Corn planting, storing and processing intelligent management and control system

Abstract

The application discloses a corn planting, storing and processing intelligent management and control system, and relates to the technical field of corn planting management and control. The system comprises a total management and control platform, which is bidirectionally connected with a planting stage decision unit, a storage intelligent monitoring unit and a processing adaptive regulation unit through communication. The total management and control platform sends a planting monitoring signal to the planting stage decision unit. The planting stage decision unit collects growth and environmental data of corn at each growth stage in the field, generates agricultural management and control instructions in stages, and sends a storage monitoring signal to the storage intelligent monitoring unit after corn is harvested and stored. The storage intelligent monitoring unit collects multi-dimensional environmental and corn quality parameters in the granary, determines the storage and preservation control effect, and connects the production site granary after the corn is harvested and matured. The storage intelligent monitoring unit collects real-time data of temperature, humidity, CO 2 and insect situation in the granary, dynamically determines the storage and preservation control efficiency, and dynamically controls the drying and crushing process parameters by matching the processing categories and processing adaptive regulation unit. The application breaks through the whole link data of planting, storage and processing, intelligently determines and reduces corn mildew at each link, and intelligently controls and improves the utilization rate of corn raw materials.

Description

Technical Field

[0001] This invention relates to the field of corn planting and management technology, specifically to an intelligent management and control system for corn planting, storage, and processing. Background Technology

[0002] Corn is the world's highest-yielding food, feed, and cash crop, with a complete industrial chain encompassing three core stages: field planting, on-site storage, and deep processing. Existing agricultural management systems only cover the corn planting stage, while storage and processing stages rely on independent manual control or isolated single-machine monitoring equipment. Data from each stage is fragmented, making it impossible to achieve coordinated control throughout the entire process.

[0003] The "Intelligent Monitoring and Control Method for Less-Tillage Maize Sowing" disclosed in application number "CN202510363047.2" is also an increasingly mature technology. In this method, "during sowing operations, a speed acquisition module obtains the operating speed, determines the seed metering speed and fertilizer application speed, and the seed metering system and fertilizer application system perform sowing and fertilization operations. The seed metering monitoring module monitors the seed placement position and provides real-time feedback on the plant spacing; the seed and fertilizer quantity monitoring system provides real-time feedback on the remaining seed and fertilizer amounts; the operation status monitoring system monitors the air duct pressure, press wheel pressure, and sowing depth in real-time; the sowing depth control system performs active contouring; the central control system comprehensively processes the information and transmits the data to the human-machine interface system for display, and uploads it to a network storage platform for recording operation information; during the operation of the seeder, a control model is used for multi-dimensional control." This invention can achieve real-time and accurate monitoring of the entire sowing process, playing a significant role in improving the transparency and intelligence level of less-tillage maize sowing operations.

[0004] The existing technology has the following core defects:

[0005] The planting end only completes field phase monitoring, but the raw material quality data at maturity cannot be synchronized to the storage stage. The quality of corn entering the warehouse is inconsistent. There is no targeted preservation plan in the early stage of storage, which makes it very easy for local mold and insects to occur. The storage loss rate remains high all year round.

[0006] The warehousing process relies solely on simple temperature and humidity monitoring, failing to dynamically adjust ventilation, dehumidification, and mold control strategies based on corn planting traceability data. It also depends on regular manual inspections, leading to delayed detection of mold and pests and the spoilage of large quantities of raw materials.

[0007] The processing stage relies solely on manual sampling to determine the quality of raw materials, making it impossible to retrieve complete planting and storage traceability data to match the processing technology. Raw materials with excessive moisture content are prone to scorching when dried directly, and moldy seeds mixed into the processing line lead to an increase in the defect rate of finished products. Processing losses cannot be used to optimize the front-end planting and storage control standards.

[0008] Therefore, an intelligent management and control system for corn planting, storage and processing is proposed. Summary of the Invention

[0009] To address the shortcomings of existing technologies, this invention provides an intelligent management and control system for corn planting, storage, and processing, which solves the problems mentioned in the background art.

[0010] To achieve the above objectives, the present invention provides the following technical solution: an intelligent management and control system for corn planting, storage, and processing, comprising a central management and control platform. The central management and control platform is bidirectionally connected to a planting stage decision-making unit, a storage intelligent monitoring unit, and a processing adaptation and control unit. The central management and control platform sends planting monitoring signals to the planting stage decision-making unit, which collects growth and environmental data of corn at various growth stages in the field and generates agricultural management and control instructions in stages. After corn is harvested and stored, the central management and control platform sends storage monitoring signals to the storage intelligent monitoring unit. The storage intelligent monitoring unit collects multi-dimensional environmental and corn quality parameters from the grain warehouse, determines the storage and preservation control effect, and outputs in-warehouse control instructions when abnormalities occur. When corn is processed after leaving the warehouse, the central management and control platform retrieves planting traceability files and storage quality data and sends processing adaptation signals to the processing adaptation and control unit. The processing adaptation and control unit dynamically matches processing technology and adjusts the operating parameters of processing equipment according to raw material quality. The central management and control platform synchronously collects data from all stages of planting, storage, and processing, generating a full-chain traceability and control report.

[0011] As a preferred embodiment of the present invention, the operation steps of the planting phased decision-making unit are as follows: the corn planting plot is divided into monitoring units, and field temperature and humidity, light, soil nutrients, corn plant height and ear weight growth parameters are collected during the seedling stage, flowering and grain-filling stage, tasseling stage and maturity stage. The growth matching coefficient of the growth period is constructed, and the matching coefficient is compared with the preset threshold of each stage. If the growth parameters deviate from the threshold, water and fertilizer, supplemental lighting and pest and disease control instructions are output. During the maturity stage, corn moisture content, ear integrity and initial mold data are collected, and raw material quality grading labels are generated and uploaded to the overall control platform.

[0012] As a preferred embodiment of the present invention, the operation steps of the intelligent monitoring unit for warehousing are as follows: Corn entering the warehouse is stored in designated zones according to harvest batches from different planting plots. Temperature and humidity sensors, carbon dioxide sensors, mold detection probes, and insect trapping and collection equipment are deployed to acquire data on the frequency of temperature and humidity exceeding standards, CO2 concentration fluctuations, mold spore detection density, and insect occurrence frequency within the warehouse, and to calculate the storage risk coefficient M. The storage risk coefficient M is compared with the storage safety threshold. If M > the storage safety threshold, it is determined that the warehouse has a risk of mold and insect infestation, and ventilation, dehumidification, and low-temperature mold suppression control signals are generated and sent to the grain warehouse execution mechanism. If M ≤ the storage safety threshold, the existing storage environment parameters are maintained stable, and raw material quality data is continuously collected periodically and bound to batch traceability codes.

[0013] In a preferred embodiment of the present invention, the intelligent warehouse monitoring unit is further configured with a warehouse loss analysis submodule, which retrieves the initial weight of corn entering the warehouse in the same batch, the daily loss weight, and the percentage of mold loss, and calculates the warehouse loss efficiency value; when the loss efficiency value exceeds the preset loss threshold, the overall control platform pushes an early warning and adjusts the warehouse ventilation and fumigation control cycle accordingly.

[0014] In a preferred embodiment of the present invention, the processing adaptation and control unit operates as follows: receiving batch corn planting traceability data and storage quality data issued by the overall control platform, extracting raw material moisture content, mold rate, grain integrity and protein content indicators, establishing a raw material adaptation score T according to the processing category (corn grits, corn flour, corn feed, edible germ), comparing the adaptation score T with the adaptation threshold of each processing category; if T is higher than the corresponding category adaptation threshold, matching standard processing parameters; if T is lower than the corresponding category adaptation threshold, automatically lowering the drying temperature, adjusting the crushing and screening mesh and adding a purification process, generating dynamic processing control instructions and issuing them to the drying unit, crushing equipment and purification production line.

[0015] In a preferred embodiment of the present invention, the processing adaptation and control unit has a built-in processing loss statistics submodule that collects the proportion of defective materials produced in real time. If the proportion of defective materials exceeds the defective threshold, the raw material quality warning is pushed back to the warehouse intelligent monitoring unit to optimize the warehouse preservation and management strategy for the next batch.

[0016] In a preferred embodiment of the present invention, the overall control platform is also connected to the production and sales linkage unit. The production and sales linkage unit retrieves historical planting yield, warehouse inventory and processed finished product capacity data, predicts finished product supply, matches downstream order demand, and dynamically outputs harvesting, warehouse release and processing scheduling instructions.

[0017] As a preferred embodiment of the present invention, the operation process of the production and sales linkage unit is as follows: statistically analyze the corn yield per mu, average storage loss rate, and finished product output rate in the same period of the past three years, construct a supply and demand forecast model, compare real-time inventory with the processing capacity and customer order demand, issue instructions for early harvesting and expedited processing when there is an inventory gap, and issue instructions for extending storage and preservation and adjusting the processing categories to increase deep processing capacity when there is an inventory backlog.

[0018] In a preferred embodiment of the present invention, the intelligent storage monitoring unit and the planting stage decision-making unit communicate with each other. If the storage risk coefficient remains high after multiple batches of corn are put into storage, the overall control platform pushes the field planting optimization instructions to the planting stage decision-making unit to adjust the water and fertilizer and harvesting and drying control standards during the maturity period, thereby reducing the initial mold probability of raw materials entering the storage.

[0019] In a preferred embodiment of the present invention, after the processing adaptation and control unit completes the processing, it sends the finished product quality inspection data and processing loss data back to the central management and control platform. Based on the planting and storage data, a complete full-chain traceability file is formed, which supports scanning the code to retrieve the plot planting record, storage environment record and processing technology record.

[0020] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0021] This invention connects the data links of planting, storage and processing through a central management platform, breaking the data silos of the traditional industrial chain. Raw material quality data during the field maturity period is directly synchronized with storage. Grain warehouses can customize preservation solutions based on the initial moisture content and mold risk of raw materials, greatly reducing mold and pest losses in corn storage and reducing raw material scrap losses.

[0022] The planting phased decision-making unit enables refined agricultural management of corn throughout its entire growth period, and simultaneously grades the quality of raw materials at maturity, providing front-end data support for downstream storage and processing. The intelligent storage monitoring unit collects real-time data on the storage environment and corn quality from multiple dimensions, dynamically calculates storage risks, and automatically adjusts ventilation and dehumidification equipment in case of abnormalities, providing more timely early warnings.

[0023] The processing adaptation and control unit dynamically matches the processing technology based on complete traceability data. It automatically adjusts the drying, impurity removal and crushing parameters for raw materials with high moisture content and slight mold, reducing the processing defect rate. At the same time, the processing loss data is fed back to the storage and planting links to form a closed-loop optimization of the whole chain and continuously improve the quality of raw materials.

[0024] 4. Add a production and sales linkage unit, and establish a supply and demand forecasting model by combining planting yield per mu, storage loss and processing output rate. Automatically schedule harvesting, warehousing and processing capacity, balance raw material inventory and finished product order demand, avoid raw material backlog or shortage, and collect data from all links to generate traceability files. Finished products can be traced with one click to the entire process of planting, storage, preservation and processing, which is compatible with the quality supervision requirements of food processing enterprises. Attached Figure Description

[0025] Figure 1 This is a block diagram illustrating the overall system architecture of the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0028] In the description of this invention, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0029] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "configuration" should be interpreted broadly. For example, they can refer to a fixed connection or configuration, a detachable connection or configuration, or an integral connection or configuration. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. Example 1

[0030] like Figure 1 As shown, this embodiment of the present invention provides a corn planting, storage and processing intelligent management and control system, including a central management and control platform, which is bidirectionally connected to a planting stage decision-making unit, a storage intelligent monitoring unit and a processing adaptation and control unit.

[0031] The overall control platform sends planting monitoring signals to the planting phased decision-making unit. The planting phased decision-making unit collects growth and environmental data of corn at each growth stage in the field and generates agricultural control instructions in stages. After the corn is harvested and stored, the overall control platform sends storage monitoring signals to the storage intelligent monitoring unit. The storage intelligent monitoring unit collects multi-dimensional environmental and corn quality parameters of the grain warehouse, judges the storage and preservation control effect, and outputs warehouse control instructions when abnormalities occur. When the corn is taken out of the warehouse for processing, the overall control platform retrieves the planting traceability file and storage quality data and sends processing adaptation signals to the processing adaptation control unit. The processing adaptation control unit dynamically matches the processing technology and adjusts the operating parameters of the processing equipment according to the raw material quality.

[0032] The central management platform simultaneously collects data from all stages of planting, storage, and processing, generating a full-chain traceability and management report.

[0033] The operational steps of the phased decision-making unit for planting are as follows:

[0034] Corn planting plots are divided into monitoring units. Field temperature and humidity, light, soil nutrients, corn plant height, and ear weight growth parameters are collected during the seedling, flowering and grain-filling, tasseling, and maturity stages. A growth matching coefficient is constructed for each growth stage. The matching coefficient is compared with the preset thresholds for each stage. If the growth parameters deviate from the thresholds, water and fertilizer, supplemental lighting, and pest and disease control instructions are output. During the maturity stage, corn moisture content, ear integrity, and initial mold growth data are collected. Raw material quality grading labels are generated and uploaded to the overall management platform.

[0035] The operation steps of the warehouse intelligent monitoring unit are as follows:

[0036] The corn harvested from different plots is stored in separate zones according to the planting area. Temperature and humidity sensors, carbon dioxide sensors, mold detection probes, and insect trapping and collection equipment are installed to obtain the frequency of temperature and humidity exceeding the standard, the fluctuation range of CO2 concentration, the detection density of mold spores, and the frequency of insect infestation in the warehouse. The storage risk coefficient M is then calculated and compared with the storage safety threshold.

[0037] If M > storage safety threshold, it is determined that the storage is at risk of mold and insect infestation, and ventilation, dehumidification and low temperature mold inhibition control signals are generated and sent to the grain warehouse execution mechanism; if M ≤ storage safety threshold, the existing storage environment parameters are kept stable, and raw material quality data is continuously collected and bound to batch traceability codes at regular intervals.

[0038] The intelligent monitoring unit for warehousing is also equipped with a warehousing loss analysis submodule, which retrieves the initial weight of corn entering the warehouse for the same batch, the daily loss weight, and the percentage of moldy loss, and calculates the warehousing loss efficiency value; when the loss efficiency value exceeds the preset loss threshold, the central control platform pushes an early warning and adjusts the warehousing ventilation and fumigation control cycle accordingly.

[0039] The operation steps of the processing adaptation and control unit are as follows:

[0040] Receive batch corn planting traceability data and storage quality data issued by the central management and control platform, extract raw material moisture content, mold rate, grain integrity and protein content indicators, establish raw material matching score T according to the processing category (corn grits, corn flour, corn feed, edible germ), and compare the matching score T with the matching threshold of each processing category.

[0041] If T is higher than the corresponding product category adaptation threshold, the standard processing parameters are matched. If T is lower than the corresponding product category adaptation threshold, the drying temperature is automatically lowered, the pulverization and screening mesh is adjusted, and the impurity removal process is added. Dynamic processing control commands are generated and sent to the drying unit, pulverizing equipment, and impurity removal production line.

[0042] The processing adaptation and control unit has a built-in processing loss statistics submodule that collects the proportion of defective materials produced in real time. If the proportion of defective materials exceeds the defective threshold, it pushes a raw material quality warning to the warehouse intelligent monitoring unit to optimize the warehouse preservation and management strategy for the next batch.

[0043] The overall control platform also connects to the production and sales linkage unit, which retrieves historical planting yield, storage inventory and processing capacity data to predict finished product supply, match downstream order demand, and dynamically output harvesting, storage and outbound and processing scheduling instructions.

[0044] The production and sales linkage unit operates as follows: statistics on corn yield per mu, average storage loss rate, and finished product output rate for the same period in the past three years are collected to build a supply and demand forecasting model. Real-time inventory, processing capacity, and customer order demand are compared. When there is an inventory gap, instructions for early harvesting and expedited processing are issued. When there is an inventory backlog, instructions for extending storage and preservation and adjusting processing categories to increase deep processing capacity are issued.

[0045] The intelligent monitoring unit for warehousing and the phased decision-making unit for planting share data. If the warehousing risk coefficient remains high after multiple batches of corn have entered the warehouse, the overall control platform will push field planting optimization instructions to the phased decision-making unit for planting to adjust the water and fertilizer management standards for the ripening period and the harvesting and drying standards, thereby reducing the probability of initial mold growth on raw materials entering the warehouse.

[0046] After the processing and adaptation control unit completes the processing, it sends the finished product quality inspection data and processing loss data back to the central control platform. The planting and storage data are bound together to form a complete traceability archive, which supports scanning codes to retrieve plot planting records, storage environment records and processing technology records. Example 2

[0047] This embodiment adds a production and sales linkage unit to the basis of embodiment 1 to realize intelligent scheduling of production and sales across the entire industrial chain.

[0048] The overall control platform communicates bidirectionally with the production and sales linkage units, and each unit has a built-in supply and demand forecasting model.

[0049] Import historical data on the yield per mu, average storage loss rate, and finished product output rate of the same plots over the past three years.

[0050] Real-time reading of current field yield awaiting harvest, total grain inventory in warehouses, daily processing capacity, and outstanding orders from downstream customers;

[0051] Supply and demand comparison assessment:

[0052] Order demand > existing finished goods inventory + raw material production capacity to be processed: issue instructions for expedited harvesting, early release of grain from warehouses, and 24-hour shift work for processing equipment;

[0053] Order demand < existing finished goods inventory: Issue instructions to delay harvesting, optimize warehousing for long-term preservation, and adjust deep-processed product categories (germ, corn starch) to reduce inventory;

[0054] The scheduling instructions output by the production and sales linkage unit are simultaneously sent to the planting, warehousing and processing units to achieve dynamic balance of production capacity in the industrial chain, reduce the capital occupation of raw material backlog and avoid order shortages and defaults. Example 3

[0055] Data-driven reverse closed-loop optimization logic

[0056] If the storage risk coefficient M continues to exceed the standard after multiple batches of corn are received into the storage unit, the overall control platform determines that the risk of mold growth before raw material harvest is too high and pushes control instructions back to the planting phase decision-making unit:

[0057] Appropriate water control during the ripening period reduces the moisture content of corn kernels;

[0058] Stop applying nitrogen fertilizer 7 days before harvest to reduce the probability of moldy ears;

[0059] Increase the drying time in the field after harvesting to reduce the initial moisture content of raw materials before storage, thereby reducing storage spoilage and loss from the source.

[0060] When the defect rate at the processing end remains high, the same reverse optimization is made to the storage ventilation and mold control cycle, forming a closed-loop control system in which the front-end planting, intermediate storage, and back-end processing provide feedback to each other for continuous optimization.

[0061] The judgment coefficients, thresholds, and proportional coefficients for each stage are obtained through simulation fitting based on a large amount of field, storage, and processing test data, and can be flexibly adjusted according to local corn varieties, storage environments, and processing categories.

[0062] Overall workflow of this invention:

[0063] The planting phased decision-making unit completes the refined monitoring and agricultural regulation of the entire corn growth period. At the maturity stage, it generates raw material quality grading data and uploads it to the central management platform. After the corn is put into storage, the intelligent storage monitoring unit customizes the in-warehouse preservation strategy based on the raw material grading data, monitors the risk of mold and pests in real time, and automatically adjusts the storage equipment. When the corn is processed, the processing adaptation and control unit retrieves the full-chain traceability data to dynamically match the processing technology, reducing processing defects and losses. The central management platform collects data from all stages and completes capacity scheduling through the production and sales linkage unit. The processing loss and storage loss data are used to optimize the front-end planting and storage management standards, realizing integrated intelligent management of the entire corn planting, storage and processing chain.

[0064] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0065] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A smart control system for corn planting, storage, and processing, characterized in that, It includes a central control platform, which has bidirectional communication connections with the planting phased decision-making unit, the warehousing intelligent monitoring unit, and the processing adaptation and control unit; The overall control platform sends planting monitoring signals to the planting phased decision-making unit. The planting phased decision-making unit collects growth and environmental data of corn at each growth stage in the field and generates agricultural control instructions in stages. After the corn is harvested and stored, the overall control platform sends storage monitoring signals to the storage intelligent monitoring unit. The storage intelligent monitoring unit collects multi-dimensional environmental and corn quality parameters of the grain warehouse, determines the storage and preservation control effect, and outputs in-warehouse control instructions when abnormalities occur. When the corn is taken out of the warehouse for processing, the overall control platform retrieves the planting traceability file and storage quality data and sends processing adaptation signals to the processing adaptation control unit. The processing adaptation control unit dynamically matches the processing technology and adjusts the operating parameters of the processing equipment according to the raw material quality. The overall management and control platform synchronously collects data from all stages of planting, storage, and processing, and generates a full-chain traceability and management report.

2. The intelligent control system for corn planting, storage, and processing according to claim 1, characterized in that, The operational steps of the planting phased decision-making unit are as follows: Corn planting plots are divided into monitoring units. Field temperature and humidity, light, soil nutrients, corn plant height, and ear weight growth parameters are collected during the seedling, flowering and grain-filling, tasseling, and maturity stages. A growth matching coefficient is constructed for each growth stage. The matching coefficient is compared with the preset thresholds for each stage. If the growth parameters deviate from the thresholds, water and fertilizer, supplemental lighting, and pest and disease control instructions are output. During the maturity stage, corn moisture content, ear integrity, and initial mold growth data are collected. Raw material quality grading labels are generated and uploaded to the overall management platform.

3. The intelligent control system for corn planting, storage, and processing according to claim 1, characterized in that, The operation steps of the intelligent warehouse monitoring unit are as follows: The corn harvested from different plots is stored in separate warehouses according to the planting area. Temperature and humidity sensors, carbon dioxide sensors, mold detection probes, and insect trapping and collection equipment are installed to obtain the frequency of temperature and humidity exceeding the standard, the fluctuation range of CO2 concentration, the detection density of mold spores, and the frequency of insect occurrence in the warehouse, and to calculate the storage risk coefficient M. Compare the warehousing risk coefficient M with the warehousing safety threshold; If M > storage safety threshold, it is determined that the storage is at risk of mold and insect infestation, and ventilation, dehumidification and low temperature mold inhibition control signals are generated and sent to the grain warehouse execution mechanism. If M ≤ storage safety threshold, the existing storage environment parameters are kept stable, and raw material quality data is continuously collected and bound to batch traceability codes at regular intervals.

4. The intelligent control system for corn planting, storage, and processing according to claim 1, characterized in that, The intelligent monitoring unit for warehousing is also equipped with a warehousing loss analysis submodule, which retrieves the initial weight of the same batch of corn entering the warehouse, the daily loss weight, and the percentage of mold loss, calculates the warehousing loss efficiency value, and when the loss efficiency value exceeds the preset loss threshold, the central control platform pushes an early warning and adjusts the warehousing ventilation and fumigation control cycle accordingly.

5. The intelligent control system for corn planting, storage, and processing according to claim 1, characterized in that, The operation steps of the processing adaptation and control unit are as follows: Receive batch corn planting traceability data and storage quality data issued by the central management and control platform, extract raw material moisture content, mold rate, grain integrity and protein content indicators, establish raw material matching score T according to the processing category (corn grits, corn flour, corn feed, edible germ), and compare the matching score T with the matching threshold of each processing category. If T is higher than the corresponding product category adaptation threshold, the standard processing parameters are matched. If T is lower than the corresponding product category adaptation threshold, the drying temperature is automatically lowered, the pulverization and screening mesh is adjusted, and the impurity removal process is added. Dynamic processing control commands are generated and sent to the drying unit, pulverizing equipment, and impurity removal production line.

6. The intelligent control system for corn planting, storage, and processing according to claim 1, characterized in that, The processing adaptation and control unit has a built-in processing loss statistics submodule that collects the proportion of defective materials produced in real time. If the proportion of defective materials exceeds the defective threshold, it pushes a raw material quality warning to the warehouse intelligent monitoring unit to optimize the warehouse preservation and management strategy for the next batch.

7. The intelligent control system for corn planting, storage, and processing according to claim 1, characterized in that, The overall control platform is also connected to the production and sales linkage unit. The production and sales linkage unit retrieves historical planting yield, warehouse inventory and processed finished product capacity data, predicts finished product supply, matches downstream order demand, and dynamically outputs harvesting, warehouse release and processing scheduling instructions.

8. The intelligent control system for corn planting, storage, and processing according to claim 1, characterized in that, The production and sales linkage unit operates as follows: it calculates the corn yield per mu, average storage loss rate, and finished product output rate for the same period in the past three years, constructs a supply and demand forecasting model, compares real-time inventory, processing capacity, and customer order demand, issues instructions for early harvesting and expedited processing when there is an inventory gap, and issues instructions for extending storage and adjusting processing categories to increase deep processing capacity when there is an inventory backlog.

9. The intelligent control system for corn planting, storage, and processing according to claim 1, characterized in that, The intelligent monitoring unit for warehousing is interconnected with the phased decision-making unit for planting. If the warehousing risk coefficient remains high after multiple batches of corn have entered the warehouse, the overall control platform will push field planting optimization instructions to the phased decision-making unit for planting, adjusting the water and fertilizer management standards for the ripening period and the harvesting and drying standards to reduce the initial mold probability of raw materials entering the warehouse.

10. The intelligent control system for corn planting, storage, and processing according to claim 1, characterized in that, After the processing adaptation and control unit completes the processing, it sends the finished product quality inspection data and processing loss data back to the central management platform. This data is then linked with planting and storage data to form a complete full-chain traceability archive, supporting the retrieval of plot planting records, storage environment records, and processing technology records via QR code scanning.

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