Grain whole-process complete-set storage and transportation method and system

Abstract

The invention relates to the field of grain storage and transportation, in particular to a whole-process complete-set grain storage and transportation method and system. The method comprises the following steps: obtaining information of types, quality and weight of grains to be warehoused; the warehousing process and equipment parameters of the to-be-warehoused grains are adjusted; carrying out grading and impurity removal treatment on the to-be-warehoused grains; the humidity of the to-be-stored grains is regulated and controlled; executing a grain warehousing process; the storage environment and the stored grains are continuously monitored; storage environment parameters are adjusted; and by constructing a deep learning ex-warehouse unit planning model and a transportation path planning model, an optimal ex-warehouse unit and an optimal transportation path are planned. According to the invention, through an intelligent adjustment mechanism, the storage process and equipment parameters are optimally configured; an optimal monitoring regulation and control technology is adopted, so that the optimal storage environment is ensured, and the grain quality is guaranteed; by constructing a deep learning warehouse-out unit planning model and a transportation path planning model, the loss of grains and equipment is effectively reduced, and the whole-process complete-set efficient storage and transportation of the grains is realized.

Description

Technical Field

[0001] The present invention relates to the field of grain storage and transportation, and specifically to a complete set of grain storage and transportation method and system for the entire process. Background Art

[0002] In the grain storage and transportation industry, ensuring grain safety, improving storage and transportation efficiency, and maintaining its original quality have always been the focus of the industry. However, the existing grain full-process complete storage and transportation methods still have many deficiencies in practical application, which are specifically manifested in the following aspects: (1) Lack of intelligent and personalized adjustment capabilities. Traditional storage and transportation methods are often based on fixed processes and parameters, and it is difficult to flexibly adjust according to differences in grain type, quality and weight. This results in grains of different qualities suffering unnecessary losses during warehousing, processing and storage, such as over-drying and residual impurities, which affects the final quality and nutritional value of the grain. In addition, the fixed operation mode cannot fully utilize the performance potential of existing equipment, resulting in low storage and transportation efficiency.

[0003] (2) Inaccurate storage environment control. Most existing storage environment control methods rely on manual experience and simple sensor data, and lack real-time, accurate environmental monitoring and control mechanisms. This not only makes it difficult to maintain key parameters such as temperature, humidity, and gas concentration in the storage environment within the optimal range, but may also cause problems such as mildew and insect pests in grain due to environmental changes, seriously affecting the storage quality and safety of grain.

[0004] (3) Outbound planning lacks intelligence and efficiency. In the grain outbound process, traditional planning methods often rely on manual experience and simple path planning algorithms, which makes it difficult to achieve optimal planning for outbound units and transportation routes. This not only increases the complexity and time cost of outbound operations, but may also lead to low transportation efficiency and increased costs due to unreasonable paths. At the same time, the lack of intelligent outbound planning also makes it difficult to effectively control the loss and waste of grain in the outbound process.

[0005] At present, there is not much research on the complete storage and transportation of grain throughout the entire process, and there is no specific, systematic, efficient and quality method for the complete storage and transportation of grain throughout the entire process. Summary of the invention

[0006] In view of the defects in the prior art, the present invention provides a complete storage and transportation method and system for grain throughout the entire process.

[0007] In the first aspect, the present invention provides a complete set of grain storage and transportation methods for the entire process, including the following steps: obtaining information on the type, quality and weight of grain to be stored; adjusting the storage process and equipment parameters of the grain to be stored based on the information to obtain the optimal adjustment result; grading and removing impurities from the grain to be stored based on the optimal adjustment result to obtain the optimal processing result; based on the optimal processing result, regulating the humidity of the grain to be stored to obtain the optimal regulation parameters; based on the optimal regulation parameters, executing the grain storage process to obtain the stored grain; obtaining the monitoring results by continuously monitoring the storage environment and the stored grain; adjusting the storage environment parameters based on the monitoring results to obtain the optimal regulation results; based on the optimal regulation results, planning the optimal outbound unit and the optimal transportation route by constructing a deep learning outbound unit planning model and a transportation route planning model. The present invention accurately collects information on the type, quality and weight of grain to be stored, and intelligently adjusts the storage process and equipment parameters to achieve accurate grading of grain and efficient removal of impurities; by introducing a humidity control mechanism and a storage environment adjustment mechanism for grain to be stored, it is beneficial to maintain the quality of the whole process of grain storage and transportation and reduce storage losses; by constructing a deep learning outbound unit planning model and an intelligent loading implementation model, the optimal outbound unit and transportation route are dynamically predicted and planned, which significantly improves the storage and transportation efficiency and the level of intelligence.

[0008] Optionally, adjusting the transportation process and equipment parameters of the grain to be stored according to the information to obtain the optimal adjustment result includes: constructing a grain storage process efficiency model and an equipment efficiency model according to the information; adjusting the storage process of the grain to be stored based on the grain storage process efficiency model to obtain the optimal adjustment result of the storage process; adjusting equipment parameters based on the equipment efficiency model to obtain the optimal adjustment result of the equipment parameters. The present invention constructs a grain storage process efficiency model to accurately predict the efficiency performance under different storage processes, thereby intelligently adjusting the storage process to achieve process optimization and efficiency maximization; by constructing an equipment efficiency model, dynamically adjusting equipment parameters based on equipment performance and grain characteristics to ensure that the equipment operates in the optimal state, which not only improves equipment utilization, but also significantly reduces energy consumption; through the optimal adjustment result, the dual optimization of the storage process and equipment parameters is achieved, which brings great efficiency improvement and cost control capabilities to the grain storage industry.

[0009] Optionally, the grain storage process efficiency model satisfies the following expression: , in, Indicates that the food type is , quality is and weight is Effective efficiency of the warehousing process, Indicates that the food type is Maximum efficiency of the warehousing process at the time Indicates that the food type is The number of steps in the warehousing process can be adjusted. Indicates Steps in food quality and weight is The weight of Indicates that the food type is The The efficiency of each step, Indicates that the food type is The equipment efficiency model satisfies the following expression: , in, Indicates that the food type is , quality is and weight is Equipment efficiency at Indicates that the food type is Optimal equipment efficiency at the time of Indicates that the food type is , quality is and weight is The change in quality loss when Indicates that the food type is The gain factor when Indicates that the food type is The present invention uses information on grain type, quality and weight to accurately calculate the effective efficiency of the warehousing process, thereby maximizing the efficiency of the warehousing process. It innovatively combines the change in quality loss with the adjustment of equipment parameters to dynamically evaluate equipment efficiency, ensuring that the equipment operates in the optimal state, effectively reducing the quality loss of grain during the warehousing process and improving the overall storage and transportation quality. It uses a highly parameterized design to flexibly adapt to different grain types and storage and transportation requirements, providing powerful customization and scalability for the grain storage and transportation industry, and promoting the development of this field in a more efficient and intelligent direction.

[0010] Optionally, the grain to be stored is graded and impurity removed through the optimal adjustment result to obtain the optimal processing result, which includes: when the effective efficiency of the storage process and the equipment efficiency are both at the maximum, using multi-layer vibration screening and magnetic separation technology, combined with high-definition cameras and deep learning algorithms, to construct a grading accuracy model; according to the grading accuracy model, the grain is graded to obtain a grading result; based on the grading result, an impurity removal rate model is constructed; according to the impurity removal rate model, various types of impurities are accurately identified and efficiently removed; using the grading accuracy model and the impurity removal rate model, an overall effect evaluation model for grading and impurity removal is constructed; according to the overall effect evaluation model, the optimal processing result is obtained. The present invention effectively improves the accuracy and efficiency of grain grading and impurity removal by combining multi-layer vibration screening and magnetic separation technology, especially when the effective efficiency of the warehousing process and the equipment efficiency are maximized, which can maximize the effectiveness and ensure the efficiency and accuracy of the processing process; by introducing high-definition cameras and deep learning algorithms, a grading accuracy model is constructed, which realizes the intelligence and automation of grain grading, significantly improves the grading accuracy, and reduces the interference of human factors; by constructing an impurity removal rate model, various impurities in grains are accurately identified and efficiently removed, which improves the purity and quality of grains and ensures the quality and safety of stored grains; by constructing an overall effect evaluation model of grading processing and impurity removal processing, the purity of grains is effectively improved, the warehousing quality is guaranteed, and the overall efficiency of the storage and transportation process is optimized.

[0011] Optionally, the humidity of the grain to be stored is regulated based on the optimal processing result, and the optimal control parameters are obtained, including: constructing an optimal control model for grain humidity based on the optimal processing result; substituting the type of grain to be stored, the initial humidity and the target humidity into the expression satisfied by the optimal control model for grain humidity to obtain the optimal control objective function value; determining the optimal control parameters through the optimal control objective function value, and the optimal control parameters include the optimal temperature and the optimal wind speed of the dryer. The present invention achieves precise control by constructing an optimal control model for grain humidity, targeting the characteristics and initial humidity of different types of grains, as well as the target humidity requirements, thereby avoiding the blindness and inefficiency of traditional control methods; by substituting the specific parameters of the grain to be stored into the model expression, the optimal control objective function value is obtained, which not only improves the accuracy of control, but also realizes the personalization and customization of grain humidity control; determining the optimal temperature and the optimal wind speed of the dryer through the optimal control objective function value ensures the high efficiency and energy saving of the control process, and at the same time, it also ensures that the humidity of the stored grain reaches the optimal state, providing strong support for the long-term storage and quality maintenance of grain.

[0012] Optionally, the grain humidity optimal control model satisfies the following expression: , in, represents the objective function for optimal control of grain humidity, Indicates the type of food. Indicates the initial moisture content of grain. Indicates the target moisture content of the grain, Indicates the temperature of the dryer. Indicates the wind speed of the dryer. represents the energy consumption function, represents the food status monitoring function, represents the time variable, Indicates the time when the drying process ends. represents the weight coefficient, Represents the efficiency function of the drying process. The present invention realizes the comprehensive optimization of the grain humidity control process by constructing the optimal grain humidity control model, and significantly improves the accuracy and efficiency of the control; by introducing the time variable and the time when the drying process ends, the control process can dynamically adapt to the changes in grain humidity, ensuring the stability and reliability of the drying effect; the model realizes the flexible adjustment of the trade-off between energy consumption and drying efficiency, which not only meets the needs of grain humidity control, but also takes into account the efficient use of energy, showing the innovation and practicality of the model in the field of grain storage management, and providing strong support for the sustainable development of the grain industry.

[0013] Optionally, adjusting the storage environment parameters according to the monitoring results to obtain the optimal adjustment results includes: constructing an optimal adjustment model for storage environment parameters according to the monitoring results of the storage environment and the stored grain; adjusting the temperature, humidity and concentration, combined with the optimal adjustment model of the storage environment parameters, to obtain the optimal adjustment objective function value; according to the optimal adjustment objective function value, determine the optimal temperature, optimal humidity and optimal The present invention integrates the monitoring data of storage environment and stored grain to construct an optimal adjustment model for storage environment parameters, accurately reflects the impact of storage environment on grain storage quality, and provides a scientific basis for the adjustment of environmental parameters; by adjusting the temperature, humidity and The concentration parameters realize the refined control of the storage environment and improve the safety and stability of grain storage. The environmental parameters determined according to the optimal adjustment objective function value not only meet the specific needs of grain storage, but also realize the efficient use of energy and sustainable development of the environment.

[0014] Optionally, the expression satisfied by the optimal adjustment model of storage environment parameters is as follows: , in, represents the objective function for optimal adjustment of storage environment parameters, Indicates the actual storage environment temperature. Indicates the actual storage environment humidity. Represents the actual storage environment concentration, Indicates the type of grain entering the warehouse. Indicates the initial humidity of grain when it enters the warehouse. Represents the total energy consumption function of the storage environment, Indicates the inspection cycle of the storage environment. represents the efficiency function of the storage environment, represents the time variable, , Represents the weight coefficient. The present invention realizes dynamic monitoring and optimization of storage environment parameters by introducing time variables and inspection cycles, ensures that environmental regulation can respond to changes in grain storage demand in real time, and improves the precision and flexibility of storage management; by comprehensively considering the total energy consumption and efficiency of the storage environment, the optimal balance between energy consumption and efficiency is achieved, which not only ensures the quality of grain storage, but also promotes energy conservation and sustainable environmental development; through the characteristics of different types of grain and initial humidity, a personalized environmental parameter adjustment scheme is constructed, which improves the pertinence and effectiveness of storage environment adjustment.

[0015] Optionally, based on the optimal adjustment result, by constructing a deep learning outbound unit planning model and a transportation path planning model, planning the optimal outbound unit and the optimal transportation path includes: constructing a deep learning outbound unit planning model based on the optimal adjustment result; planning the optimal outbound unit according to the deep learning outbound unit planning model; constructing an intelligent loading implementation model based on the optimal outbound unit; quickly weighing and efficiently loading grain according to the intelligent loading implementation model; constructing a transportation path planning model based on the optimal outbound unit; based on the transportation path planning model, planning the optimal transportation path using grain type, weight, transportation cost, transportation starting point and end point information. The present invention realizes the accurate prediction and planning of warehouse grain outbound units by constructing a deep learning outbound unit planning model, thereby improving outbound efficiency and accuracy and reducing labor costs; by constructing an intelligent loading implementation model, it realizes the rapid weighing and efficient loading of grain, further optimizes the outbound process and improves logistics efficiency; by constructing a transportation route planning model, it comprehensively considers the type, weight, transportation cost, transportation starting point and end point information of grain, realizes the optimal selection of transportation route, reduces transportation costs, and enhances the safety and timeliness of grain transportation.

[0016] In the second aspect, the present invention provides a complete set of grain storage and transportation system, including an input device, a processor, an output device and a memory, wherein the input device, the processor, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, the processor is configured to call the program instructions, and the system uses the complete set of grain storage and transportation method. The system achieves high precision and high efficiency of grain grading and impurity removal by integrating multi-layer vibration screening, magnetic separation technology, high-definition camera and deep learning algorithm, providing high quality guarantee for grain storage; by utilizing the optimal adjustment model of storage environment parameters, combined with the deep learning outbound unit planning model and the intelligent loading implementation model, the intelligent and automated outbound and transportation of grain is realized, which not only greatly improves the storage and transportation efficiency, but also reduces energy consumption and cost; by constructing a path planning model, the optimal transportation path is planned by comprehensively considering multiple factors, ensuring the safety, speed and low cost of grain transportation, and providing strong technical support for the sustainable development of the grain industry.

[0017] Compared with the prior art, the beneficial effects of the present invention include: by introducing an intelligent and personalized adjustment mechanism, flexible adjustments can be made according to the different types, qualities and weights of grain to achieve the optimal warehousing process and equipment parameter configuration; by adopting creative real-time monitoring and control technology, it is ensured that the storage environment parameters are always kept within the optimal range, effectively ensuring the storage quality and safety of grain; by constructing a deep learning outbound unit planning model and a transportation route planning model, the optimal planning of outbound units and transportation routes can be achieved, the efficiency and accuracy of outbound operations can be improved, and the loss of grain and equipment can be effectively reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow chart of a complete set of grain storage and transportation method in the whole process according to an embodiment of the present invention; Figure 2 It is a structural schematic diagram of a complete set of grain storage and transportation system for the entire process according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are only for illustration and are not intended to limit the present invention. In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that these specific details do not need to be adopted to implement the present invention. In other examples, in order to avoid confusing the present invention, known circuits, software or methods are not specifically described.

[0020] Throughout the specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment of the present invention. Therefore, the phrases "in one embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily all refer to the same embodiment or example. In addition, particular features, structures, or characteristics may be combined in one or more embodiments or examples in any suitable combination and / or subcombination. In addition, it should be understood by those of ordinary skill in the art that the figures provided herein are for illustrative purposes and that the figures are not necessarily drawn to scale.

[0021] See also Figure 1 The embodiment of the present invention provides a complete set of grain storage and transportation method, which includes the following steps: S1. Obtain information on the type, quality and weight of grain to be stored.

[0022] In one embodiment, information about the types of grain to be stored is obtained through storage notification.

[0023] In another embodiment, a reasonable sampling plan is formulated according to the type and quantity of grain, and the sampling plan should clearly specify the sampling quantity, sampling location and sampling time.

[0024] Furthermore, according to the sampling plan, samples are taken from the grain to be stored, and the samples should be representative and able to reflect the quality status of the entire set of grain.

[0025] Furthermore, the samples taken are subjected to quality inspection, including appearance, impurity content, moisture content and mildew.

[0026] Furthermore, the quality inspection information is recorded as a basis for subsequent evaluation and processing.

[0027] In another embodiment, a modern grain management system, such as a ground-based automated metering device, is used to accurately measure the grain to be stored and obtain information on the weight of the grain to be stored.

[0028] S2. According to the information, the storage process and equipment parameters of the grain to be stored are adjusted to obtain the adjustment result.

[0029] Among them, S2 includes the following steps: S21. Based on the information, construct a grain storage process efficiency model and an equipment efficiency model.

[0030] In one embodiment, first, the existing grain storage process is comprehensively reviewed to identify bottleneck links and redundant steps.

[0031] Furthermore, by removing bottlenecks and redundant steps, the grain storage process is optimized and the grain storage area is effectively allocated, thereby achieving efficient grain storage. Based on this idea, a grain storage process efficiency model is constructed, which satisfies the following expression: , in, Indicates that the food type is , quality is and weight is Effective efficiency of the warehousing process, Indicates that the food type is Maximum efficiency of the warehousing process at the time Indicates that the food type is The number of steps in the warehousing process can be adjusted. Indicates Steps in food quality and weight is The weight of Indicates that the food type is The The efficiency of each step, Indicates that the food type is The sum of the efficiencies of all steps at .

[0032] In another embodiment, the target optimization problem of equipment parameters is solved by constructing an equipment efficiency model. The equipment parameters include but are not limited to the conveying speed, which has a certain impact on the quality of grain. The equipment efficiency model satisfies the following expression: , in, Indicates that the food type is , quality is and weight is Equipment efficiency at Indicates that the food type is Optimal equipment efficiency at the time of Indicates that the food type is , quality is and weight is The change in quality loss when Indicates that the food type is The gain factor when Indicates that the food type is The device parameter adjustment amount when

[0033] S22. Based on the grain storage process efficiency model, adjust the storage process of the grain to be stored and obtain the optimal adjustment result.

[0034] In one embodiment, based on the grain warehousing process efficiency model constructed in step S21, the warehousing process of the grain to be stored is adjusted to obtain the maximum effective efficiency of the grain warehousing process, thereby determining the optimal warehousing steps.

[0035] Specifically, first, set the maximum effective target efficiency The maximum effective target efficiency is obtained by using historical data to train the grain storage process efficiency model.

[0036] Furthermore, based on the information of the type, quality and weight of the grain to be stored obtained in step S1.

[0037] Furthermore, the warehousing steps are adjusted, and the grain warehousing process efficiency model is used to solve the effective efficiency of the warehousing process. .

[0038] Furthermore, when When the condition is met, that is, when the difference between the effective efficiency of the warehousing process and the maximum effective target efficiency is the smallest, the corresponding warehousing step is the optimal warehousing step, which is also the optimal adjustment result of the warehousing step.

[0039] S23. Based on the equipment efficiency model, adjust equipment parameters and obtain optimal adjustment results.

[0040] In one embodiment, based on the equipment efficiency model constructed in step S21, the equipment parameters are adjusted to obtain the maximum efficiency of the equipment, thereby determining the optimal equipment parameters.

[0041] Specifically, first, set the maximum target efficiency of the equipment , the maximum target efficiency is obtained by training the equipment efficiency model using historical data.

[0042] Further, based on the information on the type, quality and weight of the grain to be stored obtained in step S1, the equipment parameters are adjusted, and the amount of adjustment is , and use the equipment efficiency model to solve the equipment efficiency .

[0043] Furthermore, when When the condition is met, that is, when the difference between the maximum target efficiency and the equipment efficiency is the smallest, the corresponding equipment parameters are the optimal equipment parameters, which are also the optimal adjustment results of the equipment parameters.

[0044] S3. Based on the adjustment results, the grain to be stored is graded and impurities are removed to obtain the optimal processing result.

[0045] Among them, S3 includes the following steps: S31. Based on the adjustment results, the grain to be stored is graded.

[0046] In one embodiment, when the effective efficiency of the warehousing process and the equipment efficiency are both at their maximum, multi-layer vibration screening and magnetic separation technology, combined with high-definition cameras and deep learning algorithms, are used to grade the incoming grain to obtain a grading result.

[0047] Specifically, the grain to be stored first enters a multi-layer vibrating screening machine, and each layer of the screen is screened according to a preset particle size range; during the screening process, the vibration frequency and the screen aperture are adjusted to ensure that grains of different particle sizes can be effectively separated; after each layer of screening, the grain that meets the particle size range is collected and enters the next processing stage.

[0048] Furthermore, the grain passes through a magnetic separation device to remove metal impurities therein; the magnetic separation device uses a strong permanent magnetic roller to ensure that the metal impurities are effectively adsorbed and removed.

[0049] Furthermore, after screening and magnetic separation, the grain enters the shooting area of ​​a high-definition camera; the high-definition camera captures image data of grain particles and transmits it to a deep learning algorithm system for analysis; the deep learning algorithm automatically classifies the grain according to its color, shape and texture characteristics; the classification results are used to guide subsequent grain storage or packaging processes to ensure that each batch of grain can be accurately classified.

[0050] Furthermore, a classification accuracy model is constructed, and according to the feedback of the classification accuracy, the parameters and training data of the deep learning algorithm are adjusted to improve the classification accuracy; the classification accuracy model satisfies the following expression: , in, represents the classification accuracy, Indicates the weight of correctly graded grain, The correct graded grain weight is the grain weight correctly identified by the high-definition camera and the deep learning algorithm, and the total graded grain weight is the total weight of all grains entering the grading process.

[0051] Furthermore, the maximum storage efficiency and the maximum equipment efficiency are obtained through monitoring, thereby realizing the parameter setting of the multi-layer vibration screening and magnetic separation devices and improving the overall processing efficiency.

[0052] S32. Based on the grading results, the grain to be stored is subjected to impurity removal.

[0053] In one embodiment, based on the classification processing results, various types of impurities are accurately identified and efficiently removed to obtain the optimal processing results.

[0054] Specifically, during the multi-layer vibration screening process, grains and impurities of different particle sizes are separated according to the preset particle size range. Large impurities, such as stones and straw, are removed during the initial screening. The magnetic separation device removes metal impurities in the grain, such as nails and wires.

[0055] Furthermore, high-definition cameras capture image data of grain particles, and deep learning algorithms analyze the images to identify impurities that do not match the shape and color characteristics of the grain. Based on the identification results, mechanical rejection devices, such as pneumatic nozzles and robotic arms, accurately separate impurities from the grain.

[0056] Furthermore, wind separation uses the difference in wind resistance of different materials to blow light impurities, such as dust and wheat husks, out of the grain. Gravity separation uses the difference in density of the materials to separate impurities of different densities from the grain through vibration or airflow.

[0057] Furthermore, for particularly important grain batches or situations where it is difficult to remove impurities, manual re-inspection is carried out to ensure that the impurities are completely removed.

[0058] Furthermore, an impurity removal rate model is constructed, and the parameters and strategies of the impurity removal method are continuously adjusted according to the feedback of the impurity removal results. The impurity removal rate model satisfies the following expression: , in, Indicates the impurity removal rate, Indicates the total amount of impurities removed. It indicates the impurity content in the initial total amount of grain. The total amount of impurities removed is the weight of impurities successfully removed through the impurity removal process, and the impurity content in the initial total amount of grain is the percentage of impurities in the grain before entering the grading process.

[0059] S33. Obtain the optimal processing result according to the classification accuracy model and the impurity removal rate model.

[0060] In one embodiment, a classification accuracy model and an impurity removal rate model are used to construct an effect evaluation model for classification processing and impurity removal processing, and the effect evaluation model satisfies the following expression: ,

[0061] in, Represents the evaluation index of the treatment effect, represents the classification accuracy, Indicates the impurity removal rate, Indicates the total weight of pure grain, Indicates the total weight of grain to be stored. The total weight of pure grain is the weight of grain that meets the quality standards after grading and impurity removal. The evaluation index comprehensively considers the grading accuracy, impurity removal efficiency and pure grain output.

[0062] Furthermore, the threshold of the evaluation index is set ,in, The following conditions must be met:

[0063] when When the grading and impurity removal process is not up to standard; when When the grading and impurity removal processes meet the standards.

[0064] Furthermore, if the classification and impurity removal treatment meet the standards, The processing result closest to 1 is taken as the optimal processing result.

[0065] S4. Based on the optimal processing result, the humidity of the grain to be stored is regulated to obtain optimal regulation parameters.

[0066] Among them, S4 includes the following steps: S41. Based on the optimal processing results, construct an optimal grain humidity control model; In one embodiment, based on step S3, after the grains to be stored are optimally graded and impurities are removed, an optimal grain humidity control model is constructed, and the optimal grain humidity control model satisfies the following expression: , in, represents the objective function for optimal control of grain humidity, Indicates the type of food. Indicates the initial moisture content of grain. Indicates the target moisture content of the grain. Indicates the temperature of the dryer. Indicates the wind speed of the dryer. represents the energy consumption function, represents the food status monitoring function, represents the time variable, Indicates the time when the drying process ends. represents the weight coefficient, Represents the efficiency function of the drying process.

[0067] S42. Substitute the type of grain to be stored, the initial humidity and the target humidity into the expression satisfied by the optimal grain humidity control model to obtain the optimal control target value; In one embodiment, MATLAB numerical software is used to substitute the type of grain to be stored, the initial humidity and the target humidity into the expression satisfied by the optimal grain humidity control model to obtain the optimal control target value.

[0068] S43. Determine optimal control parameters through the optimal control target value, and the optimal control parameters include the optimal temperature and optimal wind speed of the dryer.

[0069] In one embodiment, based on the optimal control target value obtained in step S42 and in combination with the grain humidity optimal control model constructed in step S41, the optimal control parameters are deduced, and the optimal control parameters include the optimal temperature and optimal wind speed of the dryer.

[0070] S5. According to the optimal control parameters, execute the grain storage process to obtain the stored grain.

[0071] In one embodiment, the grain storage process is executed according to the optimal control parameters obtained in step S43 and the optimal adjustment result obtained in step S2.

[0072] Specifically, first, prepare storage containers, such as grain bags, for complete storage; the storage containers are clean, undamaged, and meet storage requirements, and a moisture-proof layer is laid inside to prevent the grain from getting damp.

[0073] Furthermore, the regulated grain is stored in sequence according to type and batch, and storage areas are allocated; during the storage process, attention is paid to maintaining the looseness of the grain to avoid compaction that causes uneven moisture.

[0074] Furthermore, the storage time, quantity and humidity information of each batch of grain are recorded; identification plates are affixed to the storage containers to indicate the type of grain, batch and storage time information.

[0075] Further, obtain complete sets of stored grain.

[0076] S6. Obtain monitoring results by monitoring the storage environment and the stored grain.

[0077] In one embodiment, first, the parameters of the monitoring equipment, such as sampling frequency and alarm threshold, are set to ensure that the monitoring parameters can accurately reflect the actual conditions of the storage environment and grain.

[0078] Furthermore, the monitoring system is started to collect and transmit monitoring data in real time to ensure the normal operation of the monitoring equipment and to record and process the monitoring data in a timely manner; the monitoring data includes the temperature, humidity and Concentration data.

[0079] Furthermore, the collected monitoring data is analyzed and processed to extract useful information and determine whether the storage environment and the state of grain are normal.

[0080] S7. According to the monitoring results, adjust the storage environment parameters to obtain the optimal adjustment results.

[0081] Wherein, S7 further comprises the following steps: S71. Based on the monitoring results of the storage environment and the stored grain, an optimal adjustment model for storage environment parameters is constructed.

[0082] In one embodiment, based on the monitoring results of the storage environment and the incoming grain, combined with the most suitable environmental target requirements for grain storage, an optimal adjustment model for storage environment parameters is constructed. The expression satisfied by the optimal adjustment model for storage environment parameters is as follows: , in, represents the objective function for optimal adjustment of storage environment parameters, Indicates the actual storage environment temperature. Indicates the actual storage environment humidity. Represents the actual storage environment concentration, Indicates the type of grain entering the warehouse. Indicates the initial humidity of grain when it enters the warehouse. Represents the total energy consumption function of the storage environment, Indicates the inspection cycle of the storage environment. represents the efficiency function of the storage environment, represents the time variable, , Represents the weight coefficient.

[0083] S72. By adjusting the temperature, humidity and The concentration is combined with the optimal adjustment model of the storage environment parameters to obtain the optimal adjustment objective function value.

[0084] In one embodiment, the appropriate temperature, humidity and Concentration adjustment equipment.

[0085] Specifically, use air conditioners, dehumidifiers, humidifiers and Generators and other equipment are used to adjust the parameters of the storage environment.

[0086] Furthermore, the adjustment equipment is reasonably arranged in the storage environment to ensure that the parameters of the storage environment can be adjusted evenly and effectively. Among them, the operating noise and energy consumption factors of the equipment are fully considered to optimize the equipment layout plan.

[0087] Furthermore, according to the optimal adjustment model and the target value, the parameters of the adjustment device are set, including the control range of the temperature and humidity, and The rate at which a concentration is replenished or discharged.

[0088] Further, the conditioning equipment is started to adjust the temperature, humidity and concentration parameters, and monitor the operating status of the equipment to ensure that the equipment operates normally and achieves the expected effect. During the adjustment process, when the minimum optimization condition of the storage environment parameter optimal adjustment model is met, the optimal adjustment objective function value is obtained. It should be noted that the optimal adjustment objective function value refers to the objective function in the storage environment parameter optimal adjustment model constructed in step S71. The value of .

[0089] S73. Determine the optimal temperature, optimal humidity and optimal storage environment according to the optimal adjustment objective function value. concentration.

[0090] In one embodiment, the temperature, humidity and concentration, determine the optimal temperature, optimal humidity and optimal concentration.

[0091] S8. Based on the optimal adjustment result, the optimal outbound unit and the optimal transportation route are planned by constructing a deep learning outbound unit planning model and a transportation route planning model.

[0092] Wherein, S8 further comprises the following steps: S81. Based on the optimal adjustment result, use the deep learning outbound unit planning algorithm to plan the optimal outbound unit.

[0093] In one embodiment, first, the warehouse management system obtains information on grain location, inventory, warehouse layout, channel width, and obstacle location.

[0094] Furthermore, the warehouse layout diagram is converted into a format suitable for deep learning model processing, and other data are normalized and feature engineered.

[0095] Furthermore, according to the characteristics of warehouse layout and path planning problems, a convolutional neural network is selected to process image data.

[0096] Furthermore, a deep learning outbound unit planning model is constructed.

[0097] Specifically, a model architecture is designed, including an input layer, a hidden layer and an output layer. The input layer receives preprocessed inventory, warehouse layout and grain location information; the hidden layer extracts features through a multi-layer neural network; and the output layer outputs the location of the optimal outbound unit.

[0098] Furthermore, for the input layer, assume that the size of the warehouse layout map is , the grain location information is represented as a two-dimensional coordinate .in, Indicates width, Indicates altitude.

[0099] Furthermore, the hidden layers include convolutional layers, pooling layers, and fully connected layers.

[0100] Specifically, the hidden layer is used to extract local features of the warehouse layout diagram and is designed as multiple convolutional layers, each of which uses a different convolution kernel size and number. The expression of the convolution operation is as follows:

[0101] in, represents the result of the convolution operation, Indicates input, represents the convolution kernel weight, represents the bias term, Represents the activation function.

[0102] The pooling layer is used to reduce the dimension of the feature map, reduce the amount of calculation, and retain important features.

[0103] The fully connected layer flattens the output of the convolutional layer or the pooling layer, and then performs feature fusion and classification through the fully connected layer. The expression of the fully connected operation is as follows:

[0104] in, Represents the result of the full connection operation. represents the weight matrix, represents the output of the hidden layer, represents the activation function, Represents the bias term.

[0105] Furthermore, for the output layer, a two-dimensional coordinate Indicates the location of the optimal outbound unit. To ensure that the output is within the boundaries of the warehouse, the sigmoid function is used to map the output to the (0, 1) interval and then multiplied by the size of the warehouse.

[0106] Furthermore, historical data is used as a training set and the model parameters are adjusted through a back-propagation algorithm so that the model can accurately predict the optimal outbound unit.

[0107] Furthermore, the latest inventory quantity, grain location and warehouse layout information are input into the trained deep learning outbound unit planning model. The model predicts the optimal outbound unit location based on the input information.

[0108] S82. Based on the optimal outbound unit, intelligent loading technology is used to quickly weigh and efficiently load grain.

[0109] In one embodiment, based on the optimal outbound unit determined in step S81, intelligent loading technology is used to quickly weigh and efficiently load the grain.

[0110] Specifically, a grabbing device is installed above the optimal outbound unit position, and a suitable grabbing method is selected according to the type and shape of the grain.

[0111] Furthermore, a high-precision weighing sensor is installed under the grabbing device. When the grabbing device contacts the grain and starts to lift it, the weighing sensor will record the weight of the grain in real time.

[0112] Furthermore, by calculating the difference between the weighing sensors before and after grabbing, the accurate weight of the grabbed food can be obtained.

[0113] Furthermore, the captured grain is quickly loaded into the transport equipment.

[0114] Furthermore, during the loading process, barcode and RFID technology are used to identify and track grains, ensuring that the source, weight and destination of each batch of grains are clearly traceable. At the same time, the weighing results and loading information are recorded in real time into the database for subsequent data analysis and report generation.

[0115] Furthermore, an intelligent loading implementation model is constructed, and the intelligent loading implementation model is used to judge the grain loading situation. The intelligent loading implementation model satisfies the following expression:

[0116] in, Indicates the implementation results. represents the implementation function, represents the optimal outbound unit position, Indicates the total weight of the loaded grain. Indicates the type and status of the transport equipment, The optimal outbound unit position refers to the two-dimensional coordinates mentioned in step S81. The types of transport equipment include AGV trolleys and robotic arms, and the status of the transport equipment includes the current position, speed and load capacity. The specific implementation details of the implementation model are as follows: By judging the type and status of the transport equipment , reasonably allocate transportation equipment, and load at a time of When the total weight after loading is of food.

[0117] S83. Based on the optimal outbound unit, plan the optimal transportation route according to the type, weight and transportation cost of grain, and the starting and ending point information.

[0118] In one embodiment, first, a transportation path planning model is constructed according to the type, weight, transportation starting point and end point information of the grain, and the transportation path planning model satisfies the following expression: , in, represents the objective function value, also known as minimizing the comprehensive transportation cost per unit weight, Indicates The unit cost of growing food, Indicates The weight of the grain, represents the total weight of all food, Represents a set of transportation paths from the starting point to the end point. Indicates The weight of the transport path.

[0119] Furthermore, the transportation path planning model is used to find an optimal transportation path from the warehouse to the destination, which minimizes the comprehensive transportation cost per unit weight after considering the type, weight and transportation cost of grain.

[0120] See also Figure 2 , Figure 2 The structure diagram of the complete set storage and transportation system for grain in the embodiment of the present invention is shown in FIG. The system includes an input device, a processor, an output device and a memory, wherein the input device, the processor, the output device and the memory are connected to each other, wherein the memory is used to store a computer program, the computer program includes program instructions, the processor is configured to call the program instructions, and the system uses the complete set storage and transportation method for grain in the whole process.

[0121] In this embodiment, the input device includes a collection module and an input module; Specifically, the acquisition module uses a variety of instruments, including temperature monitors, humidity monitors, and gas concentration monitors, to obtain real-time temperature, humidity, and Concentration data; the input module includes a display screen for inputting storage and transportation information of grain, including information on grain type, quality and weight.

[0122] The processor, as the core of the system, includes a calculation module and an analysis module; Specifically, the calculation module has built-in multiple calculation models, including an effect evaluation model of grading and impurity removal, a grain warehousing process efficiency model, an equipment efficiency model, an optimal grain humidity control model, an optimal storage environment parameter adjustment model, a deep learning outbound unit planning model, an intelligent loading implementation model, and a transportation route planning model; the analysis module is used to analyze the situation of grain entry, storage, and outbound delivery.

[0123] The output device includes a display screen for displaying the processing results of the processor and the actual situation of the whole process of complete storage and transportation of grain.

[0124] The memory uses a high-speed solid-state hard drive, which has the characteristics of fast reading and writing speed, large capacity and high reliability. It is mainly used to store the original data obtained by the input device and the acquisition device and the result data processed by the processor, and can meet the needs of large data storage.

[0125] In summary, the present invention provides a complete set of grain storage and transportation methods for the entire process. By utilizing intelligent adjustment mechanisms and processing measures, the storage process and equipment parameters are optimized according to the different types, qualities and weights of grains, so as to achieve accurate grading of grains and efficient removal of impurities. By adopting creative real-time monitoring and control technologies, it is ensured that the storage environment parameters are always kept within the optimal range, effectively ensuring the storage quality and safety of grains. By constructing a deep learning outbound unit planning model and a transportation route planning model, the optimal planning of outbound units and transportation routes is achieved, thereby improving the efficiency and accuracy of outbound operations and effectively reducing grain and equipment losses.

[0126] The present invention provides a complete set of grain storage and transportation system for the entire process, which realizes high-precision and high-efficiency grain grading and impurity removal by integrating multi-layer vibration screening, magnetic separation technology, high-definition cameras and deep learning algorithms, and provides high-quality guarantee for grain storage; by utilizing the optimal adjustment model of storage environment parameters, combined with the deep learning outbound unit planning model and the intelligent loading implementation model, the intelligent and automated grain outbound transportation is realized, which not only greatly improves the storage and transportation efficiency, but also reduces energy consumption and costs; by planning the optimal transportation route, the safety, speed and low cost of grain transportation are ensured, which provides strong technical support for the sustainable development of the grain industry.

[0127] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A complete set of grain storage and transportation method, characterized in that: The method comprises the following steps: Obtain information on the type, quality and weight of grain to be stored; According to the information, the storage process and equipment parameters of the grain to be stored are adjusted to obtain the optimal adjustment result; Based on the optimal adjustment result, the grain to be stored is graded and impurities are removed to obtain the optimal processing result; Based on the optimal processing result, the humidity of the grain to be stored is adjusted to obtain the optimal adjustment parameters; According to the optimal control parameters, the grain storage process is executed to obtain the stored grain; By continuously monitoring the storage environment and the stored grain, monitoring results are obtained; According to the monitoring results, adjust the storage environment parameters to obtain the optimal adjustment results; Based on the optimal adjustment results, the optimal outbound unit and the optimal transportation route are planned by constructing a deep learning outbound unit planning model and a transportation route planning model.

2. A complete set of grain storage and transportation method according to claim 1, characterized in that: The step of adjusting the storage process and equipment parameters of the grain to be stored according to the information to obtain the optimal adjustment result includes: Based on the information, a grain storage process efficiency model and an equipment efficiency model are constructed; Based on the grain storage process efficiency model, the storage process of the grain to be stored is adjusted to obtain the optimal adjustment result of the storage process; Based on the equipment efficiency model, equipment parameters are adjusted to obtain optimal adjustment results of the equipment parameters.

3. A complete set of grain storage and transportation method according to claim 2, characterized in that: The grain storage process efficiency model satisfies the following expression: , in, Indicates that the food type is , quality is and weight is Effective efficiency of the warehousing process, Indicates that the food type is Maximum efficiency of the warehousing process at the time Indicates that the food type is The number of steps in the warehousing process can be adjusted. Indicates Steps in food quality and weight is The weight of Indicates that the food type is The The efficiency of each step, Indicates that the food type is The equipment efficiency model satisfies the following expression: , in, Indicates that the food type is , quality is and weight is Equipment efficiency at Indicates that the food type is Optimal equipment efficiency at the time of Indicates that the food type is , quality is and weight is The change in quality loss when Indicates that the food type is The gain factor when Indicates that the food type is The device parameter adjustment amount when 4. A complete set of grain storage and transportation method according to claim 1, characterized in that: The optimal adjustment result is used to grade and remove impurities from the grain to be stored, and the optimal processing result is obtained, which includes: When the effective efficiency of the warehousing process and the equipment efficiency are both at their maximum, a grading accuracy model is constructed using multi-layer vibration screening and magnetic separation technology, combined with high-definition cameras and deep learning algorithms; Grading the grains according to the grading accuracy model to obtain grading results; Based on the classification processing results, constructing an impurity removal rate model; According to the impurity removal rate model, various impurities are accurately identified and efficiently removed; Using the classification accuracy model and the impurity removal rate model, a general effect evaluation model of classification processing and impurity removal processing is constructed; The optimal treatment result is obtained through the overall effect evaluation model.

5. A complete set of grain storage and transportation method according to claim 1, characterized in that: Based on the optimal processing result, the humidity of the grain to be stored is adjusted to obtain the optimal adjustment parameters, which includes: Based on the optimal processing results, constructing an optimal grain humidity control model; Substituting the type of grain to be stored, initial humidity and target humidity into the expression satisfied by the optimal grain humidity control model to obtain the optimal control objective function value; The optimal control parameters are determined by the optimal control objective function value, and the optimal control parameters include the optimal temperature and the optimal wind speed of the dryer.

6. A complete set of grain storage and transportation method according to claim 5, characterized in that: The optimal grain humidity control model satisfies the following expression: , in, represents the objective function for optimal control of grain humidity, Indicates the type of food. Indicates the initial moisture content of grain. Indicates the target moisture content of the grain. Indicates the temperature of the dryer. Indicates the wind speed of the dryer. represents the energy consumption function, represents the food status monitoring function, represents the time variable, Indicates the time when the drying process ends. represents the weight coefficient, Represents the efficiency function of the drying process.

7. A complete set of grain storage and transportation method according to claim 1, characterized in that: The step of adjusting the storage environment parameters according to the monitoring results to obtain the optimal adjustment results includes: According to the monitoring results of storage environment and incoming grain, an optimal adjustment model for storage environment parameters is constructed; By adjusting the temperature, humidity and concentration, combined with the optimal adjustment model of the storage environment parameters, to obtain the optimal adjustment objective function value; According to the optimal adjustment objective function value, the optimal temperature, optimal humidity and optimal concentration.

8. A complete set of grain storage and transportation method according to claim 7, characterized in that: The expression satisfied by the optimal adjustment model of storage environment parameters is as follows: , in, represents the objective function for optimal adjustment of storage environment parameters, Indicates the actual storage environment temperature. Indicates the actual storage environment humidity. Represents the actual storage environment concentration, Indicates the type of grain entering the warehouse. Indicates the initial humidity of grain when it enters the warehouse. Represents the total energy consumption function of the storage environment, Indicates the inspection cycle of the storage environment. represents the efficiency function of the storage environment, represents the time variable, , Represents the weight coefficient.

9. A complete set of grain storage and transportation method according to claim 1, characterized in that: Based on the optimal adjustment result, by constructing a deep learning outbound unit planning model and a transportation path planning model, planning the optimal outbound unit and the optimal transportation path includes: Based on the optimal adjustment result, a deep learning outbound unit planning model is constructed; Planning the optimal outbound unit according to the deep learning outbound unit planning model; Based on the optimal outbound unit, an intelligent loading implementation model is constructed; According to the intelligent loading implementation model, the grain is weighed quickly and loaded efficiently; Based on the optimal outbound unit, a transportation path planning model is constructed; Based on the transportation route planning model, the optimal transportation route is planned using the food type, weight, transportation cost, transportation starting point and transportation end point information.

10. A complete set of grain storage and transportation system, the system using a complete set of grain storage and transportation method according to any one of claims 1 to 9, characterized in that: The system includes an input device, a processor, an output device and a memory, wherein the input device, the processor, the output device and the memory are connected to each other, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions.

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