CN109544631A - A kind of detection system and method for cargo conveying equipment operating status - Google Patents
A kind of detection system and method for cargo conveying equipment operating status Download PDFInfo
- Publication number
- CN109544631A CN109544631A CN201910010151.8A CN201910010151A CN109544631A CN 109544631 A CN109544631 A CN 109544631A CN 201910010151 A CN201910010151 A CN 201910010151A CN 109544631 A CN109544631 A CN 109544631A
- Authority
- CN
- China
- Prior art keywords
- cargo
- image
- transport plane
- series
- coverage rate
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/70—Determining position or orientation of objects or cameras
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/18—Status alarms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- Emergency Management (AREA)
- Image Analysis (AREA)
Abstract
Present applicant proposes the detection methods and system of a kind of cargo conveying equipment operating status, wherein this method comprises: obtaining a series of consecutive images of the cargo transport plane of the cargo conveying equipment in the process of running；Distribution of the cargo in the cargo transport plane is identified according to a series of consecutive images；And the cargo coverage rate in the cargo transport plane judges unit exception when being greater than the set value, and issues warning information.The method can timely and effectively can carry out early warning according to cargo coverage rate to determine whether cargo accumulation occurs by detecting the distribution situation of cargo on cargo transport platform in real time in unit exception.
Description
Technical field
The present invention relates to field of video monitoring more particularly to a kind of system for detecting cargo conveying equipment operating status and sides
Method.
Background technique
Either in traditional factory, or in logistics field booming now, cargo conveying equipment is to improve life
Produce the essential equipment of efficiency.For example, in thermal power plant coal can be conveyed with belt transmission equipment；In logistics storehouse
Library can transmit package etc. with assembly line.These cargos are during conveying, it may occur however that accumulation, thus defeated to entire cargo
The normal operation of equipment is sent to bring hidden danger.Such as accumulation of the coal on belt is likely to result in the load exacerbation of equipment, cargo
It drops, coal dust enters the adverse effects such as cargo conveying equipment inside.
It would therefore be desirable to a kind of condition monitoring system and method for new cargo conveying equipment, with can be as early as possible
Ground finds the abnormal conditions such as cargo accumulation on cargo conveying equipment.
Summary of the invention
The condition monitoring system and method for being designed to provide a kind of new cargo conveying equipment of the application, with solution
Cargo accumulation occurs when certainly cargo conveying equipment is run, in cargo transport plane, cannot timely early warning the problem of.
The one side of the application proposes a kind of detection method of cargo conveying equipment operating status, may include: to obtain institute
State a series of consecutive images of the cargo transport plane of cargo conveying equipment in the process of running；According to a series of sequential charts
As identifying distribution of the cargo in the cargo transport plane；And wherein, the goods in the cargo transport plane
When object coverage rate is greater than the set value, unit exception is judged, issue warning information.
By detecting the distribution situation of cargo on cargo transport platform in real time, according to cargo coverage rate to determine whether hair
The accumulation of raw products object, can timely and effectively carry out early warning in unit exception.
In some embodiments, a series of consecutive images are the corresponding image of multiple frames in video image, described
Video image is obtained by the cargo transport plane that video recording equipment shoots the cargo conveying equipment.
By way of shooting video, the image of cargo transport plane can be continuously obtained, is consolidated between every frame of video
Analytical judgment after fixed time interval is is provided convenience.
In some embodiments, described to identify that the cargo is flat in the cargo transport according to a series of consecutive images
Distribution on face may include: whenever obtaining an image in a series of consecutive images: identify in the image
Distribution of the cargo in the cargo transport plane；The method further includes: according to the current goods in the image
The current cargo coverage rate of at least one image before object coverage rate and the image, determines the goods in the cargo transport plane
Object coverage rate.
Whenever receiving an image, that is, its image for the previous period is combined to carry out comprehensive descision, it is ensured that early warning
Instantaneity, while accumulation bring false alarm can also be generated to avoid at some of short duration moment.
In some embodiments, described to identify that the cargo is flat in the cargo transport according to a series of consecutive images
Distribution on face may include: for a series of each of consecutive images image: identify the goods in the image
Distribution of the object in the cargo transport plane；The method further includes: according in a series of consecutive images
The corresponding current cargo coverage rate of each image, determines the cargo coverage rate in the cargo transport plane.
By way of focusing on primary ground for a period of time, the instantaneity of early warning equally can be realized, while can also keep away
Exempt to generate accumulation bring false alarm at some of short duration moment.
In some embodiments, the setting value may range from 70%~100%.
In some embodiments, the cargo can transport on the transport level along particular path.
In some embodiments, the cargo transport plane may include belt, crawler belt, steel band, chain, gear set, rolling
At least one of cylinder group.System and method described herein can be applied on extensive cargo conveying equipment.
In some embodiments, the distribution for identifying the cargo in the image in the cargo transport plane can
To include: that the image is inputted trained machine learning model, to identify the corresponding transport level of the cargo transport plane
Region and the corresponding cargo area of the cargo.
By way of shifting to an earlier date training pattern, so that in application process, can quickly recognize out the difference in image
Region, with enhancing early warning instantaneity.
In some embodiments, the cargo coverage rate in the cargo transport plane may include: the cargo area
The ratio of area and the area in the transport level region.
The another aspect of the application proposes a kind of electronic equipment, may include memory, processor and is stored in described deposit
On reservoir and the computer program that can run on the processor, which is characterized in that the processor executes the computer
The step of detection method of cargo conveying equipment operating status described in the application first aspect is realized when program.
The another aspect of the application proposes that a kind of detection system of cargo conveying equipment operating status may include: that data obtain
Unit is taken, for obtaining a series of consecutive images of the cargo transport plane of the cargo conveying equipment in the process of running；Know
Other unit, for identifying distribution of the cargo in the cargo transport plane according to a series of consecutive images；
Judging unit judges unit exception when the cargo coverage rate in the cargo transport plane is greater than the set value；Early warning list
Member, for issuing warning information.
In some embodiments, a series of consecutive images can be the corresponding image of multiple frames in video image,
The video image is obtained by the cargo transport plane that video recording equipment shoots the cargo conveying equipment.
In some embodiments, described to identify that the cargo is flat in the cargo transport according to a series of consecutive images
Distribution on face may include: the image obtained in a series of consecutive images whenever the data capture unit
When: the recognition unit identifies distribution of the cargo in the image in the cargo transport plane；The system can be with
Further comprise: the recognition unit according in the image current cargo coverage rate and the image before at least one image
Current cargo coverage rate, determine the cargo coverage rate in the cargo transport plane.
In some embodiments, described to identify that the cargo is flat in the cargo transport according to a series of consecutive images
Distribution on face may include: for a series of each of consecutive images image: the recognition unit identification
Distribution of the cargo in the cargo transport plane in the image；The system may further include: the identification
Unit determines the cargo transport plane according to the corresponding current cargo coverage rate of image each in a series of consecutive images
On cargo coverage rate.
In some embodiments, the distribution for identifying the cargo in the image in the cargo transport plane can
To include: the recognition unit by the trained machine learning model of image input, to identify the cargo transport plane pair
The transport level region answered and the corresponding cargo area of the cargo.
Other feature will be set forth in part in the description in the application.By the elaboration, make the following drawings and
The content of embodiment narration becomes apparent for those of ordinary skills.Inventive point in the application can pass through
Practice is sufficiently illustrated using method described in detailed example discussed below, means and combinations thereof.
Detailed description of the invention
Exemplary embodiment disclosed in this application is described in detail in the following drawings.Wherein identical appended drawing reference is in attached drawing
Several views in indicate similar structure.Those of ordinary skill in the art will be understood that these embodiments be non-limiting,
Exemplary embodiment, the purpose that attached drawing is merely to illustrate and describes, it is no intended to it limits the scope of the present disclosure, other modes
Embodiment may also similarly complete the intention of the invention in the application.It should be appreciated that the drawings are not drawn to scale.Wherein:
Fig. 1 shows the usage scenario figure according to shown in some embodiments of the present application.
Fig. 2 is the schematic diagram of an image in a series of consecutive images in the application.
Fig. 3 is the exemplary process diagram of the detection method of the cargo conveying equipment operating status in the application.
Fig. 4 is the schematic diagram of one embodiment of the detection system of the cargo conveying equipment operating status in the application.
Specific embodiment
Following description provides the specific application scene of the application and requirements, it is therefore an objective to those skilled in the art be enable to make
It makes and using the content in the application.To those skilled in the art, to the various partial modifications of the disclosed embodiments
Be it will be apparent that and without departing from the spirit and scope of the disclosure, the General Principle that will can be defined here
Applied to other embodiments and application.Therefore, the embodiment the present disclosure is not limited to shown in, but it is consistent most wide with claim
Range.
Term used herein is only used for the purpose of description specific example embodiments, rather than restrictive.For example, unless
Context is expressly stated otherwise, used herein above, singular " one ", and " one " and " being somebody's turn to do " also may include plural form.
When used in this manual, term " including ", " including " and/or " containing " is meant that associated integer, step, behaviour
Make, element and/or component exist, but be not excluded for other one or more features, integer, step, operation, element, component and/or
Group presence or can be added in the system/method other features, integer, step, operation, element, component and/or.
In view of being described below, the operation of the related elements of these features of the disclosure and other features and structure and
The economy of combination and the manufacture of function and component may be significantly raising.With reference to attached drawing, all these formation disclosure
A part.It is to be expressly understood, however, that the purpose that attached drawing is merely to illustrate and describes, it is no intended to limit the disclosure
Range.
Fig. 1 shows the usage scenario figure according to shown in some embodiments of the present application.It as shown in the figure include video recording equipment
1, cargo conveying equipment 4, processing equipment 7 and prior-warning device 8.The cargo conveying equipment 4 includes cargo transport plane 2.Cargo 3
It can be carried in the cargo transport plane 2, in the cargo conveying equipment 4 operation, be moved along particular path.
The cargo conveying equipment 4 may include it is any can with bearing goods, and by the movement of its transport level 2 so that
The mobile equipment in position occurs for the cargo being carried thereon.For example cargo conveying equipment 4 shown in FIG. 1 can be for by two rotations
The rotation of device 6 drives its cargo transport plane 2 (to can be belt, crawler belt, steel band, chain, gear set, roller group or similar
Object, or combinations thereof) sliding, so that the cargo 3 being carried in the cargo transport plane 2 can be mobile from right side (end A)
To left side (end B).4 structure of cargo conveying equipment shown in figure is used as just the explanation to teachings herein, without answering
It is considered the limitation to the application application range, any cargo conveying equipment with cargo transport plane can apply this Shen
Please in disclose condition monitoring system be monitored.The driving method of the cargo transport plane 2 is in addition to shown in Fig. 1
Outside mode, any driving method that can drive cargo transport plane is all within the scope of the disclosure of the application.
The cargo transport plane 2 also may include multiple branch's planes.Such as in the case where logistics sorts scene, the cargo
Transport level 2 may include multiple branches, and each branch can correspond to a kind of series of lot, and the cargo carried thereon can transported
Enter different branch's planes during defeated to achieve the purpose that sorting.
The cargo transport plane 2 may include it is any can be with bearing goods, and can be under the drive of the drive
The plane moved along particular path.For example, the cargo transport plane may include belt, crawler belt, steel band, chain, gear
Group, roller group, or the like, or combinations thereof.
The cargo 3 can be any cargo that can be conveyed with the cargo conveying equipment 4.Coal can be used in the application
Illustrate herein disclosed content as an example, it should be appreciated that any cargo that such equipment conveying can be used is in this Shen
Within the scope of disclosure please.The cargo 3 can be transported to the end B from the end A of cargo conveying equipment 4 shown in FIG. 1.In the goods
When object conveying equipment 4 is run, can constantly there be the input of cargo 3 at the end A, for example set by manually adding, or with the conveying of other cargos
Standby outlet connection etc..In the ideal situation, the cargo transport plane 2 can be followed together from the cargo 3 that the diagram end A inputs
It is mobile, until the end B.But when actual motion, it is understood that there may be many reasons transport the motion delay of the cargo 3 in the cargo
The movement of defeated plane 2.For example, the cargo 3 may skid between the cargo transport plane 2.In such cases,
The case where cargo 3 in cargo transport plane 2 may be accumulated, and this cargo is accumulated may be to entire cargo conveying equipment
4 normal operation causes a hidden trouble.
The video recording equipment 1 may be mounted at the top of the cargo conveying equipment 4.The video recording equipment 1, which can be, appoints
What with filmed image function equipment, such as camera, video camera, or the like, or combinations thereof.The video recording equipment 1 can
To be assemblied in the top of the cargo conveying equipment 4 with any assembly method, angle of assembling, so that the view of the video recording equipment 1
Wild 5 (ranges for the scene that the i.e. described video recording equipment 1 can be shot) can include the target area in the cargo transport plane 3
Domain.In some embodiments, the target area can be the whole of the cargo transport plane 3, at this time entire cargo transport
Plane 3 is all the range for needing to monitor.In some embodiments, the target area is also possible to the cargo transport plane of part
3.Such as in some scenes, cargo accumulation may occur for the cargo transport plane 3 of only part, then the transport of this partial cargo is flat
Face 3 can be the target area, need to only be monitored to this partial region.
In some embodiments, the video recording equipment 1 can shoot a series of continuous images.A series of sequential charts
As can be a series of images with serial number, the time interval between the image of two neighboring serial number can be preset value.Than
If the video recording equipment 1 can be camera, the camera can take the photograph a photo every 1 second beats.It is described a series of continuous
Image is also possible to the series of successive frames in a video image.For example the video recording equipment 1 can be video camera, it is described to take the photograph
Shadow machine can shoot image with the frame per second of 24FPS (Frames Per Second), then between the image of the two neighboring serial number
Time interval can be 1/24 second.
A series of consecutive images taken can be transferred to processing shown in Fig. 1 and set by the video recording equipment 1
Standby 7 carry out image procossing.The processing equipment 7 judges the goods after can analyzing a series of consecutive images received
Whether the operation of object conveying equipment 4 is normal.If the operating status of the cargo conveying equipment 4 is abnormal, the processing
Equipment 7 can send warning information to prior-warning device 8.The prior-warning device 8 can be made pre- after being connected to the warning information
Alert response.About being analyzed a series of consecutive images and generate the details of the warning information see Fig. 3 and its phase
Close description.
In some embodiments, the processing equipment 7 can be computer or other are any with data-handling capacity
Processor.In some embodiments, the processing equipment 7 is also possible to the processing module being integrated in the video recording equipment 1.Than
As the video recording equipment 1 can be the equipment with graphics processor and/or central processing unit.A series of consecutive images exist
The video recording equipment 1 is local can to complete analysis work.
In some embodiments, the prior-warning device 8 can be alarm bell, or the similar device that can be sounded an alarm.It is described
Alarm bell can be sounded an alarm when receiving the warning information.In some embodiments, the prior-warning device 8 also can integrate
On the video recording equipment 1.For example installing loudspeaker additional on the video recording equipment 1, the loudspeaker is receiving the early warning
Preset alert audio can be played when information.
Fig. 2 is the schematic diagram of an image in a series of consecutive images in the application.Rectangular profile shown in Fig. 2
2 can be the cargo transport plane 2.With the assembly method of the video recording equipment 1 of embodiment shown in Fig. 1, take
In image, the transporting direction of the cargo transport plane 2 is the direction in figure shown in arrow from bottom to top.Cargo is transported in Fig. 2
Texture part in defeated plane 2 can be the cargo 3.Distribution of the cargo 3 in the cargo transport plane 2 can
To reflect whether the cargo conveying equipment 4 runs well.For example, distribution of the cargo 3 in the cargo transport plane 2
Region account for the surface area of the cargo transport plane 2 ratio can for cargo in the cargo transport plane 2 coverage rate (i.e.
Cargo coverage rate).When the cargo coverage rate is excessive, show that the cargo 3 may occur in the cargo transport plane 2
Accumulation, has an impact so as to the normal operation to the cargo conveying equipment 4.In some embodiments, the cargo
There may be the cargo coverage rate of regional area (such as region 21) excessive on transport level 2, then show that this partial region may be sent out
The accumulation of part is given birth to.The equally possible operation to equipment generates some bad shadows in some cases for this local accumulation
It rings.For example, if generating the accumulation of part, dropping for coal dust may can occur in the region when the cargo 3 is coal,
It causes that the mechanical transmission component of cargo conveying equipment 4 may also be generated certain influence while loss of goods.
Fig. 3 is the exemplary process diagram of the detection method of the cargo conveying equipment operating status in the application.Process master
To include the processing equipment 7 issues warning information to the prior-warning device 8 from receiving a series of consecutive images
Process.
In the step 310, the transport level of the available cargo conveying equipment of the processing equipment 7 is in operational process
In a series of consecutive images.Embodiment according to figure 1, a series of consecutive images can be by the video recording equipments 1
The cargo transport plane 2 is shot to obtain.When the processing equipment 7 is independent equipment, the processing equipment 7 can be with institute
It states video recording equipment 1 and establishes data transmission link (such as wire transmission or wireless transmission).The video recording equipment 1 can be shot
A series of consecutive images arrived are sent by the data transmission link to the processing equipment 7.When the processing equipment 7
For be integrated in inside the video recording equipment 1 processor when, the video recording equipment 1 can be by its internal bus to the processing
Device sends a series of consecutive images.
When the rigging position of the cargo conveying equipment 4 and the video recording equipment 1 is fixed, in a series of consecutive images
Cargo transport plane 2 position be it is fixed.With the operation of the cargo conveying equipment 4, the shifting of cargo transport plane 2
The movement of the cargo 3 of dynamic drive on it.For the different images in a series of consecutive images, wherein corresponding cargo 3
Part shows different distributions in different shooting times.The processing equipment 7 can be further according to different moments
The distribution of cargo determines the case where cargo 3 described in cargo is with the presence or absence of accumulation.
In step 320, the processing equipment 7 can identify the cargo described according to a series of consecutive images
Distribution in cargo transport plane.In some embodiments, the processing equipment 7 can identify a series of sequential charts
Distribution of the cargo 3 in the cargo transport plane 2 as on each image.
When a series of consecutive images are continuously to input the processing equipment 7, the processing equipment 7 can whenever
When it receives an image, the primary identification operation is carried out.For example, the video recording equipment 1 is shot with the frame per second of 24FPS
Image, then it can be with every 1/24 second image transmitting just taken into the processing equipment 7.The processing equipment 7 can
Just the image that it is received once was identified with every 1/24 second.This identification method can be the first identification method.
When a series of consecutive images are to input the processing equipment 7 in batches, the processing equipment 7 can be at it
When receiving the image of a batch, the identification is carried out to a series of consecutive images in the batch respectively and is operated.For example, institute
State video recording equipment 1 can every an early warning period to the processing equipment 7 send a batch image.Wherein, described pre-
The alert period can refer to, when the cargo coverage rate identified in a series of images continuous time a period of time is all exception into
Row early warning, described continuous a period of time is the early warning period.The early warning period can for 2 seconds, 5 seconds, 10 seconds or its
He is any time.When the early warning period is 5 seconds, indicates that the unit exception situation maintains 5 seconds durations, just need to issue
Alarm.It is described for a series of consecutive images (for example the video recording equipment 1 shoots all images in 5 seconds) of a batch
Processing equipment 7 can be unified to carry out each of these image the identification operation.This identification method can be second
Identification method.
Under first identification method or second identification method, the processing equipment 7 is directed to single image
Identification operation.In some embodiments, the processing equipment 7 can be based on a trained machine learning model to described
Single image is identified.The machine learning model can be used as a program and be stored in the processing equipment 7.The machine
Device learning model can be trained with the image of some calibration.For example, may include the goods in the image of the calibration
Object 3 also may include the cargo transport plane 2.By some spies for extracting the cargo 3 or the cargo transport plane 2
It levies (such as color, texture, profile etc.), and is input in the machine learning model, the machine learning model is known
It Chu not cargo area and cargo transport plane domain in single image.For the processing equipment 7 obtain single image,
It can extract feature (such as pixel value of each pixel etc., with the feature phase acquired when training in the single image
Together), it and is input in the trained machine learning model.The machine learning model can identify that the cargo 3 is right
The region (cargo area) and the corresponding region of the cargo transport plane 2 (transport level region) answered.The cargo covering
Rate can be the area of the cargo area and the ratio in the transport level region, indicate quilt in the cargo transport plane 2
The percentage that cargo 3 covers.For single image, corresponding cargo coverage rate indicates the cargo in the shooting time of the image
Coverage rate (hereinafter referred to as current cargo coverage rate).
For the first identification method, the processing equipment 7 identifies goods therein when it receives an image
Distribution of the object in the cargo transport plane.The distribution includes shooting the image moment corresponding current cargo
Coverage rate.Under first identification method, the processing equipment 7 can be covered further according to the current cargo in the image
The current cargo coverage rate of at least one image before rate and the image determines that cargo covers in the cargo transport plane
Rate.In some embodiments, the cargo coverage rate can be the image current cargo coverage rate and the image before at least
Minimum value in the current cargo coverage rate of one image indicates that at least one image is corresponding before the image and the image
The shooting period, the minimum value of the cargo coverage rate.For example, the cargo coverage rate of the image is 80%, when corresponding shooting
Between be the 5th second.The corresponding current cargo coverage rate of 4 width images and shooting time before the image be respectively 81%, 82%,
84%, 88% and the 4th second, the 3rd second, the 2nd second and the 1st second.Then the cargo coverage rate can be 80%, indicate from the 1st second to
5th second, the ratio of cargo area and transport level region in the cargo transport plane 2 was at least 80%.If early warning is all
Phase is 5 seconds, and the cargo coverage rate threshold value of early warning is 70%, then the cargo coverage rate in the example at least 5 seconds is all 70%
More than, early warning can be triggered.
For the second identification method, when the processing equipment 7 receives a collection of image, each image therein can be known
Not Chu wherein distribution of the cargo in the cargo transport plane, the current goods of its shooting time is corresponded to including each image
Object coverage rate.Under second identification method, the processing equipment 7 can be further according in a series of consecutive images
The corresponding current cargo coverage rate of each image, determines cargo coverage rate in the cargo transport plane.In some embodiments,
The cargo coverage rate can be the minimum value in the corresponding current cargo coverage rate of each image, indicate in this batch of image
The minimum value of cargo coverage rate in the corresponding shooting period.For example, this batch of image corresponding shooting period is 0~5 second.This 5
In second, the video recording equipment 1 can shoot 5 images, corresponding current cargo coverage rate can for 80%, 81%, 82%,
84%, 88%, it indicates in this 5 seconds, the ratio of cargo area and the transport level region in the cargo transport plane 2
At least 80%.Likewise, if the early warning period be 5 seconds, and the cargo coverage rate of early warning be 70% if, then in this example embodiment
Cargo coverage rate at least 5 seconds can trigger early warning all 70% or more.
In the embodiment of the foregoing description, the early warning period can be any time length, depend on and actual engineering
It needs.For the first identification method, the shooting time span of the image being currently received and at least one image before it
It can be the early warning period.It can guarantee within an early warning period in this way, when abnormal conditions persistently meet a period of time,
Trigger early warning.For example, the early warning period is 1 second, and when shooting frame rate is 24FPS, 23 before the image being currently received
A image can be used as at least one described image.For the second identification method, the corresponding shooting time of a batch image across
Degree can be the early warning period.Can equally it guarantee within an early warning period, when abnormal conditions persistently meet a period of time
When, trigger early warning.Such as the early warning period be 5 seconds when, it is described a batch image may include all images taken in 5 seconds.
In a step 330, when the coverage rate of cargo is greater than the set value in the cargo transport plane, judge unit exception,
Issue warning information.It is in step 320, described either according to first identification method or second identification method
Processing equipment 7 can determine the cargo coverage rate.When the cargo coverage rate is greater than the set value, the processing equipment 7 can
To judge that the cargo conveying equipment 4 is operating abnormally.The setting value can according to different scenes, different type of merchandize, with
And difference transport level property is empirically determined, such as the setting value can be any value in 70%~100%.
After the processing equipment 7 judges unit exception, warning information can be sent to the prior-warning device 8.The early warning
Device 8 can carry out early warning after being connected to the warning information.If the prior-warning device 8 and the processing equipment 7 are all collection
At the module on the video recording equipment 1, then the processing equipment 7 can be sent by bus to the prior-warning device 8 described
Warning information.
Fig. 4 is the schematic diagram of one embodiment of the detection system of the cargo conveying equipment operating status in the application.Institute
The detection system 400 for stating cargo conveying equipment operating status may include data capture unit 410, recognition unit 420, judgement list
Member 430 and prewarning unit 440.
The cargo transport plane that the data capture unit 410 can be used for obtaining the cargo conveying equipment was being run
A series of consecutive images in journey.The recognition unit 420 can be used for identifying the goods according to a series of consecutive images
Distribution of the object in the cargo transport plane.The judging unit 430 can be used in the cargo transport plane
When cargo coverage rate is greater than the set value, unit exception is judged.The prewarning unit 440 can be used for issuing warning information.
In some embodiments, described to identify that the cargo is flat in the cargo transport according to a series of consecutive images
Distribution on face may include: the image obtained in a series of consecutive images whenever the data capture unit
When: the recognition unit 420 can identify distribution of the cargo in the image in the cargo transport plane.The knowledge
Other unit 420 can be according to the current cargo coverage rate in the image and the current cargo of at least one image before the image
Coverage rate determines the cargo coverage rate in the cargo transport plane.
In some embodiments, described to identify that the cargo is flat in the cargo transport according to a series of consecutive images
Distribution on face includes: for a series of each of consecutive images image: the recognition unit 420 can be known
Distribution of the cargo in the cargo transport plane not in the image.The recognition unit 420 can be according to described one
The corresponding current cargo coverage rate of each image in serial consecutive image determines the cargo covering in the cargo transport plane
Rate.
In some embodiments, which can also be inputted trained machine learning model by the recognition unit 420,
To identify the corresponding transport level region of the cargo transport plane and the corresponding cargo area of the cargo.
In conclusion after reading this detailed disclosures, it will be understood by those skilled in the art that aforementioned detailed disclosure
Content can be only presented in an illustrative manner, and can not be restrictive.Although not explicitly described or shown herein, this field skill
Art personnel are understood that improve and modify it is intended to include the various reasonable changes to embodiment.These change, improve and
It modifies and is intended to be proposed by the disclosure, and in the spirit and scope of the exemplary embodiment of the disclosure.
In addition, certain terms in the application have been used for describing implementation of the disclosure example.For example, " one embodiment ", "
Embodiment " and/or " some embodiments " means to combine the special characteristic of embodiment description, structure or characteristic may include
In at least one embodiment of the disclosure.Therefore, can emphasize and it is to be understood that in the various pieces of this specification to "
Embodiment " or " one embodiment " or " alternate embodiment " two or more references be not necessarily all referring to identical implementation
Example.In addition, special characteristic, structure or characteristic can be appropriately combined in one or more other embodiments of the present disclosure.
It should be appreciated that in the foregoing description of embodiment of the disclosure, in order to help to understand a feature, originally for simplification
Disclosed purpose, the application sometimes combine various features in single embodiment, attached drawing or its description.Alternatively, the application is again
Be by various characteristic dispersions in multiple the embodiment of the present invention.However, this be not to say that the combination of these features be it is necessary,
Those skilled in the art are entirely possible to come out a portion feature extraction as individual when reading the application
Embodiment understands.That is, embodiment in the application it can be appreciated that multiple secondary embodiments integration.And it is each
The content of secondary embodiment is also to set up when being less than individually all features of aforementioned open embodiment.
In some embodiments, the quantity or property for certain embodiments of the application to be described and claimed as are expressed
The number of matter is interpreted as in some cases through term " about ", " approximation " or " substantially " modification.For example, unless otherwise saying
Bright, otherwise " about ", " approximation " or " substantially " can indicate ± 20% variation of the value of its description.Therefore, in some embodiments
In, the numerical parameter listed in written description and the appended claims is approximation, can be tried according to specific embodiment
Scheme the required property obtained and changes.In some embodiments, numerical parameter should be according to the quantity of the effective digital of report simultaneously
It is explained by the common rounding-off technology of application.Although illustrating that some embodiments of the application list broad range of numerical value
Range and parameter are approximations, but numerical value reported as precisely as possible is all listed in specific embodiment.
Herein cited each patent, patent application, the publication and other materials of patent application, such as article, books,
Specification, publication, file, article etc. can be incorporated herein by reference.Full content for all purposes, in addition to
Its relevant any prosecution file history, may or conflicting any identical or any possibility inconsistent with this document
On any identical prosecution file history of the restrictive influence of the widest range of claim.Now or later and this document
It is associated.For example, if in description, definition and/or the use of term associated with any included material and this
The relevant term of document, description, definition and/or between there are it is any inconsistent or conflict when, be using the term in this document
It is quasi-.
Finally, it is to be understood that the embodiment of application disclosed herein is the explanation to the principle of the embodiment of the application.
Other modified embodiments are also within the scope of application.Therefore, herein disclosed embodiment it is merely exemplary rather than
Limitation.Those skilled in the art can take alternative configuration according to the embodiment in the application to realize the invention in the application.
Therefore, embodiments herein is not limited to which embodiment accurately described in application.
Claims (15)
1. a kind of detection method of cargo conveying equipment operating status characterized by comprising
Obtain a series of consecutive images of the cargo transport plane of the cargo conveying equipment in the process of running；
Distribution of the cargo in the cargo transport plane is identified according to a series of consecutive images；And
Wherein, when the cargo coverage rate in the cargo transport plane is greater than the set value, judge unit exception, issue early warning letter
Breath.
2. the method according to claim 1, wherein a series of consecutive images are multiple in video image
The corresponding image of frame, the video image are obtained by the cargo transport plane that video recording equipment shoots the cargo conveying equipment.
3. the method according to claim 1, wherein described identify the goods according to a series of consecutive images
Distribution of the object in the cargo transport plane include:
Whenever obtaining an image in a series of consecutive images:
Identify distribution of the cargo in the cargo transport plane in the image；
The method further includes: according in the image current cargo coverage rate and the image before at least one image
Current cargo coverage rate, determine the cargo coverage rate in the cargo transport plane.
4. the method according to claim 1, wherein described identify the goods according to a series of consecutive images
Distribution of the object in the cargo transport plane include:
For a series of each of consecutive images image:
Identify distribution of the cargo in the cargo transport plane in the image；
The method further includes: according to the corresponding current cargo coverage rate of image each in a series of consecutive images,
Determine the cargo coverage rate in the cargo transport plane.
5. the method according to claim 1, wherein the range of the setting value is 70%~100%.
6. the method according to claim 1, wherein the cargo is on the transport level along particular path
Transport.
7. the method according to claim 1, wherein the cargo transport plane include belt, crawler belt, steel band,
At least one of chain, gear set, roller group.
8. according to the method described in claim 3, it is characterized in that, the cargo identified in the image is in the cargo transport
Distribution in plane includes:
The image is inputted into trained machine learning model, to identify the corresponding transport level area of the cargo transport plane
Domain and the corresponding cargo area of the cargo.
9. according to the method described in claim 8, it is characterized in that, the cargo coverage rate in the cargo transport plane includes:
The ratio of the area of the cargo area and the area in the transport level region.
10. a kind of electronic equipment, including memory, processor and it is stored on the memory and can transports on the processor
Capable computer program, which is characterized in that the processor is realized when executing the computer program as in claim 1 to 9
The step of detection method of described in any item cargo conveying equipment operating statuses.
11. a kind of detection system of cargo conveying equipment operating status characterized by comprising
Data capture unit, for obtaining a series of companies of the cargo transport plane of the cargo conveying equipment in the process of running
Continuous image；
Recognition unit, for identifying distribution of the cargo in the cargo transport plane according to a series of consecutive images
State；
Judging unit judges unit exception when the cargo coverage rate in the cargo transport plane is greater than the set value；
Prewarning unit, for issuing warning information.
12. system according to claim 11, which is characterized in that a series of consecutive images are more in video image
The corresponding image of a frame, the video image are obtained by the cargo transport plane that video recording equipment shoots the cargo conveying equipment.
13. system according to claim 11, which is characterized in that described according to a series of consecutive image identifications
Distribution of the cargo in the cargo transport plane include:
When the data capture unit obtains an image in a series of consecutive images:
The recognition unit identifies distribution of the cargo in the image in the cargo transport plane；
The system further comprises: the recognition unit according in the image current cargo coverage rate and the image before
The current cargo coverage rate of at least one image determines the cargo coverage rate in the cargo transport plane.
14. system according to claim 11, which is characterized in that described according to a series of consecutive image identifications
Distribution of the cargo in the cargo transport plane include:
For a series of each of consecutive images image:
The recognition unit identifies distribution of the cargo in the image in the cargo transport plane；
The system further comprises: the recognition unit is corresponding current according to each image in a series of consecutive images
Cargo coverage rate determines the cargo coverage rate in the cargo transport plane.
15. system according to claim 13, which is characterized in that described to identify that the cargo in the image is transported in the cargo
Distribution in defeated plane includes:
The image is inputted trained machine learning model by the recognition unit, to identify that the cargo transport plane is corresponding
Transport level region and the corresponding cargo area of the cargo.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910010151.8A CN109544631A (en) | 2019-01-03 | 2019-01-03 | A kind of detection system and method for cargo conveying equipment operating status |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910010151.8A CN109544631A (en) | 2019-01-03 | 2019-01-03 | A kind of detection system and method for cargo conveying equipment operating status |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109544631A true CN109544631A (en) | 2019-03-29 |
Family
ID=65834410
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910010151.8A Pending CN109544631A (en) | 2019-01-03 | 2019-01-03 | A kind of detection system and method for cargo conveying equipment operating status |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109544631A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110084798A (en) * | 2019-04-25 | 2019-08-02 | 广州富港万嘉智能科技有限公司 | A kind of orientation detection and method of adjustment, device and storage medium of melon and fruit |
| CN110519566A (en) * | 2019-08-28 | 2019-11-29 | 山东科技大学 | A kind of belt movement state monitoring method based on video processing |
| CN112990189A (en) * | 2019-12-17 | 2021-06-18 | 顺丰科技有限公司 | Cargo congestion identification method and device, electronic equipment and storage medium |
| CN114308680A (en) * | 2021-12-31 | 2022-04-12 | 中科微至智能制造科技江苏股份有限公司 | Control method for stack separation |
| WO2023185234A1 (en) * | 2022-04-02 | 2023-10-05 | 北京京东乾石科技有限公司 | Image processing method and apparatus, and electronic device and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170200082A1 (en) * | 2014-07-14 | 2017-07-13 | Gerrit Böhm | Capacity prediction for public transport vehicles |
| CN107555120A (en) * | 2017-09-30 | 2018-01-09 | 徐州东南钢铁工业有限公司 | A kind of anti-coal piling early warning protector of ribbon conveyer |
| CN108334861A (en) * | 2018-03-02 | 2018-07-27 | 北京未来媒体科技股份有限公司 | A kind of video content recognition method and device based on machine learning |
| CN108664874A (en) * | 2018-02-14 | 2018-10-16 | 北京广天夏科技有限公司 | Underground coal flow rate testing methods based on image recognition |
| CN108960207A (en) * | 2018-08-08 | 2018-12-07 | 广东工业大学 | A kind of method of image recognition, system and associated component |
-
2019
- 2019-01-03 CN CN201910010151.8A patent/CN109544631A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170200082A1 (en) * | 2014-07-14 | 2017-07-13 | Gerrit Böhm | Capacity prediction for public transport vehicles |
| CN107555120A (en) * | 2017-09-30 | 2018-01-09 | 徐州东南钢铁工业有限公司 | A kind of anti-coal piling early warning protector of ribbon conveyer |
| CN108664874A (en) * | 2018-02-14 | 2018-10-16 | 北京广天夏科技有限公司 | Underground coal flow rate testing methods based on image recognition |
| CN108334861A (en) * | 2018-03-02 | 2018-07-27 | 北京未来媒体科技股份有限公司 | A kind of video content recognition method and device based on machine learning |
| CN108960207A (en) * | 2018-08-08 | 2018-12-07 | 广东工业大学 | A kind of method of image recognition, system and associated component |
Non-Patent Citations (3)
| Title |
|---|
| 关丙火: "基于激光扫描的带式输送机瞬时煤量检测方法", 《工矿自动化》 * |
| 张立亚: "矿山智能视频分析与预警系统研究", 《工矿自动化》 * |
| 许军: "基于图像处理的溜槽堆煤预警研究", 《煤炭技术》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110084798A (en) * | 2019-04-25 | 2019-08-02 | 广州富港万嘉智能科技有限公司 | A kind of orientation detection and method of adjustment, device and storage medium of melon and fruit |
| CN110084798B (en) * | 2019-04-25 | 2021-10-22 | 广州富港万嘉智能科技有限公司 | Orientation detection and adjustment method and device for melons and fruits and storage medium |
| CN110519566A (en) * | 2019-08-28 | 2019-11-29 | 山东科技大学 | A kind of belt movement state monitoring method based on video processing |
| CN112990189A (en) * | 2019-12-17 | 2021-06-18 | 顺丰科技有限公司 | Cargo congestion identification method and device, electronic equipment and storage medium |
| CN114308680A (en) * | 2021-12-31 | 2022-04-12 | 中科微至智能制造科技江苏股份有限公司 | Control method for stack separation |
| WO2023185234A1 (en) * | 2022-04-02 | 2023-10-05 | 北京京东乾石科技有限公司 | Image processing method and apparatus, and electronic device and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109544631A (en) | A kind of detection system and method for cargo conveying equipment operating status | |
| KR102579783B1 (en) | Vision inspection system by using remote learning of product defects image | |
| CN102348128B (en) | Surveillance camera system having camera malfunction detection function | |
| Chauhan et al. | A comparative study of machine vision based methods for fault detection in an automated assembly machine | |
| US20130265423A1 (en) | Video-based detector and notifier for short-term parking violation enforcement | |
| CN105139397B (en) | A kind of pcb board detection method and device | |
| CN106454330B (en) | The fuzzy method for detecting abnormality of vision signal | |
| CN113942805A (en) | Coal block transportation monitoring method and device | |
| CN111539265A (en) | Method for detecting abnormal behaviors in elevator car | |
| CN101426128B (en) | Detection system and method for stolen and lost packet | |
| Malhi et al. | Vision based intelligent traffic management system | |
| CN109974686A (en) | Transfer robot path planning householder method based on monitoring camera detection | |
| CN113297885A (en) | Belt conveyor surface state detection method and device based on convolutional neural network | |
| Thirde et al. | A Real-Time Scene Understanding System for Airport Apron Monitoring. | |
| CN112232107A (en) | Image type smoke detection system and method | |
| CN115131738A (en) | Conveyor belt tearing early warning method and device | |
| JPH05143714A (en) | Method and system for monitoring abnormality of traveling vehicle utilizing image processing | |
| JP2017030380A (en) | Train detection system and train detection method | |
| CN116503811A (en) | Image-based forklift violation monitoring method, medium and device | |
| JP3974015B2 (en) | Item inspection system | |
| JP6806176B2 (en) | Method for determining mist around lumpy substances on conveyor and method for measuring properties of lumpy substance on conveyor | |
| CN107609498A (en) | A kind of data processing method of computer supervisory control system | |
| Khosla et al. | Automated scheduling of radar-cued camera system for optimizing visual inspection and detection of radar targets | |
| EP2625670B1 (en) | Image processing | |
| CN110175483A (en) | A kind of recognition methods based on label |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20190329 |
|
| WD01 | Invention patent application deemed withdrawn after publication |