Temperature variation coefficient regulation and control method for storage grain pile uniform temperature ventilation partition management

Abstract

The invention discloses a temperature variation coefficient regulation and control method for storage grain pile uniform-temperature ventilation partition management, is suitable for high and large bungalow storage grain piles, and constructs a three-dimensional partition management mode based on a grain uniform-temperature ventilation process. In combination with an octahedron spatial data compensation scheme, effective acquisition and analysis of temperature data in a uniform-temperature ventilation process are completed, statistics and calculation of spatial temperature variation coefficients of different partition points are completed based on spatial adjacent membership calculation, and definitions of different uniform-temperature ventilation modes are clarified. The execution management of the grain pile uniform temperature ventilation partition is completed, and the accurate and efficient operation of the uniform temperature ventilation process is ensured. By means of the method, uniform temperature distribution in the grain storage process of the tall and large horizontal warehouse can be achieved, the stability of the microecology of the grain pile in the storage process is improved, the risks such as micro-airflow flowing and moisture condensation in the grain pile are reduced, and the method plays an important role in promoting grain green storage and improvement of mechanical ventilation uniformity; and technical reserve is provided for high-quality development of the grain industry.

Description

Technical Field

[0001] The present invention relates to the technical field of grain engineering, and specifically to a method for regulating the coefficient of variation of temperature in the uniform temperature ventilation zoning management of a stored grain pile. Background Art

[0002] At present, the uniformity of heat and moisture transfer and the optimization of ventilation monitoring in post-harvest grain storage are bottleneck problems that plague large-scale low-temperature fresh storage. During the grain storage process, it is crucial to maintain a good grain storage environment. Among them, the balanced management of temperature not only affects the quality and freshness period of grains but also directly relates to the safety of grain storage. Therefore, researching and developing a method for regulating the coefficient of variation of temperature applicable to the uniform temperature ventilation zoning management of grain storage has important practical significance and application prospects. In recent years, various countries have increased their investment in grain storage technology and adopted advanced control technologies to improve the efficiency and safety of grain storage. Taking European and American countries as an example, they have multiple patented technologies in temperature and humidity monitoring, ventilation system optimization, and grain pile management. Among them, some patents involve ventilation control systems based on intelligent perception and mechatronics. However, considering the actual industry situation, most of the above technical methods only stay at the mechanized operation of storage steel silos, vertical silos, and shallow silos, etc., and there is a problem of insufficient temperature control ability for different grain storage zones.

[0003] Regarding the currently main type of grain storage, the tall flat warehouse, it has a relatively large plane span and a sufficient single-warehouse storage scale. The unified temperature and ventilation control lack refined management, and it is extremely easy to have the existence of overcooling and ventilation dead corners, which poses a major hidden danger to grain pile condensation, mildew, and pest occurrence. Usually, there are a large number of temperature measurement points in existing granaries. In a standard warehouse bay, usually more than 200 grain condition temperature measurement points will be set, and it is extremely easy to have individual mechanical failures, resulting in abnormal data at the detection points. If no compensation and correction are carried out, these abnormal data may be misinterpreted as abnormal grain pile temperature. In fact, it is only a fault of the monitoring equipment itself, and there is no real abnormality in the grain pile temperature. In this case, only manual experience can be relied on for judgment, and the current technology lacks an effective data cleaning and spatial compensation mechanism when dealing with such abnormal situations. However, the existing uniform temperature ventilation methods often ignore this actual objective existence, and it is extremely easy to cause an operating mode where the ventilation program does not match the actual grain condition. Summary of the Invention

[0004] In order to solve the problems of the prior art, the present invention provides a method for controlling the temperature variation coefficient of uniform temperature and ventilation zoning management of stored grain piles, so as to realize the zoning of ventilation areas in tall flat warehouses, the mainstream type of grain storage, and carry out statistics and analysis of the average value, standard deviation and variation coefficient of temperature data of grain piles in different zones of the whole warehouse, thereby improving the accuracy of subsequent data analysis, forming an intelligent uniform temperature ventilation adjustment system, improving the stability of the microecology of the grain pile during storage, and reducing the risks of micro-air flow and condensation inside the grain pile, which plays an important 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] The present invention comprises the following specific steps:

[0006] Step 1: After clarifying the span of the granary, divide it into zones and design the temperature sensor collection location: take the center point of the granary as the origin, set different quadrants according to the horizontal and vertical symmetry axes of the warehouse, and use the ground as the basic plane to set different zones with fixed vertical distance heights. It is required to set an appropriate number of temperature collection sensors at each zone temperature measurement point;

[0007] Step 2: Abnormal determination and effective retention of original grain pile temperature data: abnormal determination of the collected original temperature data, and elimination of invalid and abnormal data;

[0008] Step 3: Blank compensation of cleaned grain pile temperature data: On the basis of abnormal judgment and effective retention of original data, the blank data after cleaning are filled by data interpolation of adjacent points in time, and 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 the acquisition sites is completed;

[0009] Step 4: Calculation of the coefficient of variation of grain pile temperature: With the target sensor as the center point, statistically analyze the average value, standard deviation and coefficient of variation of the grain pile temperature data of the six adjacent points in the space; if the target sensor is a 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 the grain pile temperature in different quadrants and vertical partitions;

[0010] Step 5: Determine the uniformity of grain pile temperature: According to the temperature variation coefficient of the grain pile in different partitions and temperature measurement points, establish the ventilation temperature uniformity logic rules to adapt to the changing ventilation air flow temperature of the grain pile and build a specific grain pile internal circulation ventilation mode;

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

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

[0013] Step 8: Effect feedback and optimization loop: Monitor the control effect in real time and continuously optimize it, regularly collect fan operation data and temperature and humidity information of the grain storage area, and based on the feedback results, adjust the abnormal value judgment criteria, the calculation method of the coefficient of variation and the fan operation strategy to achieve continuous optimization.

[0014] As a further improvement, in step 1, the quadrants are set based on the granary ground as the basic plane, and four quadrants are symmetrically designed based on the physical symmetry center point of the basic plane as the basic origin, with independent partitions set every 1m in the vertical direction; the adjacent horizontal intervals of the temperature monitoring sites are required to be less than 5.0m, and the vertical intervals are required to be less than 1.75m; each independent partition is required to have no less than 9 temperature monitoring sites.

[0015] Further improvement, in step 2, the invalid data includes data on accidental failures, abnormal operation and data transmission loss caused by non-human factors of the temperature sensor itself, and the abnormal data is single-point temperature data with long-term over-detection range values, repeated values, blank values ​​and excessive difference between two consecutive time acquisitions. In step 2, the judgment standard for the statistical data of accidental failures caused by non-human factors of the temperature sensor is that the corresponding value of the sensor exceeds the maximum detection range of the sensor; the judgment standard for repeated values ​​is that the temperature values ​​collected for more than 3 consecutive times are exactly the same to 3 decimal places, which is regarded as abnormal repetition; the sensor collection data is empty or is indeed regarded as blank abnormality; the difference between two consecutive valid temperature collection values ​​exceeds the set threshold of 10°C, which is regarded as abnormally large difference.

[0016] Further improvement, in step 3, before interpolating the data, it is necessary to ensure that the original data has been judged as abnormal and invalid data has been eliminated. The interpolation compensation process of the data uses the octahedral space adjacent 6-point interpolation method to compensate for blank data, and the interpolation algorithm uses the weighted average method:

[0017]

[0018] D fill represents interpolation data, D i Represented as adjacent point data, w i Represents the corresponding weights, where among the 6 adjacent points in space, 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, compensation with equal weight coefficients is performed according to the adjacent points; when the blank data of all acquisition sites are filled, the blank compensation of the cleaned data is considered to be completed.

[0019] For further improvement, in step 4, using the target sensor as the center point, statistical analysis of the temperature data of adjacent points is carried out around it. The statistical indicators include the average value, standard deviation, and coefficient of variation of the target site and 6 adjacent points in space. The calculation formulas are as follows:

[0020]

[0021] Mean represents the average value of the target temperature measurement point and adjacent points; StdDev represents the standard deviation; CV represents the coefficient of variation of this point corresponding to the 6 adjacent points. Similarly, the overall average value, standard deviation, and coefficient of variation of different quadrants and their partitions are calculated synchronously to evaluate the overall temperature uniformity of the partition.

[0022] For further improvement, in step 5, if the CV corresponding to the temperature measurement point < 10%, it is defined as relatively stable temperature; if 10% ≤ CV < 20%, it is defined as the "to be monitored" state of temperature; if 20% ≤ CV, it is defined as "needs adjustment"; if the average CV value of the temperature measurement quadrant partition < 15%, it is defined as "average temperature", and if the average CV value of the temperature measurement quadrant partition ≥ 15%, it is defined as "abnormal average temperature". According to the average temperature logic rules, ventilation modes for different partitions and ventilation states are formulated, including dynamic wind speed adjustment: based on the temperature fluctuation situation, different wind speed and wind direction configurations are adopted in different regions to optimize the air flow distribution; and regular adjustment cycle: setting the timing monitoring and adjustment frequency to ensure real-time adaptation and adjustment to the air flow temperature.

[0023] For further improvement, in step 6, when the partition is in the abnormal average temperature state, the ventilation fan operates at a constant rated power; when the partition is in the average temperature state, and there is 1 or more "needs adjustment" states or 3 or more "to be monitored" states in the CV of the temperature measurement points, the fan is in the deceleration mode, and the operating power is 0.8 of the previous detected operation; when the partition is in the normal temperature state, and the CV of all temperature measurement points is in the relatively stable temperature or "to be monitored" state less than 3 points, the average temperature fan is in the low-frequency state.

[0024] For further improvement, in step 7, the specific process of integrating the fan and monitoring data is to transmit the collected data to the PLS controller, and use the PLC programmable logic controller to realize the integration of the fan and monitoring data.

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

[0026] 1. Realize the zoning division of the ventilation area in the mainstream bin type of large flat warehouses for grain storage;

[0027] 2. On the basis of constructing a temperature monitoring and acquisition system, complete the statistics, cleaning, and compensation of the original data, improve the accuracy of later data analysis, and overcome the misjudgment of the actual grain situation caused by mechanical failures of temperature sensors themselves, abnormal data transmission interfaces, and loss of degree display signals, etc.

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

[0029] 4. Link the Internet of Things technology to seamlessly connect with the on - ground cage ventilation system of the high - rise bungalow warehouse, which is applicable to all ground - type vertical ventilation methods. It replaces the manual judgment operation of the original mechanical ventilation system of the granary, completes real - time monitoring, judgment, and control of the temperature of the entire granary, and can improve the digital and intelligent level of granary ventilation;

[0030] 5. Accurately determine the uniformity of the grain pile temperature distribution. Combining point - surface - volume - type multi - dimensional coefficient of variation analysis, it completes the variable - frequency energy - saving control during the uniform - temperature ventilation process of the granary, improves the stability of the micro - ecosystem in the grain pile during storage, reduces the risks of micro - air - flow movement and condensation inside the grain pile, plays an important 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. Description of the Drawings

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0032] Figure 1 It is the distribution setting scheme of the temperature sensors in the granary applicable to the present invention;

[0033] Figure 2 It is the layout diagram of the on - ground cage ventilation duct and temperature sensors inside the granary of the present invention;

[0034] Figure 3 It is the octahedron space data compensation distribution diagram of the temperature measurement points in the granary of the present invention;

[0035] Figure 4 It is the original distribution and compensation distribution diagram of the abnormal temperature measurement data in the high - rise bungalow granary of the present invention;

[0036] Figure 5 It is the coefficient of variation of the grain layer temperature of the 4th and 5th layers in the high - rise bungalow granary of the present invention;

[0037] Figure 6 It is the change of the grain pile temperature before and after the uniform - temperature ventilation in the high - rise bungalow granary of the present invention. Detailed Embodiments

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] The invention discloses a temperature variation coefficient control strategy method suitable for the uniform temperature ventilation zone management of grain storage. According to the corresponding zone and temperature monitoring site variation coefficient technology, the ventilation fan operating power is linked to control the control idea of ​​uniform temperature distribution inside the grain pile. The scheme has completed the application of the internal circulation ventilation system in a tall flat warehouse with a length of 30m, a width of 24m and a height of 7m for grain storage. The corresponding temperature sensor distribution scheme is shown in the attached figure. Figure 1 As shown in the figure, including the ground 1, warehouse wall 2, temperature measuring cable 3-1, temperature measuring point 3-2, window 4, roof 5, grain stacking line 6, online monitoring and real-time control of frequency conversion are realized, and the uniform adjustment of the temperature inside the grain pile is completed, avoiding condensation, compaction and insect and mildew breeding inside the grain pile caused by "cold core and hot skin" and "micro airflow".

[0040] The layout of the ventilation duct and temperature sensor of the ground cage inside the granary is as follows Figure 2 As shown, it includes a warehouse wall 2, a temperature measuring point 3-2, a fan interface 7 and a ground cage ventilation duct 8. The figure can be divided into four quadrants: the first quadrant 9-1, the second quadrant 9-2, the third quadrant 9-3, and the fourth quadrant 9-4.

[0041] A specific implementation of the present invention includes the following specific steps:

[0042] Step 1: After clarifying the span of the granary, the zoning is carried out and the temperature sensor collection position is designed: taking the center point of the granary as the origin, different quadrants are set according to the horizontal and vertical symmetry axes of the warehouse, and the ground is used as the basic plane, and different partitions are set at a fixed vertical distance height. It is required to set a suitable number of temperature collection sensors at each partition temperature measuring point; the setting quadrant is based on the granary ground as the basic plane, and the physical symmetry center point of the basic plane as the basic origin. Four quadrants are designed symmetrically, and independent partitions are set every 1m in the vertical direction; the adjacent horizontal intervals of the temperature monitoring sites are required to be less than 5.0m, and the vertical intervals are required to be less than 1.75m; each independent partition is required to have no less than 9 temperature monitoring sites.

[0043] Step 2: Determine the abnormality of the original grain pile temperature data and retain it effectively: Determine the abnormality of the collected original temperature data, eliminate the accidental failures, operation abnormalities and data transmission losses caused by non-human factors of the temperature sensor itself, and regard them as invalid data. The single-point temperature data with long-term out-of-detection range values, repeated values, blank values ​​and excessive difference between two consecutive time collections are regarded as abnormal data to ensure the correctness of the back-end result judgment and strategy formulation; the statistical data judgment standard for accidental failures caused by non-human factors of the temperature sensor is that the corresponding value of the sensor exceeds the maximum detection range of the sensor; the repeated value judgment standard is that the temperature values ​​collected for more than 3 consecutive times are exactly the same to 3 decimal places, which is regarded as abnormal repetition; the sensor collection data is empty or true (the numerical data shows NULL or NaN) is regarded as blank abnormality; the difference between two consecutive valid temperature collection values ​​exceeding the set threshold of 10℃ is regarded as excessively large difference.

[0044] Step 3: Blank compensation of cleaned grain pile temperature data: Based on the abnormal judgment and effective retention of the original data, the blank data after cleaning are filled by data interpolation of adjacent points in time, and blank compensation is performed based on the data interpolation of 6 adjacent points in the octahedron space. The compensation is considered complete only after the blank filling of all the collection points is completed.

[0045] Before data interpolation, it is necessary to ensure that the original data has been judged as abnormal and invalid data has been eliminated. The adjacent 6-point interpolation method of the octahedron space is used to compensate for the blank data. The interpolation algorithm uses the weighted average method:

[0046]

[0047] Dfill represents interpolation data, Di represents adjacent point data, and wi represents corresponding weights. Among the 6 adjacent points in space, 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, compensation with equal weight coefficients is performed according to the adjacent points. When the blank data of all acquisition sites are filled, the blank compensation of the cleaning data is considered completed.

[0048] Figure 3 This is the octahedral spatial data compensation distribution diagram of the granary temperature measurement point of the present invention

[0049] Step 4: Temperature variation of the grain pile. In the figure, there are seven points set. Point No.1 is the temperature compensation data point; points No.3, No.4, No.5, and No.6 are the adjacent points on the plane of the temperature compensation data points; points No.2 and No.7 are the adjacent points vertically to the temperature compensation data points. Coefficient calculation: Taking the target sensor as the central point, statistically analyze the average value, standard deviation, and coefficient of variation of the grain pile temperature data of the 6 adjacent points in the surrounding space; if the target sensor is a temperature measurement point at the edge of the grain pile, calculate according to the actual adjacent points; synchronously calculate the average value, standard deviation, and coefficient of variation of the grain pile temperature in different quadrants and different vertical partitions.

[0050] Taking the target sensor as the central point, conduct a statistical analysis of the temperature data of the adjacent points around it. The statistical indicators include the average value, standard deviation, and coefficient of variation of the target point and the 6 adjacent points in the space. The calculation formulas are as follows:

[0051]

[0052]

[0053] Mean represents the average value of the target temperature measurement point and the adjacent points; StdDev represents the standard deviation; CV represents the coefficient of variation of this point corresponding to the 6 adjacent points. Similarly, the overall average value, standard deviation, and coefficient of variation of different quadrants and their partitions are calculated synchronously to evaluate the overall temperature uniformity of the partitions.

[0054] Step 5: Judgment of the temperature uniformity of the grain pile: According to the coefficient of variation of the grain pile temperature of different partitions and temperature measurement points, establish the ventilation temperature equalization logic rules to adapt to the continuously changing temperature of the grain pile ventilation air flow, and construct a specific internal circulation ventilation mode of the grain pile.

[0055] If the CV corresponding to the temperature measurement point < 10%, it is defined as relatively stable temperature; if 10% ≤ CV < 20%, it is defined as the "to be monitored" state of temperature; 20% ≤ CV, it is defined as "needs adjustment"; if the average CV value of the temperature measurement quadrant partition < 15%, it is defined as "average temperature", and if the average CV value of the temperature measurement quadrant partition ≥ 15%, it is defined as "abnormal temperature equalization". According to the temperature equalization logic rules, formulate ventilation modes for different partitions and ventilation states, including dynamic wind speed adjustment: based on the temperature fluctuation situation, adopt different wind speed and wind direction configurations in different regions to optimize the air flow distribution; and regular adjustment cycle: set the timing monitoring and adjustment frequency to ensure real-time adaptation and adjustment to the air flow temperature.

[0056] Step 6: Matching of the operation mode of the inner circulation fan: Based on the analysis results of the coefficient of variation and temperature uniformity, formulate a strategy for adjusting the operation power of the fan, and set the working mode (rated operation, decelerated operation, and low-frequency operation) of the fan for each partition. The power adjustment is based on the partition state and the size of the coefficient of variation of the temperature of the independent temperature measurement point.

[0057] When the partition is in a state of uniform temperature anomaly, the ventilation fan operates at a constant rated power; when the partition is at an average temperature and one or more "needs adjustment" states or three or more "awaiting monitoring" states appear at the temperature measurement points CV, the fan is in a deceleration mode and the operating power is 0.8 times that of the previous detected operation; when the partition is at a normal temperature and all temperature measurement points CV are in a relatively stable temperature state or the number of "awaiting monitoring" states is less than 3, the uniform temperature fan is in a low-frequency state.

[0058] Step 7: Automation of the inner circulation fan control system: Through real-time monitoring of the environmental temperature, humidity, and the temperature distribution inside the grain pile, the collected data is transmitted to the PLS controller, and the PLC programmable logic controller is used to integrate the fan and the monitoring data to ensure that the fan can automatically adjust the operating power according to the real-time data to meet the requirement of uniform temperature inside the stored grain.

[0059] Step 8: Effect feedback and optimization cycle: Real-time monitor the control effect and continuously optimize it. Regularly collect the fan operation data and the temperature and humidity information of the stored grain area. Based on the feedback results, adjust the abnormal value judgment criteria, the calculation method of the coefficient of variation, and the fan operation strategy to achieve continuous optimization.

[0060] Each implementation in this specification

[0061] Example 1:

[0062] In view of the special situations such as the loss of temperature detection data transmission and the malfunction of some sensors that are prone to occur in the grain condition temperature measurement system during the long-term storage of grain in tall flat warehouses, which have caused continuous interruption of sensor data and seriously affected the judgment of the ventilation opportunity for the grain condition. In response to this, this case intercepted the grain condition temperature measurement of the No. 8 rice reserve warehouse of a directly affiliated reserve grain depot. The grain entry time was February 7, 2022, and the grain condition temperature detection time was October 9, 2023. There were a total of 3,502 tons of japonica rice with a moisture content of 14.0%. Among them, the warehouse temperature was 22.0 °C, the warehouse humidity was 49.0%, the outside temperature was 16.5%, and the outside humidity was 74.2%.

[0063] Figure 4 It is the original distribution and compensation distribution diagram of the abnormal temperature measurement data for the tall flat warehouse. The squares in the figure represent the data abnormal points.

[0064] There are a total of 4 ventilation fan inlets in the north and south of the granary. A 1-machine 4-channel above-ground cage ventilation system is adopted. A total of 6 rows and 7 columns of temperature measurement cables are designed. 5 temperature acquisition points are set on each cable. The layout of the above-ground cage ventilation ducts and temperature sensors is as Figure 2 shown. Vertically and longitudinally, the 4th layer (2.0 m from the grain surface) temperature measurement layer is selected for data analysis. The original data is as 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, the signals of 3 temperature measurement points are missing due to transmission loss or failure, showing NaN, and 1 is detected below the detection limit and determined to be invalid data.

[0065] According to this case, the method of interpolating 6 adjacent points in the octahedral space is proposed to compensate for the blank data, and the interpolation algorithm is calculated by the weighted average method. Among the 6 adjacent points in space, the weight coefficients of the 4 points in the horizontal direction are 0.2 respectively, and the weight coefficients of the 2 points in the vertical direction are 0.1 respectively. If the compensated data point is a boundary point, it is compensated with equal weight coefficients according to the adjacent points. Therefore, the compensation values of the abnormal data points are 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 point:

[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 compensated data is as Figure 4 shown in Area B.

[0075] Example 2:

[0076] Based on the daily data collection status of the No. 8 rice storage warehouse of a certain reserve grain warehouse, this experiment continued to carry out the calculation of the temperature variation coefficient of the grain pile and the evaluation of the temperature uniformity of the grain layer plane based on the data anomaly judgment and cleaning in Example 1. This case is aimed at a tall flat warehouse grain pile with a length, width and height of 30m×24m×7m, which is divided into 4 quadrants and 5 vertical layers, a total of 20 quadrant partitions; among them, there are a total of 210 temperature sampling points, and each quadrant partition is set with 3 rows and 3 columns of grain temperature data collection sensors, and an independent grain pile temperature collection data is set on the vertical line of the center of the grain warehouse corresponding to the location of the warehouse door.

[0077] Figure 5 It is the temperature variation coefficient of the 4th and 5th grain layers of the tall flat warehouse. The square boxes in the figure represent the locations where the coefficient of variation of the temperature measuring points exceeds 20%.

[0078] According to the specific content of the present invention, the proposed octahedral coefficient of variation calculation scheme uses the target sensor as the center point, and performs statistical analysis of the temperature data of the adjacent points around it. The statistical indicators include the average, standard deviation and coefficient of variation of the target site and the 6 adjacent points in space; at the same time, the average grain temperature, standard deviation and average coefficient of variation of the partition are statistically calculated, and the results are shown in Table 1. The temperature uniformity of the first layer of the grain pile is good, and the deviation of the average temperature of different quadrant partitions does not exceed 1°C, and the corresponding CV coefficient is within 2.3%; as the grain pile moves upward to the grain surface layer (5th layer), the average temperature of the grain pile in different quadrant partitions exceeds 2.1°C, and the corresponding CV coefficient even exceeds 20%. According to the content of the present invention, the proposed average CV value of the temperature measuring quadrant partition is <15%, which is defined as "temperature average", and the average CV value of the temperature measuring quadrant partition is ≥15%, which is defined as "average temperature abnormality". In comparison, the 1st, 2nd and 3rd floors are in an average temperature state as a whole, while the second and third quadrants of the 4th floor have "average temperature abnormalities", and the second and third quadrants of the 5th floor also have "average temperature abnormalities". According to the orientation analysis diagram, they are mainly concentrated in the western half of the granary.

[0079] The temperature uniformity of the grain piles on the 4th and 5th layers was further evaluated and analyzed. If the CV corresponding to the temperature measuring point was less than 10%, the temperature was defined as "relatively stable"; if 10%≤CV<20%, the temperature was defined as "to be monitored"; and 20%≤CV was defined as the "need to be adjusted" principle. There were "need to be adjusted" temperature measuring points in the first and second quadrants of the 4th layer, and a total of 5 "need to be adjusted" variation points appeared on the central vertical axis and the second quadrant of the 5th layer. This indicated that the temperature uniformity distribution of the grain pile in this area was abnormal. During the ventilation process, the corresponding cage fan at the location needed to operate at rated full power to ensure uniform temperature of the grain pile.

[0080] Table 1 Temperature compensation data set of grain pile in experimental granary and detection and analysis of grain temperature uniformity index in different partitions

[0081]

[0082]

[0083]

[0084] Example 3:

[0085] Based on the above analysis, combined with the principle of fan operation power matching, the working modes of the fans (rated operation, deceleration operation, and low-frequency operation) are set for each partition, and the power adjustment is based on the partition status and the magnitude of the temperature variation coefficient of the independent temperature measurement points. Through the real-time monitoring of the ambient temperature, humidity, and the temperature distribution inside the grain pile, the collected data is transmitted to the PLS controller, and the integration of the fan and the monitoring data is realized by using the PLC programmable logic controller. The grain pile cyclic temperature equalization ventilation was carried out based on the above variation coefficient calculation and uniformity analysis, and the results before and after temperature equalization are as Figure 6 shown. Before temperature equalization, the temperature distribution of different grain layer depths was significantly different, and even the temperature distribution in the same quadrant was significantly different. Among them, the average temperature difference between the fourth quadrants of the second layer and the fifth layer was as high as 10.2 °C. After temperature equalization ventilation, the average temperature interpolation in this area has been reduced to 2.34 °C. In addition, this difference is not only reflected in the average temperature distribution between different grain layer depths, but also in different quadrant areas of the same grain layer, and the temperature uniformity has also been significantly improved, and the temperature difference has been reduced to within 2.0 °C, indicating that the above uniform ventilation strategy has a significant improvement on the temperature uniformity of large flat warehouses.

[0086] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. For any person skilled in the art in the technical field disclosed by the present invention, any changes or substitutions that can be easily thought of by those of ordinary skill in the technical field should be covered within the protection scope of the present invention without departing from the principle of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for controlling the temperature variation coefficient of a grain storage pile for uniform temperature ventilation zone management, characterized in that: The specific steps include: Step 1: After clarifying the span of the granary, divide it into zones and design the temperature sensor collection location: take the center point of the granary as the origin, set different quadrants according to the horizontal and vertical symmetry axes of the warehouse, and use the ground as the basic plane to set different zones with fixed vertical distance heights. It is required to set an appropriate number of temperature collection sensors at each zone temperature measurement point; Step 2: Abnormal determination and effective retention of original grain pile temperature data: abnormal determination of the collected original temperature data, and elimination of invalid and abnormal data; Step 3: Blank compensation of cleaned grain pile temperature data: On the basis of abnormal judgment and effective retention of original data, the blank data after cleaning are filled by data interpolation of adjacent points in time, and 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 the acquisition sites is completed; Step 4: Calculation of the coefficient of variation of grain pile temperature: With the target sensor as the center point, statistically analyze the average value, standard deviation and coefficient of variation of the grain pile temperature data of the six adjacent points in the space; if the target sensor is a 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 the grain pile temperature in different quadrants and vertical partitions; Step 5: Determine the uniformity of grain pile temperature: According to the temperature variation coefficient of the grain pile in different partitions and temperature measurement points, establish the ventilation temperature uniformity logic rules to adapt to the changing ventilation air flow temperature of the grain pile and build a specific grain pile internal circulation ventilation mode; Step 6: Matching the operation mode of the internal circulation fan: Based on the analysis results of the coefficient of variation and temperature uniformity, formulate a strategy for adjusting the fan operation power, set the fan operation mode for each partition, and adjust its power according to the partition status and the temperature coefficient of variation of the independent temperature measurement point; Step 7: Automation of the internal circulation fan control system: Through real-time monitoring of ambient temperature, humidity and temperature distribution inside the grain pile, the fan and monitoring data are integrated to ensure that the fan can automatically adjust the operating power according to real-time data to meet the uniform temperature requirements inside the stored grain; Step 8: Effect feedback and optimization loop: Monitor the control effect in real time and continuously optimize it, regularly collect fan operation data and temperature and humidity information of the grain storage area, and based on the feedback results, adjust the abnormal value judgment criteria, the calculation method of the coefficient of variation and the fan operation strategy to achieve continuous optimization.

2. The temperature variation coefficient control method for uniform temperature ventilation zone management of stored grain pile according to claim 1 is characterized by: In step 1, the quadrant setting is based on the granary ground as the basic plane, and four quadrants are designed symmetrically based on the physical symmetry center point of the basic plane as the basic origin. Independent partitions are set every 1 m in the vertical direction; the adjacent horizontal intervals of temperature monitoring sites are required to be less than 5.0 m, and the vertical intervals are required to be less than 1.75 m; each independent partition is required to have no less than 9 temperature monitoring sites.

3. The temperature variation coefficient control method for uniform temperature ventilation zone management of stored grain pile according to claim 1, characterized in that: In step 2, the invalid data includes data due to accidental failures, abnormal operation and data transmission loss caused by non-human factors of the temperature sensor itself, and the abnormal data is single-point temperature data with long-term out-of-detection range values, repeated values, blank values ​​and excessive difference between two adjacent time collections.

4. The temperature variation coefficient control method for uniform temperature ventilation zone management of stored grain pile according to claim 3 is characterized by: In step 2, the statistical data judgment standard for accidental failures of the temperature sensor caused by non-human factors is that the corresponding value of the sensor exceeds the maximum detection range of the sensor; the repeated value judgment standard is that the temperature values ​​collected for more than 3 consecutive times are exactly the same to 3 decimal places, which is considered an abnormal repetition; the sensor collection data is empty or is indeed considered a blank abnormality; the difference between two adjacent valid temperature collection values ​​exceeds the set threshold of 10℃, which is considered to be too large an abnormality.

5. The temperature variation coefficient control method for uniform temperature ventilation zone management of stored grain pile 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 judged as abnormal and invalid data has been eliminated.

6. The temperature variation coefficient control method for uniform temperature ventilation zone management of stored grain piles according to claim 1 or 5, characterized in that: In step 3, the data interpolation compensation process uses the octahedral space adjacent 6-point interpolation method to compensate for blank data, and the interpolation algorithm uses the weighted average method: ； D fill represents interpolation data, D i Represented as adjacent point data, w i Represents the corresponding weights, where among the 6 adjacent points in space, 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, compensation with equal weight coefficients is performed according to the adjacent points; when the blank data of all acquisition sites are filled, the blank compensation of the cleaned data is considered to be completed.

7. The temperature variation coefficient control method for uniform temperature ventilation zone management of stored grain pile according to claim 1, characterized in that: In step 4, the target sensor is used as the center point, and the temperature data of the adjacent points are statistically analyzed around it. The statistical indicators include the mean, standard deviation and coefficient of variation of the target site and the six adjacent points in space. 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 between the point and the six adjacent points; similarly, the overall mean, standard deviation and coefficient of variation of different quadrants and their partitions are calculated synchronously to evaluate the overall temperature uniformity of the partitions.

8. The temperature variation coefficient control method for uniform temperature ventilation zone management of stored grain pile according to claim 7, characterized in that: In step 5, if the CV corresponding to the temperature measurement point is less than 10%, it is defined as a relatively stable temperature; if 10%≤CV<20%, it is defined as a temperature "to be monitored" state; 20%≤CV, it is defined as "need to be adjusted"; if the average CV value of the temperature measurement quadrant partition is less than 15%, it is defined as "average temperature", and the average CV value of the temperature measurement quadrant partition is ≥15%, it is defined as "abnormal average temperature"; according to the average temperature logic rules, ventilation modes for different partitions and ventilation states are formulated, including dynamic wind speed adjustment: based on temperature fluctuations, different wind speed and wind direction configurations are used in different areas to optimize airflow distribution; and regular adjustment cycle: set the regular monitoring and adjustment frequency to ensure real-time adaptation and adjustment of airflow temperature.

9. The temperature variation coefficient control method for uniform temperature ventilation zone management of stored grain piles according to claim 7 or 8, characterized in that: In step 6, when the partition is in an abnormal average temperature state, the ventilation fan is in constant operation at rated power; when the partition is in an average temperature state, one or more temperature measurement points CV appear in the "need to be adjusted" state or three or more "to be monitored" state, the fan is in deceleration mode, and the operating power is 0.8 of the last detection operation; when the partition is in a normal temperature state, and all temperature measurement points CV are in a relatively stable temperature or the "to be monitored" state is lower than 3 points, the average temperature fan is in a low frequency state.

10. The temperature variation coefficient control method for uniform temperature ventilation zone management of stored grain pile according to claim 1, characterized in that: In step 7, the fan and monitoring data integration process specifically transmits the collected data to the PLS controller, and uses the PLC programmable logic controller to realize the fan and monitoring data integration.

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