Crop storage dynamic management method and system based on intelligent Internet of Things

Abstract

The invention provides a crop storage dynamic management method and system based on the intelligent Internet of Things, and belongs to the technical field of storage management, and the method comprises the steps: generating a first gas concentration distribution diagram based on the position information of a plurality of gas sensors and the first gas concentration detection information of each gas sensor in a set time period; fusing the first gas concentration distribution map based on the environmental information of potato storage to obtain a second gas concentration distribution map; the second gas concentration distribution diagram is segmented, target gas distribution image blocks are obtained, and the target gas distribution image blocks are image blocks with the gas concentration larger than a preset concentration value in the second gas concentration distribution diagram; based on the potato storage area image data corresponding to the target gas distribution image block, determining the germination rate of the potatoes; determining the ventilation quantity of storage based on the germination rate. According to the crop storage dynamic management method and system based on the intelligent Internet of Things, the quality of crops in the storage period can be guaranteed.

Description

Technical Field

[0001] The present application belongs to the field of warehouse management technology, and more specifically, relates to a method and system for dynamic management of crop warehouses based on the intelligent Internet of Things. Background Art

[0002] Potatoes are a key agricultural commodity in the field of crop storage, and their storage quality is crucial to farmers' profits and market supply. Traditional potato storage management methods rely on fixed ventilation, temperature, and humidity control methods, failing to fully consider the actual state of the potatoes and the dynamic changes in the storage environment. This makes it difficult to accurately adjust environmental parameters based on the real-time condition of the potatoes, resulting in high germination and rot rates, seriously affecting storage quality. Summary of the Invention

[0003] The purpose of this application is to provide a method and system for dynamic management of crop storage based on intelligent Internet of Things to ensure the quality of crops during storage.

[0004] A first aspect of the embodiments of the present application provides a method for dynamic management of crop storage based on the intelligent Internet of Things, comprising: generating a first gas concentration distribution map based on position information of the plurality of gas sensors and first gas concentration detection information of each gas sensor within a set time period; fusing the first gas concentration distribution map based on environmental information of potato storage to obtain a second gas concentration distribution map, wherein the environmental information includes temperature information and humidity information; Segmenting the second gas concentration distribution map to obtain a target gas distribution image block, wherein the target gas distribution image block is an image block in the second gas concentration distribution map where the gas concentration is greater than a preset concentration value; determining a potato germination rate based on the potato storage area image data corresponding to the target gas distribution image block; The ventilation rate of the storage is determined based on the germination rate.

[0005] A second aspect of the embodiments of the present application provides a crop storage dynamic management system based on the intelligent Internet of Things, comprising: a gas concentration distribution module, configured to generate a first gas concentration distribution map based on position information of a plurality of gas sensors and first gas concentration detection information of each gas sensor within a set time period; a data fusion module for fusing the first gas concentration distribution map based on environmental information of potato storage to obtain a second gas concentration distribution map, wherein the environmental information includes temperature information and humidity information; a data segmentation module, configured to segment the second gas concentration distribution map to obtain a target gas distribution image block, wherein the target gas distribution image block is an image block in the second gas concentration distribution map in which the gas concentration is greater than a preset concentration value; a germination rate calculation module, configured to determine the germination rate of potatoes based on the potato storage area image data corresponding to the target gas distribution image block; A ventilation module is used to determine the ventilation volume of the storage based on the germination rate.

[0006] According to a third aspect of an embodiment of the present application, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the above-mentioned method for dynamic management of crop storage based on the intelligent Internet of Things are implemented.

[0007] In a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the steps of the above-mentioned method for dynamic management of crop storage based on the intelligent Internet of Things are implemented.

[0008] The beneficial effects of the intelligent IoT-based crop storage dynamic management method and system provided in the embodiments of the present application are as follows: The present application generates a gas concentration distribution map using gas sensor data and integrates temperature and humidity environmental information to accurately present the actual distribution of gas within the storage. The target gas distribution image blocks are segmented and the potato germination rate is determined based on this, enabling effective monitoring of the potato status. The ventilation volume is then determined based on the germination rate, enabling targeted adjustment of the storage environment gas composition, inhibiting potato germination and improving storage quality. Dynamic management is also achieved, effectively reducing labor costs, enhancing the intelligence and refinement of storage management, and ensuring the quality of crops during storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0010] Figure 1 A flowchart of a method for dynamic crop storage management based on the intelligent Internet of Things provided in one embodiment of the present application; Figure 2 A flow chart for obtaining a second gas concentration distribution map provided in one embodiment of the present application; Figure 3 A flowchart for determining potato germination rate provided in one embodiment of the present application; Figure 4 This is a structural diagram of a dynamic crop storage management system based on the intelligent Internet of Things provided in one embodiment of the present application; Figure 5 A schematic block diagram of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0011] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, systems, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0012] In order to make the purpose, technical solutions and advantages of this application clearer, specific embodiments will be described below with reference to the accompanying drawings.

[0013] Please refer to Figure 1 , Figure 1 A flowchart of a method for dynamic crop storage management based on the intelligent Internet of Things provided in one embodiment of the present application can be executed by an electronic device. The method may include: S101: Generate a first gas concentration distribution map based on position information of multiple gas sensors and first gas concentration detection information of each gas sensor within a set time period.

[0014] In this embodiment, the gas sensor can detect specific gas components in the environment (such as O2, CO2, etc.), and is used to monitor the gas concentration in the storage space in real time, providing a data basis for generating a gas concentration distribution map.

[0015] The first gas concentration detection information refers to the gas concentration data at the corresponding position detected by each gas sensor within a set time period. This data is the basic raw data for generating the first gas concentration distribution map.

[0016] The first gas concentration distribution map is a two-dimensional distribution map generated by combining the location information of multiple gas sensors with the first gas concentration detection information in a spatial mapping manner, showing the distribution of gas concentration in the storage space within a set time period.

[0017] In this embodiment, multiple gas sensors are placed at different locations within the potato storage space. Each sensor continuously detects the gas concentration at its location over a set period of time. This detection information serves as first gas concentration detection information. By combining the location information of each sensor, the gas concentration data at different locations is spatially mapped.

[0018] For example, using a coordinate system, the concentration values detected by the sensor are mapped to specific locations in the storage space, thereby generating a first gas concentration distribution map. This gas concentration distribution map can visualize the two-dimensional distribution of gas concentration in the storage space during a set time period, allowing managers to intuitively understand the distribution of gas concentration in the storage space.

[0019] During storage, potatoes consume nutrients and oxygen through respiration, producing carbon dioxide. Excessive carbon dioxide in the environment and poor ventilation can cause potato tissue suffocation, affecting seed potato vitality and reducing germination rates.

[0020] For example, multiple gas sensors capable of detecting carbon dioxide concentrations can be deployed within a potato storage space. Over a set period of time (e.g., one week), these sensors continuously monitor the carbon dioxide concentration at their respective locations. This data serves as the first gas concentration detection information. The location information of each sensor is combined with the detection information and spatially mapped using a coordinate system to generate a first carbon dioxide concentration distribution map, visually displaying the two-dimensional distribution of carbon dioxide concentrations within the storage space over that week.

[0021] S102: The first gas concentration distribution map is fused based on environmental information of potato storage to obtain a second gas concentration distribution map, where the environmental information includes temperature information and humidity information.

[0022] In this embodiment, the environmental information refers to the temperature information and humidity information in the potato storage environment, which is used to be integrated with the first gas concentration distribution map to obtain a second gas concentration distribution map that more accurately reflects the actual situation.

[0023] The second gas concentration distribution map is a gas concentration distribution map obtained by integrating environmental information such as temperature and humidity on the basis of the first gas concentration distribution map. Compared with the first gas concentration distribution map, it can more accurately reflect the actual gas concentration distribution status in the storage space.

[0024] In this embodiment, the environmental information of potato storage has a significant impact on gas diffusion, reaction, and potato respiration. Temperature changes alter the velocity of gas molecules, thereby affecting the gas diffusion rate. Humidity can affect processes such as gas adsorption and desorption within the storage space, while also affecting the physiological state of the potatoes, indirectly influencing gas production and consumption. Fusion of temperature and humidity information with the first gas concentration distribution map comprehensively considers the impact of these factors on gas concentration distribution. The concentration data for each location in the first gas concentration distribution map can be adjusted based on the weight of the impact of temperature and humidity on gas concentration.

[0025] For example, in areas with high temperature and humidity, considering that potato respiration may be enhanced, the gas concentration data in this area is corrected accordingly, so that the generated second gas concentration distribution map more accurately reflects the actual gas distribution conditions, providing a more accurate data basis for subsequent analysis based on gas distribution.

[0026] S103: Segment the second gas concentration distribution map to obtain a target gas distribution image block, where the target gas distribution image block is an image block in the second gas concentration distribution map where the gas concentration is greater than a preset concentration value.

[0027] In this embodiment, the preset concentration value is a standard concentration value determined based on factors such as the critical concentration of gases associated with potato sprouting during storage. When segmenting the second gas concentration distribution image, this value is used as a threshold, and areas with a concentration greater than this value are segmented as target gas distribution image blocks.

[0028] The target gas distribution image blocks are image blocks segmented from the second gas concentration distribution map, where the gas concentration is greater than a preset concentration value. The areas corresponding to these image blocks are considered to be key areas where gas concentration may affect potato sprouting.

[0029] In this embodiment, after obtaining the second gas concentration distribution map, a preset concentration value is set. This preset concentration value is determined based on factors such as the critical gas concentration value associated with potato sprouting during storage. Using image segmentation technology, portions of the map where the gas concentration exceeds the preset concentration value are segmented to form target gas distribution image blocks.

[0030] This embodiment uses a threshold segmentation method to compare the gas concentration value of each pixel with a preset concentration value. Pixels with concentrations above this value are segmented into target gas distribution image blocks. The areas corresponding to these image blocks are key areas where gas concentrations may affect potato sprouting. For example, if the preset carbon dioxide concentration value is a, areas in the image with carbon dioxide concentrations above a will be segmented as target gas distribution image blocks, allowing further analysis of the relationship between these areas and potato sprouting.

[0031] S104: Determine the germination rate of potatoes based on the potato storage area image data corresponding to the target gas distribution image block.

[0032] In this embodiment, the potato storage area image data refers to the image information of the potato area corresponding to the target gas distribution image block, including visual feature data such as the potato's shape and color. The potato germination rate is determined by processing and analyzing this data.

[0033] The germination rate is the percentage of sprouted potatoes relative to the total number of potatoes within a specific potato storage area. It is calculated through image processing and analysis of image data from the potato storage area. The germination rate is a key indicator of the storage environment's impact on potato storage and is used to determine regulatory measures such as required ventilation.

[0034] In this embodiment, the target gas distribution image blocks correspond to potato storage areas. Image data from these areas is acquired and image processing and analysis techniques are used to determine the potato germination rate. Image processing techniques can include image preprocessing, such as denoising and contrast enhancement, to improve image quality and facilitate subsequent analysis. An image recognition algorithm is then used to identify the number of sprouted potatoes in the image. For example, sprouted potatoes will have features such as protruding buds and color changes. This embodiment uses these features to determine whether a potato has sprouted. The germination rate is then calculated by comparing the count with the total number of potatoes. For example, if the number of sprouted potatoes is N and the total number of potatoes is M, the germination rate is N / M × 100%. In this way, the gas concentration distribution is linked to the potato germination status.

[0035] S105: Determine the ventilation volume of the storage based on the germination rate.

[0036] In this embodiment, ventilation volume refers to the volume of air introduced into or exhausted from the storage space by ventilation equipment per unit time. The appropriate ventilation volume is determined based on the potato germination rate to adjust the gas composition within the storage area, inhibit potato germination, and improve storage quality.

[0037] In this embodiment, the germination rate reflects the degree to which the current storage environment affects potato germination. A higher germination rate indicates that the gas composition in the storage environment is unfavorable for potato storage, and ventilation is needed to improve the environment. In this embodiment, a model for the relationship between germination rate and ventilation volume can be pre-established. This model can be derived from experimental data, theoretical analysis, or empirical experience.

[0038] For example, through extensive experiments, we found that when the germination rate is within a certain range, a specific ventilation rate range corresponds to ensure that ventilation adjusts the gas composition in the warehouse, inhibits further germination of potatoes, and maintains a good storage environment. Based on this relationship model and the calculated germination rate, the required ventilation rate can be determined, thereby achieving dynamic control of the storage environment and ensuring the storage quality of potatoes.

[0039] As can be seen from the above, this embodiment generates a gas concentration distribution map using gas sensor data and integrates temperature and humidity environmental information to accurately present the actual gas distribution within the warehouse. The target gas distribution image blocks are segmented and used to determine the potato germination rate, effectively monitoring the potato's condition. The ventilation rate is then determined based on the germination rate, enabling targeted adjustments to the storage environment's gas composition, suppressing potato germination and improving storage quality. This also enables dynamic management, effectively reducing labor costs, enhancing the intelligence and refinement of warehouse management, and ensuring the quality of crops during storage.

[0040] In one embodiment of the present application, the first gas concentration distribution map is fused based on the environmental information of potato storage to obtain a second gas concentration distribution map, including: Divide the potato storage area into multiple sub-storage areas; Based on the density of potatoes stacked in each sub-storage area, determine the weight of the impact of temperature and humidity information in each sub-storage area on the gas concentration in the area; Based on the weights and the temperature information difference and humidity information difference in each sub-storage area, the corresponding fusion parameters of each sub-storage area are obtained. The temperature information difference is the difference between the current temperature and the standard temperature in the sub-storage area, and the humidity information difference is the difference between the current humidity and the standard humidity in the sub-storage area. Adjust the first gas concentration detection information corresponding to each sub-storage area based on the fusion parameters corresponding to the area to obtain the second gas concentration detection information corresponding to the area; The second gas concentration detection information corresponding to each sub-storage area is spliced together to obtain a second gas concentration distribution map.

[0041] In this embodiment, the sub-storage area is a smaller area unit obtained by dividing the entire potato storage area.

[0042] The fusion parameter is calculated by comprehensively considering the temperature and humidity differences within each sub-storage area, as well as the weighted impact of temperature and humidity on gas concentration. This parameter represents the combined impact of environmental factors on gas concentration in each sub-storage area and is used to adjust the original gas concentration measurement information to obtain gas concentration data that more accurately reflects actual conditions.

[0043] In this example, the potato storage area can be divided into multiple sub-storage areas because environmental conditions and potato stacking conditions vary across the storage space. For example, the temperature, humidity distribution, and potato stacking density may differ between the edge and center of the storage area, and between areas near and far from ventilation openings. This segmentation allows for tailored management of each sub-area, improving refinement.

[0044] The different potato stacking densities in each sub-storage area will result in different degrees of influence of temperature and humidity on gas concentration. Expressed in terms of formula, let the weight of the influence of temperature on gas concentration be , the weight of humidity on gas concentration is (i represents different sub-storage areas), these weights can be determined through experiments and data analysis. For example, in the sub-area with high potato stacking density, since potato respiration is relatively concentrated, the impact of temperature and humidity changes on gas concentration may be greater, and accordingly and The value will be higher.

[0045] Based on a determined weight and , and the temperature information difference within each sub-storage area ( ,in, is the current temperature, is the standard temperature) and humidity information difference ( ),in, is the current humidity, is the standard humidity) to calculate the fusion parameters The calculation formula of the fusion parameter is:

[0046] This formula comprehensively considers the degree to which temperature and humidity deviate from the standard values and the weight of their impact on gas concentration, and quantifies the comprehensive impact of environmental factors in each sub-storage area on gas concentration.

[0047] For example, if the temperature of a sub-storage area is higher than the standard temperature and the humidity is lower than the standard humidity, and the temperature and humidity have a greater influence on the gas concentration, the calculated The value will reflect the extent of this combined impact.

[0048] In this embodiment, the fusion parameters calculated are used The first gas concentration detection information corresponding to the sub-storage area Adjust to obtain the second gas concentration detection information , the adjustment formula is This is because the fusion parameters This reflects the impact of environmental factors on gas concentration. Through this adjustment, the first gas concentration detection information can be made more consistent with the actual gas concentration situation, and the deviation based only on gas sensor detection without considering the impact of environmental factors can be corrected.

[0049] The second gas concentration detection information obtained after adjustment in each sub-storage area By stitching these data together, a second gas concentration distribution map for the entire storage area is formed. This map comprehensively considers the impact of environmental factors at different locations within the storage area on gas concentration. Compared to the first map generated solely based on gas sensor data, it more accurately reflects the actual gas concentration distribution within the warehouse, providing more reliable data support for subsequent gas distribution-based analyses, such as determining potato germination rates.

[0050] As can be seen from the above, this embodiment subdivides the storage area, determines the weights of the impact of temperature and humidity on gas concentration based on potato stacking density, calculates fusion parameters, and adjusts the gas concentration detection information accordingly, ultimately forming a second gas concentration distribution map. This embodiment achieves a refined analysis of the storage environment, making the gas concentration distribution map more realistic.

[0051] In one embodiment of the present application, segmenting the second gas concentration distribution map to obtain a target gas distribution image block includes: Mapping the second gas concentration distribution map into a grayscale value, and mapping the preset concentration value into a grayscale threshold; The image block with a grayscale value greater than the grayscale threshold is regarded as the target gas distribution image block.

[0052] In this embodiment, a grayscale value represents the brightness of a pixel in an image. For black and white images (grayscale images), the grayscale value typically ranges from 0 (black) to 255 (white), with different grayscale values representing varying shades of gray. In this embodiment, the second gas concentration distribution map is converted into a grayscale image, and the gas concentrations are mapped to corresponding grayscale values using a certain mapping relationship to facilitate image processing and analysis.

[0053] The grayscale threshold is a key parameter used in grayscale image segmentation. It is a specific grayscale value used to distinguish different areas in an image. In this embodiment, a preset concentration value is mapped to a grayscale threshold. By comparing the grayscale value of each pixel in the image with the threshold, the pixels belonging to the target gas distribution image block are determined.

[0054] In this embodiment, the second gas concentration distribution map is first mapped to grayscale values. This is because grayscale images are more convenient for subsequent analysis and manipulation in image processing. Using specific mapping rules, different gas concentration values in the map are mapped to different grayscale levels, resulting in a grayscale representation of the gas concentration distribution. Simultaneously, a preset concentration value is mapped to a grayscale threshold, which serves as a key criterion for distinguishing the target gas distribution area from other areas.

[0055] After mapping is complete, image blocks with grayscale values greater than the grayscale threshold are identified and designated as target gas distribution blocks. Regions above a preset concentration value (corresponding to the grayscale threshold) are areas of concern, as their gas concentrations affect potato sprouting. This threshold segmentation method allows for rapid extraction of target regions from the entire second gas concentration distribution map.

[0056] From the above, it can be concluded that this embodiment achieves rapid positioning of key gas distribution areas by mapping the second gas concentration distribution map to grayscale values, mapping the preset concentration value to a grayscale threshold, and thereby segmenting the image blocks with grayscale values greater than the threshold as target gas distribution image blocks.

[0057] In one embodiment of the present application, determining a potato germination rate based on potato storage area image data corresponding to a target gas distribution image block includes: Perform feature extraction on the image data to obtain the actual color features of the potatoes within a set time period; determining the number of sprouted potatoes based on the rate of change of the initial color characteristic and the actual color characteristic; The potato germination rate is determined based on the number of sprouted potatoes relative to the total amount of potatoes stored.

[0058] In this embodiment, the actual color features refer to a series of parameters reflecting the current color state of the potato obtained by performing feature extraction on the image data of the potato storage area within a set time period.

[0059] The initial color profile is the color characteristic parameter of potatoes in their initial storage state before they sprout. By comparing this with the actual color profile and analyzing the color changes, the number of sprouted potatoes can be determined.

[0060] In this embodiment, feature extraction is performed on the potato storage area image data corresponding to the target gas distribution image block to obtain key information reflecting the potato's condition. In this embodiment, the actual color features of the potatoes within a set time period can be extracted. Color is an important external manifestation of changes in the potato's physiological state, and the color of potatoes changes during the germination process. The actual color features are extracted through specific image processing algorithms, such as color space conversion (e.g., from RGB to HSV or Lab color space), statistical calculation of color component averages, and construction of color histograms. For example, the average value of hue (H) in the HSV color space is calculated as a parameter reflecting color characteristics.

[0061] In this embodiment, the number of sprouted potatoes is determined based on the rate of change of the initial color feature and the actual color feature. The initial color feature refers to the color feature of the potato before it sprouts in the early stage of storage. Assume that we have obtained the initial color feature of the potato through the previous image acquisition and processing, for example, in the Lab color space, the initial brightness , green-red component a0 and blue-yellow component b0. As time goes by, the actual color characteristics are obtained within the set time period , a1, b1. By calculating the rate of change of color features, such as: Brightness change rate:

[0062] Green-red component change rate:

[0063] Blue-yellow component change rate:

[0064] Combining these change rates, according to the pre-set judgment rules (for example, when 、 、 ) when both exceed a certain threshold, the potato is determined to have sprouted), and the number of sprouted potatoes is counted.

[0065] In this embodiment, after the number of sprouted potatoes is determined, the germination rate is calculated based on the proportion of the number of sprouted potatoes in the total amount of potatoes stored. Assuming that the number of sprouted potatoes is N and the total amount of potatoes stored is M, the calculation formula for the germination rate is Germination rate = This calculation result can intuitively reflect the germination status of potatoes under current storage conditions, providing important data basis for storage management.

[0066] As can be seen from the above, this embodiment extracts the actual color features of the potato storage area image and combines them with the rate of change of the initial color features to determine the number of sprouts, thereby calculating the germination rate. This embodiment uses color-derived features to effectively determine the germination rate, quickly and accurately reflecting the potato germination status.

[0067] In one embodiment of the present application, determining the storage ventilation time includes: Determine the degree of storage ventilation required based on germination rate; Determine environmental correction factors based on environmental information about potato storage; Determine the ventilation time of the warehouse based on the degree of ventilation demand and environmental correction factors; The formula for determining storage ventilation time is: t=D+E+C Among them, t represents the warehouse ventilation time, D represents the degree of warehouse ventilation demand, E represents the environmental correction factor, and C represents a constant.

[0068] In this embodiment, the ventilation requirement is a quantitative indicator determined based on the potato germination rate, reflecting the urgency of ventilation required to improve the storage condition of potatoes under the current storage environment. It can be calculated by correlating a specific functional relationship with the germination rate.

[0069] The environmental correction factor (EF) is a parameter derived by comprehensively considering the impact of potato storage environment information (such as temperature and humidity) on ventilation effectiveness. It is calculated using a formula that incorporates these factors. It is used to adjust ventilation time variations caused by environmental differences, ensuring that ventilation time is more accurately tailored to the actual storage environment.

[0070] In this embodiment, germination rate is a key indicator of the suitability of the potato storage environment. Generally speaking, a higher germination rate indicates a more unfavorable storage environment for potatoes, which in turn means a higher ventilation requirement. This embodiment uses a previously established model for the relationship between germination rate and ventilation requirement (e.g., a functional relationship D = aG + b between ventilation requirement D and germination rate G derived from experimental data fitting) to determine the storage ventilation requirement D based on the real-time germination rate.

[0071] In this embodiment, environmental conditions such as temperature and humidity during potato storage significantly impact potato respiration and ventilation. This embodiment analyzes the relationship between these environmental factors and germination rate to derive an environmental correction factor (E): for example, E = k1T + k2H + k3, where T is temperature, H is humidity, and k1, k2, and k3 are weighting coefficients determined experimentally. This formula comprehensively considers the impact of temperature and humidity on ventilation, quantifying the effect of these environmental factors on ventilation time.

[0072] In this embodiment, the ventilation time t is determined based on the ventilation demand D and the environmental correction factor E, combined with the constant C, using the formula t = D + E + C. Constant C can be determined based on factors such as the inherent characteristics of the storage facility and the basic operating time of the ventilation equipment. Constant C is a relatively fixed adjustment parameter used to balance and calibrate the calculated ventilation time. This formula comprehensively considers the ventilation demand reflected by potato germination and the impact of the storage environment on ventilation effectiveness, making the calculated ventilation time more consistent with actual storage needs, more effectively adjusting the storage environment, inhibiting potato germination, and ensuring storage quality.

[0073] As can be seen from the above, this embodiment determines the degree of ventilation requirement based on the germination rate, combines environmental information to determine the environmental correction factor, and then calculates the ventilation time according to a formula. This embodiment comprehensively considers the potato germination status and storage environment factors to accurately determine the ventilation duration. This effectively meets the ventilation requirement and inhibits potato germination, while avoiding excessive ventilation and waste of resources, thus achieving scientific regulation of the storage environment.

[0074] In one embodiment of the present application, determining the ventilation rate of storage based on the germination rate includes: In response to the germination rate being greater than a first threshold and less than a second threshold, ventilating the storage based on a first ventilation amount; In response to the germination rate being greater than a second threshold, updating the first ventilation rate based on the first formula to obtain a second ventilation rate, and ventilating the warehouse based on the second ventilation rate; The second threshold is greater than the first threshold; The first formula is:

[0075] in, Indicates the second ventilation volume, Indicates the first ventilation volume, Indicates germination rate.

[0076] In this embodiment, the first threshold is a pre-set germination rate limit used to demarcate germination rate intervals. When the germination rate exceeds this threshold, it indicates that potato germination is beyond the normal range and requires ventilation control measures. The first threshold is determined based on experimental data and storage experience and serves as an important reference indicator for switching ventilation strategies.

[0077] The second threshold is also a pre-set germination rate limit and is greater than the first threshold. When the germination rate is greater than the second threshold, it indicates that the potato sprouting situation is serious and more powerful ventilation measures are needed. In other words, the second ventilation rate is obtained by adjusting the first ventilation rate using the formula.

[0078] The first ventilation rate is a fixed ventilation rate used when the germination rate is between a first threshold and a second threshold. This ventilation rate was determined through previous experiments or empirical research and has been shown to be effective in suppressing potato sprouting within this germination rate range.

[0079] The second ventilation rate is calculated by updating the first ventilation rate using the first formula when the germination rate exceeds the second threshold. This is the ventilation rate adjusted to more effectively improve the storage environment and inhibit germination based on the current high germination rate.

[0080] In this embodiment, two key thresholds are first set: a first threshold and a second threshold. These thresholds are determined based on extensive experimental data and in-depth research on potato storage environments. They are used to define different germination rate intervals, corresponding to different ventilation strategies.

[0081] When the germination rate is greater than the first threshold and less than the second threshold, indicating that the potatoes are germinating at a moderate level, the warehouse is ventilated based on the first ventilation rate. The first ventilation rate is determined based on experience or preliminary experiments and is considered to be a ventilation rate that can control germination to a certain extent within this germination rate range and is relatively energy-efficient and efficient. For example, through previous monitoring of potato germination at different ventilation rates, it was found that when the germination rate is within this range, a fixed first ventilation rate can effectively suppress the rapid increase in germination rate.

[0082] When the germination rate is greater than the second threshold, it means that the potato germination is serious and the original first ventilation volume may not be enough to effectively improve the storage environment.

[0083] Update the first ventilation volume V to obtain the second ventilation volume The principle of this formula is that the higher the germination rate G, the greater the increase in ventilation volume. For example, if the germination rate is 20% (G=0.2), the first ventilation volume V is 100 cubic meters / hour, and the second ventilation volume is 100 cubic meters / hour. In this way, the ventilation volume is dynamically adjusted according to the germination rate to more effectively regulate the gas composition in the storage and inhibit further germination of potatoes.

[0084] As can be seen from the above, this embodiment precisely responds to different germination levels by setting dual thresholds. When the germination rate is within the normal range, the first ventilation rate is used, effectively suppressing germination and saving energy. When the germination rate is too high, the second ventilation rate is dynamically adjusted according to a formula, enhancing ventilation to curb the worsening of germination. This allows for on-demand ventilation adjustment, safeguarding the storage environment and improving potato storage quality.

[0085] In one embodiment of the present application, the method further includes: Determine the utilization of storage space based on the storage volume and the volume occupied by existing potatoes; Determine the unit energy storage capacity based on the operating energy consumption of the ventilation equipment; Generate potato storage layout strategy based on storage space utilization and storage capacity per unit energy consumption.

[0086] In this embodiment, the storage space utilization rate refers to the ratio of the volume currently occupied by potatoes to the total storage volume, reflecting the actual utilization of the storage space.

[0087] Storage capacity per unit of energy consumption indicates the amount of potatoes that can be stored in a warehouse for each unit of energy consumed by the ventilation system. It is used to measure the relationship between energy consumption and storage capacity in a warehouse system.

[0088] In this embodiment, the storage space utilization rate is an important indicator to measure the efficiency of storage space use. By calculating the ratio of the total storage volume to the volume occupied by existing potatoes, the actual utilization of the storage space can be clearly understood. For example, if the storage volume is V t , the existing potato occupies a volume of V o , then the storage space utilization rate is:

[0089] If the utilization rate is low, it means that more space can be used to optimize potato storage; if the utilization rate is too high, you need to consider expanding storage space or adjusting storage methods.

[0090] In this embodiment, the ventilation equipment consumes energy during operation, and the unit energy consumption storage capacity is the number of potatoes that can be stored under the condition of consuming unit energy. By counting the operating energy consumption of the ventilation equipment (such as the amount of electricity consumed in a period of time U) and the amount of potatoes stored in the warehouse during this period M, the unit energy consumption storage capacity can be calculated. The unit energy consumption storage capacity reflects the balance between energy consumption and storage capacity in the storage system, which helps to evaluate the economy and energy efficiency of ventilation equipment operation.

[0091] In this embodiment, the two key indicators of storage space utilization and storage capacity per unit of energy consumption are comprehensively considered to generate a potato storage layout strategy. Storage space utilization reflects the utilization of space resources, and storage capacity per unit of energy consumption reflects energy utilization efficiency. If the storage space utilization is low but the storage capacity per unit of energy consumption is high, you can consider appropriately increasing the potato storage capacity to improve space utilization while maintaining energy utilization efficiency; conversely, if the storage space utilization is high but the storage capacity per unit of energy consumption is low, you need to adjust the storage layout, optimize the use of ventilation equipment, and increase the storage capacity per unit of energy consumption without reducing space utilization. By weighing and analyzing these two indicators, a potato storage layout strategy is formulated that can fully utilize the storage space, reduce energy consumption, and improve storage efficiency, thereby achieving optimized operation of the storage system.

[0092] In this embodiment, Figure 2To create a flow chart for the second gas concentration distribution map, the initial gas concentration distribution and environmental information are first input. The potato storage area is then divided into multiple sub-areas. Next, based on the potato stacking density within each sub-area, the weights of the impact of temperature and humidity information on the gas concentration in that area are determined. The temperature difference (the difference between the current temperature and the reference temperature) and humidity difference (the difference between the current humidity and the reference humidity) for each sub-area are then combined with the aforementioned weights to generate the corresponding fusion parameters for each sub-area. Based on the fusion parameters, the first gas concentration detection information for each sub-area is then adjusted to generate the second gas concentration detection information. Finally, the second gas concentration detection information from all sub-areas is combined to generate the second gas concentration distribution map. By integrating environmental information, this process ensures that the gas concentration distribution more closely reflects the actual storage conditions.

[0093] In this embodiment, Figure 3 To determine the potato germination rate, the flowchart first inputs the potato image data corresponding to the target gas concentration distribution image as the basis for analysis. Then, the image data is preprocessed (denoising, enhancement) to optimize the image quality to facilitate subsequent feature extraction. Subsequently, the actual color features of the potato image within a certain time period are extracted (covering multiple color spaces such as RGB, HSV, and Lab), and the initial color features are obtained at the same time (storing the color data of the initial ungerminated state). By calculating the color feature change rate (such as 、 、 The rate of change is compared with the preset threshold: if it exceeds the threshold, it is determined to be a sprouted potato and the number is counted (recorded as N); if it does not exceed the threshold, it is classified as non-sprouting. Then, the total amount of potato storage (M) is obtained and the formula is used Calculate the germination rate and output the result.

[0094] In this embodiment, preprocessing improves image quality, multi-color space feature extraction ensures comprehensive capture of color information, initial features serve as a baseline for comparison, and a rate-of-change threshold enables automated identification of germination status. Germination rate is calculated through quantitative statistics and total volume calculation. By linking color change with germination and combining image processing techniques, this embodiment efficiently and accurately quantifies potato germination. This provides a standardized, automated solution for germination rate monitoring in warehouse management, effectively improving detection efficiency and accuracy. It is suitable for germination status assessment in large-scale potato storage scenarios.

[0095] Corresponding to the above embodiment of the crop storage dynamic management method based on the intelligent Internet of Things, Figure 4 This is a structural diagram of a crop storage dynamic management system based on the intelligent Internet of Things provided in one embodiment of the present application. For ease of explanation, only the parts related to the embodiment of the present application are shown. Figure 4The crop storage dynamic management system 20 based on the intelligent Internet of Things includes: a gas concentration distribution module 21, a data fusion module 22, a data segmentation module 23, a germination rate calculation module 24, and a ventilation module 25.

[0096] The gas concentration distribution module 21 is configured to generate a first gas concentration distribution map based on the position information of the plurality of gas sensors and the first gas concentration detection information of each gas sensor within a set time period; a data fusion module 22 for fusing the first gas concentration distribution map based on environmental information of potato storage to obtain a second gas concentration distribution map, wherein the environmental information includes temperature information and humidity information; A data segmentation module 23 is configured to segment the second gas concentration distribution map to obtain a target gas distribution image block, where the target gas distribution image block is an image block in the second gas concentration distribution map where the gas concentration is greater than a preset concentration value; a germination rate calculation module 24 for determining the germination rate of potatoes based on the potato storage area image data corresponding to the target gas distribution image block; The ventilation module 25 is used to determine the ventilation volume of the storage based on the germination rate.

[0097] In one embodiment of the present application, the data fusion module 22 is specifically configured to: Divide the potato storage area into multiple sub-storage areas; Based on the density of potatoes stacked in each sub-storage area, determine the weight of the impact of temperature and humidity information in each sub-storage area on the gas concentration in the area; Based on the weights and the temperature information difference and humidity information difference in each sub-storage area, the corresponding fusion parameters of each sub-storage area are obtained. The temperature information difference is the difference between the current temperature and the standard temperature in the sub-storage area, and the humidity information difference is the difference between the current humidity and the standard humidity in the sub-storage area. Adjust the first gas concentration detection information corresponding to each sub-storage area based on the fusion parameters corresponding to the area to obtain the second gas concentration detection information corresponding to the area; The second gas concentration detection information corresponding to each sub-storage area is spliced together to obtain a second gas concentration distribution map.

[0098] In one embodiment of the present application, the data segmentation module 23 is specifically configured to: Mapping the second gas concentration distribution map into a grayscale value, and mapping the preset concentration value into a grayscale threshold; The image block with a grayscale value greater than the grayscale threshold is regarded as the target gas distribution image block.

[0099] In one embodiment of the present application, the germination rate calculation module 24 is specifically used to: Perform feature extraction on the image data to obtain the actual color features of the potatoes within a set time period; determining the number of sprouted potatoes based on the rate of change of the initial color characteristic and the actual color characteristic; The potato germination rate is determined based on the number of sprouted potatoes relative to the total amount of potatoes stored.

[0100] In one embodiment of the present application, the crop storage dynamic management system 20 based on the intelligent Internet of Things further includes: a ventilation time calculation module; the ventilation time calculation module is specifically used to: Determine the degree of storage ventilation required based on germination rate; Determine environmental correction factors based on environmental information about potato storage; Determine the ventilation time of the warehouse based on the degree of ventilation demand and environmental correction factors; The formula for determining storage ventilation time is: t=D+E+C Among them, t represents the warehouse ventilation time, D represents the degree of warehouse ventilation demand, E represents the environmental correction factor, and C represents a constant.

[0101] In one embodiment of the present application, the ventilation module 25 is specifically used to: In response to the germination rate being greater than a first threshold and less than a second threshold, ventilating the storage based on a first ventilation amount; In response to the germination rate being greater than a second threshold, updating the first ventilation rate based on the first formula to obtain a second ventilation rate, and ventilating the warehouse based on the second ventilation rate; The second threshold is greater than the first threshold; The first formula is:

[0102] in, Indicates the second ventilation volume, Indicates the first ventilation volume, Indicates germination rate.

[0103] In one embodiment of the present application, the crop storage dynamic management system 20 based on the intelligent Internet of Things further includes: an adjustment module; the adjustment module is specifically used to: Determine the utilization of storage space based on the storage volume and the volume occupied by existing potatoes; Determine the unit energy storage capacity based on the operating energy consumption of the ventilation equipment; Generate potato storage layout strategy based on storage space utilization and storage capacity per unit energy consumption.

[0104] See also Figure 5 , Figure 5 This is a schematic block diagram of an electronic device provided in one embodiment of the present application. Figure 5 The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of the modules in the above-mentioned system embodiments, such as Figure 4 The functions of the gas concentration distribution module 21, the data fusion module 22, the data segmentation module 23, the germination rate calculation module 24 and the ventilation module 25 are shown.

[0105] It should be understood that in the embodiment of the present application, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0106] The input device 302 may include a touchpad, a fingerprint collection sensor (for collecting user fingerprint information and fingerprint direction information), a microphone, etc. The output device 303 may include a display (LCD, etc.), a speaker, etc.

[0107] The memory 304 may include a read-only memory and a random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information such as the first threshold value and the second threshold value.

[0108] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiment of the present application can execute the implementation method described in the dynamic management method of crop storage based on the intelligent Internet of Things provided in the embodiment of the present application, and can also execute the implementation method of the electronic device described in the embodiment of the present application, which will not be repeated here.

[0109] In another embodiment of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, all or part of the process of the method in the above embodiment is implemented. The computer program can also be used to instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above method embodiments are implemented. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can include: any entity or system capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium.

[0110] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the computer-readable storage medium can include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or is about to be output.

[0111] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0112] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0113] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or can be an electrical, mechanical or other form of connection.

[0114] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0115] In addition, the functional modules in the various embodiments of the present application may be integrated into a single processing unit, or each module may exist physically separately, or two or more modules may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0116] The above are only specific embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for dynamic management of crop storage based on intelligent Internet of Things, characterized in that: include: generating a first gas concentration distribution map based on position information of the plurality of gas sensors and first gas concentration detection information of each gas sensor within a set time period; fusing the first gas concentration distribution map based on environmental information of potato storage to obtain a second gas concentration distribution map, wherein the environmental information includes temperature information and humidity information; Segmenting the second gas concentration distribution map to obtain a target gas distribution image block, wherein the target gas distribution image block is an image block in the second gas concentration distribution map where the gas concentration is greater than a preset concentration value; determining a potato germination rate based on the potato storage area image data corresponding to the target gas distribution image block; The ventilation rate of the storage is determined based on the germination rate.

2. The method for dynamic crop storage management based on intelligent Internet of Things according to claim 1, characterized in that: The step of fusing the first gas concentration distribution map based on the potato storage environment information to obtain a second gas concentration distribution map includes: Divide the potato storage area into multiple sub-storage areas; Based on the density of potatoes stacked in each sub-storage area, determine the weight of the impact of temperature and humidity information in each sub-storage area on the gas concentration in the area; Based on the weights and the temperature information difference and humidity information difference in each sub-storage area, a fusion parameter corresponding to each sub-storage area is obtained. The temperature information difference is the difference between the current temperature and the standard temperature in the sub-storage area, and the humidity information difference is the difference between the current humidity and the standard humidity in the sub-storage area. Adjust the first gas concentration detection information corresponding to each sub-storage area based on the fusion parameters corresponding to the area to obtain the second gas concentration detection information corresponding to the area; The second gas concentration detection information corresponding to each sub-storage area is spliced to obtain the second gas concentration distribution map.

3. The method for dynamic crop storage management based on intelligent Internet of Things according to claim 1, characterized in that: The step of segmenting the second gas concentration distribution map to obtain a target gas distribution image block includes: Mapping the second gas concentration distribution map into a grayscale value, and mapping the preset concentration value into a grayscale threshold; The image block whose grayscale value is greater than the grayscale threshold is used as the target gas distribution image block.

4. The method for dynamic crop storage management based on intelligent Internet of Things according to claim 1, characterized in that: The determining of the potato germination rate based on the potato storage area image data corresponding to the target gas distribution image block includes: Performing feature extraction on the image data to obtain actual color features of the potatoes within a set time period; determining a number of sprouted potatoes based on the initial color characteristic and the rate of change of the actual color characteristic; The germination rate of potatoes is determined based on the proportion of the number of the germinated potatoes to the total amount of potatoes stored.

5. The method for dynamic crop storage management based on intelligent Internet of Things according to claim 1, characterized in that: Determine storage ventilation time, including: determining the degree of storage ventilation required based on the germination rate; Determine environmental correction factors based on environmental information about potato storage; Determining a ventilation time for the warehouse based on the ventilation requirement level and the environmental correction factor; The formula for determining storage ventilation time is: t=D+E+C Among them, t represents the warehouse ventilation time, D represents the degree of warehouse ventilation demand, E represents the environmental correction factor, and C represents a constant.

6. The method for dynamic crop storage management based on intelligent Internet of Things according to claim 1, characterized in that: The method of determining the ventilation volume of the storage based on the germination rate includes: In response to the germination rate being greater than a first threshold and less than a second threshold, ventilating the warehouse based on a first ventilation amount; In response to the germination rate being greater than a second threshold, updating the first ventilation rate based on a first formula to obtain a second ventilation rate, and ventilating the warehouse based on the second ventilation rate; The second threshold is greater than the first threshold; The first formula is: in, Indicates the second ventilation volume, Indicates the first ventilation volume, Indicates germination rate.

7. The method for dynamic crop storage management based on intelligent Internet of Things according to claim 1, characterized in that: Also includes: Determine the utilization of storage space based on the storage volume and the volume occupied by existing potatoes; Determine the unit energy storage capacity based on the operating energy consumption of the ventilation equipment; A potato storage layout strategy is generated based on the utilization rate of the storage space and the storage capacity per unit energy consumption.

8. A crop storage dynamic management system based on intelligent Internet of Things, characterized by: include: a gas concentration distribution module, configured to generate a first gas concentration distribution map based on position information of a plurality of gas sensors and first gas concentration detection information of each gas sensor within a set time period; a data fusion module for fusing the first gas concentration distribution map based on environmental information of potato storage to obtain a second gas concentration distribution map, wherein the environmental information includes temperature information and humidity information; a data segmentation module, configured to segment the second gas concentration distribution map to obtain a target gas distribution image block, wherein the target gas distribution image block is an image block in the second gas concentration distribution map in which the gas concentration is greater than a preset concentration value; a germination rate calculation module, configured to determine the germination rate of potatoes based on the potato storage area image data corresponding to the target gas distribution image block; A ventilation module is used to determine the ventilation volume of the storage based on the germination rate.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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