Automatic management method of soil moisture monitoring sensor combined with Internet of Things technology

Abstract

The present invention relates to the field of soil management, and discloses an automated management method for soil moisture monitoring sensors in combination with Internet of Things technology, comprising the following steps: real-time monitoring and analysis of soil moisture data by means of soil moisture monitoring sensors, thereby determining irrigation optimization schemes on different target soil sub-regions, and analyzing the irrigation optimization effects after the irrigation optimization schemes are output, and finally adjusting the target soil sub-regions with problems based on the analysis results. At the same time, abnormal real-time analysis and correction are performed on the soil moisture data monitored by the soil moisture monitoring sensors. The present invention can monitor soil moisture data in real time by means of soil moisture monitoring sensors, which helps to realize the refinement and intelligence of agricultural production, and achieve the purpose of improving agricultural production efficiency, ensuring sustainable agricultural development and the quality of agricultural products.

Description

Technical Field

[0001] The present invention relates to the field of soil management, and in particular to an automated management method for soil moisture monitoring sensors in combination with Internet of Things technology. Background Art

[0002] Soil moisture refers to the moisture content in the soil, which is one of the important factors of soil fertility and plant growth. Soil moisture directly reflects the amount and distribution of water in the soil, and is a comprehensive reflection of soil moisture. By monitoring soil moisture, we can timely understand the soil moisture status, provide a scientific basis for agricultural production, and reasonably arrange agricultural activities such as irrigation and fertilization to improve agricultural production efficiency. The soil moisture monitoring sensor is an intelligent device specially used to monitor soil moisture status (i.e. soil moisture), which has a wide range of application value in agricultural production, environmental monitoring, soil science research and water resources management. In agricultural production, it can help farmers achieve precise irrigation and fertilization, and improve the yield and quality of crops; in environmental monitoring, it can be used to evaluate the impact of soil moisture status on the ecological environment; in soil science research, it can provide data support for soil improvement and governance; in water resources management, it can be used to monitor and evaluate the utilization efficiency and sustainability of water resources. Therefore, an automated management method for soil moisture monitoring sensors combined with Internet of Things technology is proposed, which will help to realize the refinement and intelligence of agricultural production, provide strong support for the sustainable development of agriculture, and achieve the goal of improving agricultural production efficiency, ensuring the sustainable development of agriculture and the quality of agricultural products. Summary of the invention

[0003] The present invention overcomes the shortcomings of the prior art and provides an automated management method for soil moisture monitoring sensors combined with Internet of Things technology.

[0004] In order to achieve the above object, the technical solution adopted by the present invention is:

[0005] The first aspect of the present invention provides an automated management method for soil moisture monitoring sensors in combination with Internet of Things technology, comprising the following steps:

[0006] The soil moisture data is monitored in real time on the target soil by using a soil moisture monitoring sensor, and the real-time soil moisture data obtained by monitoring is stored;

[0007] Analyze the soil moisture data of the target soil, classify the target soil sub-areas based on the analysis results, and obtain irrigation optimization plans for different target soil sub-areas;

[0008] Output the target irrigation optimization plan, perform secondary soil moisture analysis in different abnormal target soil sub-areas, and make adjustments in the abnormal target soil sub-areas based on the secondary soil moisture analysis results;

[0009] The soil moisture data monitored by the soil moisture monitoring sensor is analyzed in real time for abnormalities, and the soil moisture monitoring sensor is corrected in real time for abnormalities based on the results of the abnormal real-time analysis.

[0010] Furthermore, in a preferred embodiment of the present invention, the soil moisture data is monitored in real time on the target soil by a soil moisture monitoring sensor, and the real-time soil moisture data obtained by monitoring is stored, specifically:

[0011] Obtain all soils that need to be included in real-time soil moisture data monitoring and mark them as target soils;

[0012] Obtaining the area and scope of the target soil, and obtaining a soil moisture monitoring sensor, wherein the soil moisture monitoring sensor is a sensor that can be placed in the target soil to monitor soil moisture data;

[0013] Obtaining a maximum monitoring range of a soil moisture monitoring sensor, and based on the maximum monitoring range of the soil moisture monitoring sensor, partitioning the target soil to obtain a target soil sub-area, wherein the target soil sub-area is not larger than the maximum monitoring range of the soil moisture monitoring sensor;

[0014] Soil moisture monitoring sensors are placed in different target soil sub-areas and run, wherein the soil moisture monitoring sensors include a sensor resistor, a sensor probe, a signal processing circuit, a wireless communication module and a data processing module;

[0015] In the target soil sub-area, the moisture content and temperature of the soil are monitored in real time by a sensor probe, which are calibrated as the real-time moisture content and the real-time temperature of the soil, respectively, and the real-time moisture content and the real-time temperature of the soil are converted into digital signals, and the signal is amplified and filtered by the signal processing circuit, and finally the real-time moisture content and the real-time temperature of the soil under the digital signal are stored in the data processing module through the wireless communication module;

[0016] In the data processing module, the digital signals of the real-time soil moisture content and the real-time soil temperature are calibrated as the real-time soil moisture content parameter and the real-time soil temperature parameter, respectively, and the matching relationship between the real-time soil moisture content parameter and the resistance value of the sensor resistor is calculated in real time. Based on the matching relationship between the real-time soil moisture content parameter and the resistance value of the sensor resistor, combined with the real-time soil temperature parameter, the soil moisture of the target soil is calculated, that is, the soil moisture data of the target soil is calculated.

[0017] Furthermore, in a preferred embodiment of the present invention, the soil moisture data of the target soil is analyzed, and the target soil sub-areas are classified based on the analysis results, and the irrigation optimization schemes for different target soil sub-areas are obtained, specifically:

[0018] Acquire plant species that need to be planted on the target soil, introduce a big data network, and retrieve appropriate soil moisture data of the plant species that need to be planted on the target soil based on the big data network, and calibrate them as standard soil moisture data;

[0019] Analyze the soil moisture data of all target soil sub-areas, calculate the Euclidean distance between the soil moisture data of different target soil sub-areas and the standard soil moisture data, mark them as the first Euclidean distance, and preset the standard Euclidean distance interval;

[0020] If the first Euclidean distance corresponding to a target soil sub-region is not within the standard Euclidean distance interval, the corresponding target soil sub-region is marked as an abnormal target soil sub-region, and other target soil sub-regions are marked as normal target soil sub-regions;

[0021] In the abnormal target soil sub-area, the difference between the corresponding soil moisture data and the standard soil moisture data is calculated and calibrated as the soil moisture data difference, and the appropriate irrigation optimization scheme under different soil moisture data differences is retrieved in the big data network and calibrated as the qualified irrigation optimization scheme;

[0022] In different abnormal target soil sub-areas, all qualified irrigation optimization plans are analyzed, and the qualified irrigation optimization plan with the shortest irrigation time during implementation is calibrated as the target irrigation optimization plan, so that different abnormal target soil sub-areas have corresponding target irrigation optimization plans.

[0023] Furthermore, in a preferred embodiment of the present invention, the output target irrigation optimization scheme performs secondary soil moisture analysis in different abnormal target soil sub-areas, and adjusts the abnormal target soil sub-areas based on the secondary soil moisture analysis results, specifically:

[0024] Based on the target irrigation optimization schemes corresponding to different abnormal target soil sub-areas, equipment for irrigation optimization of different abnormal target soil sub-areas is obtained and calibrated as target irrigation optimization equipment, and the target irrigation optimization schemes corresponding to different abnormal target soil sub-areas are imported into the irrigation optimization equipment, so that the irrigation optimization equipment outputs the corresponding target irrigation optimization scheme according to the abnormal target soil sub-areas, and the abnormal target soil sub-areas after the initial irrigation optimization are obtained, which are calibrated as the initial irrigation optimization soil sub-areas;

[0025] The soil moisture monitoring sensors are used to monitor the soil moisture of all the first irrigation optimized soil sub-areas, and the soil moisture data of all the first irrigation optimized soil sub-areas are obtained, which are calibrated as the first irrigation optimized soil moisture data;

[0026] The Euclidean distance between the first irrigation optimized soil moisture data and the standard soil moisture data is calculated and marked as the second Euclidean distance, and the first irrigation optimized soil sub-region whose second Euclidean distance is not in the standard Euclidean distance interval is marked as the optimized abnormal soil sub-region;

[0027] Soil sampling is carried out in all optimized abnormal soil sub-areas to obtain soil samples to be analyzed, and climate parameters of the optimized abnormal soil sub-areas are obtained, and climate parameter simulation equipment is introduced at the same time;

[0028] Placing the soil sample to be analyzed in a climate parameter simulation device, adjusting the initial climate parameters in the climate parameter simulation device to the climate parameters of the optimized abnormal soil sub-region, and performing real-time climate parameter adjustment in the climate parameter simulation device;

[0029] Monitor the change rate of the soil moisture data of the soil sample to be analyzed during the real-time adjustment of the climate parameters. If the change rate of the soil moisture data of the soil sample to be analyzed during the real-time adjustment of the climate parameters is greater than a preset value, retrieve the climate parameters that make the initial irrigation optimized soil moisture data equal to the standard soil moisture data in the big data network, calibrate them as standard climate parameters, and simultaneously retrieve and output the solution that makes the climate parameters of the initial irrigation optimized soil sub-area equal to the standard climate parameters;

[0030] If the change rate of the soil moisture data of the soil sample to be analyzed during the real-time adjustment of climate parameters is not greater than the preset value, the irrigation optimization equipment will continue to output the corresponding target irrigation optimization plan on the initial irrigation optimization soil sub-area, and at the same time, the soil in the initial irrigation optimization soil sub-area will be loosened, so that all the initial irrigation optimization soil sub-areas are normal target soil sub-areas.

[0031] Furthermore, in a preferred embodiment of the present invention, the soil moisture data monitored by the soil moisture monitoring sensor is analyzed in real time, and the soil moisture monitoring sensor is corrected in real time based on the abnormal real-time analysis result, specifically:

[0032] When the soil moisture data of all target soil sub-areas are monitored in real time by the soil moisture monitoring sensor, the data fluctuation range of the soil moisture data is obtained in real time by the data processing module of the soil moisture monitoring sensor, and is calibrated as the fluctuation range of the soil moisture data to be analyzed;

[0033] Preset the data fluctuation range analysis time, and preset the minimum range of soil moisture data fluctuation. During the data fluctuation range analysis time, calculate the overlap rate between the soil moisture data fluctuation range to be analyzed obtained by the soil moisture monitoring sensor and the minimum range of soil moisture data fluctuation, and preset the minimum overlap rate;

[0034] The soil moisture monitoring sensor whose overlap rate between the fluctuation range of the soil moisture data to be analyzed and the minimum fluctuation range of the soil moisture data is greater than the minimum overlap rate is calibrated as a normal soil moisture monitoring sensor, and the soil moisture monitoring sensor whose overlap rate between the fluctuation range of the soil moisture data to be analyzed and the minimum fluctuation range of the soil moisture data is less than the minimum overlap rate is calibrated as an abnormal soil moisture monitoring sensor;

[0035] Conduct sensor abnormality tracing analysis on abnormal soil moisture monitoring sensors, and repair the abnormal soil moisture monitoring sensors based on the abnormality tracing analysis results.

[0036] Furthermore, in a preferred embodiment of the present invention, the abnormal soil moisture monitoring sensor is subjected to sensor abnormality tracing analysis, and the abnormal soil moisture monitoring sensor is repaired based on the abnormality tracing analysis result, specifically:

[0037] Perform sensor probe cleaning and power supply replacement on the abnormal soil moisture monitoring sensor, and determine whether the overlap rate between the fluctuation range of the soil moisture data to be analyzed and the minimum fluctuation range of the soil moisture data obtained after the abnormal soil moisture monitoring sensor performs sensor probe cleaning and power supply replacement is greater than the minimum overlap rate;

[0038] If yes, the abnormal soil moisture monitoring sensor after the sensor probe cleaning process and power supply replacement process is calibrated as a normal soil moisture monitoring sensor;

[0039] If not, then obtain the communication parameters of the wireless communication module in the abnormal soil moisture monitoring sensor, calibrate them as target communication parameters, and obtain the standard range of the communication parameters of the wireless communication module in the abnormal soil moisture monitoring sensor during operation, calibrate them as standard communication parameter range;

[0040] If the target communication parameters are not maintained within the standard communication parameter range, the target communication parameters are analyzed based on the Bayesian network to determine the fault location of the wireless communication module in the abnormal soil moisture monitoring sensor, and the maintenance plan output of the fault location of the wireless communication module in the abnormal soil moisture monitoring sensor is retrieved in the big data network to maintain the target communication parameters within the standard communication parameter range;

[0041] When the target communication parameters are maintained within the standard communication parameter range, but the overlap rate between the fluctuation range of the soil moisture data to be analyzed and the minimum fluctuation range of the soil moisture data is still not greater than the minimum overlap rate, a protective cover is installed on the abnormal soil moisture monitoring sensor to ensure that when the abnormal soil moisture monitoring sensor is working, the overlap rate between the fluctuation range of the soil moisture data to be analyzed and the minimum fluctuation range of the soil moisture data is greater than the minimum overlap rate.

[0042] The second aspect of the present invention further provides an automatic management system for soil moisture monitoring sensors combined with Internet of Things technology, wherein the automatic management system includes a memory and a processor, wherein an automatic management method is stored in the memory, and when the automatic management method is executed by the processor, the following steps are implemented:

[0043] The soil moisture data is monitored in real time on the target soil by using a soil moisture monitoring sensor, and the real-time soil moisture data obtained by monitoring is stored;

[0044] Analyze the soil moisture data of the target soil, classify the target soil sub-areas based on the analysis results, and obtain irrigation optimization plans for different target soil sub-areas;

[0045] Output the target irrigation optimization plan, perform secondary soil moisture analysis in different abnormal target soil sub-areas, and make adjustments in the abnormal target soil sub-areas based on the secondary soil moisture analysis results;

[0046] The soil moisture data monitored by the soil moisture monitoring sensor is analyzed in real time for abnormalities, and the soil moisture monitoring sensor is corrected in real time for abnormalities based on the results of the abnormal real-time analysis.

[0047] The present invention solves the technical defects existing in the background technology, and the present invention has the following beneficial effects: the soil moisture data is monitored and analyzed in real time through the soil moisture monitoring sensor, so as to determine the irrigation optimization plan for different target soil sub-areas, and the irrigation optimization effect is analyzed after the irrigation optimization plan is output, and finally the target soil sub-area with problems is adjusted based on the analysis result. At the same time, the soil moisture data monitored by the soil moisture monitoring sensor is analyzed and corrected in real time. The present invention can monitor the soil moisture data in real time through the soil moisture monitoring sensor, which is helpful to realize the refinement and intelligence of agricultural production, and achieve the purpose of improving agricultural production efficiency, ensuring the sustainable development of agriculture and the quality of agricultural products. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0049] Figure 1 A flow chart of an automated management method for soil moisture monitoring sensors in combination with Internet of Things technology is shown;

[0050] Figure 2 A flow chart of a method for analyzing and correcting the monitoring effect of a soil moisture monitoring sensor is shown;

[0051] Figure 3 The program view of the soil moisture monitoring sensor automation management system combined with the Internet of Things technology is shown. DETAILED DESCRIPTION

[0052] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0053] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0054] Figure 1 The flowchart of the automatic management method of soil moisture monitoring sensor combined with Internet of Things technology is shown, which includes the following steps:

[0055] S102: Real-time monitoring of soil moisture data on target soil is performed using a soil moisture monitoring sensor, and the real-time soil moisture data obtained through monitoring is stored;

[0056] S104: analyzing soil moisture data of the target soil, classifying the target soil sub-areas based on the analysis results, and obtaining irrigation optimization plans for different target soil sub-areas;

[0057] S106: Outputting a target irrigation optimization plan, performing secondary soil moisture analysis in different abnormal target soil sub-areas, and making adjustments in the abnormal target soil sub-areas based on the secondary soil moisture analysis results;

[0058] S108: Performing real-time abnormal analysis on the soil moisture data monitored by the soil moisture monitoring sensor, and performing real-time abnormal correction on the soil moisture monitoring sensor based on the results of the real-time abnormal analysis.

[0059] Furthermore, in a preferred embodiment of the present invention, the soil moisture data is monitored in real time on the target soil by a soil moisture monitoring sensor, and the real-time soil moisture data obtained by monitoring is stored, specifically:

[0060] Obtain all soils that need to be included in real-time soil moisture data monitoring and mark them as target soils;

[0061] Obtaining the area and scope of the target soil, and obtaining a soil moisture monitoring sensor, wherein the soil moisture monitoring sensor is a sensor that can be placed in the target soil to monitor soil moisture data;

[0062] Obtaining a maximum monitoring range of a soil moisture monitoring sensor, and based on the maximum monitoring range of the soil moisture monitoring sensor, partitioning the target soil to obtain a target soil sub-area, wherein the target soil sub-area is not larger than the maximum monitoring range of the soil moisture monitoring sensor;

[0063] Soil moisture monitoring sensors are placed in different target soil sub-areas and run, wherein the soil moisture monitoring sensors include a sensor resistor, a sensor probe, a signal processing circuit, a wireless communication module and a data processing module;

[0064] In the target soil sub-area, the moisture content and temperature of the soil are monitored in real time by a sensor probe, which are calibrated as the real-time moisture content and the real-time temperature of the soil, respectively, and the real-time moisture content and the real-time temperature of the soil are converted into digital signals, and the signal is amplified and filtered by the signal processing circuit, and finally the real-time moisture content and the real-time temperature of the soil under the digital signal are stored in the data processing module through the wireless communication module;

[0065] In the data processing module, the digital signals of the real-time soil moisture content and the real-time soil temperature are calibrated as the real-time soil moisture content parameter and the real-time soil temperature parameter, respectively, and the matching relationship between the real-time soil moisture content parameter and the resistance value of the sensor resistor is calculated in real time. Based on the matching relationship between the real-time soil moisture content parameter and the resistance value of the sensor resistor, combined with the real-time soil temperature parameter, the soil moisture of the target soil is calculated, that is, the soil moisture data of the target soil is calculated.

[0066] It should be noted that the soil moisture monitoring sensor is an intelligent device specifically used to monitor soil moisture conditions. It can monitor the moisture content and state of the soil in real time and continuously. It converts the moisture content in the soil into a readable digital signal to provide key data support for agricultural production, environmental monitoring and other fields. Soil moisture data refers to the moisture status data in the soil, that is, humidity data. After determining the location of the target soil, the area occupied and other data, the purpose of zoning the target soil is that since the target soil area may be large, the monitoring range of a single soil moisture monitoring sensor is limited, so it is necessary to divide the target soil area according to the monitoring range of the soil moisture monitoring sensor, so that any position in the target soil area can be monitored. The working principle of the soil moisture monitoring sensor is to use resistive technology to infer the soil moisture content by measuring the relationship between soil moisture and resistance. As the soil moisture content increases, the conductivity of the soil increases, resulting in a decrease in the resistance of the sensor resistor. On the contrary, the resistance value will increase. After measuring the soil moisture content and temperature data, it needs to be converted into a digital signal, because only digital signals can be used for signal transmission. The purpose of signal amplification and signal filtering is that the collected digital signal may contain noise, and it is more convenient to filter it after amplification. Filtering can eliminate noise and improve accuracy. Finally, according to the matching relationship between the real-time soil moisture content parameter and the resistance value of the sensor resistor, combined with the temperature data, the soil humidity, that is, the soil moisture data, can be generated.

[0067] Furthermore, in a preferred embodiment of the present invention, the soil moisture data of the target soil is analyzed, and the target soil sub-areas are classified based on the analysis results, and the irrigation optimization schemes for different target soil sub-areas are obtained, specifically:

[0068] Acquire plant species that need to be planted on the target soil, introduce a big data network, and retrieve appropriate soil moisture data of the plant species that need to be planted on the target soil based on the big data network, and calibrate them as standard soil moisture data;

[0069] Analyze the soil moisture data of all target soil sub-areas, calculate the Euclidean distance between the soil moisture data of different target soil sub-areas and the standard soil moisture data, mark them as the first Euclidean distance, and preset the standard Euclidean distance interval;

[0070] If the first Euclidean distance corresponding to a target soil sub-region is not within the standard Euclidean distance interval, the corresponding target soil sub-region is marked as an abnormal target soil sub-region, and other target soil sub-regions are marked as normal target soil sub-regions;

[0071] In the abnormal target soil sub-area, the difference between the corresponding soil moisture data and the standard soil moisture data is calculated and calibrated as the soil moisture data difference, and the appropriate irrigation optimization scheme under different soil moisture data differences is retrieved in the big data network and calibrated as the qualified irrigation optimization scheme;

[0072] In different abnormal target soil sub-areas, all qualified irrigation optimization plans are analyzed, and the qualified irrigation optimization plan with the shortest irrigation time during implementation is calibrated as the target irrigation optimization plan, so that different abnormal target soil sub-areas have corresponding target irrigation optimization plans.

[0073] It should be noted that in the target soil area, the soil moisture data of different target soil sub-areas are different, and the plants to be planted in different target soil sub-areas may also be different, so the methods of optimizing soil irrigation for different target soil sub-areas are different. Under different soil irrigation schemes, the soil moisture data changes differently, corresponding to different plants that can be planted. The similarity between the soil moisture data of different target soil sub-areas and the soil moisture data of the plant fitness of the target planting is analyzed in the current state, in order to determine whether the current target soil sub-area is suitable for the requirements of planting plants. If the similarity is high, it proves that the target soil sub-area is suitable for planting plants and is marked as a normal target soil sub-area, otherwise it is an abnormal target soil sub-area. Among them, the similarity between the calculated data can be determined by calculating the Euclidean distance between the data, and the smaller the Euclidean distance, the higher the data similarity. When the first Euclidean distance is not within the Euclidean distance interval, it proves that the soil moisture data of the current target soil sub-area is not suitable for planting plants, and irrigation optimization is required. The irrigation optimization plan is retrieved from the big data network. The irrigation optimization plan is to perform watering and other treatments in the soil. Different irrigation optimization plans may have different watering amounts and watering times. Select the qualified irrigation optimization plan with the shortest irrigation time but the same effect for output, so that different abnormal target soil sub-areas have corresponding target irrigation optimization plans.

[0074] Furthermore, in a preferred embodiment of the present invention, the output target irrigation optimization scheme performs secondary soil moisture analysis in different abnormal target soil sub-areas, and adjusts the abnormal target soil sub-areas based on the secondary soil moisture analysis results, specifically:

[0075] Based on the target irrigation optimization schemes corresponding to different abnormal target soil sub-areas, equipment for irrigation optimization of different abnormal target soil sub-areas is obtained and calibrated as target irrigation optimization equipment, and the target irrigation optimization schemes corresponding to different abnormal target soil sub-areas are imported into the irrigation optimization equipment, so that the irrigation optimization equipment outputs the corresponding target irrigation optimization scheme according to the abnormal target soil sub-areas, and the abnormal target soil sub-areas after the initial irrigation optimization are obtained, which are calibrated as the initial irrigation optimization soil sub-areas;

[0076] The soil moisture monitoring sensors are used to monitor the soil moisture of all the first irrigation optimized soil sub-areas, and the soil moisture data of all the first irrigation optimized soil sub-areas are obtained, which are calibrated as the first irrigation optimized soil moisture data;

[0077] The Euclidean distance between the first irrigation optimized soil moisture data and the standard soil moisture data is calculated and marked as the second Euclidean distance, and the first irrigation optimized soil sub-region whose second Euclidean distance is not in the standard Euclidean distance interval is marked as the optimized abnormal soil sub-region;

[0078] Soil sampling is carried out in all optimized abnormal soil sub-areas to obtain soil samples to be analyzed, and climate parameters of the optimized abnormal soil sub-areas are obtained, and climate parameter simulation equipment is introduced at the same time;

[0079] Placing the soil sample to be analyzed in a climate parameter simulation device, adjusting the initial climate parameters in the climate parameter simulation device to the climate parameters of the optimized abnormal soil sub-region, and performing real-time climate parameter adjustment in the climate parameter simulation device;

[0080] Monitor the change rate of the soil moisture data of the soil sample to be analyzed during the real-time adjustment of the climate parameters. If the change rate of the soil moisture data of the soil sample to be analyzed during the real-time adjustment of the climate parameters is greater than a preset value, retrieve the climate parameters that make the initial irrigation optimized soil moisture data equal to the standard soil moisture data in the big data network, calibrate them as standard climate parameters, and simultaneously retrieve and output the solution that makes the climate parameters of the initial irrigation optimized soil sub-area equal to the standard climate parameters;

[0081] If the change rate of the soil moisture data of the soil sample to be analyzed during the real-time adjustment of climate parameters is not greater than the preset value, the irrigation optimization equipment will continue to output the corresponding target irrigation optimization plan on the initial irrigation optimization soil sub-area, and at the same time, the soil in the initial irrigation optimization soil sub-area will be loosened, so that all the initial irrigation optimization soil sub-areas are normal target soil sub-areas.

[0082] It should be noted that, through the target irrigation optimization equipment, the corresponding target irrigation optimization scheme is executed in different abnormal target soil sub-areas, the purpose is to optimize the irrigation of different abnormal target soil sub-areas so that they can plant the required plants. After applying the target irrigation optimization scheme, the primary irrigation optimized soil sub-area is obtained. The Euclidean distance between the soil moisture data of the primary irrigation optimized soil sub-area and the soil standard moisture data is calculated to determine whether the preset optimization purpose is achieved after the target irrigation optimization scheme is output. If the second Euclidean distance is still not within the standard interval, it proves that there are still problems in the primary irrigation optimized soil sub-area, and it is necessary to analyze the region, determine the existing problems and make adjustments. The reason for judging that the output target irrigation optimization scheme still cannot meet the conditions may be the influence of climate parameters. Climate conditions such as precipitation, evaporation, and temperature directly affect the moisture status of the soil, and indirectly the type of soil and preparation production conditions. Different types of soil have different water retention capacity and water release characteristics, and the growth status of vegetation will affect the evaporation and water absorption of the soil. Therefore, it is judged that the climate conditions may affect the soil moisture data. The soil is first sampled and then introduced into a climate simulation device, which can simulate the climate conditions of the soil position in reality, making the sampling analysis more accurate. After adjusting the climate parameters to the climate parameters that optimize the abnormal soil sub-area, continue to adjust the climate parameters to determine the rate of change of the soil moisture data of the soil sample to be analyzed during the real-time adjustment of the climate parameters. If the rate of change is greater than the preset value, it proves that the probability of soil changing with the change of climate parameters is high. At this time, it is necessary to determine the climate parameters in the corresponding area that maintain the soil moisture data equal to the standard value, that is, the standard climate parameters, and search for solutions in which the climate parameters are equal to the standard climate parameters. Including but not limited to installing a constant temperature greenhouse, appropriate rain and watering, etc. If the rate of change is less than the preset value, it proves that the relationship with the climate parameters is small. At this time, the soil can be loosened at the same time during the process of optimizing the irrigation treatment of the soil to achieve the purpose of fully absorbing soil moisture, adjust the soil moisture data, and obtain the normal target soil sub-area.

[0083] Figure 2 A flow chart of a method for analyzing and correcting the monitoring effect of a soil moisture monitoring sensor is shown, comprising the following steps:

[0084] S202: Calculating and analyzing the overlap rate between the fluctuation range of the soil moisture data to be analyzed obtained by the soil moisture monitoring sensor and the minimum fluctuation range of the soil moisture data;

[0085] S204: Perform sensor abnormality tracing analysis on the abnormal soil moisture monitoring sensor, and repair the abnormal soil moisture monitoring sensor based on the abnormality tracing analysis result.

[0086] Furthermore, in a preferred embodiment of the present invention, the calculation and analysis of the overlap rate between the fluctuation range of the soil moisture data to be analyzed obtained by the soil moisture monitoring sensor and the minimum fluctuation range of the soil moisture data is specifically as follows:

[0087] When the soil moisture data of all target soil sub-areas are monitored in real time by the soil moisture monitoring sensor, the data fluctuation range of the soil moisture data is obtained in real time by the data processing module of the soil moisture monitoring sensor, and is calibrated as the fluctuation range of the soil moisture data to be analyzed;

[0088] Preset the data fluctuation range analysis time, and preset the minimum range of soil moisture data fluctuation. During the data fluctuation range analysis time, calculate the overlap rate between the soil moisture data fluctuation range to be analyzed obtained by the soil moisture monitoring sensor and the minimum range of soil moisture data fluctuation, and preset the minimum overlap rate;

[0089] The soil moisture monitoring sensor whose overlap rate between the fluctuation range of the soil moisture data to be analyzed and the minimum fluctuation range of the soil moisture data is greater than the minimum overlap rate is calibrated as a normal soil moisture monitoring sensor, and the soil moisture monitoring sensor whose overlap rate between the fluctuation range of the soil moisture data to be analyzed and the minimum fluctuation range of the soil moisture data is less than the minimum overlap rate is calibrated as an abnormal soil moisture monitoring sensor.

[0090] It should be noted that when the soil moisture monitoring sensor monitors data in real time, if the detected soil moisture data maintains a constant value and lasts for a period of time, it is judged that the soil moisture monitoring sensor may have a fault. The soil moisture monitoring sensor after the fault cannot monitor the soil moisture data in real time, and may maintain a constant value, reducing the accuracy, so it is necessary to perform defect detection and maintenance on the soil moisture monitoring sensor. To determine whether the soil moisture monitoring sensor is abnormal, the data fluctuation range of the data obtained from its monitoring is analyzed, that is, the fluctuation range of the soil moisture data to be analyzed is analyzed. If the overlap rate between the fluctuation range of the soil moisture data to be analyzed and the minimum fluctuation range of the soil moisture data within the specified time is greater than the minimum overlap rate, it proves that the soil moisture monitoring sensor does not have a constant data situation during monitoring, and it is calibrated as a normal soil moisture monitoring sensor, otherwise it is an abnormal soil moisture monitoring sensor.

[0091] Furthermore, in a preferred embodiment of the present invention, the abnormal soil moisture monitoring sensor is subjected to sensor abnormality tracing analysis, and the abnormal soil moisture monitoring sensor is repaired based on the abnormality tracing analysis result, specifically:

[0092] Perform sensor probe cleaning and power supply replacement on the abnormal soil moisture monitoring sensor, and determine whether the overlap rate between the fluctuation range of the soil moisture data to be analyzed and the minimum fluctuation range of the soil moisture data obtained after the abnormal soil moisture monitoring sensor performs sensor probe cleaning and power supply replacement is greater than the minimum overlap rate;

[0093] If yes, the abnormal soil moisture monitoring sensor after the sensor probe cleaning process and power supply replacement process is calibrated as a normal soil moisture monitoring sensor;

[0094] If not, then obtain the communication parameters of the wireless communication module in the abnormal soil moisture monitoring sensor, calibrate them as target communication parameters, and obtain the standard range of the communication parameters of the wireless communication module in the abnormal soil moisture monitoring sensor during operation, calibrate them as standard communication parameter range;

[0095] If the target communication parameters are not maintained within the standard communication parameter range, the target communication parameters are analyzed based on the Bayesian network to determine the fault location of the wireless communication module in the abnormal soil moisture monitoring sensor, and the maintenance plan output of the fault location of the wireless communication module in the abnormal soil moisture monitoring sensor is retrieved in the big data network to maintain the target communication parameters within the standard communication parameter range;

[0096] When the target communication parameters are maintained within the standard communication parameter range, but the overlap rate between the fluctuation range of the soil moisture data to be analyzed and the minimum fluctuation range of the soil moisture data is still not greater than the minimum overlap rate, a protective cover is installed on the abnormal soil moisture monitoring sensor to ensure that when the abnormal soil moisture monitoring sensor is working, the overlap rate between the fluctuation range of the soil moisture data to be analyzed and the minimum fluctuation range of the soil moisture data is greater than the minimum overlap rate.

[0097] It should be noted that there are many reasons why the soil moisture monitoring sensor fails and becomes an abnormal soil moisture monitoring sensor. First of all, the sensor probe needs to be cleaned and the power supply needs to be replaced. Among them, the sensor probe will be covered with dust or pollutants due to long-term work in the soil, affecting the measurement accuracy and even causing damage. If the power supply is insufficient or the battery energy is exhausted, the sensor will not work properly, which will cause errors in the monitoring data of the soil moisture monitoring sensor. If the overlap rate is still less than the minimum overlap rate after the sensor probe is cleaned and the power supply is replaced, it may be that the digital signal detected by the sensor has an error in the communication process. Analyze the wireless communication module to determine whether the target communication parameters are maintained within the normal parameter range. If not, it is determined that the overlap rate is small because the digital signal has an error in the communication process. The Bayesian network can analyze the target communication parameters to accurately locate the fault location. After obtaining the fault location, the corresponding maintenance plan is retrieved to maintain the target communication parameters within the standard communication parameter range. If the overlap rate is still less than the standard range at this time, it is judged to be affected by external factors. For example, the sensor is exposed to severe weather conditions for a long time, such as high temperature, high humidity, severe cold, etc., which may cause equipment performance degradation or damage. At this time, the purpose of maintenance can be achieved by installing a protective cover or protective cover for the sensor to avoid direct exposure to severe weather.

[0098] In addition, the automatic management method of soil moisture monitoring sensors combined with Internet of Things technology also includes the following steps:

[0099] Acquire a soil moisture data analysis module, wherein the soil moisture data analysis module can collect soil moisture data monitored by all normal soil moisture monitoring sensors for the same analysis;

[0100] All normal soil moisture monitoring sensors are connected to the soil moisture data analysis module respectively to construct a soil moisture data analysis system. In the soil moisture data analysis system, soil moisture data monitored by different normal soil moisture monitoring sensors are extracted to obtain a soil moisture data distribution map of the target soil area, which is calibrated as a type of soil moisture data distribution map;

[0101] A convolutional neural network model is introduced, and the soil moisture data distribution map of the first type is imported into the convolutional neural network model to construct a target soil area prediction model, wherein the target soil area prediction model can predict the changes of other target soil sub-areas in the target soil area when the soil moisture data of one target soil sub-area changes;

[0102] In the target soil area prediction model, different model nodes are obtained, wherein different model nodes represent different target soil sub-areas, and parameters of different model nodes are actively adjusted respectively, wherein actively adjusting parameters of model nodes is equivalent to actively adjusting soil moisture data of target soil sub-areas;

[0103] During the process of actively adjusting the parameters of the model nodes, if the parameters of other model nodes change, the other model nodes whose parameter change rate is greater than the preset value are marked as associated model nodes, and the model nodes whose parameters are actively adjusted and the corresponding associated model nodes are combined into associated model nodes;

[0104] Based on the association model node combination, the associated target soil sub-area combination is obtained. In the process of outputting a qualified irrigation optimization plan through the target irrigation optimization equipment, the target irrigation optimization equipment is controlled to simultaneously output a qualified irrigation optimization plan to the target soil sub-areas within the same associated target soil sub-area combination.

[0105] It should be noted that different target soil sub-areas may be interrelated. For example, when the soil moisture data of one target soil sub-area changes, the water in the soil may penetrate into the target soil sub-area next door, causing it to change. The reason is that the moisture in the soil is unbalanced, and the soil with high moisture will penetrate into the soil with low moisture. Therefore, a type of soil moisture data distribution map is constructed to uniformly analyze the changes in the soil moisture data of all target soil sub-areas in the target soil area. Convolutional neural network is a prediction network. After combining a type of soil moisture data distribution map, the target soil area prediction model obtained can predict the changes in other target soil sub-areas in the target soil area when the soil moisture data of one target soil sub-area changes. The model node in the model is the target soil sub-area. After the node parameter analysis of the target soil sub-area is performed, the associated node combination is determined, and the associated target soil sub-area combination is obtained based on the associated model node combination. All target soil sub-areas in the associated target soil sub-area combination will change due to changes in each other's moisture content. Therefore, when outputting a plan for a target soil sub-area in the same associated target soil sub-area combination, it is necessary to simultaneously output it to other sub-areas to improve the accuracy of the soil moisture data after output.

[0106] like Figure 3 As shown, the second aspect of the present invention also provides an automatic management system for soil moisture monitoring sensors combined with Internet of Things technology, wherein the automatic management system includes a memory 31 and a processor 32, wherein the memory 31 stores an automatic management method, and when the automatic management method is executed by the processor 32, the following steps are implemented:

[0107] The soil moisture data is monitored in real time on the target soil by using a soil moisture monitoring sensor, and the real-time soil moisture data obtained by monitoring is stored;

[0108] Analyze the soil moisture data of the target soil, classify the target soil sub-areas based on the analysis results, and obtain irrigation optimization plans for different target soil sub-areas;

[0109] Output the target irrigation optimization plan, perform secondary soil moisture analysis in different abnormal target soil sub-areas, and make adjustments in the abnormal target soil sub-areas based on the secondary soil moisture analysis results;

[0110] The soil moisture data monitored by the soil moisture monitoring sensor is analyzed in real time for abnormalities, and the soil moisture monitoring sensor is corrected in real time for abnormalities based on the results of the abnormal real-time analysis.

[0111] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. The automatic management method of soil moisture monitoring sensor combined with Internet of Things technology is characterized by: The following steps are involved: The soil moisture data is monitored in real time on the target soil by using a soil moisture monitoring sensor, and the real-time soil moisture data obtained by monitoring is stored; Analyze the soil moisture data of the target soil, classify the target soil sub-areas based on the analysis results, and obtain irrigation optimization plans for different target soil sub-areas; Output the target irrigation optimization plan, perform secondary soil moisture analysis in different abnormal target soil sub-areas, and make adjustments in the abnormal target soil sub-areas based on the secondary soil moisture analysis results; Performing real-time analysis of abnormal soil moisture data monitored by the soil moisture monitoring sensor, and performing real-time correction of abnormal soil moisture monitoring sensors based on the results of the real-time analysis; The method for automatically managing soil moisture monitoring sensors in combination with Internet of Things technology further includes the following steps: Acquire a soil moisture data analysis module, wherein the soil moisture data analysis module can collect soil moisture data monitored by all normal soil moisture monitoring sensors for the same analysis; All normal soil moisture monitoring sensors are connected to the soil moisture data analysis module respectively to construct a soil moisture data analysis system. In the soil moisture data analysis system, soil moisture data monitored by different normal soil moisture monitoring sensors are extracted to obtain a soil moisture data distribution map of the target soil area, which is calibrated as a type of soil moisture data distribution map; A convolutional neural network model is introduced, and the soil moisture data distribution map of the first type is imported into the convolutional neural network model to construct a target soil area prediction model, wherein the target soil area prediction model can predict the changes of other target soil sub-areas in the target soil area when the soil moisture data of one target soil sub-area changes; In the target soil area prediction model, different model nodes are obtained, wherein different model nodes represent different target soil sub-areas, and parameters of different model nodes are actively adjusted respectively, wherein actively adjusting parameters of model nodes is equivalent to actively adjusting soil moisture data of target soil sub-areas; During the process of actively adjusting the parameters of the model nodes, if the parameters of other model nodes change, the other model nodes whose parameter change rate is greater than the preset value are marked as associated model nodes, and the model nodes whose parameters are actively adjusted and the corresponding associated model nodes are combined into associated model nodes; Based on the association model node combination, the associated target soil sub-area combination is obtained. In the process of outputting a qualified irrigation optimization plan through the target irrigation optimization equipment, the target irrigation optimization equipment is controlled to simultaneously output a qualified irrigation optimization plan to the target soil sub-areas within the same associated target soil sub-area combination.

2. The method for automatically managing soil moisture monitoring sensors in combination with Internet of Things technology according to claim 1, characterized in that: The real-time monitoring of soil moisture data on the target soil by using a soil moisture monitoring sensor and storing the real-time soil moisture data obtained by monitoring are specifically as follows: Obtain all soils that need to be included in real-time soil moisture data monitoring and mark them as target soils; Obtaining the area and scope of the target soil, and obtaining a soil moisture monitoring sensor, wherein the soil moisture monitoring sensor is a sensor that can be placed in the target soil to monitor soil moisture data; Obtaining a maximum monitoring range of a soil moisture monitoring sensor, and based on the maximum monitoring range of the soil moisture monitoring sensor, partitioning the target soil to obtain a target soil sub-area, wherein the target soil sub-area is not larger than the maximum monitoring range of the soil moisture monitoring sensor; Soil moisture monitoring sensors are placed in different target soil sub-areas and run, wherein the soil moisture monitoring sensors include a sensor resistor, a sensor probe, a signal processing circuit, a wireless communication module and a data processing module; In the target soil sub-area, the moisture content and temperature of the soil are monitored in real time by a sensor probe, which are calibrated as the real-time moisture content and the real-time temperature of the soil, respectively, and the real-time moisture content and the real-time temperature of the soil are converted into digital signals, and the signal is amplified and filtered by the signal processing circuit, and finally the real-time moisture content and the real-time temperature of the soil under the digital signal are stored in the data processing module through the wireless communication module; In the data processing module, the digital signals of the real-time soil moisture content and the real-time soil temperature are calibrated as the real-time soil moisture content parameter and the real-time soil temperature parameter, respectively, and the matching relationship between the real-time soil moisture content parameter and the resistance value of the sensor resistor is calculated in real time. Based on the matching relationship between the real-time soil moisture content parameter and the resistance value of the sensor resistor, combined with the real-time soil temperature parameter, the soil moisture of the target soil is calculated, that is, the soil moisture data of the target soil is calculated.

3. The method for automatic management of soil moisture monitoring sensors combined with Internet of Things technology according to claim 1, characterized in that: The soil moisture data of the target soil is analyzed, and the target soil sub-areas are classified based on the analysis results, and the irrigation optimization schemes for different target soil sub-areas are obtained, specifically: Acquire plant species that need to be planted on the target soil, introduce a big data network, and retrieve appropriate soil moisture data of the plant species that need to be planted on the target soil based on the big data network, and calibrate them as standard soil moisture data; Analyze the soil moisture data of all target soil sub-areas, calculate the Euclidean distance between the soil moisture data of different target soil sub-areas and the standard soil moisture data, mark them as the first Euclidean distance, and preset the standard Euclidean distance interval; If the first Euclidean distance corresponding to a target soil sub-region is not within the standard Euclidean distance interval, the corresponding target soil sub-region is marked as an abnormal target soil sub-region, and other target soil sub-regions are marked as normal target soil sub-regions; In the abnormal target soil sub-area, the difference between the corresponding soil moisture data and the standard soil moisture data is calculated and calibrated as the soil moisture data difference, and the appropriate irrigation optimization scheme under different soil moisture data differences is retrieved in the big data network and calibrated as the qualified irrigation optimization scheme; In different abnormal target soil sub-areas, all qualified irrigation optimization plans are analyzed, and the qualified irrigation optimization plan with the shortest irrigation time during implementation is calibrated as the target irrigation optimization plan, so that different abnormal target soil sub-areas have corresponding target irrigation optimization plans.

4. The method for automatic management of soil moisture monitoring sensors combined with Internet of Things technology according to claim 1, characterized in that: The output target irrigation optimization plan performs secondary soil moisture analysis in different abnormal target soil sub-areas, and adjusts the abnormal target soil sub-areas based on the secondary soil moisture analysis results, specifically: Based on the target irrigation optimization schemes corresponding to different abnormal target soil sub-areas, equipment for irrigation optimization of different abnormal target soil sub-areas is obtained and calibrated as target irrigation optimization equipment, and the target irrigation optimization schemes corresponding to different abnormal target soil sub-areas are imported into the irrigation optimization equipment, so that the irrigation optimization equipment outputs the corresponding target irrigation optimization scheme according to the abnormal target soil sub-areas, and the abnormal target soil sub-areas after the initial irrigation optimization are obtained, which are calibrated as the initial irrigation optimization soil sub-areas; The soil moisture monitoring sensors are used to monitor the soil moisture of all the first irrigation optimized soil sub-areas, and the soil moisture data of all the first irrigation optimized soil sub-areas are obtained, which are calibrated as the first irrigation optimized soil moisture data; The Euclidean distance between the first irrigation optimized soil moisture data and the standard soil moisture data is calculated and marked as the second Euclidean distance, and the first irrigation optimized soil sub-region whose second Euclidean distance is not in the standard Euclidean distance interval is marked as the optimized abnormal soil sub-region; Soil sampling is carried out in all optimized abnormal soil sub-areas to obtain soil samples to be analyzed, and climate parameters of the optimized abnormal soil sub-areas are obtained, and climate parameter simulation equipment is introduced at the same time; Placing the soil sample to be analyzed in a climate parameter simulation device, adjusting the initial climate parameters in the climate parameter simulation device to the climate parameters of the optimized abnormal soil sub-region, and performing real-time climate parameter adjustment in the climate parameter simulation device; Monitor the change rate of the soil moisture data of the soil sample to be analyzed during the real-time adjustment of the climate parameters. If the change rate of the soil moisture data of the soil sample to be analyzed during the real-time adjustment of the climate parameters is greater than a preset value, retrieve the climate parameters that make the initial irrigation optimized soil moisture data equal to the standard soil moisture data in the big data network, calibrate them as standard climate parameters, and simultaneously retrieve and output the solution that makes the climate parameters of the initial irrigation optimized soil sub-area equal to the standard climate parameters; If the change rate of the soil moisture data of the soil sample to be analyzed during the real-time adjustment of climate parameters is not greater than the preset value, the irrigation optimization equipment will continue to output the corresponding target irrigation optimization plan on the initial irrigation optimization soil sub-area, and at the same time, the soil in the initial irrigation optimization soil sub-area will be loosened, so that all the initial irrigation optimization soil sub-areas are normal target soil sub-areas.

5. The method for automatic management of soil moisture monitoring sensors combined with Internet of Things technology according to claim 1, characterized in that: The abnormal real-time analysis of the soil moisture data monitored by the soil moisture monitoring sensor and the abnormal real-time correction of the soil moisture monitoring sensor based on the abnormal real-time analysis result are specifically as follows: When the soil moisture data of all target soil sub-areas are monitored in real time by the soil moisture monitoring sensor, the data fluctuation range of the soil moisture data is obtained in real time by the data processing module of the soil moisture monitoring sensor, and is calibrated as the fluctuation range of the soil moisture data to be analyzed; Preset the data fluctuation range analysis time, and preset the minimum range of soil moisture data fluctuation. During the data fluctuation range analysis time, calculate the overlap rate between the soil moisture data fluctuation range to be analyzed obtained by the soil moisture monitoring sensor and the minimum range of soil moisture data fluctuation, and preset the minimum overlap rate; The soil moisture monitoring sensor whose overlap rate between the fluctuation range of the soil moisture data to be analyzed and the minimum fluctuation range of the soil moisture data is greater than the minimum overlap rate is calibrated as a normal soil moisture monitoring sensor, and the soil moisture monitoring sensor whose overlap rate between the fluctuation range of the soil moisture data to be analyzed and the minimum fluctuation range of the soil moisture data is less than the minimum overlap rate is calibrated as an abnormal soil moisture monitoring sensor; Conduct sensor abnormality tracing analysis on abnormal soil moisture monitoring sensors, and repair the abnormal soil moisture monitoring sensors based on the abnormality tracing analysis results.

6. The method for automatic management of soil moisture monitoring sensors combined with Internet of Things technology according to claim 5, characterized in that: The abnormal soil moisture monitoring sensor is subjected to sensor abnormality tracing analysis, and the abnormal soil moisture monitoring sensor is repaired based on the abnormality tracing analysis result, specifically: Perform sensor probe cleaning and power supply replacement on the abnormal soil moisture monitoring sensor, and determine whether the overlap rate between the fluctuation range of the soil moisture data to be analyzed and the minimum fluctuation range of the soil moisture data obtained after the abnormal soil moisture monitoring sensor performs sensor probe cleaning and power supply replacement is greater than the minimum overlap rate; If yes, the abnormal soil moisture monitoring sensor after the sensor probe cleaning process and power supply replacement process is calibrated as a normal soil moisture monitoring sensor; If not, then obtain the communication parameters of the wireless communication module in the abnormal soil moisture monitoring sensor, calibrate them as target communication parameters, and obtain the standard range of the communication parameters of the wireless communication module in the abnormal soil moisture monitoring sensor during operation, calibrate them as standard communication parameter range; If the target communication parameters are not maintained within the standard communication parameter range, the target communication parameters are analyzed based on the Bayesian network to determine the fault location of the wireless communication module in the abnormal soil moisture monitoring sensor, and the maintenance plan output of the fault location of the wireless communication module in the abnormal soil moisture monitoring sensor is retrieved in the big data network to maintain the target communication parameters within the standard communication parameter range; When the target communication parameters are maintained within the standard communication parameter range, but the overlap rate between the fluctuation range of the soil moisture data to be analyzed and the minimum fluctuation range of the soil moisture data is still not greater than the minimum overlap rate, a protective cover is installed on the abnormal soil moisture monitoring sensor to ensure that when the abnormal soil moisture monitoring sensor is working, the overlap rate between the fluctuation range of the soil moisture data to be analyzed and the minimum fluctuation range of the soil moisture data is greater than the minimum overlap rate.

7. The automatic management system of soil moisture monitoring sensor combined with Internet of Things technology is characterized by: The automated management system includes a memory and a processor. The memory stores an automated management method program. When the automated management method program is executed by the processor, the automated management method steps as described in any one of claims 1 to 6 are implemented.

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