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Rice Cloud-Controlled Production Line Solution
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Rice Cloud-Controlled Production Line

Jiexun Cloud Control utilizes self-developed S+ intelligent analyzers and cloud servers to capture real-time quality data during continuous production, enabling data-driven decisions and intelligent regulation of entire processing lines.

Description

Jiexun cloud control, through independent research and development of S+ intelligent analyzer and cloud control server, obtains real-time quality data indicators in the continuous production process,and provides feedback to regulate the entire production process. Relying on real and accurate data sources, it makes data-driven decisions and real-time regulates the entire processing process, truly achieving intelligent control solution for processing production line.

Advantage

Jiexun's Cloud-Controlled AI Super Agent Production Line Solution is designed to reduce losses that may occur in each processing stage and preserve the optimal form of rice. Leveraging large AI models, it customizes optimal processing parameters for different raw grains, enabling the intelligent realization of "grain conservation, loss reduction, quality improvement and efficiency enhancement" throughout the entire grain processing process.
We are committed to maximizing the utilization of every single rice grain, fully advancing grain conservation and loss reduction as well as quality and efficiency improvement. Meanwhile, the solution retains high-value by-products generated during rice processing, achieving an all-round increase in resource utilization efficiency.
Amid the increasingly fierce competition in the grain processing industry, it serves as a new value growth driver for grain processing enterprises to turn losses into profits and achieve leaping development. We put food security into practical action—for genuine cloud control, choose Jiexun.

1.Digitalization: Clear Expenses | Defined Returns | Transparent Processes | Full-index Quality Data of Products


2.Standardization: Precise Process Section Control | Interconnected Production Equipment Control | Graded Classification for Diverse Product Standards


3.Intellectualization: Rapid Response for Quality Control Adjustment | AI Self-learning Standard Judgment | Intelligent Analysis of Processing Raw Materials


4.Visualization: On-site Visualization | Process Operation Visualization | Quality Analysis Visualization


5.Customization: Customizable Factory Management Metrics | Customizable Product Standards | Real-time Mastery of Intelligent Production Line Output Data


6.Safety Assurance: Monitoring Data Management | Accurate Data | Stage-by-stage Control of Sorted Products

Traceability

1.Husking Section
On-line analyzers detect the husking rate and brown rice breakage rate of the husker, and adjust the corresponding parameters of the husker according to the set target values to avoid fluctuations in the husker's husking performance. This enables precise control of the husker and a reduction in the brown rice breakage rate. We have also carried out process transformation of quality sorting for re-husking materials to improve the head rice yield. Meanwhile, brown rice purification can be implemented on demand, realizing the comprehensive utilization of by-products.


2.Milling Section
Cooperating with rice mills, on-line analyzers detect the milling precision and whiteness of milled rice, and adjust the corresponding parameters of rice mills according to the set target values to ultimately achieve moderate processing of rice by the mills. Qualified rice is controlled to enter the color sorting section in advance, while unqualified rice is sent back for re-milling. This effectively avoids over-milling, under-milling and high broken rice rate, and reduces processing energy consumption. The section also enables the processing of value-added rice products such as germ rice, boosting the added value of rice processing.


3.Color Sorting and Polishing Section
In collaboration with length graders, on-line analyzers allow the input of target broken rice processing values for length graders into the cloud control server. The analyzers adjust the angles of length graders accordingly, effectively controlling the broken rice rate of grains transferred from length graders to subsequent equipment. It enables real-time coordinated control of quality sorters to regulate various indicators including off-color rice, chalky rice and broken rice, ensuring that color sorting maintains precise performance at optimal indicators. Our world's pioneering discolor reverse sorting technology reduces the loss of good grains in by-products and increases the finished product yield.


4.Outloading and Packaging Section
On-line analyzers control the broken rice rate of rice discharged from the silo to meet the standard, reducing the loss of whole rices. In collaboration with rice blending scales, the target values for finished products can be input into the cloud control server; the analyzers adjust the output and other relevant parameters of the rice blending scales to achieve order matching. Moreover, based on the final order targets, the system can feed back the relevant data to the upstream husking section for corresponding processing parameter operation, thus realizing full-process control of the entire production line.

Core Technologies

imgi_5_image.jpgMultimodal AI Quality Sorting Large Model

ANYSORT multimodal AI quality sorting large model leverages powerful computing to achieve faster sorting model setup, more accurate discrimination, quicker model training and deployment, and advanced automated data analysis.

imgi_6_image.jpgPLOV Platform 3.0

The intelligent uniform distribution sorting structure has been innovatively upgraded to PLOV 3.0, ensuring materials are always spread evenly without overlapping during high-speed, high-yield sorting processes.

imgi_7_image.jpgHawkeye Camera Technology 4.0

AI + Hawkeye camera technology enables clearer high-speed dynamic imaging with more precise and intelligent identification.

imgi_8_image.jpgMPF Multi-Pixel Fusion Technology

It essentially distinguishes different substances based on the spectral fingerprint characteristics of each material.

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