Multi-sensor based rice milling force and environment control system

Abstract

The application discloses a multi-sensor-based rice milling intensity and environment regulation system, and belongs to the technical field of intelligent control of grain processing machinery. The system comprises a data acquisition unit for collecting state data of incoming rice through different types of sensors, a rice milling intensity analysis unit for generating theoretical rice milling intensity according to the state data of the incoming rice, an intensity adjustment unit for adjusting the rice milling intensity of a rice mill, a temperature prediction unit for obtaining state data of outgoing rice and state data of a whitening chamber, generating a whitening chamber temperature prediction value according to the state data of the outgoing rice and the state data of the whitening chamber, an environment adjustment analysis unit for generating a whitening chamber heat dissipation power adjustment value according to the whitening chamber temperature prediction value, and an environment adjustment unit for adjusting the environment of the whitening chamber. The application solves the problem of experience-dependent adjustment and temperature lag control in the prior art, improves rice milling accuracy, reduces the broken rice rate, and thus maintains a stable processing environment.

Description

Technical Field

[0001] This invention belongs to the field of intelligent control technology for grain processing machinery, and particularly relates to a rice milling force and environmental control system based on multiple sensors. Background Technology

[0002] Rice milling is the process of removing the outer layer of brown rice using physical or chemical methods to produce rice that meets food standards. Its core purpose is to improve the quality of the rice, increase the yield, and retain nutrients.

[0003] In traditional rice milling processes, the control of milling intensity and environmental regulation mainly rely on a single sensor or fixed parameter settings. Milling intensity adjustments are typically based on operator experience or simple feedback mechanisms (such as broken rice rate monitoring), achieved through manual adjustment of the milling roller pressure. Regarding environmental regulation, existing technologies mostly employ basic ventilation or constant temperature equipment to maintain the temperature of the whitening chamber. However, existing milling intensity control and environmental regulation technologies have three significant drawbacks: First, the precision of milling intensity control is low; fixed parameters or experience-based adjustments cannot adapt to differences in rice varieties, moisture content, etc., easily leading to over-milling (high broken rice rate) or under-milling (high bran rate), affecting rice quality and yield. Second, environmental regulation lacks predictability; adjustments are triggered only by real-time temperature thresholds, failing to predict the impact of rice flow heat accumulation or equipment load changes on the whitening chamber temperature, resulting in energy waste and the risk of heat damage, thus affecting milling efficiency. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a multi-sensor-based rice milling force and environmental control system, which solves the aforementioned problems.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a multi-sensor-based rice milling force and environmental control system, comprising:

[0006] The data acquisition unit is used to collect status data of the rice entering the machine through different types of sensors; where rice entering the machine refers to unhulled rice grains that have entered the rice milling machine.

[0007] The rice milling force analysis unit is used to establish a theoretical analysis model and generate theoretical rice milling force based on the state data of the rice entering the machine.

[0008] The force adjustment unit is used to adjust the rice milling force of the rice milling machine according to the theoretical rice milling force.

[0009] The temperature prediction unit is used to acquire the state data of the rice leaving the mill and the state data of the whitening chamber. Based on the theoretical milling force, the state data of the rice leaving the mill and the state data of the whitening chamber, a temperature prediction model is established to generate the predicted temperature value of the whitening chamber. Among them, the rice leaving the mill refers to the rice grains output from the rice mill after dehulling.

[0010] The environmental adjustment analysis unit is used to generate adjustment values ​​for the heat dissipation power of the whitening chamber based on the predicted temperature value of the whitening chamber;

[0011] The environmental adjustment unit is used to adjust the environment of the whitening chamber according to the heat dissipation power adjustment value of the whitening chamber.

[0012] Based on the above technical solutions, the present invention also provides the following optional technical solutions:

[0013] Further technical solution: The expression of the theoretical analysis model is specifically as follows:

[0014] ;

[0015] Where F represents the theoretical rice milling force. This indicates the average hardness of the rice grains, L represents the length of the rice entering the machine, W represents the width of the rice entering the machine, and H represents the width of the rice entering the machine. in This represents the moisture content of the rice entering the machine, 'a' represents the humidity correction coefficient, and 'k' represents the structural coefficient of the rice milling machine.

[0016] Further technical solution: The temperature prediction unit specifically includes:

[0017] The data acquisition module is used to acquire the status data of the rice leaving the mill and the status data of the whitening chamber. The status data of the rice leaving the mill includes the moisture content of the rice leaving the mill, and the status data of the whitening chamber includes the linear speed of the whitening roller, the volume of rice grains in the whitening chamber, the distribution density of rice grains in the whitening chamber, the total mass of rice grains in the whitening chamber, the wind speed at the air outlet of the whitening chamber, the area of ​​the air outlet of the whitening chamber, and the current heat dissipation power of the whitening chamber.

[0018] The frictional heat analysis module is used to generate frictional temperature rise values ​​based on theoretical rice milling force, rice grain volume in the whitening chamber, rice grain distribution density in the whitening chamber, and roller linear speed.

[0019] The evaporation heat analysis module is used to generate evaporation temperature drop values ​​based on the moisture content of the rice leaving the mill and the total mass of rice grains in the whitening chamber.

[0020] The forced heat dissipation analysis module is used to generate forced heat dissipation temperature drop values ​​based on the current heat dissipation power of the whitening chamber, the air velocity at the whitening chamber outlet, and the area of ​​the whitening chamber outlet.

[0021] The temperature prediction model building module is used to build a temperature prediction model based on the friction temperature rise, evaporation temperature drop and forced heat dissipation temperature drop, and generate the predicted temperature value of the whitening chamber.

[0022] Further technical solution: The method for generating the frictional temperature rise specifically includes:

[0023] Through the formula:

[0024] ;

[0025] Generating frictional temperature rise value ;

[0026] In the formula, F represents the theoretical rice milling force, μ is the coefficient of friction, and V represents the volume of rice grains in the whitening chamber. This represents the linear velocity of the milling roller, and c represents the specific heat capacity of the rice grain. This indicates the predicted time duration.

[0027] Further technical solution: The method for generating the evaporation temperature drop specifically includes:

[0028] Through the formula:

[0029] ;

[0030] Evaporation temperature drop ;

[0031] In the formula, This refers to the latent heat of water vaporization. This indicates the moisture content of the rice entering the machine. This indicates the moisture content of the rice leaving the machine. This indicates the total mass of rice grains from the milling room. This represents the specific heat capacity of air. This indicates the air quality in the bleaching chamber.

[0032] Further technical solution: The method for generating the forced heat dissipation temperature drop value specifically includes:

[0033] Through the formula:

[0034] ;

[0035] Generates forced heat dissipation temperature drop value △T cool ;

[0036] In the formula, This indicates the current heat dissipation power of the whitening chamber. This represents the specific heat capacity of air. This indicates the air quality in the bleaching chamber. This indicates heat dissipation efficiency. This indicates the predicted time duration.

[0037] Further technical solution: The specific expression of the temperature prediction model is as follows:

[0038] Through the formula:

[0039] ;

[0040] Generate predicted temperature values ​​for the whitening chamber ;

[0041] In the formula, This indicates the current temperature value of the whitening chamber. This represents the frictional temperature rise value. This represents the evaporation temperature drop value. This represents the temperature drop value due to forced heat dissipation.

[0042] Further technical solution: the heat dissipation efficiency The specific methods of obtaining it include:

[0043] ;

[0044] Generating heat dissipation efficiency ;

[0045] In the formula, This represents the characteristic constant of the cooling fan. This indicates the effective area of ​​the air outlet. This indicates the airflow speed at the air outlet. This indicates the current heat dissipation power.

[0046] Further technical solution: The method for generating the heat dissipation power adjustment value of the whitening chamber specifically includes:

[0047] Through the formula:

[0048] ;

[0049] Generate heat dissipation power adjustment value for whitening chamber ;

[0050] In the formula, This indicates heat dissipation efficiency. This represents the predicted temperature of the whitening chamber. This indicates the optimal operating temperature of the whitening chamber. This represents the total heat capacity of the system. This indicates the predicted time duration.

[0051] This invention provides a multi-sensor-based rice milling force and environmental control system, which has the following advantages compared with the prior art:

[0052] This invention dynamically collects data through multiple sensors and establishes a theoretical model to adjust the rice milling force in real time. It combines a comprehensive predictive model of frictional heat, evaporative heat absorption, and forced heat dissipation to achieve advanced temperature control. This solves the problems of relying on experience for adjustment and lagging temperature control in traditional technologies, improves rice milling accuracy, reduces broken rice rate, and thus maintains a stable processing environment. Attached Figure Description

[0053] Figure 1This is a schematic diagram of the structure of a multi-sensor-based rice milling force and environmental control system provided in an embodiment of the present invention.

[0054] Figure 2 This is a block diagram of a temperature prediction unit provided in an embodiment of the present invention.

[0055] Figure 3 This is a flowchart illustrating a multi-sensor-based rice milling intensity and environmental control method provided in an embodiment of the present invention.

[0056] Figure 4 The flowchart of step S4 provided in the embodiment of the present invention is shown. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0058] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0059] Please see Figure 1 The rice milling force and environmental control system based on multiple sensors provided in one embodiment of the present invention specifically includes:

[0060] Data acquisition unit 10 is used to collect status data of rice entering the machine through different types of sensors; where rice entering the machine refers to unhulled rice grains entering the rice milling machine.

[0061] The rice milling force analysis unit 20 is used to establish a theoretical analysis model and generate theoretical rice milling force based on the state data of the rice entering the machine.

[0062] The force adjustment unit 30 is used to adjust the rice milling force of the rice milling machine according to the theoretical rice milling force;

[0063] The temperature prediction unit 40 is used to acquire the state data of the rice leaving the mill and the state data of the whitening chamber. Based on the theoretical milling force, the state data of the rice leaving the mill and the state data of the whitening chamber, a temperature prediction model is established to generate the predicted temperature value of the whitening chamber. Among them, the rice leaving the mill refers to the rice grains output from the rice mill after dehulling.

[0064] The environmental adjustment analysis unit 50 is used to generate the heat dissipation power adjustment value of the whitening chamber based on the predicted temperature value of the whitening chamber;

[0065] The environmental adjustment unit 60 is used to adjust the environment of the whitening chamber according to the heat dissipation power adjustment value of the whitening chamber;

[0066] Among them, the data acquisition unit 10 refers to the device that acquires the physical property parameters of rice grains through multiple types of sensors. Specifically, it can be implemented by using a hardness sensor, an optical size measuring instrument, and a moisture content detector. It is used to collect data on the hardness, length, width, and moisture content of rice grains in real time, providing a basis for subsequent analysis.

[0067] The rice milling force analysis unit 20 refers to a processor that establishes a milling intensity calculation model based on rice grain parameters. Specifically, it can be implemented using an embedded system or an industrial computer. By analyzing the correlation between rice grain hardness and geometric dimensions, it dynamically generates a milling pressure that is adapted to the current state of the rice grain.

[0068] Temperature prediction unit 40 refers to a prediction module that integrates the effects of frictional heat, evaporative heat dissipation and forced heat dissipation. Specifically, it can be implemented using thermodynamic simulation algorithms. By calculating the temperature change trend at future time points, it provides a predictive basis for environmental regulation.

[0069] The environmental adjustment analysis unit 50 is a calculation module that calculates heat dissipation demand based on the difference between the predicted temperature and the target temperature. Its implementation methods include, but are not limited to, PID control algorithms, and it is used to dynamically adjust the power of the heat dissipation equipment to maintain temperature stability.

[0070] Specifically, the data acquisition unit 10 acquires the hardness, size, and moisture content parameters of the rice entering the mill in real time through multiple sensors. The milling intensity analysis unit 20 substitutes the rice grain hardness and geometric size into a theoretical model to calculate the theoretical value of the milling intensity suitable for the current state of the rice grains. The intensity adjustment unit 30 precisely adjusts the milling roller pressure according to the theoretical value to ensure that the milling intensity matches the physical properties of the rice grains. The temperature prediction unit 40 predicts the temperature value at future time points based on the changes in rice grain volume and moisture content in the whitening chamber and the current heat dissipation power, using frictional heat, evaporative heat dissipation, and forced heat dissipation models. The environmental adjustment analysis unit 50 compares the predicted temperature with the preset optimal temperature and generates a heat dissipation power adjustment command. The environmental adjustment unit 60 adjusts the output power of the heat dissipation equipment according to the command, completing heat dissipation control before the temperature rises abnormally, forming a closed-loop control.

[0071] Compared to existing technologies, traditional methods rely on single sensor data or fixed parameters, failing to dynamically adapt to differences in rice grain characteristics, resulting in insufficient milling accuracy. This solution achieves real-time dynamic adjustment of milling intensity through multi-sensor fusion and theoretical model calculation. Traditional environmental control relies on threshold triggering, resulting in response lag; this solution uses a temperature prediction model to calculate heat accumulation trends in advance and actively adjusts heat dissipation power to avoid temperature overshoot. In traditional methods, milling intensity and environmental control are independent; this solution coordinates and optimizes milling intensity and heat dissipation control to form a closed-loop control system.

[0072] Through the above technical solutions, this application can dynamically adjust the milling intensity according to the differences in rice grain hardness, size and moisture content, thereby reducing abnormal rice breakage rate and bran retention rate; by predicting future temperature change trends, it can adjust the heat dissipation power in advance, thereby reducing the risk of heat damage and energy consumption; and by using multi-sensor data fusion and closed-loop control mechanism, it can improve the stability and adaptability of rice milling process.

[0073] Preferably, the present invention further proposes the following expression for the theoretical analysis model:

[0074] ;

[0075] Where F represents the theoretical rice milling force. This indicates the average hardness of the rice grains, L represents the length of the rice entering the machine, W represents the width of the rice entering the machine, and H represents the width of the rice entering the machine. in This represents the moisture content of the rice entering the machine, 'a' represents the humidity correction coefficient, and 'k' represents the structural coefficient of the rice milling machine.

[0076] Among them, the average hardness of rice grains Pa refers to the ability of a rice grain per unit area to resist plastic deformation; its unit is Pa, or N / m³. 2 Specifically, this can be achieved by using a hardness tester to measure the rice grains at multiple points and taking the average value. This parameter is used to reflect the hardness differences of different rice varieties and to avoid overloading or underloading the rice milling force due to excessively high or low hardness.

[0077] The length of the rice grain entering the mill refers to the maximum longitudinal dimension of a single rice grain, while the width of the rice grain entering the mill refers to the maximum transverse dimension of a single rice grain. Both are measured in meters (m). This can be achieved by using an image recognition system to scan the contours of the rice grains and calculate their geometric features. The square root product is used to characterize the influence of the rice grain surface area on the milling contact area. Furthermore, the square root product is used primarily because the contact between the rice grain and the milling roller is not a point contact, but an elliptical surface contact; according to Hertzian contact theory... This represents the equivalent contact diameter (characteristic length) of a rice grain.

[0078] It should be noted that, This is a simplified calculation for the equivalent contact diameter when the equivalent contact diameter R is greater than the grain length L (common in practical engineering) and the grain length-to-width ratio is greater than 3. The calculation of the equivalent contact diameter can be rigorously solved using Hertzian contact theory, or simplified when the roller curvature radius is greater than 3 times the grain length. ;

[0079] The moisture content of rice entering the mill refers to the percentage of the internal water mass of the rice grain to the total mass of the rice grain. It is a dimensionless value and can be achieved by online detection of the moisture content of rice grains using a near-infrared spectrometer. This parameter is added to a constant as a denominator to reflect the control logic of reducing the milling intensity when the moisture content increases.

[0080] The structural coefficient k of the rice milling machine refers to the adaptation parameter related to the mechanical characteristics of the rice milling machine. Its unit is the length unit m. Specifically, it can be realized by establishing a mapping relationship through calibration experiments on different models to ensure that the model is suitable for different rice milling equipment.

[0081] Specifically, this model establishes a quantitative relationship between milling force and the physical properties of rice grains by integrating parameters such as rice grain hardness, geometric size, and moisture content. The average hardness of the rice grains directly determines the mechanical stress required for milling; the square root product of the geometric size reflects the nonlinear influence of the milling contact area on the force; and the moisture content parameter adjusts the rate of decrease in milling force as moisture changes through the denominator. When rice grain hardness increases, the model automatically increases the theoretical milling force to ensure milling effectiveness; when rice grain size decreases or moisture content increases, the model dynamically reduces the milling force to avoid excessive mechanical stress leading to broken rice. Through the synergistic effect of multiple parameters, the model can adapt in real time to the differences in physical properties of different rice varieties, eliminating the lag and one-sidedness of traditional empirical adjustments.

[0082] Compared to existing technologies, traditional rice milling force control relies on a single parameter or operational experience, failing to quantify the coupled effects of rice grain hardness, size, and moisture content. This solution, however, establishes a mathematical model incorporating multidimensional parameters, transforming the physical properties of rice grains into calculable mechanical parameters, thus shifting the rice milling force control from experience-based judgment to mechanism-based derivation. Existing technologies with fixed parameter settings cannot distinguish the hardness differences between different rice varieties, while this solution introduces a rice grain hardness parameter to achieve variety adaptability. Traditional methods ignore the influence of rice grain size on the milling contact area, while this solution establishes an area correlation model through the square root product of geometric dimensions. Conventional control lacks a dynamic moisture content compensation mechanism, while this solution achieves automatic force attenuation through the moisture content denominator.

[0083] Through the above technical solution, this application can automatically match the optimal milling force according to the differences in rice varieties. When the rice grains are hard, it provides sufficient milling force to avoid excessive bran retention, and reduces the milling force to prevent broken rice when the rice grains are small or have high moisture content. This solution effectively solves the problem of uneven milling caused by single parameters in traditional control methods, improving rice quality while maintaining rice yield.

[0084] For preferred options, please refer to [link / reference]. Figure 2 The present invention further proposes that the temperature prediction unit specifically includes:

[0085] The data acquisition module 41 is used to acquire the status data of the rice leaving the mill and the status data of the whitening chamber. The status data of the rice leaving the mill includes the moisture content of the rice leaving the mill, and the status data of the whitening chamber includes the linear speed of the milling roller, the volume of rice grains in the whitening chamber, the distribution density of rice grains in the whitening chamber, the total mass of rice grains in the whitening chamber, the wind speed at the air outlet of the whitening chamber, the area of ​​the air outlet of the whitening chamber, and the current heat dissipation power of the whitening chamber.

[0086] Frictional heat analysis module 42 is used to generate frictional temperature rise values ​​based on theoretical rice milling force, rice grain volume in whitening chamber, rice grain distribution density in whitening chamber, and roller linear speed.

[0087] Evaporation heat analysis module 43 is used to generate evaporation temperature drop value based on the moisture content of the rice leaving the mill and the total mass of rice grains in the whitening chamber;

[0088] Forced heat dissipation analysis module 44 is used to generate forced heat dissipation temperature drop value based on the current heat dissipation power of the whitening chamber, the air velocity at the whitening chamber outlet, and the area of ​​the whitening chamber outlet.

[0089] The temperature prediction model building module 45 is used to build a temperature prediction model based on the friction temperature rise, evaporation temperature drop and forced heat dissipation temperature drop, and generate the predicted temperature value of the whitening chamber.

[0090] Among them, the frictional heat analysis module 42 is a functional module used to calculate the temperature rise caused by friction between rice grains and equipment during the rice milling process. Specifically, it can be implemented using a physical model based on the friction coefficient, rice milling force, rice grain volume and specific heat capacity. By quantifying the process of mechanical energy being converted into heat energy, the contribution of friction to temperature change can be determined.

[0091] The evaporation heat absorption analysis module 43 refers to the functional module used to calculate the temperature drop caused by the evaporation of rice grain moisture. Specifically, it can be implemented using a mathematical model based on changes in moisture content, latent heat of phase change, and air heat capacity. By establishing the correspondence between the amount of moisture evaporation and the amount of heat absorbed, the inhibitory effect of the evaporation process on temperature change can be determined.

[0092] The forced heat dissipation analysis module 44 is a functional module used to calculate the temperature drop value generated by active heat dissipation measures. Specifically, it can be implemented by using dynamic equations based on heat dissipation power, efficiency parameters and air heat capacity. By quantifying the correlation between the operating parameters of the heat dissipation equipment and the environmental regulation capability, the regulatory effect of forced heat dissipation on temperature changes can be determined.

[0093] Specifically, the temperature prediction unit 40 constructs a temperature prediction model by decomposing the temperature change in the whitening chamber into three independent components: frictional temperature rise, evaporation temperature drop, and forced heat dissipation temperature drop. The frictional heat analysis module 42 calculates the cumulative heat generated by friction per unit time based on the rice milling force and rice grain volume parameters. The evaporation heat absorption analysis module 43 calculates the heat absorbed by water evaporation based on the difference in moisture content between the rice entering and leaving the mill, combined with the total mass of rice grains and air heat capacity parameters. The forced heat dissipation analysis module 44 calculates the heat removed by active heat dissipation measures based on the current heat dissipation power and efficiency coefficient. The output values ​​of the three modules are superimposed to generate a temperature prediction value, realizing the prediction of temperature change trends based on the coupling of multiple physical processes.

[0094] Compared to existing technologies, traditional methods rely solely on real-time monitoring by temperature sensors to trigger threshold regulation, failing to distinguish the causes and interaction mechanisms of temperature changes. This solution decomposes temperature changes into three independent components: frictional heat generation, evaporative heat absorption, and forced heat dissipation. It establishes a predictive model that can analyze the contribution of each factor, enabling precise temperature regulation targeting different heat sources and avoiding the lag or over-adjustment caused by single threshold control.

[0095] Through the above technical solution, this application realizes the early prediction of the temperature change trend of the whitening chamber, effectively solves the problem of temperature runaway caused by heat accumulation or sudden changes in equipment load, reduces the risk of rice grain heat damage caused by temperature fluctuations, and reduces energy waste caused by ineffective heat dissipation operations, ensuring that the rice milling process operates stably within the optimal temperature range.

[0096] Preferably, the present invention further proposes a method for generating the frictional temperature rise value, specifically including:

[0097] Through the formula:

[0098] ;

[0099] Generating frictional temperature rise value ;

[0100] In the formula, F represents the theoretical rice milling force, μ is the coefficient of friction, and V represents the volume of rice grains in the whitening chamber. This represents the linear velocity of the milling roller, and c represents the specific heat capacity of the rice grain. This indicates the predicted duration. This indicates the grain distribution density of rice in the whitening room;

[0101] Among them, the friction coefficient μ refers to the friction characteristics between rice grains and milling rollers. Specifically, it can be achieved by experimentally measuring the sliding friction coefficient between different milling roller materials and rice grains, which is used to quantify the intensity of frictional heat generation.

[0102] Rice milling force F refers to the mechanical force exerted by the milling rollers on the rice grains, and its unit is N, i.e., kg·m / s. 2 Specifically, this can be achieved by collecting real-time roller pressure data through a pressure sensor, which is used to correlate rice milling intensity with the rate of frictional heat generation.

[0103] Time increment It refers to the duration of frictional heat accumulation, and its unit is time (s). Specifically, it can be achieved by the system clock module collecting data at a preset cycle, which is used to control the timing accuracy of temperature rise calculation.

[0104] Roller linear velocity This refers to the linear speed of the rice milling machine's rollers, measured in m / s. It can be measured using a speed sensor or similar means.

[0105] Rice grain density This refers to the mass of rice grains per unit volume, and its unit is kg / m³. 3 Specifically, this can be achieved by combining the weighing method with the volume measurement of the whitening chamber, which is used to characterize the heat capacity characteristics of rice grains.

[0106] The volume V of rice grains in the whitening chamber refers to the spatial volume occupied by the rice grains inside the whitening chamber, and its unit is the volume unit m. 3 Specifically, this can be achieved by combining a rice milling machine material level sensor with a volume conversion model, which is used to dynamically reflect the impact of the amount of rice milling material on heat absorption.

[0107] Specific heat capacity c refers to the heat absorption capacity parameter of rice grains, and its unit is J / (kg·℃). After unit conversion, the unit can be m² / (s²·℃), that is, J / (kg·℃) = kg·m² / (s²·kg·℃) = m² / (s²·℃). Specifically, it can be achieved by measuring the specific heat capacity value of rice grains with a thermal property tester, which is used to calculate the temperature rise after a group of rice grains absorbs frictional heat.

[0108] Compared to existing technologies, traditional methods rely on experience or simple feedback mechanisms, which can only trigger heat dissipation control based on real-time temperature and cannot predict the trend of frictional heat accumulation. This solution establishes a quantitative calculation model, combining parameters such as rice milling intensity, rice grain characteristics, and material quantity, to predict the temperature rise in advance, providing data support for adjusting heat dissipation power and avoiding temperature fluctuations and energy waste caused by delayed response.

[0109] Through the above technical solution, this application can accurately predict the temperature rise caused by friction during rice milling, solving the problem of rice grain heat damage caused by the inability to dynamically predict heat accumulation in traditional processes. By calculating the frictional temperature rise value in real time, the heat dissipation power can be adjusted in advance to prevent the temperature of the whitening chamber from exceeding the optimal operating range, while reducing unnecessary heat dissipation energy consumption and achieving heat balance control in the rice milling process.

[0110] Preferably, the present invention further proposes a method for generating the evaporation temperature drop value, specifically including:

[0111] Through the formula:

[0112] ;

[0113] Evaporation temperature drop ;

[0114] In the formula, This refers to the latent heat of water vaporization. This indicates the moisture content of the rice entering the machine. This indicates the moisture content of the rice leaving the machine. This indicates the total mass of rice grains from the milling room. This represents the specific heat capacity of air. This indicates the air quality in the bleaching chamber;

[0115] in, It refers to the latent heat of vaporization of water, and its unit is J / kg. After unit conversion, J / kg = m² / s². Specifically, it can be achieved by experimental calibration or standard values ​​in the thermodynamic parameter library. It is used to convert the amount of water evaporated into the amount of temperature change.

[0116] It refers to the moisture content of rice entering the mill, which is a dimensionless value. It can be measured in real time by a near-infrared sensor or a resistance moisture meter to characterize the initial moisture state of rice grains before milling.

[0117] It refers to the moisture content of milled rice, which is a dimensionless value. It can be obtained through an online moisture detection device installed at the outlet of the whitening chamber, and is used to reflect the total amount of moisture evaporated during the rice milling process.

[0118] It refers to the total mass of milled rice, and its unit is weight (kg). It can be monitored in real time by weighing sensors or flow meters to correlate the total mass of rice grains with the scale effect of evaporation.

[0119] It refers to the specific heat capacity of air, and its unit is J / (kg·℃). Specifically, it can be obtained by using standard atmospheric parameters or calibration values ​​of environmental temperature and humidity sensors, which are used to quantify the heat absorption capacity of the air medium.

[0120] It refers to air mass, and its unit is weight (kg). It can be calculated by combining air volume sensor with air density and is used to characterize the total amount of air involved in heat exchange.

[0121] Specifically, this formula calculates the total heat absorbed during the water evaporation process by multiplying the latent heat of vaporization coefficient by the change in moisture content. Then, it incorporates the air heat capacity parameter to convert the heat change into a temperature drop. During rice milling, some of the heat generated by the friction between the rice grains and the milling rollers is offset by water evaporation. It directly reflects the total amount of moisture evaporated in the whitening chamber, while the total mass of rice being milled... With air parameters , The heat load per unit mass of air is determined jointly; by calculating the evaporation temperature drop in real time, the temperature change trend of the whitening chamber can be dynamically predicted, providing a basis for preliminary calculation of heat dissipation power adjustment;

[0122] Compared to existing technologies, traditional methods only trigger heat dissipation control based on real-time data feedback from temperature sensors, failing to quantify the impact of moisture evaporation on temperature, resulting in a lag in regulation. This solution establishes a physical model of evaporation temperature drop, incorporating moisture content changes, rice grain mass, and air parameters into the calculation, enabling proactive prediction of temperature changes and avoiding a passive response mode that relies on threshold triggers.

[0123] Through the above technical solution, this application can predict the temperature change range caused by moisture evaporation in the whitening chamber in advance, so that the heat dissipation power adjustment is synchronized with the heat accumulation process, reducing energy waste caused by temperature fluctuations; at the same time, by quantifying the evaporation heat absorption effect, the ratio of friction heat generation to evaporation heat dissipation can be accurately distinguished, avoiding misjudgment that may be caused by single temperature monitoring, thereby reducing the risk of heat damage to rice grains.

[0124] Preferably, the present invention further proposes a method for generating the forced heat dissipation temperature drop value, specifically including:

[0125] Through the formula:

[0126] ;

[0127] Generates forced heat dissipation temperature drop value △T cool ;

[0128] In the formula, This indicates the current heat dissipation power of the whitening chamber. This represents the specific heat capacity of air. This indicates the air quality in the bleaching chamber. This indicates heat dissipation efficiency. This indicates the predicted time duration;

[0129] Among them, heat dissipation efficiency It refers to the ratio between the actual working efficiency of the heat dissipation system and the theoretical maximum efficiency. It is dimensionless data and is used to dynamically correct the impact of the performance degradation of heat dissipation equipment on temperature drop calculation.

[0130] Current heat dissipation power It refers to the real-time power output of the heat dissipation equipment during operation. Its unit is W. After unit conversion, W = J / s = kg·m² / s³. Specifically, it can be calculated by collecting electrical parameters in real time through a power sensor, and is used to quantify the instantaneous working status of the heat dissipation system.

[0131] air specific heat capacity It refers to the amount of heat required to raise the temperature of a unit mass of air by 1 degree Celsius. Its unit is J / (kg·℃). Specifically, the thermodynamic parameters of air under standard atmospheric pressure can be used as a reference value to correlate the physical relationship between heat dissipation power and temperature drop.

[0132] air quality It refers to the total mass of air participating in heat exchange in the whitening chamber, and its unit is weight (kg). It can be calculated from the volume of the whitening chamber and the air density, and is used to characterize the heat load capacity of the heat dissipation system.

[0133] Specifically, this technical solution establishes a forced heat dissipation temperature drop calculation model that includes heat dissipation efficiency parameters. Using current heat dissipation power, heat dissipation efficiency, and time intervals as input variables, and combining air thermodynamic parameters, it dynamically calculates the heat dissipation system's ability to regulate the temperature of the whitening chamber per unit time. The dynamic correction of heat dissipation efficiency reflects the impact of actual operating conditions such as dust accumulation on the radiator surface and changes in airflow resistance on heat dissipation performance, thus avoiding prediction deviations caused by changes in equipment status in traditional fixed-parameter models. By collecting heat dissipation power data in real time and substituting it into the formula, the actual temperature drop contribution of the heat dissipation system under different operating conditions can be accurately quantified, providing a precise predictive basis for subsequent heat dissipation power adjustments.

[0134] Compared with existing technologies, traditional methods usually use a fixed heat dissipation efficiency coefficient or ignore the impact of the performance degradation of heat dissipation equipment, resulting in a deviation between the predicted temperature drop and the actual heat dissipation effect. This application introduces heat dissipation status data and incorporates it into the calculation model, so that the temperature drop prediction results can adapt to changes in equipment performance, which significantly improves the accuracy of temperature control.

[0135] Through the above technical solution, this application can accurately predict the actual impact of the forced cooling system on the temperature of the whitening chamber, avoiding overcooling or overheating caused by errors in the estimation of heat dissipation efficiency. By dynamically correcting the heat dissipation efficiency parameters, it can effectively adapt to complex operating conditions such as performance degradation of heat dissipation equipment and blockage of air ducts, thereby reducing energy waste and minimizing the risk of rice grain thermal damage caused by temperature fluctuations.

[0136] Preferably, the present invention further proposes the following expression for the temperature prediction model:

[0137] Through the formula:

[0138] ;

[0139] Generate predicted temperature values ​​for the whitening chamber ;

[0140] In the formula, This indicates the current temperature value of the whitening chamber. This represents the frictional temperature rise value. This represents the evaporation temperature drop value. This represents the temperature drop value due to forced heat dissipation;

[0141] Among them, the current temperature value of the whitening chamber It refers to the real-time temperature of the whitening chamber at time point t, which can be achieved in real time by a temperature sensor and used as the initial reference value for temperature prediction.

[0142] Frictional temperature rise It refers to the temperature rise caused by the heat generated by the friction between rice grains and milling rollers during the rice milling process. It can be calculated by measuring the milling force, the contact area of ​​rice grains, and the coefficient of friction. It is used to quantify the process of mechanical energy being converted into heat energy.

[0143] Evaporation temperature drop This refers to the temperature drop caused by the evaporation of moisture from rice grains and the absorption of heat. It can be calculated by monitoring the difference in moisture content between the rice entering and leaving the machine and combining it with the latent heat of vaporization coefficient. It is used to offset the heat generated by friction.

[0144] Forced heat dissipation temperature drop It refers to the amount of temperature reduction achieved through active cooling equipment, which can be calculated by measuring the current heat dissipation power and heat dissipation efficiency, and is used to characterize the effect of human intervention on temperature control.

[0145] Specifically, the temperature prediction model starts with the current temperature and reflects the heat accumulation generated by mechanical friction during rice milling by superimposing frictional temperature rise values. It also subtracts evaporation temperature drop values ​​to reflect the heat absorption effect of moisture evaporation, and further subtracts forced heat dissipation temperature drop values ​​to quantify the effectiveness of active heat dissipation measures. By dynamically calculating the superimposed effects of these three types of temperature change factors, the model can predict future time points. The model can predict the temperature change trend in the whitening chamber after the process; for example, when the frictional temperature rise exceeds the combined cooling capacity of evaporation and forced heat dissipation, the model can anticipate the risk of temperature rise in advance and trigger the heat dissipation power adjustment command.

[0146] Compared with existing technologies, traditional methods rely solely on real-time temperature thresholds to trigger the start and stop of heat dissipation equipment, failing to predict the impact of frictional heat accumulation or equipment load changes on temperature, resulting in lag in regulation. This solution establishes a predictive model that includes three elements: friction, evaporation, and forced heat dissipation, enabling dynamic prediction of temperature changes and transforming heat dissipation power adjustment from a passive response to active intervention.

[0147] Through the above technical solution, this application solves the problem of lack of predictability in environmental control in the prior art. It can identify the temperature fluctuation trend of the whitening chamber in advance, avoid energy waste or heat damage to rice grains caused by control lag, and thus improve the stability and energy efficiency of the rice milling process.

[0148] Preferably, the present invention further improves the heat dissipation efficiency. The specific methods of obtaining it include:

[0149] ;

[0150] Generating heat dissipation efficiency ;

[0151] In the formula, This represents the characteristic constant of the cooling fan. This indicates the effective area of ​​the air outlet. This indicates the airflow speed at the air outlet. This indicates the current heat dissipation power;

[0152] Among them, the effective area of ​​the air outlet It refers to the cross-sectional area of ​​the air outlet channel of the heat dissipation system, which can be measured using a laser rangefinder or mechanical calipers. This parameter is used to characterize the physical constraints of the heat dissipation structure.

[0153] Air outlet wind speed This refers to the airflow speed through the air outlet during forced cooling, which can be monitored in real time using a wind speed sensor. This parameter reflects the direct impact of airflow dynamics on cooling efficiency.

[0154] Current heat dissipation power This refers to the instantaneous power value of the heat dissipation equipment during operation. It can be measured using a power meter or current transformer. This parameter is used to quantify the real-time energy consumption status of the heat dissipation system.

[0155] Cooling fan characteristic constants It refers to the proportionality constant related to the equipment model and environmental conditions. Its unit is kg / m³. The specific value can be obtained through experimental calibration or equipment parameter manual. This parameter is used to adapt to the differences in heat dissipation characteristics under different working conditions.

[0156] Specifically, this technical solution dynamically calculates heat dissipation efficiency by using the cube of the effective area of ​​the air outlet and the air velocity as the numerator, and the current heat dissipation power as the denominator, thus constructing a correlation model between physical parameters and equipment operating status. During the operation of the rice milling machine, the wind speed sensor collects air velocity data in real time, and the power meter simultaneously obtains the heat dissipation power value. Combined with the preset effective area of ​​the air outlet and the heat dissipation coefficient, the dynamic heat dissipation efficiency is calculated by substituting into the formula. After this efficiency value is input into the temperature prediction model, it can accurately reflect the temperature drop contribution of the forced heat dissipation module, thereby correcting the predicted temperature value of the whitening chamber. Compared with a fixed efficiency coefficient, this model enhances the sensitivity of dynamic changes in airflow to heat dissipation efficiency by introducing a cube of the air velocity, and at the same time, it realizes a negative feedback correlation between the operating status and efficiency of the heat dissipation equipment through the reciprocal of the current heat dissipation power.

[0157] Compared with existing technologies, traditional methods use a fixed heat dissipation efficiency coefficient and do not consider the impact of differences in air outlet structure, airflow fluctuations and equipment power changes on heat dissipation efficiency, resulting in deviations in temperature drop calculation. This solution establishes a dynamic calculation model that includes multi-dimensional parameters, incorporating physical structure parameters, airflow dynamic parameters and equipment operating parameters into a unified formula, so that the heat dissipation efficiency calculation results can respond in real time to changes in the environment and equipment status.

[0158] Through the above technical solution, this application solves the problem of inaccurate efficiency parameters when calculating the temperature drop value of forced heat dissipation, realizes dynamic matching between the predicted temperature value of the whitening chamber and the actual working conditions, reduces the lag in temperature control caused by the estimation deviation of heat dissipation efficiency, and thus ensures the accuracy and timeliness of temperature control in the whitening chamber during rice milling.

[0159] Preferably, the present invention further proposes a method for generating the heat dissipation power adjustment value of the whitening chamber, specifically including:

[0160] Through the formula:

[0161] ;

[0162] Generate heat dissipation power adjustment value for whitening chamber ;

[0163] In the formula, This indicates heat dissipation efficiency. This represents the predicted temperature of the whitening chamber. This indicates the optimal operating temperature of the whitening chamber. This represents the total heat capacity of the system. This indicates the predicted time duration;

[0164] This refers to heat dissipation efficiency, which can be achieved by combining the performance parameters of the radiator with the measurement data of the wind speed sensor. It is used to reflect the actual performance of the current heat dissipation system and ensure that the heat dissipation power adjustment value matches the heat dissipation capacity of the equipment.

[0165] It refers to the predicted temperature, which can be calculated and generated by the temperature prediction unit based on frictional heat, evaporative heat absorption and forced heat dissipation, and is used to characterize the degree to which the temperature of the whitening chamber deviates from the ideal value at future moments.

[0166] This refers to the optimal operating temperature, which can be determined through experimental calibration or optimization using historical data, and serves as the benchmark target value for temperature control.

[0167] It refers to the total heat capacity of the system, which is measured in J / ℃. It can be calculated by combining the heat capacity parameters of rice grains, air and equipment materials in the milling room with volume measurement data. It is used to quantify the conversion relationship between temperature changes and heat dissipation energy requirements.

[0168] This refers to the prediction time, which can be set through the system control cycle or the step size of the prediction model to balance the response speed and calculation accuracy of heat dissipation power adjustment.

[0169] Specifically, the formula uses the difference between the predicted and optimal temperatures as its core input, combined with heat dissipation efficiency and total heat capacity parameters, to dynamically calculate the heat dissipation power that needs to be compensated per unit time. When the predicted temperature is higher than the optimal temperature, the formula outputs a positive heat dissipation power adjustment value, driving the cooling system to increase power in advance to offset heat accumulation; when the predicted temperature is lower than the optimal temperature, the adjustment value decreases or returns to zero to avoid excessive heat dissipation. Through real-time iterative calculation, the heat dissipation power adjustment value can proactively compensate for thermal disturbances based on the predicted temperature change trend, while combining heat dissipation efficiency parameters to ensure that the adjustment value is adapted to the current heat dissipation capacity of the equipment, avoiding control failure due to fluctuations in radiator performance.

[0170] Compared to existing technologies, which rely on real-time temperature monitoring and trigger heat dissipation adjustments based on thresholds, resulting in delayed heat dissipation response and an inability to predict heat accumulation trends, this solution uses a predictive temperature model to calculate future temperature deviations in advance. By combining this with heat capacity parameters, the temperature difference is converted into heat dissipation power requirements, enabling the heat dissipation system to proactively adjust power before temperature fluctuations occur. This reduces the risk of temperature overshoot and minimizes ineffective heat dissipation energy consumption.

[0171] Through the above technical solution, this application can dynamically adjust the heat dissipation power based on the predicted temperature, avoiding temperature fluctuations and energy waste caused by response lag in traditional methods. At the same time, through the coupled calculation of heat capacity parameters and heat dissipation efficiency, it ensures that the heat dissipation power adjustment value matches the actual heat dissipation capacity and heat load characteristics of the equipment, thereby optimizing heat dissipation energy consumption while maintaining the stable temperature of the whitening chamber.

[0172] For preferred options, please refer to [link / reference]. Figure 3 This invention further proposes a multi-sensor-based method for controlling rice milling intensity and environment. This method is applied to the aforementioned multi-sensor-based rice milling intensity and environment control system, and specifically includes the following steps:

[0173] S1: Collect status data of rice entering the machine through different types of sensors; where rice entering the machine refers to unhulled rice grains that have entered the rice milling machine.

[0174] S2: Based on the state data of the rice entering the machine, establish a theoretical analysis model to generate theoretical rice milling power;

[0175] S3: Adjust the rice milling force of the rice milling machine according to the theoretical rice milling force;

[0176] S4: Obtain the state data of the rice leaving the mill and the state data of the whitening chamber. Based on the theoretical milling force, the state data of the rice leaving the mill and the state data of the whitening chamber, establish a temperature prediction model and generate the predicted temperature value of the whitening chamber. Among them, the rice leaving the mill refers to the rice grains output from the rice mill after dehulling.

[0177] S5: Generate the heat dissipation power adjustment value of the whitening chamber based on the predicted temperature value of the whitening chamber;

[0178] S6: Adjust the environment of the whitening chamber according to the heat dissipation power adjustment value of the whitening chamber.

[0179] For preferred options, please refer to [link / reference]. Figure 4 The present invention further proposes a method for generating the predicted temperature value of the whitening chamber, which specifically includes the following steps:

[0180] S4.1: Obtain the status data of the rice leaving the mill and the status data of the whitening chamber; wherein, the status data of the rice leaving the mill includes the moisture content of the rice leaving the mill, and the status data of the whitening chamber includes the linear speed of the whitening roller, the volume of rice grains in the whitening chamber, the distribution density of rice grains in the whitening chamber, the total mass of rice grains in the whitening chamber, the wind speed at the air outlet of the whitening chamber, the area of ​​the air outlet of the whitening chamber, and the current heat dissipation power of the whitening chamber.

[0181] S4.2: Generate the frictional temperature rise value based on the theoretical rice milling force, rice grain volume in the whitening chamber, rice grain distribution density in the whitening chamber, and the linear speed of the milling roller;

[0182] S4.3: Generate the evaporation temperature drop value based on the moisture content of the rice leaving the mill and the total mass of rice grains in the whitening chamber;

[0183] S4.4: Generate a forced heat dissipation temperature drop value based on the current heat dissipation power of the whitening chamber, the air velocity at the whitening chamber outlet, and the area of ​​the whitening chamber outlet.

[0184] S4.5: Based on the friction temperature rise, evaporation temperature drop and forced heat dissipation temperature drop, establish a temperature prediction model and generate the predicted temperature value of the whitening chamber.

[0185] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A rice milling force and environmental control system based on multiple sensors, characterized in that, The system specifically includes: The data acquisition unit is used to collect status data of the rice entering the machine through different types of sensors; where rice entering the machine refers to unhulled rice grains that have entered the rice milling machine. The rice milling force analysis unit is used to establish a theoretical analysis model and generate theoretical rice milling force based on the state data of the rice entering the machine. The force adjustment unit is used to adjust the rice milling force of the rice milling machine according to the theoretical rice milling force. The temperature prediction unit is used to acquire the state data of the rice leaving the mill and the state data of the whitening chamber. Based on the theoretical milling force, the state data of the rice leaving the mill and the state data of the whitening chamber, a temperature prediction model is established to generate the predicted temperature value of the whitening chamber. Among them, the rice leaving the mill refers to the rice grains output from the rice mill after dehulling. The environmental adjustment analysis unit is used to generate adjustment values ​​for the heat dissipation power of the whitening chamber based on the predicted temperature value of the whitening chamber; An environmental adjustment unit is used to adjust the environment of the whitening chamber according to the heat dissipation power adjustment value of the whitening chamber; The temperature prediction unit specifically includes: The data acquisition module is used to acquire the status data of the rice leaving the mill and the status data of the whitening chamber. The status data of the rice leaving the mill includes the moisture content of the rice leaving the mill, and the status data of the whitening chamber includes the linear speed of the whitening roller, the volume of rice grains in the whitening chamber, the distribution density of rice grains in the whitening chamber, the total mass of rice grains in the whitening chamber, the wind speed at the air outlet of the whitening chamber, the area of ​​the air outlet of the whitening chamber, and the current heat dissipation power of the whitening chamber. The frictional heat analysis module is used to generate frictional temperature rise values ​​based on theoretical rice milling force, rice grain volume in the whitening chamber, rice grain distribution density in the whitening chamber, and roller linear speed. The evaporation heat analysis module is used to generate evaporation temperature drop values ​​based on the moisture content of the rice leaving the mill and the total mass of rice grains in the whitening chamber. The forced heat dissipation analysis module is used to generate forced heat dissipation temperature drop values ​​based on the current heat dissipation power of the whitening chamber, the air velocity at the whitening chamber outlet, and the area of ​​the whitening chamber outlet. The temperature prediction model building module is used to build a temperature prediction model based on the friction temperature rise, evaporation temperature drop and forced heat dissipation temperature drop, and generate the predicted temperature value of the whitening chamber.

2. The rice milling force and environmental control system based on multiple sensors according to claim 1, characterized in that, The specific expression of the theoretical analysis model is as follows: ； Where F represents the theoretical rice milling force. This indicates the average hardness of the rice grains, L represents the length of the rice entering the machine, W represents the width of the rice entering the machine, and H represents the width of the rice entering the machine. in This represents the moisture content of the rice entering the machine, 'a' represents the humidity correction coefficient, and 'k' represents the structural coefficient of the rice milling machine.

3. The rice milling force and environmental control system based on multiple sensors according to claim 1, characterized in that, The specific methods for generating the frictional temperature rise include: Through the formula: ； Generating frictional temperature rise value ; In the formula, F represents the theoretical rice milling force, μ is the coefficient of friction, and V represents the volume of rice grains in the whitening chamber. This represents the linear velocity of the milling roller, and c represents the specific heat capacity of the rice grain. This indicates the predicted duration. This indicates the distribution density of rice grains in the whitening room.

4. The rice milling force and environmental control system based on multiple sensors according to claim 1, characterized in that, The specific methods for generating the evaporation temperature drop include: Through the formula: ； Evaporation temperature drop ; In the formula, This refers to the latent heat of water vaporization. This indicates the moisture content of the rice entering the machine. This indicates the moisture content of the rice leaving the machine. This indicates the total mass of rice grains from the milling room. This represents the specific heat capacity of air. This indicates the air quality in the bleaching chamber.

5. The rice milling force and environmental control system based on multiple sensors according to claim 1, characterized in that, The specific methods for generating the forced heat dissipation temperature drop value include: Through the formula: ； Generates forced heat dissipation temperature drop value △T cool ; In the formula, This indicates the current heat dissipation power of the whitening chamber. This represents the specific heat capacity of air. This indicates the air quality in the bleaching chamber. This indicates heat dissipation efficiency. This indicates the predicted time duration.

6. The rice milling force and environmental control system based on multiple sensors according to claim 1, characterized in that, The specific expression of the temperature prediction model is as follows: Through the formula: ； Generate predicted temperature values ​​for the whitening chamber ; In the formula, This indicates the current temperature value of the whitening chamber. This represents the frictional temperature rise value. This represents the evaporation temperature drop value. This represents the temperature drop value due to forced heat dissipation.

7. The rice milling force and environmental control system based on multiple sensors according to claim 5, characterized in that, The heat dissipation efficiency The specific methods of obtaining it include: ； Generating heat dissipation efficiency ; In the formula, This represents the characteristic constant of the cooling fan. This indicates the effective area of ​​the air outlet. This indicates the airflow speed at the air outlet. This indicates the current heat dissipation power.

8. The rice milling force and environmental control system based on multiple sensors according to claim 1, characterized in that, The specific method for generating the heat dissipation power adjustment value of the whitening chamber includes: Through the formula: ； Generate heat dissipation power adjustment value for whitening chamber ; In the formula, This indicates heat dissipation efficiency. This represents the predicted temperature of the whitening chamber. This indicates the optimal operating temperature of the whitening chamber. This represents the total heat capacity of the system. This indicates the predicted time duration.

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