CN112604977A - Target classification platform and method using big data service - Google Patents
Target classification platform and method using big data service Download PDFInfo
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- CN112604977A CN112604977A CN202011234309.9A CN202011234309A CN112604977A CN 112604977 A CN112604977 A CN 112604977A CN 202011234309 A CN202011234309 A CN 202011234309A CN 112604977 A CN112604977 A CN 112604977A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
- B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
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- A—HUMAN NECESSITIES
- A23—FOODS OR FOODSTUFFS; TREATMENT THEREOF, NOT COVERED BY OTHER CLASSES
- A23N—MACHINES OR APPARATUS FOR TREATING HARVESTED FRUIT, VEGETABLES OR FLOWER BULBS IN BULK, NOT OTHERWISE PROVIDED FOR; PEELING VEGETABLES OR FRUIT IN BULK; APPARATUS FOR PREPARING ANIMAL FEEDING- STUFFS
- A23N15/00—Machines or apparatus for other treatment of fruits or vegetables for human purposes; Machines or apparatus for topping or skinning flower bulbs
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
- B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
- B07C5/02—Measures preceding sorting, e.g. arranging articles in a stream orientating
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
- B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
- B07C5/34—Sorting according to other particular properties
- B07C5/342—Sorting according to other particular properties according to optical properties, e.g. colour
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
- B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
- B07C5/36—Sorting apparatus characterised by the means used for distribution
- B07C5/361—Processing or control devices therefor, e.g. escort memory
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/56—Extraction of image or video features relating to colour
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
- G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
- G06V10/752—Contour matching
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- A—HUMAN NECESSITIES
- A23—FOODS OR FOODSTUFFS; TREATMENT THEREOF, NOT COVERED BY OTHER CLASSES
- A23N—MACHINES OR APPARATUS FOR TREATING HARVESTED FRUIT, VEGETABLES OR FLOWER BULBS IN BULK, NOT OTHERWISE PROVIDED FOR; PEELING VEGETABLES OR FRUIT IN BULK; APPARATUS FOR PREPARING ANIMAL FEEDING- STUFFS
- A23N15/00—Machines or apparatus for other treatment of fruits or vegetables for human purposes; Machines or apparatus for topping or skinning flower bulbs
- A23N2015/008—Sorting of fruit and vegetables
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
- B07C2501/00—Sorting according to a characteristic or feature of the articles or material to be sorted
- B07C2501/009—Sorting of fruit
Abstract
The invention relates to a target classification platform and a method using big data service, wherein the platform comprises: the big data service node is arranged at a big data network end and used for storing the outline picture of each type of mango; the motor conveying mechanism is used for conveying each mango in sequence by adopting a conveying belt, the distance between every two adjacent mangoes on the conveying belt exceeds a preset distance threshold value, and the motor conveying mechanism sequentially passes through a first detection position and a second detection position; and the visual capture device is arranged above the first detection position and used for executing visual capture action on the mango below the visual capture device so as to obtain a corresponding mango capture image. The target classification platform and the method utilizing the big data service are effective in operation and stable in operation. Due to the fact that the on-site classification and sorting processing of the immature mangos and other types of mangos can be performed by taking the assembly line conveying mechanism as a platform, the automation level of mango assembly line operation is improved.
Description
Technical Field
The invention relates to the field of big data service, in particular to a target classification platform and a method utilizing big data service.
Background
Technically, the relation between big data and cloud computing is as inseparable as the front and back of a coin. The large data cannot be processed by a single computer necessarily, and a distributed architecture must be adopted. The method is characterized in that distributed data mining is carried out on mass data. But it must rely on distributed processing of cloud computing, distributed databases and cloud storage, virtualization technologies. With the advent of the cloud era, Big data (Big data) has attracted more and more attention. The team of analysts believes that large data (Big data) is often used to describe the large amount of unstructured and semi-structured data created by a company that can take excessive time and money to download to a relational database for analysis. Big data analysis is often tied to cloud computing because real-time large dataset analysis requires a MapReduce-like framework to distribute work to tens, hundreds, or even thousands of computers. Large data requires special techniques to efficiently process large amounts of data that are tolerant of elapsed time. Technologies applicable to big data include Massively Parallel Processing (MPP) databases, data mining, distributed file systems, distributed databases, cloud computing platforms, the internet, and scalable storage systems.
Currently, there are two phenomena requiring targeted processing during the execution of mango pipelining, the first being that some offenders have mixed other cheap types of mangos into the current mango batch to gain greater economic benefit, and the second being that some immature mangos are present in the pipeline and can flow into the market after the processing such as ripening is required.
Disclosure of Invention
In order to solve the technical problems in the prior art, the invention provides a target classification platform using big data service, which can perform field classification and sorting processing on immature mangos and other types of mangos by using a production line transmission mechanism as a platform, thereby improving the automation level of mango production line operation.
Therefore, the invention needs to have the following two important points:
(1) identifying whether the current mango belongs to a sorting type or not and carrying out corresponding sorting processing based on a big data storage mode by taking the assembly line conveying mechanism as a platform;
(2) the intelligent linkage control method is characterized in that the assembly line conveying mechanism is used as a platform, field sorting actions are carried out on the immature mangos on the basis of red component imaging characteristics, and the key point is that the trigger time for carrying out the field sorting actions on the immature mangos is calculated on the basis of the distance between two sorting positions and the conveying speed, so that the intelligent linkage control of electronic equipment in the assembly line conveying mechanism is realized.
According to an aspect of the present invention, there is provided a target classification platform using big data service, the platform including:
the big data service node is arranged at a big data network end and used for storing the outline picture of each type of mango, and the outline picture only comprises the imaging content of the mango of the corresponding type;
the motor conveying mechanism is used for conveying each mango in sequence by adopting a conveying belt, the distance between every two adjacent mangoes on the conveying belt exceeds a preset distance threshold value, and the motor conveying mechanism sequentially passes through a first detection position and a second detection position;
a visual capture device disposed above the first detection position for performing a visual capture action on the mango therebelow to obtain a corresponding mango captured image;
the first identification mechanism is respectively connected with the big data service node and the vision capture equipment and is used for sending a first control signal when an image area matched with any one contour picture stored in the big data service node does not exist in the received mango captured image;
the first sorting mechanism is arranged at the first detection position, is connected with the first identification mechanism and is used for sorting the mangos at the first detection position into other types of containers on the side of the first detection position when receiving the first control signal;
the second identification mechanism is connected with the first identification mechanism and used for taking an image area which is matched with one contour picture stored in the big data service node and exists in the received mango captured image as an area to be analyzed, acquiring an integral red component of the area to be analyzed and sending a third control signal when the numerical value of the integral red component is lower than a preset component threshold value;
and the second sorting mechanism is arranged at the second detection position, is connected with the second identification mechanism and is used for calculating a time interval for triggering the action of sorting the mangos at the second detection position into the immature fruit containers at the side of the second detection position based on the distance between the first detection position and the second detection position and the conveying speed of the conveying belt when the third control signal is received.
According to another aspect of the invention, there is also provided a method of object classification using big data services, the method comprising using an object classification platform using big data services as described above to perform a live classification process on other types of mangos and immature mangos based on appearance imaging features and color imaging features of the mangos.
The target classification platform and the method utilizing the big data service are effective in operation and stable in operation. Due to the fact that the on-site classification and sorting processing of the immature mangos and other types of mangos can be performed by taking the assembly line conveying mechanism as a platform, the automation level of mango assembly line operation is improved.
Detailed Description
Embodiments of the present invention of a target classification platform and method using big data services will be described in detail below.
Mango is an original Indian evergreen big arbor of Anacardiaceae, leafy leatheroid, intergrown; small, miscellaneous, yellow or yellowish, forming a terminal panicle. Big stone, squashed, 5-10 cm long, 3-4.5 cm wide, yellow when ripe, sweet in taste, and hard stone.
Mango is one of the famous tropical fruits, contains sugar, protein and crude fiber, contains a particularly high content of carotene, a precursor of vitamin A, and is rare in all fruits. Secondly, the content of vitamin C is not low. Minerals, proteins, fats, carbohydrates, etc., are also the main nutritional components. Can be used for preparing fruit juice, jam, canned food, pickled vegetable, sour and spicy pickle, mango milk powder, preserved fruit, etc.
The mango producing trees are evergreen big trees and are 10-20 meters high; the bark is grey brown, twig brown and hairless. The leaf is thin and leathery, and is often gathered with branch tops, the shape and size of the leaf are greatly changed, the leaf is usually in a long round shape or long round shape and is in a needle shape, the length is 12-30 cm, the width is 3.5-6.5 cm, the tip is gradually sharp, the length is gradually sharp or sharp, the base part is wedge-shaped or nearly round, the edge is wrinkled and wavy, the leaf is hairless, the leaf surface is slightly glossy, 20-25 pairs of side veins are obliquely lifted, two surfaces are protruded, a net vein is not shown, the leaf stalk is 2-6 cm long, a groove is arranged on the leaf, and the base part is expanded.
Currently, there are two phenomena requiring targeted processing during the execution of mango pipelining, the first being that some offenders have mixed other cheap types of mangos into the current mango batch to gain greater economic benefit, and the second being that some immature mangos are present in the pipeline and can flow into the market after the processing such as ripening is required.
In order to overcome the defects, the invention builds a target classification platform and a method using big data service, and can effectively solve the corresponding technical problems.
The target classification platform utilizing big data services according to the embodiment of the invention comprises:
the big data service node is arranged at a big data network end and used for storing the outline picture of each type of mango, and the outline picture only comprises the imaging content of the mango of the corresponding type;
the motor conveying mechanism is used for conveying each mango in sequence by adopting a conveying belt, the distance between every two adjacent mangoes on the conveying belt exceeds a preset distance threshold value, and the motor conveying mechanism sequentially passes through a first detection position and a second detection position;
a visual capture device disposed above the first detection position for performing a visual capture action on the mango therebelow to obtain a corresponding mango captured image;
the first identification mechanism is respectively connected with the big data service node and the vision capture equipment and is used for sending a first control signal when an image area matched with any one contour picture stored in the big data service node does not exist in the received mango captured image;
the first sorting mechanism is arranged at the first detection position, is connected with the first identification mechanism and is used for sorting the mangos at the first detection position into other types of containers on the side of the first detection position when receiving the first control signal;
the second identification mechanism is connected with the first identification mechanism and used for taking an image area which is matched with one contour picture stored in the big data service node and exists in the received mango captured image as an area to be analyzed, acquiring an integral red component of the area to be analyzed and sending a third control signal when the numerical value of the integral red component is lower than a preset component threshold value;
and the second sorting mechanism is arranged at the second detection position, is connected with the second identification mechanism and is used for calculating a time interval for triggering the action of sorting the mangos at the second detection position into the immature fruit containers at the side of the second detection position based on the distance between the first detection position and the second detection position and the conveying speed of the conveying belt when the third control signal is received.
Next, a detailed description will be made of a specific structure of the target classification platform using big data service according to the present invention.
In the target classification platform using big data service:
calculating a time interval triggering an action of sorting mangoes at the second detection position into immature fruit receptacles lateral to the second detection position based on a distance between the first detection position and the second detection position and a conveying speed of the conveyor belt comprises: the larger the distance between the first detection position and the second detection position, the longer the time interval obtained by calculation.
In the target classification platform using big data service:
calculating a time interval triggering an action of sorting mangoes at the second detection position into immature fruit receptacles lateral to the second detection position based on a distance between the first detection position and the second detection position and a conveying speed of the conveyor belt comprises: the slower the conveying speed of the conveyor belt, the longer the time interval obtained by the calculation.
In the target classification platform using big data service:
calculating a time interval triggering an action of sorting mangoes at the second detection position into immature fruit receptacles lateral to the second detection position based on a distance between the first detection position and the second detection position and a conveying speed of the conveyor belt comprises: the time interval obtained by calculation is a timing starting point of the time when the visual capture device performs the visual capture action.
In the target classification platform using big data service:
the second sorting mechanism is further configured to, upon receiving the third control signal, perform an action of sorting the mango at the second detection position into an immature fruit container at the side of the second detection position when the calculated time interval is reached, starting with a time at which the visual capturing device performs the visual capturing action as a timing point.
In the target classification platform using big data service:
and the second identification mechanism is also used for sending out a fourth control signal when the numerical value of the integral red component is higher than or equal to the preset component threshold value.
In the target classification platform using big data service:
acquiring the integral red component of the region to be analyzed comprises: and acquiring the red component of each pixel point in the region to be analyzed, and taking the average value of each pixel point in the region to be analyzed as the integral red component, wherein the red component is the R component in the RGB color space.
In the target classification platform using big data service:
the first identification mechanism is used for sending a second control signal when an image area matched with a certain outline picture stored in the big data service node exists in the received mango captured image.
The target classification platform using big data service may further include:
the parallel communication interface is respectively connected with the first identification mechanism, the second identification mechanism, the first sorting mechanism and the second sorting mechanism;
wherein the parallel communication interface is configured to provide the first identification mechanism, the second identification mechanism, the first sorting mechanism and the second sorting mechanism with respective required operating configuration parameters.
Meanwhile, in order to overcome the defects, the invention also builds a target classification method using big data service, and the method comprises the step of using the target classification platform using big data service to perform field classification processing on other types of mangos and immature mangos based on appearance imaging characteristics and color imaging characteristics of the mangos.
In addition, in the target classification platform using big data service, the vision capture device is built in with a CMOS sensor. CMOS image sensors have several advantages: 1) random window reading capability. Random window read operation is one aspect of CMOS image sensors that is functionally superior to CCDs, also referred to as region of interest selection. In addition, the high integration characteristics of the CMOS image sensor make it easy to implement a function of opening a plurality of tracking windows simultaneously. 2) And radiation resistance. In general, the potential radiation resistance of CMOS image sensors is significantly enhanced relative to CCD performance. 3) System complexity and reliability. The use of CMOS image sensors can greatly simplify the system hardware architecture. 4) And a non-destructive data reading method. 5) Optimized exposure control. It is noted that CMOS image sensors also have several disadvantages, mainly two indicators of noise and fill factor, due to the integration of multiple functional transistors in the pixel structure. In view of the relatively superior performance of the CMOS image sensor, the CMOS image sensor has been widely used in various fields.
Those of ordinary skill in the art will understand that: all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: Read-Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disk, and other various media capable of storing program codes.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A target classification platform utilizing big data services, the platform comprising:
and the big data service node is arranged at a big data network end and used for storing the outline picture of each type of mango, and the outline picture only comprises the imaging content of the corresponding type of mango.
2. The target classification platform utilizing big data services of claim 1, wherein the platform further comprises:
the motor transmission mechanism is used for sequentially transmitting each mango by adopting a transmission belt, and the distance between every two adjacent mangoes on the transmission belt exceeds a preset distance threshold;
the motor conveying mechanism sequentially passes through the first detection position and the second detection position.
3. The target classification platform utilizing big data services of claim 2, wherein the platform further comprises:
a visual capture device disposed above the first detection position for performing a visual capture action on the mango therebelow to obtain a corresponding mango captured image;
the first identification mechanism is respectively connected with the big data service node and the vision capture equipment and is used for sending a first control signal when an image area matched with any one contour picture stored in the big data service node does not exist in the received mango captured image;
the first sorting mechanism is arranged at the first detection position, is connected with the first identification mechanism and is used for sorting the mangos at the first detection position into other types of containers on the side of the first detection position when receiving the first control signal;
the second identification mechanism is connected with the first identification mechanism and used for taking an image area which is matched with one contour picture stored in the big data service node and exists in the received mango captured image as an area to be analyzed, acquiring an integral red component of the area to be analyzed and sending a third control signal when the numerical value of the integral red component is lower than a preset component threshold value;
a second sorting mechanism, disposed at the second detection position, connected to the second recognition mechanism, for calculating, upon receipt of the third control signal, a time interval for triggering an action of sorting the mangos at the second detection position into the immature fruit containers to the side of the second detection position based on the distance between the first detection position and the second detection position and the conveying speed of the conveyor belt;
wherein calculating a time interval triggering an action of sorting mangoes at the second detection position into immature fruit receptacles lateral to the second detection position based on the distance between the first detection position and the second detection position and the conveying speed of the conveyor belt comprises: the larger the distance between the first detection position and the second detection position is, the longer the time interval obtained by calculation is;
wherein calculating a time interval triggering an action of sorting mangoes at the second detection position into immature fruit receptacles lateral to the second detection position based on the distance between the first detection position and the second detection position and the conveying speed of the conveyor belt comprises: the slower the conveying speed of the conveyor belt, the longer the time interval obtained by the calculation.
4. The target classification platform utilizing big data services of claim 3, wherein:
calculating a time interval triggering an action of sorting mangoes at the second detection position into immature fruit receptacles lateral to the second detection position based on a distance between the first detection position and the second detection position and a conveying speed of the conveyor belt comprises: the time interval obtained by calculation is a timing starting point of the time when the visual capture device performs the visual capture action.
5. The target classification platform utilizing big data services of claim 4, wherein:
the second sorting mechanism is further configured to, upon receiving the third control signal, perform an action of sorting the mango at the second detection position into an immature fruit container at the side of the second detection position when the calculated time interval is reached, starting with a time at which the visual capturing device performs the visual capturing action as a timing point.
6. The target classification platform utilizing big data services of claim 5, wherein:
and the second identification mechanism is also used for sending out a fourth control signal when the numerical value of the integral red component is higher than or equal to the preset component threshold value.
7. The target classification platform utilizing big data services of claim 6, wherein:
acquiring the integral red component of the region to be analyzed comprises: and acquiring the red component of each pixel point in the region to be analyzed, and taking the average value of each pixel point in the region to be analyzed as the integral red component, wherein the red component is the R component in the RGB color space.
8. The target classification platform utilizing big data services of claim 7, wherein:
the first identification mechanism is used for sending a second control signal when an image area matched with a certain outline picture stored in the big data service node exists in the received mango captured image.
9. The target classification platform utilizing big data services of claim 8, wherein the platform further comprises:
the parallel communication interface is respectively connected with the first identification mechanism, the second identification mechanism, the first sorting mechanism and the second sorting mechanism;
wherein the parallel communication interface is configured to provide the first identification mechanism, the second identification mechanism, the first sorting mechanism and the second sorting mechanism with respective required operating configuration parameters.
10. A method of object classification using big data services, the method comprising using the object classification platform using big data services of any of claims 1-9 to perform a live classification process on other types of mangos and immature mangos based on appearance imaging features and color imaging features of the mangos.
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| CN113435726A (en) * | 2021-06-22 | 2021-09-24 | 布瑞克农业大数据科技集团有限公司 | Commodity management method and system |
| CN115907622A (en) * | 2022-11-30 | 2023-04-04 | 布瑞克(苏州)农业互联网股份有限公司 | Bulk agricultural product warehousing and ex-warehouse management method and system |
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