Network connection pipeline irrigation control system for intelligent agriculture

Abstract

The invention relates to the technical field of agricultural irrigation, and particularly discloses a network connection pipeline irrigation control system for intelligent agriculture, and the system specifically comprises a data acquisition module which monitors soil humidity data in real time, carries out the data analysis, and outputs a humidity abnormality judgment value; wherein the acquisition process of the humidity abnormity judgment value comprises the following steps: analyzing soil humidity data, and 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, abnormal conditions of soil humidity can be found in time, abnormal influence factors can be judged, abnormal reasons can be positioned, and an irrigation strategy can be adjusted according to the abnormal degree, so that the problem of excessive irrigation or insufficient irrigation is effectively avoided, the yield and quality of crops are improved, and the irrigation efficiency is improved. Water resources can be remarkably saved, sustainable development of agricultural production is promoted, meanwhile, the frequency and difficulty of manual intervention are reduced, and powerful technical support is provided for development of intelligent agriculture.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural irrigation, and in particular to a networked pipeline irrigation control system for smart agriculture. Background Art

[0002] With the increasing shortage of global water resources and the acceleration of agricultural modernization, how to efficiently and accurately manage farmland irrigation systems to ensure the water supply required for crop growth while reducing water waste has become a key issue that needs to be urgently addressed 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 smart agriculture has gradually emerged, providing new solutions for farmland irrigation management. Smart agriculture integrates sensors, network communications, cloud computing and other technologies to achieve real-time monitoring and data analysis of farmland environment, making precision irrigation possible.

[0003] However, most existing smart agricultural irrigation systems are still at the level of data collection and simple display. The in-depth analysis and intelligent response mechanism for abnormal soil moisture are relatively insufficient, and the ability to flexibly adjust irrigation strategies according to actual conditions is poor, which may affect the yield and quality of crops.

[0004] In view of this, we proposed a networked pipeline irrigation control system for smart agriculture. Summary of the invention

[0005] The purpose of the present invention is to provide a networked pipeline irrigation control system for smart agriculture to solve the technical problems in the above background.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] The present invention provides a network-connected pipeline irrigation control system for smart agriculture, which specifically includes:

[0008] Data acquisition module: real-time monitoring of soil moisture data, data analysis, and output of moisture anomaly judgment values;

[0009] The process of obtaining the humidity anomaly judgment value is as follows: analyzing the soil moisture 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 anomaly 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] Humidity anomaly judgment module: obtains the output humidity anomaly judgment value and judges the soil humidity state during the analysis period;

[0011] Among them, the soil moisture state includes generating a signal of high moisture abnormality;

[0012] Abnormal correlation analysis module: based on the abnormal high humidity signal, obtain the abnormal irrigation amount, perform data analysis, and output the synchronous correlation value;

[0013] Abnormal correlation judgment module: compares the synchronization correlation value with the synchronization correlation threshold to judge the degree of synchronization correlation;

[0014] wherein the degree of synchronization association includes generating a synchronization association signal;

[0015] Irrigation optimization module: Based on the synchronous correlation signal, the optimization coefficient is calculated and the irrigation amount is optimized and adjusted.

[0016] As a further solution of the present invention: the process of obtaining the abnormal judgment value is:

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

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

[0019] As a further solution of the present invention: the acquisition process of the abnormal area ratio, the humidity extreme value ratio total value and the abnormal time ratio is as follows:

[0020] Preset analysis period to obtain humidity anomaly areas and soil humidity values;

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

[0022] In each analysis period, the soil moisture extreme value of each humidity anomaly area is extracted, the difference between the soil moisture extreme value and the soil moisture standard value is calculated, the absolute value of the difference is taken, and the ratio is calculated with the soil moisture standard value to obtain the humidity extreme value ratio, and the soil moisture extreme value ratios of all humidity anomaly areas are 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 summed to obtain the total humidity abnormal time value, and the ratio of the total humidity abnormal time value to the total time value of the analysis period is calculated to obtain the humidity abnormal time ratio SZ.

[0024] As a further solution of the present invention: the process of obtaining the humidity abnormal area and the soil humidity value is:

[0025] Real-time monitoring of soil moisture values ​​through soil moisture monitoring equipment;

[0026] With time as the X-axis and soil moisture as the Y-axis, a plane rectangular coordinate system is established to draw a real-time soil moisture change curve;

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

[0028] The area where the real-time soil moisture change curve is located above the first humidity standard line and below the second humidity standard line is marked as an abnormal humidity area.

[0029] As a further solution of the present invention: the process of generating the abnormally high humidity signal is:

[0030] Obtaining a humidity abnormality judgment value, and comparing the humidity abnormality judgment value with a humidity abnormality judgment threshold;

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

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

[0033] Get the proportion of the same-direction sub-curve group TX, the slope deviation mean XJ, and the proportion of the approximate sub-curve group JS

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

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

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

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

[0038] The abnormal period is divided into several abnormal sub-periods, the soil moisture change sub-curve and the irrigation quantum curve of each abnormal sub-period are obtained respectively, and the slope value of each soil moisture change sub-curve and the irrigation quantum curve is calculated respectively;

[0039] The soil moisture variation 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 same-direction sub-curve groups, count the number of same-direction sub-curve groups, and perform ratio processing with the total number of sub-curve groups to obtain the same-direction sub-curve group ratio TX;

[0041] The sub-curve group with the same slope refers to a sub-curve group with the same positive slope or the same negative slope.

[0042] Extract the slope values ​​corresponding to the same-direction sub-curve group, perform difference calculation, take the absolute value of the difference to obtain the slope deviation value, sum and average the slope deviation values ​​of all the same-direction sub-curves to obtain the slope deviation mean value XJ;

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

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

[0045] As a further solution of the present invention: the acquisition process of generating the synchronous association signal is:

[0046] obtaining a synchronization association value, and comparing the synchronization association value with a 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 solution of the present invention: the process of obtaining the optimization coefficient is:

[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 processing is performed with the humidity anomaly judgment value to obtain the optimization coefficient.

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

[0051] Obtain the current irrigation amount, multiply the irrigation amount by the optimization coefficient to obtain the optimized adjustment value;

[0052] Extract the soil moisture value corresponding to the abnormal period, sum and average them 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] Beneficial effects of the present invention:

[0056] Through real-time monitoring and data analysis, the present invention can promptly detect abnormal conditions of soil moisture, determine abnormal influencing factors, locate abnormal causes, and adjust irrigation strategies according to the degree of abnormality, thereby effectively avoiding problems such as over-irrigation or under-irrigation. This not only helps to increase the yield and quality of crops, but also can significantly save water resources and promote 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The present invention will be further described below in conjunction with the accompanying drawings.

[0058] Figure 1 It is a flow chart of a networked pipeline irrigation control system for smart agriculture according to the present invention. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] Embodiment 1:

[0061] See also Figure 1 As shown, a networked pipe irrigation control system for smart agriculture according to an embodiment of the present invention 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, the soil moisture value is 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] With time as the X-axis and soil moisture as the Y-axis, a plane rectangular coordinate system is established to draw a real-time soil moisture change curve;

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

[0066] The soil moisture threshold range is set by technicians in this field based on historical experimental data.

[0067] The area where the real-time soil moisture change curve is located above the first humidity standard line and below the second humidity standard line is marked as an abnormal humidity area;

[0068] The area where the real-time soil moisture change curve is located between the first humidity standard line and the second humidity standard line is marked as a normal humidity area;

[0069] Preset analysis period, wherein the analysis period is set by technicians in this field based on experience and analysis requirements;

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

[0071] It should be noted that the calculation process of the area standard value is: the end time point and the start time point of the analysis period are calculated to obtain the time difference, the maximum value and the minimum value of the soil moisture threshold range are calculated to obtain the humidity standard deviation, and the humidity standard deviation is multiplied by the time difference to obtain the area standard value;

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

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

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

[0075] In each analysis period, the time value of each humidity abnormal area is extracted and summed to obtain the total humidity abnormal time value, and the total humidity abnormal time value is calculated by ratio with the total time value of the analysis period to obtain the humidity abnormal time ratio SZ;

[0076] Substitute the abnormal area ratio MZ, humidity extreme value ratio total value JZ, and abnormal time ratio SZ into the formula The humidity abnormality judgment value SP is calculated, where a1, a2, and a3 are preset proportional coefficients, and the value of a1 is 1.374, the value of a2 is 1.268, and the value of a3 is 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 soil humidity anomaly, which can evaluate the abnormal condition of soil humidity more comprehensively and accurately. The abnormal area ratio reflects the proportion of the area of ​​soil humidity deviating from the normal range in the analysis period to the area of ​​the normal area. The larger the abnormal area ratio, the more abnormal the soil humidity. The total value of the humidity extreme value ratio reflects the sum of the deviations between the extreme value (maximum or minimum value) of soil humidity deviating from the normal range and the standard value of soil humidity in the analysis period. The larger the humidity extreme value ratio, the more abnormal the soil humidity. The abnormal time ratio reflects the proportion of the time when soil humidity is in an abnormal state in the analysis period to the entire analysis period. The larger the abnormal time ratio, the more abnormal the soil humidity.

[0078] Humidity anomaly judgment module: obtains the output humidity anomaly judgment value and judges the soil humidity state during the analysis period;

[0079] Among them, the soil moisture state includes generating a low moisture abnormality signal and a high moisture abnormality signal;

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

[0081] If the humidity anomaly judgment value is less than or equal to the humidity anomaly judgment threshold, the soil humidity anomaly degree in the analysis period is low, and a humidity anomaly low signal is generated;

[0082] If the humidity anomaly judgment value is greater than the humidity anomaly judgment threshold, the soil humidity anomaly degree in the analysis period is high, and a humidity anomaly high signal is generated;

[0083] The technical solution of the embodiment of the present invention is mainly: real-time monitoring of soil moisture data by a data acquisition module, and in-depth analysis to calculate a humidity anomaly judgment value, which comprehensively considers the area, extreme value and abnormal duration of the humidity anomaly area, thereby comprehensively and accurately evaluating the abnormal condition of soil moisture, and then timely understanding the abnormal soil moisture situation, and formulating optimization strategies based on the abnormal situation to improve the yield and quality of crops.

[0084] Embodiment 2:

[0085] Based on Example 1, please refer to Figure 1As shown, a networked pipe irrigation control system for smart agriculture according to an embodiment of the present invention specifically includes:

[0086] Abnormal correlation analysis module: Based on the abnormal humidity high degree signal, obtain the abnormal related influencing parameter value, perform data analysis, and output the synchronous correlation value;

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

[0088] In this embodiment, the abnormality-related influencing parameter value is taken as an example of irrigation amount: based on the humidity abnormality high signal, the analysis period corresponding to the humidity abnormality high signal is obtained and marked as the abnormal period;

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

[0090] The abnormal period is divided into several abnormal sub-periods, the soil moisture change sub-curve and the irrigation quantum curve of each abnormal sub-period are obtained respectively, and the slope value of each soil moisture change sub-curve and the irrigation quantum curve is calculated respectively;

[0091] The soil moisture variation 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 same-direction sub-curve groups, count the number of same-direction sub-curve groups, and perform ratio processing with the total number of sub-curve groups to obtain the same-direction sub-curve group ratio TX;

[0093] The sub-curve group with the same slope refers to a sub-curve group with the same positive slope or the same negative slope.

[0094] Extract the slope values ​​corresponding to the same-direction sub-curve group, perform difference calculation, take the absolute value of the difference to obtain the slope deviation value, sum and average the slope deviation values ​​of all the same-direction sub-curves to obtain the slope deviation mean value XJ;

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

[0096] Count the number of approximate sub-curve groups and calculate the ratio with the number of sub-curves in the same direction to obtain the approximate sub-curve group ratio JS;

[0097] Substitute the proportion of the same-direction sub-curve group TX, the mean value of the slope deviation XJ, and the proportion of the approximate sub-curve group JS into the formula The synchronization correlation value GL is calculated, where s1, s2, and s3 are weight coefficients, and the value of s1 is 0.32, the value of s2 is 0.35, and the value of s3 is 0.33;

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

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

[0100] The synchronous correlation degree includes generating synchronous correlation signals and generating asynchronous correlation signals;

[0101] In some embodiments, a synchronization correlation value is obtained, and the synchronization correlation value is compared with a synchronization correlation threshold value, wherein the synchronization correlation threshold value is set by a person skilled in the art based on a summary of multiple historical experimental data, and is used to determine the degree of synchronization correlation;

[0102] If the synchronous correlation value is less than or equal to the synchronous correlation threshold, 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 the embodiment of the present invention is mainly as follows: after detecting a high signal of abnormal humidity, further analyzing the impact of irrigation amount, calculating the synchronous correlation value by drawing a change curve, calculating the slope value, and analyzing the sub-curve group, comprehensively reflecting the degree of synchronous change between soil moisture change and irrigation amount, comparing the synchronous correlation value with the synchronous correlation threshold, generating a synchronous correlation signal or an asynchronous correlation signal, so as to more accurately adjust the irrigation strategy and improve irrigation efficiency and crop yield.

[0105] Embodiment 3:

[0106] Based on Example 1 and Example 2, please refer to Figure 1 As shown, a networked pipe irrigation control system for smart agriculture according to an embodiment of the present invention specifically includes:

[0107] Irrigation optimization module: optimizes the irrigation amount based on synchronous correlation signals;

[0108] In some embodiments, based on the synchronous correlation signal, a corresponding humidity abnormality judgment value is obtained, a difference is calculated between the humidity abnormality judgment value and the humidity abnormality judgment threshold, and a ratio is processed with the humidity abnormality judgment value to obtain an optimization coefficient;

[0109] Obtain the current irrigation amount, multiply the irrigation amount by the optimization coefficient to obtain the optimized adjustment value;

[0110] Extract the soil moisture value corresponding to the abnormal period, sum and average them 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 means that the soil is 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 current irrigation amount is calculated by the difference between the optimized adjustment value 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 and is adjusted according to other factors, including but not limited to: the growth cycle of crops and weather changes;

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

[0115] The technical solution of the embodiment of the present 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 value, and the current irrigation amount is adjusted accordingly. At the same time, the irrigation target value is determined in combination with the comparison result of the soil moisture adjustment value and the standard value, thereby improving irrigation efficiency and reducing water resource waste. It can also be dynamically adjusted according to the soil moisture status and crop growth requirements to ensure that crops grow in the best growth environment.

[0116] Embodiment 4:

[0117] On the basis of Example 1, Example 2 and Example 3, a method for controlling irrigation using a networked pipeline for smart agriculture according to an embodiment of the present invention specifically includes the following steps:

[0118] Step 1: Real-time monitoring of soil moisture data, data analysis, and output of moisture anomaly judgment values;

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

[0120] Among them, the soil moisture state includes generating a low moisture abnormality signal and a high moisture abnormality signal;

[0121] Step 3: Based on the abnormal humidity high degree signal, obtain the abnormal related influencing parameter value, perform data analysis, and output the synchronous correlation value;

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

[0123] The synchronous correlation degree includes generating synchronous correlation signals and generating asynchronous correlation signals;

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

[0125] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A networked pipeline irrigation control system for smart agriculture, characterized in that: Specifically include: 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: analyzing the soil moisture 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 anomaly judgment value SP according to the abnormal area ratio MZ, the humidity extreme value ratio total value JZ, and the abnormal time ratio SZ; Humidity anomaly judgment module: obtains the output humidity anomaly judgment value and judges the soil humidity state during the analysis period; Among them, the soil moisture state includes generating a signal of high moisture abnormality; Abnormal correlation analysis module: based on the abnormal high humidity signal, obtain the irrigation amount, perform data analysis, and output the synchronous correlation value; Abnormal correlation judgment module: compares the synchronization correlation value with the synchronization correlation threshold to determine the degree of synchronization correlation; wherein the degree of synchronization association includes generating a synchronization association signal; Irrigation optimization module: Based on the synchronous correlation signal, the optimization coefficient is calculated and the irrigation amount is optimized and adjusted.

2. A networked pipe irrigation control system for smart agriculture according to claim 1, characterized in that: The process of obtaining the abnormal judgment value is as follows: Obtain the abnormal area ratio MZ, humidity extreme value ratio total value JZ, and abnormal time ratio SZ; Substitute the abnormal area ratio MZ, humidity extreme value ratio total value JZ, and abnormal time ratio SZ into the formula The humidity abnormality judgment value SP is calculated, wherein a1, a2, and a3 are preset proportional coefficients.

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

4. The smart agricultural network-connected pipe irrigation control system according to claim 3 is characterized in that: The process of obtaining the humidity anomaly area and soil humidity value is as follows: Real-time monitoring of soil moisture values ​​through soil moisture monitoring equipment; With time as the X-axis and soil moisture as the Y-axis, a plane rectangular coordinate system is established to draw a real-time soil moisture change curve; With the maximum and minimum values ​​of the soil moisture threshold range as limits, two standard lines parallel to the X-axis are drawn, marked as the first humidity standard line and the second humidity standard line respectively; The area where the real-time soil moisture change curve is located above the first humidity standard line and below the second humidity standard line is marked as an abnormal humidity area.

5. The network-connected pipe irrigation control system for smart agriculture according to claim 1, characterized in that: The process of generating the abnormally high humidity signal is as follows: Obtaining a humidity abnormality judgment value, and comparing the humidity abnormality judgment value with a humidity abnormality judgment threshold; If the humidity abnormality judgment value is greater than the humidity abnormality judgment threshold, a humidity abnormality high signal is generated.

6. The smart agricultural network-connected pipe irrigation control system according to claim 1, characterized in that: The process of obtaining the synchronization association value is as follows: Obtain the proportion of the same-direction sub-curve group TX, the mean value of the slope deviation XJ, and the proportion of the approximate sub-curve group JS. Substitute the proportion of the same-direction sub-curve group TX, the mean value of the slope deviation XJ, and the proportion of the approximate sub-curve group JS into the formula The synchronization correlation value GL is calculated, where s1, s2, and s3 are weight coefficients.

7. A networked pipe irrigation control system for smart agriculture according to claim 6, characterized in that: The process of obtaining the proportion of the same-direction sub-curve group, the slope deviation mean and the proportion of the approximate sub-curve group is as follows: Based on the abnormally high humidity signal, an analysis period corresponding to the abnormally high humidity signal is obtained and marked as an abnormal period; Extract the irrigation amount corresponding to the abnormal period, draw the irrigation amount change curve with time as the X-axis and irrigation amount as the Y-value; The abnormal period is divided into several abnormal sub-periods, the soil moisture change sub-curve and irrigation quantum curve of each abnormal sub-period are obtained respectively, and the slope value of each soil moisture change sub-curve and irrigation amount is calculated respectively; The soil moisture variation sub-curve and irrigation quantum curve corresponding to the same abnormal sub-period are marked as a sub-curve group; Extract sub-curve groups with the same slope direction, mark them as same-direction sub-curve groups, count the number of same-direction sub-curve groups, and perform ratio processing with the total number of sub-curve groups to obtain the same-direction sub-curve group ratio TX; The sub-curve group with the same slope refers to a sub-curve group with the same positive slope or the same negative slope. Extract the slope values ​​corresponding to the same-direction sub-curve group, perform difference calculation, take the absolute value of the difference to obtain the slope deviation value, sum and average the slope deviation values ​​of all the same-direction sub-curves to obtain the slope deviation mean value XJ; Compare all slope deviation values ​​with the slope deviation mean respectively. If the slope deviation value is greater than or equal to the slope deviation mean, it is marked as a difference sub-curve group. If the slope deviation value is less than the slope deviation mean, it is marked as an approximate sub-curve group. The number of approximate sub-curve groups is counted, and the ratio is calculated with the number of sub-curves in the same direction to obtain the approximate sub-curve group ratio JS.

8. The smart agricultural network-connected pipe irrigation control system according to claim 1, characterized in that: The acquisition process of generating the synchronization association signal is as follows: obtaining a synchronization association value, and comparing the synchronization association value with a synchronization association threshold; If the synchronization correlation value is greater than the synchronization correlation threshold, a synchronization correlation signal is generated.

9. The smart agricultural network-connected pipe irrigation control system according to claim 1, characterized in that: The process of obtaining the optimization coefficient 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 processing is performed with the humidity anomaly judgment value to obtain the optimization coefficient.

10. The smart agricultural network-connected pipeline irrigation control system according to claim 1, characterized in that: The process of optimizing the irrigation amount is as follows: Obtain the current irrigation amount, multiply the irrigation amount by the optimization coefficient to obtain the optimized adjustment value; Extract the soil moisture values ​​corresponding to the abnormal period, sum and average them 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.

Images