CN110910151A - Quality tracing system and method - Google Patents
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Abstract
The invention discloses a quality tracing system and a tracing method, wherein the system comprises: the system comprises an information acquisition module, a data storage system, a data analysis module and a user feedback system; the information acquisition module comprises a server, a switch connected with the server, an automatic labeling machine and a recognition device which are arranged on a barn packaging line, a sensor detection element arranged in the barn, and video monitoring modules arranged inside and outside the barn. Compared with the prior art, the method can realize early warning threshold values in aspects of grain production quantity, safety, storage parameters and the like, combines the time and frequency of sampling inspection, quick inspection and enterprise self-inspection and the size of a food tracing unit on the basis of detection data accumulated in each link of a grain storage and transportation supply chain, constructs an overall process evidence chain of the food supply chain on the basis of a video trajectory tracking technology, and provides food overall process quality safety information query service for consumers.
Description
Technical Field
The invention belongs to the field of grain condition quality tracing, and particularly relates to a quality tracing system and a tracing method.
Background
In the grain production process, the safety problem of grains is always a major problem related to the living counting of the nation, the existing quality tracing system in the current market is lack of factors in the aspects of grain safety, grain production quantity safety, early warning threshold values and the like, the reliability and effectiveness of threshold value setting are disputed, and the cross repetition problem exists in evaluation indexes. Secondly, the grain data volume early warning mainly takes static data as a main part, the real-time inventory information of each warehouse is less utilized, the authenticity of the inventory data needs to be further enhanced, and the problems of false reporting, missing reporting, grain rotation and the like are difficult to avoid.
Disclosure of Invention
The purpose of the invention is as follows: provides a comprehensive grain condition detection system to solve the problems in the prior art.
The technical scheme is as follows: a quality traceability system, the system comprising: the system comprises an information acquisition module, a data storage system, a data analysis module and a user feedback system;
the information acquisition module comprises a server, a switch connected with the server, an automatic labeling machine and a recognition device which are arranged on a barn packaging line, a sensor detection element arranged in the barn, and video monitoring modules arranged inside and outside the barn.
In a further embodiment, the video surveillance module includes a multisource aggregation module and a spatio-temporal dynamics module; the multi-source aggregation module extracts effective information from video monitoring data acquired by a plurality of monitoring cameras; the space-time dynamic module is the effective information of the detected motion trail of a single target.
In a further embodiment, the information collecting device installed in the field comprises: the field information acquisition equipment is arranged in the field and supports the collection of field atmospheric temperature and humidity, atmospheric pressure, illumination intensity, wind speed, wind direction, rainfall, PM2.5, PM10 and PM100 environmental elements.
In a further embodiment, the method comprises the steps of:
step one, an information acquisition module senses comprehensive data of grain conditions from planting, harvesting and storing, and stores the sensed data in a data storage system for further data analysis;
the video monitoring module automatically detects a target in a video, analyzes the motion track of the target, establishes a grain depot three-dimensional model, realizes data aggregation of multiple cameras, and monitors links of grain processing, packaging, transportation and sale;
thirdly, the data analysis module performs data analysis and data fusion on the information gathered in the first step and the second step, and extracts traceability data which can be used for grain food safety tracing;
and fourthly, performing risk early warning analysis on the food safety according to the source tracing data in the data storage system, and sending the analysis to a user feedback system.
In a further embodiment, the fourth step is further:
s1, extracting data characteristics through analysis of the comparison relation among the spatial topology, the time sequence change and the environment variables on a multi-detail level, and describing through a characteristic relation graph;
s2, on the basis of the characteristic relation graph, defining and detecting different tracing states by using a clustering algorithm based on graph mining;
s3, analyzing the change rule of the tracing state on the basis of the association rule mining algorithm, constructing an environment state automaton and describing the change of the monitored environment state;
and S4, mapping the real-time monitoring data and the state automaton by using a matching analysis method, analyzing possible future changes of the environment, and performing early warning and intelligent response according to a prediction result so as to realize the project system target.
In a further embodiment, the data fusion is divided into two sub-modules of coordinate mapping and matching fusion, the coordinate mapping sub-module is a two-dimensional-three-dimensional mapping relation established for each camera, and two-dimensional pixel coordinates of the target can be converted into three-dimensional coordinates of the target in a real space based on the two-dimensional-three-dimensional mapping relation; and the matching and fusing submodule performs matching and track fusion on all the targets according to the position information and the color characteristic information of the targets so as to obtain final tracing data.
Has the advantages that: compared with the prior art, the invention has the following advantages:
1, early warning on the aspects of grain production, quantity, safety, storage parameters and the like can be realized, and data acquisition, storage, recording and analysis are carried out in the processes of grain production, storage, packaging, transportation and sale;
and 2, on the basis of detection data accumulated in each link of the grain storage, transportation and supply chain, combining the time and frequency of sampling inspection, quick inspection and enterprise self-inspection and the size of a food source tracing unit, and constructing a food supply chain overall process evidence chain by using a video trajectory tracking technology, so that a supervision department can conveniently provide supply chain panoramic quality safety information monitoring service, provide quality safety information management service for enterprises, and provide food overall process quality safety information query service for consumers. The problem of difficulty in tracing and blank space after the fact is solved, and the safety tracing efficiency is improved.
And 3, establishing a source tracing data abnormity judgment deep neural network. And establishing an abnormal state evaluation model based on the abnormal analysis.
Drawings
Fig. 1 is a schematic structural diagram of the quality tracing system of the present invention.
FIG. 2 is a flow chart of a control cycle of step three of the present invention.
Fig. 3 is a network topology structure diagram of the quality tracing system of the present invention.
FIG. 4 is a flow chart of a control cycle of step four of the present invention.
FIG. 5 is a flow chart of a control cycle of the steps of the present invention.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the present invention. It will be apparent, however, to one skilled in the art, that the present invention may be practiced without one or more of these specific details. In other instances, well-known features have not been described in order to avoid obscuring the invention.
The applicant finds that the existing quality tracing system related to grain safety in the current market is lack of factors in aspects of grain production quantity safety, early warning threshold values and the like, the reliability and effectiveness of threshold value setting are disputed, and the cross repetition problem exists in evaluation indexes. Secondly, the grain data volume early warning mainly takes static data as a main part, the real-time inventory information of each warehouse is less utilized, the authenticity of the inventory data needs to be further enhanced, and the problems of false reporting, missing reporting, grain rotation and the like are difficult to avoid. Temperature distribution and prediction model under the large-scale environment, and a new generation grain condition monitoring and early warning system based on Ethernet and mobile internet technology are less researched, so that efficient and accurate information sharing and risk early warning are difficult to carry out on the time scale in grain condition detection under multi-regional and spatial distribution.
Examples
Fig. 1 discloses a quality traceability system, the system comprising: the system comprises an information acquisition module, a data storage system, a data analysis module and a user feedback system;
the information acquisition module comprises a server, a switch connected with the server, an automatic labeling machine and a recognition device which are arranged on a barn packaging line, a sensor detection element arranged in the barn, and video monitoring modules arranged inside and outside the barn. The information acquisition module collects traceability data inside and outside the granary through the video monitoring module and the sensor detection element, and transmits the data to the data storage system server through the network; as shown in fig. 5, the information acquisition device installed in the field includes: the field information acquisition equipment is arranged in the field and supports the collection of field atmospheric temperature and humidity, atmospheric pressure, illumination intensity, wind speed, wind direction, rainfall, PM2.5, PM10 and PM100 environmental elements. The device supports one Ethernet communication interface and can transmit data to a plurality of server addresses. The system supports the function of capturing the alarm images of noise and dust, and when the system detects noise or abnormal dust, the spherical camera is called to capture and upload pictures. The system can acquire related environmental physical quantities through the external sensor, and uploads the acquired physical quantities to a remote cloud platform through GPRS (general packet radio service), a wired network, WIFI (wireless fidelity), a data transmission radio station and the like. The cloud platform is combined with big data technology and information such as on-site video images to perform analysis and early warning. The multi-parameter grain condition integrated sensing data is transmitted to an integrated online grain condition monitoring software system, so that the signal difference of different types of sensors is eliminated, and a uniform and standard shared hardware access interface is provided. And the data generated by the video monitoring module is stored in a data storage system as visual tracing data, and the video monitoring module is communicated with the server.
The method comprises the following steps:
step one, an information acquisition module senses comprehensive data of grain conditions from planting, harvesting and storing, and stores the sensed data in a data storage system for further data analysis;
the video monitoring module automatically detects a target in a video, analyzes the motion track of the target, establishes a grain depot three-dimensional model, realizes data aggregation of multiple cameras, and monitors links of grain processing, packaging, transportation and sale;
thirdly, the data analysis module performs data analysis and data fusion on the information gathered in the first step and the second step, and extracts traceability data which can be used for grain food safety tracing;
and fourthly, performing risk early warning analysis on the food safety according to the source tracing data in the data storage system, and sending the analysis to a user feedback system.
Further, the visualization tracing data storage process shown in fig. 2 includes: multi-source aggregated visual and spatio-temporal dynamic visual data; the multi-source polymerization visualization is to arrange a plurality of cameras in the grain processing industry to realize multi-source shooting, and the time-space dynamic visualization data is to automatically identify and detect targets in the video, monitor the motion track of the targets and establish a grain depot three-dimensional model.
The multi-source polymerization visualization can realize the identification and detection of the moving target in the grain depot, thereby providing a proof for the authenticity of the data of the grain depot in and out of the grain depot. The space-time dynamic visualization data realizes the functions of supporting track extraction and three-dimensional track synthesis, can dynamically judge the behavior characteristics of vehicles and personnel in a grain depot, and sends out early warning on abnormal behaviors.
Furthermore, the multi-source aggregation visualization and space-time dynamic visualization data are obtained from the video monitoring data of the food and grain processing enterprises in a large amount through a video data analysis and fusion technology, so that the traceability data which can be used for food and grain safety tracing can be obtained.
As shown in fig. 3, the structure diagram for acquiring the visual traceability data is shown, the whole system is composed of two modules, namely, an information extraction module and a data fusion module, effective information is extracted from video monitoring data acquired by a plurality of monitoring cameras, and then the effective information is fused to realize acquisition of the traceability data. The information extraction module is divided into two sub-modules of target detection and target identification: the object detection sub-module extracts valid frames containing moving objects from a large amount of video stream data, and the object identification module determines the object types contained in the valid frames and the positions of each object in the current valid frame. The data fusion module is divided into two submodules of coordinate mapping and matching fusion. The coordinate mapping submodule establishes a two-dimensional-three-dimensional mapping relation for each camera, and can convert the two-dimensional pixel coordinate of the target into the three-dimensional coordinate of the target in a real space based on the two-dimensional-three-dimensional mapping relation; and finally, the matching and fusing submodule performs matching and track fusion on all the targets according to the position information and the color characteristic information of the targets so as to obtain final traceability data.
Further, as shown in fig. 4, the data analysis module utilizes a clustering analysis and association rule mining algorithm, extracts data features through analysis of a comparison relationship among spatial topology, time sequence variation and environment variables on a multi-level of detail, and describes the data features through an Attribute Relational Graph (ARG); on the basis of the characteristic relation graph, defining and detecting different traceability states by using a clustering algorithm based on graph mining; analyzing the change rule of the tracing state on the basis of an association rule mining algorithm, constructing an environment state automaton and describing the change of the monitored environment state; and mapping the real-time monitoring data and the state automaton by using a matching analysis method, analyzing possible future changes of the environment, and performing early warning and intelligent response according to a prediction result so as to realize a project system target.
On the basis of multi-source aggregated visual traceability data, a multi-detail level model of the traceability data is established by utilizing a space-time analysis and clustering method, multi-source dynamic visualization is further realized, and a comprehensive simplified algorithm is adjusted and optimized through a visual result, so that the structural characteristics of the data are reserved, and effective data support is provided for risk early warning and mining.
Compared with the prior art, the method can realize early warning on the aspects of grain production quantity, safety, storage parameters and the like, construct a user interaction platform, acquire user feedback information, evaluate visual mining, state analysis and monitoring early warning results, summarize project achievements, retrain a grain quality traceability data visual analysis scientific method and form a final report. The method is used for collecting, storing, recording and analyzing data in the processes of grain production, storage, packaging, transportation and sale, providing supply chain panoramic quality safety information monitoring service for supervision departments, providing quality safety information management service for enterprises and providing food overall process quality safety information query service for consumers. The problem of retrospective safety tracing is avoided, the safety tracing efficiency is improved, and the daily supervision requirements of grains in circulation, storage and transportation are met without the space-time limitation. The quantity and quality tracing system and the risk early warning analysis method in the whole process from production, processing, storage, transportation to sale are constructed, various services such as information inquiry, product tracking, department supervision, industry early warning, complaint right-to-maintenance, index analysis and the like can be provided for supervision departments, enterprises and consumers, and the system has good industrialization prospect and comprehensive benefits.
The preferred embodiments of the present invention have been described in detail with reference to the accompanying drawings, however, the present invention is not limited to the specific details of the embodiments, and various equivalent changes can be made to the technical solution of the present invention within the technical idea of the present invention, and these equivalent changes are within the protection scope of the present invention.
Claims (6)
1. A quality traceability system, comprising: the system comprises: the system comprises an information acquisition module, a data storage system, a data analysis module and a user feedback system;
the information acquisition module comprises a server, a switch connected with the server, an automatic labeling machine and a recognition device which are arranged on a barn packaging line, a sensor detection element arranged in the barn, information acquisition equipment arranged in the field and video monitoring modules arranged inside and outside the barn.
2. A quality traceability system as claimed in claim 1, wherein the video surveillance module comprises a multisource aggregation module and a spatiotemporal dynamics module; the multi-source aggregation module extracts effective information from video monitoring data acquired by a plurality of monitoring cameras; the space-time dynamic module is the effective information of the detected motion trail of a single target.
3. The quality traceability system of claim 1, wherein the information collecting device disposed in the field comprises: the field information acquisition equipment is arranged in the field and supports the collection of field atmospheric temperature and humidity, atmospheric pressure, illumination intensity, wind speed, wind direction, rainfall, PM2.5, PM10 and PM100 environmental elements.
4. A quality tracing system tracing method is characterized by comprising the following steps:
step one, an information acquisition module senses comprehensive data of grain conditions from planting, harvesting and storing, and stores the sensed data in a data storage system for further data analysis;
the video monitoring module automatically detects a target in a video, analyzes the motion track of the target, establishes a grain depot three-dimensional model, realizes data aggregation of multiple cameras, and monitors links of grain processing, packaging, transportation and sale;
thirdly, the data analysis module performs data analysis and data fusion on the information gathered in the first step and the second step, and extracts traceability data which can be used for grain food safety tracing;
and fourthly, performing risk early warning analysis on the food safety according to the source tracing data in the data storage system, and sending the analysis to a user feedback system.
5. A quality traceability system, as claimed in claim 4, wherein said step four further comprises:
s1, extracting data characteristics through analysis of the comparison relation among the spatial topology, the time sequence change and the environment variables on a multi-detail level, and describing through a characteristic relation graph;
s2, on the basis of the characteristic relation graph, defining and detecting different tracing states by using a clustering algorithm based on graph mining;
s3, analyzing the change rule of the tracing state on the basis of the association rule mining algorithm, constructing an environment state automaton and describing the change of the monitored environment state;
and S4, mapping the real-time monitoring data and the state automaton by using a matching analysis method, analyzing possible future changes of the environment, and performing early warning and intelligent response according to a prediction result so as to realize the project system target.
6. The quality tracing system tracing method according to claim 4, wherein the data fusion is divided into two sub-modules of coordinate mapping and matching fusion, the coordinate mapping sub-module is a two-dimensional-three-dimensional mapping relation established for each camera, based on which a two-dimensional pixel coordinate of a target can be converted into a three-dimensional coordinate of the target in a real space; and the matching and fusing submodule performs matching and track fusion on all the targets according to the position information and the color characteristic information of the targets so as to obtain final tracing data.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113029252A (en) * | 2021-04-16 | 2021-06-25 | 中科海拓(无锡)科技有限公司 | Industrial park air quality detection system based on data processing |
| CN113344728A (en) * | 2021-04-30 | 2021-09-03 | 华中农业大学 | Intelligent monitoring system and method for food production full-chain information |
| CN113507491A (en) * | 2021-04-30 | 2021-10-15 | 华中农业大学 | Method and system for uploading all-chain information of clean egg production in real time |
| CN113987240A (en) * | 2021-12-27 | 2022-01-28 | 智器云南京信息科技有限公司 | Customs inspection sample tracing method and system based on knowledge graph |
| CN115136799A (en) * | 2022-08-15 | 2022-10-04 | 安徽荣夏智能科技有限责任公司 | Grain storage intelligent management system |
| CN116523466A (en) * | 2023-05-06 | 2023-08-01 | 福建凯邦锦纶科技有限公司 | Production data tracing system and method based on big data |
| CN117575171A (en) * | 2024-01-09 | 2024-02-20 | 湖南工商大学 | Grain situation intelligent evaluation system based on data analysis |
| CN117688049A (en) * | 2024-02-04 | 2024-03-12 | 问策师信息科技南京有限公司 | Visual data management system and method for tracing data |
-
2019
- 2019-11-20 CN CN201911142290.2A patent/CN110910151A/en not_active Withdrawn
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113029252A (en) * | 2021-04-16 | 2021-06-25 | 中科海拓(无锡)科技有限公司 | Industrial park air quality detection system based on data processing |
| CN113344728A (en) * | 2021-04-30 | 2021-09-03 | 华中农业大学 | Intelligent monitoring system and method for food production full-chain information |
| CN113507491A (en) * | 2021-04-30 | 2021-10-15 | 华中农业大学 | Method and system for uploading all-chain information of clean egg production in real time |
| CN113987240A (en) * | 2021-12-27 | 2022-01-28 | 智器云南京信息科技有限公司 | Customs inspection sample tracing method and system based on knowledge graph |
| CN113987240B (en) * | 2021-12-27 | 2022-04-08 | 智器云南京信息科技有限公司 | Customs inspection sample tracing method and system based on knowledge graph |
| CN115136799A (en) * | 2022-08-15 | 2022-10-04 | 安徽荣夏智能科技有限责任公司 | Grain storage intelligent management system |
| CN116523466A (en) * | 2023-05-06 | 2023-08-01 | 福建凯邦锦纶科技有限公司 | Production data tracing system and method based on big data |
| CN116523466B (en) * | 2023-05-06 | 2023-11-03 | 福建凯邦锦纶科技有限公司 | Production data tracing system and method based on big data |
| CN117575171A (en) * | 2024-01-09 | 2024-02-20 | 湖南工商大学 | Grain situation intelligent evaluation system based on data analysis |
| CN117575171B (en) * | 2024-01-09 | 2024-04-05 | 湖南工商大学 | Grain situation intelligent evaluation system based on data analysis |
| CN117688049A (en) * | 2024-02-04 | 2024-03-12 | 问策师信息科技南京有限公司 | Visual data management system and method for tracing data |
| CN117688049B (en) * | 2024-02-04 | 2024-04-12 | 问策师信息科技南京有限公司 | Visual data management system and method for tracing data |
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