Methods for controlling the coefficient of temperature variation in the temperature uniformity and ventilation zone management of grain storage piles

Abstract

This invention discloses a method for controlling the temperature variation coefficient in the temperature-uniform ventilation zoning management of grain storage piles. Applicable to tall, flat-roofed grain storage piles, it constructs a three-dimensional zoning management model based on the temperature-uniform ventilation process. Combined with an octahedral spatial data compensation scheme, it effectively collects and analyzes temperature data during the temperature-uniform ventilation process. Based on spatial adjacency and membership calculations, it statistically calculates the temperature variation coefficients at different zoning points, clarifies the definitions of different temperature-uniform ventilation modes, and completes the execution management of the temperature-uniform ventilation zoning of the grain pile, ensuring the precise and efficient operation of the temperature-uniform ventilation process. This method can achieve uniform temperature distribution during grain storage in tall, flat-roofed warehouses, improve the stability of the grain pile's micro-ecology during storage, reduce the risks of micro-airflow and condensation within the grain pile, and play a significant role in promoting green grain storage and improving the uniformity of mechanical ventilation. It also provides technical reserves for the high-quality development of the grain industry.

Description

Technical Field

[0001] This invention relates to the field of grain engineering technology, specifically a method for controlling the temperature variation coefficient in the temperature uniformity and ventilation zone management of stored grain piles. Background Technology

[0002] Currently, the uniformity of heat and humidity transfer and the optimization of ventilation and temperature control in post-harvest grain storage are bottlenecks hindering large-scale low-temperature preservation storage. Maintaining a good storage environment is crucial during grain storage. Temperature uniformity management not only affects grain quality and shelf life but also directly relates to grain safety. Therefore, researching and developing temperature variation coefficient control strategies suitable for temperature and ventilation zone management in grain storage has significant practical implications and application prospects. In recent years, countries have increased investment in grain storage technology, adopting advanced control technologies to improve the efficiency and safety of grain storage. For example, European and American countries have numerous patented technologies in temperature and humidity monitoring, ventilation system optimization, and grain pile management. Some of these patents involve ventilation control systems based on intelligent sensing and mechatronics. However, in practice, most of these technologies are limited to the mechanized operation of steel silos, vertical silos, and shallow circular silos, lacking sufficient capacity for temperature control in different grain storage zones.

[0003] Currently, the main type of post-harvest grain storage is the large, flat warehouse. These warehouses have a large floor area and sufficient storage capacity per unit. However, the lack of refined management in temperature and ventilation control makes them prone to overcooling and ventilation dead zones, posing significant risks of condensation, mold, and pests on the grain piles. Typically, existing grain warehouses have numerous temperature monitoring points—standard warehouses usually have over 200—making them susceptible to individual mechanical malfunctions that can lead to abnormal data. Without compensation and correction, these abnormal data may be mistaken for abnormal grain pile temperatures when they are actually just equipment malfunctions. In such cases, judgment relies solely on human experience, and current technology lacks effective data cleaning and spatial compensation mechanisms for handling such anomalies. Furthermore, existing temperature equalization and ventilation methods often overlook these objective realities, easily resulting in ventilation programs that do not match the actual grain conditions. Summary of the Invention

[0004] To address the problems of existing technologies, this invention provides a method for controlling the coefficient of variation of temperature in the temperature and ventilation zones of grain storage. This method enables the division of ventilation zones in the mainstream grain storage type of tall, flat warehouses, and allows for the statistical analysis of the average value, standard deviation, and coefficient of variation of temperature data in different zones of the entire warehouse. This improves the accuracy of subsequent data analysis, forms an intelligent temperature and ventilation control system, enhances the stability of the grain pile's micro-ecology during storage, and reduces the risks of micro-airflow and condensation within the grain pile. This method plays a significant role in promoting green grain storage and improving the uniformity of mechanical ventilation, and also provides technical reserves for the high-quality development of the grain industry.

[0005] This invention includes the following specific steps:

[0006] Step 1: After determining the span of the grain warehouse, divide the area into zones and design the temperature sensor collection locations: take the center point of the grain warehouse as the origin, set different quadrants according to the horizontal and vertical symmetry axes of the warehouse, and set different zones with the ground as the base plane and a fixed vertical distance. Each zone should be equipped with an appropriate number of temperature collection sensors.

[0007] Step 2: Anomaly detection and effective retention of raw grain pile temperature data: Anomaly detection is performed on the collected raw temperature data to eliminate invalid and abnormal data;

[0008] Step 3: Blank compensation for temperature data of cleaned grain pile: Based on the original data anomaly judgment and effective retention, the blank data after cleaning is filled by data interpolation of nearby points in time. Blank compensation is performed according to the data interpolation of 6 adjacent points in octahedral space. The compensation is considered to be completed after the blank filling of all collection points is completed.

[0009] Step 4: Calculation of temperature variation coefficient of grain pile: Taking the target sensor as the center point, statistically analyze the average value, standard deviation and coefficient of variation of grain pile temperature data of 6 adjacent points in the space; if the target sensor is the temperature measurement point at the edge of the grain pile, calculate according to the actual adjacent points; simultaneously calculate the average value, standard deviation and coefficient of variation of grain pile temperature in different quadrants and different vertical zones.

[0010] Step 5: Grain pile temperature uniformity determination: Based on the temperature variation coefficient of the grain pile in different zones and at different temperature measurement points, a ventilation temperature uniformity logic rule is established to adapt to the constantly changing temperature of the grain pile ventilation airflow and to construct a specific grain pile internal circulation ventilation mode.

[0011] Step 6: Matching the operating mode of the internal circulation fan: Based on the analysis results of the coefficient of variation and temperature uniformity, formulate a strategy to adjust the operating power of the fan, set the operating mode of the fan for each zone, and adjust its power according to the zone status and the magnitude of the temperature variation coefficient of the independent temperature measurement point.

[0012] Step 7: Automation of the internal circulation fan control system: By real-time monitoring of ambient temperature, humidity and internal temperature distribution of the grain pile, the fan and monitoring data are integrated to ensure that the fan can automatically adjust its operating power according to real-time data to meet the uniform temperature requirements inside the grain storage.

[0013] Step 8: Effect Feedback and Optimization Cycle: Monitor and continuously optimize the control effect in real time, collect fan operation data and temperature and humidity information of the grain storage area at regular intervals, and adjust the outlier judgment criteria, the calculation method of the coefficient of variation and the fan operation strategy based on the feedback results to achieve continuous optimization.

[0014] Further improvements are made in step 1, where the quadrant setting is based on the grain warehouse floor as the base plane, and four quadrants are symmetrically designed with the physical symmetry center point of the base plane as the base origin. Vertically, independent zones are set at 1m intervals. The horizontal interval between adjacent temperature monitoring points is required to be less than 5.0m, and the vertical interval is required to be less than 1.75m. Each independent zone is required to have no less than 9 temperature monitoring points.

[0015] Further improvements are made in step 2. The invalid data includes data caused by accidental malfunctions, operational abnormalities, and data transmission loss due to non-human factors affecting the temperature sensor. Abnormal data refers to single-point temperature data that consistently exceeds the detection range, contains duplicate values, is blank, or has excessively large differences between adjacent temperature acquisitions. In step 2, the criteria for judging accidental malfunctions caused by non-human factors affecting the temperature sensor are: the corresponding value exceeds the sensor's maximum detection range; the criteria for judging duplicate values ​​are: three or more consecutive temperature acquisitions with identical values ​​to three decimal places are considered abnormal duplicates; blank or actual sensor data is considered an abnormal blank; and the difference between two adjacent valid temperature acquisitions exceeding a set threshold of 10°C is considered an excessively large difference.

[0016] Further improvements are made in step 3. Before performing data interpolation compensation, it must be ensured that the original data has undergone anomaly detection and invalid data has been removed. The data interpolation compensation process uses an octahedral space 6-point adjacent interpolation method to compensate for blank data. The interpolation algorithm uses a weighted average method.

[0017]

[0018] D fill D represents interpolated data. i Represented as adjacent point data, w i The corresponding weights are indicated, where among the 6 spatially adjacent points, the weight coefficients for the 4 points in the horizontal direction are 0.2 and the weight coefficients for the 2 points in the vertical direction are 0.1 respectively; if the compensation data is a boundary point, it is compensated with equal weight coefficients according to its adjacent points; when the blank data of all collection points has been filled, the blank compensation of the cleaned data is considered to be completed.

[0019] In a further improvement, step 4 involves using the target sensor as the center point and performing statistical analysis on the temperature data of adjacent points around it. The statistical indicators include the mean, standard deviation, and coefficient of variation of the target location and its six spatially adjacent points. The calculation formula is as follows:

[0020]

[0021] Mean represents the average value of the target temperature measurement point and its adjacent points; StdDev represents the standard deviation; CV represents the coefficient of variation of the point and its six adjacent points; similarly, the overall average value, standard deviation and coefficient of variation of different quadrants and their sub-regions are calculated simultaneously to evaluate the overall temperature uniformity of the sub-regions.

[0022] Further improvements are made in step 5. If the CV corresponding to the temperature measurement point is less than 10%, it is defined as relatively stable temperature; if 10% ≤ CV < 20%, it is defined as temperature "to be monitored" state; if 20% ≤ CV, it is defined as "needs adjustment"; if the average CV value of the temperature measurement quadrant is less than 15%, it is defined as "average temperature"; if the average CV value of the temperature measurement quadrant is greater than or equal to 15%, it is defined as "abnormal temperature"; based on the temperature uniformity logic rules, ventilation modes are formulated for different zones and ventilation states, including dynamic wind speed adjustment: based on temperature fluctuations, different wind speeds and wind directions are configured in different areas to optimize airflow distribution; and periodic adjustment cycle: a timed monitoring and adjustment frequency is set to ensure real-time adaptation and adjustment of airflow temperature.

[0023] Further improvements are made in step 6. When the zone is in an abnormal temperature condition, the ventilation fan operates at its rated power at constant speed. When the zone is in an average temperature condition, and the temperature measurement point CV shows one or more "needs adjustment" status or three or more "awaiting monitoring" statuses, the fan is in deceleration mode, with its operating power being 0.8 times that of the last test. When the zone is in a normal temperature condition, and all temperature measurement points CV are in a relatively stable temperature condition or have fewer than three "awaiting monitoring" statuses, the temperature equalization fan operates at a low frequency.

[0024] In a further improvement, step 7 involves integrating the wind turbine and monitoring data by transmitting the collected data to the PLS controller and using the PLC programmable logic controller to integrate the wind turbine and monitoring data.

[0025] The beneficial effects of this invention are as follows:

[0026] 1. Implement zoned ventilation areas for the mainstream grain storage warehouse type, tall, flat-roofed warehouses;

[0027] 2. Based on the construction of the temperature monitoring and acquisition system, the raw data is statistically analyzed, cleaned and compensated to improve the accuracy of subsequent data analysis and overcome the misjudgment of the actual grain condition caused by mechanical failure of the temperature sensor itself, abnormal data transmission interface and loss of degree display signal.

[0028] 3. Based on the principle of spatial distribution, conduct statistical analysis of the average value, standard deviation, and coefficient of variation of grain pile temperature data in different zones of the entire warehouse;

[0029] 4. By linking with IoT technology and the ground-level ventilation system of tall, flat warehouses, it can be seamlessly integrated with the existing mechanical ventilation system of grain warehouses. It is applicable to all ground-level vertical ventilation methods, replacing the manual judgment and operation of the original mechanical ventilation of grain warehouses. It can realize the real-time monitoring, judgment and control of the temperature of the entire grain warehouse, and improve the digitalization and intelligence of grain warehouse ventilation.

[0030] 5. Accurate determination of temperature distribution uniformity in grain piles, combined with multi-dimensional coefficient of variation analysis of point-surface-volume, completed the frequency conversion energy-saving control of the temperature uniformity ventilation process in grain warehouses, improved the stability of the micro-ecology of grain piles during storage, reduced the risk of micro-airflow and condensation inside grain piles, played an important role in promoting green grain storage and improving the uniformity of mechanical ventilation, and also provided technical reserves for the high-quality development of the grain industry. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This invention provides a distribution scheme for temperature sensors in grain warehouses to which the present invention is applicable.

[0033] Figure 2 This is a diagram showing the arrangement of ventilation ducts and temperature sensors inside the grain warehouse according to the present invention.

[0034] Figure 3 This is an octahedral spatial data compensation distribution diagram of the grain warehouse temperature measuring points according to the present invention;

[0035] Figure 4 This is a diagram showing the original and compensated distribution of abnormal temperature measurement data in the tall, flat warehouse of this invention.

[0036] Figure 5 The temperature variation coefficients of the 4th and 5th grain layers in the tall, flat warehouse of this invention;

[0037] Figure 6 The temperature change of the grain pile before and after the ventilation of the tall, flat warehouse of this invention. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] The invention discloses a temperature variation coefficient control strategy and method applicable to temperature uniformity and ventilation zone management of grain storage. Based on the variation coefficient technology of the corresponding zones and temperature monitoring points, the method uses coordinated adjustment of ventilation fan power to achieve uniform temperature distribution within the grain pile. This scheme has been successfully applied to the internal circulation ventilation system of a tall, flat grain storage warehouse (30m long, 24m wide, and 7m high). The corresponding temperature sensor distribution scheme is shown in the attached figure. Figure 1 As shown in the figure, the ground 1, warehouse wall 2, temperature measuring cable 3-1, temperature measuring point 3-2, window 4, roof 5, and grain stacking line 6 are included. The variable frequency online monitoring and real-time control are realized, and the uniform regulation of the internal temperature of the grain pile is completed, avoiding the phenomena of condensation, compaction and insect and mold growth inside the grain pile caused by "cold core and hot skin" and "micro airflow".

[0040] The layout diagram of the ventilation ducts and temperature sensors inside the grain warehouse is as follows: Figure 2 As shown, the structure includes the warehouse wall 2, temperature measuring point 3-2, fan interface 7, and ground cage ventilation duct 8. The diagram can be divided into four quadrants: Quadrant 1 9-1, Quadrant 2 9-2, Quadrant 3 9-3, and Quadrant 4 9-4.

[0041] One specific embodiment of the present invention includes the following specific steps:

[0042] Step 1: After determining the span of the grain storage area, divide the area into zones and design the temperature sensor acquisition locations: Using the center point of the grain storage area as the origin, set different quadrants according to the horizontal and vertical axes of symmetry of the storage area. With the ground as the base plane, set different zones at fixed vertical distances. Each zone should have an appropriate number of temperature acquisition sensors. The quadrant setting is based on the grain storage area ground plane, with the physical center point of the base plane as the origin. Four quadrants are symmetrically designed, with independent zones set at 1m intervals in the vertical direction. The horizontal interval between adjacent temperature monitoring points should be less than 5.0m, and the vertical interval should be less than 1.75m. Each independent zone should have no less than 9 temperature monitoring points.

[0043] Step 2: Anomaly Detection and Retention of Raw Grain Pile Temperature Data: The raw temperature data collected is anomaly detected. Data caused by accidental malfunctions, operational abnormalities, or data transmission loss due to non-human factors affecting the temperature sensor is considered invalid. Single-point temperature data showing consistently exceeding the detection range, duplicate values, blank values, or excessively large differences between adjacent data collections are considered abnormal data to ensure the accuracy of backend result determination and strategy formulation. The statistical standard for accidental malfunctions caused by non-human factors affecting the temperature sensor is that the corresponding sensor value exceeds the sensor's maximum detection range. The standard for duplicate values ​​is that three or more consecutive temperature data collections are identical to the last three decimal places; data that is empty or missing (numerical data showing NULL or NaN) is considered blank anomaly; and the difference between two adjacent valid temperature data collections exceeding a set threshold of 10℃ is considered an excessively large difference.

[0044] Step 3: Blank compensation for temperature data of cleaned grain pile: Based on the original data anomaly judgment and effective retention, the blank data after cleaning is filled by data interpolation of nearby points in time. Blank compensation is performed according to the data interpolation of 6 adjacent points in octahedral space. The compensation is considered to be completed only after the blank filling of all collection points is completed.

[0045] Before performing data interpolation, it is necessary to ensure that the original data has undergone anomaly detection and invalid data has been removed. An octahedral space interpolation method with six adjacent points is used to compensate for blank data. The interpolation algorithm uses a weighted average method.

[0046]

[0047] Dfill represents interpolated data, Di represents adjacent point data, and wi represents the corresponding weight. Among the 6 spatially adjacent points, the weight coefficients of the 4 points in the horizontal direction are 0.2 and the weight coefficients of the 2 points in the vertical direction are 0.1. If the compensation data is a boundary point, it is compensated with equal weight coefficients according to its adjacent points. When the blank data of all collection points has been filled, the blank compensation of the cleaned data is considered to be completed.

[0048] Figure 3 This is an octahedral spatial data compensation distribution diagram of the grain warehouse temperature measurement points according to the present invention.

[0049] Step 4: Grain pile temperature variation. In the diagram, seven points are set: point No. 1 is the temperature compensation data point; points No. 3, No. 4, No. 5, and No. 6 are the plane adjacent points of the temperature compensation data point; points No. 2 and No. 7 are the vertically adjacent points of the temperature compensation data point. Coefficient calculation: Using the target sensor as the center point, statistically analyze the average value, standard deviation, and coefficient of variation of the grain pile temperature data from six adjacent points in the space. If the target sensor is a temperature measurement point at the edge of the grain pile, the calculation is performed based on the actual adjacent points. Simultaneously calculate the average value, standard deviation, and coefficient of variation of the grain pile temperature in different quadrants and different vertical zones.

[0050] Using the target sensor as the center point, statistical analysis was performed on the temperature data of adjacent points around it. The statistical indicators included the mean, standard deviation, and coefficient of variation of the target location and its six spatially adjacent points. The calculation formulas are as follows:

[0051]

[0052]

[0053] Mean represents the average value of the target temperature measurement point and its adjacent points; StdDev represents the standard deviation; CV represents the coefficient of variation of the point and its six adjacent points; similarly, the overall average value, standard deviation and coefficient of variation of different quadrants and their sub-regions are calculated simultaneously to evaluate the overall temperature uniformity of the sub-regions.

[0054] Step 5: Grain pile temperature uniformity determination: Based on the temperature variation coefficient of the grain pile in different zones and at different temperature measurement points, a ventilation temperature uniformity logic rule is established to adapt to the constantly changing temperature of the grain pile ventilation airflow and to construct a specific grain pile internal circulation ventilation mode.

[0055] If the CV corresponding to the temperature measurement point is <10%, it is defined as relatively stable temperature; if 10%≤CV<20%, it is defined as temperature "to be monitored" state; if 20%≤CV, it is defined as "needs adjustment"; if the average CV value of the temperature measurement quadrant is <15%, it is defined as "average temperature"; if the average CV value of the temperature measurement quadrant is ≥15%, it is defined as "abnormal average temperature"; based on the average temperature logic rules, ventilation modes are formulated for different zones and ventilation states, including dynamic wind speed adjustment: based on temperature fluctuations, different wind speeds and wind directions are configured in different areas to optimize airflow distribution; and periodic adjustment cycle: set the timed monitoring and adjustment frequency to ensure real-time adaptation and adjustment of airflow temperature.

[0056] Step 6: Matching the operating mode of the internal circulation fan: Based on the analysis results of the coefficient of variation and temperature uniformity, formulate a strategy to adjust the operating power of the fan, and set the operating mode of the fan (rated operation, deceleration operation and low frequency operation) for each zone. The power adjustment is based on the zone status and the magnitude of the temperature variation coefficient of the independent temperature measurement point.

[0057] When a zone is in an abnormal temperature range, the ventilation fan operates at its rated power at constant speed. When a zone is in an average temperature range, and the CV values ​​at the temperature measuring points show one or more "adjustment required" states or three or more "monitoring pending" states, the fan is in deceleration mode, with its operating power being 0.8 times that of the last monitored operation. When a zone is in a normal temperature range, and all temperature measuring points have relatively stable CV values ​​or fewer than three "monitoring pending" states, the temperature equalization fan operates at a low frequency.

[0058] Step 7: Automation of the internal circulation fan control system: By real-time monitoring of ambient temperature, humidity and internal temperature distribution of the grain pile, the collected data is transmitted to the PLS controller. The PLC programmable logic controller is used to integrate the fan with the monitoring data, ensuring that the fan can automatically adjust its operating power according to the real-time data to meet the uniform temperature requirements inside the grain storage.

[0059] Step 8: Effect Feedback and Optimization Cycle: Monitor and continuously optimize the control effect in real time, collect fan operation data and temperature and humidity information of the grain storage area at regular intervals, and adjust the outlier judgment criteria, the calculation method of the coefficient of variation and the fan operation strategy based on the feedback results to achieve continuous optimization.

[0060] Various implementations in this specification

[0061] Example 1:

[0062] In response to the special circumstances that easily occur in the grain condition monitoring system during long-term storage of grain in tall, flat warehouses, such as loss of temperature detection data transmission and malfunction of some sensors, resulting in continuous interruption of sensor data, this case study retrieves grain condition temperature monitoring data from the No. 8 rice storage warehouse of a certain reserve grain depot. The grain was stored on February 7, 2022, and the grain condition temperature was monitored on October 9, 2023, totaling 3,502 tons of japonica rice with a moisture content of 14.0%. The warehouse temperature was 22.0℃, the warehouse humidity was 49.0%, the external temperature was 16.5%, and the external humidity was 74.2%.

[0063] Figure 4 The diagram shows the original and compensated distribution of abnormal temperature measurement data in tall, flat warehouses. The boxes in the diagram represent data anomalies.

[0064] The grain silo has a total of four ventilation fan inlets on the north and south sides, employing a one-unit-four-channel above-ground cage ventilation system. A total of six rows and seven columns of temperature sensing cables are designed, with five temperature acquisition points on each cable. The arrangement of the above-ground cage ventilation ducts and temperature sensors is as follows: Figure 2 As shown. In terms of vertical direction, the fourth temperature measuring layer (2.0m above the grain surface) was selected for data analysis. The raw data is as follows: Figure 4As shown in area A, there are a total of 42 temperature measurement points, 38 valid temperature measurement data, and 4 abnormal points. Among them, 3 temperature measurement points have signal loss due to transmission loss or failure, displaying NaN, and 1 point is determined to be invalid data because the detection is below the detection limit.

[0065] This case study proposes an octahedral spatial interpolation method using six adjacent points to compensate for missing data. The interpolation algorithm employs a weighted average method. Among the six adjacent points, the four horizontal points have a weight coefficient of 0.2, and the two vertical points have a weight coefficient of 0.1. If a data point to be compensated is a boundary point, it is compensated with equal weight coefficients according to its adjacent points. Therefore, the compensation value for outlier data points is calculated as follows:

[0066] NaN1 point:

[0067] 12.4×0.2+18.1×0.2+12.0×0.2+11.1×0.2+15.9×0.1+11.1×0.1=13.4℃;

[0068] NaN2 point:

[0069] 12.0×0.2+11.1×0.2+11.6×0.2+11.1×0.2+16.6×0.1+16.7×0.1=12.5℃;

[0070] NaN3 point:

[0071] 13.1×0.2+11.6×0.2+13.9×0.2+17.9×0.2+15.4×0.2=14.4℃;

[0072] 0.0 points:

[0073] 10.4×0.2+12.5×0.2+12.4×0.2+11.4×0.2+18×0.1+10.8×0.1=12.2℃;

[0074] The corresponding compensation data is as follows Figure 4 As shown in section B.

[0075] Example 2:

[0076] This experiment, based on the daily data collection status of No. 8 rice storage warehouse of a certain grain reserve, and building upon the data anomaly identification and cleaning in Example 1, further calculated the temperature variation coefficient of the grain pile and evaluated the uniformity of the grain layer's planar temperature. This case study focuses on a tall, flat-roofed grain pile measuring 30m × 24m × 7m, divided into 4 quadrants and 5 vertical layers, totaling 20 quadrant zones. A total of 210 temperature sampling points were established, with 3 rows and 3 columns of grain temperature data acquisition sensors set up in each quadrant zone. Independent grain pile temperature data was collected along the vertical line from the center of the grain warehouse corresponding to the location of the warehouse door.

[0077] Figure 5 The figure shows the temperature variation coefficients of the 4th and 5th grain layers in the tall, flat warehouse. The boxes in the figure represent the points where the temperature measurement coefficients exceed 20%.

[0078] According to the specific content of this invention, the proposed octahedral coefficient of variation calculation scheme uses the target sensor as the center point and performs statistical analysis on the temperature data of adjacent points around it. The statistical indicators include the mean, standard deviation, and coefficient of variation of the target point and six spatially adjacent points. At the same time, the average grain temperature, standard deviation, and average coefficient of variation of the corresponding zone are statistically calculated, and the results are shown in Table 1. Among them, the temperature uniformity of the first layer of grain pile is good, and the average temperature deviation of different quadrants does not exceed 1℃, and the corresponding CV coefficient is within 2.3%. However, as the grain pile moves upward, until the grain surface layer (5 layers), the average temperature of the grain pile in different quadrants exceeds 2.1℃, and the corresponding CV coefficient even exceeds 20%. According to the present invention, the average CV value of the temperature measurement quadrant is <15%, which is defined as "temperature average", and the average CV value of the temperature measurement quadrant is ≥15%, which is defined as "temperature anomalous". In comparison, the first, second and third floors are in a temperature average state, while the second and third quadrants of the fourth floor show "temperature anomalous". The fifth floor also shows "temperature anomalous" in the second and third quadrants. According to the orientation analysis map, it is mainly concentrated in the western half of the grain warehouse area.

[0079] Further evaluation and analysis of the temperature uniformity of the 4th and 5th layers of grain piles were conducted. Based on the principle that CV < 10% for the temperature measurement points, the temperature was defined as "relatively stable"; if 10% ≤ CV < 20%, the temperature was defined as "to be monitored"; and if 20% ≤ CV, the temperature was defined as "needs adjustment". There were temperature measurement points "needing adjustment" in the first and second quadrants of the 4th layer, and a total of 5 variation points "needing adjustment" appeared in the central longitudinal axis and the second quadrant of the 5th layer. This indicates that the temperature uniformity of the grain pile in this area is abnormal. During the ventilation process, the corresponding location of the top fan needs to operate at full power to ensure the temperature uniformity of the grain pile.

[0080] Table 1. Data set of temperature compensation for grain piles in the experimental grain warehouse and analysis of grain temperature uniformity index in different zones.

[0081]

[0082]

[0083]

[0084] Example 3:

[0085] Based on the above analysis and in accordance with the principle of matching fan operating power, the operating modes of the fans in each zone (rated operation, deceleration operation, and low-frequency operation) were set. The power adjustment was based on the zone status and the temperature variation coefficient of independent temperature measurement points. Real-time monitoring of ambient temperature, humidity, and the internal temperature distribution of the grain pile was conducted, and the collected data was transmitted to a PLS controller. A PLC programmable logic controller was used to integrate the fan and monitoring data. Based on the above calculation of the variation coefficient and uniformity analysis, the experiment involved circulating temperature equalization ventilation of the grain pile. The results before and after temperature equalization are as follows: Figure 6 As shown, before temperature equalization, the temperature distribution varied significantly across different grain depths, even within the same quadrant. The average temperature difference between the second and fifth layers in the fourth quadrant reached as high as 10.2℃. After temperature equalization and ventilation, the average temperature in this area decreased to 2.34℃. Furthermore, this difference was not only reflected in the average temperature distribution across different grain depths, but also significantly improved in temperature uniformity across different quadrants within the same grain layer, with the temperature difference reduced to within 2.0℃. This demonstrates that the aforementioned uniform ventilation strategy significantly improves the temperature uniformity of tall, flat warehouses.

[0086] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention, without departing from the principle of the present invention, should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for controlling the temperature variation coefficient in a temperature-controlled and ventilated zone management system for stored grain piles, characterized in that: The specific steps include the following: Step 1: After determining the span of the grain warehouse, divide the area into zones and design the temperature sensor collection locations: take the center point of the grain warehouse as the origin, set different quadrants according to the horizontal and vertical symmetry axes of the warehouse, and set different zones with the ground as the base plane and a fixed vertical distance. Each zone should be equipped with an appropriate number of temperature collection sensors. Step 2: Anomaly detection and effective retention of raw grain pile temperature data: Anomaly detection is performed on the collected raw temperature data to eliminate invalid and abnormal data; Step 3: Blank compensation for temperature data of cleaned grain pile: Based on the original data anomaly judgment and effective retention, the blank data after cleaning is filled by data interpolation of nearby points in time. Blank compensation is performed according to the data interpolation of 6 adjacent points in octahedral space. The compensation is considered to be completed after the blank filling of all collection points is completed. Step 4: Calculation of temperature variation coefficient of grain pile: Taking the target sensor as the center point, statistically analyze the average value, standard deviation and coefficient of variation of grain pile temperature data of 6 adjacent points in the space; if the target sensor is the temperature measurement point at the edge of the grain pile, calculate according to the actual adjacent points; simultaneously calculate the average value, standard deviation and coefficient of variation of grain pile temperature in different quadrants and different vertical zones. Step 5: Grain pile temperature uniformity determination: Based on the temperature variation coefficient of the grain pile in different zones and at different temperature measurement points, a ventilation temperature uniformity logic rule is established to adapt to the constantly changing temperature of the grain pile ventilation airflow and to construct a specific grain pile internal circulation ventilation mode. Step 6: Matching the operating mode of the internal circulation fan: Based on the analysis results of the coefficient of variation and temperature uniformity, formulate a strategy to adjust the operating power of the fan, set the operating mode of the fan for each zone, and adjust its power according to the zone status and the magnitude of the temperature variation coefficient of the independent temperature measurement point. Step 7: Automation of the internal circulation fan control system: By real-time monitoring of ambient temperature, humidity and internal temperature distribution of the grain pile, the fan and monitoring data are integrated to ensure that the fan can automatically adjust its operating power according to real-time data to meet the uniform temperature requirements inside the grain storage. Step 8: Effect Feedback and Optimization Cycle: Monitor and continuously optimize the control effect in real time, collect fan operation data and temperature and humidity information of the grain storage area at regular intervals, and adjust the outlier judgment criteria, the calculation method of the coefficient of variation and the fan operation strategy based on the feedback results to achieve continuous optimization.

2. The method for controlling the temperature variation coefficient of grain storage piles under temperature uniformity and ventilation zone management according to claim 1, characterized in that: In step 1, the quadrant setting is based on the grain warehouse ground as the base plane, and four quadrants are designed symmetrically with the physical symmetry center point of the base plane as the base origin. Vertically, independent zones are set at 1 m intervals. The horizontal interval between adjacent temperature monitoring points is required to be less than 5.0 m, and the vertical interval is required to be less than 1.75 m. Each independent zone is required to have no less than 9 temperature monitoring points.

3. The method for controlling the temperature variation coefficient of grain storage piles under temperature uniformity and ventilation zone management according to claim 1, characterized in that: In step 2, the invalid data includes data caused by accidental failures, operational abnormalities, and data transmission loss due to non-human factors of the temperature sensor itself. The abnormal data refers to single-point temperature data that consistently exceeds the detection range, contains duplicate values, blank values, or has excessively large differences between adjacent time acquisitions.

4. The method for controlling the temperature variation coefficient of grain storage piles under temperature uniformity and ventilation zone management according to claim 3, characterized in that: In step 2, the statistical criteria for judging the accidental failure of the temperature sensor caused by non-human factors are as follows: the corresponding value of the sensor exceeds the maximum detection range of the sensor; the criteria for judging repeated values ​​are as follows: the temperature values ​​collected for three or more consecutive times are exactly the same to three decimal places, which is considered abnormal repetition; the sensor data is empty or indeed empty, which is considered blank abnormal; the difference between two adjacent valid temperature data collection values ​​exceeds the set threshold of 10℃, which is considered an abnormally large difference.

5. The method for controlling the temperature variation coefficient of grain storage piles under temperature uniformity and ventilation zone management according to claim 1, characterized in that: In step 3, before performing data interpolation compensation on the data, it is necessary to ensure that the original data has been anomaly detected and invalid data has been removed.

6. The method for controlling the temperature variation coefficient of grain storage piles under temperature uniformity and ventilation zone management according to claim 1 or 5, characterized in that: In step 3, the data interpolation compensation process uses an octahedral space 6-point adjacent interpolation method to compensate for blank data. The interpolation algorithm uses a weighted average method. ； D fill D represents interpolated data. i Represented as adjacent point data, w i The corresponding weights are indicated, where among the 6 spatially adjacent points, the weight coefficients for the 4 points in the horizontal direction are 0.2 and the weight coefficients for the 2 points in the vertical direction are 0.1 respectively; if the compensation data is a boundary point, it is compensated with equal weight coefficients according to its adjacent points; when the blank data of all collection points has been filled, the blank compensation of the cleaned data is considered to be completed.

7. The method for controlling the temperature variation coefficient of grain storage piles under temperature uniformity and ventilation zone management according to claim 1, characterized in that: In step 4, using the target sensor as the center point, statistical analysis is performed on the temperature data of adjacent points around it. The statistical indicators include the mean, standard deviation, and coefficient of variation of the target location and its six spatially adjacent points. The calculation formula is as follows: ； Mean represents the average value of the target temperature measurement point and its adjacent points; StdDev represents the standard deviation; CV represents the coefficient of variation of the point and its six adjacent points; similarly, the overall average value, standard deviation and coefficient of variation of different quadrants and their sub-regions are calculated simultaneously to evaluate the overall temperature uniformity of the sub-regions.

8. The method for controlling the temperature variation coefficient of grain storage piles under temperature uniformity and ventilation zone management according to claim 7, characterized in that: In step 5, if the CV corresponding to the temperature measurement point is <10%, it is defined as relatively stable temperature; if 10%≤CV<20%, it is defined as temperature "to be monitored" state; if 20%≤CV, it is defined as "needs adjustment"; if the average CV value of the temperature measurement quadrant is <15%, it is defined as "average temperature"; if the average CV value of the temperature measurement quadrant is ≥15%, it is defined as "abnormal temperature"; based on the temperature uniformity logic rules, ventilation modes are formulated for different zones and ventilation states, including dynamic wind speed adjustment: based on temperature fluctuations, different wind speeds and wind directions are configured in different areas to optimize airflow distribution; and periodic adjustment cycle: set the timed monitoring and adjustment frequency to ensure real-time adaptation and adjustment of airflow temperature.

9. The method for controlling the temperature variation coefficient of grain storage piles under temperature uniformity and ventilation zone management according to claim 7 or 8, characterized in that: In step 6, when the zone is in an abnormal temperature condition, the ventilation fan operates at its rated power at constant speed; when the zone is in an average temperature condition, and the temperature measurement point CV shows one or more "needs adjustment" status or three or more "awaiting monitoring" statuses, the fan is in deceleration mode, with its operating power being 0.8 times that of the last test operation; when the zone is in a normal temperature condition, and all temperature measurement points CV are in a relatively stable temperature condition or have fewer than three "awaiting monitoring" statuses, the temperature equalization fan is in a low-frequency state.

10. The method for controlling the temperature variation coefficient of grain storage piles under temperature uniformity and ventilation zone management according to claim 1, characterized in that: In step 7, the process of integrating the wind turbine and monitoring data specifically involves transmitting the collected data to the PLS controller and using the PLC programmable logic controller to integrate the wind turbine and monitoring data.

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