CN116402518A - Fruit processing management method, equipment and medium based on identification analysis - Google Patents
Fruit processing management method, equipment and medium based on identification analysis Download PDFInfo
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- G06Q30/00—Commerce
- G06Q30/018—Certifying business or products
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06395—Quality analysis or management
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The application discloses a fruit processing management method, equipment and medium based on identification analysis, wherein the method comprises the following steps: transporting the fruits to a cleaning room through a vehicle; collecting a first running state of the cleaning equipment in the cleaning process; transporting the cleaned fruits to a packaging workshop, and tracking the circulation state of the fruits in the transportation process; and (3) carrying out quality detection on the fruits in the packaging process, packaging the fruits meeting the quality requirements, and coding the packaged fruits. The method is based on an identification analysis platform, and the value flows of fruits from the ground to the bin, from the bin to the fruits and from the fruits to the products in the process of harvesting, production, processing and sales are used as cores, and the method is matched with equipment such as an industrial personal computer to realize the modern, digital and intelligent fruit processing and product quality tracing system which covers the whole industrial chain and the whole value chain by comprehensively connecting quality control elements such as people, production raw materials, technological processes, equipment states and the like, so that the traceability of production, processing and manageability of the production process are realized.
Description
Technical Field
The application relates to the field of industrial Internet, in particular to a fruit processing management method, equipment and medium based on identification analysis.
Background
With the recent improvement of the life quality of people, the requirements for fruit products are continuously improved, and the earlier 'eating ability' is changed into the today 'eating ability'. The transition of consumption concepts also adversely affects the field of fruit planting and processing production: the method is gradually changed from original self-production and self-marketing of farmers to standardized planting, unified acquisition and standardized production of the current technology. This puts higher demands on the current fruit planting and production management process. Most fruit processing enterprises are still in a pipeline and manual detection mode, and the standard is difficult to control.
Disclosure of Invention
In order to solve the above problems, the present application provides a fruit processing management method based on identification analysis, including:
transporting the fruits to a cleaning room through a vehicle, and uploading vehicle information and the information of the places of production of the fruits to an identification analysis platform;
collecting a first running state of cleaning equipment in the cleaning process, and uploading the cleaning state corresponding to the fruits and the first running state to the identification analysis platform;
transporting the cleaned fruits to a packaging workshop, tracking the circulation state of the fruits in the transportation process, and uploading the circulation state to the identification analysis platform, wherein the transportation process comprises transportation processes in the cleaning workshop and the packaging workshop and transportation processes across workshops;
and in the packaging process, quality detection is carried out on the fruits, the fruits meeting the quality requirements are packaged, codes are assigned to the packaged fruits, and corresponding code information is uploaded to the identification analysis platform.
In one example, the method further comprises, prior to transporting the fruit to the cleaning facility by the vehicle:
setting a radio frequency identification tag at a fruit producing area, and marking a corresponding scanning point according to the radio frequency identification tag;
vehicle information of a vehicle for harvesting fruits and a destination block are recorded in an identification analysis platform in advance;
and at the fruit harvesting time, uploading the destination block harvested at this time to the identification analysis platform by the vehicle through the scanning point so as to carry out registration verification.
In one example, uploading the vehicle information and the information of the origin of the fruit to an identification analysis platform specifically includes:
when the vehicle enters a processing factory, carrying out weighing detection and production place detection on the vehicle, and carrying out anti-cheating detection in the weighing detection process, wherein the processing factory is internally provided with the cleaning room and the packaging workshop, the anti-cheating detection comprises infrared detection, chassis shooting and vehicle number identification, and the production place detection is determined based on registration verification in the identification analysis platform;
uploading the vehicle information obtained after the weighing detection and the production place information of the fruits obtained after the production place detection to an identification analysis platform;
guiding the vehicle to enter a stock area of a cleaning room for unloading;
and when the unloading of the vehicle is finished and leaves the processing factory, the vehicle is subjected to weighing detection and anti-cheating detection, and the vehicle information obtained after the weighing detection is uploaded to an identification analysis platform.
In one example, the quality detection is performed on the fruits in the packaging process, and the fruits meeting the quality requirements are packaged, which specifically comprises:
collecting appearance photos of the fruits in the packaging process;
judging the quality of the fruits according to the appearance photo;
if the quality of the fruits is lower than the preset degree, ejecting the fruits from the production line through an air cylinder controlled by the industrial personal computer;
and if the quality of the fruits is higher than the preset degree, packaging.
In one example, coding the packaged fruits, and uploading corresponding code information to the identification analysis platform, which specifically includes:
generating a corresponding two-dimensional code aiming at the fruit, so that a user can access the fruit information corresponding to the fruit in the identification analysis platform through the two-dimensional code, wherein the fruit information comprises production place information, cleaning state, circulation state and fruit quality;
and coding the packaged fruits in the form of the two-dimensional code through a two-dimensional code printer, and uploading code information corresponding to the two-dimensional code to the identification analysis platform.
In one example, the determining the quality of the fruit according to the appearance photo specifically includes:
determining the fruit type of the fruit, and determining a neural network model corresponding to the fruit type trained in advance;
inputting the appearance photo into the neural network model, and judging the quality of the fruits through the neural network model;
the training process of the neural network model comprises the following steps:
acquiring a training sample, wherein the training sample is a first image containing fruits corresponding to the fruit type;
labeling the region corresponding to the fruit in the first image so as to obtain a position identification model through the first image training after the region is labeled;
and giving corresponding quality labels to the fruits so as to obtain a quality identification model through the quality label training.
In one example, the corresponding quality label is given to the fruit, so that a quality recognition model is obtained through the training of the quality label, and the method specifically comprises the following steps:
determining a second image identified by the position identification model;
performing cluster analysis on the second image to determine clusters after the cluster analysis;
assigning a corresponding quality label to each cluster, selecting an abnormal point which does not accord with the corresponding quality label from each cluster, and assigning a new quality label to the abnormal point;
and performing model training of a neural network model through the first image given with the quality label, and keeping the duty ratio of the training sample corresponding to the abnormal point in a preset range in each training process.
In one example, tracking the circulation state of the fruit in the transportation process specifically includes:
determining a production line of the fruits, and determining a transfer tray and a portal frame which are positioned on the production line;
and the transfer tray and the portal frame are provided with radio frequency identification tags so as to track the circulation state of the fruits through the transfer tray and the portal frame.
On the other hand, the application also provides fruit processing management equipment based on identification analysis, which comprises:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform operations such as:
transporting the fruits to a cleaning room through a vehicle, and uploading vehicle information and the information of the places of production of the fruits to an identification analysis platform;
collecting a first running state of cleaning equipment in the cleaning process, and uploading the cleaning state corresponding to the fruits and the first running state to the identification analysis platform;
transporting the cleaned fruits to a packaging workshop, tracking the circulation state of the fruits in the transportation process, and uploading the circulation state to the identification analysis platform, wherein the transportation process comprises transportation processes in the cleaning workshop and the packaging workshop and transportation processes across workshops;
and in the packaging process, quality detection is carried out on the fruits, the fruits meeting the quality requirements are packaged, codes are assigned to the packaged fruits, and corresponding code information is uploaded to the identification analysis platform.
In another aspect, the present application also proposes a non-volatile computer storage medium storing computer-executable instructions configured to:
transporting the fruits to a cleaning room through a vehicle, and uploading vehicle information and the information of the places of production of the fruits to an identification analysis platform;
collecting a first running state of cleaning equipment in the cleaning process, and uploading the cleaning state corresponding to the fruits and the first running state to the identification analysis platform;
transporting the cleaned fruits to a packaging workshop, tracking the circulation state of the fruits in the transportation process, and uploading the circulation state to the identification analysis platform, wherein the transportation process comprises transportation processes in the cleaning workshop and the packaging workshop and transportation processes across workshops;
and in the packaging process, quality detection is carried out on the fruits, the fruits meeting the quality requirements are packaged, codes are assigned to the packaged fruits, and corresponding code information is uploaded to the identification analysis platform.
The fruit processing management method based on identification analysis can bring the following beneficial effects:
the method is based on an identification analysis platform, and the value flows of fruits from the ground to the bin, from the bin to the fruits and from the fruits to the products in the process of harvesting, production, processing and sales are used as cores, and the method is matched with equipment such as an industrial personal computer to realize the modern, digital and intelligent fruit processing and product quality tracing system which covers the whole industrial chain and the whole value chain by comprehensively connecting quality control elements such as people, production raw materials, technological processes, equipment states and the like, so that the traceability of production, processing and manageability of the production process are realized.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
fig. 1 is a schematic flow chart of a fruit processing management method based on identification analysis in an embodiment of the application;
fig. 2 is a schematic flow chart of a fruit processing management method based on identification analysis in a scenario in the embodiment of the present application;
FIG. 3 is a schematic diagram of an apparatus for vehicle detection in an embodiment of the present application;
FIG. 4 is a schematic diagram of an apparatus for quality inspection in an embodiment of the present application;
fig. 5 is a schematic diagram of an apparatus for fruit removal in an embodiment of the present application;
fig. 6 is a schematic diagram of a fruit processing management device based on identification resolution in an embodiment of the application.
Detailed Description
For the purposes, technical solutions and advantages of the present application, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments of the present application and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The following describes in detail the technical solutions provided by the embodiments of the present application with reference to the accompanying drawings.
As shown in fig. 1, an embodiment of the present application provides a fruit processing management method based on identification resolution, including:
s101: and transporting the fruits to a cleaning room through a vehicle, and uploading vehicle information and the information of the places of production of the fruits to an identification analysis platform.
And taking the vehicles as units, taking fruits of the same train number as a batch to enter a cleaning room, and collecting the running state of the production line through the terminal of the Internet of things. Based on the identification analysis system, all data are uploaded to the national identification analysis platform, the full-chain data cannot be tampered, the true credibility of the data is ensured, and the real-time sharing of the data can be realized.
As shown in fig. 2, in the whole process of fruit production and processing, firstly, a radio frequency identification tag RFID is set at a fruit producing area, and corresponding scanning points are marked according to the radio frequency identification tag, for example, in the fruit producing area, a corresponding scanning point is set in each land, the radio frequency identification tag is set in the scanning point, and after the fruits are picked, a worker can perform corresponding scanning.
Vehicle information and destination blocks of a vehicle for harvesting fruits are recorded in the identification analysis platform in advance, the vehicle uploads the destination blocks harvested this time to the identification analysis platform through scanning points at the harvesting time of the fruits so as to conduct registration verification, and if no abnormality exists in verification, the vehicle can allow the harvesting this time. The tracing process is tracked to the field plots, and the consumer can see the complete industrial chain during inquiry, so that the added value of the product can be improved. And marking and identifying farmland plots based on RFID, and carrying out association binding on the harvesting vehicles and the farmland through a production area management system, so that traceability of production plot information is realized.
As shown in fig. 3, when a vehicle enters a processing factory (a cleaning room, a packaging room, and the like are provided in the processing factory), the vehicle is detected by a wagon balance and the place of production is detected, and the vehicle information obtained after the wagon balance detection includes the entering net weight, and naturally includes license plate information and the like, and the vehicle information is uploaded to the identification analysis platform. The origin detection is based on the check-in verification determination in the identification analysis platform, and the origin information is also uploaded to the identification analysis platform.
Of course, anti-cheating detection is performed in the process of detecting the wagon balance, for example, as shown in fig. 3, the anti-cheating detection comprises infrared detection (an infrared light curtain is arranged on the front side and the rear side of the wagon balance), chassis shooting and wagon number identification (realized by a license plate identification camera arranged beside the wagon balance), and a card reader is further arranged to read the swipe card of a worker of the wagon, so that the wagon balance can be controlled to enter and exit the wagon balance. And the traffic lights and the road gates are also arranged to control the forward and stop of the vehicle. And acquiring the corresponding information through the local data acquisition equipment so as to be convenient to upload to the identification analysis platform.
And guiding the vehicle to enter a goods storage area of the cleaning room for unloading, carrying out weighing detection and anti-cheating detection on the vehicle again when the unloading of the vehicle is completed and leaves the processing factory, and uploading the vehicle information obtained after the weighing detection to the identification analysis platform. Wherein, the fruits after unloading are stored in a refrigeration house so as to be convenient for long-time storage.
S102: and collecting a first running state of the cleaning equipment in the cleaning process, and uploading the cleaning state corresponding to the fruits and the first running state to the identification analysis platform.
The first running state comprises the working parameters of the equipment, and if the equipment is abnormal, the equipment can be overhauled at any time. When the fruits are cleaned, the cleaning state can comprise the net weight of the fruits, the water consumption of the current cleaning and the like. After unloading, the vehicles are stored in the refrigeration house, the fruits in the refrigeration house are transported to a cleaning workshop for cleaning, and after the cleaning is finished, the fruits can be continuously placed in the refrigeration house for storage.
S103: transporting the cleaned fruits to a packaging workshop, tracking the circulation state of the fruits in the transportation process, uploading the circulation state to the identification analysis platform, and transporting the fruits in the packaging workshop, wherein the transportation process comprises a transportation process in the cleaning workshop and a transportation process across workshops.
In the transportation process of fruits, the fruits are transported based on the transport trays arranged on the assembly line, and the portal frames are further arranged at the positions of the entrance and the exit of the refrigeration house, the entrance and the exit of the packaging workshop and the like. The transfer tray and the portal frame are provided with radio frequency identification tags so as to track the circulation state of the fruits through the transfer tray and the portal frame and identify the weight of the fruits through the transfer tray.
The RFID and portal frame combined mode is adopted in the processing factory, automatic identification is achieved in the transferring process, manual intervention is not needed for scanning codes, products with abnormal stay can be found in time, and circulation efficiency is improved. And the production line is paved with the internet of things equipment such as a transfer tray and a cleaning device, and the operation data are collected and summarized in real time, so that the productivity operation state and the energy consumption expenditure state can be judged more effectively and more accurately.
S104: and in the packaging process, quality detection is carried out on the fruits, the fruits meeting the quality requirements are packaged, codes are assigned to the packaged fruits, and corresponding code information is uploaded to the identification analysis platform.
The cleaning workshop and the packaging workshop are provided with the sensing equipment of the Internet of things and the industrial control integrated machine, so that the real-time acquisition of production line data, the automatic identification of abnormal products and the code giving and delivery of qualified products can be realized, and the unification of quality inspection standards is realized. Based on actual requirements and corresponding packaging equipment, the fruits can be packaged in bags, boxes and the like, and the packaged fruits are sent to a next sales node.
As shown in fig. 4, a display bracket, a scanner bracket, a touch screen and other devices for quality detection are arranged on the assembly line, the display bracket and the scanner bracket are respectively used for placing a display and a scanner, the scanner is used for scanning and shooting appearance photos of fruits, the display is used for displaying the shot appearance photos, and the touch screen is used for setting the working states (such as start and stop, scanning frequency, display size and the like) of the display bracket and the scanner bracket.
In the packaging process, the appearance photo of the fruit is collected through the camera, and the quality of the fruit is judged through the appearance photo. If the quality of the fruits is lower than the preset level, the fruits are ejected out of the production line through the air cylinders controlled by the industrial personal computer, as shown in fig. 5, the removing air cylinders are arranged on the production line, and the fruits which do not meet the requirements are removed out of the production line through the air cylinders. And if the quality of the fruits is higher than the preset degree, packaging the fruits by packaging equipment.
Specifically, a corresponding two-dimensional code is generated aiming at the fruits, and the two-dimensional code is used as an access entry, so that a user can access the corresponding fruit information of the fruits through the two-dimensional code in the identification analysis platform. Wherein, the fruit information comprises the information of the producing place, the cleaning state, the circulation state and the fruit quality.
The packaged fruits are coded in a two-dimensional code mode through a two-dimensional code printer, for example, the packaged fruits are printed on the outer package of the fruits. And uploading code information corresponding to the two-dimensional code to an identification analysis platform.
In addition, in making the fruit quality determination, the fruit type of the fruit may be determined, for example, it belongs to apples, bananas, oranges, and the like. And determining a neural network model corresponding to the pre-trained fruit type, wherein different neural network models are adopted for different fruit types.
Inputting the appearance photo into a neural network model, and judging the quality of the fruits through the neural network model. By means of a machine learning technology, inferior fruits are automatically identified and products which do not meet standards are automatically identified, manual operation is reduced, and standard unification is easy to achieve.
The training process of the neural network model comprises the following steps: firstly, a training sample is obtained, wherein the training sample is a first image containing fruits corresponding to the types of the fruits. For example, the quality of training is ensured by acquiring images from a corresponding image library and on-site photographs, and the quality of training samples is consistent with a certain proportion of the quality of the training samples.
Marking a region corresponding to the fruit in the first image so as to obtain a position identification model through the first image training after marking the region, wherein the position identification model belongs to a sub-model in the neural network model, and can be used for carrying out position identification on the fruit in the image so as to extract a second image corresponding to the fruit.
Corresponding quality labels (such as good, bad, etc. which can be manually labeled) are assigned to the fruits so as to obtain a quality recognition model through the training of the quality labels in a supervision mode. The quality recognition model also belongs to a sub-model of the neural network model, and performs quality recognition on the fruits in the second image, and finally outputs the corresponding quality of the fruits.
Further, determining a second image identified by the position identification model, and performing cluster analysis on the second image to determine clusters after cluster analysis. The general appearance of the fruit in each cluster can be observed, and the corresponding quality label is given to each cluster, so that the quality label of each training sample is given. The outliers that do not fit the corresponding quality labels are selected in each cluster (e.g., by manual screening) and new quality labels are assigned to the outliers. The influence of the background is avoided by clustering the second image, the similar fruits belong to the same cluster through the clustering, the research personnel can conveniently and rapidly find the same points in the cluster, the differences in different clusters can be conveniently found, the fruits are conveniently and hierarchically classified, the quality labels which are endowed for the first time can be used for assisting research personnel in labeling, and the possibility of errors of the research personnel is reduced.
Model training of the neural network model is performed by the first image to which the quality label is assigned (of course, if there is an outlier therein, training is performed according to the new quality label assigned). In the training process of each round, the duty ratio of the training sample corresponding to the abnormal point is kept in a preset range, and the abnormal point is different from other samples in the cluster, so that the neural network model is required to learn and distinguish the abnormal point, the duty ratio is required to be higher than a certain degree, but the abnormal point is usually few, and the duty ratio is required to be controlled not to be higher than a certain degree in order to accord with an objective rule, so that the duty ratio is in the preset range.
As shown in fig. 6, the embodiment of the present application further provides a fruit processing management device based on identification resolution, including:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform operations such as:
transporting the fruits to a cleaning room through a vehicle, and uploading vehicle information and the information of the places of production of the fruits to an identification analysis platform;
collecting a first running state of cleaning equipment in the cleaning process, and uploading the cleaning state corresponding to the fruits and the first running state to the identification analysis platform;
transporting the cleaned fruits to a packaging workshop, tracking the circulation state of the fruits in the transportation process, and uploading the circulation state to the identification analysis platform, wherein the transportation process comprises transportation processes in the cleaning workshop and the packaging workshop and transportation processes across workshops;
and in the packaging process, quality detection is carried out on the fruits, the fruits meeting the quality requirements are packaged, codes are assigned to the packaged fruits, and corresponding code information is uploaded to the identification analysis platform.
The embodiments also provide a non-volatile computer storage medium storing computer executable instructions configured to:
transporting the fruits to a cleaning room through a vehicle, and uploading vehicle information and the information of the places of production of the fruits to an identification analysis platform;
collecting a first running state of cleaning equipment in the cleaning process, and uploading the cleaning state corresponding to the fruits and the first running state to the identification analysis platform;
transporting the cleaned fruits to a packaging workshop, tracking the circulation state of the fruits in the transportation process, and uploading the circulation state to the identification analysis platform, wherein the transportation process comprises transportation processes in the cleaning workshop and the packaging workshop and transportation processes across workshops;
and in the packaging process, quality detection is carried out on the fruits, the fruits meeting the quality requirements are packaged, codes are assigned to the packaged fruits, and corresponding code information is uploaded to the identification analysis platform.
All embodiments in the application are described in a progressive manner, and identical and similar parts of all embodiments are mutually referred, so that each embodiment mainly describes differences from other embodiments. In particular, for the apparatus and medium embodiments, the description is relatively simple, as it is substantially similar to the method embodiments, with reference to the section of the method embodiments being relevant.
The devices and media provided in the embodiments of the present application are in one-to-one correspondence with the methods, so that the devices and media also have similar beneficial technical effects as the corresponding methods, and since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media are not described in detail herein.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.
Claims (10)
1. The fruit processing management method based on identification analysis is characterized by comprising the following steps of:
transporting the fruits to a cleaning room through a vehicle, and uploading vehicle information and the information of the places of production of the fruits to an identification analysis platform;
collecting a first running state of cleaning equipment in the cleaning process, and uploading the cleaning state corresponding to the fruits and the first running state to the identification analysis platform;
transporting the cleaned fruits to a packaging workshop, tracking the circulation state of the fruits in the transportation process, and uploading the circulation state to the identification analysis platform, wherein the transportation process comprises transportation processes in the cleaning workshop and the packaging workshop and transportation processes across workshops;
and in the packaging process, quality detection is carried out on the fruits, the fruits meeting the quality requirements are packaged, codes are assigned to the packaged fruits, and corresponding code information is uploaded to the identification analysis platform.
2. The method of claim 1, wherein prior to transporting the fruit to the cleaning booth by vehicle, the method further comprises:
setting a radio frequency identification tag at a fruit producing area, and marking a corresponding scanning point according to the radio frequency identification tag;
vehicle information of a vehicle for harvesting fruits and a destination block are recorded in an identification analysis platform in advance;
and at the fruit harvesting time, uploading the destination block harvested at this time to the identification analysis platform by the vehicle through the scanning point so as to carry out registration verification.
3. The method according to claim 2, wherein uploading the vehicle information and the information of the origin of the fruit to an identification resolution platform, in particular comprises:
when the vehicle enters a processing factory, carrying out weighing detection and production place detection on the vehicle, and carrying out anti-cheating detection in the weighing detection process, wherein the processing factory is internally provided with the cleaning room and the packaging workshop, the anti-cheating detection comprises infrared detection, chassis shooting and vehicle number identification, and the production place detection is determined based on registration verification in the identification analysis platform;
uploading the vehicle information obtained after the weighing detection and the production place information of the fruits obtained after the production place detection to an identification analysis platform;
guiding the vehicle to enter a stock area of a cleaning room for unloading;
and when the unloading of the vehicle is finished and leaves the processing factory, the vehicle is subjected to weighing detection and anti-cheating detection, and the vehicle information obtained after the weighing detection is uploaded to an identification analysis platform.
4. Method according to claim 1, characterized in that the quality of the fruit is checked during the packaging process and the fruit meeting the quality requirements is packaged, in particular comprising:
collecting appearance photos of the fruits in the packaging process;
judging the quality of the fruits according to the appearance photo;
if the quality of the fruits is lower than the preset degree, ejecting the fruits from the production line through an air cylinder controlled by the industrial personal computer;
and if the quality of the fruits is higher than the preset degree, packaging.
5. The method of claim 4, wherein the coding of the packaged fruit and uploading the corresponding code information to the identification resolution platform, comprises:
generating a corresponding two-dimensional code aiming at the fruit, so that a user can access the fruit information corresponding to the fruit in the identification analysis platform through the two-dimensional code, wherein the fruit information comprises production place information, cleaning state, circulation state and fruit quality;
and coding the packaged fruits in the form of the two-dimensional code through a two-dimensional code printer, and uploading code information corresponding to the two-dimensional code to the identification analysis platform.
6. The method according to claim 4, wherein the determination of the fruit quality of the fruit is performed by the appearance photo, specifically comprising:
determining the fruit type of the fruit, and determining a neural network model corresponding to the fruit type trained in advance;
inputting the appearance photo into the neural network model, and judging the quality of the fruits through the neural network model;
the training process of the neural network model comprises the following steps:
acquiring a training sample, wherein the training sample is a first image containing fruits corresponding to the fruit type;
labeling the region corresponding to the fruit in the first image so as to obtain a position identification model through the first image training after the region is labeled;
and giving corresponding quality labels to the fruits so as to obtain a quality identification model through the quality label training.
7. The method according to claim 6, wherein the fruit is given a corresponding quality label to be trained by the quality label to obtain a quality recognition model, comprising:
determining a second image identified by the position identification model;
performing cluster analysis on the second image to determine clusters after the cluster analysis;
assigning a corresponding quality label to each cluster, selecting an abnormal point which does not accord with the corresponding quality label from each cluster, and assigning a new quality label to the abnormal point;
and performing model training of a neural network model through the first image given with the quality label, and keeping the duty ratio of the training sample corresponding to the abnormal point in a preset range in each training process.
8. Method according to claim 1, characterized in that the tracking of the circulation state of the fruit during transportation comprises in particular:
determining a production line of the fruits, and determining a transfer tray and a portal frame which are positioned on the production line;
and the transfer tray and the portal frame are provided with radio frequency identification tags so as to track the circulation state of the fruits through the transfer tray and the portal frame.
9. Fruit processing management equipment based on identification analysis, characterized by comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform operations such as:
transporting the fruits to a cleaning room through a vehicle, and uploading vehicle information and the information of the places of production of the fruits to an identification analysis platform;
collecting a first running state of cleaning equipment in the cleaning process, and uploading the cleaning state corresponding to the fruits and the first running state to the identification analysis platform;
transporting the cleaned fruits to a packaging workshop, tracking the circulation state of the fruits in the transportation process, and uploading the circulation state to the identification analysis platform, wherein the transportation process comprises transportation processes in the cleaning workshop and the packaging workshop and transportation processes across workshops;
and in the packaging process, quality detection is carried out on the fruits, the fruits meeting the quality requirements are packaged, codes are assigned to the packaged fruits, and corresponding code information is uploaded to the identification analysis platform.
10. A non-transitory computer storage medium storing computer-executable instructions, the computer-executable instructions configured to:
transporting the fruits to a cleaning room through a vehicle, and uploading vehicle information and the information of the places of production of the fruits to an identification analysis platform;
collecting a first running state of cleaning equipment in the cleaning process, and uploading the cleaning state corresponding to the fruits and the first running state to the identification analysis platform;
transporting the cleaned fruits to a packaging workshop, tracking the circulation state of the fruits in the transportation process, and uploading the circulation state to the identification analysis platform, wherein the transportation process comprises transportation processes in the cleaning workshop and the packaging workshop and transportation processes across workshops;
and in the packaging process, quality detection is carried out on the fruits, the fruits meeting the quality requirements are packaged, codes are assigned to the packaged fruits, and corresponding code information is uploaded to the identification analysis platform.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310245266.1A CN116402518A (en) | 2023-03-10 | 2023-03-10 | Fruit processing management method, equipment and medium based on identification analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310245266.1A CN116402518A (en) | 2023-03-10 | 2023-03-10 | Fruit processing management method, equipment and medium based on identification analysis |
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| Publication Number | Publication Date |
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| CN116402518A true CN116402518A (en) | 2023-07-07 |
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| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310245266.1A Pending CN116402518A (en) | 2023-03-10 | 2023-03-10 | Fruit processing management method, equipment and medium based on identification analysis |
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| Country | Link |
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| CN (1) | CN116402518A (en) |
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- 2023-03-10 CN CN202310245266.1A patent/CN116402518A/en active Pending
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