Intelligent storage management system before paddy hulling

Abstract

The application discloses a kind of intelligent storage management systems before paddy husking, including warehouse environment detection module, storage model building module, storage quality evolution estimation module, interference intervention evaluation module and conduction migration analysis module, the temperature and humidity of the position where the storage environment detection unit is in model building is carried out in the present application, obtains the temperature and humidity storage model of paddy storage warehouse, and by the temperature and humidity variation of each position in storage warehouse and the duration of paddy storage, the moisture content of paddy storage into paddy storage warehouse is analyzed, to analyze out the real-time moisture content in the internal of paddy storage warehouse along with temperature and humidity change in the process of paddy storage, and then predict the quality of paddy under real-time moisture content change Preplay situation, can accurately preplay the quality of paddy in the process of storage, and estimate the pre-interference intervention duration required for paddy to reach quality control lower limit under paddy quality preplay, realize intelligent control in the process of paddy storage, with the characteristics of intelligent dynamic regulation.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of rice storage and relates to an intelligent storage management system before rice hulling. BACKGROUND

[0002] With the increase of rice production in China, the problems of drying and storage of rice are increasingly valued, and grain storage safety is an important factor related to food safety. As a grain crop in China, the storage safety of rice is particularly important. The temperature and humidity of the storage environment of rice are important factors affecting the storage quality of rice. High temperature and moisture in the rice storage warehouse can cause rice to heat up, and different degrees of changes such as point jade, mold, and rot will occur. At the same time, the heat consumption of grain dry matter will cause the weight of grain to decrease, and the nutrition to decrease or be lost.

[0003] The existing conventional detection method has problems such as frequent detection of rice in the storage warehouse, long detection time, and consumption of a large amount of manpower and material resources for obtaining storage environment parameters in the storage warehouse. In order to ventilate the storage warehouse, there is a problem of insufficient ventilation time or excessive ventilation volume in the ventilation process. The temperature and humidity at any position in the rice storage warehouse cannot be accurately predicted, and the moisture content of the rice cannot be analyzed according to the detected temperature and humidity in the storage warehouse and the storage conditions of the rice. Therefore, the quality change of the rice during the storage process cannot be analyzed according to the change of the moisture content of the rice. In addition, in the process of ventilating the rice storage warehouse in the prior art, there is a problem of continuous ventilation, which causes energy waste or insufficient ventilation time, so that the temperature and humidity in the storage warehouse do not reach the standard storage environment, affecting the quality control of the rice during the storage process, and the intelligent dynamic ventilation control adjustment of the rice storage process cannot be realized. SUMMARY

[0004] The purpose of the present application is to provide an intelligent storage management system before rice hulling, which solves the problems existing in the prior art.

[0005] The purpose of the present application can be realized by the following technical solutions:

[0006] An intelligent storage management system before rice hulling, comprising a warehouse environment detection module, a storage model building module, a storage quality evolution estimation module, and an interference intervention evaluation module.

[0007] The warehouse environment detection module comprises a plurality of warehouse environment detection units distributed in the storage warehouse for detecting the temperature and humidity and air volume parameters at the placed position.

[0008] The storage model building module is configured to obtain the temperature and humidity detected by each position number corresponding warehouse environment detection unit, and to establish a mapping relationship between the temperature and humidity detected by each warehouse environment detection unit and the spatial position of the warehouse environment detection unit in the paddy storage bin, train the temperature and humidity at each spatial position in the paddy storage bin, and build a temperature and humidity storage model in the paddy storage bin.

[0009] The storage quality evolution estimation module is configured to extract the temperature and humidity parameters at any position in the paddy storage bin, and to select the storage duration of the paddy and the moisture content of the paddy when stored in the paddy storage bin, analyze the temperature and humidity parameters of the paddy with the moisture content changing with the storage duration, and obtain the paddy quality evolution estimation coefficient at each position in the paddy storage bin.

[0010] The interference intervention evaluation module is configured to obtain the paddy quality evolution estimation coefficient at each position in the paddy storage bin, select the position of the paddy storage bin with the largest paddy quality evolution estimation coefficient in each paddy storage bin, analyze the largest paddy quality evolution estimation coefficient, evaluate the pre-interference intervention duration corresponding to the paddy reaching the lower limit of quality control when the largest paddy quality evolution estimation coefficient is reached, and control the ventilation of the storage bin according to the pre-interference intervention duration.

[0011] Further, when building the temperature storage model and the humidity storage model, the position of the warehouse environment detection unit located at the center of the bottom of the paddy storage bin is taken as the origin to establish an xy coordinate, i.e., (x 01 ,y 01 ) as the coordinate origin.

[0012] Further, the paddy storage quality is evolved and analyzed under the influence of the moisture content when stored in the paddy storage bin, the storage duration, and the temperature and humidity parameters in the storage process, and specifically includes the following steps:

[0013] Step A1, extracting the temperature and humidity at each position in the storage bin at each equal-interval sampling time point within the storage duration of the paddy;

[0014] Step A2, analyzing the unit change rate of the temperature and humidity at each position in the storage bin, the temperature unit change rate and the humidity unit change rate W k (x,y) is the temperature at the (x, y) position at the kth equal-interval sampling time point, Q k (x,y) is the humidity at the (x, y) position at the kth equal-interval sampling time point, k = 2, 3,..., and T is the duration between adjacent equal-interval sampling time points.

[0015] Step A3, analyzing the temperature unit change rate and humidity unit change rate in step A2, and estimating the real-time moisture content L of the rice in the rice storage bin n (x,y) ;

[0016] Step A4, extracting the real-time moisture content of the rice at each position in the rice storage bin in step A3, and estimating the quality evolution of the rice at the position in the rice storage bin to obtain the quality evolution estimation coefficient of the rice.

[0017] Further, it also includes a conduction migration analysis module, which is used to judge whether the pre-interference duration analyzed by the interference intervention evaluation module is less than or equal to the set time threshold, and if it is less than or equal to the set time threshold, the weight of the rice stacked in the storage bin, the stacking height of the rice, and the temperature and humidity parameters at each position in the storage bin built by the storage model building module are obtained, and a ventilation conduction migration model is used to analyze the conduction migration of the temperature and humidity at each position in the rice storage bin, and the conduction migration coefficient of each position in the storage bin is obtained.

[0018] Further, the ventilation conduction migration model in the conduction migration analysis module is μ (x,y) μ is the conduction migration coefficient at the (x, y) position in the rice storage bin, μ is the conduction migration coefficient at the (x, y) position in the rice storage bin, G is the total weight of the rice stored in the rice storage bin, g is the gravitational constant, which is 9.8, x and y are the position coordinates at the (x, y) position, D is the radius of the rice storage bin, the rice storage bin in this embodiment is cylindrical, h is the storage height of the rice storage bin, v represents the volume of gas introduced into the rice storage bin per unit time through the ventilation pipe, s is the cross-sectional area of the ventilation pipe, σ (x，y) μ is the conduction migration coefficient at the (x, y) position in the rice storage bin, β is the temperature and humidity ratio, which is 0.47, θ (x,y) μ is the conduction migration coefficient at the (x, y) position in the rice storage bin, (x,y) μ is the conduction migration coefficient at the (x, y) position in the rice storage bin, the values of the temperature and humidity dispersion guide coefficients are obtained by training, W (x，y) W is the temperature at the (x, y) position in the rice storage bin, W0 is the set standard temperature, Q (x，y) Q is the humidity at the (x, y) position in the rice storage bin, Q0 is the set standard humidity, and the set standard temperature and humidity are the temperature and humidity input by the air inlet pipe.

[0019] Further, the evacuation migration prediction module is further included for obtaining the conduction migration coefficient of each position in the storage bin, and predicting the conduction migration time length of the temperature and humidity evacuation at each position according to the conduction migration coefficient, extracting the maximum conduction migration time length, judging whether the maximum conduction migration time length is greater than the set evacuation standard time length, and if greater than the set evacuation standard time length, dynamically adjusting the volume of the gas introduced into the paddy storage bin by the ventilation duct per unit time until the conduction migration time length corresponding to the conduction migration coefficient of each position in the storage bin is less than the pre-disturbance intervention time length.

[0020] Further, the evacuation training analysis module is further included for obtaining the air volume parameter at the position of each storage environment detection unit in the storage bin, and experimentally training the air volume parameter of the storage environment detection unit at each position in the storage bin to obtain the correlation interference factor between the air volume parameter at each position and the distance from the center axis of the storage bin, and further obtaining the air volume parameter at any position in the storage bin.

[0021] Further, the evacuation spread evaluation module is further included for extracting the air volume parameter at any position in the storage bin obtained by the evacuation training analysis module, and simultaneously obtaining the temperature and humidity parameters at each position in the storage bin built by the storage model building module, and estimating the temperature evacuation guide coefficient and the humidity evacuation guide coefficient corresponding to each position through the air volume parameter at each position in the storage bin and the temperature and humidity parameters at each position.

[0022] The beneficial effects of the present application are as follows:

[0023] The present application builds a model for the temperature and humidity at the positions of a limited number of storage environment detection units in the storage bin, obtains the temperature and humidity storage model of the paddy storage bin, so as to obtain the temperature and humidity parameters at any position in the storage bin according to the temperature and humidity storage model, reduces the number of storage environment detection units required for detecting the environmental parameters at each position in the storage bin, and improves the accuracy of detecting the temperature and humidity parameters at each position in the storage bin, thereby providing reliable basic data for analyzing the influence of the temperature and humidity in the storage environment on the quality of the paddy.

[0024] This invention analyzes the temperature and humidity changes at various locations within the storage silo, the storage time of the rice, and the moisture content of the rice upon arrival at the silo. This analysis reveals the real-time moisture content within the silo as temperature and humidity change during storage, allowing for the prediction of rice quality under varying moisture content. This enables quantitative analysis of rice quality under different storage conditions, accurately predicting rice quality during storage and assessing the pre-interference intervention time required for the rice to reach its quality control limit. This facilitates early assessment of the storage environment for rice that is most likely to yellow, allowing for timely intervention and minimizing quality degradation. Furthermore, the invention allows for timely control of ventilation devices based on the pre-interference intervention time, ensuring the quality of the rice within the storage silo.

[0025] This invention analyzes the weight of rice stored in a storage silo, the height of the rice stack, and the temperature and humidity parameters at various locations using a ventilation conduction migration model. It obtains the conduction migration coefficient for each location within the silo and compares the predicted conduction migration time for temperature and humidity dissipation at each location with a set standard dissipation time. This allows for dynamic adjustment of the volume of gas introduced into the rice storage silo per unit time through the ventilation ducts, achieving intelligent management of rice storage. This ensures intelligent heat dissipation and dehumidification of the storage silo, avoids continuous ventilation which wastes energy, or insufficient ventilation time which results in suboptimal temperature and humidity levels within the storage silo, thus improving the quality of stored rice. Attached Figure Description

[0026] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a schematic diagram of the internal distribution of the storage compartment in this invention. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Rice storage requires timely ventilation: Newly grown rice often has vigorous respiration, and the temperature and moisture inside the rice storage silo are relatively high. It is necessary to ventilate in a timely manner. If ventilation is not carried out in time, the storage silo will heat up and mold, which will affect the quality of the stored rice.

[0030] Several rice storage bins are distributed within the warehouse. Each rice storage bin has an air inlet at the bottom and an air outlet at the top. The air inlet at the bottom is connected to an air supply duct, which is connected to a fan. When the fan is working, the air generated by the fan is dried by a dryer and then enters the bottom of the rice storage bin through the air inlet duct and the air inlet of the storage bin in sequence. Finally, it is discharged from the air outlet at the top of the rice storage bin, which can effectively remove moisture and heat from the rice storage bin.

[0031] An intelligent storage management system for rice before hulling includes a storage environment monitoring module, a storage model building module, a storage quality evolution prediction module, an interference intervention assessment module, and a storage ventilation control module.

[0032] The storage environment monitoring module consists of several storage environment monitoring units, which are distributed at three different storage heights: bottom, middle, and top of the rice storage silo. The storage environment monitoring unit at the bottom is 5cm away from the bottom of the rice storage silo, the storage environment monitoring unit at the top is 5cm away from the top of the rice storage silo, and the storage environment monitoring unit in the middle of the rice storage silo is located at half the height of the rice storage silo.

[0033] like Figure 1 As shown, a storage environment detection unit is installed at the central axis position of the rice storage silo at the same storage height, and at half the distance between the central axis of the storage silo and the edge of the rice storage silo. By acquiring the temperature and humidity at different storage heights at the bottom, middle and top of the rice storage silo, as well as at different positions at the same storage height from the central axis of the storage silo, the temperature and humidity parameters at each position inside the rice storage silo can be uniformly obtained.

[0034] The storage environment detection unit detects the temperature, humidity, and airflow parameters at the placement location, and feeds back the detected temperature and humidity at the placement location and the location number corresponding to the storage environment detection unit at that location to the storage model building module. It also sends the detected airflow parameters at the location of the storage environment detection unit to the evacuation training and analysis module. Specifically, in this embodiment, the airflow parameter is the volume of gas flowing through the location per unit time.

[0035] The storage model building module is used to obtain the temperature and humidity detected by the storage environment detection unit corresponding to each location number, and to establish a mapping relationship between the temperature and humidity detected by the storage environment detection unit under each location number and the spatial location of the storage environment detection unit in the rice storage warehouse. The module trains the temperature and humidity at each spatial location in the rice storage warehouse to build a temperature and humidity storage model in the rice storage warehouse. By building the temperature and humidity storage model of the rice storage warehouse, the temperature and humidity parameters at any location in the rice storage warehouse can be obtained, realizing the convenience of obtaining the temperature and humidity at any location in the rice storage warehouse, reducing the number of storage environment detection units used to obtain the temperature and humidity parameters at each location in the rice storage warehouse, and featuring high accuracy in storage model building.

[0036] When building the temperature and humidity storage models, the origin is set at the location of the storage environment monitoring unit at the center of the bottom of the rice storage silo, and the xy coordinates are established, i.e., (xy = xyy)y ... 01 ,y 01 () is the origin of the coordinate system. Since the temperature and humidity of rice at the same height and distance from the central axis of the rice storage silo are relatively similar, in order to reduce the amount of experimental data, it is stipulated that the temperature and humidity of the rice at the same height and distance from the central axis of the rice storage silo are the same.

[0037] Among them, the temperature storage model:

[0038]

[0039] W (x,y) W represents the temperature value at (x, y) inside the storage compartment. 01 and W 02 These are represented as the temperature value at the bottom center and the temperature value at the midpoint of the line connecting the bottom center and the edge of the storage compartment, respectively, W. 11 and W 12 These are the temperature values ​​at the center of the storage compartment and the temperature value at the midpoint of the line connecting the center of the storage compartment and the edge of the storage compartment, respectively. W 21 and W 22 These are the temperature values ​​at the center of the top of the storage bin and the temperature values ​​at the midpoint of the line connecting the top of the storage bin to the edge of the storage bin, respectively. (x 01 ,y 01 (x) represents the coordinates of the bottom center. 02 ,y 02 (x) represents the coordinates of the midpoint of the line connecting the bottom center and the bottom edge of the storage compartment. 11 ,y 11 (x) represents the coordinates of the center of the storage compartment. 12 ,y 12 (x) represents the coordinates of the midpoint of the line connecting the center of the storage bin to its edge.21 ,y 21 (x) represents the coordinates of the center of the top of the storage bin. 22 ,y 22 ) represents the coordinates of the midpoint of the line connecting the top of the storage bin and the top edge of the storage bin.

[0040] The humidity storage model:

[0041]

[0042] Q (x,y) Q represents the humidity value at (x, y) inside the storage compartment. 01 and Q 02 Q represents the temperature value at the bottom center and the humidity value at the midpoint of the line connecting the bottom center and the bottom edge of the storage compartment, respectively. 11 and Q 12 These represent the temperature value at the center of the storage compartment and the humidity value at the midpoint of the line connecting the center of the storage compartment to its edge, respectively. 21 and Q 22 These are the humidity values ​​at the center of the top of the storage compartment and the humidity value at the midpoint of the line connecting the top of the storage compartment to its edge.

[0043] The storage quality evolution prediction module is used to extract temperature and humidity parameters at any location within the rice storage silo, and to filter out the storage time of the rice and the moisture content of the rice when it is stored in the silo. The module analyzes the temperature and humidity parameters of the rice at the given moisture content as the storage time changes, in order to obtain the rice quality evolution prediction coefficient at each location within the rice storage silo. By comprehensively analyzing the rice storage environment parameters, the module can predict the quality of the rice under the current storage environment, which facilitates the improvement of the rice storage environment based on the predicted rice quality, thereby ensuring the quality of the rice during the storage process.

[0044] The evolution of rice storage quality was analyzed based on the influence of moisture content upon storage, storage duration, and temperature and humidity parameters during storage. The analysis included the following steps:

[0045] Step A1: Extract the temperature and humidity at various locations within the storage chamber at equally spaced sampling points during the rice storage period;

[0046] Step A2: Analyze the unit rate of change of temperature and humidity at various locations within the storage chamber, including the unit rate of change of temperature. Humidity unit change rate W k (x,y) Let Q be the temperature at position (x, y) at the k-th equally spaced sampling time point. k (x,y)Let be the humidity at position (x, y) at the k-th equally spaced sampling time point, where k = 2, 3, ..., and T is the duration between two adjacent equally spaced sampling time points;

[0047] Step A3: Analyze the rate of change of temperature and humidity in step A2 to estimate the real-time moisture content of the rice in the rice storage silo. L n (x,y) The real-time moisture content of rice at position (x, y) at the nth equally spaced sampling time point, where n takes values ​​of 1, 2, 3, ..., χ is the moisture content of rice when it is stored in the storage bin, α is the moisture absorption coefficient of rice, with a value of 0.0317, obtained from multiple experiments, ΔηW is the maximum allowable temperature change rate per unit time, 0.12-0.3℃ / h, and ΔηQ is the maximum allowable humidity change rate per unit time, 0.01-0.025% / h. To ensure the security of core data, this invention does not disclose the data of the maximum allowable temperature change rate and maximum humidity change rate per unit time.

[0048] Step A4: Extract the real-time moisture content of rice at various locations within the rice storage silo from Step A3, and predict the quality evolution of the rice at those locations within the silo to obtain the rice quality evolution prediction coefficient φ. n (x,y) , The rice quality evolution prediction coefficient reflects the degree of quality degradation of rice under the interference of temperature and humidity inside the rice during storage. As the rice quality evolution prediction coefficient gradually increases, the quality of rice stored in the storage warehouse gradually decreases.

[0049] The interference intervention assessment module is used to obtain the predicted coefficient of rice quality evolution at various locations within the rice storage silos. It then identifies the rice storage silos with the highest predicted coefficient and analyzes this coefficient to determine the pre-interference intervention duration corresponding to when the rice reaches the lower limit of quality control. For rice with a predicted coefficient equal to the set risk threshold and no spoilage occurring below this threshold, yellowing gradually occurs once the predicted coefficient exceeds the risk threshold. Using the pre-interference intervention duration allows for early time assessment of the rice storage environment at which yellowing is most likely to occur, facilitating early intervention and minimizing quality degradation. This improves the quality of rice in the storage environment. The ventilation system can be controlled promptly based on the pre-interference intervention duration to ensure the quality of the rice within the storage silos.

[0050] The duration of pre-interference intervention is E is the rice quality evolution prediction coefficient corresponding to the lower limit of quality control, φ max It is the largest predictor of rice quality evolution in the rice storage silo.

[0051] In one embodiment, the ventilation device is combined to make intelligent prediction of the ventilation in the storage warehouse, and the humidity and heat emission of the rice in the storage warehouse are analyzed. Specifically, this embodiment also includes a conduction migration analysis module, an evacuation migration prediction module, an evacuation training analysis module, and an evacuation spread assessment module.

[0052] The conduction migration analysis module is used to determine whether the pre-interference intervention duration analyzed by the interference intervention assessment module is less than or equal to the set time threshold. If it is less than or equal to the set time threshold (the set time threshold is the longest time for ventilation of the rice storage silo that has reached the lower limit of quality control), the module obtains the weight of the rice piled in the storage silo, the height of the rice pile, and the temperature and humidity parameters at various locations in the storage silo built by the storage model building module. The ventilation conduction migration model is used to conduct conduction migration analysis on the temperature and humidity at various locations in the rice storage silo to obtain the conduction migration coefficient at each location in the storage silo. The conduction migration coefficient reflects the ventilation device's ability to ventilate, dissipate heat, and remove moisture at various locations in the rice storage silo. Based on the conduction migration coefficient, the module analyzes the time required for the temperature and humidity in the rice storage silo to decrease to below the standard storage environment (standard storage temperature and standard storage humidity) under ventilation, thereby facilitating intelligent control of the ventilation device.

[0053] The ventilation conduction migration model is μ (x,y) Let be the conduction migration coefficient at position (x, y) inside the rice storage silo. For conduction migration interference factor, G represents the total weight of rice stored in the rice storage silo, g is the gravitational constant with a value of 9.8, x and y are the position coordinates at (x, y), D is the radius of the rice storage silo (in this embodiment, the rice storage silo is cylindrical), h is the storage height of the rice storage silo, v represents the volume of gas introduced into the rice storage silo by the ventilation duct per unit time, s is the cross-sectional area of ​​the ventilation duct, and σ (x，y) Let (x, y) be the environmental evacuation barrier factor at location (x, y) inside the rice storage silo. β represents the temperature-humidity ratio, with a value of 0.47, and θ (x,y) ψ is the temperature evacuation guiding coefficient. (x,y) The values ​​of the humidity evacuation guidance coefficient, temperature evacuation guidance coefficient, and humidity evacuation guidance coefficient are given by W. (x，y) Q represents the temperature at position (x, y) inside the rice storage silo, where W0 is the set standard temperature. (x，y)The humidity at position (x, y) inside the rice storage silo is Q0, which is the set standard humidity. The set standard temperature and humidity are the temperature and humidity input from the air inlet duct.

[0054] The evacuation migration prediction module is used to obtain the conduction migration coefficients at various locations within the storage warehouse, and to predict the conduction migration duration for temperature and humidity evacuation at each location based on these coefficients. The maximum conduction migration time is extracted, and it is determined whether the maximum conduction migration time is greater than the set evacuation standard time. If it is greater than the set evacuation standard time, the volume of gas introduced into the rice storage silo by the ventilation duct per unit time is dynamically adjusted until the conduction migration time corresponding to the conduction migration coefficient at each location in the storage silo is less than the set evacuation standard time, so as to achieve the best heat dissipation and ventilation of the storage silo and effectively ensure the storage quality of the rice.

[0055] The evacuation migration prediction module analyzes the conduction migration coefficients of various locations within the storage chamber affected by factors such as temperature, humidity, and airflow parameters in the ventilation ducts. This yields the predicted conduction migration time for temperature and humidity evacuation at each location. Based on this predicted conduction migration time, the volume of gas flowing into the storage chamber through the ventilation ducts can be intelligently and dynamically adjusted. This ensures intelligent heat dissipation and dehumidification of the storage chamber, avoiding continuous ventilation that could lead to energy waste or insufficient ventilation time, resulting in the storage chamber's temperature and humidity failing to meet standard storage conditions and ultimately affecting storage quality.

[0056] In addition, this embodiment also describes an evacuation training analysis module and an evacuation spread assessment module for obtaining the temperature evacuation guidance coefficient and humidity evacuation guidance coefficient in the ventilation conduction migration model. Based on the real-time changes in air volume and temperature and humidity at each location, the module can accurately guide and predict the temperature and humidity evacuation in the storage chamber based on the air volume.

[0057] Since the number and location of each storage environment detection unit are limited, it cannot be guaranteed that the temperature, humidity and air volume parameters at any location in the storage chamber can be obtained through the storage environment detection unit. Based on the air volume parameters detected by the limited storage environment detection units in the storage chamber, and by training the limited storage environment detection units to obtain the air volume parameters at any location in the storage chamber.

[0058] Specifically, the evacuation training and analysis module is used to obtain the airflow parameters at the locations of each storage environment monitoring unit within the storage warehouse. Experimental training is then conducted on the airflow parameters of the monitoring units at each location within the storage warehouse to obtain the correlation interference factor between the airflow parameters at each location and the distance from the central axis of the storage warehouse; that is, the airflow interference factor e is obtained. -x This allows us to obtain airflow parameters at any location within the storage chamber. Because the resistance to airflow varies depending on the location of the rice grains in the storage chamber, it can be determined by the location of the air intake pipes and the distance of each location in the storage chamber from the central axis of the storage chamber in the horizontal plane. x is the x-coordinate of the storage chamber.

[0059] By conducting experimental training on the airflow parameters detected by the storage environment detection unit at a limited number of collection points within the storage chamber, patterns can be found to identify the correlation interference factors between the airflow parameters at each location and the distance from the central axis of the storage chamber. Subsequently, the airflow parameters at any location within the storage chamber can be analyzed based on these correlation interference factors.

[0060] The evacuation and spread assessment module extracts airflow parameters at any location within the storage chamber obtained from the evacuation training and analysis module. Simultaneously, it acquires temperature and humidity parameters at various locations within the storage chamber constructed by the storage model building module. Based on these airflow and temperature / humidity parameters, the temperature-based evacuation guidance coefficient for each location is estimated. Humidity evacuation guidance coefficient F is the set volume of standard gas passing through a unit area per unit time, and E W E represents the temperature evacuated per unit area per unit time by the volume of standard gas passing through it. Q This refers to the humidity value dissipated by the standard gas volume passing through a unit area per unit time. The dissipation and spread assessment module analyzes the degree of temperature and humidity dissipation in the storage chamber, thereby obtaining accurate data on the temperature and humidity dissipation process. This facilitates the accurate acquisition of the conduction and migration coefficients at various locations within the rice storage chamber, thereby improving the accuracy of the conduction and migration time of temperature and humidity dissipation at each location.

[0061] In addition, this system also includes a warehouse ventilation control module. The intelligent ventilation device includes a gas drying cylinder, a fan, a ventilation duct, and a controller. The controller is connected to the fan and is used to control the volume of air flowing through the ventilation duct generated by the fan per unit time. The fan is connected to the ventilation duct and the gas drying cylinder in sequence. The gas drying cylinder is connected to the air inlet of the rice storage silo through the ventilation duct. The air generated by the fan flows into the gas drying cylinder through the ventilation duct between the fan and the gas drying cylinder (the volume of gas flowing into the gas drying cylinder and the volume of gas flowing out of the gas drying cylinder per unit time are the same). The gas dried by the gas drying cylinder is connected to the air inlet at the center of the bottom of the storage silo through the ventilation duct, which facilitates the introduction of dried gas and the discharge of humid gas and high-temperature environment in the storage silo from the air outlet of the storage silo.

[0062] The above formulas are all dimensionless calculations. The formulas are derived from software simulations using a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art based on the actual situation. The magnitudes of the proportionality coefficient and weighting coefficient are to quantify each parameter to obtain a specific value for easy comparison later. Regarding the magnitudes of the proportionality coefficient and weighting coefficient, it is acceptable as long as they do not affect the proportional relationship between the parameter and the quantified value.

[0063] The above description is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in the claims, they should all fall within the protection scope of the present invention.

Claims

1. An intelligent storage management system for rice before hulling, characterized in that: It includes a warehouse environment monitoring module, a storage model building module, a storage quality evolution prediction module, and an interference intervention assessment module; The storage environment monitoring module includes several storage environment monitoring units distributed within the storage warehouse to monitor the temperature, humidity, and airflow parameters at the placement location. The storage model building module is used to obtain the temperature and humidity detected by the storage environment detection unit corresponding to each location number, and to establish a mapping relationship between the temperature and humidity detected by each storage environment detection unit and the spatial location of the storage environment detection unit in the rice storage warehouse. The module trains the temperature and humidity at each spatial location in the rice storage warehouse to build a temperature and humidity storage model in the rice storage warehouse. The storage quality evolution prediction module is used to extract temperature and humidity parameters at any location in the rice storage silo, and to filter out the storage time of the rice and the moisture content of the rice when it is stored in the rice storage silo. The module analyzes the temperature and humidity parameters of the rice at the moisture content as the storage time changes, so as to obtain the rice quality evolution prediction coefficient at each location in the rice storage silo. The interference intervention assessment module is used to obtain the predicted coefficient of rice quality evolution at various locations within the rice storage silo, screen out the location of the rice storage silo with the highest predicted coefficient of rice quality evolution, analyze the highest predicted coefficient of rice quality evolution, and assess the pre-interference intervention time corresponding to when the rice with the highest predicted coefficient of rice quality evolution reaches the lower limit of quality control, so as to control the ventilation of the storage silo according to the pre-interference intervention time. When building the temperature and humidity storage models, the origin is set at the location of the storage environment monitoring unit at the center of the bottom of the rice storage silo, and the xy coordinates are established as follows: The origin of the coordinate system; The evolution of rice storage quality was analyzed based on the influence of moisture content upon storage, storage duration, and temperature and humidity parameters during storage. The analysis included the following steps: Step A1: Extract the temperature and humidity at various locations within the storage chamber at equally spaced sampling points during the rice storage period; Step A2: Analyze the unit rate of change of temperature and humidity at various locations within the storage chamber, including the unit rate of change of temperature. Humidity unit change rate , Let be the temperature at position (x, y) at the k-th equally spaced sampling time point. Let represent the humidity at position (x, y) at the k-th equally spaced sampling time point, where k = 2, 3, ..., and T is the duration between two adjacent equally spaced sampling time points; Step A3: Analyze the rate of change of temperature and humidity in step A2 to estimate the real-time moisture content of the rice in the rice storage silo. ; Step A4: Extract the real-time moisture content of rice at various locations within the rice storage silo from Step A3, and predict the quality evolution of the rice at those locations within the rice storage silo to obtain the rice quality evolution prediction coefficient.

2. The intelligent storage management system for rice before hulling according to claim 1, characterized in that: It also includes a conduction migration analysis module, which is used to determine whether the pre-interference intervention duration analyzed by the interference intervention assessment module is less than or equal to the set time threshold. If it is less than or equal to the set time threshold, it obtains the weight of rice piled in the storage warehouse, the height of rice pile, and the temperature and humidity parameters at various locations in the storage warehouse built by the storage model building module. It then uses a ventilation conduction migration model to conduct conduction migration analysis on the temperature and humidity at various locations in the rice storage warehouse to obtain the conduction migration coefficient at each location in the storage warehouse.

3. The intelligent storage management system for rice before hulling according to claim 2, characterized in that: The ventilation conduction migration model in the conduction migration analysis module is as follows: , Let be the conduction migration coefficient at position (x, y) inside the rice storage silo. For conduction migration interference factor, G represents the total weight of rice stored in the rice storage silo, g is the gravitational constant with a value of 9.8, x and y are the coordinates of the position at (x, y), D is the radius of the rice storage silo (which is cylindrical), h is the storage height of the rice storage silo, v represents the volume of gas introduced into the rice storage silo by the ventilation duct per unit time, and s is the cross-sectional area of ​​the ventilation duct. Let (x, y) be the environmental evacuation barrier factor at location (x, y) inside the rice storage silo. , This represents the temperature and humidity ratio, with a value of 0.

47. This is the temperature evacuation guiding coefficient. The values ​​of the humidity evacuation guidance coefficient, temperature evacuation guidance coefficient, and humidity evacuation guidance coefficient are obtained through training. Let be the temperature at position (x, y) inside the rice storage silo. For the set standard temperature, Let be the humidity at position (x, y) inside the rice storage silo. The set standard humidity, standard temperature, and standard humidity are the temperature and humidity input from the air inlet duct.

4. The intelligent storage management system for rice before hulling according to claim 2, characterized in that: It also includes an evacuation migration prediction module, which is used to obtain the conduction migration coefficient of each location in the storage warehouse, and predict the conduction migration time of temperature and humidity evacuation at each location based on the conduction migration coefficient. The maximum conduction migration time is extracted, and it is determined whether the maximum conduction migration time is greater than the set evacuation standard time. If it is greater than the set evacuation standard time, the volume of gas introduced into the rice storage warehouse by the ventilation duct per unit time is dynamically adjusted until the conduction migration time corresponding to the conduction migration coefficient of each location in the storage warehouse is less than the pre-interference intervention time.

5. The intelligent storage management system for rice before hulling according to claim 3, characterized in that: It also includes an evacuation training and analysis module, which is used to obtain the air volume parameters at the locations of each storage environment detection unit in the storage warehouse. The module performs experimental training on the air volume parameters of the storage environment detection units at each location in the storage warehouse to obtain the correlation interference factor between the air volume parameters at each location and the distance from the central axis of the storage warehouse, thereby obtaining the air volume parameters at any location in the storage warehouse.

6. The intelligent storage management system for rice before hulling according to claim 5, characterized in that: It also includes an evacuation and spread assessment module, which extracts the air volume parameters at any location within the storage chamber obtained by the evacuation training and analysis module, and simultaneously obtains the temperature and humidity parameters at each location within the storage chamber built by the storage model building module. Based on the air volume parameters and temperature and humidity parameters at each location within the storage chamber, the temperature evacuation guidance coefficient and humidity evacuation guidance coefficient corresponding to each location are estimated.

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