A networked pipeline irrigation control system for smart agriculture

Abstract

The application relates to the field of agricultural irrigation technology, and particularly discloses a networked pipeline irrigation control system for intelligent agriculture, which specifically comprises a data acquisition module: real-time monitoring of soil humidity data, data analysis, and output of humidity anomaly judgment value; wherein the humidity anomaly judgment value is obtained by analyzing the soil humidity data, calculating an abnormal area ratio MZ, a humidity extreme value ratio total value JZ, and an abnormal time ratio SZ. Through real-time monitoring and data analysis, the application can timely find abnormal conditions of soil humidity, judge abnormal influencing factors, locate abnormal causes, and adjust irrigation strategies according to the abnormal degree, thereby effectively avoiding the problems of excessive irrigation or insufficient irrigation, helping to improve the yield and quality of crops, significantly saving water resources, promoting the sustainable development of agricultural production, reducing the frequency and difficulty of manual intervention, and providing strong technical support for the development of intelligent agriculture.

Description

TECHNICAL FIELD

[0001] The application relates to the field of agricultural irrigation technology, and particularly relates to a network-connected pipeline irrigation control system for intelligent agriculture. BACKGROUND

[0002] With the global water resources becoming increasingly scarce and the acceleration of the agricultural modernization process, how to efficiently and accurately manage the farmland irrigation system, ensure the water supply required for crop growth, and reduce water waste has become a key problem to be solved in the current agricultural field. In recent years, with the rapid development of technologies such as the Internet of Things, big data, and artificial intelligence, the concept of intelligent agriculture has gradually emerged, providing a new solution for farmland irrigation management. Intelligent agriculture realizes real-time monitoring and data analysis of farmland environment by integrating technologies such as sensors, network communication, and cloud computing, making precise irrigation possible.

[0003] However, most of the existing intelligent agricultural irrigation systems still remain at the level of data collection and simple display, and the depth analysis and intelligent response mechanism of soil humidity abnormality are insufficient. The ability to flexibly adjust the irrigation strategy according to the actual situation is poor, which may affect the yield and quality of crops.

[0004] In view of this, the application provides a network-connected pipeline irrigation control system for intelligent agriculture. SUMMARY

[0005] The application aims to provide a network-connected pipeline irrigation control system for intelligent agriculture to solve the technical problems in the background.

[0006] The application can be achieved by the following technical solutions.

[0007] The application provides a network-connected pipeline irrigation control system for intelligent agriculture, which specifically comprises:

[0008] The data acquisition module: real-time monitoring of soil humidity data and data analysis, output of humidity abnormality judgment value;

[0009] The humidity abnormality judgment value is obtained by analyzing the soil humidity data, calculating the abnormal area ratio MZ, the humidity extreme value ratio total value JZ, and the abnormal time ratio SZ, and calculating the humidity abnormality judgment value SP according to the abnormal area ratio MZ, the humidity extreme value ratio total value JZ, and the abnormal time ratio SZ.

[0010] The humidity abnormality judgment module: obtaining the output humidity abnormality judgment value and judging the soil humidity state of the analysis period;

[0011] The soil humidity state includes generating a high humidity abnormality degree signal.

[0012] Abnormal correlation analysis module: based on the high humidity abnormal degree signal, the abnormal irrigation amount is obtained, and data analysis is carried out, and the synchronous correlation value is output;

[0013] Abnormal correlation judgment module: compare the synchronous correlation value with the synchronous correlation threshold value, judge the synchronous correlation degree;

[0014] Among them, the synchronous correlation degree includes generating a synchronous correlation signal;

[0015] Irrigation optimization module: based on the synchronous correlation signal, calculate the optimization coefficient, and optimize and adjust the irrigation amount.

[0016] As a further scheme of the application: the abnormal judgment value acquisition process is:

[0017] Obtain the abnormal area ratio MZ, the humidity extreme value ratio total value JZ and the abnormal time ratio SZ;

[0018] Substitute the abnormal area ratio MZ, the humidity extreme value ratio total value JZ and the abnormal time ratio SZ into the formula The humidity abnormality judgment value SP is calculated, wherein a1, a2 and a3 are preset proportion coefficients.

[0019] As a further scheme of the application: the abnormal area ratio, humidity extreme value ratio total value and abnormal time ratio acquisition process is:

[0020] Preset analysis period, obtain the humidity abnormal area and soil humidity value;

[0021] In each analysis period, the area value of all humidity abnormal areas is obtained, and is summed to obtain the total value of the humidity abnormal area, and is compared with the area standard value to obtain the humidity abnormal area ratio MZ;

[0022] In each analysis period, the soil humidity extreme value of each humidity abnormal area is extracted, the soil humidity extreme value is compared with the soil humidity standard value, the absolute value of the difference value is taken, and the difference value is compared with the soil humidity standard value to obtain the humidity extreme value ratio, the soil humidity extreme value ratio of all humidity abnormal areas is summed to obtain the total value of the humidity extreme value ratio JZ;

[0023] In each analysis period, the time value of each humidity abnormal area is extracted, and is summed to obtain the total value of the humidity abnormal time, and the total value of the humidity abnormal time is compared with the total value of the analysis period to obtain the humidity abnormal time ratio SZ.

[0024] As a further scheme of the application: the humidity abnormal area and soil humidity value acquisition process is:

[0025] The soil humidity value is monitored in real time by the soil humidity monitoring equipment;

[0026] Establish a Cartesian coordinate system with time as the X-axis and soil moisture as the Y-axis, and plot the real-time soil moisture change curve.

[0027] Two standard lines parallel to the X-axis are drawn, with the maximum and minimum values ​​of the soil moisture threshold range as the limits, and are marked as the first moisture standard line and the second moisture standard line, respectively.

[0028] Areas where the real-time soil moisture change curve lies above the first humidity standard line and below the second humidity standard line are marked as humidity abnormal areas.

[0029] As a further aspect of the present invention: the process of generating the high humidity anomaly signal is as follows:

[0030] Obtain the humidity anomaly judgment value and compare it with the humidity anomaly judgment threshold;

[0031] If the humidity anomaly judgment value is greater than the humidity anomaly judgment threshold, a high humidity anomaly signal is generated.

[0032] As a further aspect of the present invention: the process of obtaining the synchronization association value is as follows:

[0033] Obtain the proportion of sub-curves in the same direction (TX), the mean slope deviation (XJ), and the proportion of approximate sub-curves (JS).

[0034] Substitute the proportion of the same-direction subcurve group (TX), the mean slope deviation (XJ), and the proportion of the approximate subcurve group (JS) into the formula. The synchronization correlation value GL is calculated, where s1, s2, and s3 are weight coefficients.

[0035] As a further aspect of the present invention: the process for obtaining the proportion of the same-direction sub-curve group is as follows:

[0036] Based on the high humidity anomaly signal, the analysis period corresponding to the high humidity anomaly signal is obtained and marked as the abnormal period;

[0037] Extract the irrigation amount corresponding to the abnormal period, and plot the irrigation amount change curve with time as the X-axis and irrigation amount as the Y-value;

[0038] The abnormal period was divided into several abnormal sub-periods. The soil moisture change sub-curve and irrigation quantum curve for each abnormal sub-period were obtained, and the slope value of each soil moisture change sub-curve and irrigation quantum curve was calculated.

[0039] The soil moisture change sub-curve and irrigation quantum curve corresponding to the same abnormal sub-period are marked as a sub-curve group;

[0040] Extract sub-curve groups with the same slope direction, mark them as sub-curve groups with the same direction, count the number of sub-curve groups with the same direction, and calculate the ratio of the number of sub-curve groups with the total number of sub-curve groups to obtain the proportion of sub-curve groups with the same direction (TX).

[0041] Among them, sub-curve groups with the same slope direction represent sub-curve groups with the same positive slope or the same negative slope.

[0042] Extract the slope values ​​corresponding to the same-direction sub-curve group and calculate the difference. Take the absolute value of the difference to obtain the slope deviation value. Sum the slope deviation values ​​of all the same-direction sub-curves and take the average value to obtain the average slope deviation value XJ.

[0043] Compare each slope deviation value with the mean slope deviation. If the slope deviation value is greater than or equal to the mean slope deviation, it is marked as a differential sub-curve group. If the slope deviation value is less than the mean slope deviation, it is marked as an approximate sub-curve group.

[0044] The number of approximate sub-curve groups is counted, and the ratio of this number to the number of sub-curves in the same direction is calculated to obtain the proportion of approximate sub-curve groups (JS).

[0045] As a further aspect of the present invention: the process of obtaining the synchronization correlation signal is as follows:

[0046] Obtain the synchronization association value and compare it with the synchronization association threshold;

[0047] If the synchronization correlation value is greater than the synchronization correlation threshold, a synchronization correlation signal is generated.

[0048] As a further aspect of the present invention: the process of obtaining the optimization coefficients is as follows:

[0049] Based on the synchronous correlation signal, the corresponding humidity anomaly judgment value is obtained. The difference between the humidity anomaly judgment value and the humidity anomaly judgment threshold is calculated, and the ratio of the difference to the humidity anomaly judgment value is processed to obtain the optimization coefficient.

[0050] As a further aspect of the present invention: the process of optimizing and adjusting the irrigation amount is as follows:

[0051] Obtain the current irrigation volume, multiply the irrigation volume with the optimization coefficient, and obtain the optimized adjustment value;

[0052] Extract the soil moisture values ​​corresponding to the abnormal period, sum them and take the average to obtain the soil moisture adjustment value, and compare the soil moisture adjustment value with the soil moisture standard value.

[0053] If the soil moisture adjustment value is less than the soil moisture standard value, the current irrigation amount is summed with the optimized adjustment value to obtain the irrigation target value.

[0054] If the soil moisture adjustment value is greater than the soil moisture standard value, the difference between the current irrigation amount and the optimized adjustment value is calculated to obtain the irrigation target value.

[0055] The beneficial effects of this invention are:

[0056] This invention, through real-time monitoring and data analysis, can promptly detect abnormal soil moisture conditions, identify the influencing factors, pinpoint the causes of the abnormalities, and adjust irrigation strategies according to the degree of abnormality. This effectively avoids problems of over-irrigation or under-irrigation, not only helping to improve crop yield and quality but also significantly saving water resources and promoting the sustainable development of agricultural production. At the same time, it reduces the frequency and difficulty of manual intervention, providing strong technical support for the development of smart agriculture. Attached Figure Description

[0057] The invention will now be further described with reference to the accompanying drawings.

[0058] Figure 1 This is a flowchart of a smart agriculture networked pipeline irrigation control system according to the present invention. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] Example 1:

[0061] Please see Figure 1 As shown in the embodiment of the present invention, a smart agricultural network-connected pipeline irrigation control system specifically includes:

[0062] Data acquisition module: Real-time monitoring of soil moisture data, data analysis, and output of moisture anomaly judgment values;

[0063] In some embodiments, soil moisture values ​​are monitored in real time by a soil moisture monitoring device, wherein the soil moisture monitoring device includes, but is not limited to, a soil moisture sensor;

[0064] Establish a Cartesian coordinate system with time as the X-axis and soil moisture as the Y-axis, and plot the real-time soil moisture change curve.

[0065] Two standard lines parallel to the X-axis are drawn, with the maximum and minimum values ​​of the soil moisture threshold range as the limits, and are marked as the first moisture standard line and the second moisture standard line, respectively.

[0066] The soil moisture threshold range was set by those skilled in the art based on historical experimental data.

[0067] Areas where the real-time soil moisture change curve lies above the first humidity standard line and below the second humidity standard line are marked as humidity abnormal areas.

[0068] The area where the real-time soil moisture change curve lies between the first and second humidity standard lines is marked as the normal humidity area.

[0069] The analysis period is preset, and the analysis period is set by those skilled in the art based on experience and analysis needs;

[0070] In each analysis period, the area values ​​of all humidity anomaly areas are obtained and summed to obtain the total humidity anomaly area value. This total area value is then compared with the area standard value to obtain the humidity anomaly area ratio MZ.

[0071] It should be noted that the calculation process of the area standard value is as follows: the difference between the end time and the start time of the analysis period is calculated to obtain the time difference; the difference between the maximum and minimum values ​​of the soil moisture threshold range is calculated to obtain the moisture standard deviation; and the product of the moisture standard deviation and the time difference is calculated to obtain the area standard value.

[0072] In each analysis period, the extreme values ​​of soil moisture in each area with abnormal humidity are extracted. The difference between the extreme values ​​of soil moisture and the standard values ​​of soil moisture is calculated. The absolute value of the difference is taken and the ratio with the standard values ​​of soil moisture is calculated to obtain the extreme value ratio of humidity. The extreme value ratios of soil moisture in all areas with abnormal humidity are summed to obtain the total extreme value ratio of humidity JZ.

[0073] It should be explained that if the humidity anomaly area is located above the first humidity standard line, the extreme value of soil humidity is the maximum value of soil humidity in that area; if the temperature anomaly area is located below the second humidity standard line, the extreme value of soil humidity is the minimum value of soil humidity in that area.

[0074] The standard value for soil moisture is the average of the maximum and minimum values ​​within the soil moisture threshold range;

[0075] In each analysis period, the time value of each humidity anomaly area is extracted and summed to obtain the total humidity anomaly time value. The ratio of the total humidity anomaly time value to the total time value of the analysis period is calculated to obtain the humidity anomaly time ratio SZ.

[0076] Substitute the abnormal area ratio MZ, the total extreme humidity ratio JZ, and the abnormal time ratio SZ into the formula. The humidity anomaly judgment value SP is calculated, where a1, a2, and a3 are preset proportional coefficients, with a1 being 1.374, a2 being 1.268, and a3 being 1.358.

[0077] It should be explained that the meaning of the humidity anomaly judgment value is as follows: The humidity anomaly judgment value is a comprehensive quantitative indicator of the abnormal state of soil moisture. It can more comprehensively and accurately assess the abnormal state of soil moisture. Among them, the abnormal area ratio reflects the proportion of the area of ​​soil moisture that deviates from the normal range during the analysis period to the normal area. The larger the abnormal area ratio, the more abnormal the soil moisture. The total value of the humidity extreme value ratio reflects the sum of the deviations between the extreme values ​​(maximum or minimum values) of soil moisture that deviate from the normal range during the analysis period and the standard value of soil moisture. The larger the humidity extreme value ratio, the more abnormal the soil moisture. The abnormal time ratio reflects the proportion of the time that soil moisture is in an abnormal state during the analysis period to the entire analysis period. The larger the abnormal time ratio, the more abnormal the soil moisture.

[0078] Humidity anomaly detection module: acquires the output humidity anomaly detection value and determines the soil moisture status during the analysis period;

[0079] Among them, soil moisture status includes signals with low degree of moisture anomaly and signals with high degree of moisture anomaly.

[0080] In some embodiments, a humidity anomaly judgment value is obtained, and the humidity anomaly judgment value is compared with a humidity anomaly judgment threshold. The humidity anomaly judgment threshold is set by a person skilled in the art based on historical experimental data and is used to judge the degree of humidity anomaly.

[0081] If the humidity anomaly judgment value is less than or equal to the humidity anomaly judgment threshold, then the soil humidity anomaly level is low during the analysis period, and a low humidity anomaly level signal is generated.

[0082] If the humidity anomaly judgment value is greater than the humidity anomaly judgment threshold, then the soil humidity anomaly level is relatively high during the analysis period, generating a high humidity anomaly level signal.

[0083] The technical solution of this invention mainly involves: monitoring soil moisture data in real time through a data acquisition module and performing in-depth analysis to calculate an abnormal moisture judgment value. This value comprehensively considers the area, extreme value, and duration of the abnormal moisture region, thereby comprehensively and accurately assessing the abnormal soil moisture situation. This allows for timely understanding of the abnormal soil moisture situation, and the development of optimization strategies based on the abnormal situation to improve crop yield and quality.

[0084] Example 2:

[0085] Based on Example 1, please refer to Figure 1As shown in the embodiment of the present invention, a smart agricultural networked pipeline irrigation control system further includes:

[0086] Anomaly Correlation Analysis Module: Based on the high degree of humidity anomaly signal, it obtains the values ​​of anomaly-related influencing parameters, performs data analysis, and outputs synchronous correlation values;

[0087] Among them, the abnormal related impact parameters include, but are not limited to: irrigation amount and rainfall;

[0088] In this embodiment, taking the irrigation amount as an example, based on the high degree of humidity anomaly signal, the analysis period corresponding to the high degree of humidity anomaly signal is obtained and marked as the abnormal period;

[0089] Extract the irrigation amount corresponding to the abnormal period, plot the irrigation amount change curve with time as the X-axis and the relevant influencing parameter value as the Y-value;

[0090] The abnormal period was divided into several abnormal sub-periods. The soil moisture change sub-curve and irrigation quantum curve for each abnormal sub-period were obtained, and the slope value of each soil moisture change sub-curve and irrigation quantum curve was calculated.

[0091] The soil moisture change sub-curve and irrigation quantum curve corresponding to the same abnormal sub-period are marked as a sub-curve group;

[0092] Extract sub-curve groups with the same slope direction, mark them as sub-curve groups with the same direction, count the number of sub-curve groups with the same direction, and calculate the ratio of the number of sub-curve groups with the total number of sub-curve groups to obtain the proportion of sub-curve groups with the same direction (TX).

[0093] Among them, sub-curve groups with the same slope direction represent sub-curve groups with the same positive slope or the same negative slope.

[0094] Extract the slope values ​​corresponding to the same-direction sub-curve group and calculate the difference. Take the absolute value of the difference to obtain the slope deviation value. Sum the slope deviation values ​​of all the same-direction sub-curves and take the average value to obtain the average slope deviation value XJ.

[0095] Compare each slope deviation value with the mean slope deviation. If the slope deviation value is greater than or equal to the mean slope deviation, it is marked as a differential sub-curve group. If the slope deviation value is less than the mean slope deviation, it is marked as an approximate sub-curve group.

[0096] The number of approximate sub-curve groups is counted, and the ratio of this number to the number of sub-curves in the same direction is calculated to obtain the proportion of approximate sub-curve groups (JS).

[0097] Substitute the proportion of the same-direction subcurve group (TX), the mean slope deviation (XJ), and the proportion of the approximate subcurve group (JS) into the formula. The synchronization correlation value GL is calculated, where s1, s2, and s3 are weight coefficients, with s1 having a value of 0.32, s2 having a value of 0.35, and s3 having a value of 0.33.

[0098] It should be explained that the synchronous correlation value is a quantitative indicator used to assess the degree of synchronous change between soil moisture changes and related influencing parameters (such as irrigation amount). The proportion of the same-direction sub-curve group reflects the degree of consistency between soil moisture changes and irrigation amount changes in direction. The larger the proportion of the same-direction sub-curve group, the more likely that the two have the same trend of change in most sub-periods, and the stronger the correlation. The mean slope deviation measures the degree of difference between the slope of soil moisture changes and irrigation amount changes within the same-direction sub-curve group. The smaller the mean slope deviation, the closer the slopes are, and the stronger the correlation. The proportion of the approximate sub-curve group reflects the proportion of the sub-curve group with smaller slope deviation among the sub-curve groups with the same slope. The larger the proportion of the approximate sub-curve group, the better the synchronicity of the two in terms of slope, and the stronger the correlation.

[0099] Anomaly correlation judgment module: compares the synchronization correlation value with the synchronization correlation threshold to determine the degree of synchronization correlation;

[0100] Among them, the degree of synchronization association includes generating synchronous association signals and generating asynchronous association signals;

[0101] In some embodiments, a synchronization association value is obtained and compared with a synchronization association threshold, wherein the synchronization association threshold is set by those skilled in the art based on historical experimental data from multiple experiments, and is used to determine the degree of synchronization association.

[0102] If the synchronization correlation value is less than or equal to the synchronization correlation threshold, then an asynchronous correlation signal is generated;

[0103] If the synchronization association value is greater than the synchronization association threshold, a synchronization association signal is generated;

[0104] The technical solution of this invention is mainly as follows: after detecting a high level of abnormal humidity, the impact of irrigation volume is further analyzed. By drawing a change curve, calculating the slope value, and analyzing the sub-curve group, a synchronous correlation value is calculated, which comprehensively reflects the degree of synchronous change between soil moisture change and irrigation volume. The synchronous correlation value is compared with the synchronous correlation threshold to generate a synchronous correlation signal or asynchronous correlation signal, thereby enabling more precise adjustment of irrigation strategies and improving irrigation efficiency and crop yield.

[0105] Example 3:

[0106] Based on Examples 1 and 2, please refer to Figure 1 As shown in the embodiment of the present invention, a smart agricultural networked pipeline irrigation control system further includes:

[0107] Irrigation optimization module: Based on synchronous correlation signals, optimizes and adjusts the irrigation amount;

[0108] In some embodiments, based on the synchronous correlation signal, the corresponding humidity anomaly judgment value is obtained, the difference between the humidity anomaly judgment value and the humidity anomaly judgment threshold is calculated, and the ratio of the difference between the humidity anomaly judgment value and the humidity anomaly judgment value is processed to obtain the optimization coefficient.

[0109] Obtain the current irrigation volume, multiply the irrigation volume with the optimization coefficient, and obtain the optimized adjustment value;

[0110] Extract the soil moisture values ​​corresponding to the abnormal period, sum them and take the average to obtain the soil moisture adjustment value, and compare the soil moisture adjustment value with the soil moisture standard value.

[0111] If the soil moisture adjustment value is less than the soil moisture standard value, it indicates that the soil is too dry and the irrigation amount needs to be increased. The current irrigation amount is summed with the optimized adjustment value to obtain the irrigation target value.

[0112] If the soil moisture adjustment value is greater than the soil moisture standard value, it indicates that the soil is too wet and the irrigation amount needs to be reduced. The difference between the current irrigation amount and the optimized adjustment value is calculated to obtain the irrigation target value.

[0113] If the soil moisture adjustment value is equal to the soil moisture standard value, it indicates that the current soil moisture is suitable. Adjustments should be made according to other factors, including but not limited to: the crop growth cycle and weather changes.

[0114] Based on the irrigation target value, the irrigation optimization module adjusts the irrigation plan of the networked pipeline irrigation system;

[0115] The technical solution of this invention is mainly as follows: based on the synchronous correlation signal, the optimization coefficient is obtained by calculating the ratio of the humidity anomaly judgment value to the threshold, and the current irrigation amount is adjusted accordingly. At the same time, the irrigation target value is determined by combining the comparison results of the soil moisture adjustment value and the standard value. This can improve irrigation efficiency, reduce water waste, and dynamically adjust according to the soil moisture status and crop growth needs to ensure that crops grow in the best growth environment.

[0116] Example 4:

[0117] Based on Examples 1, 2, and 3, the smart agriculture networked pipeline irrigation control method described in this embodiment of the invention specifically includes the following steps:

[0118] Step 1: Monitor soil moisture data in real time, perform data analysis, and output moisture anomaly judgment values;

[0119] Step 2: Obtain the output humidity anomaly judgment value and determine the soil moisture status during the analysis period;

[0120] Among them, soil moisture status includes signals with low degree of moisture anomaly and signals with high degree of moisture anomaly.

[0121] Step 3: Based on the high humidity anomaly signal, obtain the values ​​of anomaly-related influencing parameters, perform data analysis, and output synchronous correlation values;

[0122] Step 4: Compare the synchronization association value with the synchronization association threshold to determine the degree of synchronization association;

[0123] Among them, the degree of synchronization association includes generating synchronous association signals and generating asynchronous association signals;

[0124] Step 5: Optimize and adjust the irrigation amount based on the synchronous correlation signal.

[0125] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A smart agricultural network-connected pipeline irrigation control system, characterized in that, Specifically, it includes: Data acquisition module: Real-time monitoring of soil moisture data, data analysis, and output of moisture anomaly judgment values; The process of obtaining the humidity anomaly judgment value is as follows: analyze the soil moisture data, calculate the abnormal area ratio MZ, the extreme humidity ratio JZ, and the abnormal time ratio SZ, and calculate the humidity anomaly judgment value SP based on the abnormal area ratio MZ, the extreme humidity ratio JZ, and the abnormal time ratio SZ. Humidity anomaly detection module: acquires the output humidity anomaly detection value and determines the soil moisture status during the analysis period; Among them, soil moisture status includes signals indicating a high degree of abnormal moisture generation; Anomaly Correlation Analysis Module: Based on the high level of humidity anomaly signal, it acquires irrigation volume, performs data analysis, and outputs synchronous correlation values; Anomaly correlation judgment module: compares the synchronization correlation value with the synchronization correlation threshold to determine the degree of synchronization correlation; Among them, the degree of synchronization association includes the generation of synchronization association signals; Irrigation optimization module: Based on synchronous correlation signals, it calculates optimization coefficients and adjusts irrigation amounts accordingly. The process for obtaining the synchronization association value is as follows: Obtain the proportion of sub-curves in the same direction (TX), the mean slope deviation (XJ), and the proportion of approximate sub-curves (JS). Substitute the proportion of the same-direction subcurve group (TX), the mean slope deviation (XJ), and the proportion of the approximate subcurve group (JS) into the formula. The synchronization correlation value GL is calculated, where s1, s2, and s3 are weight coefficients. The process for obtaining the proportion of the same-direction sub-curve group, the average slope deviation, and the proportion of the approximate sub-curve group is as follows: Based on the high humidity anomaly signal, the analysis period corresponding to the high humidity anomaly signal is obtained and marked as the abnormal period; Extract the irrigation amount corresponding to the abnormal period, and plot the irrigation amount change curve with time as the X-axis and irrigation amount as the Y-value; The abnormal period was divided into several abnormal sub-periods. The soil moisture change sub-curve and irrigation quantum curve for each abnormal sub-period were obtained, and the slope value of each soil moisture change sub-curve and irrigation amount was calculated. The soil moisture change sub-curve and irrigation quantum curve corresponding to the same abnormal sub-period are marked as a sub-curve group; The process of obtaining the optimization coefficients is as follows: Based on the synchronous correlation signal, the corresponding humidity anomaly judgment value is obtained. The difference between the humidity anomaly judgment value and the humidity anomaly judgment threshold is calculated, and the ratio of the difference to the humidity anomaly judgment value is processed to obtain the optimization coefficient.

2. The smart agricultural network-connected pipeline irrigation control system according to claim 1, characterized in that, The process for obtaining the anomaly judgment value is as follows: Obtain the abnormal area ratio MZ, the extreme humidity ratio JZ, and the abnormal time ratio SZ; Substitute the abnormal area ratio MZ, the total extreme humidity ratio JZ, and the abnormal time ratio SZ into the formula. The humidity anomaly judgment value SP is calculated, where a1, a2, and a3 are preset proportional coefficients.

3. The smart agricultural network-connected pipeline irrigation control system according to claim 1, characterized in that, The process for obtaining the total value of the abnormal area ratio, the extreme humidity ratio, and the abnormal time ratio is as follows: Preset the analysis period to obtain areas with abnormal humidity and soil moisture values; In each analysis period, the area values ​​of all humidity anomaly areas are obtained and summed to obtain the total humidity anomaly area value. This total area value is then compared with the area standard value to obtain the humidity anomaly area ratio MZ. In each analysis period, the extreme values ​​of soil moisture in each area with abnormal humidity are extracted. The difference between the extreme values ​​of soil moisture and the standard values ​​of soil moisture is calculated. The absolute value of the difference is taken and the ratio with the standard values ​​of soil moisture is calculated to obtain the extreme value ratio of humidity. The extreme value ratios of soil moisture in all areas with abnormal humidity are summed to obtain the total extreme value ratio of humidity JZ. In each analysis period, the time value of each humidity anomaly area is extracted and summed to obtain the total humidity anomaly time value. The ratio of the total humidity anomaly time value to the total time value of the analysis period is calculated to obtain the humidity anomaly time ratio SZ.

4. A smart agricultural networked pipeline irrigation control system according to claim 3, characterized in that, The process for obtaining the abnormal humidity areas and soil moisture values ​​is as follows: Soil moisture values ​​are monitored in real time using soil moisture monitoring equipment; Establish a Cartesian coordinate system with time as the X-axis and soil moisture as the Y-axis, and plot the real-time soil moisture change curve. Two standard lines parallel to the X-axis are drawn, with the maximum and minimum values ​​of the soil moisture threshold range as the limits, and are marked as the first moisture standard line and the second moisture standard line, respectively. Areas where the real-time soil moisture change curve lies above the first humidity standard line and below the second humidity standard line are marked as humidity abnormal areas.

5. A smart agricultural networked pipeline irrigation control system according to claim 1, characterized in that, The process of generating a signal indicating a high degree of humidity abnormality is as follows: Obtain the humidity anomaly judgment value and compare it with the humidity anomaly judgment threshold; If the humidity anomaly judgment value is greater than the humidity anomaly judgment threshold, a high humidity anomaly signal is generated.

6. A smart agricultural networked pipeline irrigation control system according to claim 1, characterized in that, Extract sub-curve groups with the same slope direction, mark them as sub-curve groups with the same direction, count the number of sub-curve groups with the same direction, and calculate the ratio of the number of sub-curve groups with the total number of sub-curve groups to obtain the proportion of sub-curve groups with the same direction (TX). Among them, sub-curve groups with the same slope direction represent sub-curve groups with the same positive slope or the same negative slope. Extract the slope values ​​corresponding to the same-direction sub-curve group and calculate the difference. Take the absolute value of the difference to obtain the slope deviation value. Sum the slope deviation values ​​of all the same-direction sub-curves and take the average value to obtain the average slope deviation value XJ. Compare each slope deviation value with the mean slope deviation. If the slope deviation value is greater than or equal to the mean slope deviation, it is marked as a differential sub-curve group. If the slope deviation value is less than the mean slope deviation, it is marked as an approximate sub-curve group. The number of approximate sub-curve groups is counted, and the ratio of this number to the number of sub-curves in the same direction is calculated to obtain the proportion of approximate sub-curve groups (JS).

7. A smart agricultural networked pipeline irrigation control system according to claim 1, characterized in that, The process of obtaining the synchronization correlation signal is as follows: Obtain the synchronization association value and compare it with the synchronization association threshold; If the synchronization correlation value is greater than the synchronization correlation threshold, a synchronization correlation signal is generated.

8. A smart agricultural networked pipeline irrigation control system according to claim 1, characterized in that, The process of optimizing and adjusting the irrigation amount is as follows: Obtain the current irrigation volume, multiply the irrigation volume with the optimization coefficient, and obtain the optimized adjustment value; Extract the soil moisture values ​​corresponding to the abnormal period, sum them and take the average to obtain the soil moisture adjustment value, and compare the soil moisture adjustment value with the soil moisture standard value. If the soil moisture adjustment value is less than the soil moisture standard value, the current irrigation amount is summed with the optimized adjustment value to obtain the irrigation target value. If the soil moisture adjustment value is greater than the soil moisture standard value, the difference between the current irrigation amount and the optimized adjustment value is calculated to obtain the irrigation target value.

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