Ferret:RFID Localization for Pervasive
Multimedia
Xiaotao Liu,Mark D.Corner,and Prashant Shenoy
Department of Computer Science
University of Massachusetts,Amherst,MA
{xiaotaol,mcorner,shenoy}@https://www.doczj.com/doc/e65450920.html,
Abstract.The pervasive nature of multimedia recording devices en-
ables novel pervasive multimedia applications with automatic,inexpen-
sive,and ubiquitous identi?cation and locationing abilities.We present
the design and implementation of Ferret,a scalable system for locating
nomadic objects augmented with RFID tags and displaying them to a
user in real-time.We present two alternative algorithms for re?ning a
postulation of an object’s location using a stream of noisy readings from
an RFID reader:an online algorithm for real-time use on a mobile device,
and an o?ine algorithm for use in post-processing applications.We also
present methods for detecting when nomadic objects move and how to
reset the algorithms to restart the re?nement process.An experimental
evaluation of the Ferret prototype shows that(i)Ferret can re?ne object
locations to only1%of the reader’s coverage region in less than2min-
utes with small error rate(2.22%);(ii)Ferret can detect nomadic objects
with100%accuracy when the nomadic distances exceed20cm;and(iii)
Ferret works with a variety of user mobility patterns.
1Introduction
Advances in digital imaging technologies have led to a proliferation of consumer devices with video capture capabilities.The pervasive nature of multimedia recording devices such as cellphones,digital camcorders,PDAs and laptops, has made it relatively simple to capture,transform,and share large volumes of personal video and image content.A concurrent trend is the emergence of low-cost identi?cation technologies such as RFID tags,designed to replace bar-codes[12].Each tag contains a numeric code that uniquely identi?es the object and can be queried by a wireless reader.It is likely that in the near future many personal objects(e.g.,books,clothing,food items,furniture)will be equipped with self-identifying RFID tags.
The con?uence of these trends—the ubiquity of RFID tags and the perva-sive nature of multimedia recording devices—enables novel pervasive multimedia applications with automatic,inexpensive,and ubiquitous identi?cation and lo-cation abilities.By equipping cameras with RFID readers,it is possible to record This research was supported in part by NSF grants National Science Foundation grants CNS-0219520,CNS-052072and EIA-0098060.
images as well as the identities and locations of all RFID-tagged objects con-tained within each image.The captured video can then be queried in real-time to display the location of a particular object.
While the inexpensive nature of RFID tags eases large-scale deployment is-sues,their passive nature raises a number of hurdles.A key limitation is that pas-sive RFID tags are self-identifying but not self-locating(i.e.,upon being queried, a tag can report its identify but not its location).Consequently,if multiple ob-jects are present in a captured image,it is not possible to distinguish between these objects or pinpoint their individual locations.Some of the applications (e.g.,pinpointing a misplaced book on a bookshelf)require location information in addition to object identities.While numerous locationing technologies such as GPS and ultrasound[10,13,14]are available,it is not possible to equip pas-sive RFID tags with these capabilities due to reasons of cost,form-factor and limited battery life.Instead,we require a locationing technology that does not depend on modi?cations to tags,is easily maintained,and scales to hundreds or thousands of tagged objects.
To address the above challenges,we have designed a system called Ferret. Ferret combines locationing technologies with pervasive multimedia applications. Ferret can locate objects using their RFID tags and displays their locations in real-time to a mobile user.As the positions of objects are uncertain,the system overlays the video display with an outline of where the object probably is.For instance,a user with a portable camera can ask the system to display the location of every new object in the room,and the display will show an outline of all of those locations.The display is constantly updated as the user moves using the continuous stream of tag readings to update locations.
Ferret uses the location and directionality of RFID readers to infer the lo-cations of nearby tags.Ferret leverages the user’s inherent mobility to produce readings of the tag from multiple vantage points.It does this through two novel algorithms that re?ne the locations of objects using a stream of noisy readings from RFID tags.One algorithm is designed for o?ine use,given a large amount of computational power,while the other is designed to operate in real-time on a mobile system.In the case of the o?ine algorithm,we also incorporate neg-ative readings—when the reader does not see the object—this greatly reduces the object’s possible locations.
We have implemented a prototype of Ferret and have used it to conduct a detailed performance evaluation.Our experiments pay speci?c attention to how fast Ferret can re?ne object locations,the error rate in locating objects,and how well it handles nomadic objects.Our results show that(i)Ferret can re?ne object locations to only1%of the reader’s coverage region in less than2minutes with small error rate(2.22%);(ii)The o?ine algorithm incorporates information about not seeing the object,outperforming the online algorithm by a factor of 13or more;(iii)Ferret can detect nomadic objects with100%accuracy when the moving distances exceed20cm;and(iv)Ferret works with a wide variety of user mobility patterns.
Ferret provides systems support for a variety of new applications.For in-stance,users can locate a misplaced book on a bookshelf.Robots can use such
https://www.doczj.com/doc/e65450920.html,e of Ferret to discover the location of a soup can in an o?ce devices to conduct real-time identi?cation and search operations.Vision-based applications can use them to quickly learn the structure or organization of a space.Inventory tracking applications can proactively generate missing object alerts upon detecting the absence of an object.We imagine that the ability to locate and identify thousands of objects in a space will enable new opportunities in vision and graphics,such as augmented reality and immersive systems.
2Ferret Design
Ferret is designed to operate on a handheld video camera with a display.To use Ferret,the user selects some set of objects she would like to locate in the room and moves around the room with the https://www.doczj.com/doc/e65450920.html,ing an RFID reader embedded in the video camera,Ferret samples for nearby tags,and in real-time updates the camera’s display with an outline of the probable location of the objects she is searching for.Ferret’s knowledge of object location can be imprecise,so rather than showing a single centroid point,Ferret displays the outline,leaving the interpretation of the precise location to the user’s cognition.For instance, if Ferret can narrow the location of a book to a small region on a shelf,a user can quickly?nd the precise location.Figure1provides a pictorial representation of how the system would work.In this scenario the user is looking for a co?ee cup in a messy o?ce.After scanning the room using a Ferret-based camera,the system highlights a small region that contains the cup.
2.1Nomadic Location with RFID
Many pervasive systems that rely on location are predicated on the assumption that the number of objects requiring location information is small and mobile. In contrast,we designed Ferret to support a massive number of mostly static, or nomadic objects—objects that change locations infrequently.As a fraction of all objects,nomadic and static ones are in the vast majority—in any given room it is likely that there are hundreds,or possibly thousands of nomadic and static objects,while there are only a few mobile ones.
The primary barrier to providing locationing information for such a large number of objects is the reliance on batteries—making objects self-locating re-quires the use of a battery-powered locationing hardware.Even though location-ing systems such as ultrasound[10]and Ultra-Wide Band(UWB)are becoming
more energy e?cient,equipping hundreds of objects in a room with self-locating capabilities simply does not scale,since it will require changing an unmanageable number of batteries.In contrast,passive RFID provides a battery-free,inexpen-sive,distributed,and easily maintained method for identifying objects;Ferret adds locationing capabilities to such objects.Ferret leverages the fact that an increasing number of objects will be equipped with RFID tags as a replacement to barcodes.Further,RFID tags continue to drop in price,and one can imagine attaching tags to a large number of household or o?ce objects.
As RFID tags are passive devices and have no notion of their own location, Ferret must continuously calculate and improve its own notion of the object locations.The system fuses a stream of noisy,and imprecise readings from an RFID reader to formulate a proposition of the object’s location.The key insight in Ferret is to exploit the location of a camera/reader to infer the location of objects in its vicinity.In essence,any tag that can be read by a reader must be contained within its sensing range;by maintaining a history of tags read by the system,Ferret can progressively narrow the region containing the object.This is a simple yet elegant technique for inferring the location of passive RFID tags without expensive,battery-powered locationing capabilities.
2.2Infrastructure Requirements
Strictly speaking,calculating and displaying object locations does not require any infrastructural support.Displaying a location on the video,as well as com-bining multiple readings of the object location,only requires relative locations, such as those from inertial navigation systems[1].However,it is likely that knowledge of object locations in relation to a known coordinate system,such as GPS or a building map,will be useful for many applications.We assume that the camera/reader uses such a locationing system,such as ultrasound or UWB, to determine its own location and then uses it to infer the location of objects in its vicinity.
As Ferret uses a directional video camera and RFID reader,it also requires an orientation system that can measure the pan(also known as heading and yaw), tilt(also known as pitch),and roll of the system.While research has proposed orientation systems for ultrasound[10],we have chosen to a use commercially available digital compass.Similar to the locationing system,Ferret bene?ts from having absolute orientation,although it can operate with only a relative orien-tation.
2.3Location Storage
For each object,Ferret must store a description of the object’s location.Consid-ering that some RFID tags are remotely rewritable,Ferret can store the location for an object directly on the tag itself.Other options are to store the locations locally in each Ferret reader,or in an online,external database.Each option provides di?erent privacy,performance,and management tradeo?s.Storing lo-cations locally on each reader means that each independent Ferret device must
start?nding objects with zero initial knowledge.As the device moves and senses the same object from di?erent vantage points,it can use a sequence of readings to infer and re?ne the object location.The advantage of this method is that it works with read-only RFID tags and does not require any information sharing across devices.However,it prevents the device from exploiting history available from other readers that have seen the object in the recent past.In contrast,if location information can be remotely written to the RFID tags then other Ferret devices can start with better initial estimates of object location.However,this option requires writable tags and the small storage available on a tag limits the amount of history that can be maintained.In both of the above options,any device that has an RFID reader can determine object locations without needing the full complexity of the Ferret system.
A third option is to store the location information in a central database.This has the advantages of allowing o?ine querying and providing initial location es-timates to mobile readers;further,since database storage is plentiful,the system can store long histories as well as past locations of nomadic objects.However, it requires readers to have connectivity to the database,the burden of manage-ment,and privacy controls on the database.Storing data on the tags also has implications for privacy control,however one must at least be proximate to the tag to query its location.
At the heart of Ferret is an RFID localization system that can infer the loca-tions of individual passive RFID tagged objects.Ferret then uses this localization system to dynamically discover,update,store,and display object locations.The following section presents the design of our RFID localization technique.
3RFID Locationing
Consider an RFID reader that queries all tags in its vicinity—the reader emits a signal and tags respond with their unique identi?er.Given all responses to a query,the reader can produce positive or negative assertions whether a particu-lar tag is present within its reading range.The reader can not directly determine the exact location of the tag in relation to the reader,or even a distance measure-ment.However,just one positive reading of a tag greatly reduces the possible locations for that particular object—a positive reading indicates that the object is contained in the volume de?ned by the read range of the reader(see Figure2). Ferret leverages the user’s mobility to produce a series of readings;the coverage region from each reading is intersected with all readings from the recent past, further reducing the possible locations for the object(see Figure3).Using this method,Ferret can continually improve its postulation of the object location.
In addition to positive readings of an object’s RFID tag,the reader implicitly indicates a negative reading whenever it fails to get a reading for a particular tag that it is looking https://www.doczj.com/doc/e65450920.html,ing a similar method to positive readings,Ferret subtracts the reader’s coverage region from the postulation of the object’s loca-tion.This also improves the postulation of the object’s location.A third method to reduce the likely positions for the object is to modulate the power output of the reader.If a particular power output produces a positive reading,and a lower
power produces a negative reading,the system has gained additional knowledge about the location of the object.
In general,whenever a tag is present in the read range,the reader is assumed to detect it with a certain probability—objects closer to the centroid of its read range are detected with higher probabilities,while objects at the boundary are detected with lower probabilities.Thus,each positive reading not only gives us a region that is likely to contain the object,it also associates probability values for each point within that region.This coverage map of a reader is shown in Figure2.The map can be determined from the antenna data sheet,or by manually mapping the probability of detecting tags at di?erent(x,y,z)o?sets from the reader.
Reader has a 95% chance
Fig.2.Coverage region
Fig.3.Re?ning location estimates
Given a three dimensional grid of the environment and assuming no prior history,Ferret starts with an initial postulate that associates an unknown prob-ability of?nding the object at each coordinate within the grid.For each positive reading,the probability values of each grid point contained within the coverage range are re?ned(by intersecting the range with past history as shown in Figure 3).Similarly,for each negative reading,the the probability values of each grid point contained within the coverage range is decreased.This results in a three-dimensional map,M(x,y,z),that contains the probability of seeing a tag at each data point in relation to the https://www.doczj.com/doc/e65450920.html,ing multiple power outputs requires building a map for each power output level.Due to several constraints in our current prototype,Ferret currently does not use power modulation;however, adding this to the system will be trivial.
The amount of computation that the system can do drastically a?ects the location algorithm that performs intersections,the compensation for false nega-
tives,and how it re?ects the map to the user.Next we describe two alternative methods,one that is computationally intense and cannot be done in realtime on current mobile hardware.Such an o?ine technique is useful for describing an eventual goal for the system,or how to use the system for analyzing the data after it is collected.However,our goal is to implement Ferret on a mobile device so we also describe an online algorithm with drastically reduced computational cost.
3.1O?ine Locationing Algorithm
Formally,if we consider Ferret’s readings as a series of readings,both positive and negative,as a series D={D1,D2,D3,...D n},and we want to derive the probability of the object being at position X,given the readings from the RFID, or P(X|D).If we assume that each reading of the RFID reader is an independent trial,we can compute the likelihood as:
P(X|D)=P(X|{D1,...D n?1})P(D n|X)
P({D1...D N)}
1
Z
,(1)
where Z is a normalization factor.We omit the proof as it is a straight-forward application of conditional probability.
If we?rst assume that the Ferret device(camera)is completely stationary, it operates as follows:i)once Ferret receives the?rst positive reading of a tag it initializes a three dimensional map,L,with the coverage map M,to track the probability that the object is at each of the coordinates in the map.ii)each successive reading multiplies each coordinate in L by M(x,y,z)if the reading was positive,or1?M(x,y,z)if the reading was negative.This approach is derived from Elfes’s work on occupancy grids for robotic navigation[3],and is equivalent to H¨a hnel’s approach for using sensor readings to locate RFID tags[6].
3.2Translation,Rotation and Projection
The basic algorithm described above assumes a stationary camera/reader;Fer-ret’s notion of object location does not improve beyond a point,even with a large number of readings—most points in the reader’s range(i.e.,within the coverage map)will continue to have a high,and equally likely probability of detecting the tag.Subsequently,multiple readings produce a large map with equally likely probabilities of the object’s location.Instead,Ferret depends on the user’s mo-tion to reduce the possibilities for the object location—as the user moves in the environment,the same object is observed by the camera from multiple vantage points and intersecting these ranges allows Ferret to narrow the region contain-ing the object.Incorporating motion is straightforward;however,the coordinate system of the coverage map M must be reconciled with that of the map L before this can be done.
The coverage map shown in Figure3is described in a three-dimensional coor-dinate system with the origin at the center of the reader’s RFID antenna,which we refer to as the reader coordinate system.The camera,although attached to
the RFID reader,is o?set from the reader,and has a slightly di?erent coordinate system.We refer to this as the camera coordinate system which has its origin at the center of the camera’s CCD sensor.To combine multiple readings from the reader,and subsequently display them to the user,each map M must be transformed into a common coordinate system.We refer to this as the world coordinate system.The world can have its origin at any point in the space—with a locationing system we can use its origin,or with an inertial location system we can use the?rst location of the reader.Performing this transformation is possible using techniques from linear algebra and computer graphics[5].Further details can be found in a technical report[15].
When computing the intersection of coverage maps,Ferret?rst transforms the coverage map,M into the world coordinate system,and computes the in-tersection according to the methods presented in Section3.1to produce a new map L containing the likelihood of an object’s location.
Once Ferret produces a three dimensional map that it believes contains a particular object,it must overlay this region onto the video screen of the camera; doing so involves projecting a3D map onto a two dimensional display.This is done in two steps:thresholding and projection.The threshold step places a minimum value for the likelihood on the map L—by using a small,but non-zero value for the threshold,Ferret reduces the volume that encompasses the likely position of the object.However,using a larger threshold may cause Ferret to shrink the volume excessively,thus missing the object.Currently this is a tunable parameter in Ferret—in the evaluation section we demonstrate how to choose a reasonable value.
Finally,Ferret projects the intersection map onto the image plane of the video display.Ferret must transform the intersection map from the world coordinate system into the camera coordinate system.Ferret performs this transformation using the camera’s current position and orientation.Assuming that the z-axis of the camera coordinate system is co-linear with the camera’s optical axis, projecting the image onto the image plane is straightforward.
For each reading the RFID reader produces,the location algorithm must perform O(n3)operations,for a three dimensional space that is n×n×n, in addition to translating and rotating the coverage map,and projecting the location map onto the display.If Ferret is searching for multiple objects,it must perform these operations for each individual object.In practice,we have found that each RFID reading consumes0.7seconds on a modern processor,while our RFID reader produces4readings per second.Given the speed at which a human may move the camera,this is not feasible to do in realtime,however it works well for an o?ine system that has less stringent latency requirements.An o?ine algorithm also has the opportunity to perform these operations for the whole video,and then use the smallest region that it computed to back-annotate the entire video stream with that region.
3.3Online Locationing Algorithm
Given that the o?ine algorithm is too computationally intensive for a mobile device to operate in real-time,we describe a greatly simpli?ed version of the locationing algorithm.The primary goal is to reduce the representation of the probability of where the object is.Instead of a full representation that describes the probability at each location,we reduce it to describing just the convex region where the object is with very high probability.Describing such a region is very compact,as we only need to track the points that describe the perimeter of the convex region.Intersecting two maps is very fast,as it is a series of line intersections.
Figure4shows this in detail for two dimensions,extending it to three di-mensions is straightforward.The?rst half of the diagram shows sample points that describe the outside of the coverage map.Ferret rotates and translates the coverage map M as described in the previous section,and intersects it with the current map L.For each constant y value,the system?nds the intersection of the two line segments and uses that as the description of the new map L. For instance in Figure4,we choose a constant y value y1.After rotating and translating the map M to match to the reader’s current position,the system in-tersects the two line segments,(x1,y1)?(x3,y1)from the current map L,with (x2,y1)?(x4,y1)from the new map M.The resulting intersection is the seg-ment(x2,y1)?(x3,y1),which describes the perimeter of the new location map L.Ferret repeats this process for all y values.Extending this to three dimensions is straightforward:intersect two line segments for each pair of constant y and z value.This means the complexity of the intersection is O(n2)rather than O(n3) as in the o?ine algorithm.
Fig.4.Online location estimation
Also,instead of using a map of probabilities for the coverage map,we reduce it to the convex shape that describes the coverage region of the RFID reader than can read tags with some probability greater than0.This virtually elim-inates the possibility of false positives.Additionally,describing the perimeter only requires two x points for each pair of y and z values,thus the represen-tation of the region is greatly reduced in size from O(n3)to O(n2).Using our prototype as an example,this reduces the storage requirement from43.5M bytes to178K bytes—each of these are highly compressible.This greatly aids Ferret’s ability to store the regions directly on the storage-poor tags.The line segment representation does mean that the system cannot incorporate negative regions,
as intersecting with a negative region can create a concave,rather than convex, region.A concave region would return the complexity of the representation and the intersection to O(n3).False negatives do not a?ect the system,as negative readings are not used at all.
3.4Dealing with Nomadic Objects
We designed Ferret to deal with objects that move infrequently—commonly referred to as nomadic—as opposed to mobile objects that move frequently. When objects do move,Ferret should adjust to deal with this.In the online algorithm,this is straightforward.When the location algorithm performs an intersection of two maps,it may produce a region of zero volume.This indicates that the maps were disjoint,and the object could not possibly be within the previously postulated region.The system then reinitializes the location map,L, to the most current reading,which is M rotated and translated to the reader’s current position.
However,the o?ine algorithm is more complicated as it produces a likelihood location map.One solution is applying a likelihood threshold to the likelihood location map and removing any location with a probability less than the thresh-old.If the resulting location map is empty,we will consider that the object has moved and reinitialize the location map,L,to the most current reading.Choos-ing an appropriate threshold is a critical factor in this https://www.doczj.com/doc/e65450920.html,ing a larger threshold will increase the likelihood that the resulting location map is empty when the object actually did not move.In Section5,we show how to choose an appropriate threshold.
4Implementation Considerations
We have implemented a prototype Ferret system as shown in Figure5.Although the prototype is quite large,this is due to the combination of many separate pieces of hardware—there is nothing that would preclude a much smaller com-mercial version.Our prototype is based on the following hardware:
A ThingMagic Mercury4RFID reader.The output power of the reader is set to30dBm(1Watt).This reader operates at the frequency range909?928MHz, and supports RFID tags of EPC Class0,EPC Class1,and ISO18000-6B. The reader is paired with a ThingMagic monostatic circular antenna that has a balloon shaped radiation pattern.An alternative is to a use a linear antenna that has a more focused radiation pattern and longer range;however,the narrower beam will produce fewer positive readings for each tag.The tradeo?in antenna choice and the possibility of future antennas with variable radiation patterns are interesting questions for future research.We used an orientation-insensitive, EPC Class1,Alien Technology“M”RFID tag operating at915MHz.
A Sony Motion Eye web-camera connected to a Sony Vaio laptop.This CMOS-based camera is set to a?xed focal length of2.75mm,and uses a sensor size of2.4mm by1.8mm.The camera provides uncompressed320x240video at 12frames-per-second.
Fig.5.Ferret Prototype System
Cricket[10]ultrasound3D locationing system to estimate the location of the camera and RFID reader.We deployed Cricket beacons on the ceiling,and attached a Cricket sensor to our prototype system.The Cricket sensor is o?set from the camera and RFID reader and we correct for this translation in software.
A Sparton SP3003D digital compass to obtain the3D orientations(pan,tilt, and roll)of the camera’s lens and the reader’s antenna.We mounted the compass, the camera’s lens,and the reader’s antenna with the same3D orientation.
5Experimental Evaluation
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Fig.6.Online re?nement of location
In this section,we evaluate Ferret by focusing on the performance of loca-tioning and projection.In particular,we concentrate on how quickly Ferret can re?ne the location of an object for a user.We show how to tune the o?ine algo-rithm to trade the size of the location region and the overall error rate.We then show a comparison of the online and o?ine systems.We demonstrate that Ferret can detect objects that move within a room and we show the computation and storage costs of our system.
We measure Ferret’s performance using two metrics:the size of the postu-lated location and the error rate.Ferret automatically provides the size,either the volume of the three-dimensional region,or the area of the two-dimensional
projection on the video screen.The three-dimensional region is not spherical, but to interpret the results,a sphere with a volume of0.01m3has a diameter of 26.7cm and a volume of0.1m3has a diameter of57.6cm.Ferret’s error rate is the number of objects that do not appear in the area projected onto the display. The error rate is determined through manual inspection of a video recording.
All of our experiments are conducted in a4m x10m x3m room equipped with a Cricket ultrasound system.We used?ve beacons mounted on the ceiling which we manually calibrated.The origin of our world-coordinate system is a corner of the room.The camera records all video at12frames/second,and the RFID reader produces4readings per second.For the online system,we use a coverage map that includes all places where the tag has a non-zero probability of reading a tag.That region is an irregular shape that is2.56m x1.74m x2.56m at the maximum and has a volume of approximately2m3.
5.1Online Re?nement Performance
The primary goal of Ferret is to quickly locate,re?ne,and display an outline on the video display that contains a particular object.As this happens online, Ferret continuously collects readings and improves its postulation of the object’s location—this is re?ected as the volume of the region shrinking over time.To demonstrate this,we placed one tag in the room,and then walked“randomly”around the room with the prototype.We plot the volume of the location esti-mation versus time in Figure6.The absolute volume tracks the total volume of the region,while the relative volume tracks the size of the region relative to the starting coverage region of the reader.In this case Ferret does not make any errors in locating the object.The time starts from the?rst positive reading of the tag and Ferret begins with no previous knowledge about object locations.
The results show that the volume size of the location estimation drops from 2m3to0.02m3which is only1%of the reader’s coverage region in less than2 minutes.The volume monotonically decreases,as intersecting positive readings only shrinks the area,while negative readings are ignored.Also,this is a pes-simistic view of the re?nement time—with prior knowledge,the process occurs much more rapidly.For instance,if the user switches to searching for another ob-ject in the same room,Ferret can take advantage of all of the previous readings. If a previous user has stored location information on the tag,this reader can also take advantage of that from the time of the?rst reading.Additionally,if some location information is stored in a centralized database,Ferret can immediately project an area onto the video without any positive readings.
In addition to the volume size of the location estimation,we also plot the projection area versus time in Figure6(c)in which the projection areas are shown as a percentage of the image plane area.Our results show that the?nal projection area is only3%of the whole image,or approximately a54pixel diameter circle on a320x240frame.However,the projection area does not monotonically decrease as the volume does.This is because the camera is constantly moving,thus the point-view constantly changes,and the same volume can project di?erent areas from di?erent orientations.
5.2O?ine Algorithm Performance
While the online algorithm is useful for current mobile devices,the o?ine algo-rithm uses more information,and a more precise representation of the object’s location likelihood.To evaluate Ferret’s precision in locating objects,we placed 30tags in a2.5m x2.5m x2m region,and we move the prototype around the room for20minutes.We repeat the experiment3times and record the volume of the postulated region,and manually verify how many objects are truly contained in the area projected onto the video plane.With30tags and3experiments,Fer-ret can make between0and90location errors.
Before evaluating the o?ine algorithm,we must set a threshold for the min-imum likelihood for the object as described in Section3.Recall that a larger threshold can reduce the volume encompassing the likely position of the object. However,a larger threshold will also increase the error rate of Ferret(the vol-ume doesn’t contain the object).In order to test the sensitivity of o?ine Ferret to the change of likelihood threshold,we varied the likelihood threshold from 0.00001to0.4,and ran the o?ine Ferret algorithm on the data we collected in the experiment.We show the results in Figure7.
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Fig.7.Performance of o?ine Ferret under
di?erent likelihood thresholds.Fig.8.CDF of locationing accuracy The results show that:(i)the number of errors almost doubles from5to9 as threshold increase from0.00001to0.4(ii)the mean volume of the location estimation is essentially constant;and(iii)for a threshold≤0.01,the number of errors doesn’t change.When using too high of a threshold Ferret incorrectly shrinks the volume,leaving out possible locations for the object.Considering the balance of error rate and mean volume,we choose a likelihood threshold of https://www.doczj.com/doc/e65450920.html,ing this threshold,we run the o?ine algorithm and compare it to the performance of the online algorithm.In Figure8,we plot the CDF of Ferret’s location accuracy for both algorithms.
The results show that(i)The online algorithm can localize an object in 0.15m3and0.05m3regions with80%and50%probability,respectively.The 0.15m3and0.05m3regions are only7.5%and2.5%of the reader’s coverage region which is2m3;(ii)The o?ine algorithm outperforms the online algorithm by localizing an object in a0.05m3region with more then90%probability and in a0.1m3region with100%probability.
However,when we verify the online algorithm’s error rate,it only makes2 errors,as compared to the o?ine algorithm’s5errors.We believe that the slightly
greater number of errors in the o?ine algorithm is due to our incorporation of negative readings in the algorithm.In this experimental setup,the prototype system is constantly moving and the tags are in the coverage region of the RFID reader for a small portion of the total time(less than5%).This scenario will generate19times the number of negative readings than positive readings,and negative readings are weighted as heavily as positive readings.Considering that we measured the performance of the reader under ideal conditions,we have overestimated the performance of the RFID reader.The online algorithm does not exhibit the same behavior as it does not ever use negative readings.As negative readings are correlated by orientation,and location,we believe that more accurate modeling of reader performance is an important direction for future research.
5.3Mobility E?ects
Ferret exploits the user’s mobility to produce a series of readings from multiple positions,and further re?ne its location estimation via intersecting the coverage regions at these positions.The previous experiment showed the results of a human,yet uncontrolled,mobility pattern.In reality users move erratically; however,their motions are composed of smaller,discrete motion patterns.To study how individual patterns a?ect the performance of Ferret we placed a single tag in the room and evaluated Ferret with a small set of semi-repeatable motion patterns shown in Figure9:(a)straight line,the prototype system moves in a straight line,tangential to the object,without changing the orientation of the camera lens and RFID reader;(b)head-on,the prototype moves straight at the object and stops when the reader reaches the object;(c)z-Line,the prototype system moves in a z-shaped line without changing its orientation;(d)rotation, the prototype system moves in an arc,while keeping the lens orientation radial to the path;(e)circle,the prototype system moves in a circle,while keeping the reader facing the object.Intuitively,the circular pattern may be the least likely of the mobility patterns,whereas the head-on is probably the most likely—once the user gets one positive reading,she will tend to head towards the object in a head-on pattern.We evaluated Ferret’s performance using the volume of the resulting region.For each movement pattern we ran three experiments,averaged the results,and compared the smallest volume size of both online and o?ine Ferret.Our results are shown in Figure11.
The results show that Ferret performs similarly for each of the movement patterns;however the circular pattern performs the worst.The circular pattern always keeps the object in view and generally in the center of the reader’s cov-erage region.This produces a set of readings that generally cover very similar regions.In each of the other cases,the mobility of the reader covers more dis-joint spaces,and thus produces smaller volumes.This is true even of the head-on pattern as the?rst reading and the last reading have very little volume in com-mon.Another result is that the o?ine algorithm widely outperforms the online algorithm,except in the case of the circular and head-on patterns,where the performance is similar.Much of the o?ine algorithm’s performance advantage
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Fig.9.Path of the Ferret device
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online Volume(m3)0.0200.00420.0230.0260.032
o?ine Volume(m3)0.00150.00300.00170.00110.026
o?ine:online13.331.4013.5223.631.23
Fig.11.Performance of Ferret under various mobility patterns.
comes from incorporating negative readings to reduce the possible locations for the object.In the case of the circular and head-on patterns,the object is always in view,producing few negative readings,yielding similar performance to the online algorithm.Although non-intuitive,this means that not seeing the object is as important as seeing it to narrow its location.
5.4Object Motion Detection
Ferret is designed to deal with objects that move infrequently,but when the object does move,Ferret should detect this and start its re?nement process over.As discussed in Section3,whenever Ferret encounters an empty location estimation,Ferret assumes that the corresponding object has moved.To evaluate Ferret’s performance in detecting these nomadic objects we place a tag in the room and use Ferret to estimate its location.We then move the tag a distance between5cm and200cm and again use Ferret to estimate its location.We repeat the experiment ten times for each distance,and record the number of times that Ferret didn’t detect a moved object.The results are shown in Figure10.
The?gure shows that the online and o?ine Ferret can detect100%object movements when the moving distance exceeds25cm and20cm,respectively. This is consistent with our previous results that show that Ferret can localize an object to within an region with a volume of hundredths of a m3—this gives a radius on the order of20cm,exactly how well Ferret can detect movement.As the object has not actually left the postulated area,Ferret is still correct about the object’s location.
5.5Spatial Requirements
The prototype has a non-zero probability of detecting tags in balloon-shaped region,with maximum dimensions of2.56m x2.56m x1.74m—this shape has a volume of approximately2m3.For the o?ine algorithm we sample this coverage
region every centimeter.As discussed in Section3,the o?ine algorithm requires every point in this space,while the online algorithm only requires a set of points that describe the exterior of the region.This reduced representation results in much smaller spatial requirements as compared to o?ine spatial requirements: (i)the o?ine algorithm uses a?oat of four bytes to describe the probability of a sample point,and the total space is256?256?174?4=43.5M bytes using a three dimensional array to store the probabilities of all sample points,and(ii) the online algorithm uses a two dimensional array(the dimensions correspond to y and z)to represent the coverage region,and consequently,it only needs two bytes to track the x value of every outside sample point,thus the total space required is256?174?2=178K bytes.Both the o?ine and online representations are highly compressible:the o?ine can be reduced to250K bytes and the online representation to5K bytes using the commonly available compression tool gzip. For the foreseeable future,RFID tags will not contain enough storage for the o?ine representation,while the online version is not unreasonable.If tags have more or less storage the number of sample points can be adjusted,although this will a?ect the precision of the system.
5.6Computational Requirements
The computational requirements of the o?ine and online algorithms have a similar relationship.We measured the computational requirements of Ferret’s locationing algorithm on an IBM X40laptop equipped with a1.5GHz Pentium-M processor:(i)the o?ine algorithm costs749.32ms per reading for each object, and(ii)the online algorithm only costs6ms per positive reading for each object, which is only1/125of the o?ine computational requirements.Our results show that the online algorithm incurs small overhead and will run online to track multiple tags simultaneously on relatively inexpensive hardware,while the o?ine algorithm incurs large overhead and can only run o?ine.
6Related Work
Researchers have developed RFID-based indoor locationing systems[8,9]using active,battery powered,RFID tags.In SpotON[8],Hightower,et.al,use the radio signal attenuation to estimate tag’s distance to the base stations,and triangulate the position of the tagged objects with the distance measurements to several base https://www.doczj.com/doc/e65450920.html,NDMARC[9]deploys multiple?xed RFID readers and reference tags as infrastructure,and measures the tracking tag’s nearness to reference tags by the similarity of their signal received in multiple readers. LANDMARC uses the weighted sum(the weight is proportional to the nearness) of the positions of reference tags to determine the2D position of the tag being tracked.
The above work use battery-powered sensors to identify and locate objects. These sensors are expensive(at least tens of dollars per sensor)and have limited lifetime(from several days to several years).These limitations prevent them
from scaling to applications dealing with hundreds and thousands of objects. In contrast,passive RFID tags are inexpensive(less than a dollar per tag and falling)and do not require battery power source.These features make passive RFID technology ideal for such applications.
Fishkin,et.al,proposed a technique to detect human interactions with passive RFID tagged objects using static RFID readers in[4].The proposed technique used the change of response rate of RFID tags to unobtrusively detect human activities on RFID tagged objects such as,rotating objects,moving objects, waving a hand in front of objects,and walking in front of objects.However, this doesn’t consider the problem of estimating the locations of RFID tagged objects.Their experimental results show that their system could nearly always detect rotations,while the system performed poorly in detecting translation-only movement.
H¨a hnel,et.al proposed a navigation,mapping and localization approach us-ing the combination of a laser-range scanner,a robot and RFID technology[6]. Their approach employed laser-based FastSLAM[7]and Monte Carlo localiza-tion[2]to generate o?ine maps of static RFID tags using mobile robots equipped with RFID readers and laser-range scanner.Through practical experiments they demonstrated that their system can build accurate2D maps of RFID tags,and they further illustrated that resulting maps can be used to accurately localize the robot and moving tags.Ferret’s o?ine algorithm uses the same underlying technique to map the detection probability and form a pose about the location of the tag.However,Ferret focuses on the novel problems posed by the integration of a handheld device and nomadic objects.In addition to the o?ine technique, Ferret also provides an online algorithm for real-time use on a mobile device—this technique greatly reduces complexity over the o?ine technique.Ferret also address the concerns of how to deal with nomadic objects,such as how to reset the locationing algorithm when objects move.Furthermore,Ferret incorporates a video display to show the tags’locations—the o?ine algorithm alone does not provide a method for displaying the uncertainty region to the user,something that Ferret adds.Our evaluation shows that Ferret can incorporate human move-ments,as opposed to robotic ones,demonstrating that these techniques will be useful for human-driven applications.
The3D RFID system uses a robot-controlled uni-directional antenna,and the3D tag consists of several combined tags[11].Two kinds of3D tags are developed:union tag and cubic tag.The proposed system can not only detect the existence of the3D tag but also estimate the orientation and position of the object.However,they require speci?c orientation-sensitive3D tags,custom-built from multiple tags.Furthermore,the system uses an expensive robot system to control the antenna’s movement and then estimate the orientation and position of the object.In contrast,Ferret only needs one standard orientation-insensitive tag per object and the user’s inherent mobility to estimate the object’s location.
7Conclusions
This paper presents the design and implementation of Ferret,a scalable system for locating nomadic objects augmented with RFID tags and displaying them to a user in real-time.We present two alternative algorithms for re?ning a postulation of an object’s location using a stream of noisy readings from an RFID reader:an online algorithm for real-time use on a mobile device,and an o?ine algorithm for use in post-processing applications.We also present methods for detecting when nomadic objects move and how to reset the algorithms to restart the re?nement process.
We present the results of experiments conducted using a fully working pro-totype.Our results show that(i)Ferret can re?ne object locations to only1%of the reader’s coverage region in less than2minutes with small error rate(2.22%); (ii)Ferret can detect nomadic objects with100%accuracy when the moving dis-tances exceed20cm;and(iii)Ferret can use a variety of user mobility patterns. References
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中国制造业正面临技术升级的关键时刻，无论是机器换人、两化融合、智能制造、还是未来将实现“工业4.0”和“中国制造2025”，目前市场项目实施的关键是：MES系统+车间设备联网。“车间设备联网”是实现工厂“两化融合“的关键，可保证自动化层实时在线数据进入MES/ERP管理层。MES是实现车间级数字化管理的功能软件，MES与AI及互联网、云计算技术的融合是未来工厂智能制造的技术基础。 杭州，在这座城市中，总是充斥着大量的MES系统}相关信息，但是人们一时难以辨别其真假。 不要被小编的慷慨陈词所打动了，和你们说好做彼此的天使，所以今天不选择套路你们。好了，言归正传，来看看小编送上的MES系统福利是否能打动你吧~ 可追溯性是MES系统的一个重要特性，可追溯数据模型不仅可以完整记录生产过程数据，还可以扩展到质量追溯、采购追溯等方面，对企业制造过程控制和制造过程改进具有重要意义。在生产车间仓工人将产品放错是很正常的事情。MES追溯管理系统主要是帮助企业进行产品生产基础数据整理、物料防错管理还有产品整个生产销售流程的追溯管理。预防人为因素造成工艺漏装。 MES防错追溯管理系统主要是使用统一的信息管理方法，在装配线上通过安装一维/二维 条码、RFID等信息载体。华磊迅拓的MES系统软件通过扫描枪的实时扫描和对比，通过一体机或触摸屏识别装配件是否符合要求，以及将装配过程中的实时数据发送到系统记录服务器。 MES防错追溯管理系统主要功能
1.生产计划导入 （1）计划管理员制订每条生产线的上线计划，把计划录入防错系统计划导入模板文件； （2）进入防错系统上线计划导入页面，选择上传计划后，系统把计划数据导入用户界面，同时后台并检查计划内容是否正确； （3）确认计划数据无误后确认，防错系统把计划导入系统数据库； （4）可以实现上线批次的顺序、计划数量操作，对于删除和更新的计划，系统记录计划变更日志，在计划追踪查询中给予标注； （5）计划调整时允许多行选择计划的删除功能； （6）提供一个操作软件，安装于办公室PC机，联网运行，由有权限的操作员执行。 2.MES商品质量防错 MES防错系统是检查在装配过程中一些关键步骤或关键操作是否成功完成，包括制造过程中重要零件符合性检查和精确追溯信息的管理，零件追溯实际对生产物流控制提出流程要求和数据要求，通过对追溯物料的状态跟踪来控制质量遏制范围。通过扫描枪读取零件条形码用以检查零件是否和显示的部件相符，以防止错装。指导车间生产，可进行生产流程完工反馈。 系统监控关键件装配，预防人为因素造成工艺漏装。可规范产品作业流程，并严格要求按产品流程作业。解决了门板装配过程中缺少装配作业的问题，做到实时监控、及时反应，立刻解决。这个小编之前还跟大家提过包装称重防错系统，也是一样的道理。 3.MES防差错操作流程 （1）同一台终端设备根据订单类型自动显示不同的装配界面指导装配作业； （2）按照工艺流程要求严格把关装配过程，装配人员只需按照系统提示进行装配，排除人为因素造成的潜在风险，促使装配过程标准化、透明化； （3）装配数据必须符合装配零件校验规则，系统对每个扫描的零件条码进行验证,若不符合零件校验规则则自动报警提醒并禁止下一步工艺装配； （4）MES系统通过解析关键件、重保件的条码与工序进行匹配，从而实现质量防遗漏、质量防差错、质量数据正、反追溯； （5）完成整个工序过程自动放行并显示下一个订单配置、装配信息。 4.零件校对规则 对装配零件与数据进行有效性较验规则进行设置。可根据供应商提供的子零件不同的条码规则(如扶手、装饰条、前门总成、后门总成)在MES系统中配置，配置数据将作为MES对装配过程中的零件进行防差错较验。
防窜货跟踪管理系统 解决方案 XXXXX科技有限公司 2017.2.8
目录 目录 (2) 1.系统概述 (3) 2.原理与流程 (3) 3.锦春堂经销商管理平台..................................................................................................................... 错误!未定义书签。 3.1.功能模块描述 (5) 3.1.1.平台管理系统 (5) 3.1.1.1.基本设置 (5) 3.1.1.2.商品管理 (5) 3.1.2.码管理 (6) 3.1.3.代理商系统 (6) 3.1.4.产品出入库管理 (6) 3.1.5.报表与查询 (7) 3.1.6.数据查询 (7) 2.5 平台运行环境 (7) 3. 系统报价及其开发周期 (7)
1.系统概述 产品防窜货跟踪管理系统是结合条码技术和计算机管理技术开发的新型物流解决方案。通过对单件产品进行数字处理，为生产制造企业在流通领域内跟踪产品提供支持，并解决分销渠道售假、窜货等长期困扰企业的难题。产品在流通过程中，系统及时将防窜货码状态记录到中心数据库，客户可以通过防窜货码查询产品流向。通过防窜货码不但可以确认产品防窜货流向，还可以鉴别产品真伪。该系统的应用有利于厂商制定不同地区的价格策略，并及时准确地掌握产品在市场上的流通情况 2.原理与流程 防伪功能：通过一窜数字防伪码实现查询认证，该防伪码无序、无规律、无重复，每个码查询一次后失效。 防窜货功能：结合条码（或二维码）对产品进行控制追踪，从而达到防窜货效果。 通过条码标签（或二维码标签）为每个产品分配一个唯一的识别码，产品入库、出货、退货时对条码（或二维码）进行扫描，并赋予特定信息（经销商、产品、时间等），最后使用系统的查询追踪功能来查询产品条码，调出入库、出货、退货等信息。 防窜货流程示意图：
RFID智能仓库管理系统方案1 基于RFID技术的智能仓库管理系统解决方案 一、系统背景 仓储管理在物流管理中占据着核心的地位。传统的仓储业是以收保管费为商业模式的，希望自己的仓库总是满满的，这种模式与物流的宗旨背道而驰。现代物流以整合流程、协调上下游为己任，静态库存越少越好，其商业模式也建立在物流总成本的考核之上。由于这两类仓储管理在商业模式上有着本质区别，但是在具体操作上如入库、出库、分拣、理货等又很难区别，所以在分析研究必须注意它们的异同之处，这些异同也会体现在信息系统的结构上。 随着制造环境的改变，产品周期越来越短，多样少量的生产方式，对库存限制的要求越来越高，因而必须建立及执行供应链管理系统，借助电脑化、信息化将供应商、制造商、客户三者紧密联合，共担库存风险。仓储管理可以简单概括为8个关键管理模式：追-收-查-储-拣-发-盘-退。 库存的最优控制部分是确定仓库的商业模式的，即要（根据上一层设计的要求）确定本仓库的管理目标和管理模式，如果是供应链上的一个执行环节，是成本中心，多以服务质量、运营成本为控制目标，追求合理库存甚至零库存。因此精确了解仓库的物品信息对系统来说至关重要，所以我们提出要解决精确的仓储管理。 仓储管理及精确定位在企业的整个管理流程中起着非常重
要的作用，如果不能保证及时准确的进货、库存控制和发货，将会给企业带来巨大损失，这不 仅表现为企业各项管理费用的增加，而且会导致客户服务质量难以得到保证，最终影响企业的市场竞争力。所以我们提出了全新基于RFID射频识别技术的仓库系统方案来解决精确仓储管理问题。 使用RFID仓储物流管理系统，对仓储各环节实施全过程控制管理，并可对货物进行货位、批次、保质期、配送等实现RFID 电子标签管理，对整个收货、发货、补货等各个环节的规范化作业，还可以根据客户的需求制作多种合理的统计报表。RFID技术引入仓储物流管理，去掉了手工书写输入的步骤，解决库房信息陈旧滞后的弊病。RFID技术与信息技术的结合帮助商业企业合理有效地利用仓库空间，以快速、准确、低成本的方式为客户提供最好的服务。 二、系统优势 1.读取方便快捷：数据的读取无需光源，甚至可以透过外包装来进行。有效识别距离更长，采用自带电池的主动标签时，有效识别距离可达到30米以上； 2.识别速度快：标签一进入磁场，阅读器就可以即时读取其中的信息，而且能够同时处理多个标签，实现批量识别； 3.穿透性和无屏碍阅读：条形码扫描机必须在近距离而且没有物体阻挡的情况下，才可以辨读条形码。RFID能够穿透纸张、木材和塑料等非金属和非透明的材质，进行穿透性通信，不需要
三、系统特点 RFID的无线即时远距离读取方式、大容量和高速数据处理能力以及高度的自动化，使它具备不可替代的技术优势； 货物，货车等数据可实时于因特网上查对、管理； 虚拟库存跟踪； 系统易于安装维护； 数据获取高效，准确； 系统实时跟踪人，货物，托盘，叉车，货车等所有物流环节； 物流的所有环节和流程都被实时监控。 四、系统组成 1、阅读器：本系统采用UHF915MHz阅读器，分别为固定安装阅读器和手持式阅读器，阅读器通过天线感应标签，并读取标签内的数据信息。固定安装阅读器对标签读取的距离可以达到八米以内，而手持式阅读器的读取距离为１米左右。（见下图）
2、标签：标签有各种款式，如卡片式，信用卡大小，安装在货车、集装箱上，安装在托盘，货物，包裹上，标签有各种封装形式，可针对性地选择。每个标签拥有一个全球唯一的ID 号码，在实际的应用中，它被赋予被安装物件、货物的信息以做识别、读取，并可回收利用。标签可以十分方便地附着安装在待识别物体上。（下图） 3、管理软件：阅读器在获取大量标签的数据后可以直接接入计算机/掌上电脑/笔记本电脑的端口，即时自动输入系统数据库，由管理软件进行管理。管理软件为网络版，这意味着在全球的任何一台因特网电脑上，管理者或者用户都可以实时对货物进行查验、管理，了解货车的所在位置等。信息上传到一个普通的Internet平台，这样货物端到端实时运动的可视化场景就能获得。 五、系统实现 5.工作人员或者叉车司机可以通过手持读卡器查在仓库内的货物进行信息收集，查找，快速便捷，大大提高了仓储盘点，查货的效率和准确率。（见附图E）在汽车的挡风玻璃上安装标签，在集装箱上安装标签，在大宗货物的容器和包装上安装标签。标签放在汽车挡风玻璃上，它可利用内部写入的唯一识别码或直接写入车辆信息（车牌号、所载货物）等来标识汽车及其装载货物。标签将内存数据调制反射给天线的微波信号上。货车经过各站点，由安置在各站点的阅读器识别经过的货车，记下货车到达该点的时间，货车内托盘及货物的信息，自动连接数据库比对，并上传因特网系统，达到实时跟踪的效果。最后，通过计算机局域网络，送至有关人员的计算机终端，实现了管理者不出门就能掌握货场车辆运用情况，控制车辆及其装载货物进出的目的。而当运载集装箱和大宗货物的交通工具经过运输网点RFID阅读器的感应范围时，集装箱和货物将被自动识别，并传输给计算机。（见附图）
智能物流跟踪与仓储管理系统 2016年9月23日
目录 1、产品方案介绍 0 1.1概述 0 1.2业务流程图 (1) 1.3 业务流程简介 0 1.4 主要硬件设备简介 (6) 2、实验内容 (10) 3、配置清单及技术指标 (11)
1、产品方案介绍 1.1概述 “互联网+”浪潮让一切传统行业和产业都在发生改变，物流业也开始进入智能时代。目前，我国物流系统虽然跨越了简单送货上门的阶段，但层次上仍是传统意义上的物流配送，先进信息技术在物流行业的应用和推广水平仍然较低，且存在物流企业规模较小且布局散乱、物流信息标准滞后等问题，物流配送的许多环节造成巨大的成本、人力、时间浪费。总体来说，我国智慧物流尚处于发展的初级阶段，运输及仓储信息化低等仍需突破。而智能物流能使整个物流系统能模仿人的智能，具有思维、感知、学习、推理判断和自行解决物流中某些问题的能力，标志着信息化在整合网络和管控流程中进入到一个新的阶段，即进入到一个动态的，实时进行选择和控制的管理水平，并成为未来发展的方向。 智能物流跟踪与仓储管理系统是利用无线射频识别技术将物联网的思想转换成实际应用的典型方案；智能物流跟踪与仓储管理系统模拟了从生产企业包装至仓储物流、商超存储，乃至消费购物、收银等整个流程。利用先进的RFID技术、AGV自动车辆导航技术和计算机网络、数据库、软件等技术，真实的展现了典型的商业运作的业务流程，实现智能仓储、智能物流、智能收银等功能，全面实现“从仓储物流到顾客购物”的智能化及零售业务管理的智能化，学生通过在智能物流跟踪与仓储物流系统实训室内各种活动及作业的模拟可以掌握现代物流信息，了解智能物流跟踪与仓储物流系统的总体流程，增强管理意识，提高团队意识和沟通能力。 智能物流输送系统可广泛应用于汽车制造、工程机械、服装家电、农用机械、
软件主要功能： RFID仓库管理系统软件平台，主要将RFID技术特性与仓库管理的流程结合，在软 件上实现更科学、可视化的管理。下面针对软件上其几个重要的功能进行介绍： 1、入库管理 在仓库的门口部署RFID固定式读写器，同时根据现场环境进行射频规划，比如可以 安装上下左右四个天线，保证RFID电子标签不被漏读。接到入库单后，按照一定的规则 将产品进行入库，当RFID电子标签(超高频)进入RFID固定式读写器的电磁波范围内会 主动激活，然后RFID电子标签与RFID固定式读写器进行通信，当采集RFID标签完成后，会与订单进行比对，核对货物数量及型号是否正确，如有错漏进行人工处理，最后将 货物运送到指定的位置，按照规则进行摆放。RFID在仓库管理应用中最主要的优势非接 触式远距离识别，且能够批量读取，提高效率与准确性。 2、出库管理 根据提货的计划，出库的货物进行分拣处理，并进行出库管理。如果出库数量较多时，将货物呈批推到仓库门口，利用固定式读写器与标签通信，对出库的货物的RFID电子标 签采集，检查是否与计划对应，如有错误，尽快的人工处理。对于少量的货物，可以使用RFID手持式终端进行RFID电子标签的信息采集(手持扫描枪或RFID平板电脑)，出现 错误时，会发出警报，工作人员应该及时的处理，最后把数据发送到管理中心更新数据库 完成出库。
3、盘点管理 按照仓库管理的要求，进行定期不定期的盘点。传统的盘点，耗时耗力，且容易出错。而这一切RFID把这些问题解决了，当有了盘点计划的时候，利用RFID手持式的终端进 行货物盘点扫描，盘点服装的信息，可以通过无线网络传入后台数据库，并与数据库中的 信息进行比对，生成差异信息实时的显示在RFID手持终端上，供给盘点工作人员核查。 在盘点完成后，盘点的信息与后台的数据库信息进行核对，盘点完成。在盘点的过程中， 系统通过RFID非接触式读取(通常可以在1~2米范围内)非常快速方便地读取服装货物信息，与传统的模式相比，会提高很多效率和盘点的准确性。
消 防 产 品 跟 踪 管 理 系 统 生 产 企 业 客 户 端 简明用户手册 公安部消防产品合格评定中心 制作单位 北京优士东方数码科技有限公司 制作日期： 二零零七年十二月
前言 首次安装消防产品跟踪管理系统，基础信息下载完毕后，请您立即通过系统升级获得最新版本软件，以保证您所使用的软件版本同服务器保持一致。 阅读手册 欢迎您使用消防产品跟踪管理系统。 通过阅读本手册，您可以快速了解如何安装和使用消防产品跟踪管理系统，以及一些产品安装和使用过程中的注意事项。为帮助您更好地阅读，现将手册的内容结构、所用约定及特定术语的含义，简单介绍如下： 手册内容结构 手册共分四部分： 第一部分 前言 介绍手册使用相关问题和系统简介，本产品使用过程中的一些注意事项； 第二部分 产品安装 介绍产品的安装方法； 第三部分 操作说明 生产厂家使用本产品时的基本概念及主要操作流程； 手册中的术语 z准入标志 公安部消防产品合格评定中心颁发，采用专业印刷技术生产出的精密数字标识。每个准入标志具有唯一性。准入标志只能由通过消防产品准入生产评定审核的产品生产厂家粘贴在被批准的产品型号、并且必须由生产厂家进行准入标志信息启用注册机才能正式使用。目前分为准入标志Ｉ和准入标志ＩＩ两种规格。 z专用读取设备 本系统采用ＵＤ笔作为准入标志的信息专用读取设备。ＵＤ笔具有与普通笔一样的纸面手写功能，而且具有在数码纸面上读取手写信息并进行存储和传输的功能。每套专用读取设备都具有唯一的设备号。只有经过评定中心发放认可的ＵＤ笔才能使用。 z数码表单 本系统使用的标签启用注册表、监督检查表是采用专用数码纸印制的，各单位根据需要购买经评定中心认可的产品。 系统简介 消防产品跟踪管理系统是以在数码纸笔基础上建立起来的标识技术为核心，结合其他防伪技术，根据消防产品在生产和监管过程中的使用特点，对消防产品进行全过程监管的综合管理系统。
RFID智能仓库管理解决方案 背景： 江山天安是一件专业的非标钢质门生产的大型企业，以非标钢质门的加工生产为主。企业生产基本是按单生产，基本是一单一门。仓库的一般情况下的出入库数量在1000套门左右，高峰期可达到2000左右。同时，在库的成品门一般保持在10000套左右。仓库面积234X30M，在这样大的库房及库容量的库房中执行出入库、盘点等业务是相对比较复杂的。人工操作的稍有不慎，就可能会造成出入库差错、帐物不符、寻找困难等情况出现，给企业的生产经营带来诸多困难。 随着企业的发展与企业生存环境的变化，对我们的管理提出了更高的要求。RFID技术是一种无线射频识别技术。具有非可视识读、远距离识读、多目标识读、环境适应性强等诸多特点，相对于生产、仓储和运输等环节对设备的要求而言具有比较高的可靠性。 一．系统结构 企业目前已经部署ERP系统，RFID系统的数据最终需要汇集到现有的ERP系统处理。
ERP 系统 固定式设备固定式设备 手持设备 W i f i 手持设备 1. 包装入库 包装入库人员在对成品门打包包装的同时将RFID 标签以合适方式固定在门上，同时采用手持机读取标签数据，并在手持终端软件系统中填写相关信息。 在仓库的各个货位上部署标签，标识货位信息，并在软件系统中建立基于RFID 标签标识的仓库货位信息表。包装入库人员在将门安放进制定货位时，先识读当前货位标签，确定（或选择）货位，并与即将放入的门绑定。这样就保障了成品门进入准确的货位。 手持终端设备通过部署的WIFI 网络实时连接到企业ERP 系统，若网络系统存在故障，手持设备可以保存数据，在网络恢复是上载，或手动上载入库数据信息。 入库完成后系统可以自动产生入库单，由仓库管理人员
国际上对MES系统的标准定义是：MES系统是“位于上层计划管理系统与底层工业控制之间，面向车间层的管理信息系统。” 东莞，在}这座繁华的都市，人们的脚步难免匆匆，但是其实有很多人想让自己的脚步能够慢下来，能够一步一个脚印的踏踏实实，MES系统的关注无意是一个好的选择。 近年来，MES系统的需求在不断提升，大家对其的要求也越来越高。当下很多人都会网上搜寻相关的信息。接下来就让小编带你走进它吧。 可追溯性是MES系统的一个重要特性，可追溯数据模型不仅可以完整记录生产过程数据，还可以扩展到质量追溯、采购追溯等方面，对企业制造过程控制和制造过程改进具有重要意义。在生产车间仓工人将产品放错是很正常的事情。MES追溯管理系统主要是帮助企业进行产品生产基础数据整理、物料防错管理还有产品整个生产销售流程的追溯管理。预防人为因素造成工艺漏装。 MES防错追溯管理系统主要是使用统一的信息管理方法，在装配线上通过安装一维/二维 条码、RFID等信息载体。华磊迅拓的MES系统软件通过扫描枪的实时扫描和对比，通过一体机或触摸屏识别装配件是否符合要求，以及将装配过程中的实时数据发送到系统记录服务器。 MES防错追溯管理系统主要功能 1.生产计划导入 （1）计划管理员制订每条生产线的上线计划，把计划录入防错系统计划导入模板文件； （2）进入防错系统上线计划导入页面，选择上传计划后，系统把计划数据导入用户界面，同时后台并检查计划内容是否正确； （3）确认计划数据无误后确认，防错系统把计划导入系统数据库； （4）可以实现上线批次的顺序、计划数量操作，对于删除和更新的计划，系统记录计划变更日志，在计划追踪查询中给予标注； （5）计划调整时允许多行选择计划的删除功能； （6）提供一个操作软件，安装于办公室PC机，联网运行，由有权限的操作员执行。
RFID智能仓库管理 应用方案 2017年2月
目录 1 项目背景 (1) 2 方案简介 (2) 2.1 应用框架流程图 (2) 2.2 软件系统简介 (3) 2.3 硬件简介 (4) 2.4 工作流程简介 (6) 3 系统优势 (7) 4 系统实施步骤 (10)
1 项目背景 当前物流环节存在的费用高、效率低的实际情况，依时利通过调研拜访第三方物流仓库经营企业、工厂自用仓库企业等仓库一线用户，发现传统仓库管理存在如下问题： 入库通知：货物都要入库了，入库单还没有送下来，不能立即投入工作。 入库完成：送货车辆都走了好久了，一查，货物还是未入库状态。 入库：货物已经搬入仓库摆放整齐了，入库完成了吗？没有，还要记录货物存放信息，但面对一批货物要放入几十上百个货架库位，普通的仓库管理系 统难以快速维护。 单据：五花八门的送货单、出库单、载货清单，没有一个标准，换个新人，先培训1个月再说。 业务：业务兴隆是好事情，可管理跟不上，只能看着客户流失。 理货：发现货物放置不合适，想调整下位置，担心造成货物存放位置不正确。 理货：一个理货员，每天理货的库位位置很多，能记清楚每个位置的货物存放么？ 理货：仓库面积大，库位数量多，理货怎么安排？加人也不能有效的解决问题。 盘点：一盘就是几天，效率真低，效率低还算了，老出错，咋整？ 面对当前物流企业市场激烈的行业竞争和社会对物流费用、物流效率提升的迫切要求，依时利RFID智能仓库系统从一开始就着手于解决物流关键环节：仓库内部的物流效率与管理效率提升，通过摆放仓库企业一线用户，调查其业务状况，管理状况，软件系统状况，我们分析出仓库管理存在以上问题的关键： 1、仓储管理数据提交不实时，导致数据与实物不符合。 2、仓库管理不够科学，没有形成标准化。 3、仓库现有机械设备没有处理自动处理业务的设备基础. 为解决这两个核心问题，依时利通过与仓库企业合作，历时2年，开发出了一套可以解决实际仓库管理难题、明显提升管理效率的软硬件集成系统。
MES系统通过智能监控自动、实时地采集生产现场的各种数据，生成质量报表和质量数据分析报告，对关键工序关键项点的数据加以分析，以便于发现车间生产中的隐性质量问题，实现及早发现处理问题、提升生产效率的功能。 广州，这座繁华又古朴的城市，想了解MES系统的人必然不少，那如何省时省力的找到自己想要的信息呢？我想下面的内容可以给您答案。 近年来，MES系统的需求在不断提升，大家对其的要求也越来越高。当下很多人都会网上搜寻相关的信息。接下来就让小编带你走进它吧。 可追溯性是MES系统的一个重要特性，可追溯数据模型不仅可以完整记录生产过程数据，还可以扩展到质量追溯、采购追溯等方面，对企业制造过程控制和制造过程改进具有重要意义。在生产车间仓工人将产品放错是很正常的事情。MES追溯管理系统主要是帮助企业进行产品生产基础数据整理、物料防错管理还有产品整个生产销售流程的追溯管理。预防人为因素造成工艺漏装。 MES防错追溯管理系统主要是使用统一的信息管理方法，在装配线上通过安装一维/二维 条码、RFID等信息载体。华磊迅拓的MES系统软件通过扫描枪的实时扫描和对比，通过一体机或触摸屏识别装配件是否符合要求，以及将装配过程中的实时数据发送到系统记录服务器。 MES防错追溯管理系统主要功能 1.生产计划导入 （1）计划管理员制订每条生产线的上线计划，把计划录入防错系统计划导入模板文件； （2）进入防错系统上线计划导入页面，选择上传计划后，系统把计划数据导入用户界面，同时后台并检查计划内容是否正确； （3）确认计划数据无误后确认，防错系统把计划导入系统数据库； （4）可以实现上线批次的顺序、计划数量操作，对于删除和更新的计划，系统记录计划变更日志，在计划追踪查询中给予标注； （5）计划调整时允许多行选择计划的删除功能； （6）提供一个操作软件，安装于办公室PC机，联网运行，由有权限的操作员执行。
昊天科技RFID仓储管理系统项目经验分享1标识方式 1.1货位标识方式 标识方式每个货位一个电子标签。 标识类型背胶粘贴。 标识位置1)高位货架的货位，将电子标签粘贴在托盘上。货架 和托盘上原有的一维条形码仍然保留，便于仓储管理员核对。 2)自动化立体库的货位，将电子标签粘贴在周转箱上。 3)阁楼式货架的货位，将电子标签粘贴在货箱上。 4)地铺和悬臂式货架的货位，将电子标签粘贴在原有 的一维条形码旁边。 如果是高位货架上的货位，将电子标签粘贴在托盘上，作为货位的唯一标识，如下图所示：
如果是周转箱或货箱，将电子标签粘贴在一侧，如下图所示： 1.1.1 叉车标识方式 在叉车的两侧处，粘贴电子标签各一枚，如下图所示：
1.1.2 备件标识方式 不可独立标识的备件，如弹簧垫圈、钢绞线等，需与供应商合同约定，提供适合张贴电子标签的物资包装方式，将备件放入包装内，如包装袋、包装箱等，电子标签固定或悬挂在包装上。 备件摆放原则： 1)备件如果叠放，电子标签不能被遮掩。 2)可独立标识的备件，电子标签应固定或悬挂在备件一侧，当 备件摆放在托盘或货位上时，应当将电子标签的朝向一致， 并朝向通道或人员，以提高读取准确率。 下图是可固定的备件标签：
下图是可悬挂的备件标签： 3)不可独立标识的备件，电子标签应固定或悬挂在外包装上， 当备件摆放在托盘或货位上时，应当将电子标签朝向上方， 以提高读取准确率。 4)周转箱内的备件，遵循上述原则。 标识方式每个可独立标识的备件，固定一个电子标签。不可独立标识的备件，更改包装方式后，固定或悬挂在外包装上。标识类型固定或悬挂。
成品仓储出入库R F I D 项目解决方案 Document serial number【NL89WT-NY98YT-NC8CB-NNUUT-NUT108】
成品仓储出入库RFID项目解决方案 编制单位（加盖公章）：XXXXXXXXXXXXXXX 单位负责人：XXXXXXXXXXXXXX 计划负责人：XXXXXXXXXXXXXX 项目负责人：XXXXXXXXXXXXXX 编制日期：年月日 目录
1项目概况 1.1项目背景 仓储管理在物流管理中占据着核心地位，传统的仓储业中存在诸多问题，如：进出库人员操作混乱、库存报告不及时、仓库货品属性不清晰、堆放混乱、盘点不准确等，都需要一个基于信息化管理的技术进行彻底的改造。 基于RFID技术的仓库管理，将改变传统的仓库管理的工作方式与流程，把所有关键的因素通过贴上RFID标签，在仓库管理的核心业务流程：出库、入库、盘点、库存控制上实现更高效精确的管理。RFID技术以识别距离远，快速，不易损坏，容量大等条码无法比拟的优势，简化繁杂的工作流程，有效改善仓库管理效率和透明度，保持企业业务运营的精益。 像世界零售巨头沃尔玛这样的大型零售商已经开始在他们的物品仓库和配货中心部署RFID仓储管理系统。一些研究机构估测，沃尔玛的RFID系统每天产生大约7兆字节的数据。因此，对于RFID仓储管理系统，如何存储和检索海量RFID数据也是一个迫切需要解决的问题 1.2项目目标 仓库管理系统（WMS）作为某一公司的核心业务系统，利用计算机软件模拟实施仓库管理的系统流程，其综合性要求达到简明实用、提高仓库管理的质量和效率的目的。 将物资集中放置在一定的场所需要利用科学的方法进行管理，并且实现高效准确的出入库操作，在保证基本效率的前提下，及时的将货物更新信息提交到服务器数据库；上层的管理系统要保证货物在存储期间数量不发生变化、维护相应的出入库记录和定期盘点信息。 在货物存储时要达到空间利用的最大化，劳动力和设备要达到最大程度和最有效的利用，货物能够方便的存取，在需要时能够做到快速的定位。另一方面，此管理系统要实现高效准确的出入货验证审核机制，使得在有限的工作时间内再无差错的基础上更多数量的完成入货和出货的操作，从最大程度上节省人力资源消耗，避免人为因素导致错误的发生。 2项目需求 2.1仓库管理系统需求 传统的仓储管理，一般依赖于一个非自动化的，以纸质文件为基础的系统与人工记忆相结合来实现库存管理。这种方式不仅费时费力，而且容易出错，
2014年第8 期 交流 ＨＥＢＥＩＮＯＮＧＪＩ河北 农 机 企业产品内部追踪系统的设计与实现 四川职业技术学院 陈垚至 １引言 内江海天气体有限公司创立于１９９２年，专业从事各种气体、液体的生产、销售、运输，产品应用范围广、销售区域覆盖周边各市区。 ２００７年伴随着公司业务量的增大，数据量的增多，公司引进了金蝶Ｋ／３系统，实现供应链物流的数据化管理。但公司各个业务部数据不能很好的相互应用、相互共享，造成信息传输不及时。 ２０１３年公司实施ＥＲＰ系统，将企业人事管理部门、计划管理部门、采购管理部门、生产管理部门、销售管理部门、仓储管理部门、财务管理部门、固定资产部门等业务部门实现信息共享。现在，管理者希望利用ＰＤＡ设备的无线通信技术、自动识别技术结合企业ＥＲＰ系统，使其能够实时追踪产品信息状态。 ２技术分析2.1E RP ＥＲＰ是以计算机信息技术为基础，将企业中的全部资源进行整合，使企业的物流、资金流、信息流一体化。 2.2P D A ＰＤＡ设备，又称为掌上电脑，它能满足移动办公、通信、存储，支持条码自动识别、ＲＦＩＤ读写、无线通信等功能。本系统主要采用ＰＤＡ设备的自动识别条码技术和ＷＬＡＮ无线通信功能进行展开。 ３系统的设计与实现3.1系统功能分析 依据业务流程分析，客户端功能结构主要包括收发台业务模块、瓶检站业务模块、充装部业务模块、质检部业务模块、仓库业务模块等五大模块，其具体的模块功能组成如图１所示。 图1 P D A 客户端功能结构图 3.2系统结构设计 借助自动识别技术、ＷＬＡＮ无线通信技术等核心的物联网技术，全面完善企业产品内部生产、仓储及配送管理。这里以收发台业务结构设计为例进行说明： 收发台的主要职责是气瓶检查和核对气瓶数量。门卫对新进入厂区的车辆发放“身份识别卡”，利用ＰＤＡ条码扫描技术扫描身份识别卡条码，登记车辆装载的气瓶总数和满瓶总数。标记当前身份识别卡信息后将数据传送到系统后台存储，从而追踪车辆的具体状态及位置。 检前登记员扫描身份识别卡条码，从而对该车辆进行追踪，如图２所示。然后，操作人员用ＰＤＡ设备对车上气瓶的唯一识别码进行扫描，并确认该气瓶为是否合格。若点击合格按钮，系统会自动忽略选择的问题，标记该气瓶为待充装状态，并将此数据实时传送后台，与ＥＲＰ系统实现数据共享，实现气瓶状态及位置追踪。在此之前，企业必须为每个气瓶粘贴唯一识别码，这有利于系统对某个气瓶的辨别与控制。 图2检前登记图 3.3系统实 现 身份识别卡信息追踪，系统对门卫发出去的身（下转６４页） 61
RFID橡胶行业仓库管理解决方案 一、方案概述： 在橡胶企业的仓库管理中运用先进的RFID技术和WMS仓储管理系统来替代人工经验管理模式，改善企业的运营效率，利用RFID技术采集数据，结合天津小蜜蜂公司领先的RFID应用平台和服务，及合作伙伴的资源，建立强大的仓库管理信息化解决方案，给橡胶企业带去真正的由RFID所发生的改变。融入企业的仓库管理的业务中，让RFID技术在入库、出库、盘点、移库、调拨等业务中发挥最大的效益，利用RFID技术保持数据的准确性与速度，达到财物相符，信息对称。企业能够从数据获取对仓储业务的洞察，从而做出科学的决策，保持合理的库存，进一步的改善仓库的管理。 二、行业洞察 此次对橡胶企业行业的洞察中主要集中在生产轮胎、履带等一般工业橡胶制品的橡胶生产型企业，在生产型企业主要涉及的仓库管理业务主要有原材料仓库管理、半成品及成品仓库管理。在行业洞察中发现，相关企业有很多良好的装备，但在整个物流环节有着一定的瓶颈，面临着诸多的问题。比如比如落后的管理方式使得库存数据不准确，信息不能实时传递，橡胶产品的规格数量不够准确，没有对计划管理的准确性与预见性，帐物不符造成数据库存数据的失真，造成为客户提供货物的供货周期不准，物资积压或延迟交货等问题。在对仓库的
橡胶制品盘点的时候，通常出现数量差异很大，经过问题分析很多是因为人工的操作的失误与管理上的一些不足。 三、方案设计 RFID应用在橡胶仓库管理解决方案是集合软件、硬件、服务为一体的，根据橡胶企业的属性来设计，最大限度的满足客户需求。在单位橡胶产品上贴上RFID电子标签，相当于给每一个货物发送一个电子的身份证，借由RFID电子标签与RFID读写器的通信来实现精确有效的数据采集，然后传输到数据库中，反映到系统平台的应用上。在粘贴标签的时候，至关重要的是根据货物种类和企业的不同，选择以不同的标签粘贴方案，可以以一个托盘、一件货物或者一箱为单位。在入库、出库、盘点等关键业务上，使用固定式、移动式的RFID读写器，根据仓库管理业务计划，来实现业务过程的高效数据采集，数据将能够实时的发送到数据库，应用软件再对数据进行显示和统计分析，管理者可以通过应用软件实现对各种数据洞察业务的情况。货物、托盘信息可以和货位信息进行绑定，仓库操作人员在查找货物和调拨等过程中可以更快捷高效的完成。在整体的解决方案构建上，确保标签读取的准确性、系统的稳定性，并能对应用进行高度的扩展，保证RFID技术应用的适用性，同时，天津小蜜蜂极为重视系统应用的用户体验。这一切都得益于我们对橡胶企业的行业洞察和众多的客户关系，并结合由我们勤劳的企业品格积累的经验和客户一起合作而发展出来的智慧。
MES系统采用不同的自动化接口方法，实现对具备采集条件数据的自动化获取，MES系统上线运行后，经评价确认满足实际要求，整个MES项目即告竣工，这时必须对整个系统的建设进行总结，并对项目进行验收。 上海，在这座城市中，总是充斥着大量的MES系统}相关信息，但是人们一时难以辨别其真假。 大家还在为如果和选择MES系统而绞尽脑汁吗？各位还在网上搜寻相关信息吗？别麻烦了，就让小编为大家一网打尽吧！ 可追溯性是MES系统的一个重要特性，可追溯数据模型不仅可以完整记录生产过程数据，还可以扩展到质量追溯、采购追溯等方面，对企业制造过程控制和制造过程改进具有重要意义。在生产车间仓工人将产品放错是很正常的事情。MES追溯管理系统主要是帮助企业进行产品生产基础数据整理、物料防错管理还有产品整个生产销售流程的追溯管理。预防人为因素造成工艺漏装。 MES防错追溯管理系统主要是使用统一的信息管理方法，在装配线上通过安装一维/二维 条码、RFID等信息载体。华磊迅拓的MES系统软件通过扫描枪的实时扫描和对比，通过一体机或触摸屏识别装配件是否符合要求，以及将装配过程中的实时数据发送到系统记录服务器。 MES防错追溯管理系统主要功能 1.生产计划导入 （1）计划管理员制订每条生产线的上线计划，把计划录入防错系统计划导入模板文件；
（2）进入防错系统上线计划导入页面，选择上传计划后，系统把计划数据导入用户界面，同时后台并检查计划内容是否正确； （3）确认计划数据无误后确认，防错系统把计划导入系统数据库； （4）可以实现上线批次的顺序、计划数量操作，对于删除和更新的计划，系统记录计划变更日志，在计划追踪查询中给予标注； （5）计划调整时允许多行选择计划的删除功能； （6）提供一个操作软件，安装于办公室PC机，联网运行，由有权限的操作员执行。 2.MES商品质量防错 MES防错系统是检查在装配过程中一些关键步骤或关键操作是否成功完成，包括制造过程中重要零件符合性检查和精确追溯信息的管理，零件追溯实际对生产物流控制提出流程要求和数据要求，通过对追溯物料的状态跟踪来控制质量遏制范围。通过扫描枪读取零件条形码用以检查零件是否和显示的部件相符，以防止错装。指导车间生产，可进行生产流程完工反馈。 系统监控关键件装配，预防人为因素造成工艺漏装。可规范产品作业流程，并严格要求按产品流程作业。解决了门板装配过程中缺少装配作业的问题，做到实时监控、及时反应，立刻解决。这个小编之前还跟大家提过包装称重防错系统，也是一样的道理。 3.MES防差错操作流程 （1）同一台终端设备根据订单类型自动显示不同的装配界面指导装配作业； （2）按照工艺流程要求严格把关装配过程，装配人员只需按照系统提示进行装配，排除人为因素造成的潜在风险，促使装配过程标准化、透明化； （3）装配数据必须符合装配零件校验规则，系统对每个扫描的零件条码进行验证,若不符合零件校验规则则自动报警提醒并禁止下一步工艺装配； （4）MES系统通过解析关键件、重保件的条码与工序进行匹配，从而实现质量防遗漏、质量防差错、质量数据正、反追溯； （5）完成整个工序过程自动放行并显示下一个订单配置、装配信息。 4.零件校对规则 对装配零件与数据进行有效性较验规则进行设置。可根据供应商提供的子零件不同的条码规则(如扶手、装饰条、前门总成、后门总成)在MES系统中配置，配置数据将作为MES对装配过程中的零件进行防差错较验。 5.质量数据追溯及条码规则 系统是采集关键件信息与门板信息关联，建立统一的关键件条码规则，可通过追溯关键件条码而得知该关键件是哪家供应商、生产日期、生产批次等信息。如此便于批次管理及后期质量追溯
创羿科技智能仓库管理系统 系统背景 仓储管理在物流管理中占据着核心的地位。传统的仓储业是以收保管费为商业模式的，希望自己的仓库总是满满的，这种模式与物流的宗旨背道而驰。现代物流以整合流程、协调上下游为己任，静态库存越少越好，其商业模式也建立在物流总成本的考核之上。由于这两类仓储管理在商业模式上有着本质区别，但是在具体操作上如入库、出库、分拣、理货等又很难区别，所以在分析研究必须注意它们的异同之处，这些异同也会体现在信息系统的结构上。 随着制造环境的改变，产品周期越来越短，多样少量的生产方式，对库存限制的要求越来越高，因而必须建立及执行供应链管理系统，借助电脑化、信息化将供应商、制造商、客户三者紧密联合，共担库存风险。仓储管理可以简单概括为8个关键管理模式：追-收-查-储-拣-发-盘-退。 库存的最优控制部分是确定仓库的商业模式的，即要（根据上一层设计的要求）确定本仓库的管理目标和管理模式，如果是供应链上的一个执行环节，是成本中心，多以服务质量、运营成本为控制目标，追求合理库存甚至零库存。因此精确了解仓库的物品信息对系统来说至关重要，所以我们提出要解决精确的仓储管理。 仓储管理及精确定位在企业的整个管理流程中起着非常重要的作用，如果不能保证及时准确的进货、库存控制和发货，将会给企业带来巨大损失，这不仅表现为企业各项管理费用的增加，而且会导致客户服务质量难以得到保证，最终影响企业的市场竞争力。所以我们提出了全新基于射频识别的仓库系统方案来解决精确仓储管理问题。下面我们来分析采用射频识别技术后给企业带来的经济效益。 系统优势 ?读取方便快捷：数据的读取无需光源，甚至可以透过外包装来进行。有效识别距离更长，采用自带电池的主动标签时，有效识别距离可达到30米以上； ?识别速度快：标签一进入磁场，阅读器就可以即时读取其中的信息，而且能够同时处理多个标签，实现批量识别； ?穿透性和无屏碍阅读：条形码扫描机必须在近距离而且没有物体阻挡的情况下，才可以辨读条形码。RFID能够穿透纸张、木材和塑料等
MES系统产品追溯跟踪管理功能 MES系统产品追溯跟踪管理功能介绍：可追溯性是MES系统的一个重要特性，可追溯数据模型不仅可以完整记录生产过程数据，还可以扩展到质量追溯、采购追溯等方面，对企业制造过程控制和制造过程改进具有重要意义。在生产车间仓工人将产品放错是很正常的事情。MES追溯管理系统主要是帮助企业进行产品生产基础数据整理、物料防错管理还有产品整个生产销售流程的追溯管理。预防人为因素造成工艺漏装。 MES防错追溯管理系统主要是使用统一的信息管理方法，在装配线上通过安装一维/二维条码、RFID等信息载体。华磊迅拓的MES系统软件通过扫描枪的实时扫描和对比，通过一体机或触摸屏识别装配件是否符合要求，以及将装配过程中的实时数据发送到系统记录服务器。 MES防错追溯管理系统主要功能 1.生产计划导入 （1）计划管理员制订每条生产线的上线计划，把计划录入防错系统计划导入模板文件； （2）进入防错系统上线计划导入页面，选择上传计划后，系统把计划数据导入用户界面，同时后台并检查计划内容是否正确；
（3）确认计划数据无误后确认，防错系统把计划导入系统数据库； （4）可以实现上线批次的顺序、计划数量操作，对于删除和更新的计划，系统记录计划变更日志，在计划追踪查询中给予标注； （5）计划调整时允许多行选择计划的删除功能； （6）提供一个操作软件，安装于办公室PC机，联网运行，由有权限的操作员执行。 2.MES商品质量防错 MES防错系统是检查在装配过程中一些关键步骤或关键操作是否成功完成，包括制造过程中重要零件符合性检查和精确追溯信息的管理，零件追溯实际对生产物流控制提出流程要求和数据要求，通过对追溯物料的状态跟踪来控制质量遏制范围。通过扫描枪读取零件条形码用以检查零件是否和显示的部件相符，以防止错装。指导车间生产，可进行生产流程完工反馈。 系统监控关键件装配，预防人为因素造成工艺漏装。可规范产品作业流程，并严格要求按产品流程作业。解决了门板装配过程中缺少装配作业的问题，做到实时监控、及时反应，立刻解决。这个小编之前还跟大家提过包装称重防错系统，也是一样的道理。 3.MES防差错操作流程
服装仓库管理解决方案 概述：天津小蜜蜂服装仓库管理解决方案，可以实现对整个仓库管理中的入库、盘点，出库等各个系统环节进行科学智能化的管理，实现对库存的实时监控和补给，提高仓库管理效率，达到数据和信息的智能化的采集和统一。 一、方案背景 中国是全世界最大的服装生产加工基地，经济全球化的形势下，服装行业的竞争日益加剧，这是新的机遇也是巨大的挑战。随着生产规模的扩大，库存盘点、仓储、物流等工作也变得越来越困难，如何提高劳动生产效率，保持市场秩序，成了管理者们面临的重要课题。 您在服装仓库管理中是否面临着这样的问题：将全部出入库各类型商品由人工统一管理，工作量过大，步骤太繁琐；箱内货物配错颜色、尺码，数量少时容易被忽略；人工检查无法做到逐个仔细检查每个项目，易漏检、错检；补货过程混乱，工作效率低等。虽然应用了条码进行数据采集的公司比人工采集节省了很多的人力资源和时间，但是，随着科技的发展，以及各个企业对高效率生产线的要求，条码的应用也已到达了它的瓶颈状态，因为它并不能做到多批量、远距离、高速度、恶环境的数据采集，于是也就限制了企业在生产过程中效率的更大化。 二、技术的应用价值 （射频识别）是一种利用射频通信实现的非接触式自动识别技术。标签体积小、容量大、寿命长、可重复使用等特点，可支持快速读写、多目标识读、非可视识别、移动识别、定位及长期跟踪管理。 技术的引入，将是您仓库管理的全新体验。利用这项技术，可以解决目前服装下线至发货出厂环节存在的数据采集不精确和效率低下，以及数据利用率不高等问题，实现精确快速的产品装箱、存库、出货配合缩短供货周期，自动化的数据采集保证仓库管理各个环节数据输入的速度和准确性，确保企业及时准确地掌握库存的真实数据，合理保持和控制企业库存，提高企业的生产效率。 三、系统结构及特点 服装仓库管理系统采用先进的电子标签，具有全球唯一编码标识。电子标签支持远距离识别和批量读取，具有环境适应性强等特点。在生产过程中，先利用发卡机将单件服