A grain condition cloud chart analysis method and system

Abstract

This invention relates to the field of grain condition analysis technology and discloses a grain condition cloud map analysis method and system. By constructing the temperature and humidity fields inside the grain pile, a visualized grain condition cloud map is generated, transforming discrete sensor data into continuous isotherm distribution maps and color rendering maps. This allows warehouse management personnel to intuitively and quickly grasp the overall grain condition status, including temperature distribution, heat core location, and humidity distribution inside the grain pile. By matching the binary correspondence between isotherms and heat core areas, combined with dew point calculation and relative humidity change analysis during micro-airflow rise, the formation process of condensation and mold growth modes can be accurately identified, issuing early warnings before threats to grain storage safety occur. Compared to existing technologies that only issue alarms after temperature or humidity exceeds limits, this invention enables early detection and proactive prevention of potential grain condition safety hazards, effectively ensuring grain storage safety.

Description

Technical Field

[0001] This invention relates to the field of grain storage condition analysis technology, specifically a grain condition cloud map analysis method and system. Background Technology

[0003] Currently, grain storage condition analysis technology mainly employs a matrix-style deployment of temperature sensors inside the grain pile. By collecting temperature values ​​at various discrete points, numerical comparisons and outlier analysis are performed to determine whether the stored grain exhibits abnormalities such as overheating or mold growth. Specifically, existing technologies typically analyze the detection data from a single sensor or multiple discrete points independently. For example, they determine whether the temperature at a certain point exceeds a set threshold or whether there are abnormal changes such as a sudden temperature rise, thereby issuing an alarm signal. Some systems also incorporate humidity sensor data for simple over-limit judgments. However, this technical solution based on discrete point numerical comparison and independent analysis has obvious defects and shortcomings. The independent analysis of discrete points cannot reflect the overall spatial distribution and continuous trend of temperature and humidity inside the grain pile, and it is difficult to detect global grain condition characteristics such as micro-airflow movement, heat core formation and migration inside the grain pile. Existing technology can only alarm for abnormal values ​​of a single point or local area, but it cannot identify the formation process and evolution law of typical grain condition modes such as condensation, mold, and heat generation, resulting in delayed warnings, which are often only discovered after the grain condition has deteriorated. Due to the lack of intuitive visualization of the temperature and humidity field inside the grain pile, warehouse managers have difficulty judging the uniformity of ventilation operations and the cooling effect, and cannot optimize ventilation process parameters according to the grain condition, resulting in energy waste or even causing new grain condition problems due to improper operation.

[0004] To address the shortcomings of the existing technologies, the technical problem to be solved by this invention is to provide a method and system based on grain condition cloud map analysis. By constructing the temperature and humidity fields inside the grain pile, a visualized grain condition cloud map is generated, and typical cloud map modes such as condensation, mold, and heat generation are identified. This enables early warning of grain condition safety and real-time evaluation of the uniformity and cooling effect of ventilation operations to guide grain storage processes and ensure grain storage safety. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for analyzing grain condition cloud maps to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for analyzing grain condition cloud maps, comprising the following steps: Step S1: Real-time acquisition of discrete temperature data detected by temperature sensors arranged in a matrix inside the grain pile, as well as corresponding humidity data; Step S2: Clean the acquired discrete temperature and humidity data to construct a temperature field reflecting the internal temperature distribution of the grain pile and a humidity field reflecting the internal humidity distribution of the grain pile. Step S3: Generate a visualized grain condition cloud map based on the temperature field and humidity field, wherein the grain condition cloud map includes at least the distribution of isotherms; Step S3.1: Generate three-section grain condition cloud maps (front view, side view, and horizontal view) to visually and intuitively represent the grain condition status and guide grain storage process operations; Step S4: During the ventilation operation, update the grain condition cloud map in real time; Step S5: Based on the real-time updated grain condition cloud map, assess the uniformity of ventilation operations and the cooling effect, and generate ventilation effect assessment results.

[0007] Preferably, the construction of the humidity field in step S2 specifically includes: Based on the CAE equilibrium humidity equation, and combined with the temperature data corresponding to each temperature value, the humidity value at that location is calculated, thereby constructing a humidity field corresponding to the temperature field space.

[0008]

[0009] Based on the grain quality test report, the corresponding humidity level is calculated using the equilibrium humidity equation.

[0010] From the temperature (and humidity) matrix, temperature and humidity fields are generated using interpolation methods. The interpolation formula for the temperature (humidity) field is as follows: double t01-(×1-x2)*(x1-x3)*(×1-x4); double tO2-(×2-×1)”(×2-x3)“(×2-x4); double t03-(x3-x1)*(x3-x2)*(×3-x4); double t04-(x4×1)*(x4-x2)*(x4-x3); double xk; t11-(xk-x2)*{xk-x3)“(xk-x4); t12-(xk-x1)°(xk-x3)*(xk-x4); t13=(xk-x1)*[xk-x2]*(xk-x4); t14=(xk-x1)*[(xk-x2)*(xk-x3); dTemp=tempo*t11 / t01+temp1*12 / t02+temp2*t13 / t03+temp3*114 / t04; Xk: Coordinates of the interpolation point; X1, x2, x3, x4: Coordinates of the four points adjacent to the interpolation point; Tempo, Temp1, Temp2, Temp4: Temperature or humidity of four adjacent points; dTemp: The value of the interpolation point: Preferably, generating the visualized grain condition cloud map in step S3 further includes: Calculate and plot isotherm distribution maps, where the isotherm plotting interval is a preset n degrees, where n is a real number greater than 0; and identify and mark the hot core region inside the grain pile based on the temperature field and humidity field, where the hot core region is the region with a temperature higher than the surrounding preset range.

[0011] Preferably, step S5, which evaluates the uniformity and cooling effect of ventilation operations, specifically includes: By comparing the changes in the shape of isotherms and the temperature changes in the hot core area in the grain condition cloud map before and after ventilation, if the isotherm distribution tends to be flat and the temperature drop rate in the hot core area reaches the preset threshold, it is determined that the ventilation uniformity is good and the cooling effect is good.

[0012] Preferably, it also includes food security early warning steps: Based on the real-time grain condition cloud map, a preset typical cloud map mode is matched, which includes condensation mode, mold growth mode and heat generation mode; when any typical cloud map mode is matched, a corresponding grain condition safety early warning information is generated.

[0013] Preferably, the method for identifying the condensation mode is as follows: A binary correspondence is established between isotherms and hot core regions. When the temperature value of an isotherm is lower than the dew point of its corresponding hot core region, it is determined that a condensation mode has formed in that region, and a condensation warning is issued. Preferably, the method for identifying the mold growth mode is as follows: When the temperature in the hot core area exceeds the preset temperature threshold, the relative humidity change of the air rising with the micro-airflow during the temperature drop process is analyzed. If the relative humidity exceeds the preset humidity threshold, it is determined that a mold growth mode has been formed and a mold growth warning is issued. This invention generates three profile grain condition cloud maps, which visually and intuitively represent the grain condition status and guide grain storage operations. The invention also provides a grain condition cloud map analysis system, comprising: The data acquisition module is used to acquire discrete point temperature data detected by temperature sensors arranged in a matrix inside the grain pile in real time, as well as the corresponding humidity data. The field construction module is used to clean the acquired discrete temperature and humidity data and construct temperature and humidity fields. A cloud map generation module is used to generate a visualized grain condition cloud map based on the temperature field and humidity field, wherein the grain condition cloud map includes at least an isotherm distribution; The ventilation assessment module is used to evaluate the uniformity and cooling effect of ventilation operations based on real-time updated grain condition cloud maps during ventilation operations, and to generate ventilation effect assessment results.

[0014] Preferred options also include: The cloud map transformation module is used to map the sensor coordinates to the standard cloud map coordinate system according to the sensor deployment points in different warehouse types, so as to generate the grain condition cloud map of the corresponding warehouse type, including flat warehouses and shallow round warehouses.

[0015] Preferred options also include: The early warning module is used to match preset typical cloud map modes based on real-time grain condition cloud maps. The typical cloud map modes include condensation mode, mold growth mode and heat generation mode. When any typical cloud map mode is matched, corresponding grain condition safety early warning information is generated.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention generates a visualized grain condition cloud map by constructing temperature and humidity fields inside the grain pile. It transforms discrete sensor data into continuous isotherm distribution maps and color rendering maps, enabling warehouse managers to intuitively and quickly grasp the overall grain condition status, including temperature distribution, hot core location, and humidity distribution inside the grain pile. This overcomes the shortcomings of existing technologies that rely solely on discrete point numerical comparisons and lack spatial distribution perception. By matching the binary correspondence between isotherms and hot core regions, combined with dew point calculation and relative humidity change analysis during micro-airflow rise, it can accurately identify the formation process of condensation and mold growth modes, issuing early warnings before grain conditions deteriorate. Compared to existing technologies that can only alarm after temperature or humidity exceeds limits, this invention achieves early detection and proactive prevention of potential grain safety hazards, effectively ensuring grain storage safety. This invention uses a grain condition cloud map to monitor the temperature change trend of the hot core area in real time. It can promptly detect abnormal heating areas inside the grain pile and issue a heating warning, which facilitates managers to take timely cooling measures to prevent the heating range from expanding and mold from occurring. Furthermore, the grain condition cloud map is updated in real time during ventilation operations. By comparing the morphological changes of isotherm distribution before and after ventilation and the rate of temperature decrease in the hot core area, the uniformity of ventilation and the cooling effect can be objectively evaluated. Attached Figure Description

[0017] Figure 1 is a flowchart of the grain condition cloud map analysis method provided by the present invention.

[0018] Figure 2 is a system framework diagram provided by the present invention.

[0019] Figure 3 is a cloud map of grain conditions under the condensation mode provided by the present invention.

[0020] Figure 4 is a cloud map of grain condition in the moldy mode provided by the present invention.

[0021] Figure 5 is a cloud map of grain temperature with heat generation provided by the present invention.

[0022] Figure 6 is a grain condition cloud map of a vertical cross-section of a flat warehouse provided by the present invention.

[0023] Figure 7 is a cloud map of the grain condition under ventilation conditions provided by the present invention.

[0024] Figure 8 is a cloud map of grain conditions in a shallow circular granary provided by the present invention. Detailed Implementation

[0025] 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.

[0026] Please refer to Figures 1-8, which illustrate a method for analyzing grain condition cloud maps, including the following steps: Step S1: Real-time acquisition of discrete temperature data detected by temperature sensors arranged in a matrix within the grain pile, and corresponding humidity data; wherein the temperature sensors are distributed in a three-dimensional matrix within the grain pile according to a preset row, column, and layer spacing. Humidity data is acquired either directly from humidity sensors arranged within the grain pile or calculated from temperature data in subsequent steps. This embodiment preferably uses a combined deployment of temperature and humidity sensors to obtain more accurate humidity data.

[0027] Step S2: Clean the acquired discrete temperature and humidity data to construct a temperature field reflecting the internal temperature distribution of the grain pile and a humidity field reflecting the internal humidity distribution of the grain pile. Specifically, when no humidity sensor is directly installed inside the grain pile, the construction of the humidity field in step S2 includes: calculating the humidity value at each temperature location based on the CAE equilibrium humidity equation and the temperature data at that location, thereby constructing a humidity field corresponding to the temperature field space.

[0028] Step S3: Based on the temperature and humidity fields, generate a visualized grain condition cloud map, which includes at least an isotherm distribution. The specific generation process is as follows: (1) Calculate the isotherm distribution: Based on the temperature field data, draw an isotherm every preset n degrees (e.g., n=2℃). The isotherm is a line connecting points with the same temperature value. The value of n can be adjusted according to the grain condition monitoring accuracy requirements. The smaller the value of n, the denser the isotherms and the richer the details of the grain condition.

[0029] (2) Marking the hot core region: Based on the temperature field, identify and mark the hot core region inside the grain pile. The hot core refers to a continuous region with a temperature higher than the surrounding preset range (e.g., more than 2°C higher than the average temperature of the surrounding adjacent areas), which is usually represented by a closed high value center in the isotherm map.

[0030] (3) Overlaying humidity information: Based on the isotherm map, humidity field information is overlaid. A pseudo-color rendering method can be used to represent different humidity ranges with different colors.

[0031] Step S3.1: Generate three-section grain condition cloud maps (front view, side view, and horizontal view) to visually and intuitively represent the grain condition status and guide grain storage process operations.

[0032] Step S4: During the ventilation operation, update the grain condition cloud map in real time; during the ventilation operation, continuously read the latest sensor data at preset time intervals (such as once per minute), repeat steps S1-S3, update the grain condition cloud map in real time, and the updated cloud map can be dynamically displayed on the display terminal for warehouse management personnel to monitor in real time.

[0033] Step S5: Based on the real-time updated grain condition cloud map, assess the uniformity and cooling effect of ventilation operations, and generate a ventilation effect assessment result. The specific assessment method is as follows: compare the changes in the morphology of isotherm distribution and the temperature changes in the hot core area in the grain condition cloud map before and after ventilation. If the isotherm distribution gradually becomes straight and sparse from dense and curved, and the temperature drop rate in the hot core area reaches the preset threshold (e.g., a drop of 0.5℃ per hour), then the ventilation uniformity is good and the cooling effect is excellent. Conversely, if there is no significant change in the isotherm morphology or the temperature drop in the hot core area is slow, then the ventilation effect is poor, and ventilation parameters (such as air volume, wind direction, ventilation duration, etc.) need to be adjusted. The assessment result can be output in the form of text, charts, or cloud map overlay labels. For example, use green to mark areas with obvious cooling and red to mark areas with insufficient cooling on the cloud map.

[0034] Based on the above method, this invention also includes a grain condition safety early warning step for early identification of typical grain condition modes such as condensation, mold, and heat generation. Specifically, in the grain condition cloud map, a series of isotherms are formed around each hot core region. Isotherms adjacent to the boundary of the hot core are selected for matching, and the dew point temperature of the hot core region is calculated. The dew point is obtained based on the temperature and humidity of the hot core region using the dew point calculation formula. k1=d / 222.0f*(exp((b1-dWater) / a1)-exp((b2-dWater) / a2)); c1=d*(1.0f-exp((b1-dWater) / a1)); xl=1737.1f-(474242.0f / (273.0f+dTemp)); Equilibrium absolute humidity at grain temperature: dPS3 dPS3=exp(((k1+0.9845f)*xl+c1-68.5f) / 87.72f); Dew point dT3 = 474242.0 / (1872.7 - 89.1 * log(dPS3)) - 273.0; dTemp: Grain temperature; dWater: Grain moisture content; “a1,a2,b1,b2,d” are the coefficients of the equilibrium humidity equation.

[0035] When the temperature value of the isotherm is lower than the dew point of its corresponding hot core region, it is determined that a condensation mode has formed in that region. A condensation mode means that water vapor condenses in the cold zone, which can easily cause condensation and mold growth on grains. At this time, the system generates a condensation warning, which includes the location, extent, and severity of the condensation, and recommends measures such as turning on exhaust fans and local ventilation.

[0036] When the temperature in the core area exceeds a preset temperature threshold (e.g., 22°C), the upward flow of micro-airflow generated in that area is analyzed. Air in the core area of ​​the grain pile rises due to heating. As the height increases, the ambient temperature gradually decreases, while the relative humidity gradually increases. Based on thermodynamic principles, the change in relative humidity at different heights is calculated. When the relative humidity exceeds a preset humidity threshold (e.g., 90%) and the temperature is greater than 22°C, a mold growth mode is determined to have formed in that area. This is because a high-temperature (22°C) and high-humidity environment is suitable for microbial growth, jeopardizing grain storage safety. At this point, the system generates a mold warning and recommends measures such as cooling, ventilation, or partial turning (the temperature and humidity thresholds in the mold growth mode should be adjusted for different grain types and storage conditions). "When the humidity is greater than 90% and the temperature is greater than 22°C, microbial growth is easy, constituting a mold growth mode. The equilibrium humidity between grains is related to the variety; the humidity calculation has already taken into account different grain varieties, and storage type is not a direct factor." The heating mode is characterized by one or more abnormally high temperature areas appearing in the grain condition cloud map. The temperature is significantly higher than the surrounding background temperature (e.g., more than 5°C higher), and the temperature shows an upward trend over time. The system automatically detects such areas, generates a heating warning, and indicates that there may be vigorous microbial respiration or pest activity. Three profile grain condition cloud maps are generated to vividly and intuitively represent the grain condition status and guide grain storage process operations.

[0037] This invention also provides a grain condition cloud map analysis system, which is used to implement the above method. The system includes the following modules: Data acquisition module: This module is used to acquire discrete temperature data detected by temperature sensors arranged in a matrix inside the grain pile in real time, as well as the corresponding humidity data. This module communicates with the sensor network deployed in the grain warehouse and supports wired or wireless data transmission.

[0038] Field Construction Module: This module is used to clean the acquired discrete temperature and humidity data and construct temperature and humidity fields using interpolation algorithms. It includes embedded data cleaning rules (such as threshold filtering, median filtering, etc.) and an interpolation algorithm library.

[0039] Cloud map generation module: used to generate a visualized grain condition cloud map based on the temperature field and humidity field. The grain condition cloud map includes at least the isotherm distribution. This module calls the drawing engine to render the field data into a 2D or 3D image.

[0040] Ventilation assessment module: During ventilation operations, based on real-time updated grain condition cloud maps, the module assesses the uniformity and cooling effect of ventilation operations and generates ventilation effect assessment results. This module has built-in assessment logic and can output assessment reports.

[0041] Cloud map transformation module: It is used to map the sensor coordinates to the standard cloud map coordinate system according to the deployment points of sensors in different types of warehouses, so as to generate the grain condition cloud map of the corresponding warehouse type. For example, the sensors of flat warehouses are usually deployed in a rectangular grid, which can be directly mapped to the plane rectangular coordinate system; the sensors of shallow circular warehouses are deployed in concentric circles or radial patterns, and coordinate transformation (such as polar coordinates to rectangular coordinates) is required to generate a standard cloud map.

[0042] Early warning module: This module matches real-time grain condition cloud maps with preset typical cloud map modes (condensation mode, mold growth mode, and heat generation mode). When a match is successful, it generates corresponding grain condition safety early warning information. This module has a built-in modality recognition algorithm library.

[0043] The above modules can be integrated into an industrial control computer or server, or they can be deployed in the cloud and accessed through a browser or mobile terminal.

[0044] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0045] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for analyzing grain condition cloud maps, characterized in that, Includes the following steps: Step S1: Real-time acquisition of discrete temperature data detected by temperature sensors arranged in a matrix inside the grain pile, as well as corresponding humidity data; Step S2: Clean the acquired discrete temperature and humidity data to construct a temperature field reflecting the internal temperature distribution of the grain pile and a humidity field reflecting the internal humidity distribution of the grain pile. Step S3: Generate a visualized grain condition cloud map based on the temperature field and humidity field, wherein the grain condition cloud map includes at least the distribution of isotherms; Step S3.1: Generate three-section grain condition cloud maps (front view, side view, and horizontal view) to visually and intuitively represent the grain condition status and guide grain storage process operations; Step S4: During the ventilation operation, update the grain condition cloud map in real time; Step S5: Based on the real-time updated grain condition cloud map, assess the uniformity of ventilation operations and the cooling effect, and generate ventilation effect assessment results.

2. The method for analyzing grain condition cloud maps according to claim 1, characterized in that, The construction of the humidity field in step S2 specifically includes: ， ; Where EAHr is the relative humidity of the air between grains; mg2 is the moisture content of the grain; A1, A2, B1, B2 and D are equation coefficients; Ts is the temperature of the grain; Based on the CAE equilibrium humidity equation, and combined with the temperature data corresponding to each temperature value, the humidity value at that location is calculated, thereby constructing a humidity field corresponding to the temperature field space.

3. The method for analyzing grain condition cloud maps according to claim 1, characterized in that, Step S3, generating a visualized grain condition cloud map, also includes: Calculate and plot isotherm distribution maps, where the isotherm plotting interval is a preset n degrees, where n is a real number greater than 0; and identify and mark the hot core region inside the grain pile based on the temperature field and humidity field, where the hot core region is the region with a temperature higher than the surrounding preset range.

4. The grain condition cloud map analysis method according to claim 1, characterized in that, Step S5, which assesses the uniformity and cooling effect of ventilation operations, specifically includes: By comparing the changes in the shape of isotherms and the temperature changes in the hot core area in the grain condition cloud map before and after ventilation, if the isotherm distribution tends to be flat and the temperature drop rate in the hot core area reaches the preset threshold, it is determined that the ventilation uniformity is good and the cooling effect is good.

5. The method for analyzing grain condition cloud maps according to claim 1, characterized in that, It also includes food security early warning procedures: Based on the real-time grain condition cloud map, a preset typical cloud map mode is matched, which includes condensation mode, mold growth mode and heat generation mode; when any typical cloud map mode is matched, a corresponding grain condition safety early warning information is generated.

6. The method for analyzing grain condition cloud maps according to claim 5, characterized in that, The method for identifying the condensation mode is as follows: The binary correspondence between isotherms and hot core regions is matched. When the temperature value of an isotherm is lower than the dew point of its corresponding hot core region, it is determined that the region has formed a condensation mode and a condensation warning is issued.

7. The method for analyzing grain condition cloud maps according to claim 5, characterized in that, The method for identifying the mold growth mode is as follows: When the temperature in the hot core region exceeds a preset temperature threshold, the relative humidity change of the air rising with the micro-airflow during the temperature drop process is analyzed. If the relative humidity exceeds a preset humidity threshold, it is determined that a hot core has formed. Mold growth mode, and issue mold growth warning; Three cross-sectional grain condition cloud maps are generated to vividly and intuitively represent the grain condition status and guide grain storage process operations.

8. A grain condition cloud map analysis system, characterized in that, include: The data acquisition module is used to acquire discrete point temperature data detected by temperature sensors arranged in a matrix inside the grain pile in real time, as well as the corresponding humidity data. The field construction module is used to clean the acquired discrete temperature and humidity data and construct temperature and humidity fields. A cloud map generation module is used to generate a visualized grain condition cloud map based on the temperature field and humidity field, wherein the grain condition cloud map includes at least an isotherm distribution; The ventilation assessment module is used to evaluate the uniformity and cooling effect of ventilation operations based on real-time updated grain condition cloud maps during ventilation operations, and to generate ventilation effect assessment results.

9. The grain condition cloud map analysis system according to claim 8, characterized in that, Also includes: The cloud image transformation module is used to transform the sensor coordinates according to the deployment locations of sensors in different warehouse types. Mapping to the standard cloud map coordinate system to generate a grain condition cloud map for the corresponding warehouse type, which includes flat warehouses and shallow round warehouses.

10. A grain condition cloud map analysis system according to claim 8, characterized in that, Also includes: The early warning module is used to match preset typical cloud map modes based on real-time grain condition cloud maps. The typical cloud map modes include condensation mode, mold growth mode and heat generation mode. When any typical cloud map mode is matched, corresponding grain condition safety early warning information is generated.

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