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lane detection and tracking using B-snake

Lane detection and tracking using B-Snake

Yue Wang a ,Eam Khwang Teoh a,*,Dinggang Shen b

School of Electrical and Electronic Engineering,Nanyang Technological University,Black S2,

Nanyang Avenue,Singapore,Singapore,639798

Department of Radiology,University of Pennsylvania,Philadelphia,PA 19104,USA

Received 17July 2002;received in revised form 26September 2003;accepted 1October 2003

Abstract

In this paper,we proposed a B-Snake based lane detection and tracking algorithm without any cameras’https://www.wendangku.net/doc/a57236691.html,pared with other lane models,the B-Snake based lane model is able to describe a wider range of lane structures since B-Spline can form any arbitrary shape by a set of control points.The problems of detecting both sides of lane markings (or boundaries)have been merged here as the problem of detecting the mid-line of the lane,by using the knowledge of the perspective parallel lines.Furthermore,a robust algorithm,called CHEVP,is presented for providing a good initial position for the B-Snake.Also,a minimum error method by Minimum Mean Square Error (MMSE)is proposed to determine the control points of the B-Snake model by the overall image forces on two sides of lane.Experimental results show that the proposed method is robust against noise,shadows,and illumination variations in the captured road images.It is also applicable to the marked and the unmarked roads,as well as the dash and the solid paint line roads.q 2003Elsevier B.V.All rights reserved.

Keywords:Lane detection;B-Spline;Snake;Lane model;Machine vision;Intelligent vehicle

1.Introduction

Autonomous Guided Vehicles (AGV)have found many applications in the industries.Their applications had been explored in areas,such as patient transportation in hospitals,automated warehouses and other hazardous related areas.In most applications,these AGVs have to navigate in the unstructured environments.Path ?ndings and navigational control under these situations are usually accomplished from the images captured by camera mounted on the vehicles.These images are also interpreted to extract meaningful information such as positions,road markings,road boundaries,and direction of vehicle’s heading.Among many extraction methods,the lane marking (or road boundary)detection from the road images had received great interest.As the captured images are usually corrupted by noises,lots of boundary-detection algorithms have been developed to achieve robustness against these noises.

The main properties that the lane marking (or boundary)detection techniques should possess are:

?The quality of lane detection should not be affected by shadows,which can be cast by trees,buildings,etc.?It should be capable of processing the painted and the unpainted roads.

?It should handle the curved roads rather than assuming that the roads are straight.

?It should use the parallel constraint as a guidance to improve the detection of both sides of lane markings (or boundaries)in the face of noises in the images.

?It should produce an explicit measurement of the reliability of the results obtained.Up to present,various vision-based lane detection algorithms have been developed.They usually utilized different lane patterns (solid or dash white painted line,etc.)or different road models (2D or 3D,straight or curve),and different techniques (Hough,template matching,neural networks,etc.).Basically,there are two classes of approaches used in lane detection:the feature-based technique and the model-based technique.The feature-based technique localizes the lanes in the road images by combining the low-level features,such as painted lines [5–10]or lane edges [1,2],https://www.wendangku.net/doc/a57236691.html,ne segments that are detected by traditional image segmentation.Accordingly,

doi:10.1016/j.imavis.2003.10.003

*Corresponding author.Tel.:t65-6790-5393;fax:t65-6791-2687.E-mail addresses:eekteoh@https://www.wendangku.net/doc/a57236691.html,.sg (E.K.Teoh),s2633175g@https://www.wendangku.net/doc/a57236691.html,.sg (Y.Wang),dgshen@https://www.wendangku.net/doc/a57236691.html, (D.Shen).

this technique requires the studied road having well-painted lines or strong lane edges,otherwise it will fail.Moreover, as it has the disadvantage of not imposing any global constraints on the lane edge shapes,this technique may suffer from occlusion or noise.

On the other hand,the model-based technique just uses a few parameters to represent the lanes.Assuming the shapes of lane can be presented by either straight line[11,12,13,16] or parabolic curve[3,4,14,15],the processing of detecting lanes is approached as the processing of calculating those model parameters.This way,the model-based technique is much more robust against noise and missing data, compared with the feature-based technique.To estimate the parameters of lane model,the likelihood function[3,4,11,12,16],Hough transform[13],and the chi-square?tting[14,15],etc.are applied into the lane detection.However,as the most lane models are only focused on certain shapes of road,thus they lack the ?exibility to modeling the arbitrary shape of road.

Motivated by the above problems,we here present a new B-Snake based lane detection and tracking algorithm for the outdoor application of AGV.The main characters of our method are the following:

1.A novel B-Snake based lane model which describes the

perspective effect of parallel lines is constructed with dual external forces for generic lane boundary or marking,it is able to describe a wider range of lane structures than other lane models such as straight and parabolic models.In addition,it is robust against shadows,noises,etc.due to the use of the parallel knowledge of roads on the ground plane.The lane detection problem is formulated by determining the set of lane model control points.

2.A robust algorithm called Canny/Hough Estimation of

Vanishing Points(CHEVP)is presented for providing a good initial position for the B-Snake lane model.This algorithm is robust to noises,shadows,and illumination variations in the captured road images,and is also applicable to both the marked and the unmarked,dash paint line and solid paint line roads.

https://www.wendangku.net/doc/a57236691.html,ing Gradient Vector Flow(GVF)to construct the

B-Snake external force?eld for lane detection,a minimum error method called Minimum Mean Square Error(MMSE)that?nds the correspondence between B-Snake and the real edge image is presented to determine the parameters of road model iteratively.

Road tracking is carried on after successful lane detection,by a simple external force?eld and MMSE method,tracking is ef?cient and speed is fast.

Besides B-Spline,other kind splines also can be used in our lane model.Our early version of lane model used Catmull-Rom spline[24,25,26].The different between the B-Spline and the other kind splines is the locations of the control points.

The remained structure of this paper is arranged as

follows.Section2introduces a novel B-Spline lane

model with dual external forces.In Section3,the

CHEVP is described for B-Snake lane model initializa-

tion.Section4presents a minimum error method,

MMSE,to determine the parameters for lane detection

and lane tracking.This section also shows some

representative results of applying the proposed algorithm

to various types of roads under different environments.

This paper concludes in Section5.

2.Road model

2.1.The modeling of lane boundaries

Lane model plays an important role in lane detection.

The lane modeling has to make some assumptions about the

road’s structure in the real world in order to fully recover3D

information from the2D static image.In this paper,we

focus on constructing the2D lane model,by assuming that

the two sides of the road boundaries are parallel on the

ground plane as shown in Fig.1(a).

In addition,let us assume that the right side of road is the

shifted version of the left side of road at a distance,

D?ex r2x lT;along the x axis in the ground plane.Here,x r and x l are the x coordinates of the two correspondence

points,P lex l;yTand P rex r;yT;in the ground plane.After projection from the ground plane to the image plane,the

horizontal distance d?ec r2c lTbetween the corresponding

points p lec l;rTand p rec r;rT;which are the projected points of P lex l;yTand P rex r;yT;is:

l2Der2hzT

HelthzT

e1T

where l is the focal length of the lens,H is the height of the camera location,hz is the position of vanish line in the image pane,and r is the vertical coordinate used in the image plane (see Fig.1(b)for reference).

The horizontal distance d can be represented as

d?ker2hzTe2Twhere

l2D

Hel2thz2T

Let us de?ne the mid-line of the road in the image plane as

L mid?ec m;r mTe3TThus the left side of the modeled road is

L left?ec l;r lT;e4T

Y.Wang et al./Image and Vision Computing22(2004)269–280 270

where c l ?c m 2

12d ?c m 21

k er l 2hz Tand r l ?r m :e5T

Similarly,the right side of the modeled road is L rights ?ec r ;r r Te6T

where c r ?c m t

12d ?c m t1

k er r 2hz Tand r r ?r m :e7TFrom the above modeling,it is easy to observe that the

problem of detecting two sides of road can be formulated as the problem of detecting the mid-line of road.In following Sections,we would show that k can be estimated directly from image data without any camera’s parameters.2.2.B-Spline snake

Snakes [17],or active contours ,are curves de?ned within an image domain which can move under the in?uence of internal forces from the curve itself and external forces from the image data.Once internal and external forces have been de?ned,the snake can detect the desired object boundaries (or other object features)within an image.Snakes have been used widely in many applications,such as edge detection [17],shape modeling [18,19],segmentation [20,21],and motion tracking [20,22].

A more economical realization of snake can be reached by using far fewer state variables by cubic B-Splines.The B-Splines are piecewise polynomial functions that provide local approximations to contours using a small number of parameters (control points).It can represent curves by four or more state variables (control points).As required,the represented curves may be open or closed.The ?exibility of the curve increases as more control points are added.Each additional control point either allows one more in?ection in

the curve or,when multiple knots are used [23],reduces

continuity at one point.

2.2.1.Uniform cubic B-splines

An open cubic B-Spline,with n t1control points {Q 0;Q 1;…;Q n };consists of en 22Tconnected curve segments,g i es T?er i es T;c i es TT;i ?1;2;…;en 22T:It is C 2continuous and has both its continuous slopes and curvatures.Each curve segment is a linear combination of four control points by the parameter s ;where s is normalized between 0and 1e0#s #1T:It can be expressed as:

g es T?X i

M i es TQ i e8T

where M i es Tare the spline basis functions.

According the B-Spline property,the B-Spline would pass through the control point by triple the corresponding control points.

https://www.wendangku.net/doc/a57236691.html,ing B-Snake to describe lane markings (or boundaries)

We use a set of control points to describe the mid-line of the road by B-Spline,and a additional parameter k (as described in Section 2.1)to determine the left and the right sides of road model.In order to make B-Splines pass through the ?rst and the last control points,we set the ?rst three control points equal and the last three control points equal.

The mid-line of road model can be expressed by a B-Spline as

L mid ?ec m ;r m T?M R es TQ i 21

Q i Q i t1Q i t22

6666

643777

7775;i ?21;0;1;2;…;n :

TFig.1.Parallel lines on ground plane and image plane.

Y.Wang et al./Image and Vision Computing 22(2004)269–280271

The mid-line of lane model can be deformed by the external forces E M_sumesT;which is the sum of the dual external forces calculated from both the left and the right sides of lane model,E LesTand E ResT:

E M_sumesT?E LesTtE ResTe10TIn Fig.2,E M_sumesTwould push the lane model to the left.

Also,the difference of horizontal components of E LesTand E ResT;denoted as E c M_difesT;would lead to adjustment of the parameter k:

E c M_difesT?E c LesT2E c ResT:e11TFig.3shows how E c M_difesTwould lead to the adjustment of the parameter k;increasing(Fig.3(b))or decreasing(Fig. 3(a)).In Fig.3(a),the left side of the estimated lane model is located at the left of the real road’s left boundary,while the right side of the estimated lane model is located at the right of the real road’s right boundary.As shown in Fig.3(a), E c M_difesTpoints to the right,we can de?ne it as leading to decreasing k.On the contrary,in Fig.3(b),the left side of the estimated lane model is located at the right of the real road’s left boundary;and the right side of the estimated lane model is located at the left of the real road’s right boundary. This way,E c M_difesTpoints to the left,which will lead to increasing k.

Compare to other lane models,there are few advantages for B-Snake lane model with dual external forces:

1.B-Snake can describe much wider range of lane shapes

while retains compact representation,since B-Spline has local controllability and can form arbitrary shape.For example,it can describe more complex road shape,such as‘S’or sharp corner turn,just by increasing the number of control points.Other lane model cannot describe those complex shapes,since they use only a single polynomial.

2.With dual external forces,B-Snake model would be

robust against shadows,noises,occasional missing and false markings,etc.since the sampling locations for calculating the dual external forces are combined with the knowledge of parallel lines on the ground plane,the external forces for deformation of B-Snake is not depended on one,but both sides of lane model at a time.

3.The processing time will be reduced since two

deformation problems for both sides of lane have been formulated to one deformation problem.

4.This B-Snake lane model is particular suitable for lane

tracking application,since the parameters of lane model for the current frame is usually similar to those in the previous frame,i.e.the movements of the control points are smaller.On the contrary,for other lane models such as the second order polynomial lane model,when the road shape has a small change,it may cause a large change in the parameters of model.

For most lanes,we found that using3control points is ef?cient to describe their shapes.Therefore, we select3control points in this paper for constructing the lane model.Fig.4(a)shows a lane model formed by a set of3 control points,Q0;Q1and Q2shapes(Q0and Q2are triple,

we actually compute a curve of two segments from the sequence of seven control points Q 0;Q 0;Q 0;Q 1;Q 2;Q 2;Q 2).Since we only concern the road in the camera’s ?eld of view,we limit the location of the ?rst control point Q 0in the vanishing line if the vanishing line is in the captured image.In the case that the vanishing line is above the top of image,Q 0will be limited in the top row of the captured image.The end control point Q 2is limited to the bottom row of image.For the case that three control points are not suf?cient to describe the shape of road,our structure-adaptive B-Snake [28]model can be implemented for auto-increasing the number of control point to adapt the shape of road.The more control points are used,the more complex shape can be formed.Fig.4(b)gives an example of using four control points to describe a ‘S’shape road.

3.Initialization of B-Snake lane model:CHEVP algorithm

Some lane detection algorithms required the operator to provide the initial estimate of the road location,while others required the speci?c road structure scene (such as straight road)as the ?rst road image.These requirements on the road initializations are clumsy for the automatic road detection task.Therefore,automatic initialization technique,able to extract the location of any type of the lane shapes,is important and necessary.

3.1.Description of the CHEVP algorithm

The CHEVP (Canny/Hough Estimation of Vanishing Points)algorithm has been developed to meet these requirements.The road is assumed to have two parallel boundaries on the ground,and in the short horizontal band of image,the road is approximately straight.As a result of

the perspective projection,the road boundaries in the image plane should intersect at a shared vanishing point on the horizon.Below we brie?y introduce this algorithm,for full details please visit:https://www.wendangku.net/doc/a57236691.html,.sg/home5/ps2633175g/chevp.htm .There are following ?ve processing stages in CHEVP algorithm:

1.Edge pixel extraction by Canny edge detection.Canny edge detection is employed to obtain edge map.

2.Straight lines detection by hough transform.

The detected edge points are used to vote for possible lines in the space of line parameters.The image is here partitioned into a small number of horizontal sections,i.e.?ve as shown in Fig.5(b),in order to accommodate the change in road vanishing point due to the bend of the road.The height of image section is gradually reduced as moving to the upper part of image.Notice that,each image section has its own space of line parameters,and edge points in each image section vote separately for possible straight lines in that section.By suitably thresholding the normalized accumulator spaces,line segments can be ?nally detected for each image section (Fig.5(c)).

3.Horizon and vanishing points detection.

The detected straight lines of each image section are paired,and the intersections of any pair of lines vote for vanishing points on another Hough space.The votes are weighted by the sum of the paired lines’normalized accumulator values produced in the Step 2.This process is repeated for each image section separately,but vote in the same Hough space.The votes on each column of the Hough space are summed for detecting possible vanishing line.The row with the maximum support is chosen as the horizon (or vanishing line)in the image plane.Fig.5(d)shows the detected vanishing line.

For each image section,its vanish point can be determined as the point around the horizon and with the strongest support.Fig.6(a)shows both the vanishing

point

Fig.4.B-Snake based lane model.

Y.Wang et al./Image and Vision Computing 22(2004)269–280273

of image Section 2and a pair of lines voting for it.The detected vanishing points for all image sections are shown in Fig.6(b).Notice that,no vanishing point exists for the image Section 5,since no lines can be detected in this image section.

4.Estimate the mid-line of road and the parameter k by the detected road lines.

The lines voting for vanishing point are assumed to be road lines in each image section.From the bottom image section upward,select the two detected road lines from the left and the right sides,which are closest to the mid column of that section.If these two road lines do not exist in the current image section,then the procedure will be repeated in the next higher image section until the required road lines are obtained.Fig.7(a)shows the two lines L 1and L 2chosen

in image Section 4,since no line exist in image Section 5.Then,connect the vanishing point evp 4Tof this image Section 4and the middle point eP m 4Tof the two points (P l 4and P r 4)which are the intersection points of the two road lines L 1and L 2at the bottom row of that section.

The line passing through points vp 4and P m 4intersects at the bottom of Section 3at P m 3:Then the parameter k can be estimated by:k ?

c right 2c left

r mid 2hz

e12T

where hz is the vertical coordinate of vanishing line.In the case of Fig.7(a),c left ?c l 4

c right ?c r 4

r mids ?r l 4?r r 4

e13

Fig.5.Detection of straight lines and vanishing line.

Y.Wang et al./Image and Vision Computing 22(2004)269–280

274

Down from image Section4,since in image Section5no

vanishing point has been detected,we assume this section’s

vanishing point follows the vanishing point vp4of Section4.

Extend the line(passing through vp4and P m4)and joint at

the bottom of image Section5at P m5:Similarly,in image Section3we can detect vanishing point vp3(vp3is the same

point as vp4in Fig.7(b)).The lineevp32P m3Tintersects at

the bottom of image Section2at P m2:(In the case the vanishing point vp3cannot be detected,we assume vp3 follows to vp4:)Image Section2has detected a vanishing point vp2;so just connect vp2and P m2and intersects at the bottom of Section1at P m1:Image Section1also has a detected vanishing point vp1;then the lineevp12P m1Tintersects at the top of Section1at P m0:Fig.7(b)shows the whole mid-line.After constructing the mid-line of road, the both sides of road boundaries can be constructed based on the mid-line of road and the estimated k:

5.Initial the control points of the lane model to approach the mid-line detected by last step.

We?rst choose P m0and P m5;respectively,as the start control point Q0and the end control point Q2for lane model(see Fig.8(a)).We know if knots of B-Spline are known,then the according control points can be gotten. The selection of the knot P1depends on the values

of Fig.6.Vanishing points detection.(a)The vanishing point of the image Section2and the lines which vote for it.(b)The detected vanishing points for all image

sections.

Fig.7.Estimate mid-line of road.(a)Two lines in image Section4are chosen from both sides of road boundaries to estimate k.(b)Estimated mid-line of road.

Y.Wang et al./Image and Vision Computing22(2004)269–280275

angles b 1and b 2de?ned in Fig.8.If angles b 1and b 2are not equal to zero,we choose P m as the knot for Q 1:That is P 1?P m ;where P m is the middle point of P m 1and P m 2:If b 1?0and b 2–0;we choose P m 1as the knot P 1(for Q 1).If {b 1–0and b 2?0}or {b 1?0and b 2?0};we choose P m 2as the knot P 1(for Q 1).Therefore,the control point Q 1can be calculeted by Q 1?

32P 121

eQ 0tQ 2T:e14T

The estimated B-Spline is shown in Fig.8(b).Notice,k

is not changed here,just taken the same values from the last step.

3.2.Experiment results on testing CHEVP algorithm The CHEVP algorithm has been applied to real road images grabbed by a camera at different locations and at different times.These images include straight and curve roads with painted or unpainted,solid or dash lines,

and

Fig.8.Initialize the lane model to approach the mid-line detected.(a)Choose control points for lane model.(b)Final result of initialization for lane

model.

Fig.9.Some results of CHEVP.

Y.Wang et al./Image and Vision Computing 22(2004)269–280

276

shadow.Some results are shown in Fig.9.Fig.9(a) shows the result of applying CHEVP to the images with the curved road.CHEVP locates the double paint lints on the left side of the lane,and the white stripe on the right side of the road,especially it remains high accuacy in the near place to camera.Notice that the location of the detected mid-line is not totally accurate in the top of image due to the curve lines being hard to be detected by straight line parameters in Hough space.However,it is well within the tolerance necessary to use them to make initial predictions for the lane model detection. Fig.9(b)shows another examples of applying the CHEVP algorithm to curve-road image with strong shadow edges.It can be observed that,even the white paint line on the right passes through noisy shadow edges,the Hough transform and the shared vanishing point constraint allow CHEVP algorithm to successfully locate the feature position.On the contrary,edge tracking algorithms would become confused by the shadow edges, which would offer multiple possible continuations. Fig.9(c)shows the results of the CHEVP algorithm on the multi-lane image taken on a divided highway with strong shadows.As CHEVP algorithm is designed to choose the lane which is closest to the centre column of the image(see Step4of CHEVP),hence,even under strong shadows,CHEVP algorithm successfully locates the solid white stripe on the left side of the lane,as well as the broken white stripe on the right side of the lane where the vehicle is located.An example applying CHEVP algorithm to the unpainted lane is shown in Fig.9(d).The road image was taken on a1-lane road for bicycle.It can be seen that the road is wet after raining. The CHEVP algorithm can only detect the vanishing points for Sections1and2.However,the estimated mid-line of road is still acceptable.As we can observe in Fig.9,combined with global constraint(vanishing line) and local constraint(straight lines and vanishing points of each section),the CHEVP algorithm shows promise to provide a robust method for extracting and identifying the lines composing the road,with an ability to reject ‘weak’local optimality in an image.Moreover,the parameters of camera are not required.Being initialized by CHEVP,the B-Snake would deform to lane boundaries more precisely by using MMSE approach, which is presented in next Section.

CHEVP algorithm assumes that the horizon(or vanishing line)appears horizontal in the image plane,although it need not be in the camera’s?eld of view.If the terrain varies in slope,CHEVP algorithm may be unable to correctly locate all the road lines due to their not having a vanishing point on the horizon row corresponding to the tangent plane at the vehicle location.However,CHEVP algorithm has ability to identify the best horizon row and vanishing point for each section of the image,but it would lead to a3D road model, so we leave it to the future work.4.B-Snake parameters updated from image data

Based on the initial location of the control points that are determined either by CHEVP algorithm or lane detection result of previous frame,the B-Snake would further approach toroadedgeaccuratelyinthecurrentframe.ThisSectiondeals with this problem.

4.1.Minimum mean square error approach

B-Snake should be updated to minimize(1)the sum of the external forces from the both sides of the road model for achieving accurate position of B-Snake,and (2)the difference of the external forces from the both sides of the road model for achieving suitable parameter k:In addition,external forces should be transmitted to each control point when updating B-Snake.

When the B-Snake approaches the road boundaries,its external force should satisfy the equation.

E ext?0e15Twhere

E ext?E M_sumesT?E LesTtE ResT:e16TIf external force of the B-Snake is zero,then there is no change in both the position and the shape of the mid-line of road.So we can de?ne the following equation for solving the requirement of external force being zero.

E ext?geL midetT2L midet21TT

?g M ResTeQetT2Qet21TT?g M ResTD QetTe17T

where g is a step-size and D QetTis de?ned as the adjustment of the control points Q in each iteration step.

QetT?Qet21TtD QetTe18TExternal force can be sampled along the B-Spline of B-Snake at a certain distance.Then Eq.(17)can be solved digitally.Here,the MMSE solution for the digital version of the Eq.(17)is given as a matrix form.

D QetT?g21?M T M 21M T

E exte19T

Y.Wang et al./Image and Vision Computing22(2004)269–280277

where

M210 0

0M00 0

·····

0···0M n210

0······0M n

;

M i?

s3i;1s2i;1s i;11

s3i;2s2i;2s i;21

····

s3i;m s2i;m s i;m1

:e20T

and m is the sampling points number in i th segment of the B-Spline.E ext is the force vector digitized on the B-Spline.Here,n?2is for the case of using three control points.

The difference of the external forces from the left and the right side of lane model would lead to changing the parameter k(as given in Section2.3).Estimation of the parameter k can be similarly given as follows.

E k?E c M_dif?eE c LesT2E c ResTTe21TWe set the right-hand side of the equation equal to the product of a step size and the negative time derivative of the left-hand side.The resulting equation is:

E k?teketT2ket21TT?t D ketTe22TketT?ket21TtD ketTe23Twhere t is a step-size for k:Thus,

D ketT?

E k=te24THere we choose GVF[27]as the external force for B-Snake to perform the lane detection,since GV

F has a larger capture range.But GVF is time consuming.So,after successful lane detection,we use the traditional external force(directly calculated from image gradient)to speed up the lane tracking.

4.2.Application in lane detection

4.2.1.Update B-snake parameters for lane detection

In order to achieve the solutions in Eqs.(18)and(23), an iterative procedure is adopted.The steps contained in this iterative minimization process are as follows:

1.Initialization Step.Initialize the control point parameters

by CHEVP algorithm introduced in Section3.

2.Calculate the GVF of the edge road image as the external

force of B-Snake.3.Calculate MMSE in Eqs.(19)and(24)for obtaining

D QetTand D ketT;respectively.

4.Obtain QetTand ketT:

5.If k D QetTk.threshold1and k D ketTk.threshold2;then

set QetTto Qet21Tand ketTto ket21T;and go to step3;

Otherwise,go to step6.

6.Stop.The last estimations of QetTand ketTare regarded as

the solutions of MMSE.

https://www.wendangku.net/doc/a57236691.html,ne detection results

This lane detection algorithm has been simulated and tested on real road images.These lane images include curve and straight road,with or without shadows and lane marks. Some of these results are shown in Fig.10.The initializations for proposed B-Snake lane model are all obtained from CHEVP algorithm.Fig.10(a)is the approach result of a curve painted road without any shadow,it can be seen that the B-Snake lane model can achieve a very good result on approaching lane markings on the both sides of the road. Three examples of applying this lane detection algorithm to curve and straight roads with strong shadows are shown in Fig.10(b)–(d).Their initialized locations of B-Snake are Figs.8(b),9(b)and(c),respectively.All results are correctly matching to the lane markings despite the shadows lay over the pained lines.Fig.10(e)shows an example of MMSE approach to an unpainted road image,whose CHEVP result has been appeared in Fig.9(d).Although its initialization is not quite correspondent,the lane model approaches the lane boundaries accurately even the both road boundary edges are not smooth near the bottom of the image.A slight curve road, which is detected by the proposed lane detection algorithm,is shown in Fig.10(f).Please notice in the top of image the result is not matching very well due to weak lane edge pixels. However,the matching is still maintaining high accuracy in the part where is near to the camera.

For a240￡256-pixel road image,the whole proces-sing time of CHEVP and lane detection on a Pentium3 system with128RAM is below4s,which is depending on the number of edge pixels.However,since it is only for initializing B-Snake and runs only one time at the start performing of lane detection,it would not effect the real-time performance of the lane detection.

4.3.Application in lane tracking

Lane tracking is much easier than lane detection. Considering there are only small changes between two consecutive frames,we can regard the estimated parameters of the lane model in the previous frame as the initial parameters for the current frame.

The algorithm for lane tracking is quite similar to lane detection except two differences:

?Instead of using CHEVP,the parameters of lane model in the current frame are initialized by the parameters estimated in previous frame.

Y.Wang et al./Image and Vision Computing22(2004)269–280 278

?The GVF is replaced by a simple external force,which is directly calculated from the image gradient.Several road sequences that include more than 700road images have been tested for lane tracking.As the algorithm is simple and ef?cient,in real practice,we can achieve a speed of at least 2frames/s.The percentage of correct lane tracking is over 95%,depended on the real road conditions.Some results of the lane tracking in one road sequence are shown in Fig.11,which seems quite good.For the

full

https://www.wendangku.net/doc/a57236691.html,ne detection

results.

Fig.11.Some results of lane tracking.

Y.Wang et al./Image and Vision Computing 22(2004)269–280279

sequence,please visit:https://www.wendangku.net/doc/a57236691.html,.sg/home5/ ps2633175g/lane_tracking.htm.

5.Conclusion

In this paper,a novel B-Snake based lane model,that describes the perspective effect of parallel lines,has been established for generic lane boundaries(or markings).It is able to describe a wider range of lane structures than other lane models,such as straight and parabolic models.The problems of detecting both sides of lane markings(or boundaries)are merged here as the problem of detecting the mid-line of the lane.A robust algorithm,called CHEVP,is presented for providing a good initial position for the B-Snake lane model.This algorithm is robust against noise, shadows,and illumination variations in the captured road images.It is also applicable to the marked and the unmarked,as well as the dash and solid paint line roads. To approach the lane edges based on the initialized location, a minimum error method,MMSE,that measures the matching degree between the model and the real edge map is presented to determine the control points of road model for lane detection and tracking.In this method,dual external forces are sampled along the B-Spline and comprehensively transmitted to each control point for its update by minimizing both the sum of the external forces and the difference of horizontal components of external forces on the two sides of the road model.The obtained results are quite good and accurate under various conditions. Several extensions of model are possible.In this paper,we mainly focused on2D lane model.However,it is easy to extend our lane model to3D lane model by just simply adding in one more component for control points to describe the hill-dale geometry of road.For initializing the3D lane model,the CHEVP algorithm has to be improved to meet the3D lane model requirement.In order to improve the lane detection,more features of the road,such as such as color, texture,saturation and re?ectance data from the laser scanner,should be used.

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计算机信息管理系统基本情况及功能说明

计算机信息管理系统基本情况及功能说明 This model paper was revised by the Standardization Office on December 10, 2020

计算机信息管理系统基本情况及功能说明山西福康源药业集团有限公司基本情况我公司使用的为用友时空医药管理软件。用友时空在多年流通领域信息化平台研发的基础上，针对当前流通企业在快速发展过程中呈现出的管理模式创新多变、大规模快速扩张、降低运营成本获取规模效益等方面的特征，引入SOA理念，采用“工具平台化、体系架构化”的研发策略设计开发了KSOA流通企业信息融通平台（下文中简称“KSOA平台”）。 KSOA平台面向国内流通企业中高端客户，旨在以面向服务的、集成一体化的信息管理平台支撑流通企业差异化竞争、持续化发展战略的贯彻执行。 KSOA平台涵盖了流通企业经营中的业务职能、财务职能、人力资源管理职能、协同办公职能和决策支持职能等等。包括批发业务系统，连锁业务系统，零售业务系统，仓储管理系统，供应商在线自助系统，客户在线自助系统，网上在线购物系统，财务管理系统，协同办公系统，人力资源管理系统，应用服务系统等核心模块。本《用户操作手册》对KSOA平台重点介绍包括KSOA平台涉及概念、通用单据操作说明、主要业务流程等内容，内容浅显易懂。用户在启用KSOA管理系统前，须仔细阅读本操作手册，了解各个子系统、各模块及功能情况，并在商品提供商的指导下实施、操作。

北京时空超越科技有限公司致各软件用户：请严格遵照本《用户操作手册》使用，对于因违反操作流程和规范所导致的系统问题，要求时空超越公司提供的任何相关的服务和支持，不列入商品售后服务的免费服务范畴。对于用户在实际系统操作中所遇到,本《用户操作手册》中未有涉及的相关操作，请与北京时空超越公司技术部取得联系，获得相应解决办法及操作指导。第一部分：平台整体概述 1.1第一章单据中出现的名词账：账的概念来源于实际业务处理和企业会计核算方法，其表现形式与会计核算所使用账簿账页格式类似。根据核算对象不同分为商品总账、货位商品账、往来账等。货位：是为了明显标出些商品所在的位置，以便规范管理、统计分析、查询分类，货位可以根据用户需要灵活设置，既可以标示商品作在物理位置，也可以标示商品所在虚拟位置。KSOA平台中货位字段西文名称是“hw”。批号：是指用于识别“批”的一组数字或字母加数字，用以追溯和审查该批药品的生产历史。KSOA平台中批号字段西文名称是“pihao”。保质期：的保质期是指商品在条件下的质量保证期限。商品的保质期由提供，标注在限时使用的商品上。在保质期内，商品的生产企业对该商品质量符合有关标准或明示担保的质量条件负责，销售者可以放心销售这些商品，消费者可以安全使用。保质期在单据明细项中相应字段是“baozhiqi”字段。商品淘汰：流通企业在经营过程中，对于因各种原因（如滞销等）不适合销售的商品

智慧医院管理信息系统HIS功能简介

智慧医院管理信息系统HIS 功能简介

目录一、产品新亮点 (3) 二、功能模块： (6) 三、系统支持 (18) 四、数据库 (18)

一、产品新亮点医院管理信息系统是我们经13年经验研制不断升级而来的，具有先进水准的医院管理软件。多层结构：主要数据处理是服务器，BS 与CS都可支持。采用WEBSERVER方式用云计算方式。在本地缓存取到的字典和配置信息，节省网络资源。如一些不变的数据（如字典）只取一次，会在客户端缓存，这样不仅提高操作响应速度并且节约网络带宽资源及网络并发连接数量，这一点在大型医院或区域医院数据量大时尤为重要。全组件化设计：系统采用全组件化设计，动态调用，及时清理内存。方便更新维护、二次开发更，以便软件保持更高的稳定性。传统的软件都有程序编译在一个EXE中，这样大的系统就会加载所有的程序，增大内存的使用资源。而动态调用，启动时不加载，用时才动态调用加载。多数据库复合组合设计：独特字典库与多个功能不同的子程序业务库多库组合设计，达到松藕合的设计理念。使业务数据与系统基础数据分离。不论是与其他系统接口，数据备份，或避免冗余数据都起来较高的效率。及提高了空间利用率和使用速度和安全性。

如LIS PACS 或手麻系统接入HIS时，不需要单独的系统基础数据接口，因为所有的子程序均共享字典库。这样不仅节省开发工时，更重要的是避免冗余数据产生，保证数据绝对的时效性唯一性和准确性。而且字典库不需时时备份，不产生大量的业务数据，无论是应用还是备份还是维护都显示了高效的运行效率。消息服务器：使用消息服务器，实现系统内消息实时的传送。传统的系统客户端想主动得到服务器消息，会用时间事件定时刷新提取数据的方式。这样不仅数据延迟，且耗费大量网络与服务器系统资源。本系统不再是拉模式的被动状态，而我们用消息的方式，实时发生传送。挂号后患者自动会出现在医生工作站的列表，处方收费后后自动出现在药房发药的列表。患者费用增加会自动显示的各医护服务站。使系统真正有了实时消息响应及处理的高效机制。缜密数据库结构：数据库结构设计在根据多年经验，经多次改革升级而来，更符合数据业务的需要。用各种约束设计避免出错几率。充份考虑多种医院的多种业务的需要，使结构设计更合理。定时处理过期的业务数据，转到历史记录表。使大量的业务数量变得更流畅医疗卡收费：

计算机信息管理系统基本情况介绍和功能说明-(41894)

计算机信息管理系统基本情况介绍和功能说明我公司门店现使用的计算机管理系统适应目前管理软件发展的最新趋势，有支持系统正常运行的服务器和终端机；内蒙古敬德医药连锁有限公司正蓝旗第八店计算机信息管理系统具体功能说明：（一）具有实现部门之间、岗位之间信息传输和数据共享的功能；（二）具有医疗器械经营业务票据生成、打印和管理功能；（三）具有记录医疗器械产品信息（名称、注册证号或者备案凭证编号、规格型号、生产批号或者序列号、生产日期或者失效日期）和生产企业信息以及实现质量追溯跟踪的功能；（四）具有包括采购、收货、验收、贮存、检查、销售、出库、复核等各经营环节的质量控制功能，能对各经营环节进行判断、控制，确保各项质量控制功能的实时和有效；（五）具有供货者、购货者以及购销医疗器械的合法性、有效性审核控制功能；（六）具有对库存医疗器械的有效期进行自动跟踪和控制功能，有近效期预警及超过有效期自动锁定等功能，防止

过期医疗器械销售。经营事项存在着互相关联性，因而数据是环环相扣的，软件利用数据的关联性可进行跟踪查询，使得查询追踪特别方便。

计算机信息管理系统功能结构主菜单二级菜单三级菜单说明计划单生成采购计划作为订货参考采购计划计划明细表详细计划医疗器械名细计划统计表计划医疗器械汇总订单生成采购订单订单明细表订单统计表采购管理收货生成接收供应商的货品，但不正式入库收货单收货明细表收货统计表进货验收录入正式验收入库，产生直接库存进货验收单进货明细表进货统计表进货退回单厂家进货明细报价生成报价单报价明细表报价统计表订货生成意向客户订单确立，不影响库存订货单订货明细表订货统计表销售出库销售管理销售单销售明细表销售统计表销售利润分析表销货退回单厂商销售明细厂商销售汇总销售分析报表销售类型排行销售对比表月 / 年销售统计期初盘点盘点录入库存盘点盘点明细盘点汇总库存管理调整单盘点后帐实相符的差异调整报损单存货明细货品明细流水帐库存查询存货统计现有存货明细现时间段实际库存

计算机信息管理系统基本情况介绍和功能介绍

三树医疗器械管理软件介绍 “三树器械信息管理系统”软件是根据新版《医疗器械经营质量管理规范》的要求，依据多年对医疗器械行业服务经验，在行业专家的指导下，专门为医疗器械经营企业开发的计算机信息管理系统软件，它能够满足医疗器械经营管理全过程及质量控制的有关要求。本软件目前已得到数百家医疗器械客户使用验证，得到各级药监检查部门的认可。一、软件的流程控制二、功能介绍 1.基础信息管理 ★系统能够通过输入用户名、密码等身份确认方式登录系统，并在权限范围内录入或查询数据。★系统能够对各岗位操作人员姓名的记录，根据专有用户名及密码自动生成。

2.采购管理 ★对首营企业和商品的证照、资质审核登记，保证合作企业和经营商品的合法性。 ★对企业各项证照自动预警管控。 ★制定合理的采购计划。 ★优化采购业务流程。 ★制定合理的管理监控方式，防止采购漏洞。首营企业流程：首营品种流程：采购流程：采购退回流程： 3.销售管理 ★严格审核控制客户资质、经营范围或者诊疗范围，按照相应的范围销售医疗器械。

本模块支持从销售订单、发货、销售出库完整的销售全业务流程，可以实现对价格、报价、信用、订单多角度管理与分析，实现销售订单的全过程跟踪管理，提高订单响应速度和产品交付率。销售流程：销售退回流程： 4.库存管理 ★对采购、销售、盘点进行全面的管理，提供准确的库存信息。 ★通过对需要进行保质监控的存货设定失效期以及保质期预警天数，自动对存货保质期、失效期提供预警。 ★通过各种库存分析数据，形成丰富的库存分析报表实现库存展望、对呆滞积压、库龄等进行分析。 ★对库房商品养护环节，提供了相应的监控办法。 ★对不合格品进行登记，用以备查；确定处理的，按照不合格品流程处理。不合格品处理流程：养护流程：

管理信息系统功能

一般说来，管理信息系统功能主要有以下几个方面： 1．及时全面提供致据和信息，支持决策活动； 2．能准备和提供统一格式的信息，使各种统计和综合工作得以简化；3．利用给定的数学方法，分析历史数据以预测未来的情况； 4．为不同的层次管理要求提出相应的报告，帮助及时分析，作出决策； 5．有效利用MIS本身．使信息系统成本最低。从整个世界范围来看，数据处理的发展不过几十年的历史，MIS 发展也还不型三十年的历史，可是它对生产管理和社会所带来的影响却震动丁世界。它所创造的神话般业绩使人类进入新的历史时期．许多大企业家通过开发适应本企业的MIS，力图提高管理水平和竞争力．使自己能处于不败之地．一大批科学家和年青人授吸引进丁管理信息系统的研究、开发行列，并满怀信心地将MIS推广应用于其他领域，各种专门的MIS一个接着一个地被开发研制出来。四十年代末五十年代初，数据处理应用的成功引起了六十年代中期庞大的MIS研究热潮，它们朝着全实时的信息反馈和无纸操作的方向发展，如IBM公司动用了一千多科技人员研制丁COPICS系统．但庞大的信息系统并没有得到预想的成功，投资过大，技术上的复杂，效果的时延使人们产生犹豫，于是各种分功能的小系统产生了如MRP—I (物料需求计划系统)、会计信息系统、生产信息系统及各种决策支持系统．随着计算机技术水平的提高及新型号敷机的出现，网络的普及，

各种办公设备的创新，更高水平的管理信息系统将会出现．本文拟就管理信息系统的发展情况作简单介绍，然后再谈一下MIS建设及其社会影响。1MIS的进展MIS是--f]新兴的学科，它是依较于管理科学、技术科学的发展而形成的，本世纪2O年代出现的泰勒科学管理学派，3o年代出现的行为科学学派，4o年代出现的数学管理学派，年代出现的计算机管理学派，7o年代出现的系统工程学派是管理信息系统科学形成的背

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月的考勤情况进行查询、统计、生成报表。图书管理：用户不需要到图书馆就可以查询图书是否存在，还是借出，直接在图书系统里的进行查询和了解情况，并且用户还可以图书进行评价及描述图书内容。会议查询：用户登录系统后会自动弹出今日的会议消息，并且在系统的桌面上也后显示今日开会的内容，用户点击会议查询，将会出现一周内所有的会议情况供用户查看。流程管理：认定了流程服务主体后，则可以进一步明确该服务主体的具体要求，从而通过业务流程的运行与控制，达到符合该要求的结果，实现业务流程端到端的流转和价值增值。通过业务流程的运转，进而识别公司全部主体业务流程的分类。采购管理：根据用户采购的寻求进行订制订单，添加采购的物品，系统会自动汇总物品数据和总金额，最终生成订单。邮箱管理：日程管理：财务管理：日程管理：二、教务管理系统：

医院管理信息系统功能介绍

大华医院管理信息系统; HIS 一、产品新亮点大华医院管理信息系统是我们经13年经验研制不断升级而来的，具有先进水准的医院管理软件。多层结构：主要数据处理是服务器，BS 与CS都可支持。采用WEBSERVER方式用云计算方式。在本地缓存取到的字典和配置信息，节省网络资源。如一些不变的数据（如字典）只取一次，会在客户端缓存，这样不仅提高操作响应速度并且节约网络带宽资源及网络并发连接数量，这一点在大型医院或区域医院数据量大时尤为重要。全组件化设计：系统采用全组件化设计，动态调用，及时清理内存。方便更新维护、二次开发更，以便软件保持更高的稳定性。传统的软件都有程序编译在一个EXE中，这样大的系统就会加载所有的程序，增大内存的使用资源。 ,而动态调用，启动时不加载，用时才动态调用加载。多数据库复合组合设计：独特字典库与多个功能不同的子程序业务库多库组合设计，达到松藕合的设计理念。使业务数据与系统基础数据分离。不论是与其他系统接口，数据备份，或避免冗余数据都起来较高的效率。及提高了空间利用率和使用速度和安全性。如LIS PACS 或手麻系统接入HIS时，不需要单独的系统基础数据接口，因为所有的子程序均共享字典库。这样不仅节省开发工时，更重要的是避免冗余数据产生，保证数据绝对的时效性唯一性和准确性。而且字典库不需时时备份，不产生大量的业务数据，无论是应用还是备份还是维护都显示了高效的运行效率。

消息服务器：使用消息服务器，实现系统内消息实时的传送。传统的系统客户端想主动得到服务器消息，会用时间事件定时刷新提取数据的方式。这样不仅数据延迟，且耗费大量网络与服务器系统资源。本系统不再是拉模式的被动状态，而我们用消息的方式，实时发生传送。挂号后患者自动会出现在医生工作站的列表，处方收费后后自动出现在药房发药的列表。患者费用增加会自动显示的各医护服务站。使系统真正有了实时消息响应及处理的高效机制。缜密数据库结构：数据库结构设计在根据多年经验，经多次改革升级而来，更符合数据业务的需要。用各种约束设计避免出错几率。充份考虑多种医院的多种业务的需要，使结构设计更合理。定时处理过期的业务数据，转到历史记录表。使大量的业务数量变得更流畅医疗卡收费：支持医疗卡自动收费管理，使医院流程更简洁。节省大理人力。更较少业务出错几率。可以支持自动挂号与收费，使用挂号和收费成为历史。使患者排队挂号交费的情况再也不发发生了。电子病历功能：自带电子病历功能，实现全结构化。病历文件为XML，每个几K,大大节约储存空间。为海量数据的存储与查询提供高效的运行方式。报表：报表多，可设计打印发票、清单、各种医疗文书、各种报表的打印样式。支持WEB打印续打。业务灵活：可自己设定操作流程。操作简单，使用快捷键与助记码、五笔码输入，支持全键盘、快捷键操作。支持模板套餐，协议处方。支持常用语常用药品快速输入。支持各种接口：支持与LIS RIS PACS 接口。支持医保系统（农合）的接口

医院管理信息系统功能介绍

RedhoeHis 医院管理信息系统功能简介红锄头软件

一、命名 (5) 二、功能特点 (5) 三、与同类产品比较 (9) 四、功能模块： (11) 五、功能介绍 (13) 销售价格与药品单位： (13) 门诊挂号： (14) 门诊医生工作站： (14) 门诊收费： (15) 采购订单： (16) 药库管理系统： (17) 药房管理系统： (18) 进销差价： (20) 护士工作站管理系统： (20) 住院医生工作站： (21) 住院收费管理系统： (22) 财务统计： (22) 病例管理： (23)

检查检验管理： (23) 院长查询： (23) 系统维护系统： (24) 医保接口： (25) 后期维护： (26) 软件升级： (26) 六、系统支持 (26) 七、数据库 (26)

一、命名 His：医院管理信息系统是我们经多年经验研制出的，具有先进水准的中小型医院管理软件。以下简称为EasyHis。客户端：是指医院办理业务的计算机，也称为工作站。以下简称为客户端。 BS：客户端用浏览器，向SERVER端提交连接，数据请求，通过网页的形式为用户提供交互。CS：客户端上安装一个应用程序，直连接数据库。二、功能特点采用了BS ＋ActiveX + WebService 多层结构。客户端不用安装软件，无接触安装部署，自动下载更新程序，更有利于统一版本系统维护。客户端应用ActiveX技术，VC语言做的程序，在浏览器中打开程序运行办理业务。程序响应速度快，实现纯BS不能现实的其它功能。克服了BS响应速度慢的难题。客户端与WebService交换数据，程序不需开

计算机信息管理系统基本情况介绍和功能说明

计算机管理系统基本情况介绍和功能说明我公司为医疗器械批发经营企业，为了结合自己企业情况和医疗器械发展要求现状，采用哈尔滨市奇安科技企业管理软件。一、目的：为适应公司现代化快速办公需要，确保公司计算机管理系统上医疗器械进、销、存及相关数据资料完整、准确、及时、安全二、范围：本制度适用于本公司的医疗器械经营计算机信息系统的管理。三、职责：综合管理部、质量管理部、各个岗位操作员对本制度的实施负责。四、内容和功能说明： 1. 公司全员树立"一切服务于公司"" 一切围绕质量" 的思想，行政部负责对公司所有电脑设备配备、医疗器械经营软件系统的配备、网络系统的配备以及故障的及时维修与排除、用户和密码的安全管理等，安全有效地做好公司网络系统畅通及医疗器械经营软件系统的维护、改造工作,保证公司经营业务正常进行。质管部负责对公司的医疗器械经营软件系统的质量管理进行技术要求和控制，负责对每个操作员的权限设置，负责监控医疗器械购销存数据。 2. 公司的计算机管理信息系统配备能够对在库医疗器械的分类、存放和相关信息进行检索，能够对医疗器械的购进、验收、养护、出库复核、销售等进行真实、完整、准确地记录和管理，具有接受医疗器械监督管理部门监管的条件，运用医疗器械经营管理软件，能对医疗器械的购进、入库验收、在库养护、销售、出库复核进行记录和管理，对质量情况能够进行及时准确的记录, 实现质量管理工作的科学信息化。 3. 公司全员要爱惜设备, 认真学习《医疗器械监督管理条例》、《中华人民共和国质量法》等有关的法律法规。努力学习先进的计算机技术,提高操作水平，规范操作相应的管理软件。 4. 各岗位人员能够按照软件操作法提供的流程作为工作流程，按照软件提供的岗位作为工作岗位，顺利完成公司的业务流程。 5.公司的各级管理者通过平台直接或间接将自己的管理贯彻到最终的实际工作上去。 6 系统管理员能定期对计算机的硬件进行检测，并对其数据、病毒进行检测和清理。

计算机信息管理系统基本情况介绍和功能说明

计算机信息管理系统的基本情况介绍和功能说明系统简介中国现在正在进行医疗体制的改革，从体制上来保障市民的健康和医疗体制的合理性。为了适应和促进医疗体制的改革，药店的经营也本着利民、便民的宗旨出发，利用现代信息社会中各种先进的高科技手段，来更好的为社会服务。为了适应这一新的历史形势，必然对药店的管理以及辅助管理的计算机系统提出了新的要求。拟采用的系统主要是引进先进的信息处理技术，提高企业的自动化程度和信息共享度，提高工作效率，降低成本；更重要的可以从根本上改变企业的战略发展，在经营和管理上更上一个台阶。系统需求系统总体目标是为企业实现六统一建立一套高效可行的信息管理系统，六统一指的是：统一品牌；统一进货策略；统一配送策略；统一财务结算；统一价格策略；统一管理流程。具体在设计上应满足下列需求： ●各独立核算企业均要实现信息管理系统，并完成有关财务、业务数据的核算，汇总和集成 ●通过有效的网络体系实现各企业间的信息共享、信息交换、信息监控 ●能为各个业务环节提供快速的信息分析决策功能 ●能为各个独立核算企业中的工作人员提供有效的绩效评价 ●实现进、销、调、存各业务环节全过程的监控和信用管理 ●满足行业规定和国家规定（如GSP管理要求） ●建立物流配送体系和电子商务平台 ●企业科室办公自动化 ●满足企业不断扩张和快速发展在信息管理上的需要

系统应用主体业务分为单体店；企业业务系统已经使用了计算机管理，系统的使用除了保证原业务的开展之外还需要拓展一些新业务（如配送系统），系统业务特点 ●药品的管理：由于药店内药品的种类繁多，数量大，手工的管理模式难以收集到有关药品的准确信息，因而也不能对药品进行科学的管理。在药品管理上系统均对药品的品名、规格、生产批号、生产单位、批准文号、经办人、购销对象、检验记录、退换货记录及有关质量方面的内容作详细的记录； ●物流的管理：由于药店数目多，药品在仓库、药店内的流动量大，手续繁琐，手工管理容易出错，并且商流与物流没有分开，影响药品的流通； ●店员素质低，现行管理不能满足对人员、销售、商品进行有效监控； ●与供应商的结算：和供应商之间的结算要按照批号，按批次，分品种进行算。系统设计的原则 ●总体可行性原则总体可行性原则包括实用性、可靠性、可推广性、可扩充性、先进性等。对所有这些性能的综合考虑，并将实用性放在首位，以保证系统能够达到预期的目标。 ●有限合理性原则我们一方面应看到，信息技术的应用将不可避免地需要对企业中一些沿袭已久的观念和思想、工作程序和方法，甚至责权关系和组织结构做一些必要的调整；另一方面也应看到，信息技术只是一种手段，因此，对企业及业务流程的重组和调整要结合实际情况有条不紊地进行。 ●柔性组合原则按信息系统的功能来设计功能子系统，用功能子系统来组合符合业务要求的部门职能子系统，以提高系统的柔性，使其能够适应职能部门的改变和用户的业务变化。这也是开放系统的概念（应用级的开放和组件设计方法）。

设备管理系统功能介绍

设备的管理工作一直是大家共同关注的问题，车间设备档案不健全，对设备的运行周期不能及时了解，设备维修计划不能及时实施，其二是设备故障的诊断不及时，造成设备得不到预防修理，以上原因是导致设备使用周期短的一个根本性原因，当然还有其他原因，如：过度运行设备，维修水平不过关，粗暴对待设备等等都是导致设备运行周期达不到预期的原因。设备管理的新潮流——全系统，全效率，全员参与全系统是指：设备寿命周期为研究对象进行系统研究与管理，做到智能化设备管理，提高设备生命周期，加强设备的使用率。全效率是指：设备的综合效率。全员参与：设备管理有关的人员和部门都要参与，加强设备定期管理意识实现设备的全系统管理推荐：设备管理系统重庆六业科技根据长期的市场调研，总结各大工厂、国企、大中型企事业单位设备管理出现的问题，自主研发的设备管理系统，具有设备文档的管理，设备缺陷分析及事故管理，维修计划排程和成本核算，预防性维修以及统计报表等功能，是目前企事业设备管理最好的帮手。设备管理系统有什么功能：设备资产及技术管理：建立设备信息库，实现设备前期的选型、采购、安装测试、转固；设备转固后的移装、封存、启封、闲置、租赁、转让、报废，设备运行过程中的技术状态、维护、保养、润滑情况记录。设备文档管理：设备相关档案的登录、整理以及与设备的挂接。设备缺陷及事故管理：设备缺陷报告、跟踪、统计，设备紧急事故处理。预防性维修：以可靠性技术为基础的定期维修、维护，维修计划分解，自动生成预防性维修工作单。维修计划排程：根据日程表中设备运行记录和维修人员工作记录，编制整体维修、维护任务进度的安排计划，根据任务的优先级和维修人员工种情况来确定维修工人。工单的生成与跟踪：对自动生成的预防性、预测性维修工单和手工录入的请求工单，进行人员、备件、工具、工作步骤、工作进度等的计划、审批、执行、检查、完工报告，跟踪工单状态。备品、备件管理：建立备件台帐，编制备件计划，处理备件日常库存事务（接受、发料、移动、盘点等），根据备件最小库存量或备件重订货点自动生成采购计划，跟踪备件与设备的关系。维修成本核算：凭借工作单上人员时间、所耗物料、工具和服务等信息，汇总维修、维护任务成本，进行实际成本与预算的分析比较。

酒店管理系统功能模块介绍

酒店管理系统功能模块简介实时房态 Δ直观显示房间状态，并且可以对房间直接进行登记、结账等操作。 1、将预定、收银、接待、咨询工作整合在一起，实现一站式服务。接待人员可轻松解决客人需求，有效的提高工作效率 2、预定时提供准确的房间状态，可轻松预知未来房态，大幅度提高客房使用率 3、客户入住时如果是熟客自动调出客户资料，轻松快捷的实现客人入住？ 4、特殊的黑名单管理机制，可及时提醒接待人员在黑名单用户入住时采取有效的措施 5、配套身份证、门锁接口，开房仅需20秒(登记客人信息、打印押金单、发房卡) 预定管理管理散客或团体客户的预订信息接待管理接待散客或团体入住。并可以进行预订转接待、入住状态变更等操作。收银结账灵活准确的结算方式 1、收押金、结账退房，支持多种支付、结算方式(现金、会员

卡、银行卡、免单、挂账签单、) 2、客房商品部分结算、反结、走结查询报表对预订、入住、商品销售、客人信息、客人账务等等数据明细、汇总查询账务管理账务核查、实现前台数据的统计，如营业日报表、交班对帐数据统计、收银查询 , 当日结帐查询 , 催帐报表等等会员管理设置会员参数，建立会员卡号以及会员卡的基本操作(支持积分、短信、积分兑换礼品等) 1、支持市场上所有常见的卡类和卡设备 2、会员积分、折扣、储值三卡合一 3、支持会员卡状态管理，轻松解决卡挂失、卡作废、补卡、退卡等操作协议客户管理办理签订协议，对协议客户帐务查询管理，对客户挂帐进行结算 ◇系统提供协议客户管理功能，可根据实际业务设置相应的协议客户，并可针对不同的协议客户设定不同的协议价格。同时不同的协议客户在不同的时期可灵活地设置不同的协议价格和挂帐限额，确保系统能更有效的与实际业务相结合客房管理设置客房状态，处理客房内基本物品营销管理提供出租率、房型、房价销售分析，时间段开房率等数据分析，为酒店提供营销活动最终端有效数据

计算机信息管理系统基本情况介绍和功能说明

计算机信息管理系统基本情况介绍和功能说明 This model paper was revised by the Standardization Office on December 10, 2020

计算机信息管理系统基本情况介绍和功能说明（一）计算机信息管理系统基本情况介绍我单位具有符合医疗器械经营质量管理要求的计算机信息管理系统：千方百计系统，保证经营的产品可追溯。 1.管理销售计算机1台，并配置了扫码枪1把。安装了收款台系统。安装了协力商霸信息管理系统。安装了小票打印机。 2计算机管理信息系统能实现医疗器械质量管理规范要求的计算机功能，记录相关记录项目和内容，对信息进行收集、记录，并具有信息查询和交换等功能，系统能对设置的经营流程进行质量控制，采购、验收、贮存、销售、出入库、退（换）货应在系统中形成内嵌式结构，对相关经营活动进行判断。具有医疗器械经营业务票据生成、打印和管理功能。（二）计算机信息管理系统功能说明 1具有实现岗位之间信息传输和数据共享的功能建立所有岗位人员信息数据库，企业负责人、质量负责人、质量管理、验收等岗位，建立各自用户名密码，医疗器械经营管理过程中，用户名密码登陆才能操作，各岗位操作后，信息自动储存，各环节可以查询、共享。 2医疗器械经营业务票据生成、打印和管理功能系统能实现采购记录、验收记录、入库记录、在库养护、出库复核记录、销售记录等记录功能及查询。支持票据打印。销售小票在收款台打印，与销售记录一致。系统具有与结算系统、开票系统对接，自动打印每笔销售票据功能。 3记录医疗器械产品信息和生产企业信息以及实现质量追溯跟踪的功能系统具有质量管理基础数据控制功能，数据应包括名称、注册证号或者备案凭证编号、规格型号、生产批号或者序列号、生产日期或者失效日期供货单位、经营品种资质等