1. The food safety big data intelligent acquisition and application system is characterized by comprising a near-end system, an edge system and a cloud-end system, wherein the near-end system comprises a field system, a data sensing system, a near-end storage system, a near-end computing system and a data feedback system; the edge system comprises an edge storage system and an edge computing system; the cloud system comprises a cloud storage system, a cloud computing system and a cloud feedback system; the near-end computing system transmits the data to an edge computing system in the edge system, and the near-end system is connected with the edge system through the edge computing system and the near-end computing system; the edge computing system feeds back the result to the near-end system through the data feedback system; the cloud computing system is connected with the edge storage system, the cloud computing data are fed back to the edge computing system through the cloud feedback system and finally fed back to the near-end system through the data feedback system, and application of the food safety big data is completed.

2. The system according to claim 1, wherein the edge system is capable of being connected to a plurality of independent near-end systems simultaneously, and the cloud system is capable of being connected to a plurality of independent edge systems simultaneously.

3. The food safety big data intelligent acquisition and application system as claimed in claim 1, wherein the data sensing system is connected with a field system, acquires field data, and transmits the data to the near-end storage system, the near-end computing system calculates and analyzes the data of the near-end storage system and uploads the data to the edge computing system, the edge computing system stores the data to the edge storage system, and meanwhile, the data can be fed back to the near-end system through the data feedback system according to the calculation result, and the near-end system controls the operation of the field system through the data sensing system according to the feedback result.

4. The food safety big data intelligent acquisition and application system according to claim 1, wherein the near-end system optimizes the data acquired by the on-site system through a near-end computing system and a data feedback system; the edge system realizes the optimized distribution of the network resources of the near-end system and the optimization of the data flow according to the calculation of the edge computing system; the cloud system realizes the optimized distribution of the network resources of the edge system and the optimization of the data flow through the cloud feedback system.

5. The intelligent acquisition and application system of food safety big data as claimed in claim 4, wherein the optimized allocation of network resources comprises network bandwidth allocation, information type optimization, information quantity optimization, and information acquisition frequency optimization.

6. The food safety big data intelligent acquisition and application system according to claim 1, wherein the data perception system comprises an intelligent sensor, an automatic control system and a monitoring system.

7. The food safety big data intelligent acquisition and application system according to claim 6, wherein the monitoring system FRID, the camera, the intelligent sensor.

8. The intelligent acquisition and application system of big data on food safety according to claim 1, wherein the field system comprises people, equipment machines, materials, regulations and environment in planting, breeding, production, sales, logistics, warehousing and business over-the-field.

9. The food safety big data intelligent acquisition and application system according to claim 3, wherein the data types acquired by the data sensing system comprise data directly acquired by the sensor sensing system and batch imported data; the categories of data collected include structured data, semi-structured data, and unstructured data.

10. The food safety big data intelligent acquisition and application system according to claim 1, wherein the application comprises the steps of optimizing edge system data and information, adjusting edge information types, information storage modes and information distribution through a cloud feedback system; the edge system adjusts the distribution, the type of the collected information, the parameter control and the state monitoring of the near-end equipment through a data feedback system.

Technical Field

The food quality safety is related to the most basic ring of people and the health of common people. The food quality safety relates to a plurality of independent industry themes such as raw material suppliers, food processors, logistics service providers, brand parties, government supervision parties and the like; relates to various links in the food planting and processing process. Every stage of the food from the source to the table, every piece of equipment, may affect the quality safety of the final food. Therefore, the collection of the related data of the food from the source to the dining table has important significance on food safety. However, the links from the source to the dining table are very many, each different link comprises a plurality of different processing nodes, each processing node relates to different materials, processing equipment, processing parameters and other contents, if the data of each link is required to be acquired, the data acquisition amount is very large, the acquisition speed of the traditional data acquisition method is low, the acquisition efficiency is low, the data updating frequency is low, and the requirement of large data acquisition cannot be met. The amount of information related in the food safety control process is large, multi-source isomerism, multi-link and multi-node are related, the data quantity, the type and the acquisition period are long, and therefore the intelligent acquisition system with higher automation degree can meet the requirement of food safety on big data. As an extremely practical electronic technique, intelligent data acquisition is widely used in a plurality of fields such as signal detection, device monitoring, signal processing, and instrument and meter detection. With the coming of the information age, information technology, especially digitization technology, is continuously developed, so that intelligent data acquisition is continuously improved and perfected, and the current data acquisition technology can meet the requirements of rapid, efficient, large-scale and multi-channel data acquisition.

In the process of data intelligent acquisition, the data accumulation amount is more and more along with the time, which brings difficulty to the processing and application of data. The screening difficulty of a large amount of data is large, the required data storage space is large, the data storage is difficult, if a large amount of data is uploaded at the same time, network paralysis can be caused, the safety condition of the whole food processing process or the food industry can not be fed back through the data, and the application of the large data can not be realized.

The data acquisition and application system is mainly used for a certain part of food or products in the existing data acquisition and application system of the Internet of things. For example, Chinese patent 201910847381.X discloses an intelligent acquisition system of an internet of things for grain production agriculture. The system can realize the environmental monitoring of the planted crops and prompt the grower to carry out proper management on the planted field. The system comprises a first data collector, a second data collector, an air pressure sensor, a rainfall sensor, a first spectrum sensor, a wind direction sensor and a wind speed sensor which are respectively connected with the first data collector and the second data collector, and a carbon dioxide sensor, a second spectrum sensor, a soil moisture sensor, a soil temperature sensor and an air humidity sensor which are respectively connected with the second data collector. The patent of the invention mainly aims at data acquisition in the field of agricultural planting, has narrow related aspects, still faces the problems of data storage, optimized distribution and the like for the whole food industry chain, and has high difficulty.

Disclosure of Invention

Based on the defects and problems in the prior art, the invention aims to provide the food safety big data intelligent acquisition and application system, which can be used for widely acquiring data in each link of food processing, classifying and grading the data, applying reverse feedback to the food production process, restraining possible risks in time, reducing the probability of food safety problems and improving the product traceability efficiency.

Because the large amount of information of the food safety big data is large, the large-scale data flow can cause insufficient network resources, the running speed of the system is slow, and the network resources need to be reasonably and optimally utilized. The invention aims to carry out high-frequency, high-speed and large-scale data acquisition on food safety big data by using the Internet of things technologies such as a sensor, an artificial intelligence system and the like, reasonably allocate network bandwidth, optimize network resources according to data sources, data characteristics and data application requirements, realize large-scale multi-source heterogeneous food safety big data acquisition, and carry out real-time online analysis and efficient utilization.

In order to achieve the technical effects, the invention is realized by the following technical scheme:

a food safety big data intelligent acquisition and application system comprises a near-end system, an edge system and a cloud-end system, wherein the near-end system comprises a field system, a data sensing system, a near-end storage system, a near-end computing system and a data feedback system; the edge system comprises an edge storage system and an edge computing system; the cloud system comprises a cloud storage system, a cloud computing system and a cloud feedback system. The near-end computing system transmits the data to an edge computing system in the edge system, and the near-end system is connected with the edge system through the edge computing system and the near-end computing system; the edge computing system feeds back the result to the near-end system through the data feedback system; the cloud computing system is connected with the edge storage system, the cloud computing data are fed back to the edge computing system through the cloud feedback system and finally fed back to the near-end system through the data feedback system, and application of the food safety big data is completed. The near-end system, the edge system and the cloud system realize data communication through the computer internet of things.

As a specific technical solution, the edge system may be connected to a plurality of mutually independent near-end systems at the same time, and the cloud system may be connected to a plurality of mutually independent edge systems at the same time.

As a specific technical scheme, the data sensing system is connected with a field system, field data are collected and transmitted to a near-end storage system, the near-end computing system calculates and analyzes the data of the near-end storage system and uploads the data to an edge computing system, the edge computing system stores the data to the edge storage system and feeds the data back to the near-end system through a data feedback system according to a calculation result, and the near-end system controls the operation of the field system through the data sensing system according to the feedback result.

As a preferred technical solution, the edge system realizes the optimal allocation of network resources of the near-end system according to the calculation of the edge computing system.

As a specific technical scheme, the optimized distribution of the network resources comprises network bandwidth distribution, information type optimization, information quantity optimization and information acquisition frequency optimization.

As a specific technical scheme, the data perception system comprises an intelligent sensor, an automatic control system and a monitoring system.

As a specific technical scheme, the monitoring system comprises an FRID, a camera and an intelligent sensor.

As a specific technical scheme, the field system comprises people, equipment machines, materials, system rules and environment for planting, breeding, production, sale, logistics, storage and business exceeding the field.

As a specific technical scheme, the data types collected by the data sensing system comprise data directly collected by the sensor sensing system and batch imported data; the categories of data collected include structured data, semi-structured data, and unstructured data.

As a specific technical scheme, the application of the food safety big data comprises the steps of optimizing edge system data and information through a cloud feedback system, and adjusting the types, information storage modes and information distribution of edge information; the edge system adjusts the distribution, the type of the collected information, the parameter control and the state monitoring of the near-end equipment through a data feedback system.

The invention integrates the acquisition and application of big data, divides the big data into a near-end system, an edge system and a cloud system according to the distance between a network and the data, optimally designs the generated data, realizes the quick transmission of data or information, and can realize the quick response to each control device through feedback systems at all levels.

Each data perception system of the system collects field system data of each production and processing unit, the data are uploaded to a near end, an edge end and a cloud end, the data are uploaded in a grading mode, stored in a grading mode, and after grading analysis, the cloud end, the edge end and the near end are fed back to people, machines, materials, methods and rings in the field system through a data feedback system to control operation of field equipment, and real-time adjustment of the field equipment data is achieved. The continuous updating and the change of the data can be realized through the feedback system, the problems existing in the food processing process can be solved timely and efficiently, the types and the quantity of the collected food safety big data are enriched, and richer and more effective data support is provided for food safety management.

Meanwhile, through the division of the near-end system, the edge system and the cloud system, the acquired system data is calculated, and the optimized distribution of network resources is realized after analysis and optimization, so that the network transmission rate and the storage capacity are improved, and network congestion and paralysis are avoided. The data acquisition and storage are more systematic and reasonable, the data acquisition amount is large, and the clustering storage is more reasonable and standard.

Drawings

FIG. 1 is a schematic diagram of the system configuration of the present invention;

FIG. 2 is a schematic diagram of the data-aware system of the present invention.

Detailed Description

The present invention is further illustrated by the following examples, which are intended to be purely exemplary of the invention and are intended to be exemplary of the invention, and equivalents thereof available to those skilled in the art without departing from the spirit of the invention are intended to be included within the scope of the invention.

Example 1

The food safety big data intelligent acquisition and application system, as shown in fig. 1, includes a near-end system, an edge system and a cloud-end system. The near-end system comprises a field system, a data perception system, a near-end storage system, a near-end computing system and a data feedback system. The edge system comprises an edge storage system and an edge computing system; the cloud system comprises a cloud storage system, a cloud computing system and a cloud feedback system. The near-end computing system transmits the data to an edge computing system in the edge system, and the near-end system is connected with the edge system through the edge computing system and the near-end computing system; the edge computing system feeds back the result to the near-end system through the data feedback system. The cloud computing system is connected with the edge storage system, the cloud computing data are fed back to the edge computing system through the cloud feedback system and finally fed back to the near-end system through the data feedback system, and the application of the food safety big data is completed. The near-end system is used as an internet of things system to be accessed to the edge system and the cloud end system.

Example 2

The food safety big data intelligent acquisition and application system, as shown in fig. 1, includes a near-end system, an edge system and a cloud-end system. The near-end system comprises a field system, a data perception system, a near-end storage system, a near-end computing system and a data feedback system. The data sensing system is connected with the field system, collects field data and transmits the data to the near-end storage system. The edge system includes an edge storage system and an edge computing system. The near-end computing system calculates and analyzes data of the near-end storage system and uploads the data to the edge computing system, the edge computing system stores the data to the edge storage system and feeds the data back to the near-end system through the data feedback system according to a computing result, and the near-end system controls the operation of the field system through the data sensing system according to the feedback result. As shown in fig. 2, the data awareness system includes an automatic control system and a monitoring system. The monitoring system comprises data, image acquisition equipment and instruments such as an FRID, a camera and an intelligent sensor which are used in the food production and processing process. The data perception system is connected with the field system, the monitoring system is used for collecting the human, machine, material, method and ring data on the field and transmitting the data to the near-end storage system, and the near-end computing system is used for computing and analyzing the data of the near-end storage system and then uploading the data to the edge computing system. Meanwhile, the near-end computing system can also directly feed back the computing result to the data feedback system, the data feedback system sends the feedback result to the automatic control system, and the automatic control system adjusts the field device to optimize the content of the collected data such as type, frequency, range, specification, unit and the like. The edge computing system stores the data into the edge storage system, meanwhile, the data can be fed back to the near-end system through the data feedback system according to the computing result, the near-end system adjusts the field system through the data perception system according to the feedback result, and the information of the type, frequency, range, specification, unit and the like of data collection is optimized. The cloud system comprises a cloud storage system, a cloud computing system and a cloud feedback system. The cloud computing system is connected with the edge storage system, the cloud computing data are fed back to the edge computing system through the cloud feedback system and finally fed back to the near-end system through the data feedback system, and the application of the food safety big data is completed. The edge system can be connected with a plurality of mutually independent near-end systems simultaneously in the system, the high in clouds system can be connected with a plurality of mutually independent edge systems simultaneously again, a plurality of near-end systems, a plurality of edge systems, the high in clouds system constitutes the big data acquisition's of food safety network system jointly, the coverage is complete, response speed is fast, the data acquisition scope is wide, can plant food, breed, production, storage, circulation, sale, the data acquisition that various field devices in the testing process produced, conclude, arrange in order, use together, realize that data passes jointly, build jointly, share.

Example 3

The food safety big data intelligent acquisition and application system, as shown in fig. 1, includes a near-end system, an edge system and a cloud-end system. The near-end system comprises a field system, a data perception system, a near-end storage system, a near-end computing system and a data feedback system. The field system comprises people, equipment machines, materials, system rules and environment for planting, breeding, production, sale, logistics, storage and business exceeding the field. Taking the collection and application of big data of rice quality safety as an example. In the rice planting process, relevant data of planting site environment, soil and the like are collected through data sensing systems such as a temperature sensor, a humidity sensor, a PH sensor, a dust sensor and a camera, the data are uploaded to a near-end storage system, the near-end computing system uploads the relevant data to an edge system after computing or feeds back the relevant data to the site through a data feedback system as required, information such as frequency and type of the site collected data is adjusted, and the collected data are optimized. The edge computing system analyzes data uploaded by a plurality of different near-end systems, and feeds back results to each near-end system after analysis and calculation so as to guide the near-end systems to optimize data storage modes, data streams, sampling frequency, sample quantity, data types and the like. The calculation processes of the near-end calculation system, the edge calculation system and the like can be calculated and uploaded according to the response speed of data acquisition, the acquisition frequency and the data generation sequence. The near-end computing system calculates the acquired data and uploads a part of the data to the edge computing system according to a preset rule, the edge computing system unifies, summarizes and analyzes the data uploaded by the multiple related near-end systems and uploads the data to the cloud computing system, and the cloud computing system unifies, summarizes, calculates and analyzes the data uploaded by the multiple related edge systems and then provides feedback or directly stores the data to the edge systems. The cloud system analyzes data of each edge system, reasonably adjusts data information needing to be acquired of each edge system according to data conditions of each edge system, optimizes the data, avoids invalid data, extracts valid data information, adjusts types of the edge information, information storage modes and information distribution, feeds results back to the edge systems, the edge systems are connected with a data feedback system of a near-end system through an edge computing system, and adjusts distribution, types of acquired information, parameter control and state monitoring of near-end equipment through the data feedback system.

Example 4

A food safety big data intelligent acquisition and application system comprises a near-end system, an edge system and a cloud-end system, wherein the near-end system comprises a field system, a data sensing system, a near-end storage system, a near-end computing system and a data feedback system; the edge system comprises an edge storage system and an edge computing system; the cloud system comprises a cloud storage system, a cloud computing system and a cloud feedback system; the near-end computing system transmits the data to an edge computing system in the edge system, and the near-end system is connected with the edge system through the edge computing system and the near-end computing system; the edge computing system feeds back the result to the near-end system through the data feedback system; the cloud computing system is connected with the edge storage system, the cloud computing data are fed back to the edge computing system through the cloud feedback system and finally fed back to the near-end system through the data feedback system, and application of the food safety big data is completed.

The near-end system realizes the optimization of the data collected by the field system through a near-end computing system and a data feedback system; the edge system realizes the optimized distribution of the network resources of the near-end system and the optimization of the data flow according to the calculation of the edge computing system; the cloud system realizes the optimized distribution of the network resources of the edge system and the optimization of the data flow through the cloud feedback system.

And the optimized distribution of network resources comprises network bandwidth distribution, information type optimization, information quantity optimization and information acquisition frequency optimization. The data perception system directly collects data or imports data in batches, and the data types comprise structured data, semi-structured data and unstructured data.

The optimization allocation algorithm of the network resources comprises the optimization allocation based on response speed, the optimization allocation based on data types and a time queue priority rule.

The optimal allocation calculation method based on the response speed comprises the following steps: the millisecond (within 500 ms) response uses local storage, local computation mode, and uses proximity signal control. Namely, the data acquisition, storage, calculation, feedback and the like are completed at a near-end system. The second-level (more than 500 ms) response adopts a cloud storage, cloud computing and remote information control mode; namely, data storage and data feedback are completed through a cloud system, and finally control and monitoring of a field system are achieved.

Optimized allocation based on data type: the data is divided into different types, including inspection and detection, environmental parameters, health monitoring, supervision and management, dynamic equipment monitoring, logistics circulation and the like. Detecting, environment parameters, health monitoring data and supervision management data, and adopting a local storage mode, a local calculation mode and a short-range signal control mode; namely, the data acquisition, storage, calculation and feedback are completed at the near end. Data with large geographical position span, such as dynamic monitoring data, circulation data and the like of the equipment adopt a cloud storage mode, a cloud computing mode and a remote information control mode; namely, the data storage, calculation and feedback are completed through the cloud system.

The time queue priority rule is that a plurality of data or information are uploaded, calculated and fed back in sequence according to the time sequence of data generation;

response speed and time series combination law: according to the requirements of data submission time and control response speed (response time), completing uploading, calculation and feedback of a plurality of data or information; and executing the response speed serial number first, and if the response serial numbers are the same, executing the data submission time serial number, and submitting first to execute first.

Example 5

Taking rice production and processing as an example, a meteorological sensor, a near infrared spectrum analyzer, a rice appearance quality detector, a multifunctional food safety detector, a soil monitoring instrument, a water quality monitoring instrument, a temperature and humidity sensor and the like are arranged on a whole rice production and processing chain to jointly form a field data sensing system, and related data are mainly collected on rice, soil, water quality, weather and environment. Each sensing system stores directly acquired data to a near-end storage system nearby, and the edge storage system processes unnecessary uploaded data, so that the data uploading time is greatly shortened, the energy consumption is reduced, and the data security is ensured. The edge system uploads the processed data to the cloud system, the cloud system is an application layer, the data transmitted by the edge system are received and services are provided, and a user can check data results by accessing the cloud system.

Taking the whole-process video monitoring of rice processing nodes as an example, by adopting the method provided by the invention, a near-end storage system and a near-end computing system are arranged on a camera (equivalent to a data sensing system) for realizing a data acquisition function. And the near-end computing system performs frame filtering, task scheduling, target detection, target identification, behavior analysis and the like on the video stream data acquired by the corresponding paths of cameras. And the near-end computing system corresponding to each camera uploads the analysis result to the edge system, and the edge system re-analyzes the data of the camera uploaded by each near-end computing system and uploads the data to the cloud system. And each user can check the data result through the cloud system.

If the bandwidth required for directly uploading the 720P (100 ten thousand pixels) video format collected by the 24-way camera to the cloud is as follows:

the bit rate of each path of camera in the 720P (100 ten thousand pixels) video format is 2Mbps, that is, the data transmission bandwidth required by each path of camera is 2Mbps, and the data transmission bandwidth required by a 24-path camera is: 2Mbps (bit rate of video format) × 24 (number of camera paths) is 48Mbps (uplink bandwidth): the network uplink bandwidth required by monitoring of each part by adopting the 720P video format is at least 48 Mbps.

The required transmission time is:

the data size of the 720P video format of the 1-channel camera 1h is D256 × 3600 ÷ 0.9 ═ 1024000KB ═ 1000MB, and the camera has a video format with a video format data size of D ═ 256 × 3600 ÷ 1000MBRate V2 Mb/s of transmission data, transmission time T_t＝1000MB÷2Mb/s＝4000s。

By adopting the method, the collected video data is subjected to frame filtering through a near-end computing system, and 10s of video segments violating the operation specification exist in the recording process of the camera. The size of the data volume of the 720P video format of 10s is D256 multiplied by 10 divided by 0.9 2844KB 2.6MB, the data transmission rate V of the video camera is 2Mbps, and the transmission time T is T_t2.6 MB/2 Mbps 10.4 s. Is far shorter than the time for directly uploading all the video data of 1 h.

The edge system calculates the data uploaded by each near-end system according to the same method and uploads the data to the cloud-end system, and the method reduces unnecessary video data transmission, greatly reduces transmission time, improves efficiency and reduces cost.