even show differently for logical sparse matrices,but the internal structure is more compact:3>Mg2<-drop0(Mg2)>str(Mg2@x)#length 13,was 16logi [1:13]NA NA TRUE TRUE TRUE NA ...For large sparse matrices,visualization (of the sparsity pattern)is important,and we provide image()methods for that,e.g.,>data(CAex,package ="Matrix")>print(image(CAex,main ="image(CAex)"))#print(.)needed for Sweaveimage(CAex)Dimensions: 72 x 72Column R o w204060204060−0.4−0.20.00.20.40.60.81.0Further,i.e.,in addition to the above implicitly mentioned "Ops"operators (+,*,...,<=,>,...,&which all work with our matrices,notably in conjunction with scalars and traditional matrices),the "Math"-operations (such as exp(),sin()or gamma())and "Math2"(round()etc)and the "Summary"group of functions,min(),range(),sum(),all work on our matrices as they should.Note that all these are implemented via so called group methods ,see e.g.,?Arith in R .The intention is that sparse matrices remain sparse whenever sensible,given the matrix classes and operators involved,but not content specifically. E.g.,<sparse>+<dense>gives <dense>even for the rare cases where it would be advantageous to get a <sparse>result.These classed matrices can be “indexed”(more technically “subset”)as traditional S language (and hence R )matrices,as partly seen above.This also includes the idiom M [M op num ]which returns simple vectors,>sM[sM >2][1]NA NA 151718NA NA 313334353738>sml <-sM[sM <=2]>sml [1]0-10-1-10NA NA 000000000NA[24]NA 0000000and “subassign”ment similarly works in the same generality as for traditional S language matrices.41.1Matrix package for numerical linear algebraLinear algebra is at the core of many statistical computing techniques and,from its inception,the S language has supported numerical linear algebra via a matrix data type and several functions and operators,such as %*%,qr,chol,and solve.Initially the numerical linear algebra functions in R called underlying Fortran routines from the Linpack(Dongarra et al.,1979)and Eispack(Smith et al.,1976)libraries but over the years most of these functions have been switched to use routines from the Lapack(Anderson et al.,1999) library which is the state-of-the-art implementation of numerical dense linear algebra.Furthermore,R can be configured to use accelerated BLAS(Basic Linear Algebra Subroutines),such as those from the Atlas(Whaley et al.,2001)project or other ones,see the R manual“Installation and Administration”.Lapack provides routines for operating on several special forms of matrices,such as triangular matrices and symmetric matrices.Furthermore,matrix decompositions like the QR decompositions produce multiple output components that should be regarded as parts of a single object.There is some support in R for operations on special forms of matrices(e.g.the backsolve,forwardsolve and chol2inv functions)and for special structures(e.g.a QR structure is implicitly defined as a list by the qr,qr.qy,qr.qty,and related functions)but it is not as fully developed as it could be.Also there is no direct support for sparse matrices in R although Koenker and Ng(2003)have developed the SparseM package for sparse matrices based on SparseKit.The Matrix package provides S4classes and methods for dense and sparse matrices.The methods for dense matrices use Lapack and BLAS.The sparse matrix methods use CHOLMOD(Davis,2005a), CSparse(Davis,2005b)and other parts(AMD,COLAMD)of Tim Davis’“SuiteSparse”collection of sparse matrix libraries,many of which also use BLAS.Todo:triu(),tril(),diag(),...and as(.,.),but of course only when they’ve seen a few different ones.Todo:matrix operators include%*%,crossprod(),tcrossprod(),solve()Todo:expm()is the matrix exponential......Todo:symmpart()and skewpart()compute the symmetric part,(x+t(x))/2and the skew-symmetric part,(x-t(x))/2of a matrix x.Todo:factorizations include Cholesky()(or chol()),lu(),qr()(not yet for dense)Todo:Although generally the result of an operation on dense matrices is a dgeMatrix,certain operations return matrices of special types.Todo: E.g.show the distinction between t(mm)%*%mm and crossprod(mm).2Matrix ClassesThe Matrix package provides classes for real(stored as double precision),logical and so-called“pattern”(binary)dense and sparse matrices.There are provisions to also provide integer and complex(stored as double precision complex)matrices.Note that in R,logical means entries TRUE,FALSE,or NA.To store just the non-zero pattern for typical sparse matrix algorithms,the pattern matrices are binary,i.e.,conceptually just TRUE or FALSE.In Matrix, the pattern matrices all have class names starting with"n"(patter n).2.1Classes for dense matricesFor the sake of brevity,we restrict ourselves to the real(d ouble)classes,but they are paralleled by l ogical and patter n matrices for all but the positive definite ones.dgeMatrix Real matrices in general storage modedsyMatrix Symmetric real matrices in non-packed storagedspMatrix Symmetric real matrices in packed storage(one triangle only)5dtrMatrix Triangular real matrices in non-packed storagedtpMatrix Triangular real matrices in packed storage(triangle only)dpoMatrix Positive semi-definite symmetric real matrices in non-packed storagedppMatrix ditto in packed storageMethods for these classes include coercion between these classes,when appropriate,and coercion to the matrix class;methods for matrix multiplication(%*%);cross products(crossprod),matrix norm(norm); reciprocal condition number(rcond);LU factorization(lu)or,for the poMatrix class,the Cholesky decom-position(chol);and solutions of linear systems of equations(solve).Whenever a factorization or a decomposition is calculated it is preserved as a(list)element in the factors slot of the original object.In this way a sequence of operations,such as determining the condition number of a matrix then solving a linear system based on the matrix,do not require multiple factorizations of the same matrix nor do they require the user to store the intermediate results.2.2Classes for sparse matricesUsed for large matrices in which most of the elements are known to be zero(or FALSE for logical and binary (“pattern”)matrices).Sparse matrices are automatically built from Matrix()whenever the majority of entries is zero(or FALSE respectively).Alternatively,sparseMatrix()builds sparse matrices from their non-zero entries and is typically recommended to construct large sparse matrices,rather than direct calls of new().Todo: E.g.model matrices created from factors with a large number of levelsTodo:or from spline basis functions(e.g.COBS,package cobs),etc.Todo:Other uses include representations of graphs.indeed;good you mentioned it!particularly since we still have the interface to the graph package.I think I’d like to draw one graph in that article—maybe the undirected graph corresponding to a crossprod()result of dimension ca.502Todo:Specialized algorithms can give substantial savings in amount of storage used and execution time of operations.Todo:Our implementation is based on the CHOLMOD and CSparse libraries by Tim Davis.2.3Representations of sparse matrices2.3.1Triplet representation(TsparseMatrix)Conceptually,the simplest representation of a sparse matrix is as a triplet of an integer vector i giving the row numbers,an integer vector j giving the column numbers,and a numeric vector x giving the non-zero values in the matrix.2In Matrix,the TsparseMatrix class is the virtual class of all sparse matrices in triplet representation.Its main use is for easy input or transfer to other classes.As for the dense matrices,the class of the x slot may vary,and the subclasses may be triangular, symmetric or unspecified(“general”),such that the TsparseMatrix class has several3‘actual”subclasses,the most typical(numeric,general)is dgTMatrix:>getClass("TsparseMatrix")#(i,j,Dim,Dimnames)slots are common to allVirtual Class"TsparseMatrix"[package"Matrix"]Slots:2For efficiency reasons,we use“zero-based”indexing in the Matrix package,i.e.,the row indices i are in0:(nrow(.)-1)and the column indices j accordingly.3the3×3actual subclasses of TsparseMatrix are the three structural kinds,namely t riangular,s ymmetric and g eneral, times three entry classes,d ouble,l ogical,and patter n.6Name:i j Dim DimnamesClass:integer integer integer listExtends:Class"sparseMatrix",directlyClass"Matrix",by class"sparseMatrix",distance2Class"mMatrix",by class"Matrix",distance3Class"replValueSp",by class"Matrix",distance3Known Subclasses:"ngTMatrix","ntTMatrix","nsTMatrix","lgTMatrix","ltTMatrix", "lsTMatrix","dgTMatrix","dtTMatrix","dsTMatrix">getClass("dgTMatrix")Class"dgTMatrix"[package"Matrix"]Slots:Name:i j Dim Dimnames x factorsClass:integer integer integer list numeric listExtends:Class"TsparseMatrix",directlyClass"dsparseMatrix",directlyClass"generalMatrix",directlyClass"dMatrix",by class"dsparseMatrix",distance2Class"sparseMatrix",by class"dsparseMatrix",distance2Class"compMatrix",by class"generalMatrix",distance2Class"Matrix",by class"TsparseMatrix",distance3Class"xMatrix",by class"dMatrix",distance3Class"mMatrix",by class"Matrix",distance4Class"replValueSp",by class"Matrix",distance4Note that the order of the entries in the(i,j,x)vectors does not matter;consequently,such matrices are not unique in their representation.42.3.2Compressed representations:CsparseMatrix and RsparseMatrixFor most sparse operations we use the compressed column-oriented representation(virtual class CsparseMatrix) (also known as“csc”,“compressed sparse column”).Here,instead of storing all column indices j,only the start index of every column is stored.Analogously,there is also a compressed sparse row(csr)representation,which e.g.is used in in the SparseM package,and we provide the RsparseMatrix for compatibility and completeness purposes,in ad-dition to basic coercion((as(.,<cl>)between the classes.These compressed representations remove the redundant row(column)indices and provide faster access to a given location in the matrix because you only need to check one row(column).There are certain advantages5to csc in systems like R,Octave and Matlab where dense matrices are stored in column-major order,therefore it is used in sparse matrix libraries such as CHOLMOD or CSparse 4Furthermore,there can be repeated(i,j)entries with the customary convention that the corresponding x entries are addedto form the matrix element m ij.5routines can make use of high-level(“level-3”)BLAS in certain sparse matrix computations7of which we make use.For this reason,the CsparseMatrix class and subclasses are the principal classes for sparse matrices in the Matrix package.The Matrix package provides the following classes for sparse matrices ...FIXME many more —maybe ex plain naming scheme?...dgTMatrix general,numeric,sparse matrices in (a possibly redundant)triplet form.This can be a conve-nient form in which to construct sparse matrices.dgCMatrix general,numeric,sparse matrices in the (sorted)compressed sparse column format.dsCMatrix symmetric,real,sparse matrices in the (sorted)compressed sparse column format.Only theupper or the lower triangle is stored.Although there is provision for both forms,the lower triangle form works best with TAUCS.dtCMatrix triangular,real,sparse matrices in the (sorted)compressed sparse column format.Todo:Can also read and write the Matrix Market and read the Harwell-Boeing representations.Todo:Can convert from a dense matrix to a sparse matrix (or use the Matrix function)but going through an intermediate dense matrix may cause problems with the amount of memory required.Todo:similar range of operations as for the dense matrix classes.3More detailed examples of “Matrix”operationsHave seen drop0()above,showe a nice double example (where you see “.”and “0”).Show the use of dim<-for resizing a (sparse)matrix.Maybe mention nearPD().Todo:Solve a sparse least squares problem and demonstrate memory /speed gain Todo:mention lme4and lmer(),maybe use one example to show the matrix sizes.4Notes about S4classes and methods implementationMaybe we could give some glimpses of implementations at least on the R level ones?Todo:The class hierarchy:a non-trivial tree where only the leaves are “actual”classes.Todo:The main advantage of the multi-level hierarchy is that methods can often be defined on a higher (virtual class)level which ensures consistency [and saves from “cut &paste”and forgetting things]Todo:Using Group Methods5Session Info>toLatex(sessionInfo())•R version 4.3.1Patched (2023-08-09r84931),x86_64-pc-linux-gnu•Locale:LC_CTYPE=de_CH.UTF-8,LC_NUMERIC=C ,LC_TIME=en_US.UTF-8,LC_COLLATE=C ,LC_MONETARY=en_US.UTF-8,LC_MESSAGES=de_CH.UTF-8,LC_PAPER=de_CH.UTF-8,LC_NAME=C ,LC_ADDRESS=C ,LC_TELEPHONE=C ,LC_MEASUREMENT=de_CH.UTF-8,LC_IDENTIFICATION=C •Time zone:Europe/Zurich •TZcode source:system (glibc)•Running under:Fedora Linux 36(Thirty Six)•Matrix products:default8•BLAS:/u/maechler/R/D/r-patched/F36-64-inst/lib/libRblas.so•LAPACK:/usr/lib64/liblapack.so.3.10.1•Base packages:base,datasets,grDevices,graphics,methods,stats,utils•Other packages:Matrix1.6-1•Loaded via a namespace(and not attached):compiler4.3.1,grid4.3.1,lattice0.21-8,tools4.3.1 ReferencesE.Anderson,Z.Bai,C.Bischof,S.Blackford,J.Demmel,J.Dongarra,J.Du Croz,A.Greenbaum,S.Ham-marling,A.McKenney,and PACK Users’Guide.SIAM,Philadelphia,PA,3rd edition, 1999.Tim Davis.CHOLMOD:sparse supernodal Cholesky factorization and update/downdate. http://www.cise.ufl.edu/research/sparse/cholmod,2005a.Tim Davis.CSparse:a concise sparse matrix package.http://www.cise.ufl.edu/research/sparse/CSparse, 2005b.Jack Dongarra,Cleve Moler,Bunch,and G.W.Stewart.Linpack Users’Guide.SIAM,1979.Roger Koenker and Pin Ng.SparseM:A sparse matrix package for R.J.of Statistical Software,8(6),2003.B.T.Smith,J.M.Boyle,J.J.Dongarra,B.S.Garbow,Y.Ikebe,V.C.Klema,and C.B.Moler.Matrix Eigensystem Routines.EISPACK Guide,volume6of Lecture Notes in Computer Science.Springer-Verlag, New York,1976.R.Clint Whaley,Antoine Petitet,and Jack J.Dongarra.Automated empirical optimization of software and the ATLAS project.Parallel Computing,27(1–2):3–35,2001.Also available as University of Tennessee LAPACK Working Note#147,UT-CS-00-448,2000(/lapack/lawns/lawn147.ps).9。
PLATO软件操作手册-Matrix模块
目录一、Matrix简介 (1)1.1 打开程序 (1)1.2 菜单栏介绍 (1)二、创建Matrix过程 (3)2.1 创建一个新项目和新的系统元素 (3)2.2 开始创建系统Matrix (4)2.3 创建组件Matrix (8)2.4 创建元件Matrix (9)2.5 规格编辑器（specification edit） (10)三、使用优先级对功能或结构进行分析 (11)四、其它操作技巧 (12)4.1 删除系统元素 (12)4.2 使用已有系统元素 (13)4.3 功能重用 (14)4.4 查找系统元素 (15)一、Matrix模块简介SCIO-Matrix模块可以对产品进行系统的结构功能分析，为后两个模块Net-Builder和FMEA提供基础。
本手册将以Iphone4s为例对Matrix模块操作步骤进行讲解。
注：在软件中进行任何操作后无需手动保存，所有数据变动都会自动保存到数据库。
1.1 打开程序从开始菜单中找到SCIO-Matrix程序（）并打开，会出现以下登录界面：默认数据库是本地，输入用户名及密码（默认用户名和密码皆为plato）即可进入Matrix 模块。
1.2 菜单栏介绍1.2.1 The Application bar ()点击此图标可以在最右侧看到三个部分：Documents，SCIO，Plug-ins。
其中documents栏下面，可以显示最近打开过的系统元素或者创建新的系统元素；SCIO 栏下面，有SCIO软件的各个模块图标，点击即可切换到该模块；Plug-ins栏下则是一些插件程序。
如若想隐藏最右侧栏，再次点击The Application bar ()图标即可。
1.2.2 Structure tree（结构树）和function tree（功能树）点击图标，则会打开结构树，如上图（左）所示，会显示所有项目及系统元素；点击图标，则会打开功能树，如上图（右）所示，会显示元素的所有功能。
Matrix 中文手册
重要的安全指导！请阅读这本手册！它提供了很重要的安全、安装和操作指导，使您的设备发挥最高的性能，并且能够延长设备的使用寿命。
请保存这本手册！它包含了安全使用UPS 的重要指导，而且还告诉您，如何在需要时获得厂家提供的服务。
在以后对UPS的维修和存放中，以及遇到问题时，都需要参看这本手册以获得正确的指导。
射频干扰重要的安全指导！警告：我们只对遵守规则的用户负责，千万不要自行更改或拆卸，否则用户将失去使用这个设备的权力。
注意：此设备被检测通过，并且符合FCC第15部分A规则，由于该设备会产生和发出射频干扰，这些限制使得设备在商用环境下运行时产生的有害干扰保持在一定限度内，使其它通讯设备得到有效的保护。
如果没有根据指导安装或使用，可能会对无线电通讯产生有害的干扰。
总而言之，我们并不保证在特殊情况下安装时不会出现干扰。
如果该设备的关闭和打开对无线电设备产生干扰和影响，我们建议用户用以下方法中的一种或几种去尝试消除干扰。
■重置天线的方向。
■加大设备和接收器的距离。
■使设备连接在与接收器不在同一电路的插座上。
■向销售商、经验丰富的无线电调试人员咨询求助。
■该产品必须使用屏蔽通讯电缆接口。
目录1.0简介(1)2.0安全(3)3.0外观(4)隔离模块(4)电子模块(6)电池组(8)4.0安装(10)开箱检查，保护措施，移动UPS，放置(10)输入电压要求(11)安装步骤(12)输入线安装(13)输入电压插头选择(15)输出线安装过程(16)模块连接(18)紧急断电接口（Emergency Power Off Interface）(20)5.0操作(21)显示与控制操作(22)6.0UPS监控(29)计算机接口(29)7.0问题(30)故障查找(31)故障信息(33)重装电子模块(33)重装或增加了电池组(35)获得服务(36)8.0UPS存放(37)9.0指标(38)1.0简介1.1概述Matrix-UPS是一种高性能在线互动式的不间断电源。
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Matrix软件操作说明一. 软件系统设置1．通讯设置通讯设置目的是为了选不同的端口,对端口通讯速率,及矩阵系统网络号进行设置,只有在与矩阵系统速率相同的情况下才能进行通信.窗口界面:具体操作如下.1.单击工具栏的"通讯方式"按钮,进入通讯方式设置窗口2.选择当前软件的通讯方式,"使用本地串口"用485线与电脑相连,远程透明串口,用IP模块通信3.选择相应的"串口","波特率".4.选择"使用远程透明串口",使用网络设备进行通信.选择对应的网络设备.5.单击"确定" 保存设置附:虚拟键盘涉及到网络号操作,在此进行设置.2．矩阵类型窗口界面具体操作1.单击"软件系统设置"->"矩阵类型",进入矩阵类型界面2.选择"矩阵类型"(此类型关系到当前所读取矩阵数据的正确性,请务必选择对应的矩阵型号).3.单击"确定" 保存设置3．修改密码窗口界面具体操作1.单击"软件系统设置"->"修改密码",进入修改密码界面2.输入"旧密码",并确认输入新密码3.单击"确定" 保存设置4．登陆/登出为防止他人任意更改系统设置,系统提供此功能,在拥有管理员权限的情况下方可操作系统.其界面如下:二. 矩阵系统设置1.时间设置设置矩阵系统时间.窗口界面具体操作:1.单击"矩阵系统设置"->"时间设置",2.进入"时间设置"界面3.可单击"同步系统时间"按钮,与计算机系统同步时间4.也可手工设置矩阵时间.5.单击"确定",保存设置2.云台协议设置云台协议窗口界面具体操作:1.单击"矩阵系统设置"->"云台协议",2.进入"云台协议"设置界面3.选择"云台协议",和对应的通信速率.4.单击"确定",保存设置3.网络编号设置矩阵系统网络号窗口界面具体操作:1.单击"矩阵系统设置"->"网络编号",2.进入"网络编号"界面3.选择"网络编号4.单击"确定",保存设置4. 键盘数量设置矩阵系统键盘数量窗口界面具体操作:1.单击"矩阵系统设置"->"键盘数量",2.进入"键盘数量"界面3.选择"键盘数量"4.单击"确定",保存设置三. 标题与显示1. 摄像机标题设置窗口界面具体操作1.单击"标题与显示"->"摄像机标题" 或工具栏上的"摄像机标题"按钮,进入摄像机标题设置界面;2.单击"读矩阵"按钮,系统从矩阵主机读取数据并显示;3.单击"写矩阵"按钮,系统将当前页数据写入矩阵主机;4.单击"读数据库",系统从数据库中读取数据,同时也可将数据库中设置的数据,写入矩阵,也可将数据写回矩阵主机;5.单击"写数据库",将当前页数据写回数据库,以备日后查看,维护.2. 监示器标题窗口界面具体操作1.单击"标题与显示"->"监示器标题" 或工具栏上的"监示器标题"按钮,进入"监示器标题"设置界面;2.单击"读矩阵"按钮,系统从矩阵主机读取数据并显示;3.单击"写矩阵"按钮,系统将当前页数据写入矩阵主机;4.单击"读数据库",系统从数据库中读取数据,同时也可将数据库中设置的数据,写入矩阵,也可将数据写回矩阵主机;5.单击"写数据库",将当前页数据写回数据库,以备日后查看,维护.3. 屏幕位置设置监示器标题/时间显示位置窗口界面具体操作:1.单击"标题与显示"->屏幕位置" ;2.进入"屏幕位置"界面;3.拖动"屏幕位置"按钮到适当位置;4.单击"确定"保存设置;5.重启矩阵系统,使设置生效.4. 标题位置设置监示器标题/时间显示位置窗口界面具体操作:1.单击"标题与显示"->标题位置"2.进入"标题位置"界面.3.拖动"标题位置"按钮到适当位置4.单击"确定"保存设置5.重启矩阵系统,使设置生效5. 时间位置设置监示器标题/时间显示位置窗口界面具体操作:1.单击"标题与显示"->时间位置" ;2.进入"时间位置"界面;3.拖动"时间位置"按钮到适当位置;4.单击"确定"保存设置;5.重启矩阵系统,使设置生效.四. 切换1. 程序切换窗口界面具体操作1.单击"切换"->"程序切换" 或工具栏上的"程序切换"按钮,进入程序切换设置界面,2.单击"读矩阵"按钮,系统从矩阵主机读取数据并显示,3.单击"写矩阵"按钮,系统将当前页数据写入矩阵主机.4.单击"读数据库",系统从数据库中读取数据,同时也可将数据库中设置的数据,写入矩阵,也可将数据写回矩阵主机.5.单击"写数据库",将当前页数据写回数据库,以备日后查看,维护.6.单击"上一页","下一页",可查看不同的切换设置2. 同步切换窗口界面具体操作1.单击"切换"->"同步切换" 或工具栏上的"同步切换"按钮,进入同步切换设置界面,2.单击"读矩阵"按钮,系统从矩阵主机读取数据并显示,3.单击"写矩阵"按钮,系统将当前页数据写入矩阵主机.4.单击"读数据库",系统从数据库中读取数据,同时也可将数据库中设置的数据,写入矩阵,也可将数据写回矩阵主机.5.单击"写数据库",将当前页数据写回数据库,以备日后查看,维护.6.单击"上一页","下一页",可查看不同的切换设置3. 群组切换窗口界面具体操作1.单击"切换"->"群组切换" 或工具栏上的"群组切换"按钮,进入群组切换设置界面,2.单击"读矩阵"按钮,系统从矩阵主机读取数据并显示,3.单击"写矩阵"按钮,系统将当前页数据写入矩阵主机.4.单击"读数据库",系统从数据库中读取数据,同时也可将数据库中设置的数据,写入矩阵,也可将数据写回矩阵主机.5.单击"写数据库",将当前页数据写回数据库,以备日后查看,维护.6.单击"上一页","下一页",可查看不同的切换设置五. 权限1. 键盘/监示器窗口界面具体操作1.单击"权限"->"键盘/监示器" ,进入"键盘/监示器"设置界面;2.单击"读矩阵"按钮,系统从矩阵主机读取数据并显示;3.单击"写矩阵"按钮,系统将当前页数据写入矩阵主机;4.单击"读数据库",系统从数据库中读取数据,同时也可将数据库中设置的数据,写入矩阵,也可将数据写回矩阵主机;5.单击"写数据库",将当前页数据写回数据库,以备日后查看,维护.2. 键盘/摄像机窗口界面具体操作1.单击"权限"->"键盘/摄像机" 进入"键盘/摄像机"设置界面;2.单击"读矩阵"按钮,系统从矩阵主机读取数据并显示;3.单击"写矩阵"按钮,系统将当前页数据写入矩阵主机;4.单击"读数据库",系统从数据库中读取数据,同时也可将数据库中设置的数据,写入矩阵,也可将数据写回矩阵主机;5.单击"写数据库",将当前页数据写回数据库,以备日后查看,维护.3. 监示器/摄像机窗口界面具体操作1.单击"权限"->"监示器/摄像机" ,进入"监示器/摄像机"设置界面;2.单击"读矩阵"按钮,系统从矩阵主机读取数据并显示;3.单击"写矩阵"按钮,系统将当前页数据写入矩阵主机;4.单击"读数据库",系统从数据库中读取数据,同时也可将数据库中设置的数据,写入矩阵,也可将数据写回矩阵主机;5.单击"写数据库",将当前页数据写回数据库,以备日后查看,维护.4. 键盘/报警窗口界面具体操作1.单击"权限"->"键盘/报警" ,进入"键盘/摄像机"设置界面;2.单击"读矩阵"按钮,系统从矩阵主机读取数据并显示;3.单击"写矩阵"按钮,系统将当前页数据写入矩阵主机;4.单击"读数据库",系统从数据库中读取数据,同时也可将数据库中设置的数据,写入矩阵,也可将数据写回矩阵主机;5.单击"写数据库",将当前页数据写回数据库,以备日后查看,维护.5. 网络/键盘窗口界面具体操作1.单击"权限"->"网络/键盘" ,进入"网络/键盘"设置界面;2.单击"读矩阵"按钮,系统从矩阵主机读取数据并显示;3.单击"写矩阵"按钮,系统将当前页数据写入矩阵主机;4.单击"读数据库",系统从数据库中读取数据,同时也可将数据库中设置的数据,写入矩阵,也可将数据写回矩阵主机;5.单击"写数据库",将当前页数据写回数据库,以备日后查看,维护.六. 报警1报警联动窗口界面具体操作1.单击"报警"->"报警联动" ,进入"报警联动"设置界面;2.单击"读矩阵"按钮,系统从矩阵主机读取数据并显示;3.单击"写矩阵"按钮,系统将当前页数据写入矩阵主机;4.单击"读数据库",系统从数据库中读取数据,同时也可将数据库中设置的数据,写入矩阵,也可将数据写回矩阵主机;5.单击"写数据库",将当前页数据写回数据库,以备日后查看,维护.2. 自动设防窗口界面具体操作1.单击"报警"->"自动设防" ,进入"自动设防"设置界面;2.单击"读矩阵"按钮,系统从矩阵主机读取数据并显示;3.单击"写矩阵"按钮,系统将当前页数据写入矩阵主机;4.单击"读数据库",系统从数据库中读取数据,同时也可将数据库中设置的数据,写入矩阵,也可将数据写回矩阵主机;5.单击"写数据库",将当前页数据写回数据库,以备日后查看,维护.3. 历史报警窗口界面具体操作1.单击"报警"->"历史报警" ,进入"历史报警"界面;2.系统自动读取矩阵系统历史报警记录;3.可单击"刷新"按钮重新读取矩阵系统数据.4. 报警端口设置窗口界面具体操作:1.单击"报警"->"报警端口设置" ;2.进入"报警端口设置"界面 ;3.选择"报警端口" ;4.单击"确定",保存设置.5. 设防/撤防状态窗口界面具体操作1.单击"报警"->"设防/撤防状态" ,进入"设防/撤防状态"界面;2.系统自动读取矩阵系统历史报警记录;3.可单击"刷新"按钮重新读取矩阵系统数据;七. 键盘设置1. 虚拟键盘虚拟键盘是为了方便用户在计算机系统操纵矩阵设计的.功能与矩阵键盘类似.可通过虚拟键盘对矩阵进行,摄像机切换等操作. 窗口界面八. 常见问题下位机无响应:可能存在以下几种情况:1.连接线路太长或线路质量存在问题.数据无法接收.2.系统波特率与矩阵主机波特率不一至.系统波特率可通过"通讯方式"进行设置;3.矩阵主机系统软件被破坏,可与厂商联系,进行系统升级.可读矩阵数据,但无法写入矩阵:1.线路或转接器存在问题.请更换好一点的通信设备.虚拟键盘无法操作:1.网络号或波特率与矩阵系统不相符;解决方法参看“通讯设置”2.线路质量问题。