Product temperature control method and system based on data analysis

Abstract

The invention discloses a product temperature control method and system based on data analysis, and relates to the technical field of rice production temperature control. According to the product temperature control method based on data analysis, rice in a drying bin is divided into a plurality of layers of drying depths, and a plurality of measurement position points are randomly selected for each layer of drying depth; obtaining rice drying state data at each measurement position point at each layer of drying depth in the drying bin in real time, and performing comprehensive analysis to obtain a rice drying temperature correction value in the drying bin; judging and analyzing with a preset rice drying temperature threshold value; according to the method, a rice drying temperature correction value with more spatial representativeness can be dynamically obtained through layered sampling and state data comprehensive analysis, so that a more accurate basis is provided for subsequent adjustment, the overall drying uniformity is improved, the quality fluctuation in the drying process is reduced, and the drying quality is improved. And the technical shortages of the existing temperature control strategy are obviously improved.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature control in rice production, and in particular to a product temperature control method and system based on data analysis. Background Art

[0002] As one of the main grain varieties, the drying treatment of rice after harvest is of great significance to ensure storage safety and stable quality. In modern rice processing and production, hot air drying equipment is generally used to dehumidify rice, among which temperature control is the core link that affects drying efficiency, energy consumption level and finished product quality. At present, temperature control in the drying process mostly relies on a single point or a small number of measuring points to obtain temperature data in the warehouse, and combines empirical rules or fixed thresholds to perform heating, ventilation and other operations. The control method is relatively extensive. Since the heat and humidity distribution in the drying warehouse is affected by factors such as layer position, airflow organization, and material stacking status, uneven temperatures often occur at different depths and regions. Traditional control methods are difficult to accurately reflect the overall drying status, resulting in frequent problems of insufficient or excessive drying in some areas.

[0003] In addition, existing temperature control strategies generally lack analysis and feedback mechanisms for real-time multi-point data, making it difficult to dynamically adjust control parameters according to different drying stages, affecting the intelligence level of the drying process and the consistency of product quality. Therefore, a new temperature control method is needed that can integrate real-time data analysis, layered perception and intelligent adjustment to serve rice drying production more efficiently and accurately.

[0004] Prior art, such as the invention patent application with announcement number: CN110334971A, discloses a rice quality detection method based on big data analysis, including: first, selecting at least two test rice samples of rice quality, respectively collecting the first evaluation data of the test rice samples of rice quality; then, solving the first quality identification parameter corresponding to the test rice samples of each rice quality; then, collecting the second evaluation data of the rice sample to be detected; then, solving the second quality identification parameter corresponding to the rice sample to be detected; finally, comparing the second quality identification parameter with the first quality identification parameter to obtain a comparison value. The present invention uses the least squares method to identify the system parameters, without knowing the physical meaning of the system parameters themselves, only needing to solve and compare with the first quality identification parameters under the test rice samples of various rice qualities; combining a variety of evaluation data, the objectivity of the detection result is strong, and the method is simple, and effectively improves the efficiency of rice quality detection.

[0005] Based on the above solutions, it is found that the limitations of the existing technologies at least include the following problems. First, traditional rice drying temperature control technologies generally use single-point measurement or overall average temperature as the control basis, lacking a detailed description of the temperature states at different depths and positions in the drying bin, resulting in obvious surface response characteristics in drying control and making it difficult to accurately reflect the true drying state of the overall rice in the bin. During the actual drying process, due to the influence of heat flow distribution and moisture migration, there are often obvious stratified temperature differences in the vertical direction of the rice. If only relying on the surface or local temperature for regulation, it is extremely easy to have the phenomenon of over-drying or under-drying in some areas, thus affecting the drying uniformity and the quality of the finished product, and even causing energy waste or quality control failure. Summary of the Invention

[0006] Aiming at the deficiencies of the existing technologies, the present invention provides a product temperature control method and system based on data analysis, which solves the problems in the existing technologies that there is a lack of fine perception and dynamic correction of the temperature spatial distribution differences in the drying bin, resulting in inaccurate temperature control adjustment and uneven drying.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: A product temperature control method based on data analysis includes the following steps: Divide the rice in the drying bin into several drying depths, and for each drying depth, randomly select several measurement position points respectively; Obtain the rice drying state data at each measurement position point at each drying depth in the drying bin in real time, and conduct comprehensive analysis to obtain the rice drying temperature correction value in the drying bin; Judge and analyze the rice drying temperature correction value in the drying bin and a preset rice drying temperature threshold; If the rice drying temperature correction value in the drying bin is lower than the preset rice drying temperature threshold, take a preset temperature increase control measure for the rice in the drying bin; If the rice drying temperature correction value in the drying bin is higher than the preset rice drying temperature threshold, take a preset temperature decrease control measure for the rice in the drying bin.

[0008] Further, the rice drying state data includes the rice drying temperature values and rice drying humidity values at several time points. The specific steps to obtain the rice drying temperature correction value in the drying bin are as follows: Based on the rice drying state data at each measurement position point at each drying depth in the drying bin, analyze the rice drying state feature sets in the drying bin respectively, including the rice drying temperature comprehensive index and the rice drying humidity comprehensive index; Conduct comprehensive analysis on the rice drying state feature sets in the drying bin to obtain the rice drying temperature correction value in the drying bin.

[0009] Further, the specific steps for analyzing the rice drying state feature set in the drying bin are as follows: Read the rice drying temperature values and rice drying humidity values at each measurement position point at each drying depth layer in the drying bin, and conduct comprehensive analysis respectively to obtain the average rice drying temperature measurement and average rice drying humidity measurement at each drying depth layer in the drying bin; Conduct comprehensive analysis on the average rice drying temperature measurement and average rice drying humidity measurement at each drying depth layer in the drying bin respectively to obtain the comprehensive rice drying temperature index and comprehensive rice drying humidity index in the drying bin.

[0010] Further, the specific formula for calculating the corrected value of the rice drying temperature in the drying bin is as follows: ; where is the corrected value of the rice drying temperature in the drying bin, is the comprehensive rice drying temperature index in the drying bin, is the comprehensive rice drying humidity index in the drying bin, is the drying humidity influence coefficient stored in the database, is the temperature-humidity interaction influence coefficient stored in the database, is the drying humidity correction coefficient stored in the database.

[0011] Further, the specific steps for taking a preset temperature increase control measure for the rice in the drying bin are as follows: Conduct a difference analysis between the corrected value of the rice drying temperature in the drying bin and the preset rice drying temperature threshold to obtain the rice drying temperature increase difference in the drying bin; Obtain the current drying state data and drying state calibration data in the drying bin, where the current drying state data includes the current drying air velocity value and the drying air velocity reference value, and the drying state calibration data includes the current drying radiation intensity value and the drying radiation intensity reference value; Input the rice drying temperature increase difference, the current drying state data, and the drying state calibration data in the drying bin into a preset temperature increase adjustment model for comprehensive analysis to obtain the rice temperature increase adjustment index in the drying bin; Take a preset temperature increase control treatment for the rice in the drying bin based on the rice temperature increase control index in the drying bin.

[0012] Further, the temperature increase adjustment model is specifically as follows: ; where is the rice temperature increase adjustment index in the drying bin, is the rice drying temperature increase difference in the drying bin, is the temperature difference increase logarithmic correction coefficient stored in the database, is the temperature difference increase weight correction coefficient stored in the database, is the temperature difference increase comprehensive adjustment coefficient stored in the database, is the first temperature difference increase threshold in the database, is the temperature difference heating power correction coefficient stored in the database, is the current drying air flow velocity value in the drying chamber, is the reference value of the drying air flow velocity in the drying chamber, is the air flow difference heating index correction coefficient stored in the database, is the air flow difference heating influence coefficient stored in the database, is the air flow difference heating adjustment coefficient stored in the database, is the second temperature difference heating threshold value in the database, is the air flow difference heating correction coefficient stored in the database, is the temperature difference radiation heating influence coefficient stored in the database, is the current drying radiation intensity value in the drying chamber, is the reference value of the drying radiation intensity in the drying chamber, is the radiation heating adjustment coefficient stored in the database, is the heating interaction adjustment coefficient stored in the database.

[0013] Further, the specific steps of taking a preset heating control measure for the rice in the drying chamber based on the rice heating control index in the drying chamber are as follows: judging and analyzing the rice heating control index in the drying chamber with several preset heating control intervals, and each heating control interval corresponds to a preset heating control measure; based on the heating control measure corresponding to the heating control interval where the rice heating control index is located, performing heating control on the rice in the drying chamber.

[0014] Further, the specific steps of taking a preset cooling control measure for the rice in the drying chamber are as follows: analyzing the difference between the corrected value of the rice drying temperature in the drying chamber and the preset rice drying temperature threshold value to obtain the cooling difference of the rice drying temperature in the drying chamber; reading the current drying state data and the reference drying state data in the drying chamber, and inputting them into a preset cooling adjustment model for comprehensive analysis with the cooling difference of the rice drying temperature in the drying chamber respectively to obtain the rice cooling adjustment index in the drying chamber; based on the rice cooling control index in the drying chamber, taking a preset cooling control measure for the rice in the drying chamber.

[0015] Further, the specific steps of taking a preset cooling control measure for the rice in the drying chamber based on the rice cooling control index in the drying chamber are as follows: judging and analyzing the rice cooling control index in the drying chamber with several preset cooling control intervals, and each cooling control interval corresponds to a preset cooling control measure; based on the cooling control measure corresponding to the cooling control interval where the rice cooling control index is located, performing cooling control on the rice in the drying chamber.

[0016] A product temperature control system based on data analysis, comprising: a division unit for dividing the rice in the drying bin into several drying depths, and for each drying depth, randomly selecting several measurement position points respectively; a real-time acquisition unit for real-time acquiring the rice drying state data at each measurement position point at each drying depth in the drying bin, and performing comprehensive analysis to obtain the rice drying temperature correction value in the drying bin; a judgment unit for judging and analyzing the rice drying temperature correction value in the drying bin with a preset rice drying temperature threshold; a heating control unit for taking a preset heating control measure for the rice in the drying bin when the rice drying temperature correction value in the drying bin is lower than the preset rice drying temperature threshold; a cooling control unit for taking a preset cooling control measure for the rice in the drying bin when the rice drying temperature correction value in the drying bin is higher than the preset rice drying temperature threshold.

[0017] The present invention has the following beneficial effects:

[0018] (1). The product temperature control method based on data analysis divides the rice in the drying bin into multiple drying depth layers, randomly selects multiple measurement points in each layer, and real-time acquires the temperature and humidity data of each layer, effectively avoiding the problem of insufficient representativeness caused by only relying on single-point or surface measurement in traditional drying temperature control. During the drying process, affected by multiple factors such as the distribution of heated air, ventilation structure, and material density, the drying degree of rice in different layers in the bin often shows inconsistency. If the average or fixed-point temperature is used as the control basis, it is often difficult to reflect the local temperature control deviation, resulting in over-drying in some areas and local moisture retention, thereby affecting the drying efficiency and product quality. Through layered sampling and comprehensive analysis of state data, a more spatially representative rice drying temperature correction value can be dynamically obtained, providing a more accurate basis for subsequent adjustment, improving the overall drying uniformity, reducing the quality fluctuation during the drying process, and significantly improving the technical shortcomings of the existing temperature control strategy.

[0019] (2) This product temperature control method based on data analysis, by fusing and analyzing the temperature and humidity data of each layer of rice measuring point, constructs a drying state feature set including a temperature comprehensive index and a humidity comprehensive index, and on this basis introduces the drying humidity influence coefficient, temperature and humidity interaction influence coefficient, and drying humidity correction coefficient stored in the database to correct the original temperature parameters and obtain a temperature correction value that is more in line with the actual drying state of the material. This correction mechanism effectively makes up for the defects of the traditional temperature control system that ignores humidity changes or cannot reflect the humidity linkage behavior. Especially in the later stage of drying, when the temperature tends to be stable and the humidity changes are still significant, control errors will occur if only relying on temperature judgment. The dynamic temperature correction mechanism in this method can not only reflect the heat and humidity coupling effect in the drying process in real time, but also make the temperature parameters closer to the actual physical situation of drying through the support of the empirical coefficients in the database, thereby realizing intelligent temperature control with higher resolution and stronger perception ability, and improving the sensitivity and accuracy of the system.

[0020] (3) This product temperature control method based on data analysis constructs a regulation model containing multiple factors such as temperature difference, airflow difference, and radiation difference in the heating and cooling control strategies, respectively. The heating control index or the cooling control index is obtained through comprehensive calculation, and the corresponding heating or cooling control strategy is further matched based on the segmented interval where the index is located. Different from the simple rule judgment or fixed value adjustment method used in the prior art, this method integrates the current difference state and the control variable parameter value through the control index, and realizes accurate modeling with the help of a variety of correction and adjustment coefficients stored in the database. It can not only determine the adjustment intensity according to the difference size, but also dynamically control the combination and amplitude of the adjustment means, thereby effectively avoiding the problem of overshoot or under-adjustment. This control system has good scalability and flexible adjustment capabilities, and is particularly suitable for refined control in complex drying scenarios such as severe temperature fluctuations, uneven material loads, and frequent airflow disturbances.

[0021] (4) The product temperature control system based on data analysis clarifies the structural composition of the division unit, real-time acquisition unit, judgment unit, temperature rise control unit and temperature drop control unit through functional unit division, realizes the complete process encapsulation from data collection, state analysis, judgment decision-making to control execution, and constitutes a highly modular intelligent temperature control architecture. Compared with the problems of high coupling between temperature control logic and hardware structure, complex deployment and difficult maintenance in the prior art, the system can be flexibly adapted to different drying equipment and different control platforms through functional zoning design, and has good software and hardware decoupling and engineering expansion capabilities. At the same time, the modular structure facilitates the independent upgrade or algorithm replacement of each functional unit in the later stage. For example, the data collection strategy of the real-time acquisition unit can be independently optimized, or the judgment algorithm in the judgment unit can be replaced without affecting the operation of other modules, which significantly improves the maintainability, scalability and industrial application deployment efficiency of the system.

[0022] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flowchart of a product temperature control method based on data analysis according to the present invention.

[0024] Figure 2 It is a broken line graph of the rice drying temperature at seven measurement position points at three drying depths in the drying bin based on data analysis according to the present invention.

[0025] Figure 3 It is a broken line graph of the rice drying humidity at seven measurement position points at three drying depths in the drying bin based on data analysis according to the present invention.

[0026] Figure 4 It is a flowchart of the specific steps of taking a preset temperature increase control measure for the rice in the drying bin in a product temperature control method based on data analysis according to the present invention.

[0027] Figure 5 It is a block diagram of a product temperature control system based on data analysis according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] Please refer to Figure 1 , an embodiment of the present invention provides a technical solution: a product temperature control method based on data analysis, including the following steps: dividing the rice in the drying bin into several drying depths, and for each drying depth, randomly selecting several measurement position points respectively; obtaining the rice drying state data at each measurement position point at each drying depth in the drying bin in real time, and performing comprehensive analysis to obtain a corrected value of the rice drying temperature in the drying bin; making a judgment and analysis on the corrected value of the rice drying temperature in the drying bin and a preset rice drying temperature threshold; if the corrected value of the rice drying temperature in the drying bin is lower than the preset rice drying temperature threshold, taking a preset temperature increase control measure for the rice in the drying bin; if the corrected value of the rice drying temperature in the drying bin is higher than the preset rice drying temperature threshold, taking a preset temperature decrease control measure for the rice in the drying bin.

[0029] Specifically, the rice drying state data includes the rice drying temperature values and rice drying humidity values at several time points, and the specific steps for obtaining the corrected value of the rice drying temperature in the drying bin are as follows: based on the rice drying state data at each measurement position point at each drying depth in the drying bin, respectively analyzing the rice drying state feature set in the drying bin, including the rice drying temperature comprehensive index and the rice drying humidity comprehensive index; performing comprehensive analysis on the rice drying state feature set in the drying bin to obtain the corrected value of the rice drying temperature in the drying bin.

[0030] Among them, the rice drying temperature value can be measured and obtained through a multi-point thermocouple or an infrared sensor.

[0031] The rice drying humidity value can be measured and obtained through a capacitive humidity sensor or a near-infrared moisture meter.

[0032] The specific steps for analyzing the rice drying state feature set in the drying bin are as follows: Read the rice drying temperature value and the rice drying humidity value at each measurement position point at each drying depth layer in the drying bin, and conduct comprehensive analysis respectively (that is, conduct mean analysis for several measurement position points) to obtain the rice drying temperature measurement mean value and the rice drying humidity measurement mean value at each drying depth layer in the drying bin; Conduct comprehensive analysis on the rice drying temperature measurement mean value and the rice drying humidity measurement mean value at each drying depth layer in the drying bin respectively (that is, conduct mean analysis for several drying depth layers) to obtain the rice drying temperature comprehensive index and the rice drying humidity comprehensive index in the drying bin.

[0033] Among them, the specific implementation examples for calculating the rice drying temperature comprehensive index and the rice drying humidity comprehensive index in the drying bin are as follows. The existing parameters are as follows: including the rice drying temperature values and the rice drying humidity values at seven measurement position points at three drying depth layers. The specific data are shown in Table 1-3 and Figures 2 - 3 as shown below: Table 1 Data example of rice drying temperature values and rice drying humidity values at seven measurement position points at the first drying depth layer Measurement location point 1 Measurement location point 2 Measurement location point 3 Measurement location point 4 Measurement location point 5 Measurement location point 6 Measurement location point 7 Rice drying temperature value 45.820 45.277 46.454 46.163 46.409 46.342 45.897 Rice drying humidity value 0.190 0.206 0.204 0.205 0.205 0.191 0.197 Table 2 Data example of rice drying temperature values and rice drying humidity values at seven measurement position points at the second drying depth layer Measurement location point 1 Measurement location point 2 Measurement location point 3 Measurement location point 4 Measurement location point 5 Measurement location point 6 Measurement location point 7 Rice drying temperature value 44.133 44.294 44.068 44.488 44.583 44.407 45.243 Rice drying humidity value 0.223 0.219 0.215 0.211 0.215 0.215 0.221 Table 3 Data example of rice drying temperature values and rice drying humidity values at seven measurement position points at the third drying depth layer Measurement location point 1 Measurement location point 2 Measurement location point 3 Measurement location point 4 Measurement location point 5 Measurement location point 6 Measurement location point 7 Rice drying temperature value 43.421 43.814 43.211 44.203 43.112 44.480 44.158 Rice drying humidity value 0.233 0.227 0.222 0.231 0.231 0.228 0.232

[0034] Conduct mean analysis on the data in Table 1-3 to obtain:

[0035] The rice drying temperature comprehensive index in the drying bin is approximately: 44.820 (unit: °C).

[0036] The rice drying humidity comprehensive index in the drying bin is approximately: 0.217 (unit: kg / kg, which is the unit of dry basis moisture content and is based on moisture content conversion).

[0037] The specific formula for calculating the rice drying temperature correction value in the drying bin is as follows: ; where is the rice drying temperature correction value in the drying bin, is the comprehensive index of the drying temperature of rice in the drying bin, is the comprehensive index of the drying humidity of rice in the drying bin, is the drying humidity influence coefficient stored in the database, is the temperature-humidity interaction influence coefficient stored in the database, is the drying humidity correction coefficient stored in the database.

[0038] It should be noted that the drying humidity influence coefficient stored in the database , the temperature-humidity interaction influence coefficient , and the drying humidity correction coefficient The specific acquisition steps are as follows: First, extract the corresponding change data of temperature and humidity in different drying batches and classify them according to time, batch or drying stage; then, use linear or non-linear regression methods to fit the influence slope of humidity change on temperature response intensity to obtain ; Next, introduce the coupling term of temperature and humidity, and through multivariate regression or partial least squares method modeling, extract the synergistic influence intensity in the interaction relationship between the two as ; Finally, through error analysis of the measured drying temperature and the predicted temperature of the ideal model, reverse correction of the systematic deviation is carried out, and the drying humidity correction coefficient is deduced back to improve the model fitting accuracy. The whole process can be completed through the automatic analysis script of the database, or can be optimized in combination with experimental data with the participation of manual.

[0039] The specific implementation of calculating the corrected value of the drying temperature of rice in the drying bin is as follows. The following parameters are available:

[0040] The comprehensive index of the drying temperature of rice in the drying bin is approximately: 44.820 (unit: °C).

[0041] The comprehensive index of the drying humidity of rice in the drying bin is approximately: 0.217 (unit: kg / kg, which is the unit of dry basis moisture content, based on moisture content conversion).

[0042] The drying humidity influence coefficient stored in the database is approximately: 1.245.

[0043] The temperature-humidity interaction influence coefficient stored in the database is approximately: 0.030.

[0044] The drying humidity correction coefficient stored in the database is approximately: 0.896.

[0045] Substitute the above data into the specific formula for the corrected value of the drying temperature of rice in the drying bin to obtain:

[0046] The rice drying temperature correction value in the drying bin = 44.820×(1 + (0.896×((ln(1 + (0.217^1.245))) / (1 + 0.030×44.820)))) ≈ 47.009 °C.

[0047] In this implementation plan, by constructing a temperature and humidity data acquisition and analysis mechanism based on multiple layers of depth, multiple measurement points, and multiple time points, a high-dimensional and full-coverage perception of the rice drying state in the drying bin is achieved. A multi-level index system of the drying temperature comprehensive index, the drying humidity comprehensive index, and the temperature correction value is introduced, making the temperature control decision no longer rely on a single measurement point or static average value, but on the dynamic and multi-source fusion data results, thus more comprehensively reflecting the true drying state of the current rice. In addition, the calculation of the temperature correction value introduces the drying humidity influence coefficient, the temperature and humidity interaction influence coefficient, and the drying humidity correction coefficient obtained from the training of historical drying data. Through methods such as regression modeling and error inversion, parameter adaptive optimization is realized, making the correction result possess the unity of model logic and real-world physics. This structure not only effectively improves the accuracy and robustness of temperature control judgment, but also has a high degree of automatic processing ability, can widely adapt to different drying bin environments and raw material conditions, and significantly improves the intelligent level of the drying process and the stability of product quality.

[0048] Specifically, as Figure 4 shown, the specific steps for taking the preset temperature increase control measures for the rice in the drying bin are as follows: Perform a difference analysis on the rice drying temperature correction value in the drying bin and the preset rice drying temperature threshold (that is, subtract the rice drying temperature correction value from the preset rice drying temperature threshold) to obtain the rice drying temperature increase difference in the drying bin; Obtain the current drying state data and the drying state reference data in the drying bin. The current drying state data includes the current drying air velocity value and the drying air velocity reference value, and the drying state reference data includes the current drying radiation intensity value and the drying radiation intensity reference value; Input the rice drying temperature increase difference, the current drying state data, and the drying state reference data in the drying bin into the preset temperature increase adjustment model for comprehensive analysis to obtain the rice temperature increase adjustment index in the drying bin; Take the preset temperature increase control treatment for the rice in the drying bin based on the rice temperature increase control index in the drying bin.

[0049] The temperature increase adjustment model is specifically as follows: ;

[0050] Among them, is the rice temperature increase adjustment index in the drying bin, is the rice drying temperature increase difference in the drying bin, is the temperature difference increase logarithmic correction coefficient stored in the database, is the temperature difference increase weight correction coefficient stored in the database, is the comprehensive regulation coefficient of temperature difference heating stored in the database, is the first temperature difference heating threshold in the database, is the power correction coefficient of temperature difference heating stored in the database, is the current drying air flow velocity value in the drying chamber, is the reference value of the drying air flow velocity in the drying chamber, is the index correction coefficient of air flow difference heating stored in the database, is the influence coefficient of air flow difference heating stored in the database, is the regulation coefficient of air flow difference heating stored in the database, is the second temperature difference heating threshold in the database, is the correction coefficient of air flow difference heating stored in the database, is the influence coefficient of temperature difference radiation heating stored in the database, is the current drying radiation intensity value in the drying chamber, is the reference value of the drying radiation intensity in the drying chamber, is the regulation coefficient of radiation heating stored in the database, is the interactive regulation coefficient of heating stored in the database (controlling the influence of temperature, air flow and radiation on the heating regulation amount).

[0051] It should be noted that the logarithmic correction coefficient of temperature difference heating stored in the database , the weight correction coefficient of temperature difference heating , and the comprehensive regulation coefficient of temperature difference heating The specific acquisition steps are as follows: By performing piecewise regression modeling on the heating efficiency data in different temperature difference ranges in historical drying batches, the fitting factors of the logarithmic response curve are extracted respectively (obtaining ), the temperature difference amplification influence factor (obtaining ), and combining the principle of the minimum sum of squared residuals between the temperature difference and the actual response of the regulation, the overall heating response degree after inverse fitting correction is used as .

[0052] The power correction coefficient of temperature difference heating stored in the database , the index correction coefficient of air flow difference heating , the influence coefficient of air flow difference heating , and the regulation coefficient of air flow difference heating The specific acquisition steps are as follows: By extracting the data of the system heating behavior under different combinations of temperature difference and air flow difference, a power function fitting model dominated by temperature difference is constructed to obtain , and then based on the change trend of the air flow velocity difference and the temperature control response, exponential regression is performed to extract , and the modulation intensity of the air flow difference on the change of the temperature rise slope is calculated to obtain Finally, the overall adjustment ability coefficient is fitted by combining the deviation between the adjustment result of the heating rate and the theoretical value with the air flow change. .

[0053] The air flow difference heating-up correction coefficient stored in the database , the temperature difference radiation heating-up influence coefficient , the radiation heating-up adjustment coefficient , the heating-up interaction adjustment coefficient The specific acquisition steps are as follows: First, compare a large number of difference models between the air flow difference and the heating-up result, and extract based on proportional correction , and then fit through the combined data of the temperature difference and the radiation intensity to obtain , which characterizes the influence degree of its response to radiation; then fit through the adjustment causal curve of the historical radiation intensity change and the heating-up rate , and finally introduce the coupled data of temperature, air flow, and radiation, and construct a multi-factor cross-regression model to extract the ternary adjustment coupling strength as .

[0054] In this implementation plan, by constructing a heating-up adjustment model driven by multiple parameters, the heating-up behavior during the rice drying process is refined and intelligently controlled, which has significant engineering application value. Different from the traditional temperature control method that relies on empirical thresholds or linear adjustment, when calculating the heating-up adjustment index, this model introduces three types of dynamic data: temperature difference, air flow difference, and radiation intensity difference, and combines the coefficients of the corresponding dynamic data extracted from the database to construct a non-linear adjustment function with response elasticity and regulation boundary perception ability. In particular, the model conducts segmented modeling for the temperature difference interval and sets different response curves for different difference intervals, effectively solving the problems of insufficient adjustment under large differences and excessive adjustment under small differences in the traditional model; at the same time, through the introduction and parameter coupling of the air flow and radiation differences, the model has a collaborative adjustment ability, and can automatically allocate the adjustment ratios of heating, ventilation, and irradiation according to different working conditions, thereby improving the heating-up efficiency and energy utilization rate. Generally speaking, this heating-up model not only improves the intelligent decision-making ability and adaptability of the temperature control system, but also significantly enhances the precision control and energy-saving level of the drying process.

[0055] Specifically, the specific steps for taking the preset heating-up regulation treatment for the rice in the drying bin based on the rice heating-up regulation index in the drying bin are as follows: Judge and analyze the rice heating-up regulation index in the drying bin with several preset heating-up regulation intervals, and each heating-up regulation interval corresponds to a preset heating-up regulation measure, such as the temperature rising by 2°C; Based on the heating-up regulation measure corresponding to the rice heating-up regulation index in the preset heating-up regulation interval, conduct heating-up regulation on the rice in the drying bin.

[0056] In this implementation, by matching and judging the heating control index with multiple preset heating control intervals, and setting corresponding heating control measures for each interval, hierarchical response and flexible adjustment of the temperature control strategy are achieved. Compared with the traditional one-size-fits-all fixed heating strategy, this method can flexibly select different control means and amplitudes according to the strength of the actual heating demand, such as only heating, heating + adding air flow, heating + air flow + radiation, etc. combinations, thus avoiding problems of over-regulation or response lag, improving control accuracy and response efficiency, and at the same time helping to reduce energy consumption and ensuring the uniformity and quality stability of the rice drying process.

[0057] Specifically, the specific steps for taking the preset cooling control measures for the rice in the drying bin are as follows: Analyze the difference between the corrected value of the rice drying temperature in the drying bin and the preset rice drying temperature threshold (that is, the corrected value of the rice drying temperature in the drying bin minus the preset rice drying temperature threshold) to obtain the cooling difference of the rice drying temperature in the drying bin; Read the current drying state data and the drying state reference data in the drying bin, and input them into the preset cooling adjustment model for comprehensive analysis together with the cooling difference of the rice drying temperature in the drying bin to obtain the rice cooling adjustment index in the drying bin; Take the preset cooling control treatment for the rice in the drying bin based on the rice cooling control index in the drying bin.

[0058] The cooling adjustment model is specifically as follows: ; where is the rice cooling adjustment index in the drying bin, is the cooling difference of the rice drying temperature in the drying bin, is the temperature difference cooling logarithmic correction coefficient stored in the database, is the temperature difference cooling weight correction coefficient stored in the database, is the temperature difference cooling comprehensive adjustment coefficient stored in the database, is the first temperature difference cooling threshold in the database, is the temperature difference cooling power correction coefficient stored in the database, is the current drying air flow velocity value in the drying bin, is the reference value of the drying air flow velocity in the drying bin, is the air flow difference cooling index correction coefficient stored in the database, is the air flow difference cooling influence coefficient stored in the database, is the air flow difference cooling adjustment coefficient stored in the database, is the second temperature difference cooling threshold in the database, is the air flow difference cooling correction coefficient stored in the database, is the temperature difference radiation cooling influence coefficient stored in the database, is the current drying radiation intensity value in the drying bin, is the reference value of the drying radiation intensity in the drying chamber, is the radiation cooling adjustment coefficient stored in the database, is the cooling interaction adjustment coefficient stored in the database (controlling the influence of temperature, air flow, and radiation on the cooling adjustment amount).

[0059] It should be explained that the temperature difference cooling logarithmic correction coefficient , the temperature difference cooling weight correction coefficient , and the temperature difference cooling comprehensive adjustment coefficient are obtained through the following specific steps: By statistically analyzing the natural cooling trend of the system when the temperature is higher than the set value in a large number of historical drying batches, using the logarithmic fitting relationship between the temperature difference and the temperature adjustment rate to determine , and performing weighted average analysis on the adjustment response intensity in different temperature difference intervals to extract the correction factor , and finally minimizing the error between the measured actual cooling change and the theoretical model fitting output to invert the comprehensive adjustment ability index .

[0060] The temperature difference cooling power correction coefficient , the air flow difference cooling index correction coefficient , the air flow difference cooling influence coefficient , and the air flow difference cooling adjustment coefficient are obtained through the following specific steps: First, perform non - linear curve fitting on the temperature difference - dominated cooling stage to extract the power sensitivity of temperature to the cooling response to obtain , then fit the relationship between the air flow difference and the temperature drop rate into an exponential form to calculate its suppression / enhancement trend to obtain , then analyze the average adjustment ratio of the air flow difference to the cooling slope per unit time to obtain the influence intensity , and finally introduce the lag matching degree between the dynamic air flow adjustment and the system temperature response to fit the air flow difference cooling adjustment coefficient .

[0061] The air flow difference cooling correction coefficient , the temperature difference radiation cooling influence coefficient , the radiation cooling adjustment coefficient , and the cooling interaction adjustment coefficient are obtained through the following specific steps: Extract the direct adjustment response curve of the air flow velocity change to the cooling rate and perform linear correction fitting to obtain the basic correction coefficient , then establish a binary regression model for the combined influence of the temperature difference and the radiation intensity during the cooling process to extract , and obtain it through non - linear fitting of the adjustment behavior of the radiation intensity change to the temperature drop amplitude Finally, a multi-factor coupling model is constructed by combining the three change data of temperature, air flow and radiation, the interactive regulation weight distribution is modeled, and the cooling interaction regulation coefficient is extracted. .

[0062] In this implementation scheme, by constructing a cooling regulation model driven by multiple factors, precise cooling control under overheating conditions during the rice drying process is achieved, significantly improving the intelligence and adaptability of the temperature control system. Different from the traditional method that only performs simple air volume regulation or heat treatment stop based on temperature overrun, when calculating the cooling regulation index in the present invention, three key variables, namely temperature difference, air flow difference and radiation difference, are comprehensively considered, and the corresponding coefficients of each variable extracted from the database are combined to form a complex regulation model that highly fits the actual cooling behavior. Especially in parameter acquisition, techniques such as regression fitting, error inversion, and multi-variable coupling modeling are respectively adopted, making the model have stronger data-drivenness and regulation logic. It can intelligently allocate regulation means and intensities for different cooling scenarios. The model also supports segmented response and multi-variable collaborative regulation, ensuring rapid response when the temperature seriously exceeds the limit and flexible convergence when approaching the threshold, effectively avoiding overshoot or temperature control oscillation problems, significantly improving drying safety, process stability and energy utilization efficiency, and providing strong control support for high-quality and low-energy intelligent drying.

[0063] Specifically, the specific steps of taking a preset cooling regulation treatment for the rice in the drying bin based on the rice cooling regulation index in the drying bin are as follows: The rice cooling regulation index in the drying bin is judged and analyzed with a plurality of preset cooling regulation intervals, and each cooling regulation interval corresponds to a preset cooling regulation measure, such as a temperature drop of 2°C; Based on the cooling regulation measure corresponding to the cooling regulation interval where the rice cooling regulation index is located, the rice in the drying bin is subjected to cooling regulation.

[0064] In this implementation scheme, by matching and analyzing the cooling regulation index with multiple preset cooling regulation intervals and setting corresponding cooling regulation measures for each interval, hierarchical response and intelligent selection of the cooling strategy are realized. Compared with the traditional fixed cooling means, this method can flexibly select different regulation combinations according to the degree of overheating, such as only ventilation cooling, enhancing air flow intensity, linked radiation cooling, etc., thus avoiding the problem of one-size-fits-all over-treatment. This mechanism not only improves the accuracy and response efficiency of temperature control regulation, but also helps to ensure the stability and safety of the drying process, while reducing energy consumption and heat loss, and improving the overall drying quality control level.

[0065] Please refer to Figure 5, an embodiment of the present invention provides a technical solution: a product temperature control system based on data analysis, including: a division unit for dividing the rice in the drying bin into several drying depths, and for each drying depth, randomly selecting several measurement position points respectively; a real-time acquisition unit for real-time acquiring the rice drying state data at each measurement position point at each drying depth in the drying bin, and performing comprehensive analysis to obtain the rice drying temperature correction value in the drying bin; a judgment unit for performing judgment analysis on the rice drying temperature correction value in the drying bin and a preset rice drying temperature threshold; a heating regulation unit for taking a preset heating regulation measure for the rice in the drying bin when the rice drying temperature correction value in the drying bin is lower than the preset rice drying temperature threshold; a cooling regulation unit for taking a preset cooling regulation measure for the rice in the drying bin when the rice drying temperature correction value in the drying bin is higher than the preset rice drying temperature threshold.

[0066] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they know the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0067] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A product temperature control method based on data analysis, characterized in that, Including the following steps: Divide the rice in the drying bin into several drying depths, and for each drying depth, randomly select several measurement position points respectively; Obtain in real time the rice drying state data at each measurement position point at each drying depth in the drying bin, and conduct comprehensive analysis to obtain the rice drying temperature correction value in the drying bin; Judge and analyze the rice drying temperature correction value in the drying bin and the preset rice drying temperature threshold; If the rice drying temperature correction value in the drying bin is lower than the preset rice drying temperature threshold, take the preset temperature increase control measures for the rice in the drying bin; If the rice drying temperature correction value in the drying bin is higher than the preset rice drying temperature threshold, take the preset temperature decrease control measures for the rice in the drying bin; The specific steps to obtain the rice drying temperature correction value in the drying bin are as follows: Based on the rice drying state data at each measurement position point at each drying depth in the drying bin, analyze the rice drying state feature set in the drying bin respectively, including the rice drying temperature comprehensive index and the rice drying humidity comprehensive index; Conduct comprehensive analysis on the rice drying state feature set in the drying bin to obtain the rice drying temperature correction value in the drying bin; The specific formula for calculating the rice drying temperature correction value in the drying bin is as follows: ； Among them, is the correction value of the rice drying temperature in the drying bin, is the comprehensive index of the rice drying temperature in the drying bin, is the comprehensive index of the rice drying humidity in the drying bin, is the drying humidity influence coefficient stored in the database, is the temperature-humidity interaction influence coefficient stored in the database, is the drying humidity correction coefficient stored in the database.

2. The product temperature control method based on data analysis according to claim 1, characterized in that The rice drying state data includes the rice drying temperature values and rice drying humidity values at several time points.

3. The product temperature control method based on data analysis according to claim 2, characterized in that The specific steps to analyze the rice drying state feature set in the drying bin are as follows: Read the rice drying temperature values and rice drying humidity values at each measurement position point at each drying depth in the drying bin, and conduct comprehensive analysis respectively to obtain the rice drying temperature measurement average value and rice drying humidity measurement average value at each drying depth in the drying bin; Conduct comprehensive analysis on the rice drying temperature measurement average value and rice drying humidity measurement average value at each drying depth in the drying bin respectively to obtain the rice drying temperature comprehensive index and rice drying humidity comprehensive index in the drying bin.

4. The product temperature control method based on data analysis according to claim 1, wherein The specific steps to take the preset temperature increase control measures for the rice in the drying bin are as follows: Conduct difference analysis on the rice drying temperature correction value in the drying bin and the preset rice drying temperature threshold to obtain the rice drying temperature increase difference in the drying bin; Obtain the current drying state data and drying state reference data in the drying bin. The current drying state data includes the current drying air flow velocity value and the drying air flow velocity reference value, and the drying state reference data includes the current drying radiation intensity value and the drying radiation intensity reference value; Input the rice drying temperature increase difference, the current drying state data and the drying state reference data in the drying bin into the preset temperature increase adjustment model for comprehensive analysis to obtain the rice temperature increase adjustment index in the drying bin; Take the preset temperature increase control treatment for the rice in the drying bin based on the rice temperature increase control index in the drying bin.

5. The product temperature control method based on data analysis according to claim 4, characterized in that, The specific temperature increase adjustment model is as follows: ； Among them, is the rice temperature increase adjustment index in the drying bin, is the temperature difference increase value of the rice drying temperature in the drying bin, is the temperature difference increase logarithmic correction coefficient stored in the database, is the temperature difference increase weight correction coefficient stored in the database, is the temperature difference increase comprehensive adjustment coefficient stored in the database, is the first temperature difference increase threshold value in the database, is the temperature difference increase power correction coefficient stored in the database, is the current drying air flow velocity value in the drying bin, is the reference value of the drying air flow velocity in the drying bin, is the air flow difference increase index correction coefficient stored in the database, is the air flow difference increase influence coefficient stored in the database, is the air flow difference increase adjustment coefficient stored in the database, is the second temperature difference increase threshold value in the database, is the air flow difference increase correction coefficient stored in the database, is the temperature difference radiation increase influence coefficient stored in the database, is the current drying radiation intensity value in the drying bin, is the reference value of the drying radiation intensity in the drying bin, is the radiation increase adjustment coefficient stored in the database, is the increase interaction adjustment coefficient stored in the database.

6. The product temperature control method based on data analysis according to claim 4, characterized in that The specific steps to take the preset temperature increase control treatment for the rice in the drying bin based on the rice temperature increase control index in the drying bin are as follows: Judge and analyze the rice temperature increase regulation index in the drying bin with several preset temperature increase regulation intervals, and each temperature increase regulation interval corresponds to a preset temperature increase regulation measure respectively; Based on the temperature increase regulation measure corresponding to the temperature increase regulation interval where the rice temperature increase regulation index is located, regulate the temperature of the rice in the drying bin.

7. The product temperature control method based on data analysis according to claim 4, characterized in that, The specific steps for taking the preset temperature decrease regulation measure for the rice in the drying bin are as follows: Perform a difference analysis on the rice drying temperature correction value in the drying bin and the preset rice drying temperature threshold to obtain the rice drying temperature decrease difference in the drying bin; Read the current drying state data and drying state calibration data in the drying bin, and input them into the preset temperature decrease adjustment model together with the rice drying temperature decrease difference in the drying bin for comprehensive analysis to obtain the rice temperature decrease adjustment index in the drying bin; Based on the rice temperature decrease regulation index in the drying bin, take the preset temperature decrease regulation treatment for the rice in the drying bin.

8. The product temperature control method based on data analysis according to claim 7, characterized in that The specific steps for taking the preset temperature decrease regulation treatment for the rice in the drying bin based on the rice temperature decrease regulation index in the drying bin are as follows: Judge and analyze the rice temperature decrease regulation index in the drying bin with several preset temperature decrease regulation intervals, and each temperature decrease regulation interval corresponds to a preset temperature decrease regulation measure respectively; Based on the temperature decrease regulation measure corresponding to the temperature decrease regulation interval where the rice temperature decrease regulation index is located, regulate the temperature of the rice in the drying bin.

9. A product temperature control system based on data analysis, which applies the product temperature control method based on data analysis according to any one of claims 1-8, characterized in that, Including: A division unit for dividing the rice in the drying bin into several drying depths, and for each drying depth, randomly select several measurement position points respectively; A real-time acquisition unit for real-time acquiring the rice drying state data at each measurement position point at each drying depth in the drying bin, and performing comprehensive analysis to obtain the rice drying temperature correction value in the drying bin; A judgment unit for judging and analyzing the rice drying temperature correction value in the drying bin and the preset rice drying temperature threshold; A temperature increase regulation unit for taking the preset temperature increase regulation measure for the rice in the drying bin when the rice drying temperature correction value in the drying bin is lower than the preset rice drying temperature threshold; A temperature decrease regulation unit for taking the preset temperature decrease regulation measure for the rice in the drying bin when the rice drying temperature correction value in the drying bin is higher than the preset rice drying temperature threshold.

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