Intelligent control method and system for green grain storage environment based on internet of things

Abstract

This invention relates to the field of intelligent control, specifically to an intelligent control method and system for green grain storage environment based on the Internet of Things (IoT). It includes: acquiring data; determining the dew point temperature of the outside air and constructing long-term potential moisture-affected prediction characteristics; constructing short-term actual moisture-affected response characteristics; determining the moisture-affected trigger intensity; calculating the number of effective diffusion areas within the grain storage facility where moisture initiation occurs; generating local control demand and global control demand; comparing the local and global control demand, determining control instructions based on the comparison results, and controlling the grain storage environment according to the control instructions. This invention can implement targeted and precise control to reduce energy consumption when the risk of moisture-affected conditions is concentrated in a localized area, and can promptly switch to global coverage control to ensure grain storage safety when the risk spreads to multiple areas.

Description

Technical Field

[0001] This invention relates to the field of intelligent control, specifically to a method and system for intelligent regulation and control of green grain storage environment based on the Internet of Things. Background Technology

[0002] In the context of green grain storage, environmental control not only needs to prevent localized dampness, moisture buildup, and condensation, but also needs to meet the appropriate storage or preservation conditions for different types of grain. For raw rice, the focus is on moisture prevention, mold prevention, and quality stability under safe storage conditions, while for finished rice products, the focus is on temperature and humidity control under suitable preservation conditions to mitigate moisture absorption, temperature rise, and quality decline. Therefore, intelligent environmental control should not only target a single cooling or dehumidification goal, but should also consider the coordinated balance of temperature and humidity in the grain storage environment, achieving green and energy-efficient operation while ensuring grain safety and quality.

[0003] Existing green grain storage environment control technologies typically collect data on internal and external temperature and humidity, grain temperature, basic storage parameters, and weather information to automatically control the start and stop of fans, windows, ventilation ducts, or temperature control equipment. The technical implementation methods mostly involve extracting features such as temperature gradients, abnormal grain temperatures, temperature differences between adjacent measuring points, historical change rates, and ventilation condition satisfaction. These features are then combined with a rule base and empirical thresholds to determine whether ventilation or temperature control conditions are met, thereby achieving equipment linkage and environmental regulation.

[0004] However, existing technologies have the following drawbacks: they lack the ability to predict the potential impact of moisture in the outside air, lack the ability to analyze the synchronous changes in the moisture content of the air layer adjacent to the grain pile surface and the grain itself, and cannot provide differentiated local or global control based on the spatial distribution of moisture risk. Summary of the Invention

[0005] This invention provides a method and system for intelligent control of green grain storage environment based on the Internet of Things, in order to solve existing problems.

[0006] The intelligent control method for green grain storage environment based on the Internet of Things of the present invention adopts the following technical solution:

[0007] One embodiment of the present invention provides a method for intelligent control of green grain storage environment based on the Internet of Things, the method comprising the following steps:

[0008] Acquire external temperature and humidity data of the grain warehouse, and acquire surface temperature, humidity, and moisture content data of the grain pile in each monitoring area of ​​the grain warehouse.

[0009] For each monitoring area, the dew point temperature of the air outside the warehouse is determined based on the temperature and humidity data outside the warehouse, and the long-term potential moisture prediction characteristics of the monitoring area are constructed based on the difference between the dew point temperature of the air outside the warehouse and the surface temperature data of the grain pile in that monitoring area.

[0010] Based on the changes in surface humidity and moisture content of grain piles in the monitoring area, the short-term actual moisture response characteristics of the monitoring area are constructed.

[0011] For each monitoring area, the intensity of moisture triggering is determined based on its long-term potential moisture prediction characteristics and short-term actual moisture response characteristics.

[0012] Based on the distribution of moisture triggering intensity in all areas, calculate the number of effective diffusion areas for moisture-induced initiation within the grain warehouse;

[0013] The local control demand is generated based on the maximum moisture trigger intensity and the number of effective diffusion areas, and the global control demand is generated based on the difference between the sum of moisture trigger intensities and the maximum moisture trigger intensity, as well as the number of effective diffusion areas.

[0014] Compare the local and global control demands, determine control instructions based on the comparison results, and then control the grain storage environment according to the control instructions.

[0015] Preferably, the dew point temperature of the air outside the warehouse is determined based on the temperature and humidity data outside the warehouse, and a long-term potential moisture-affected prediction feature for the monitoring area is constructed based on the difference between the dew point temperature of the air outside the warehouse and the surface temperature data of the grain pile in the monitoring area, specifically including:

[0016] Based on the outside temperature and humidity data, the dew point temperature of the outside air was calculated using the dew point temperature calculation formula.

[0017] The difference between the dew point temperature of the air outside the warehouse and the surface temperature of the grain pile in the monitoring area is determined as the long-term potential moisture prediction characteristic of the monitoring area.

[0018] Preferably, based on the changes in surface humidity data and surface moisture content data of the grain pile in the monitoring area, a short-term actual moisture response characteristic of the monitoring area is constructed, specifically including:

[0019] For any monitoring time in the monitoring area, obtain the difference between the surface humidity data of the grain pile at that time and the surface humidity data of the grain pile at the previous time, as well as the difference between the surface moisture content data of the grain pile at that time and the surface moisture content data of the grain pile at the previous time.

[0020] When both differences are positive, the product of the two differences is taken as the short-term actual humidity response characteristic of the monitoring area; otherwise, the short-term actual humidity response characteristic of the monitoring area is set to zero.

[0021] Preferably, the moisture triggering intensity of the monitoring area is determined based on its long-term potential moisture prediction characteristics and short-term actual moisture response characteristics, specifically including:

[0022] When both the long-term potential moisture prediction characteristic and the short-term actual moisture response characteristic are greater than zero, the ratio of their product to their sum is taken as the moisture trigger intensity of the monitoring area; otherwise, the moisture trigger intensity of the monitoring area is set to zero.

[0023] Preferably, based on the distribution of moisture triggering intensity in all areas, the number of effective diffusion areas for moisture initiation within the grain silo is calculated, specifically including:

[0024] Obtain the moisture trigger intensity of each monitoring area;

[0025] Calculate the sum of the moisture triggering intensities of all monitored areas, and then square the sum as the first intermediate value;

[0026] Calculate the square of the moisture triggering intensity of each monitoring area, and sum the square values ​​of all areas to obtain the second intermediate value;

[0027] The ratio of the first median value to the second median value is determined as the number of effective diffusion regions.

[0028] Preferably, the local control demand is generated based on the maximum moisture triggering intensity and the number of effective diffusion areas, specifically including:

[0029] The ratio of the maximum moisture-triggered intensity to the number of effective diffusion areas is determined as the local control demand.

[0030] Preferably, the global control demand is generated based on the difference between the sum of moisture-triggered intensities and the maximum moisture-triggered intensities, as well as the number of effective diffusion regions. Specifically, this includes:

[0031] Subtract the maximum moisture triggering intensity from the sum of the moisture triggering intensities to obtain the difference value;

[0032] Subtract one from the number of effective diffusion regions to obtain the diffusion region increment;

[0033] The ratio of the product of the difference value and the increment of the diffusion area to the total number of monitored areas is determined as the global control demand.

[0034] Preferably, the local control demand is compared with the global control demand, control instructions are determined based on the comparison results, and the grain storage environment is controlled according to the control instructions, specifically including:

[0035] When the local control demand is greater than or equal to the global control demand, a local control command is output. The local control command is used to control the operation of the ventilation control equipment corresponding to the dominant area, so that the airflow preferentially covers the dominant area. The dominant area is the monitoring area with the maximum moisture trigger intensity.

[0036] When the local control demand is less than the global control demand, a global control command is output. The global control command is used to control the ventilation control equipment, expand the air supply coverage, or adjust the overall airflow organization to suppress the trend of moisture expansion in each monitoring area.

[0037] Preferably, the monitoring area includes at least one of the following: central area, surrounding area, wall-adjacent area, corner area, near-wind area, and far-wind area.

[0038] This invention proposes an intelligent control system for green grain storage environment based on the Internet of Things, comprising:

[0039] The data acquisition module is used to collect external temperature and humidity data of the grain warehouse, and to obtain surface temperature data, surface humidity data, and surface moisture content data of the grain pile in each monitoring area of ​​the grain warehouse.

[0040] The data processing module is used to execute the steps of the IoT-based intelligent control method for green grain storage environment.

[0041] The instruction execution module is used to control the ventilation and control equipment to regulate the environment of the grain warehouse according to the control instructions.

[0042] The beneficial effects of the technical solution of the present invention are:

[0043] In this embodiment of the invention, by constructing long-term potential moisture prediction characteristics and short-term actual moisture response characteristics of the surface area of ​​the grain pile, it is possible to simultaneously identify the potential moisture risk of the outside air and the actual moisture evolution of the local microenvironment inside the warehouse. Based on this, the moisture trigger intensity of each area and the number of effective diffusion areas within the warehouse are determined, and local control demand and global control demand are generated accordingly. By comparing the magnitudes of the two, local control instructions or global control instructions are automatically output, thereby achieving a technological leap from "passive response" to "active prediction" and from "single control" to "differential control". It can implement targeted and precise control to reduce energy consumption when the moisture risk is concentrated in a local area, and can switch to global coverage control in a timely manner to ensure grain storage safety when the risk spreads to multiple areas. At the same time, it takes into account the safe storage needs of raw rice and the freshness quality requirements of finished rice, achieving multiple goals of moisture-proof safety, temperature and humidity coordination, and energy efficiency. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art 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.

[0045] Figure 1A flowchart illustrating an embodiment of the intelligent control method for green grain storage environment based on the Internet of Things (IoT) provided by the present invention.

[0046] Figure 2 This is a structural diagram of an IoT-based intelligent control system for green grain storage environment provided in one embodiment of the present invention. Detailed Implementation

[0047] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the IoT-based intelligent control method for green grain storage environment proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0049] The following description, in conjunction with the accompanying drawings, details the specific scheme of the intelligent control method for green grain storage environment based on the Internet of Things provided by this invention.

[0050] This invention provides an intelligent control method and system for green grain storage environment based on the Internet of Things. Please refer to [link / reference]. Figure 1 The diagram illustrates a flowchart of an IoT-based intelligent control method for green grain storage environment according to an embodiment of the present invention. The method includes the following steps:

[0051] S101. Obtain the outside temperature and humidity data of the grain warehouse, and obtain the surface temperature data, surface humidity data, and surface moisture content data of the grain pile in each monitoring area of ​​the grain warehouse.

[0052] In this embodiment, the monitoring area includes at least one of the following: central area, surrounding area, wall-adjacent area, corner area, near-wind area, and far-wind area.

[0053] For example, an existing flat-roofed grain warehouse is used as the analysis object. Based on the warehouse's structural shape, ventilation path distribution, and differences in the location of the grain pile surface, the grain pile surface can be divided into six typical monitoring areas: the central area (the area in the center of the grain surface), the peripheral area (within 2 meters of the wall), the wall-adhesive area (the area close to the wall), the corner area (the area at the corner of the wall), the near-ventilation area (within 2 meters of the ventilation opening), and the far-ventilation area (the area beyond 8 meters of the ventilation opening). Corresponding sensor measuring points are deployed in each monitoring area.

[0054] The specific data collection method is as follows:

[0055] External temperature data (Also known as outside air temperature) and outside humidity data (Also known as relative humidity of the air outside the warehouse): The temperature and humidity are collected in real time by temperature and humidity sensors placed in the external environment of the grain warehouse, 3-5 meters away from the warehouse body and not exposed to direct sunlight, with a sampling frequency of once per minute.

[0056] Grain pile surface temperature data Temperature data is obtained by temperature sensors deployed in the shallow layer of the grain surface (10-30cm below the grain surface). Three measuring points are set up in each monitoring area, and the average value is taken as the surface temperature of that area. The sampling frequency is once every 10 minutes.

[0057] Grain pile surface moisture data (Also known as relative humidity of the air layer adjacent to the grain pile surface): It is obtained by a humidity sensor set at a height of 20-50cm above the grain surface. Two measuring points are set up in each monitoring area, and the average value is taken as the humidity of the air layer adjacent to the surface of the area. The sampling frequency is once every 10 minutes.

[0058] Data on surface moisture content of grain piles (Also known as the surface moisture content of grain pile): It is obtained in real time by moisture content sensors deployed in the shallow layer of grain (10-20cm below the grain surface). Two measuring points are set up in each monitoring area, and the average value is taken as the surface moisture content of the area. The sampling frequency is once every 30 minutes. Manual sampling can also be used as a supplement or calibration method.

[0059] The collected data undergoes time alignment and preprocessing. Specifically, this includes: data integrity checks (missing values ​​are filled using linear interpolation), outlier identification and correction (data exceeding 3 times the standard deviation are marked as outliers and replaced with the mean of the preceding and following time points), and data denoising and smoothing (using moving average filtering with a window size of 3).

[0060] Simultaneously, a unified timeline was constructed, with a sampling period of 10 minutes, and data from different sampling frequencies were resampled: temperature and humidity data were matched using nearest-neighbor time, and moisture content data were processed using previous value hold-up, so that various monitoring data could form a corresponding relationship under a unified time benchmark. The operating status of ventilation equipment (fan start / stop, damper opening, etc.) was synchronously associated and labeled with the monitoring data at the corresponding time, forming a standardized dataset.

[0061] S102. For each monitoring area, determine the dew point temperature of the air outside the warehouse based on the temperature data and humidity data outside the warehouse, and construct the long-term potential moisture prediction characteristics of the monitoring area based on the difference between the dew point temperature of the air outside the warehouse and the surface temperature data of the grain pile in the monitoring area.

[0062] In this embodiment, the dew point temperature of the air outside the warehouse is determined based on the temperature and humidity data outside the warehouse. Furthermore, based on the difference between the dew point temperature of the air outside the warehouse and the surface temperature data of the grain pile in the monitoring area, a long-term potential moisture-affected prediction feature for the monitoring area is constructed, specifically including:

[0063] Based on the outside temperature and humidity data, the dew point temperature of the outside air was calculated using the dew point temperature calculation formula.

[0064] The difference between the dew point temperature of the air outside the warehouse and the surface temperature of the grain pile in the monitoring area is determined as the long-term potential moisture prediction characteristic of the monitoring area.

[0065] For example, in the scenario of green rice storage, whether the surface grain will experience dampness, moisture absorption, or condensation precursors is essentially affected by two levels of thermal and humidity effects. The first level is the impact of outside air entering the storage area on the surface of the grain pile, representing the external environment's influence on the storage area's state. The second level is the impact of the adjacent air layer above the grain pile on the grain surface, representing the direct contact between the local microenvironment inside the storage and the grain surface. In this embodiment, a long-term potential moisture prediction feature corresponding to the first level is first constructed to characterize the external moisture-carrying trend of each area under the influence of the external environment.

[0066] For each monitoring area, the dew point temperature of the air outside the warehouse is determined based on the temperature and humidity data outside the warehouse. Based on the difference between the dew point temperature of the air outside the warehouse and the surface temperature data of the grain pile in that monitoring area, the long-term potential moisture prediction characteristics of that monitoring area are constructed.

[0067] At a given monitoring moment, the dew point temperature of the air outside the warehouse is first calculated using existing dew point temperature calculation methods based on the collected relative humidity and temperature of the air outside the warehouse. In one specific implementation, the dew point temperature is calculated using an empirical formula based on the Magnus-Tetens equation.

[0068] Whether outside air poses a risk of moisture absorption to the surface of grain piles in a monitoring area depends crucially on the relative relationship between the dew point temperature of the outside air and the surface temperature of that area. Therefore, the long-term potential moisture absorption prediction characteristics of this monitoring area are crucial. The calculation formula can be:

[0069]

[0070] in, Indicates the first Each monitoring area at any time Long-term potential moisture prediction characteristics, This indicates the monitoring area at time... The dew point temperature of the air outside the warehouse. Indicates the first Each monitoring area at any time The surface temperature of the grain pile.

[0071] The physical meaning of this feature is as follows: When When the dew point temperature of the air outside the warehouse is higher than the surface temperature of the grain pile in the monitored area, from a thermo-humidity perspective, the air outside the warehouse, after entering the warehouse and acting on the area, has the potential to cause surface dampness, moisture reabsorption, or condensation precursors. This indicates that the current humidity and heat carried by the air outside the warehouse are not sufficient to cause significant potential moisture impact on the surface of the area.

[0072] Through the above calculations, the long-term potential humidity prediction characteristics of N monitoring areas at the current time are obtained. These characteristics are used to characterize the long-term potential humidity impact of the external air environment on each area.

[0073] S103. Based on the changes in surface humidity and moisture content of the grain pile in the monitoring area, construct the short-term actual moisture response characteristics of the monitoring area.

[0074] In this embodiment, based on the changes in surface humidity data and surface moisture content data of the grain pile in the monitoring area, a short-term actual moisture response characteristic of the monitoring area is constructed, specifically including:

[0075] For any monitoring time in the monitoring area, obtain the difference between the surface humidity data of the grain pile at that time and the surface humidity data of the grain pile at the previous time, as well as the difference between the surface moisture content data of the grain pile at that time and the surface moisture content data of the grain pile at the previous time.

[0076] When both differences are positive, the product of the two differences is taken as the short-term actual humidity response characteristic of the monitoring area; otherwise, the short-term actual humidity response characteristic of the monitoring area is set to zero.

[0077] For example, in the local microenvironment within a warehouse, if the humidity of the air layer adjacent to the surface of a monitoring area increases, and the moisture content of the grain on the surface of that area also increases synchronously within a short period of time, it indicates that the air layer above that area is directly exerting a moisturizing effect on the grain surface. Since this effect occurs between the grain surface and its adjacent air layer, the spatial distance is short and the response speed is fast; therefore, it should be considered a short-timescale actual moisture response characteristic. This embodiment constructs this characteristic to characterize the direct moisturizing effect of the local microenvironment within the warehouse on the grain surface.

[0078] For a given monitoring area, first calculate the monitoring area at time [time]. The changes in humidity of the air layer adjacent to the surface and the changes in moisture content of the surface grain.

[0079] Define the monitoring area at time Humidity change of the air layer adjacent to the surface For a moment Difference between the surface moisture data of the grain pile and the previous time point:

[0080]

[0081] Define the monitoring area at time The change in moisture content of the surface grain at time t is The difference between the surface moisture content of the grain pile and the previous time point:

[0082]

[0083] Based on this, the monitoring area is constructed at any time. Short-time actual moisture response characteristics :

[0084]

[0085] in, .

[0086] The judgment logic for this feature is as follows: when This indicates that actual humidification evolution on a short timescale has been observed in the region, meaning that the humidity of the adjacent air layer and the moisture content of the grain have changed synchronously and positively, indicating that the upper air layer is exerting an actual humidification effect on the grain surface; when At this time, it indicates that synchronous moisture changes between the air layer above the surface and the grain surface have not yet been observed. There may only be unilateral fluctuations, ineffective disturbances, or synchronous dehumidification. Therefore, it cannot be concluded that the area has entered a short-term actual moisture stage.

[0087] It should be noted that, through Introducing truncation functions This ensures that only when the change in humidity of the adjacent air layer and the change in moisture content of the grain are both positive values... Only then will a positive response be generated. This design can effectively avoid the misjudgment problem of "negative times negative equals positive" caused by the simultaneous decrease of the two, and eliminate the interference of unidirectional changes or short-term disturbances on the judgment of the moisture trend.

[0088] The above calculations yield the short-term actual humidity response characteristics of each monitoring area at the current time (time t). These characteristics are used to characterize the direct humidity effect of the local microenvironment within the warehouse on each area at the current time.

[0089] S104. For each monitoring area, determine the moisture triggering intensity of the monitoring area based on its long-term potential moisture prediction characteristics and short-term actual moisture response characteristics.

[0090] In this embodiment, the moisture triggering intensity of the monitoring area is determined based on its long-term potential moisture prediction characteristics and short-term actual moisture response characteristics, specifically including:

[0091] When both the long-term potential moisture prediction characteristic and the short-term actual moisture response characteristic are greater than zero, the ratio of their product to their sum is taken as the moisture trigger intensity of the monitoring area; otherwise, the moisture trigger intensity of the monitoring area is set to zero.

[0092] For example, in the process of green rice storage, the aforementioned steps have already constructed long-term potential moisture prediction characteristics for each monitoring area. and short-time actual moisture response characteristics However, in control decision-making, simply knowing whether there is a potential trend of humidity or an actual humidity response in each region is insufficient to directly determine whether to adopt local or global control. This is because: firstly, even if a region has a high humidity prediction characteristic, it may only indicate the presence of external humidity conditions, not that the region has entered a true humidity evolution stage; secondly, even if a region shows a short-term actual humidity response characteristic greater than 0, it may only be a short-term local fluctuation and does not necessarily have an evolutionary trend continuously supported by external air conditions.

[0093] Therefore, the determination of local or global control should not be based on a single indicator, but rather on whether the long-term potential moisture absorption trend and the short-term actual moisture absorption response jointly contribute to the moisture absorption initiation in the same area, and how much of the moisture absorption initiation extends across the entire warehouse. In this embodiment, the moisture absorption trigger intensity of each monitoring area is first determined to characterize whether each area has transitioned from a potential moisture absorption trend to an actual moisture absorption initiation state.

[0094] For the i-th monitoring area, the moisture prediction characteristics obtained from the aforementioned steps are... and short-time actual moisture response characteristics Construct the moisture triggering intensity of this area. First, we analyze the predictive characteristics of moisture. Perform phased processing, that is, when When the value is zero, The original value is taken, and it is denoted as . Moisture triggering strength The calculation formula can be:

[0095]

[0096] in, It is a very small positive number, and its value can be 0.00001.

[0097] The physical meaning of this computational logic is as follows:

[0098] when At this time, it indicates that the external air conditions have not yet created an effective potential moisture impact on the area. Even if there are localized short-term disturbances at this point, they should not be considered as an effective moisture initiation driven by an external moisture trend. .

[0099] when At this time, it indicates that a synchronous short-term moisture response has not yet occurred between the adjacent air layer above the grain surface and the grain surface in that area. Even if... A higher reading only indicates that the area is in a potential warning stage, and has not yet entered the actual state of being affected by moisture. .

[0100] Only when and At the same time, A positive value indicates that the area has reached a state of humidification initiation, driven by both the potential for moisture absorption outside the warehouse and the actual localized moisture absorption response inside the warehouse.

[0101] Therefore, For the first This indicates whether the monitored area has entered the actual moisture-induced start-up phase. Specifically, while the previous embodiments calculated the "potential trend" and "actual response," this embodiment further determines whether both have jointly contributed to the actual moisture-induced start-up within the area.

[0102] The above calculations yield the current moisture trigger intensity of each monitoring area. This feature is used to characterize whether each area has entered the actual moisture trigger state and its strength.

[0103] S105. Based on the distribution of moisture triggering intensity in all areas, calculate the number of effective diffusion areas for moisture-induced initiation within the grain warehouse.

[0104] In this embodiment, based on the distribution of moisture triggering intensity in all areas, the number of effective diffusion areas for moisture-induced initiation within the grain silo is calculated, specifically including:

[0105] Obtain the moisture trigger intensity of each monitoring area;

[0106] Calculate the sum of the moisture triggering intensities of all monitored areas, and then square the sum as the first intermediate value;

[0107] Calculate the square of the moisture triggering intensity of each monitoring area, and sum the square values ​​of all areas to obtain the second intermediate value;

[0108] The ratio of the first median value to the second median value is determined as the number of effective diffusion regions.

[0109] For example, after obtaining the moisture triggering intensity of each region, it is necessary to determine the dominant moisture triggering region at the current moment. and its corresponding dominant moisture triggering intensity They are defined as follows:

[0110]

[0111]

[0112] in, This indicates the number of the area with the highest moisture triggering intensity at the current moment. This indicates the intensity of moisture triggering corresponding to the dominant moisture triggering area.

[0113] Simply identifying the dominant moisture-inducing area is insufficient to directly determine whether local or global control measures should be adopted. In actual grain storage scenarios, the control mode depends not only on the strength of the strongest initiating area but also on whether multiple other areas have simultaneously entered a moisture-inducing state. If only one or a few areas show significant moisture induction, while other areas remain largely untriggered, localized targeted control is more suitable. However, if multiple areas are triggered simultaneously, it indicates that the potential moisture-inducing trend outside the storage facility has already translated into actual responses in multiple locations within the facility. In this case, continuing to apply localized control to only a single area will be insufficient to suppress the synchronous development of other areas, making global coverage control more appropriate.

[0114] Therefore, it is necessary to further quantify the effective diffusion range of moisture-triggered events within the chamber at the current moment. To this end, a time frame is defined. The total amount of moisture-induced damage to the entire warehouse is: Where N represents the number of monitored areas. Also, to represent the effective area actually expanded to within the warehouse by moisture triggering at the current moment, therefore, at time... Number of effective diffusion regions The calculation formula can be:

[0115]

[0116] The physical meaning of this computational logic is as follows:

[0117] If the moisture triggering is mainly concentrated in a single area, that is, only one or a few areas If the value is significantly higher than in other regions, then although the numerator is largely contributed by this dominant region, the square term in the denominator is also mainly dominated by this region. A value close to 1 indicates that the current moisture-induced activation is mainly manifested as a single point or a few localized points. If multiple areas... At the same time, if the sum of squares in the numerator is at a high level, the sum of squares in the numerator will increase significantly, while the denominator will not be completely dominated by the square term of a single region. This will increase significantly, indicating that the current moisture-induced startup is no longer limited to a single area, but has expanded to multiple areas. Therefore, Based on the actual distribution of moisture triggering intensity in each area, the effective number of areas within the current moisture triggering range is adaptively represented. This definition avoids using a fixed threshold to delineate high-risk areas and is beneficial for improving adaptability under different warehouse types, seasons, and ventilation conditions.

[0118] S106. Generate local control demand based on the maximum moisture trigger intensity and the number of effective diffusion areas, and generate global control demand based on the difference between the sum of moisture trigger intensities and the maximum moisture trigger intensity, as well as the number of effective diffusion areas.

[0119] In this embodiment, the local control demand is generated based on the maximum moisture triggering intensity and the number of effective diffusion regions, specifically including:

[0120] The ratio of the maximum moisture-triggered intensity to the number of effective diffusion areas is determined as the local control demand.

[0121] The global control demand is generated based on the difference between the sum of moisture-triggered intensities and the maximum moisture-triggered intensities, as well as the number of effective diffusion regions. Specifically, this includes:

[0122] Subtract the maximum moisture triggering intensity from the sum of the moisture triggering intensities to obtain the difference value;

[0123] Subtract one from the number of effective diffusion regions to obtain the diffusion region increment;

[0124] The ratio of the product of the difference value and the increment of the diffusion area to the total number of monitored areas is determined as the global control demand.

[0125] For example, in the context of grain storage, the essential difference between local and global regulation lies in the fact that the former targets a localized, dominant situation with strong triggers but a small scope, while the latter targets a multi-regional diffusion situation with an increasing number of triggering areas and a wider range of expansion. Therefore, it is necessary to construct demand quantities for local and global regulation respectively.

[0126] time Local regulation demand The calculation formula can be:

[0127]

[0128] Local control demand is used to characterize the local priority control demand of the dominant moisture-triggered region at the current moment. Its physical meaning is: when the moisture triggering intensity of a certain region is very high, but the number of effective diffusion regions is small, meaning most moisture initiation is still concentrated in a few regions, The magnitude of the risk indicates that the current risk is mainly localized, and it is appropriate to prioritize the application of regulatory resources to the areas primarily affected by humidity. Conversely, if An increase indicates that the moisture-induced activation has spread to more areas, thus relatively weakening the local priority of a single area. decline.

[0129] Furthermore, at any time Global control demand The calculation formula can be:

[0130]

[0131] The global regulation demand is used to characterize the extended regulation demand formed by other regions besides the dominant moisture-triggered area. Its physical meaning is: Used to describe the total cumulative moisture triggering in areas other than the dominant area. This describes the extent to which moisture-induced activation spreads to more areas beyond the dominant region. Therefore, when a significant number of areas beyond the dominant region are simultaneously triggered, and the number of effective diffusion areas continues to increase, The significant increase indicates that current regulation should no longer be limited to the dominant region, but should adopt a global regulation approach covering a wider spatial range.

[0132] S107. Compare the local control demand with the global control demand, determine the control instructions based on the comparison results, and control the grain storage environment according to the control instructions.

[0133] In this embodiment, the local control demand is compared with the global control demand, control instructions are determined based on the comparison results, and the grain warehouse environment is controlled according to the control instructions. Specifically, this includes:

[0134] When the local control demand is greater than or equal to the global control demand, a local control command is output. The local control command is used to control the operation of the ventilation control equipment corresponding to the dominant area, so that the airflow preferentially covers the dominant area. The dominant area is the monitoring area with the maximum moisture trigger intensity.

[0135] When the local control demand is less than the global control demand, a global control command is output. The global control command is used to control the ventilation control equipment, expand the air supply coverage, or adjust the overall airflow organization to suppress the trend of moisture expansion in each monitoring area.

[0136] For example, based on the above steps, at time The control mode function can be:

[0137]

[0138] in, Indicates time Control mode function; Output a signal to maintain the monitoring status. Output local control commands. : Output global control commands.

[0139] when When this value is zero, it indicates that the total amount of moisture triggering in all areas is zero, meaning that no effective initiation area has been identified that simultaneously possesses external potential moisture drive and local actual moisture response. This indicates that a moisture-affected start-up area already exists, and there is a localized demand for regulation. Not less than the overall control demand This indicates that although moisture-induced activation has occurred, it is mainly concentrated in the dominant moisture-triggered region. In some areas, or even a few, the overall trend remains one of local dominance. This indicates that a moisture-affected start-up area already exists, and the overall control demand is [increased / increased / increased]. Greater than the demand for local regulation This indicates that not only is there a clear humidification-induced start-up in the dominant region, but multiple other regions are also simultaneously experiencing a humidification-induced start-up that is transforming from a potential trend into an actual response, showing an overall trend of multi-regional diffusion.

[0140] Based on the control mode function, control instructions that match the current risk space distribution can be generated and output to the fan, valve, window actuator or flow guide mechanism.

[0141] When the control output is 0, the system maintains its monitoring state, continuing to collect and update data on temperature, humidity, and grain moisture content in each area, without actively issuing control actions. When this occurs, local control commands should be output targeting the dominant moisture-triggered area. For example, priority should be given to activating fans, dampers, local window actuators, or flow guiding mechanisms corresponding to that area, allowing airflow to preferentially cover that area and thus preferentially suppress the continued intensification of moisture development in that area. When necessary, a global control command should be output, and by linking multiple fans and air valves, expanding the air supply coverage, or adjusting the overall airflow organization, the airflow can be extended to a larger space to achieve overall suppression of the trend of moisture expansion in multiple areas.

[0142] During the execution of control commands, the system not only suppresses the spread of localized humidity based on the regional humidity trigger state, but also takes into account the coordination between temperature and humidity in the storage environment. This avoids introducing high-humidity air due to a simple pursuit of cooling, or reducing control efficiency due to a simple pursuit of dehumidification. For raw rice, priority is given to ensuring safe storage conditions; for finished rice products, priority is given to ensuring suitable preservation conditions, thereby achieving balanced environmental control for different grain storage objects.

[0143] After the control command is executed, the system continues to collect data on temperature, humidity and moisture content of grain in each area, and enters the next control cycle to form a dynamic closed-loop control for the grain storage process.

[0144] This invention also proposes an intelligent control system for green grain storage environment based on the Internet of Things (IoT). Please refer to [link / reference]. Figure 2 The diagram shows a structural diagram of an IoT-based intelligent control system for green grain storage environment provided in an embodiment of the present invention. The system includes: a data acquisition module 101, a data processing module 102, and an instruction execution module 103.

[0145] The data acquisition module 101 is used to collect the outside temperature and humidity data of the grain warehouse, and to obtain the surface temperature data, surface humidity data and surface moisture content data of the grain pile in each monitoring area of ​​the grain warehouse.

[0146] The data processing module 102 is used to execute the steps of the intelligent control method for green grain storage environment based on the Internet of Things;

[0147] The instruction execution module 103 is used to control the ventilation control equipment to regulate the grain warehouse environment according to the control instructions.

[0148] It should be noted that the system provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the IoT-based intelligent control system for green grain storage environment and the IoT-based intelligent control method for green grain storage environment provided in the above embodiments belong to the same concept. The specific implementation process is detailed in the method embodiments and will not be repeated here.

[0149] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0150] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0151] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent control of green grain storage environment based on the Internet of Things, characterized in that, include: Acquire external temperature and humidity data of the grain warehouse, and acquire surface temperature, humidity, and moisture content data of the grain pile in each monitoring area of ​​the grain warehouse. For each monitoring area, the dew point temperature of the air outside the warehouse is determined based on the temperature and humidity data outside the warehouse, and the long-term potential moisture prediction characteristics of the monitoring area are constructed based on the difference between the dew point temperature of the air outside the warehouse and the surface temperature data of the grain pile in that monitoring area. Based on the changes in surface humidity and moisture content of grain piles in the monitoring area, the short-term actual moisture response characteristics of the monitoring area are constructed. For each monitoring area, the intensity of moisture triggering is determined based on its long-term potential moisture prediction characteristics and short-term actual moisture response characteristics. Based on the distribution of moisture triggering intensity in all areas, calculate the number of effective diffusion areas for moisture-induced initiation within the grain warehouse; The local control demand is generated based on the maximum moisture trigger intensity and the number of effective diffusion areas, and the global control demand is generated based on the difference between the sum of moisture trigger intensities and the maximum moisture trigger intensity, as well as the number of effective diffusion areas. Compare the local and global control demands, determine control instructions based on the comparison results, and then control the grain storage environment according to the control instructions.

2. The intelligent control method for green grain storage environment based on the Internet of Things according to claim 1, characterized in that, The process involves determining the dew point temperature of the air outside the warehouse based on external temperature and humidity data, and constructing long-term potential moisture prediction characteristics for the monitoring area based on the difference between the external air dew point temperature and the surface temperature data of the grain pile in the monitoring area. Specifically, this includes: Based on the outside temperature and humidity data, the dew point temperature of the outside air was calculated using the dew point temperature calculation formula. The difference between the dew point temperature of the air outside the warehouse and the surface temperature of the grain pile in the monitoring area is determined as the long-term potential moisture prediction characteristic of the monitoring area.

3. The intelligent control method for green grain storage environment based on the Internet of Things according to claim 1, characterized in that, The method of constructing short-term actual moisture response characteristics of the monitoring area based on changes in surface humidity and moisture content data of the grain pile in the monitoring area specifically includes: For any monitoring time in the monitoring area, obtain the difference between the surface humidity data of the grain pile at that time and the surface humidity data of the grain pile at the previous time, as well as the difference between the surface moisture content data of the grain pile at that time and the surface moisture content data of the grain pile at the previous time. When both differences are positive, the product of the two differences is taken as the short-term actual humidity response characteristic of the monitoring area; otherwise, the short-term actual humidity response characteristic of the monitoring area is set to zero.

4. The intelligent control method for green grain storage environment based on the Internet of Things according to claim 1, characterized in that, The determination of the moisture triggering intensity of the monitoring area based on its long-term potential moisture prediction characteristics and short-term actual moisture response characteristics specifically includes: When both the long-term potential moisture prediction characteristic and the short-term actual moisture response characteristic are greater than zero, the ratio of their product to their sum is taken as the moisture trigger intensity of the monitoring area; otherwise, the moisture trigger intensity of the monitoring area is set to zero.

5. The intelligent control method for green grain storage environment based on the Internet of Things according to claim 1, characterized in that, The calculation of the effective diffusion area of ​​moisture-induced initiation within the grain warehouse, based on the moisture triggering intensity distribution across all regions, specifically includes: Obtain the moisture trigger intensity of each monitoring area; Calculate the sum of the moisture triggering intensities of all monitored areas, and then square the sum as the first intermediate value; Calculate the square of the moisture triggering intensity of each monitoring area, and sum the square values ​​of all areas to obtain the second intermediate value; The ratio of the first median value to the second median value is determined as the number of effective diffusion regions.

6. The method for intelligent control of green grain storage environment based on the Internet of Things according to claim 1, characterized in that, The generation of local control demand based on the maximum moisture triggering intensity and the number of effective diffusion regions specifically includes: The ratio of the maximum moisture-triggered intensity to the number of effective diffusion areas is determined as the local control demand.

7. The intelligent control method for green grain storage environment based on the Internet of Things according to claim 1, characterized in that, The process of generating the global control demand based on the difference between the sum of moisture-triggered intensities and the maximum moisture-triggered intensities, as well as the number of effective diffusion regions, specifically includes: Subtract the maximum moisture triggering intensity from the sum of the moisture triggering intensities to obtain the difference value; Subtract one from the number of effective diffusion regions to obtain the diffusion region increment; The ratio of the product of the difference value and the increment of the diffusion area to the total number of monitored areas is determined as the global control demand.

8. The intelligent control method for green grain storage environment based on the Internet of Things according to claim 1, characterized in that, The process of comparing local and global control demands, determining control instructions based on the comparison results, and controlling the grain storage environment according to these instructions specifically includes: When the local control demand is greater than or equal to the global control demand, a local control command is output. The local control command is used to control the operation of the ventilation control equipment corresponding to the dominant area, so that the airflow preferentially covers the dominant area. The dominant area is the monitoring area with the maximum moisture trigger intensity. When the local control demand is less than the global control demand, a global control command is output. The global control command is used to control the ventilation control equipment, expand the air supply coverage, or adjust the overall airflow organization to suppress the trend of moisture expansion in each monitoring area.

9. The intelligent control method for green grain storage environment based on the Internet of Things according to claim 1, characterized in that, The monitoring area includes at least one of the following: central area, surrounding area, wall-adjacent area, corner area, near-wind area, and far-wind area.

10. An IoT-based intelligent control system for green grain storage environment, including: The data acquisition module is used to collect external temperature and humidity data of the grain warehouse, and to obtain surface temperature data, surface humidity data, and surface moisture content data of the grain pile in each monitoring area of ​​the grain warehouse. A data processing module is used to perform the steps of the intelligent control method for green grain storage environment based on the Internet of Things as described in any one of claims 1-9; The instruction execution module is used to control the ventilation and control equipment to regulate the environment of the grain warehouse according to the control instructions.

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