Automatic management system for ventilation in intelligent grain depot production

Abstract

The invention relates to the field of grain depot ventilation management, and discloses an automatic management system for ventilation in intelligent grain depot production, which comprises an environmental parameter monitoring module, a data processing and analysis module, an intelligent ventilation decision module, a ventilation equipment linkage control module and a dynamic feedback optimization module, the environmental parameter monitoring module is used for collecting temperature, humidity and gas concentration data in the bin in real time through the Internet of Things communication unit; the data processing and analyzing module is connected with the environment parameter monitoring module; the intelligent ventilation decision-making module receives the multi-dimensional data atlas, analyzes the temperature gradient, the humidity sensitivity index and the mildew risk value in the atlas through a dynamic classification algorithm, and fuses external meteorological data; the ventilation equipment linkage control module is used for receiving the instruction sequence of the intelligent ventilation decision module; the dynamic feedback optimization module is used for monitoring the efficiency coefficient and the grain pile environment change rate in the ventilation process; the system has the advantages that the ventilation precision and efficiency are improved, and granary environment management is optimized.

Description

Technical Field

[0001] The present invention relates to the field of ventilation management of grain depots, and in particular to an automated management system for ventilation in production of smart grain depots. Background Art

[0002] In the grain storage process, ventilation management is a key technology to ensure grain quality and reduce losses. However, the traditional grain warehouse ventilation system mainly relies on manual experience or simple timing control mode, which has obvious defects. Manual judgment of ventilation timing and duration is often affected by the operator's experience, and it is difficult to accurately identify critical ventilation conditions, resulting in over-ventilation or under-ventilation. Although the existing automated ventilation system reduces manual intervention, its data analysis only relies on preset static thresholds, and fails to combine multi-dimensional dynamic parameters such as grain pile bioheat and microbial activity to build a prediction model, which makes the ventilation strategy less compatible with the actual grain situation. In addition, the data island phenomenon of the sensor network and the inconsistency of equipment communication protocols further limit the global optimization ability of the ventilation system. Therefore, it is necessary to design an automated management system for ventilation in the production of smart grain warehouses that integrates intelligent perception, multi-objective optimization and autonomous decision-making. Summary of the invention

[0003] In view of the shortcomings of the prior art, the present invention provides an automated management system for ventilation in smart grain warehouse production, which has the advantages of improving ventilation accuracy and efficiency and optimizing grain warehouse environmental management, thus solving the problems in the above-mentioned background technology.

[0004] In order to achieve the above-mentioned purpose of improving ventilation accuracy and efficiency and optimizing granary environmental management, the present invention provides the following technical solutions: an automated management system for ventilation in smart granary production, including an environmental parameter monitoring module, a data processing and analysis module, an intelligent ventilation decision module, a ventilation equipment linkage control module and a dynamic feedback optimization module;

[0005] The environmental parameter monitoring module collects real-time data on temperature, humidity and gas concentration in the warehouse through the IoT communication unit and uploads it to the central controller.

[0006] Preferably, the environmental parameter monitoring module further includes a system using wireless communication technology, equipped with high-precision temperature and humidity sensors and a variety of gas sensors, supporting digital signal output and automatic calibration, the system has a self-calibration mechanism, adjusts the sensors during drastic environmental changes or within a specific period, the data acquisition module supports continuous and accurate data reading, and local storage when the network is interrupted, and automatic uploading after recovery. The built-in intelligent algorithm performs multi-dimensional analysis of temperature, humidity and gas concentration data, identifies environmental change trends, and uploads data to the cloud. The central control system has edge computing capabilities to perform preliminary processing of data and respond to exceptions.

[0007] The data processing and analysis module is connected to the environmental parameter monitoring module, and has a built-in grain condition database and an AI computing engine. It normalizes and extracts features from the data based on a deep neural network model, and generates a multi-dimensional data map representing the in-warehouse state in combination with the characteristics of the grain variety.

[0008] Preferably, the data processing and analysis module further includes accessing the environmental parameter monitoring module using a standardized communication protocol to obtain temperature, humidity, and gas concentration in real time. The system has a built-in grain condition database that stores historical data and characteristic data of different grain varieties using a multi-level data structure. Based on the convolutional neural network in deep learning, the AI computing engine fuses multi-dimensional time series data, extracts key features, analyzes the correlations between environmental factors, and generates a multi-dimensional data map of the in-warehouse state. The map combines the characteristics of the grain variety, intuitively shows the storage environment conditions, and supports visual presentation and risk assessment.

[0009] The intelligent ventilation decision-making module receives the multi-dimensional data map, analyzes the temperature gradient, humidity sensitivity index, and mildew risk value in the map through a dynamic classification algorithm. After fusing external meteorological data, it uses a reinforcement learning strategy to generate an optimal instruction sequence for the opening and closing of ventilation openings, fan regulation, and ventilation duration.

[0010] Preferably, the intelligent ventilation decision-making module further includes analyzing the temperature gradient, humidity sensitivity index, and mildew risk value through a dynamic classification algorithm, fusing external meteorological data, using a dynamic classification algorithm to identify the warehouse environment pattern in real time and adaptively update the ventilation strategy, using a reinforcement learning algorithm to generate an optimal instruction sequence for the opening and closing of ventilation openings, fan regulation, and ventilation duration. The system transmits instructions in real time through the Internet of Things communication unit, continuously monitors and gives feedback during the execution process, supports seamless integration with existing ventilation equipment, is compatible with multiple control protocols, and provides a standardized API interface.

[0011] The temperature gradient calculation formula is as follows:

[0012]

[0013] In the formula, TG is the temperature gradient, representing the temperature change rate per unit distance; T i , T i+1 are the temperatures at different positions in the warehouse; d i , d i+1 are the spatial positions of the corresponding measurement points;

[0014] The humidity sensitivity index calculation formula is as follows:

[0015]

[0016] In the formula, HI is the humidity sensitivity index, measuring the degree to which the environmental humidity deviates from the appropriate range; RH is the current humidity; RH opt is the current appropriate humidity; RHmax is the maximum allowable humidity;

[0017] The calculation formula for the mildew risk value is as follows:

[0018] MRI = α * RH + β * T + γ * t - δ * V

[0019] In the formula, MRI is the mildew risk value, reflecting the influence of temperature and humidity on the growth of mildew; RH is the relative humidity; T is the temperature; t is the time dimension; V is the air flow velocity; α, β, γ, δ are weight parameters.

[0020] The dynamic classification algorithm is adopted, including using K-means dynamic clustering to identify the warehouse environment pattern. The calculation formula is as follows:

[0021]

[0022] In the formula, C k is the center point of the k-th class; S k is the set of data points of the k-th class; x is the warehouse environment data point.

[0023] The ventilation equipment linkage control module receives the instruction sequence of the intelligent ventilation decision module and drives the ventilation equipment to perform dynamic regulation. Preferably, the ventilation equipment linkage control module further includes receiving the optimization instruction of the intelligent ventilation decision module through the Internet of Things communication protocol, decoding it into an executable control signal, supporting real-time instruction execution and dynamic adjustment, optimizing the ventilation strategy according to the change of the warehouse environment, having multi-channel control capabilities, independently managing the ventilation states of different areas, and at the same time fine-tuning the opening and closing degree of the ventilation openings according to the real-time feedback.

[0024] The dynamic feedback optimization module monitors the efficiency coefficient and the change rate of the grain pile environment during the ventilation process, corrects the ventilation strategy in real time through the adaptive PID algorithm and the energy consumption evaluation model, and feeds back the optimized parameters to the intelligent ventilation decision module.

[0025] Preferably, the dynamic feedback optimization module further includes real-time monitoring of the efficiency coefficient and the change rate of the grain pile environment during the ventilation process, evaluating the ventilation effect in combination with the environmental sensor data, dynamically adjusting the ventilation strategy by using the adaptive PID control algorithm, calculating the equipment energy consumption in real time, optimizing the energy consumption management in combination with the efficiency coefficient, automatically correcting the strategy when the ventilation effect deviates from the expectation, and feeding back the optimized parameters to the intelligent ventilation decision module.

[0026] Compared with the prior art, the present invention provides an automatic management system for ventilation in the production of intelligent grain depots, having the following beneficial effects:

[0027] Through the integration of modules such as environmental parameter monitoring, data processing and analysis, intelligent ventilation decision-making, equipment linkage control, and dynamic feedback optimization, this system realizes the automated management and optimization of the ventilation process. The AI computing engine accurately analyzes the warehouse environmental data through a deep learning model. The intelligent ventilation decision-making module dynamically adjusts the ventilation strategy according to real-time data and combines with an energy consumption assessment model to achieve energy-saving optimization. Through the adaptive PID control algorithm and real-time feedback adjustment, the system not only improves the ventilation effect but also effectively reduces energy consumption, ensuring the balance between the grain pile storage environment and energy efficiency, thereby improving the production efficiency and management level of the grain depot. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a schematic structural diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0030] The present invention provides a technical solution: an automated management system for ventilation in the production of an intelligent grain depot, including an environmental parameter monitoring module, a data processing and analysis module, an intelligent ventilation decision-making module, a ventilation equipment linkage control module, and a dynamic feedback optimization module;

[0031] The environmental parameter monitoring module uses the Internet of Things communication unit to collect data on the temperature, humidity, and gas concentration in the warehouse in real time and uploads it to the central controller.

[0032] Adopt wireless communication technology to ensure that the device can stably and with low latency interact with the central controller or cloud platform under different environmental conditions. Support two-way communication, not only can upload real-time data, but also support sending control commands from the central controller to the monitoring device to achieve remote control and adjustment. Equip with high-precision temperature and humidity sensors, and use digital signal output mode to improve the accuracy of data collection. The sensors can be automatically calibrated to reduce errors. Configure gas sensors (such as carbon dioxide sensor, ammonia sensor, oxygen concentration sensor, etc.), which can accurately measure the concentration of harmful gases in the warehouse to ensure environmental safety. When the environment changes violently or within a specific period, the system automatically calibrates the sensors by itself to avoid data drift. The system continuously monitors the temperature, humidity and gas concentration in the warehouse, and realizes continuous and accurate data reading through the integrated data acquisition module. When the network is interrupted, the device can locally store the data and automatically upload it when the network is restored to avoid data loss. Through algorithms, multi-dimensional analysis is carried out on temperature and humidity data and gas concentration data to identify potential environmental change trends. The system can upload the collected data to the cloud platform, support big data analysis and visualization display, and provide functions such as historical data query, real-time monitoring, and alarm. The central control system has edge computing capabilities, can preliminarily process and analyze the collected real-time data, quickly respond to abnormal situations, and reduce the processing pressure on the cloud. Adopt advanced data encryption protocols to ensure the security of data during transmission, preventing data leakage and tampering.

[0033] Through high-precision sensors and real-time data transmission, it can provide real-time data on temperature, humidity and gas concentration in the warehouse, effectively ensuring the monitoring accuracy of the storage environment. Realize continuous monitoring of the warehouse environment, and any environmental abnormality can be detected in time and trigger corresponding alarms. The system can automatically collect and upload data, eliminating the instability and time delay of manual inspections, and greatly reducing human resource costs. Reduce the management risks brought by incomplete monitoring equipment, data loss or incorrect records during the traditional manual inspection process. Real-time monitoring of harmful gas concentrations (such as carbon dioxide, ammonia, etc.) can effectively prevent safety accidents (such as fires, explosions, poisoning of personnel, etc.) caused by gas leakage or excessive concentrations. Monitoring of temperature and humidity ensures a suitable storage environment for stored items, avoiding damage or quality degradation of items caused by environmental factors.

[0034] The data processing and analysis module is connected to the environmental parameter monitoring module, with a grain condition database and an AI calculation engine built-in. Based on the deep neural network model, it normalizes and extracts features from the data, and generates a multi-dimensional data map representing the state in the warehouse in combination with the characteristics of the grain variety.

[0035] The data processing and analysis module accesses the environmental parameter monitoring module through a standardized communication protocol to obtain real-time temperature and humidity, gas concentration, and other environmental data. It performs preprocessing operations such as denoising, normalization, and missing value filling on the received raw environmental data to ensure data quality and prepare for subsequent analysis. The system has a dedicated grain condition database built-in, which is used to store historical environmental data, characteristic data of different grain types, and the long-term evolution of storage conditions, providing rich database support. The grain condition database adopts a multi-level data structure for management (such as time series data, regional data, grain type data, etc.), facilitating rapid retrieval and analysis. Based on data such as the growth characteristics and storage requirements of different grain types, the database records the suitable temperature and humidity ranges, gas concentration standards, and other characteristics of various grain types, providing targeted references for data analysis. The data processing and analysis module has a powerful AI computing engine built-in, which uses convolutional neural networks in deep learning to process multi-dimensional, time series data. The AI engine performs data normalization on the collected environmental data, making various data comparable, and extracts important features from it (such as temperature fluctuations, humidity change trends, gas concentration fluctuations, etc.), enhancing the data's expressive ability. The AI computing engine fuses data from different sensors, extracts the relationships between different types of environmental factors through multi-layer neural networks, and generates more representative characteristic data. Combining grain type characteristics and environmental data, the AI model generates a representation of the in-warehouse state based on a multi-dimensional data map. The map not only includes basic temperature and humidity, gas concentration data but also incorporates the response characteristics of grain types to the environment, such as the suitable storage temperature range and humidity tolerance of different grain types. The generated multi-dimensional data map can be presented through a visualization interface to help management personnel quickly understand the matching degree between the in-warehouse environment and grain types and potential risks. The AI model in the data processing module supports online learning. As the amount of data increases, the system can automatically retrain the model, gradually optimizing the prediction accuracy of the model to ensure accurate judgment of the grain condition state. As the warehouse environment changes continuously, the model can adaptively adjust various parameters to adapt to new environmental patterns and maintain long-term prediction stability. Through the analysis of the multi-dimensional data map, the AI model can predict the future environmental change trends in the warehouse and give early warning information, such as the change trends of temperature and humidity, the increase in gas concentration, etc.

[0036] The AI engine automatically extracts effective features from vast multi-dimensional data through deep neural networks, eliminates noise, and improves the accuracy of data analysis. Combining the characteristics of grain varieties with environmental data further refines the understanding of the in-warehouse environment. The multi-dimensional data map can comprehensively reflect the comprehensive state inside the warehouse, enabling managers to understand the warehousing environment from multiple perspectives and providing more in-depth information for subsequent management and decision-making. Deep learning-based multi-dimensional data analysis can help the system predict potential environmental changes in advance, avoid problems such as inappropriate temperature and humidity and excessive gas concentration, take adjustment measures in advance, and prevent the warehouse environment from getting out of control. When the environmental data deviates from the preset safe range, the system can trigger an alarm in real time and push alarm information to the administrator, respond quickly, and avoid safety hazards such as grain mildew and spoilage. Through the grain condition database and multi-dimensional data map generated by the system, the warehouse environment conditions can be adjusted more precisely according to the characteristics of grain varieties, ensuring that each type of grain can be stored under the best conditions, thus extending the shelf life. The visual display of the multi-dimensional data map and the AI analysis results provide a scientific basis for warehousing management, helping managers make more accurate management decisions, such as adjusting temperature and humidity, gas concentration, etc., and minimizing grain waste to the greatest extent. The data processing module can automatically suggest or execute adjustment measures based on the AI analysis results, reduce the need for manual intervention, reduce the risk brought by human errors, and improve management efficiency. The system automatically alarms and provides suggestions for response measures, enabling managers to take appropriate measures in a shorter time and improving the response speed of warehousing environment management. Through the online learning ability of deep neural networks, the system can make adaptive adjustments according to new environmental data, maintain the accuracy and adaptability of the model, and further improve the intelligent level of the system. The increase in data volume and continuous monitoring of environmental changes enable the AI engine to continuously optimize its prediction ability to cope with a more complex and dynamic warehousing environment. The grain condition database supports the integration and analysis of environmental data from multiple regions and multiple warehouses, facilitating the unified management and monitoring of large-scale warehousing environments. The multi-dimensional data map and AI analysis results can be shared across regions, promoting collaborative work between different warehouses and improving the overall warehousing management efficiency.

[0037] The intelligent ventilation decision-making module receives a multi-dimensional data map, analyzes the temperature gradient, humidity sensitivity index, and mildew risk value in the map through a dynamic classification algorithm. After integrating external meteorological data, it uses a reinforcement learning strategy to generate an optimal instruction sequence for the opening and closing of ventilation openings, fan regulation, and ventilation duration. The intelligent ventilation decision-making module receives the multi-dimensional data map generated by the environmental parameter monitoring module and the data processing module, including environmental data such as temperature gradient, humidity sensitivity index, and mildew risk value. The multi-dimensional data map integrates the environmental parameters in the warehouse and the characteristics of grain varieties, providing comprehensive warehouse environmental information for ventilation decision-making. A deep learning model is used to dynamically classify the data map to help extract important features related to ventilation (such as temperature fluctuation trend, humidity change, mildew risk index, etc.). A dynamic classification algorithm is used to perform real-time classification and clustering of the environmental data in the map. The algorithm automatically identifies different environmental patterns (such as high temperature and high humidity, low temperature and high humidity, etc.) by analyzing historical data and real-time environmental status, and classifies the corresponding ventilation strategies. The dynamic classification algorithm can be adaptively updated according to the real-time changes in the environment to ensure that the ventilation decision always matches the current environmental status of the warehouse and the requirements of grain varieties. The system accesses external meteorological data sources (such as temperature, humidity, wind speed, etc.) and integrates the external meteorological conditions with the environmental data in the warehouse. This can more comprehensively analyze and predict the interaction effects between the internal and external environments of the warehouse, improving the accuracy of ventilation decision-making. The timeliness and efficiency of ventilation decision-making are supplemented and optimized using meteorological data. For example, if the external meteorological conditions match the situation of high humidity inside the warehouse, the system will decide whether to increase the ventilation intensity or extend the ventilation time. A reinforcement learning algorithm is used to generate an optimal instruction sequence for the opening and closing of ventilation openings, fan regulation, and ventilation duration. Reinforcement learning continuously optimizes the control strategy through interaction with the environment to ensure finding the best ventilation solution under different environmental conditions. A reasonable reward function is designed in reinforcement learning, such as giving priority to goals like reducing humidity and minimizing mildew risk. Through long-term feedback learning, the system can automatically adjust the ventilation strategy to achieve the maximum environmental optimization effect with the minimum energy consumption. The reinforcement learning algorithm generates an optimal instruction sequence for the ventilation system based on the current environmental state and combined with historical learning experience. These instructions include the opening and closing operations of ventilation openings, the speed of fan regulation, and the ventilation duration. The generated ventilation instructions are transmitted to the ventilation system for execution in real time through the Internet of Things communication unit. During the execution process, the system continuously monitors environmental changes and feeds back new state information to ensure the continuous optimization of the decision-making. The intelligent ventilation decision-making module can be seamlessly integrated with the existing ventilation equipment in the warehouse (such as fans, windows, ventilation openings, etc.), supporting multiple control protocols (such as Modbus, ZigBee, etc.) to ensure compatibility with existing hardware. Standardized API interfaces are provided to support data exchange and integration with other warehouse management systems, meteorological service platforms, or automation control systems.

[0038] The reinforcement learning model automatically adjusts the ventilation strategy through precise analysis of factors such as temperature and humidity gradients inside and outside the warehouse and mildew risk, thereby effectively avoiding the risks of uneven temperature and humidity and mildew inside the warehouse and optimizing the grain storage environment. By monitoring and adjusting ventilation conditions in real time (such as strengthening ventilation when the humidity is too high), the system can significantly reduce the mildew risk, protect the quality of grains, and reduce grain losses. Through the reinforcement learning strategy, the system can minimize the opening duration and intensity of the fans and vents while ensuring the warehouse environmental conditions, saving energy consumption. According to external meteorological conditions, the system can dynamically adjust the ventilation strategy. For example, when the external climate is suitable, reduce the ventilation time or lower the fan power to achieve the optimization of energy use. Reinforcement learning realizes the full automation of ventilation control by automatically optimizing the strategy, avoiding delays and errors in manual operations, and improving the immediacy and accuracy of decision-making. The system can obtain environmental data and meteorological information in real time and automatically adjust the ventilation plan according to changes without manual intervention, greatly improving the automation level of warehousing management. The system can continuously adapt to the needs of different seasons, climates, and grain varieties through continuous reinforcement learning processes, accurately formulate the most suitable ventilation strategy, and enhance the system's adaptive ability. With the accumulation of data, the decision-making ability of the system will continue to improve, enabling it to handle the requirements of more complex warehousing environments and maintain efficient and stable operation. The integration of external meteorological data (such as temperature, humidity, wind speed, etc.) enables the system to more accurately predict and adjust the ventilation strategy. The system will determine the ventilation intensity and duration according to external climate conditions, avoid over-ventilation or under-ventilation, and optimize the environment inside the warehouse. External meteorological data provides a more comprehensive perspective on environmental changes for the system, helping the ventilation system make decisions in advance and avoid passive responses. The intelligent ventilation decision-making module can automatically generate and execute ventilation instructions based on environmental data, reducing the intervention of warehouse staff and improving the efficiency of warehousing management. The system supports remote monitoring and adjustment functions, and managers can view the ventilation status in real time through the platform and make necessary interventions or adjustments. The system supports flexible configuration according to different warehousing environments and requirements, can quickly adapt to the regulation of various warehouse ventilation equipment, and has strong scalability. The integration ability with existing warehousing management systems enables the system to share data with other automation devices and warehousing management platforms, providing a wider range of intelligent application scenarios.

[0039] The ventilation equipment linkage control module receives the instruction sequence from the intelligent ventilation decision-making module and drives the ventilation equipment to perform dynamic regulation. The ventilation equipment linkage control module receives the optimized instruction sequence from the intelligent ventilation decision-making module through Internet of Things communication protocols (such as MQTT, Modbus, ZigBee, etc.). The instructions include specific operations such as the opening and closing of ventilation openings, fan regulation, and ventilation duration. The module decodes these instructions and converts them into control signals executable by the hardware. The ventilation equipment linkage control module supports real-time receiving and executing the instructions sent by the intelligent ventilation decision-making module, and can update the ventilation strategy in a timely manner according to the dynamic changes of the warehouse environment to ensure that the adjustment of the equipment is consistent with the environmental requirements. The ventilation equipment linkage control module can control multiple devices simultaneously, including fans, ventilation openings, windows, etc., to achieve precise environmental regulation. For example, when more ventilation is required, the system will synchronously adjust the operating speeds of multiple fans according to the instructions and adjust the opening and closing degrees of the ventilation openings according to the temperature and humidity distribution in the warehouse. It supports multi-channel control and can manage the ventilation requirements of different areas simultaneously, such as zoning to control the on / off of fans in different areas to ensure environmental balance in each area of the warehouse. The system dynamically adjusts the device status according to the real-time instructions of the intelligent ventilation decision-making module. For example, if the humidity is too high, the system can automatically start more fans and increase the opening degree of the ventilation openings; if the temperature change is small, only a slight adjustment of the fan speed may be required. The adjustment of the ventilation equipment is based on the feedback of real-time environmental data. The system can intelligently evaluate the effect of the device response and fine-tune the control strategy according to the feedback signal to achieve precise regulation. According to the fan regulation requirements in the instruction sequence, the module can adjust the start / stop status, speed, etc. of the fan to optimize the air flow speed. By adjusting the fan speed, the system can save energy consumption on the premise of ensuring ventilation efficiency. Control the opening and closing degree of the ventilation openings to regulate the air circulation volume inside and outside the warehouse and ensure the uniformity of air circulation. When higher ventilation volume is required by the environmental conditions, the ventilation openings can be fully opened; when the environmental demand decreases, the ventilation openings can be appropriately closed to save energy.

[0040] The ventilation equipment linkage control module can, according to the instruction sequence of the intelligent ventilation decision-making module, precisely control the start and stop of each ventilation equipment, adjust the fan speed, and adjust the opening degree of the ventilation opening, etc., so as to achieve precise adjustment of environmental parameters such as temperature, humidity, and gas concentration in the warehouse. The module can coordinate the work of multiple devices to ensure that various environmental parameters such as temperature, humidity, and gas concentration are maintained within the optimal range, thus avoiding uneven or unsuitable environmental conditions in the warehouse. By dynamically adjusting the fan speed and the opening degree of the ventilation opening, the system can ensure that the ventilation equipment operates under appropriate loads, avoiding energy waste caused by excessive ventilation. The adaptive ability of reinforcement learning can also be adjusted in real time according to actual needs, reducing energy consumption and operating costs. Combining the coordinated control of the fan and the ventilation opening, the system can, on the premise of ensuring the safety of the warehouse environment, intelligently adjust the operation time and intensity of the equipment, avoiding unnecessary energy waste. The real-time regulation and closed-loop feedback mechanism of the ventilation equipment ensure the stability of the warehouse environment. For example, when the environmental humidity is too high, the system will automatically turn on more fans or adjust the opening degree of the ventilation opening to avoid problems such as mildew caused by excessive humidity and ensure the quality of grain storage. The equipment status monitoring and fault diagnosis function ensure the normal operation of the ventilation equipment. In case of a fault, the system will immediately alarm, reducing the downtime of the ventilation equipment and ensuring the continuity and stability of environmental regulation. The ventilation equipment linkage control module can automatically receive instructions and execute them without manual intervention, improving the automation level of warehousing management. Managers only need to intervene in abnormal situations, greatly reducing the workload of daily operation and maintenance. As the system continues to operate, the module can automatically learn and optimize the ventilation strategy through historical data and feedback information, improving the intelligence level of equipment regulation.

[0041] The dynamic feedback optimization module monitors the efficiency coefficient and the change rate of the grain pile environment during the ventilation process, corrects the ventilation strategy in real time through the adaptive PID algorithm and the energy consumption evaluation model, and feeds back the optimized parameters to the intelligent ventilation decision-making module.

[0042] The dynamic feedback optimization module monitors the efficiency coefficient of the equipment in real time during the ventilation process, which is the ratio of the actual efficiency to the theoretical efficiency of the ventilation system within a certain period of time. This efficiency coefficient is calculated by collecting real-time equipment operation data and environmental data, reflecting the working efficiency of the ventilation system. The change rate of the grain pile environment in the warehouse is monitored in real time through environmental sensors, especially the fluctuations of humidity, temperature, and mildew risk. Through the tracking and analysis of these data, the system evaluates the impact of ventilation operations on the environment and adjusts the ventilation strategy in combination with the characteristics of the grain pile. The dynamic feedback optimization module adopts an adaptive PID control algorithm to adjust the ventilation strategy in real time according to the monitored efficiency coefficient and environmental change rate during the ventilation process. The PID algorithm automatically adjusts control parameters such as the fan speed and the opening degree of the ventilation port by calculating the error value (the difference between the current environmental state and the target environmental state). The adaptive characteristics of the PID algorithm enable the module to automatically adjust the algorithm parameters according to the dynamic changes of the actual environment, optimize the system response speed and stability, and ensure the rapid and accurate correction of the control strategy. The dynamic feedback optimization module is built with an energy consumption evaluation model to calculate and evaluate the energy consumption of the ventilation system in real time. By tracking the fan operation time, fan speed, and air volume, the actual energy consumption of the ventilation equipment is calculated. Combining the efficiency coefficient and energy consumption evaluation, the system can adjust the equipment operation state on the premise of maintaining a good ventilation effect to achieve the best ventilation effect with the minimum energy consumption. Through continuous optimization, the system can dynamically adjust the operation strategy, reduce unnecessary energy consumption, and improve the overall energy efficiency. When the efficiency coefficient during the ventilation process deviates from the expected value, or the change of the grain pile environment does not meet the target value, the dynamic feedback optimization module will correct the ventilation strategy through the PID control algorithm, adjust the fan speed, start-stop time, and the opening degree of the ventilation port, etc., to ensure that the optimized ventilation strategy better meets the actual needs of the warehouse. The corrected optimization parameters (such as the control parameters after PID adjustment, energy efficiency data, etc.) will be fed back to the intelligent ventilation decision-making module in real time to further adjust the instruction sequence and ensure that the entire system can quickly respond to environmental changes and achieve dynamic and real-time optimization. The dynamic feedback optimization module integrates data from environmental monitoring, energy consumption evaluation, and efficiency coefficient. By fusing and analyzing these data from different sources, the system can comprehensively evaluate the ventilation effect, identify potential problems, and propose improvement measures. The module combines the characteristics of the grain pile to comprehensively analyze the ventilation effect from multiple dimensions, provides more accurate optimization parameter feedback, and assists the intelligent ventilation decision-making module in making real-time decisions.

[0043] The dynamic feedback optimization module can finely adjust the operating state of the ventilation equipment in real time through the PID control algorithm and energy consumption assessment, ensuring maximum ventilation efficiency while avoiding energy waste. This enables the environment in the warehouse to remain in the best state continuously, effectively reducing quality problems such as mildew and improving the quality of grain storage. The module continuously corrects the ventilation strategy, keeping environmental factors such as temperature and humidity around the grain pile within the preset safe range, reducing the risk of environmental instability to grain storage. By monitoring energy consumption in real time and comparing it with environmental changes and efficiency coefficients, the system can dynamically adjust the operating parameters of the ventilation equipment to minimize energy consumption and reduce energy costs to the greatest extent while meeting ventilation requirements. The energy consumption assessment model helps the system formulate the optimal ventilation strategy, avoiding over-operation of equipment or long-term inefficient operation, thus achieving energy efficiency improvement while ensuring environmental quality. The adaptive PID algorithm and energy consumption assessment model of the dynamic feedback optimization module enable the system to automatically optimize the ventilation strategy according to real-time changes in the environment, ensuring that the system can adapt to the different needs of different seasons, climates, and grain varieties, and improving the flexibility and adaptability of overall environmental management. As the operation time increases, the system can learn from historical data and optimize the PID algorithm and control strategy, improving the adjustment accuracy and response speed and maintaining continuous system optimization. By optimizing the fan speed and the opening and closing time of the ventilation ports, the system reduces the overuse and high-load operation of equipment, extending the service life of the equipment. At the same time, it ensures that the equipment always operates in its best working state, avoiding failures caused by frequent switching or abnormal operation. The fault detection mechanism of the dynamic feedback optimization module can detect problems in equipment operation in real time, adjust system control in a timely manner, avoid environmental fluctuations caused by equipment failures, and improve the stability of the system and the reliability of the equipment. The automatic optimization ability of the dynamic feedback optimization module reduces the need for manual intervention. Managers only need to monitor the system status and abnormal warnings, without frequently adjusting the equipment operating parameters, improving the automation level of management. Through real-time feedback optimization and fault detection, the system can actively warn of potential equipment problems, provide decision-making support for managers, and ensure that the ventilation system remains efficient and stable throughout the operation cycle.

[0044] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0045] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An automated management system for ventilation in smart grain depot production, characterized in that: It includes an environmental parameter monitoring module, a data processing and analysis module, an intelligent ventilation decision module, a ventilation equipment linkage control module and a dynamic feedback optimization module; the environmental parameter monitoring module collects the temperature, humidity and gas concentration data in the warehouse in real time through the Internet of Things communication unit, and uploads it to the central controller; The data processing and analysis module is connected to the environmental parameter monitoring module, has a built-in grain condition database and an AI computing engine, performs normalization and feature extraction on the data based on a deep neural network model, and generates a multidimensional data map representing the state in the warehouse in combination with the characteristics of the grain varieties; The intelligent ventilation decision module receives a multidimensional data map, analyzes the temperature gradient, humidity sensitivity index and mildew risk value in the map through a dynamic classification algorithm, integrates external meteorological data, and uses a reinforcement learning strategy to generate an optimal instruction sequence for opening and closing the vents, fan control and ventilation duration; The ventilation equipment linkage control module receives the instruction sequence of the intelligent ventilation decision module and drives the ventilation equipment to perform dynamic regulation; The dynamic feedback optimization module monitors the efficiency coefficient and the grain pile environment change rate during the ventilation process, corrects the ventilation strategy in real time through an adaptive PID algorithm and an energy consumption evaluation model, and feeds back the optimization parameters to the intelligent ventilation decision module.

2. According to claim 1, the automatic management system for ventilation in smart grain depot production is characterized in that: The environmental parameter monitoring module further includes a system that uses wireless communication technology, is equipped with high-precision temperature and humidity sensors and a variety of gas sensors, supports digital signal output and automatic calibration, and the system has a self-calibration mechanism to adjust the sensors during drastic environmental changes or within a specific period. The data acquisition module supports continuous and accurate data reading, and performs local storage when the network is interrupted, and automatically uploads after recovery. The built-in intelligent algorithm performs multi-dimensional analysis of temperature, humidity and gas concentration data, identifies environmental change trends, and uploads data to the cloud. The central control system has edge computing capabilities to perform preliminary processing of data and respond to exceptions.

3. According to claim 1, the automatic management system for ventilation in smart grain depot production is characterized in that: The data processing and analysis module further includes accessing the environmental parameter monitoring module using a standardized communication protocol to obtain temperature, humidity, and gas concentration in real time. The system has a built-in grain condition database and uses a multi-level data structure to store historical data and feature data of different grain types. Based on the convolutional neural network in deep learning, the AI ​​computing engine fuses multi-dimensional time series data, extracts key features and analyzes the correlation between environmental factors, and generates a multi-dimensional data map of the warehouse status. The map combines the characteristics of the grain types to intuitively display the storage environment status and supports visualization and risk assessment.

4. According to claim 1, the automatic management system for ventilation in smart grain depot production is characterized in that: The intelligent ventilation decision module further includes analyzing the temperature gradient, humidity sensitivity index and mildew risk value through a dynamic classification algorithm, integrating external meteorological data, using a dynamic classification algorithm, identifying warehouse environmental patterns in real time and adaptively updating ventilation strategies, and using a reinforcement learning algorithm to generate optimal instruction sequences for vent opening and closing, fan control and ventilation duration. The system transmits instructions in real time through the Internet of Things communication unit, and continuously monitors feedback during the execution process. It supports seamless integration with existing ventilation equipment, is compatible with multiple control protocols, and provides a standardized API interface.

5. According to claim 4, the automatic management system for ventilation in smart grain depot production is characterized in that: The temperature gradient calculation formula is as follows: Where TG is the temperature gradient, which represents the rate of temperature change per unit distance; T i , T i+1 is the temperature at different locations in the warehouse; d i , d i+1 is the spatial position of the corresponding measurement point; The calculation formula of humidity sensitivity index is as follows: Where HI is the humidity sensitivity index, which measures the degree to which the ambient humidity deviates from the appropriate range; RH is the current humidity; RH opt is the current suitable humidity; RH max is the maximum permissible humidity; The formula for calculating the mold risk value is as follows: MRI=α*RH+β*T+γ*t-δ*V Where MRI is the mold risk value, which reflects the influence of temperature and humidity on mold growth; RH is the relative humidity; T is the temperature; t is the time dimension; V is the air circulation speed; α, β, γ, δ are weight parameters.

6. According to claim 4, the automatic management system for ventilation in smart grain depot production is characterized in that: The dynamic classification algorithm includes using K-means dynamic clustering to identify warehouse environment patterns, and the calculation formula is as follows: In the formula, C k is the center point of the kth class; S k is the set of data points of the kth category; x is the warehouse environment data point.

7. According to claim 1, the automatic management system for ventilation in smart grain depot production is characterized in that: The ventilation equipment linkage control module further includes receiving optimization instructions from the intelligent ventilation decision-making module through the Internet of Things communication protocol, and decoding them into executable control signals, supporting real-time instruction execution and dynamic adjustment, optimizing ventilation strategies according to changes in the warehouse environment, having multi-channel control capabilities, independently managing the ventilation status of different areas, and fine-tuning the degree of opening and closing of the vents based on real-time feedback.

8. The automatic management system for ventilation in smart grain depot production according to claim 1 is characterized in that: The dynamic feedback optimization module further includes real-time monitoring of the efficiency coefficient and the rate of change of the grain pile environment during the ventilation process, evaluating the ventilation effect in combination with environmental sensor data, and dynamically adjusting the ventilation strategy using an adaptive PID control algorithm, calculating equipment energy consumption in real time, and optimizing energy consumption management in combination with the efficiency coefficient. When the ventilation effect deviates from expectations, the system automatically corrects the strategy and feeds back the optimization parameters to the intelligent ventilation decision module.

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