Electronic information identification method and system based on Internet of Things

Abstract

The invention is suitable for the technical field of agricultural Internet of Things, and provides an Internet of Things-based electronic information identification method and system, and the method comprises the steps: obtaining the real-time estimated irrigation water amount, the real-time soil temperature, the current soil permeability and the current soil organic matter content of a target field area; acquiring a preset standard soil temperature corresponding to the target field area, and comparing the real-time soil temperature with the preset standard soil temperature to generate a first correction index; according to the invention, through multi-level dynamic adjustment, accurate management of irrigation water is realized. Firstly, in combination with key factors such as soil temperature, permeability and organic matter content, the system can respond to the difference between external air temperature change and soil internal characteristics in real time, so that flexible irrigation control is kept. The double correction method not only ensures the scientificity of water regulation, but also can effectively prevent excessive irrigation and water waste, and greatly improves the resource utilization efficiency in the environment of water resource shortage.

Description

Technical Field

[0001] The present invention belongs to the technical field of agricultural Internet of Things, and in particular relates to an electronic information identification method and system based on the Internet of Things. Background Art

[0002] Existing agricultural irrigation control systems mostly rely on single or limited environmental data to determine the amount of irrigation water, mainly including basic information such as soil moisture, meteorological data, and crop water demand models. These systems usually monitor soil moisture content in real time based on soil moisture sensors, and trigger irrigation operations when the moisture is lower than the set threshold. Another type of system relies on meteorological data for predictive irrigation, and roughly adjusts the irrigation frequency and amount of water by judging external conditions such as temperature, precipitation, and wind speed. Some intelligent irrigation systems also regulate water volume according to the crop growth cycle model, and implement irrigation at different times according to the typical water demand characteristics of crops. Overall, traditional irrigation control solutions mostly use a single-layer data source, fail to achieve refined irrigation demand assessment, and the system has weak adaptability to changes in the actual environment.

[0003] However, existing technologies still have major deficiencies in accurately controlling irrigation water volume. Relying solely on simple feedback from soil moisture or meteorological data fails to fully consider the dynamic changes in soil water absorption and water retention. Especially under different soil types and crop conditions, changes in soil permeability and organic matter content have a significant impact on water demand, and a single data source may lead to problems such as frequent, excessive or insufficient irrigation. In addition, traditional meteorological-based control methods lack a response mechanism to internal changes in the soil. When soil temperature, permeability and organic matter content fluctuate greatly, traditional systems are difficult to respond in a timely manner, resulting in low water resource utilization, especially in water-scarce environments. The effect is limited. Summary of the invention

[0004] The purpose of the present invention is to provide an electronic information identification method and system based on the Internet of Things, aiming to solve the problems raised in the background technology.

[0005] The present invention is implemented in this way: an electronic information identification method based on the Internet of Things, the method comprising: Obtain real-time estimated irrigation water volume, real-time soil temperature, current soil permeability, and current soil organic matter content for target field areas; Acquiring a preset standard soil temperature corresponding to a target field area, comparing the real-time soil temperature with the preset standard soil temperature, and generating a first correction index; Call the historical irrigation records of the target field area, obtain all soil permeability and soil organic matter content within a preset time period, calculate their respective representative averages, compare the current soil permeability and current soil organic matter content with their representative averages, and combine the two differences to generate a second correction index; The first correction index and the second correction index are sequentially applied to adjust the real-time estimated irrigation water volume to obtain the final actual irrigation water volume.

[0006] As a further limitation of the technical solution of the embodiment of the present invention, the steps of obtaining a preset standard soil temperature corresponding to the target field area, comparing the real-time soil temperature with the preset standard soil temperature, and generating a first correction index include: Obtaining a preset standard soil temperature corresponding to a target field area; The first correction index calculation formula is retrieved, and the first correction index is generated by combining the preset standard soil temperature and the real-time soil temperature.

[0007] As a further limitation of the technical solution of the embodiment of the present invention, the calculation formula of the first correction index is: , where I 第一 Refers to the first correction index, T 实时 Refers to the real-time soil temperature, T 标准 refers to the preset standard soil temperature, and X refers to the lower limit value of the first correction index.

[0008] As a further limitation of the technical solution of the embodiment of the present invention, the steps of calling the historical irrigation records of the target field area, obtaining all soil permeability and soil organic matter content within a preset time period, calculating their respective representative average values, comparing the current soil permeability and the current soil organic matter content with their representative average values, and generating a second correction index by combining the two differences include: Call the historical irrigation records of the target field area to obtain all soil permeability and soil organic matter content within a preset time period; Calculate the average value of all soil permeability and the average value of all soil organic matter content within a preset time period, and set them as the representative average value of soil permeability and the representative average value of soil organic matter content in the target field area, respectively; The current soil permeability and the current soil organic matter content are compared with the representative average soil permeability and the representative average soil organic matter content respectively, and the two differences are combined to generate the second correction index.

[0009] As a further limitation of the technical solution of the embodiment of the present invention, the steps of comparing the current soil permeability and the current soil organic matter content with the representative average value of the soil permeability and the representative average value of the soil organic matter content, and generating a second correction index by combining the two differences include: Obtain the current soil permeability and the current soil organic matter content, and compare them with the representative average values ​​of soil permeability and the representative average values ​​of soil organic matter content, respectively, and calculate the permeability-related deviation rate and the organic matter content-related deviation rate; Assigning weighting coefficients to the permeability-related deviation rate and the organic matter content-related deviation rate, and calculating a weighted combination value based on the weighting coefficients; The weighted combination value is set as a second correction index.

[0010] As a further limitation of the technical solution of the embodiment of the present invention, the steps of sequentially applying the first correction index and the second correction index to adjust the real-time estimated irrigation water volume to obtain the final actual irrigation water volume include: The real-time estimated irrigation water volume is multiplied by the first correction index to obtain the initially adjusted irrigation water volume; The final actual irrigation water volume is obtained by multiplying the initially adjusted irrigation water volume by the second correction index.

[0011] An electronic information identification system based on the Internet of Things, the system comprises a data acquisition module, a first correction index generation module, a second correction index generation module and an actual irrigation water volume calculation module, wherein: A data acquisition module for obtaining the real-time estimated irrigation water volume, real-time soil temperature, current soil permeability, and current soil organic matter content of the target field area; A first correction index generating module, used for obtaining a preset standard soil temperature corresponding to a target field area, comparing the real-time soil temperature with the preset standard soil temperature, and generating a first correction index; The second correction index generation module is used to call the historical irrigation records of the target field area, obtain all soil permeability and soil organic matter content within a preset time period, calculate their respective representative average values, compare the current soil permeability and current soil organic matter content with their representative average values, and generate the second correction index by combining the two differences; The actual irrigation water volume calculation module is used to sequentially apply the first correction index and the second correction index to adjust the real-time estimated irrigation water volume to obtain the final actual irrigation water volume.

[0012] As a further limitation of the technical solution of the embodiment of the present invention, the first correction index generation module specifically includes: A standard temperature acquisition unit, used to acquire a preset standard soil temperature corresponding to a target field area; A first correction index calculation unit, used to retrieve a first correction index calculation formula and generate a first correction index in combination with a preset standard soil temperature and a real-time soil temperature; The calculation formula of the first correction index is: , where I 第一 Refers to the first correction index, T 实时 Refers to the real-time soil temperature, T 标准 refers to the preset standard soil temperature, and X refers to the lower limit value of the first correction index.

[0013] As a further limitation of the technical solution of the embodiment of the present invention, the second correction index generation module specifically includes: A historical data acquisition unit, used to call the historical irrigation records of the target field area and obtain all soil permeability and soil organic matter content within a preset time period; an average value calculation unit, used to calculate the average value of all soil permeability and the average value of all soil organic matter content within a preset time period, which are set as the representative average value of soil permeability and the representative average value of soil organic matter content in the target field area respectively; The second correction index generating unit is used to compare the current soil permeability and the current soil organic matter content with the representative average value of soil permeability and the representative average value of soil organic matter content respectively, and generate the second correction index by combining the two differences.

[0014] As a further limitation of the technical solution of the embodiment of the present invention, the actual irrigation water volume calculation module specifically includes: An irrigation water quantity preliminary adjustment unit, used for multiplying the real-time estimated irrigation water quantity by a first correction index to obtain a preliminary adjusted irrigation water quantity; The actual irrigation water volume generating unit is used to multiply the initially adjusted irrigation water volume by the second correction index to obtain the final actual irrigation water volume.

[0015] Compared with the prior art, the present invention has the following beneficial effects: Through multi-level dynamic adjustment, precise management of irrigation water is achieved. First, by combining key factors such as soil temperature, permeability and organic matter content, the system can respond to external temperature changes and differences in soil internal properties in real time, thereby maintaining flexible irrigation control. The dual correction method not only ensures the scientific nature of water regulation, but also effectively prevents over-irrigation and water waste, greatly improving resource utilization efficiency in an environment with tight water resources.

[0016] Through the real-time data recognition and intelligent correction index of the Internet of Things, the system can adapt to different crops and planting environments, continuously monitor and optimize irrigation plans, and provide stable irrigation effects even under changing climatic conditions. This intelligent, efficient and adaptable solution greatly improves the accuracy of crop water management and is suitable for agricultural planting applications of all sizes. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flowchart of a method provided by an embodiment of the present invention; Figure 2 A flow chart of calculating a first correction index in a method provided in an embodiment of the present invention; Figure 3A flow chart of obtaining a representative average value of soil permeability and a representative average value of soil organic matter content in the method provided in an embodiment of the present invention; Figure 4 A flow chart of calculating a second correction index in a method provided in an embodiment of the present invention; Figure 5 A flow chart of calculating the final actual irrigation water volume in the method provided in an embodiment of the present invention; Figure 6 An application architecture diagram of a system provided by an embodiment of the present invention; Figure 7 A structural block diagram of a first correction index generating module in a system provided by an embodiment of the present invention; Figure 8 A structural block diagram of a second correction index generating module in a system provided by an embodiment of the present invention; Fig. 9 This is a structural block diagram of an actual irrigation water volume calculation module in a system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0020] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0021] Specifically, the electronic information identification method based on the Internet of Things includes the following steps: Step S100, obtaining the real-time estimated irrigation water volume, real-time soil temperature, current soil permeability and current soil organic matter content of the target field area.

[0022] In an embodiment of the present invention, the target field area refers to the agricultural plot or field selected in the Internet of Things monitoring system. The environmental data in the area will be continuously monitored by the system for precise irrigation and resource management. The definition of this area is usually based on the size of the planting land, terrain characteristics or crop distribution, which is convenient for targeted irrigation control and management. Focusing on the real-time environmental data in the target area, the system can dynamically adjust the irrigation demand to make resource allocation more accurate. The electronic information recognition method based on the Internet of Things plays a key role in this process. The data collection and real-time recognition of various sensors enable the system to capture the soil conditions, temperature changes and water requirements in the area, providing data support for precise irrigation.

[0023] The means of obtaining various data depends on the IoT sensors and data processing systems installed in the fields. Real-time estimates of irrigation water volume are usually calculated by the agricultural management platform based on crop type, soil moisture conditions and environmental conditions, and may be dynamically generated in combination with historical data, weather forecasts and other information. Real-time soil temperature is obtained through temperature sensors, which are usually buried at different depths in the soil to capture changes in temperature distribution. The current acquisition of soil permeability relies on soil moisture sensors, which infer permeability by measuring the rate of change of moisture at different depths. At the same time, the data source of soil organic matter content is usually regular soil testing reports or real-time estimates based on sensors, the latter of which may be combined with technologies such as near-infrared spectroscopy. Through high-frequency recognition and dynamic analysis of real-time data, the system can continuously provide refined irrigation adjustment basis to make resource utilization more efficient.

[0024] Furthermore, the electronic information identification method based on the Internet of Things also includes the following steps: Step S200, obtaining a preset standard soil temperature corresponding to the target field area, comparing the real-time soil temperature with the preset standard soil temperature, and generating a first correction index.

[0025] Specifically, Figure 2 A flow chart for calculating the first correction index is shown.

[0026] The process of obtaining a preset standard soil temperature corresponding to the target field area, comparing the real-time soil temperature with the preset standard soil temperature, and generating a first correction index specifically includes the following steps: Step S201, obtaining a preset standard soil temperature corresponding to a target field area; Step S202, retrieve a first correction index calculation formula, and generate a first correction index in combination with a preset standard soil temperature and a real-time soil temperature.

[0027] The calculation formula of the first correction index is: , where I 第一Refers to the first correction index, T 实时 Refers to the real-time soil temperature, T 标准 refers to the preset standard soil temperature, and X refers to the lower limit value of the first correction index.

[0028] In the embodiment of the present invention, the preset standard soil temperature refers to a reference temperature used to evaluate whether the current soil temperature is within a suitable range. Generally, the preset standard soil temperature is set according to the optimal growth temperature of the crop, soil type, seasonal climate conditions, and local long-term climate data. Different crops have different requirements for soil temperature, so the preset standard soil temperature will vary according to the type of crop and the planting season, ensuring that the temperature benchmark is targeted and representative.

[0029] Comparing the real-time soil temperature with the preset standard soil temperature can accurately reflect the deviation between the current ambient temperature and the ideal temperature. The first correction index generated by the calculation formula is used to dynamically adjust the irrigation amount. Its significance lies in flexibly adjusting the irrigation water amount according to the degree of temperature deviation. For example, when the current soil temperature is high, the correction index will guide the system to appropriately increase the irrigation water amount to compensate for water evaporation under high temperature; conversely, the irrigation amount will be appropriately reduced at lower temperatures to avoid excess water. This dynamic adjustment process ensures that irrigation decisions respond to environmental changes and makes water resource allocation more accurate.

[0030] The application of the first correction index is based on the close relationship between temperature and crop water demand. Through dynamic control of index correction, it can ensure that crops get appropriate water supply under different temperature conditions, thereby achieving more refined irrigation management. Such settings not only meet the needs of crop growth, but also effectively save water resources and improve water utilization efficiency in agricultural production.

[0031] The following example illustrates that on a summer day, the standard soil temperature of the tomato crop in the target field area is set to 25 degrees Celsius, which is the most suitable soil temperature for tomatoes during the growing period. During the high temperature period of the day, the system detected that the real-time soil temperature had risen to 30 degrees Celsius, 5 degrees Celsius higher than the standard temperature. At this time, the system generated the first correction index based on the temperature deviation. The specific calculation method is: the temperature deviation rate is "the difference between the current temperature and the standard temperature divided by the standard temperature", and then 1 is added to the temperature deviation rate to obtain the correction index.

[0032] Since the current temperature is 5 degrees Celsius higher than the standard temperature, the temperature deviation rate is 0.2, and the first correction index is 1.2. Assuming that the estimated irrigation water volume in the current area is 10 mm water depth, the system will multiply it by the correction index of 1.2, resulting in an actual irrigation volume of 12 mm water depth. This is a 20% increase over the basic volume, which is equivalent to an appropriate increase in water supply to compensate for water evaporation caused by high temperature and ensure that the roots of tomatoes can still get enough water under high temperature conditions.

[0033] At night, the temperature drops and the real-time soil temperature drops to 20 degrees Celsius, 5 degrees Celsius lower than the standard temperature. The system recalculates the correction index. At this time, the temperature deviation rate is -0.2, and the correction index becomes 0.8. Therefore, the system multiplies the estimated irrigation water volume of 10 mm by 0.8 to obtain an irrigation volume of 8 mm. This reduced water volume adjustment effectively prevents the risk of excessive water retention in low temperature environments at night, avoids root hypoxia due to excessive water, and provides a more suitable water environment for crops.

[0034] The lower limit value X is preferably set between 0.5 and 0.8, which can effectively ensure that the irrigation amount will not be reduced to an unreasonable low level in the case of extremely low temperature or large deviation of soil temperature. The specific value can be fine-tuned according to the water tolerance and temperature sensitivity of the crop.

[0035] The main purpose of setting the lower limit value X is to prevent the correction index from being too low under low temperature or other extreme conditions, avoid a significant reduction in irrigation water, and ensure a minimum water supply. For example, when the soil temperature is significantly lower than the standard temperature, the index calculation may be close to zero, causing the system to reduce the irrigation volume to a very low level or even stop irrigation completely, which is unreasonable. Therefore, setting a lower limit value X can ensure that even under undesirable conditions, crops can still obtain a certain amount of water, avoiding the problem of insufficient water supply caused by extreme temperatures.

[0036] The lower limit value X is set based on crop water tolerance and environmental conditions. For crops with high water requirements (such as tomatoes, cucumbers, etc.), it is appropriate to set X to a higher value, such as 0.8, to ensure that the system can still provide appropriate water even at low temperatures, avoiding the adverse effects of severe water shortage on crop growth. For drought-tolerant crops such as wheat, X can be appropriately lower, close to 0.5, to reduce over-irrigation. In arid areas or areas with high evaporation rates, X is usually set higher to ensure that irrigation is not completely cut off even when the temperature is below the standard; in humid areas or areas with sufficient precipitation, X can be set to a lower value, because even if irrigation is reduced under low temperature conditions, the needs of crops can be met with the help of environmental moisture.

[0037] Furthermore, the electronic information identification method based on the Internet of Things also includes the following steps: Step S300, call the historical irrigation records of the target field area, obtain all soil permeability and soil organic matter content within a preset time period, calculate their respective representative averages, compare the current soil permeability and current soil organic matter content with their representative averages, and combine the two differences to generate a second correction index.

[0038] Specifically, Figure 3 A flow chart for obtaining representative average values ​​of soil permeability and soil organic matter content is shown.

[0039] The historical irrigation records of the target field area are called to obtain all soil permeability and soil organic matter content within a preset time period, calculate their respective representative average values, compare the current soil permeability and current soil organic matter content with their representative average values, and generate the second correction index by combining the two differences. Specifically, the following steps are included: Step S301, calling the historical irrigation records of the target field area to obtain all soil permeability and soil organic matter content within a preset time period; Step S302, calculating the average value of all soil permeability and the average value of all soil organic matter content within a preset time period, and setting them as the representative average value of soil permeability and the representative average value of soil organic matter content of the target field area respectively; Step S303, comparing the current soil permeability and the current soil organic matter content with the representative average soil permeability and the representative average soil organic matter content, respectively, and generating a second correction index by combining the differences between the two.

[0040] In an embodiment of the present invention, historical irrigation records refer to irrigation and soil environmental data collected and accumulated by the Internet of Things system over a long period of time in the target field area, including soil permeability and soil organic matter content within a specific time period. The predetermined time period is usually set according to the growth cycle of the crop, soil characteristics or environmental conditions, such as one month, one quarter or one crop growth stage. Selecting an appropriate time period can ensure that the calculated average value can represent the long-term characteristics of the current soil and effectively reflect the recent change trend of the soil. These historical data are automatically collected by sensors buried in the field and uploaded to the central database for storage, and the system can retrieve them at any time for analysis.

[0041] When calculating the average values ​​of soil permeability and soil organic matter content within a predetermined time period, the system first extracts all relevant data points within that time period from the database. For each indicator, the system accumulates all the measured values ​​within that time period and then divides by the total number of data to obtain the average values ​​of soil permeability and soil organic matter content. After the calculation is completed, these two average values ​​will be set as the "Representative Average Value of Soil Permeability" and "Representative Average Value of Soil Organic Matter Content" for the target field area, which will be used as a benchmark comparison value for the current soil status.

[0042] Using the deviation of current soil permeability and organic matter content from the representative average to generate the second correction index has important regulatory significance. Soil permeability reflects the penetration rate of water in the soil, while soil organic matter content affects the water retention capacity of the soil. The comprehensive deviation of the two helps to judge the soil's absorption and retention characteristics of water. This correction method based on the internal conditions of the soil complements the temperature regulation function of the first correction index. Temperature affects water evaporation and is an external environmental factor, while permeability and organic matter content reflect the internal water dynamics of the soil. This "internal and external" correction method ensures that irrigation decisions can adapt to current temperature changes (first correction index) and are more in line with the actual water absorption and retention capacity of the soil (second correction index).

[0043] This combined correction technology brings unexpected benefits. It can automatically adapt to the dynamic needs of the field under different climatic conditions and soil characteristics, improving the accuracy of irrigation and the efficiency of water resource utilization. When the temperature is high, the system will increase the amount of irrigation, but if the permeability and organic matter content of the soil indicate that it has a high water retention capacity, it will avoid over-irrigation; conversely, it can also reduce unnecessary water supply under low temperature conditions. This multi-layer regulation method provides the irrigation system with highly intelligent regulation capabilities.

[0044] Specifically, Figure 4 A flow chart for calculating the second correction index is shown.

[0045] Among them, comparing the current soil permeability and the current soil organic matter content with the representative average of soil permeability and the representative average of soil organic matter content respectively, and comprehensively generating the second correction index by combining the two differences specifically includes the following steps: Step S3031, obtaining the current soil permeability and the current soil organic matter content, and comparing them with the representative average values ​​of the soil permeability and the representative average values ​​of the soil organic matter content, respectively, to calculate the permeability-related deviation rate and the organic matter content-related deviation rate; Step S3032, assigning weighting coefficients to the permeability-related deviation rate and the organic matter content-related deviation rate, and calculating a weighted combination value based on the weighting coefficients; Step S3033, setting the weighted combination value as the second correction index.

[0046] In the embodiment of the present invention, first, the current soil permeability and the current soil organic matter content are monitored in real time by sensors in the field and uploaded to the management system. These real-time data are compared with the "representative average value of soil permeability" and "representative average value of soil organic matter content" of the target field area to calculate their respective deviation rates.

[0047] In practice, the management system compares the current soil permeability with the representative average and calculates the "permeability-related deviation rate", that is, the deviation between the current value and the representative average, which is used to determine whether the current soil permeability characteristics deviate significantly from the long-term average. Similarly, the current soil organic matter content is compared with its representative average to obtain the "organic matter content-related deviation rate". These two deviation rates can specifically quantify the real-time changes in soil permeability and water retention, providing a basis for the system to adjust irrigation volume.

[0048] Next, the system assigns weighting factors to the deviations associated with permeability and the deviations associated with organic matter content. The weighting factors are usually set based on soil type, crop type, and the weight of the impact of different factors on irrigation water demand. For example, in highly permeable sandy soils, permeability deviations may have a more significant impact on irrigation demand, so the weighting factor for permeability can be higher; while for organic-rich loam, water retention has a greater impact on irrigation demand, so the weighting factor for organic matter content can be relatively high. The choice of weighting factors can be pre-set based on specific field conditions and crop characteristics.

[0049] Once the weighting coefficient is determined, the system will perform a weighted calculation on the two deviation rates based on the weighting coefficient to obtain the final weighted combination value. This weighted combination value becomes the second correction index, which is used to comprehensively reflect the current soil permeability and water retention requirements. The system applies this second correction index to the adjustment of irrigation water volume to ensure that the water supply is more adapted to the dynamic characteristics of the soil.

[0050] Combining the previous example, assume that the tomato crop in a target field needs to ensure adequate water supply under high temperatures in summer. According to the system settings, the representative average soil permeability of the field is 30%, and the representative average soil organic matter content is 5%. The current soil permeability detected is 40%, and the current soil organic matter content has dropped to 3%. In this case: The system first calculates the permeability-related deviation rate. The current soil permeability is 40%, while the representative average is 30%, so the permeability-related deviation rate is calculated as the current permeability minus the representative average, divided by the representative average, which is 0.33%, or a deviation rate of 33%, indicating that the current soil permeability is higher than the average level.

[0051] Next, the organic matter content-related deviation rate is calculated. The current soil organic matter content is 3%, and the representative average is 5%. Therefore, the organic matter content-related deviation rate is calculated by subtracting the representative average from the current organic matter content and then dividing it by the representative average, which is -0.4, or a deviation rate of -40%, indicating that the current soil water retention is lower than the average level.

[0052] Assuming that the system weights the deviation rates of permeability and organic matter content as 0.6 and 0.4 respectively, the weighted combination value is calculated as follows: permeability deviation rate (33%) multiplied by 0.6, plus organic matter deviation rate (-40%) multiplied by 0.4, resulting in a comprehensive weighted value of 3.8%.

[0053] This comprehensive weighted value is set as the second correction index, which further affects the actual irrigation water volume. Since the current soil permeability is high and the organic matter content is low, the system will increase the irrigation volume appropriately to better maintain the appropriate soil moisture.

[0054] Furthermore, the electronic information identification method based on the Internet of Things also includes the following steps: Step S400, applying the first correction index and the second correction index in sequence to adjust the real-time estimated irrigation water volume to obtain the final actual irrigation water volume.

[0055] Specifically, Figure 5 A flow chart for calculating the final actual irrigation water volume is shown.

[0056] The first correction index and the second correction index are sequentially applied to adjust the real-time estimated irrigation water volume to obtain the final actual irrigation water volume, which specifically includes the following steps: Step S401, multiplying the real-time estimated irrigation water volume by the first correction index to obtain a preliminary adjusted irrigation water volume; Step S402: multiply the initially adjusted irrigation water volume by the second correction index to obtain the final actual irrigation water volume.

[0057] In the embodiment of the present invention, in the above example, by calculating the first correction index and the second correction index, we have obtained the correction parameter for adjustment. Assume that the initial real-time estimated irrigation water volume is 10 mm water depth.

[0058] First, the real-time estimated irrigation water volume is multiplied by the first correction index. Based on the previous example, the soil temperature measured during the high temperature period in summer is 30 degrees Celsius, and the standard soil temperature is 25 degrees Celsius, resulting in a first correction index of 1.2. Therefore, the initial adjusted irrigation water volume is calculated as 10 mm multiplied by 1.2, resulting in a water depth of 12 mm.

[0059] Next, the second correction index is applied. Based on the deviation rate of infiltration rate and organic matter content and the calculation of the weighted combination value, the second correction index is 1.038 (i.e., 3.8% deviation). The initial adjusted irrigation water volume of 12 mm is multiplied by the second correction index of 1.038 to calculate the final actual irrigation water volume of 12.456 mm water depth. The system will round off and set the final actual irrigation water volume to 12.5 mm water depth to adapt to the current water demand of the field.

[0060] In summary, the technical solution of the present invention achieves precise management of irrigation water through multi-level dynamic adjustment. First, by combining key factors such as soil temperature, permeability and organic matter content, the system can respond to external temperature changes and differences in soil internal properties in real time, thereby maintaining flexible irrigation control. The dual correction method not only ensures the scientific nature of water regulation, but also effectively prevents over-irrigation and water waste, greatly improving resource utilization efficiency in an environment with tight water resources.

[0061] Through the real-time data recognition and intelligent correction index of the Internet of Things, the system can adapt to different crops and planting environments, continuously monitor and optimize irrigation plans, and provide stable irrigation effects even under changing climatic conditions. This intelligent, efficient and adaptable solution greatly improves the accuracy of crop water management and is suitable for agricultural planting applications of all sizes.

[0062] Furthermore, Figure 6 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0063] Among them, in another preferred embodiment provided by the present invention, the electronic information identification system based on the Internet of Things includes: The data acquisition module 100 is used to obtain the real-time estimated irrigation water volume, real-time soil temperature, current soil permeability and current soil organic matter content of the target field area.

[0064] In an embodiment of the present invention, the target field area refers to the agricultural plot or field selected in the Internet of Things monitoring system. The environmental data in the area will be continuously monitored by the system for precise irrigation and resource management. The definition of this area is usually based on the size of the planting land, terrain characteristics or crop distribution, which is convenient for targeted irrigation control and management. Focusing on the real-time environmental data in the target area, the system can dynamically adjust the irrigation demand to make resource allocation more accurate. The electronic information recognition method based on the Internet of Things plays a key role in this process. The data collection and real-time recognition of various sensors enable the system to capture the soil conditions, temperature changes and water requirements in the area, providing data support for precise irrigation.

[0065] The means of obtaining various data depends on the IoT sensors and data processing systems installed in the fields. Real-time estimates of irrigation water volume are usually calculated by the agricultural management platform based on crop type, soil moisture conditions and environmental conditions, and may be dynamically generated in combination with historical data, weather forecasts and other information. Real-time soil temperature is obtained through temperature sensors, which are usually buried at different depths in the soil to capture changes in temperature distribution. The current acquisition of soil permeability relies on soil moisture sensors, which infer permeability by measuring the rate of change of moisture at different depths. At the same time, the data source of soil organic matter content is usually regular soil testing reports or real-time estimates based on sensors, the latter of which may be combined with technologies such as near-infrared spectroscopy. Through high-frequency recognition and dynamic analysis of real-time data, the system can continuously provide refined irrigation adjustment basis to make resource utilization more efficient.

[0066] Furthermore, the electronic information identification system based on the Internet of Things also includes: The first correction index generating module 200 is used to obtain a preset standard soil temperature corresponding to the target field area, compare the real-time soil temperature with the preset standard soil temperature, and generate a first correction index.

[0067] Specifically, Figure 7 It shows a structural block diagram of the first correction index generating module 200 in the system provided by the embodiment of the present invention.

[0068] Among them, in the preferred embodiment provided by the present invention, the first correction index generating module 200 specifically includes: The standard temperature acquisition unit 201 is used to acquire a preset standard soil temperature corresponding to a target field area; A first correction index calculation unit 202, used to retrieve a first correction index calculation formula and generate a first correction index in combination with a preset standard soil temperature and a real-time soil temperature; The calculation formula of the first correction index is: , where I 第一Refers to the first correction index, T 实时 Refers to the real-time soil temperature, T 标准 refers to the preset standard soil temperature, and X refers to the lower limit value of the first correction index.

[0069] In the embodiment of the present invention, the preset standard soil temperature refers to a reference temperature used to evaluate whether the current soil temperature is within a suitable range. Generally, the preset standard soil temperature is set according to the optimal growth temperature of the crop, soil type, seasonal climate conditions, and local long-term climate data. Different crops have different requirements for soil temperature, so the preset standard soil temperature will vary according to the type of crop and the planting season, ensuring that the temperature benchmark is targeted and representative.

[0070] Comparing the real-time soil temperature with the preset standard soil temperature can accurately reflect the deviation between the current ambient temperature and the ideal temperature. The first correction index generated by the calculation formula is used to dynamically adjust the irrigation amount. Its significance lies in flexibly adjusting the irrigation water amount according to the degree of temperature deviation. For example, when the current soil temperature is high, the correction index will guide the system to appropriately increase the irrigation water amount to compensate for water evaporation under high temperature; conversely, the irrigation amount will be appropriately reduced at lower temperatures to avoid excess water. This dynamic adjustment process ensures that irrigation decisions respond to environmental changes and makes water resource allocation more accurate.

[0071] The application of the first correction index is based on the close relationship between temperature and crop water demand. Through dynamic control of index correction, it can ensure that crops get appropriate water supply under different temperature conditions, thereby achieving more refined irrigation management. Such settings not only meet the needs of crop growth, but also effectively save water resources and improve water utilization efficiency in agricultural production.

[0072] The following example illustrates that on a summer day, the standard soil temperature of the tomato crop in the target field area is set to 25 degrees Celsius, which is the most suitable soil temperature for tomatoes during the growing period. During the high temperature period of the day, the system detected that the real-time soil temperature had risen to 30 degrees Celsius, 5 degrees Celsius higher than the standard temperature. At this time, the system generated the first correction index based on the temperature deviation. The specific calculation method is: the temperature deviation rate is "the difference between the current temperature and the standard temperature divided by the standard temperature", and then 1 is added to the temperature deviation rate to obtain the correction index.

[0073] Since the current temperature is 5 degrees Celsius higher than the standard temperature, the temperature deviation rate is 0.2, and the first correction index is 1.2. Assuming that the estimated irrigation water volume in the current area is 10 mm water depth, the system will multiply it by the correction index of 1.2, resulting in an actual irrigation volume of 12 mm water depth. This is a 20% increase over the basic volume, which is equivalent to an appropriate increase in water supply to compensate for water evaporation caused by high temperature and ensure that the roots of tomatoes can still get enough water under high temperature conditions.

[0074] At night, the temperature drops and the real-time soil temperature drops to 20 degrees Celsius, 5 degrees Celsius lower than the standard temperature. The system recalculates the correction index. At this time, the temperature deviation rate is -0.2, and the correction index becomes 0.8. Therefore, the system multiplies the estimated irrigation water volume of 10 mm by 0.8 to obtain an irrigation volume of 8 mm. This reduced water volume adjustment effectively prevents the risk of excessive water retention in low temperature environments at night, avoids root hypoxia due to excessive water, and provides a more suitable water environment for crops.

[0075] The lower limit value X is preferably set between 0.5 and 0.8, which can effectively ensure that the irrigation amount will not be reduced to an unreasonable low level in the case of extremely low temperature or large deviation of soil temperature. The specific value can be fine-tuned according to the water tolerance and temperature sensitivity of the crop.

[0076] The main purpose of setting the lower limit value X is to prevent the correction index from being too low under low temperature or other extreme conditions, avoid a significant reduction in irrigation water, and ensure a minimum water supply. For example, when the soil temperature is significantly lower than the standard temperature, the index calculation may be close to zero, causing the system to reduce the irrigation volume to a very low level or even stop irrigation completely, which is unreasonable. Therefore, setting a lower limit value X can ensure that even under undesirable conditions, crops can still obtain a certain amount of water, avoiding the problem of insufficient water supply caused by extreme temperatures.

[0077] The lower limit value X is set based on crop water tolerance and environmental conditions. For crops with high water requirements (such as tomatoes, cucumbers, etc.), it is appropriate to set X to a higher value, such as 0.8, to ensure that the system can still provide appropriate water even at low temperatures, avoiding the adverse effects of severe water shortage on crop growth. For drought-tolerant crops such as wheat, X can be appropriately lower, close to 0.5, to reduce over-irrigation. In arid areas or areas with high evaporation rates, X is usually set higher to ensure that irrigation is not completely cut off even when the temperature is below the standard; in humid areas or areas with sufficient precipitation, X can be set to a lower value, because even if irrigation is reduced under low temperature conditions, the needs of crops can be met with the help of environmental moisture.

[0078] Furthermore, the electronic information identification system based on the Internet of Things also includes: The second correction index generation module 300 is used to call the historical irrigation records of the target field area, obtain all soil permeability and soil organic matter content within a preset time period, calculate their respective representative average values, compare the current soil permeability and the current soil organic matter content with their representative average values, and generate a second correction index by combining the two differences.

[0079] Specifically, Figure 8 It shows a structural block diagram of the second correction index generating module 300 in the system provided by the embodiment of the present invention.

[0080] In a preferred embodiment of the present invention, the second correction index generating module 300 specifically includes: The historical data acquisition unit 301 is used to call the historical irrigation records of the target field area to obtain all soil permeability and soil organic matter content within a preset time period; An average value calculation unit 302 is used to calculate the average value of all soil permeabilities and the average value of all soil organic matter contents within a preset time period, which are set as the representative average value of soil permeability and the representative average value of soil organic matter content in the target field area respectively; The second correction index generating unit 303 is used to compare the current soil permeability and the current soil organic matter content with the representative average value of soil permeability and the representative average value of soil organic matter content respectively, and generate a second correction index by combining the differences between the two.

[0081] In an embodiment of the present invention, historical irrigation records refer to irrigation and soil environmental data collected and accumulated by the Internet of Things system over a long period of time in the target field area, including soil permeability and soil organic matter content within a specific time period. The predetermined time period is usually set according to the growth cycle of the crop, soil characteristics or environmental conditions, such as one month, one quarter or one crop growth stage. Selecting an appropriate time period can ensure that the calculated average value can represent the long-term characteristics of the current soil and effectively reflect the recent change trend of the soil. These historical data are automatically collected by sensors buried in the field and uploaded to the central database for storage, and the system can retrieve them at any time for analysis.

[0082] When calculating the average values ​​of soil permeability and soil organic matter content within a predetermined time period, the system first extracts all relevant data points within that time period from the database. For each indicator, the system accumulates all the measured values ​​within that time period and then divides by the total number of data to obtain the average values ​​of soil permeability and soil organic matter content. After the calculation is completed, these two average values ​​will be set as the "Representative Average Value of Soil Permeability" and "Representative Average Value of Soil Organic Matter Content" for the target field area, which will be used as a benchmark comparison value for the current soil status.

[0083] Using the deviation of current soil permeability and organic matter content from the representative average to generate the second correction index has important regulatory significance. Soil permeability reflects the penetration rate of water in the soil, while soil organic matter content affects the water retention capacity of the soil. The comprehensive deviation of the two helps to judge the soil's absorption and retention characteristics of water. This correction method based on the internal conditions of the soil complements the temperature regulation function of the first correction index. Temperature affects water evaporation and is an external environmental factor, while permeability and organic matter content reflect the internal water dynamics of the soil. This "internal and external" correction method ensures that irrigation decisions can adapt to current temperature changes (first correction index) and are more in line with the actual water absorption and retention capacity of the soil (second correction index).

[0084] This combined correction technology brings unexpected benefits. It can automatically adapt to the dynamic needs of the field under different climatic conditions and soil characteristics, improving the accuracy of irrigation and the efficiency of water resource utilization. When the temperature is high, the system will increase the amount of irrigation, but if the permeability and organic matter content of the soil indicate that it has a high water retention capacity, it will avoid over-irrigation; conversely, it can also reduce unnecessary water supply under low temperature conditions. This multi-layer regulation method provides the irrigation system with highly intelligent regulation capabilities.

[0085] First, the current soil permeability and current soil organic matter content are monitored in real time by sensors in the field and uploaded to the management system. These real-time data are compared with the "representative average soil permeability" and "representative average soil organic matter content" of the target field area to calculate their respective deviation rates.

[0086] In practice, the management system compares the current soil permeability with the representative average and calculates the "permeability-related deviation rate", that is, the deviation between the current value and the representative average, which is used to determine whether the current soil permeability characteristics deviate significantly from the long-term average. Similarly, the current soil organic matter content is compared with its representative average to obtain the "organic matter content-related deviation rate". These two deviation rates can specifically quantify the real-time changes in soil permeability and water retention, providing a basis for the system to adjust irrigation volume.

[0087] Next, the system assigns weighting factors to the deviations associated with permeability and the deviations associated with organic matter content. The weighting factors are usually set based on soil type, crop type, and the weight of the impact of different factors on irrigation water demand. For example, in highly permeable sandy soils, permeability deviations may have a more significant impact on irrigation demand, so the weighting factor for permeability can be higher; while for organic-rich loam, water retention has a greater impact on irrigation demand, so the weighting factor for organic matter content can be relatively high. The choice of weighting factors can be pre-set based on specific field conditions and crop characteristics.

[0088] Once the weighting coefficient is determined, the system will perform a weighted calculation on the two deviation rates based on the weighting coefficient to obtain the final weighted combination value. This weighted combination value becomes the second correction index, which is used to comprehensively reflect the current soil permeability and water retention requirements. The system applies this second correction index to the adjustment of irrigation water volume to ensure that the water supply is more adapted to the dynamic characteristics of the soil.

[0089] Combining the previous example, assume that the tomato crop in a target field needs to ensure adequate water supply under high temperatures in summer. According to the system settings, the representative average soil permeability of the field is 30%, and the representative average soil organic matter content is 5%. The current soil permeability detected is 40%, and the current soil organic matter content has dropped to 3%. In this case: The system first calculates the permeability-related deviation rate. The current soil permeability is 40%, while the representative average is 30%, so the permeability-related deviation rate is calculated as the current permeability minus the representative average, divided by the representative average, which is 0.33%, or a deviation rate of 33%, indicating that the current soil permeability is higher than the average level.

[0090] Next, the organic matter content-related deviation rate is calculated. The current soil organic matter content is 3%, and the representative average is 5%. Therefore, the organic matter content-related deviation rate is calculated by subtracting the representative average from the current organic matter content and then dividing it by the representative average, which is -0.4, or a deviation rate of -40%, indicating that the current soil water retention is lower than the average level.

[0091] Assuming that the system weights the deviation rates of permeability and organic matter content as 0.6 and 0.4 respectively, the weighted combination value is calculated as follows: permeability deviation rate (33%) multiplied by 0.6, plus organic matter deviation rate (-40%) multiplied by 0.4, resulting in a comprehensive weighted value of 3.8%.

[0092] This comprehensive weighted value is set as the second correction index, which further affects the actual irrigation water volume. Since the current soil permeability is high and the organic matter content is low, the system will increase the irrigation volume appropriately to better maintain the appropriate soil moisture.

[0093] Furthermore, the electronic information identification system based on the Internet of Things also includes: The actual irrigation water volume calculation module 400 is used to sequentially apply the first correction index and the second correction index to adjust the real-time estimated irrigation water volume to obtain the final actual irrigation water volume.

[0094] Specifically, Fig. 9 It shows a structural block diagram of the actual irrigation water volume calculation module 400 in the system provided by the embodiment of the present invention.

[0095] Among them, in the preferred embodiment provided by the present invention, the actual irrigation water volume calculation module 400 specifically includes: The irrigation water volume preliminary adjustment unit 401 is used to multiply the real-time estimated irrigation water volume by the first correction index to obtain the preliminary adjusted irrigation water volume; The actual irrigation water volume generating unit 402 is used to multiply the initially adjusted irrigation water volume by the second correction index to obtain the final actual irrigation water volume.

[0096] In the embodiment of the present invention, in the above example, by calculating the first correction index and the second correction index, we have obtained the correction parameter for adjustment. Assume that the initial real-time estimated irrigation water volume is 10 mm water depth.

[0097] First, the real-time estimated irrigation water volume is multiplied by the first correction index. Based on the previous example, the soil temperature measured during the high temperature period in summer is 30 degrees Celsius, and the standard soil temperature is 25 degrees Celsius, resulting in a first correction index of 1.2. Therefore, the initial adjusted irrigation water volume is calculated as 10 mm multiplied by 1.2, resulting in a water depth of 12 mm.

[0098] Next, the second correction index is applied. Based on the deviation rate of infiltration rate and organic matter content and the calculation of the weighted combination value, the second correction index is 1.038 (i.e., 3.8% deviation). The initial adjusted irrigation water volume of 12 mm is multiplied by the second correction index of 1.038 to calculate the final actual irrigation water volume of 12.456 mm water depth. The system will round off and set the final actual irrigation water volume to 12.5 mm water depth to adapt to the current water demand of the field.

[0099] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0100] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0101] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0102] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0103] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An electronic information identification method based on the Internet of Things, characterized in that: The method comprises: Obtain real-time estimated irrigation water volume, real-time soil temperature, current soil permeability, and current soil organic matter content for target field areas; Acquiring a preset standard soil temperature corresponding to a target field area, comparing the real-time soil temperature with the preset standard soil temperature, and generating a first correction index; Call the historical irrigation records of the target field area, obtain all soil permeability and soil organic matter content within a preset time period, calculate their respective representative averages, compare the current soil permeability and current soil organic matter content with their representative averages, and combine the two differences to generate a second correction index; The first correction index and the second correction index are sequentially applied to adjust the real-time estimated irrigation water volume to obtain the final actual irrigation water volume.

2. The method for identifying electronic information based on the Internet of Things according to claim 1, characterized in that: The steps of obtaining a preset standard soil temperature corresponding to the target field area, comparing the real-time soil temperature with the preset standard soil temperature, and generating a first correction index include: Obtaining a preset standard soil temperature corresponding to a target field area; The first correction index calculation formula is retrieved, and the first correction index is generated by combining the preset standard soil temperature and the real-time soil temperature.

3. The method for identifying electronic information based on the Internet of Things according to claim 2, characterized in that: The calculation formula of the first correction index is: , where I 第一 Refers to the first correction index, T 实时 Refers to the real-time soil temperature, T 标准 refers to the preset standard soil temperature, and X refers to the lower limit value of the first correction index.

4. The method for identifying electronic information based on the Internet of Things according to claim 1, characterized in that: The steps of calling the historical irrigation records of the target field area, obtaining all soil permeability and soil organic matter content within a preset time period, calculating their respective representative average values, comparing the current soil permeability and the current soil organic matter content with their representative average values, and synthesizing the two differences to generate the second correction index include: Call the historical irrigation records of the target field area to obtain all soil permeability and soil organic matter content within a preset time period; Calculate the average value of all soil permeability and the average value of all soil organic matter content within a preset time period, and set them as the representative average value of soil permeability and the representative average value of soil organic matter content in the target field area, respectively; The current soil permeability and the current soil organic matter content are compared with the representative average soil permeability and the representative average soil organic matter content respectively, and the two differences are combined to generate the second correction index.

5. The method for identifying electronic information based on the Internet of Things according to claim 4, characterized in that: The steps of comparing the current soil permeability and the current soil organic matter content with the representative average values ​​of soil permeability and soil organic matter content, respectively, and synthesizing the two differences to generate the second correction index include: Obtain the current soil permeability and the current soil organic matter content, and compare them with the representative average values ​​of soil permeability and the representative average values ​​of soil organic matter content, respectively, and calculate the permeability-related deviation rate and the organic matter content-related deviation rate; Assign weighting coefficients to the permeability-related deviation rate and the organic matter content-related deviation rate, and calculate a weighted combination value based on the weighting coefficients; The weighted combination value is set as a second correction index.

6. The method for identifying electronic information based on the Internet of Things according to claim 1, characterized in that: The steps of sequentially applying the first correction index and the second correction index to adjust the real-time estimated irrigation water volume to obtain the final actual irrigation water volume include: The real-time estimated irrigation water volume is multiplied by the first correction index to obtain the initially adjusted irrigation water volume; The final actual irrigation water volume is obtained by multiplying the initially adjusted irrigation water volume by the second correction index.

7. The electronic information identification system based on the Internet of Things is characterized by: The system comprises a data acquisition module, a first correction index generation module, a second correction index generation module and an actual irrigation water volume calculation module, wherein: A data acquisition module for obtaining the real-time estimated irrigation water volume, real-time soil temperature, current soil permeability, and current soil organic matter content of the target field area; A first correction index generating module is used to obtain a preset standard soil temperature corresponding to a target field area, compare the real-time soil temperature with the preset standard soil temperature, and generate a first correction index; The second correction index generation module is used to call the historical irrigation records of the target field area, obtain all soil permeability and soil organic matter content within a preset time period, calculate their respective representative average values, compare the current soil permeability and current soil organic matter content with their representative average values, and generate the second correction index by combining the two differences; The actual irrigation water volume calculation module is used to sequentially apply the first correction index and the second correction index to adjust the real-time estimated irrigation water volume to obtain the final actual irrigation water volume.

8. The electronic information identification system based on the Internet of Things according to claim 7 is characterized in that: The first correction index generation module specifically includes: A standard temperature acquisition unit, used to acquire a preset standard soil temperature corresponding to a target field area; A first correction index calculation unit, used to retrieve a first correction index calculation formula and generate a first correction index in combination with a preset standard soil temperature and a real-time soil temperature; The calculation formula of the first correction index is: , where I 第一 Refers to the first correction index, T 实时 Refers to the real-time soil temperature, T 标准 refers to the preset standard soil temperature, and X refers to the lower limit value of the first correction index.

9. The electronic information identification system based on the Internet of Things according to claim 8 is characterized in that: The second correction index generating module specifically includes: A historical data acquisition unit, used to call the historical irrigation records of the target field area and obtain all soil permeability and soil organic matter content within a preset time period; an average value calculation unit, used to calculate the average value of all soil permeability and the average value of all soil organic matter content within a preset time period, which are set as the representative average value of soil permeability and the representative average value of soil organic matter content in the target field area respectively; The second correction index generating unit is used to compare the current soil permeability and the current soil organic matter content with the representative average value of soil permeability and the representative average value of soil organic matter content respectively, and generate the second correction index by combining the two differences.

10. The electronic information identification system based on the Internet of Things according to claim 9 is characterized in that: The actual irrigation water volume calculation module specifically includes: An irrigation water quantity preliminary adjustment unit, used for multiplying the real-time estimated irrigation water quantity by a first correction index to obtain a preliminary adjusted irrigation water quantity; The actual irrigation water volume generating unit is used to multiply the initially adjusted irrigation water volume by the second correction index to obtain the final actual irrigation water volume.

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