A method, device, medium and equipment for early warning of condensation on grain piles

Abstract

This invention discloses a method, apparatus, medium, and equipment for early warning of condensation in grain piles. The method includes: acquiring grain condition data and ambient temperature of the grain pile through a grain condition system; constructing a feature dataset based on the grain condition data and ambient temperature, and determining the inter-layer temperature difference, intra-layer temperature difference, and average inter-layer temperature difference for each layer of grain based on the feature dataset; determining the condensation state of each layer of grain based on the feature dataset, inter-layer temperature difference, intra-layer temperature difference, average inter-layer temperature difference, and a pre-set condensation temperature difference threshold range; determining the storage suitability state of each layer of grain based on moisture data and a pre-set moisture threshold range; determining the heating state of each layer of grain based on the average inter-layer temperature difference and a pre-set heating threshold; and determining the early warning level based on the condensation state, storage suitability state, and heating state of each layer of grain.

Description

Technical Field

[0001] This invention relates to the field of grain pile condensation early warning technology, and more specifically, to a grain pile condensation early warning method, apparatus, medium and equipment. Background Technology

[0002] Condensation in stored grain is a typical example of the impact of environmental variables on stored grain within the grain storage ecosystem, and it is related to moisture, temperature, and the thermal properties of grain.

[0003] When the water vapor content in the air remains constant, and the temperature drops to a certain level, the water vapor in the air reaches saturation and begins to condense; this phenomenon is called dew. The temperature at which dew begins to appear is called the "dew point." When the temperature of a certain layer in a grain pile drops to a certain level, causing the water vapor in the pores of the grain to reach saturation, the water vapor begins to condense into small water droplets on the surface of the grain particles; this phenomenon is called grain pile dew.

[0004] The main cause of condensation on grain piles is the temperature difference between different parts of the grain pile or between the grain pile surface and the environment. Generally speaking, the greater the temperature difference, the greater the likelihood of condensation. At the same time, the moisture content of the grain pile also affects condensation; grain piles with high moisture content may experience condensation even with a small temperature difference. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method, device, medium, and equipment for early warning of condensation on grain piles.

[0006] According to one aspect of the present invention, a method for early warning of condensation on grain piles is provided, comprising:

[0007] The grain condition data and ambient temperature of the grain pile to be tested are obtained through the grain condition system.

[0008] Based on grain condition data and ambient temperature, a feature dataset is constructed, and based on the feature dataset, the interlayer temperature difference, intralayer temperature difference, and average interlayer temperature difference of each layer of grain in the grain pile are determined.

[0009] Based on the feature dataset, interlayer temperature difference, intralayer temperature difference, average interlayer temperature difference, and the pre-set dew point temperature difference threshold range, the dew state of each layer of grain in the grain pile is determined.

[0010] Based on moisture data and a pre-set moisture threshold range, determine the storage suitability of each layer of grain in the grain pile.

[0011] The heating status of each layer of grain in the grain pile is determined based on the average temperature difference between layers and the pre-set heating threshold.

[0012] The warning level is determined based on the condensation state, storage suitability, and heat generation status of each layer of grain in the grain pile.

[0013] Optionally, the operation of constructing a feature dataset based on grain condition data and ambient temperature includes:

[0014] Based on the grain condition data, determine the highest temperature, lowest temperature, average temperature, and average moisture content of each layer of grain in the grain pile;

[0015] A feature dataset is constructed based on the highest temperature, lowest temperature, average temperature, average moisture content of each layer of grain in the grain pile, as well as the ambient temperature.

[0016] Optionally, the operation of determining the interlayer temperature difference, intralayer temperature difference, and average interlayer temperature difference of each layer of grain in the grain pile based on the feature dataset includes:

[0017] The ambient temperature difference is determined by subtracting the highest and lowest temperatures of the first layer of grain in the feature dataset from the ambient temperature.

[0018] The interlayer temperature difference is determined by subtracting the highest and lowest temperatures of the grain in other layers of the grain pile from the temperature of the previous layer of grain in the characteristic data. The average temperature difference between the layers is determined by subtracting the average temperature of the grain in the previous layer of grain.

[0019] The temperature difference within a layer is determined by subtracting the highest and lowest temperatures within the grain in other layers of the grain pile from the feature dataset.

[0020] Optionally, the operation of determining the condensation state of each layer of grain in the grain pile based on the feature dataset, interlayer temperature difference, intralayer temperature difference, average interlayer temperature difference, and a pre-set condensation temperature difference threshold range includes:

[0021] When the ambient temperature difference is within the condensation temperature difference threshold range, the condensation state is surface condensation;

[0022] When the temperature difference within the layer is within the condensation temperature difference threshold range, the condensation state is condensation within the layer;

[0023] When the interlayer temperature difference or the average interlayer temperature difference is within the condensation temperature difference threshold range, the condensation state is interlayer condensation;

[0024] When the temperature difference between the layers of grain at the bottom of the grain pile, the average temperature difference between the layers, or the temperature difference within the layers is within the dew-forming temperature threshold range, the dew-forming state is bottom-layer dew.

[0025] Optionally, the survival suitability status includes high risk, risk, survival suitability, and low risk, and the fever status includes fever and no fever.

[0026] Optionally, the operation of determining the warning level based on the condensation state, storage suitability, and heat generation status of each layer of grain in the grain pile includes:

[0027] When the bottom layer of the grain pile is condensed, the storage condition is high-risk or risky, and the grain is heating up, the warning level is determined to be hot grain condensation;

[0028] If the grain pile has condensation at the bottom, is in a low-risk or suitable storage condition, and does not generate heat, the warning level is determined to be suspected condensation.

[0029] If the grain pile has surface condensation, its storage suitability status is suitable for storage, or it is at risk or high risk, and it does not generate heat, the warning level is determined to be surface condensation;

[0030] If the grain pile has surface condensation, its storage condition is deemed suitable, risky, or high-risk, and it is also heating up, the warning level is determined to be high-risk.

[0031] If the grain pile has surface condensation and the storage condition is low risk, the warning level is determined to be suspected condensation;

[0032] If the grain pile exhibits interlayer or intralayer condensation, a storage suitability status of suitable, risky, or high-risk, and is generating heat, the warning level is determined to be high-risk.

[0033] If the grain pile exhibits interlayer or intralayer condensation, and its storage suitability status is deemed suitable, risky, or high-risk, and it does not generate heat, the warning level is determined to be internal condensation.

[0034] If the grain pile shows interlayer or intralayer condensation and the storage condition is low-risk, the warning level is determined to be suspected condensation.

[0035] According to another aspect of the present invention, a grain pile condensation early warning device is provided, comprising:

[0036] The acquisition module is used to acquire grain condition data and ambient temperature of the grain pile under test through the grain condition system.

[0037] The first determination module is used to construct a feature dataset based on grain condition data and ambient temperature, and to determine the interlayer temperature difference, intralayer temperature difference, and average interlayer temperature difference of each layer of grain in the grain pile based on the feature dataset.

[0038] The second determining module is used to determine the condensation state of each layer of grain in the grain pile based on the feature dataset, interlayer temperature difference, intralayer temperature difference, average interlayer temperature difference, and a pre-set condensation temperature difference threshold range.

[0039] The third determination module is used to determine the storage suitability of each layer of grain in the grain pile based on moisture data and a pre-set moisture threshold range.

[0040] The fourth determining module is used to determine the heating state of each layer of grain in the grain pile based on the average temperature difference between layers and the preset heating threshold.

[0041] The fifth determining module is used to determine the warning level based on the condensation state, storage suitability, and heat generation status of each layer of grain in the grain pile.

[0042] According to another aspect of the present invention, a computer-readable storage medium is provided, the storage medium storing a computer program for performing the methods described in any of the above aspects of the present invention.

[0043] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: a processor; a memory for storing executable instructions of the processor; the processor being configured to read the executable instructions from the memory and execute the instructions to implement the method described in any of the preceding aspects of the present invention.

[0044] Therefore, this invention provides a grain pile condensation early warning method based on grain condition and environmental data. It is based on the experience of the grain industry, uses grain condition data and characteristic data in the ambient temperature to perform multiple calculations and cross-validation and comparisons to determine whether condensation is likely to occur. It can automatically trigger five types of early warnings: suspected condensation, surface condensation, internal condensation, hot grain condensation, and high risk, providing intelligent decision support for grain storage management. Attached Figure Description

[0045] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures:

[0046] Figure 1 This is a flowchart illustrating a grain pile condensation early warning method provided in an exemplary embodiment of the present invention;

[0047] Figure 2 This is a schematic diagram of the structure of a grain pile condensation early warning system provided in an exemplary embodiment of the present invention;

[0048] Figure 3 This is a schematic diagram of a data processing module provided in an exemplary embodiment of the present invention;

[0049] Figure 4 This is a schematic diagram of a data calculation module provided in an exemplary embodiment of the present invention;

[0050] Figure 5 This is a schematic diagram of a cross-validation module provided in an exemplary embodiment of the present invention;

[0051] Figure 6 This is a schematic diagram of an alarm triggering module provided in an exemplary embodiment of the present invention;

[0052] Figure 7 This is a schematic diagram of the structure of a grain pile condensation early warning device provided in an exemplary embodiment of the present invention;

[0053] Figure 8 This is the structure of an electronic device provided in an exemplary embodiment of the present invention. Detailed Implementation

[0054] Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0055] It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention.

[0056] Those skilled in the art will understand that the terms "first," "second," etc., in the embodiments of the present invention are only used to distinguish different steps, devices, or modules, and do not represent any specific technical meaning, nor do they indicate a necessary logical order between them.

[0057] It should also be understood that in the embodiments of the present invention, "multiple" can refer to two or more, and "at least one" can refer to one, two or more.

[0058] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more unless explicitly defined or given contrary instructions in the context.

[0059] Furthermore, the term "and / or" in this invention is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this invention generally indicates that the preceding and following related objects have an "or" relationship.

[0060] It should also be understood that the description of the various embodiments in this invention emphasizes the differences between the various embodiments, and the similarities or similarities can be referred to each other. For the sake of brevity, they will not be described in detail.

[0061] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0062] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use.

[0063] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, they should be considered part of the specification.

[0064] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0065] The embodiments of this invention can be applied to electronic devices such as terminal devices, computer systems, and servers, and can operate together with a wide range of other general-purpose or special-purpose computing system environments or configurations. Well-known examples of terminal devices, computing systems, environments, and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, and servers include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments including any of the above systems, etc.

[0066] Electronic devices such as terminal devices, computer systems, and servers can be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. Typically, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in distributed cloud computing environments, where tasks are executed by remote processing devices linked through communication networks. In distributed cloud computing environments, program modules can reside on local or remote computing system storage media, including storage devices.

[0067] Exemplary methods

[0068] Figure 1 This is a schematic flowchart of a grain pile condensation early warning method provided in an exemplary embodiment of the present invention. This embodiment can be applied to electronic devices, such as... Figure 1 As shown, the grain pile condensation early warning method 100 includes the following steps:

[0069] Step 101: Obtain grain condition data and ambient temperature of the grain pile under test through the grain condition system;

[0070] Step 102: Construct a feature dataset based on grain condition data and ambient temperature, and determine the interlayer temperature difference, intralayer temperature difference, and average interlayer temperature difference of each layer of grain in the grain pile based on the feature dataset.

[0071] Step 103: Determine the condensation state of each layer of grain in the grain pile based on the feature dataset, interlayer temperature difference, intralayer temperature difference, average interlayer temperature difference, and the preset condensation temperature difference threshold range.

[0072] Step 104: Determine the storage suitability of each layer of grain in the grain pile based on the moisture data and the preset moisture threshold range.

[0073] Step 105: Determine the heating status of each layer of grain in the grain pile based on the average temperature difference between layers and the preset heating threshold.

[0074] Step 106: Determine the warning level based on the condensation state, storage suitability, and heat generation status of each layer of grain in the grain pile.

[0075] Optionally, the operation of constructing a feature dataset based on grain condition data and ambient temperature includes:

[0076] Based on the grain condition data, determine the highest temperature, lowest temperature, average temperature, and average moisture content of each layer of grain in the grain pile;

[0077] A feature dataset is constructed based on the highest temperature, lowest temperature, average temperature, average moisture content of each layer of grain in the grain pile, as well as the ambient temperature.

[0078] Optionally, the operation of determining the interlayer temperature difference, intralayer temperature difference, and average interlayer temperature difference of each layer of grain in the grain pile based on the feature dataset includes:

[0079] The ambient temperature difference is determined by subtracting the highest and lowest temperatures of the first layer of grain in the feature dataset from the ambient temperature.

[0080] The interlayer temperature difference is determined by subtracting the highest and lowest temperatures of the grain in other layers of the grain pile from the temperature of the previous layer of grain in the characteristic data. The average temperature difference between the layers is determined by subtracting the average temperature of the grain in the previous layer of grain.

[0081] The temperature difference within a layer is determined by subtracting the highest and lowest temperatures within the grain in other layers of the grain pile from the feature dataset.

[0082] Optionally, the operation of determining the condensation state of each layer of grain in the grain pile based on the feature dataset, interlayer temperature difference, intralayer temperature difference, average interlayer temperature difference, and a pre-set condensation temperature difference threshold range includes:

[0083] When the ambient temperature difference is within the condensation temperature difference threshold range, the condensation state is surface condensation;

[0084] When the temperature difference within the layer is within the condensation temperature difference threshold range, the condensation state is condensation within the layer;

[0085] When the interlayer temperature difference or the average interlayer temperature difference is within the condensation temperature difference threshold range, the condensation state is interlayer condensation;

[0086] When the temperature difference between the layers of grain at the bottom of the grain pile, the average temperature difference between the layers, or the temperature difference within the layers is within the dew-forming temperature threshold range, the dew-forming state is bottom-layer dew.

[0087] Optionally, the survival suitability status includes high risk, risk, survival suitability, and low risk, and the fever status includes fever and no fever.

[0088] Optionally, the operation of determining the warning level based on the condensation state, storage suitability, and heat generation status of each layer of grain in the grain pile includes:

[0089] When the bottom layer of the grain pile is condensed, the storage condition is high-risk or risky, and the grain is heating up, the warning level is determined to be hot grain condensation;

[0090] If the grain pile has condensation at the bottom, is in a low-risk or suitable storage condition, and does not generate heat, the warning level is determined to be suspected condensation.

[0091] If the grain pile has surface condensation, its storage suitability status is suitable for storage, or it is at risk or high risk, and it does not generate heat, the warning level is determined to be surface condensation;

[0092] If the grain pile has surface condensation, its storage condition is deemed suitable, risky, or high-risk, and it is also heating up, the warning level is determined to be high-risk.

[0093] If the grain pile has surface condensation and the storage condition is low risk, the warning level is determined to be suspected condensation;

[0094] If the grain pile exhibits interlayer or intralayer condensation, a storage suitability status of suitable, risky, or high-risk, and is generating heat, the warning level is determined to be high-risk.

[0095] If the grain pile exhibits interlayer or intralayer condensation, and its storage suitability status is deemed suitable, risky, or high-risk, and it does not generate heat, the warning level is determined to be internal condensation.

[0096] If the grain pile shows interlayer or intralayer condensation and the storage condition is low-risk, the warning level is determined to be suspected condensation.

[0097] Specifically, refer to Figure 2 As shown, the system of the present invention includes a data processing module, a data calculation module, a cross-validation module, and an alarm triggering module. Wherein:

[0098] like Figure 3 As shown, the data processing module needs to integrate with the grain industry grain condition data interface to collect grain condition data and extract feature data for calculation and verification.

[0099] like Figure 4 As shown, the data calculation module mainly calculates the extracted feature data, and the result data is used for cross-validation.

[0100] like Figure 5 As shown, the cross-validation module compares and validates various feature data and calculation results data according to specific logic.

[0101] like Figure 6 As shown, the verification results are finally output to the alarm triggering module, which pushes different warning items to the application.

[0102] 1. Data processing module, see Figure 3

[0103] Integrating grain industry data standards, the system collects grain condition data during the grain storage process through a grain condition system. This includes two main categories of data: multi-layer temperature and humidity within the grain pile and ambient temperature in the grain silo. After acquiring the data, it is organized into a feature dataset for easy calculation and comparative verification.

[0104] Feature dataset includes

[0105] Ambient temperature, T

[0106] The highest temperature H, lowest temperature L, average temperature A, and average moisture content W of each layer of the grain pile can be used to form:

[0107] Ambient temperature: T

[0108] Layer 0: 0H, 0L, 0A, 0W

[0109] Floor 1: 1H, 1L, 1A, 1W

[0110] 2 floors: 2H, 2L, 2A, 2W

[0111] ...

[0112] x layers: xH, xL, xA, xW

[0113] Layer 0 is the first layer of the grain pile, and layer x is the bottom layer of the grain pile.

[0114] 2. Data calculation module, see Figure 4

[0115] The most direct causes of mold growth in grain piles are temperature and humidity differences. The compiled data will be used to calculate environmental temperature difference, internal temperature difference, and average temperature difference, and the results will be used for comparison and verification.

[0116] The main method of this invention is to calculate the temperature difference inside each layer of the grain pile and between adjacent layers, and then determine whether the conditions for condensation are met based on the humidity level.

[0117] 1) Calculation of ambient temperature difference

[0118] Temperature difference data is denoted by D. The temperature difference D0 between the surface data and the ambient data is the difference between the highest and lowest surface temperatures and the ambient temperature, i.e.:

[0119] D0-1=|0H-T|

[0120] D0-2=|T-0L|

[0121] 2) Internal temperature difference calculation

[0122] The difference between the highest and lowest temperatures within each layer is calculated layer by layer from the surface downwards.

[0123] D1-1=|1H-1L|

[0124] The difference between the highest and lowest temperatures between layers, i.e.:

[0125] D1-2=|0H-1L|

[0126] D1-3=|1H-0L|

[0127] 3) Calculation of average temperature difference

[0128] The average temperature difference between layers is calculated layer by layer from the surface downwards, i.e.:

[0129] DA1 = |0A-1A|

[0130] In summary, the surface layer generates a temperature difference between the grain surface and the environment; downwards, each layer generates three temperature differences: within the layer itself and between the layer above it; each layer also generates an average temperature difference between itself and the layer above it. There are a total of three sets of temperature difference values.

[0131] 3. Cross-validation module, see Figure 5

[0132] Data verification and comparison methods were used to detect condensation, storage suitability, and calorific value, identifying the different dimensions of the grain's condition at each layer.

[0133] Condensation detection

[0134] The moisture data W recorded in the data processing module for each layer is compared with the environmental difference and internal difference results (D0, D1-1, D1-2, D1-3, D2-1, D2-2, D2-3... Dx-1, Dx-2, Dx-3) in the data calculation module to determine whether the area is in a condensation state, as shown in Table 1.

[0135] Among them, there is a set of differences that fall within the range of condensation difference values, and that layer is in the condensation state.

[0136] Environmental differences trigger condensation, resulting in surface condensation.

[0137] The difference within the layer triggers the condensation state, which is condensation within the layer.

[0138] The interlayer difference triggers condensation, which is interlayer condensation.

[0139] The bottom layer or the difference between layers triggers condensation, which is condensation at the bottom layer.

[0140] Table 1

[0141] Grain moisture Condensation temperature difference W<11％ D≥12℃ 11％≤W＜12％ D≥10℃ 12％≤W＜13％ D≥8℃ 13％≤W＜14％ D≥7℃ 14％≤W＜15％ D≥6℃ 15％≤W＜16％ D≥4℃ 16％≤W＜17％ D≥3℃ 17％≤W＜18％ D≥2℃ 18％≤W D≥1℃

[0142] The survival rate test results are shown in Table 2.

[0143] Using the moisture data W recorded in the data processing module for each layer, the storage suitability of each layer of grain is verified, that is, four storage suitability states are formed: low risk, suitable for storage, risk, and high risk.

[0144] Table 2

[0145]

[0146]

[0147] Fever detection

[0148] The data calculation module calculates the average temperature difference between layers, i.e., DA1, DA2, DA3...DAX, and sets the temperature difference range for the heat output. The default setting for the heat output difference is 5℃. This results in two heating states: heating and no heating.

[0149] 4. Alarm triggering module, see Figure 6

[0150] Based on the condensation status, storage suitability status, and heating status provided by the cross-validation module, the system progressively determines and triggers five alarms: suspected condensation, hot grain condensation, surface condensation, internal condensation, and high risk. See the appendix for specific rules. Figure 6 .

[0151] The system has a data interface function, which pushes alarm results, corresponding feature data and calculation results to the application system.

[0152] The investigation method provided by this invention, combined with manual inspection, can achieve the following effects:

[0153] 1. This invention minimizes reliance on human experience, effectively identifying the risk of condensation in stored grain piles solely through data accumulated in the business system and a special algorithm.

[0154] 2. With the early warning effect provided by this invention, supervisors can conduct on-site warehouse inspections, checks, and patrols in a targeted manner, improving efficiency and reducing costs.

[0155] 3. Improved efficiency and reduced costs in on-site supervision help regulatory agencies allocate limited resources to other areas, thereby improving the overall level of grain reserve management.

[0156] Therefore, this invention provides a grain pile condensation early warning method based on grain condition and environmental data. It is based on the experience of the grain industry, uses grain condition data and characteristic data in the ambient temperature to perform multiple calculations and cross-validation and comparisons to determine whether condensation is likely to occur. It can automatically trigger five types of early warnings: suspected condensation, surface condensation, internal condensation, hot grain condensation, and high risk, providing intelligent decision support for grain storage management.

[0157] Exemplary device

[0158] Figure 7 This is a schematic diagram of the structure of a grain pile condensation early warning device provided in an exemplary embodiment of the present invention. Figure 7 As shown, the device 700 includes:

[0159] The acquisition module 710 is used to acquire grain condition data and ambient temperature of the grain pile under test through the grain condition system.

[0160] The first determining module 720 is used to construct a feature dataset based on grain condition data and ambient temperature, and to determine the interlayer temperature difference, intralayer temperature difference and average interlayer temperature difference of each layer of grain in the grain pile based on the feature dataset.

[0161] The second determining module 730 is used to determine the condensation state of each layer of grain in the grain pile based on the feature dataset, interlayer temperature difference, intralayer temperature difference, average interlayer temperature difference, and a pre-set condensation temperature difference threshold range.

[0162] The third determining module 740 is used to determine the storage suitability status of each layer of grain in the grain pile based on moisture data and a pre-set moisture threshold range.

[0163] The fourth determining module 750 is used to determine the heating state of each layer of grain in the grain pile based on the average temperature difference between layers and the preset heating threshold.

[0164] The fifth determining module 760 is used to determine the warning level based on the condensation state, storage suitability, and heat generation state of each layer of grain in the grain pile.

[0165] Optionally, the first determining module 720 includes:

[0166] The first determination submodule is used to determine the highest temperature, lowest temperature, average temperature and average moisture content of each layer of grain in the grain pile based on the grain condition data.

[0167] A submodule is constructed to build a feature dataset based on the highest temperature, lowest temperature, average temperature, average moisture content of each layer of grain in the grain pile, as well as the ambient temperature.

[0168] Optionally, the first determining module 720 includes:

[0169] The second determination submodule is used to determine the ambient temperature difference by subtracting the highest and lowest temperatures of the first layer of grain in the feature dataset from the ambient temperature.

[0170] The third determination submodule is used to determine the interlayer temperature difference by subtracting the highest and lowest temperatures of other layers of grain in the grain pile from the previous layer of grain in the feature data, and to determine the interlayer average temperature difference by subtracting the average temperature of the grain in the previous layer from the average temperature of the grain in the current layer.

[0171] The fourth determination submodule is used to determine the temperature difference within a layer by subtracting the highest and lowest temperatures within the grain in other layers of the grain pile from the feature dataset.

[0172] Optionally, the second determining module 730 includes:

[0173] The first determination submodule is used to determine the condensation state as surface condensation when the ambient temperature difference is within the condensation temperature difference threshold range.

[0174] The second determination submodule is used to determine the condensation state as condensation within the layer when the temperature difference within the layer is within the condensation temperature difference threshold range.

[0175] The third determination submodule is used to determine the condensation state as interlayer condensation when the interlayer temperature difference or the average interlayer temperature difference is within the condensation temperature difference threshold range.

[0176] The fourth determination submodule is used to determine the condensation state as bottom-layer condensation when the temperature difference between layers, the average temperature difference between layers, or the temperature difference within layers of the grain at the bottom of the grain pile is within the condensation temperature difference threshold range.

[0177] Optionally, the survival suitability status includes high risk, risk, survival suitability, and low risk, and the fever status includes fever and no fever.

[0178] Optionally, the fifth determining module 760 includes:

[0179] The fifth judgment submodule is used to determine the warning level as hot grain condensation when the grain pile has condensation at the bottom, the storage suitability status is high risk or risk and it is heating up.

[0180] The sixth judgment submodule is used to determine the warning level as suspected condensation when the grain pile has condensation at the bottom, the storage condition is low risk or suitable for storage, and there is no heat generation.

[0181] The seventh judgment submodule is used to determine the warning level as surface condensation when the grain pile has surface condensation, the storage suitability status is suitable for storage or risk or high risk, and there is no heat generation.

[0182] The eighth judgment submodule is used to determine the warning level as high risk when the grain pile has surface condensation, the storage condition is suitable for storage or risk or high risk, and it is heating up.

[0183] The ninth judgment submodule is used to determine the warning level as suspected condensation when the grain pile has surface condensation and the storage condition is low risk.

[0184] The tenth judgment submodule is used to determine the warning level as high risk when the grain pile has interlayer or intralayer condensation, the storage suitability status is suitable for storage, risky or high risk, and heat is generated.

[0185] The eleventh judgment submodule is used to determine the warning level as internal condensation when the grain pile has interlayer or intralayer condensation, the storage suitability status is suitable for storage, risk or high risk, and no heat generation.

[0186] The twelfth judgment submodule is used to determine the warning level as suspected condensation when the grain pile has interlayer or intralayer condensation and the storage suitability status is low risk.

[0187] Exemplary electronic devices

[0188] Figure 8 This is the structure of an electronic device provided in an exemplary embodiment of the present invention. For example... Figure 8 As shown, the electronic device 80 includes one or more processors 81 and memory 82.

[0189] The processor 81 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0190] The memory 82 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 81 may execute the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above, and / or other desired functions. In one example, the electronic device may also include an input device 83 and an output device 84, these components being interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0191] In addition, the input device 83 may also include, for example, a keyboard, a mouse, etc.

[0192] The output device 84 can output various information to the outside. The output device 84 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0193] Of course, for the sake of simplicity, Figure 8 Only some of the components of the electronic device relevant to the present invention are shown, omitting components such as buses, input / output interfaces, etc. In addition, the electronic device may include any other suitable components depending on the specific application.

[0194] Exemplary computer program products and computer-readable storage media

[0195] In addition to the methods and apparatus described above, embodiments of the present invention may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above.

[0196] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of the present invention. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0197] Furthermore, embodiments of the present invention may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps of the methods for information mining of historical change records according to various embodiments of the present invention as described in the "Exemplary Methods" section above.

[0198] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0199] The basic principles of the present invention have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in the present invention are merely examples and not limitations, and should not be considered as essential features of each embodiment of the present invention. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the present invention to the necessity of employing the aforementioned specific details.

[0200] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system embodiments, since they largely correspond to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0201] The block diagrams of devices, systems, devices, and systems involved in this invention are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, systems, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0202] The methods and systems of the present invention may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the methods is for illustrative purposes only, and the steps of the methods of the present invention are not limited to the order specifically described above unless otherwise specifically stated. Furthermore, in some embodiments, the present invention may also be implemented as a program recorded on a recording medium, the program comprising machine-readable instructions for implementing the methods according to the present invention. Thus, the present invention also covers recording media storing programs for performing the methods according to the present invention.

[0203] It should also be noted that in the systems, apparatus, and methods of the present invention, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered equivalents of the present invention. The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the invention. Therefore, the invention is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0204] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the invention to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A method for early warning of condensation on grain piles, characterized in that, include: The grain condition data and the ambient temperature of the grain pile to be tested are obtained through the grain condition system. Based on the grain condition data and the ambient temperature, a feature dataset is constructed, and based on the feature dataset, the interlayer temperature difference, intralayer temperature difference, and average interlayer temperature difference of each layer of grain in the grain pile are determined. The condensation state of each layer of grain in the grain pile is determined based on the feature dataset, the interlayer temperature difference, the intralayer temperature difference, the average interlayer temperature difference, and the preset condensation temperature difference threshold range. Based on the moisture data in the feature dataset and the preset moisture threshold range, the storage suitability status of each layer of grain in the grain pile is determined. The heating state of each layer of grain in the grain pile is determined based on the average temperature difference between the layers and the preset heating threshold. The warning level is determined based on the condensation state, storage suitability, and heat generation state of each layer of grain in the grain pile; The operation of determining the condensation state of each layer of grain in the grain pile based on the feature dataset, the interlayer temperature difference, the intralayer temperature difference, the average interlayer temperature difference, and the preset condensation temperature difference threshold range includes: When the ambient temperature difference is within the dew condensation temperature difference threshold range, the dew condensation state is surface dew condensation; When the temperature difference within the layer is within the condensation temperature difference threshold range, the condensation state is condensation within the layer; When the interlayer temperature difference or the average interlayer temperature difference is within the condensation temperature difference threshold range, the condensation state is interlayer condensation. When the temperature difference between layers, the average temperature difference between layers, or the temperature difference within layers of the bottom grains in the grain pile is within the dew condensation temperature difference threshold range, the dew condensation state is bottom layer dew condensation. The survival suitability status includes high risk, risk, survival suitability, and low risk, and the heat generation status includes heat generation and no heat generation; The operation of determining the warning level based on the condensation state, storage suitability state, and heat generation state of each layer of grain in the grain pile includes: If the grain pile has condensation at the bottom, a high or low storage condition, and is heating up, the warning level is determined to be hot grain condensation. If the grain pile has condensation at the bottom layer, is in a low-risk or suitable storage condition, and does not generate heat, the warning level is determined to be suspected condensation. If the grain pile has surface condensation, its storage suitability status is suitable for storage, risky, or high risk, and it does not generate heat, then the warning level is determined to be surface condensation. If the grain pile has surface condensation, its storage condition is deemed suitable for storage, risky, or high-risk, and it is generating heat, then the warning level is determined to be high-risk. If the grain pile has surface condensation and its storage suitability is low-risk, the warning level is determined to be suspected condensation. If the grain pile exhibits interlayer or intralayer condensation, its storage suitability status is deemed suitable, risky, or high-risk, and it generates heat, then the warning level is determined to be high-risk. If the grain pile exhibits interlayer or intralayer condensation, its storage suitability status is deemed suitable, risky, or high-risk, and it does not generate heat, then the warning level is determined to be internal condensation. If the grain pile exhibits interlayer or intralayer condensation and its storage suitability is deemed low-risk, the warning level is determined to be suspected condensation.

2. The method according to claim 1, characterized in that, The operation of constructing a feature dataset based on the grain condition data and the ambient temperature includes: Based on the grain condition data, determine the highest temperature, lowest temperature, average temperature, and average moisture content of each layer of grain in the grain pile; The feature dataset is constructed based on the highest temperature, lowest temperature, average temperature, average moisture content, and ambient temperature of each layer of grain in the grain pile.

3. The method according to claim 2, characterized in that, The operation of determining the interlayer temperature difference, intralayer temperature difference, and average interlayer temperature difference of each layer of grain in the grain pile based on the feature dataset includes: The ambient temperature difference is determined by subtracting the highest and lowest temperatures of the first layer of grain in the feature dataset from the ambient temperature. The interlayer temperature difference is determined by subtracting the highest and lowest temperatures of the other layers of grain in the grain pile from the previous layer of grain in the characteristic data, and the average temperature difference between the average temperature of the grain in the current layer and the average temperature of the grain in the previous layer of grain. The temperature difference within a layer is determined by subtracting the highest and lowest temperatures within the grain in other layers of the grain pile from the feature dataset.

4. A grain pile condensation early warning device, used to implement the method described in any one of claims 1-3, characterized in that, include: The acquisition module is used to acquire grain condition data and ambient temperature of the grain pile under test through the grain condition system. The first determining module is used to construct a feature dataset based on the grain condition data and the ambient temperature, and to determine the interlayer temperature difference, intralayer temperature difference and average interlayer temperature difference of each layer of grain in the grain pile based on the feature dataset. The second determining module is used to determine the condensation state of each layer of grain in the grain pile based on the feature dataset, the interlayer temperature difference, the intralayer temperature difference, the average interlayer temperature difference, and a preset condensation temperature difference threshold range. The third determining module is used to determine the storage suitability status of each layer of grain in the grain pile based on the moisture data in the feature dataset and the preset moisture threshold range. The fourth determining module is used to determine the heating state of each layer of grain in the grain pile based on the average temperature difference between the layers and the preset heating threshold. The fifth determining module is used to determine the warning level based on the condensation state, storage suitability state, and heat generation state of each layer of grain in the grain pile.

5. The apparatus according to claim 4, characterized in that, The first determining module includes: The first determining submodule is used to determine the highest temperature, lowest temperature, average temperature and average moisture content of each layer of grain in the grain pile based on the grain condition data. A submodule is constructed to build the feature dataset based on the highest temperature, lowest temperature, average temperature, average moisture content, and ambient temperature of each layer of grain in the grain pile.

6. A computer-readable storage medium, characterized in that, The storage medium stores a computer program for performing the method described in any one of claims 1-3.

7. An electronic device, characterized in that, The electronic device includes: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method described in any one of claims 1-3.

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