Straw returning-to-field deep scarification cultivation regulation and control method based on soil temperature and humidity

Abstract

The invention discloses a straw returning deep scarification cultivation regulation and control method based on soil temperature and humidity, and particularly relates to the technical field of cultivation regulation and control. A plurality of soil temperature and humidity sensors and meteorological sensors are arranged in a field, soil temperature and humidity and meteorological environment data are collected in real time, preprocessing and feature extraction are carried out on the data, then a data prediction model is constructed to evaluate the accuracy of monitoring data, and if the accuracy of the monitoring data is high, the system directly adjusts the operation time and depth; if the accuracy is low, the data acquisition frequency is automatically increased by analyzing the severity of the deviation, so that the monitoring precision and the farming decision are optimized, farming errors caused by misjudgment of the soil humidity are effectively avoided, the farming effect and the crop yield are improved, and meanwhile, the soil quality is improved.

Description

Technical Field

[0001] The invention relates to the technical field of tillage regulation, and in particular to a straw returning to field deep loosening regulation method based on soil temperature and humidity. Background Art

[0002] Straw return to the field and deep tillage are important technical means to improve agricultural soil quality and crop yields. Straw return to the field returns crop straw to the soil, providing organic fertilizer for the soil, improving soil structure, increasing organic matter content, and enhancing soil water retention capacity. Deep tillage loosens the soil through mechanical means, improves soil permeability and drainage capacity, helps the root system grow deeper, enhances crop resistance and promotes yield increase.

[0003] In traditional agriculture, the timing and methods of returning straw to the field and deep tillage often rely on the experience of farmers. However, changes in factors such as climate change, soil moisture and temperature make it particularly complicated to accurately grasp the best time for tillage. Therefore, a control method based on soil temperature and humidity has emerged, relying on real-time monitoring of soil temperature and humidity changes to dynamically adjust tillage operations to achieve the best results. By monitoring the temperature and humidity of the soil in real time, the system can perform deep tillage and return straw to the field at the appropriate time, avoiding operations when the soil is too wet or too dry, thereby improving the tillage effect and reducing resource waste. For example, when the soil moisture is too high, deep tillage too early may aggravate soil compaction, while returning straw to the field in drought conditions may not be ideal. Therefore, adjusting the operation time and depth based on soil temperature and humidity data is an important technical path to improve agricultural production efficiency and resource utilization.

[0004] The prior art has the following deficiencies:

[0005] The key to accurately monitoring soil temperature and humidity lies in high-precision sensors and monitoring equipment, but these devices may have errors, failures, or improper calibration. Sensors may drift due to long-term use, resulting in inaccurate data, which in turn affects farming decisions. In addition, if the soil moisture sensor reports that the soil is too dry or too wet, the actual situation does not match it, which may lead to the wrong choice of farming time. For example, if the moisture sensor fails, the system may mistakenly believe that the soil is too wet, thereby delaying deep loosening and causing soil compaction, affecting the growth of crop roots. Summary of the invention

[0006] The purpose of the present invention is to provide a method for regulating straw return to field deep loosening and tillage based on soil temperature and humidity to solve the shortcomings of the background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a straw return to field deep loosening and tillage control method based on soil temperature and humidity, comprising the following steps:

[0008] S1: Several soil temperature and humidity sensors are deployed in different areas of the field to collect soil temperature and humidity data in real time over multiple time periods, and different types of meteorological sensors are deployed to collect soil meteorological environment data in real time;

[0009] S2: preprocessing the collected soil temperature and humidity data and soil meteorological environment data, and extracting features from the preprocessed soil temperature and humidity data and soil meteorological environment data, respectively extracting soil temperature and humidity mutation features and soil meteorological prediction deviation features;

[0010] S3: Based on the extracted soil temperature and humidity mutation characteristics and soil meteorological forecast deviation characteristics, a data prediction model is constructed, and the accuracy of the soil temperature and humidity monitoring data is determined based on the output results of the data prediction model;

[0011] S4: If the accuracy of the soil temperature and humidity monitoring data is high, it will be directly input into the operation decision-making system to dynamically adjust the operation time and depth of straw return and deep tillage. If the accuracy of the soil temperature and humidity monitoring data is low, the severity of the deviation of the soil temperature and humidity monitoring data will be judged. If the severity is high, the data collection frequency of the soil temperature and humidity sensor will be increased.

[0012] Preferably, in S2, the soil temperature and humidity anomaly index is generated after analyzing the extracted soil temperature and humidity variation trend characteristics over time. The soil temperature and humidity anomaly index is obtained by:

[0013] For each soil temperature and humidity data point, define its neighborhood and select a parameter K to represent the neighborhood size of each data point. For a data point p, calculate the distance d(p,q) to point q. The expression is: Where p=(p1,p2,…,p n ) and q=(q1,q2,…,q n ) is the characteristic vector of the soil temperature and humidity data point. For the data point p and the point q in the neighborhood, the reachable distance r is defined k (p,q) is: r k (p,q)=max(d(p,q),k dist (q)); where k dist (q) is the distance from point q to its K-neighbors, that is, the distance from point q to its K-th nearest neighbor, the local reachability density LRD k (p) represents the density of data point p in its neighborhood, and the calculation formula is: Among them, N k (p) is the K neighbors of data point p, r k(p,q) is the reachable distance between the data point p and its neighboring point q. The local outlier factor is calculated, that is, the soil temperature and humidity anomaly index is calculated. The calculation formula is: Where QSD is the soil temperature and humidity anomaly index.

[0014] Preferably, in S2, the deviation characteristics between the extracted real-time meteorological data and the meteorological forecast data are analyzed to generate a meteorological data deviation index. The method for obtaining the meteorological data deviation index is as follows: Let x t represents the real-time meteorological data at time t, and Represents the weather forecast data at the same time t, and the difference is the error: Among them, e t is the absolute error at the tth moment, and the error e at each moment t It will be multiplied by a weight w t , the weighted error is: For all collected meteorological data points, the meteorological data deviation index is calculated, and the expression is: Where N is the total number of data points and WMAE is the meteorological data deviation index.

[0015] Preferably, in S3, a data prediction model is constructed based on the extracted soil temperature and humidity mutation characteristics and soil meteorological forecast deviation characteristics, and the accuracy of the soil temperature and humidity monitoring data is determined based on the output results of the data prediction model, specifically:

[0016] The soil temperature and humidity anomaly index and the meteorological data deviation index are normalized, and the accuracy value of the soil temperature and humidity monitoring data is calculated by the normalized soil temperature and humidity anomaly index and the meteorological data deviation index.

[0017] Preferably, the accuracy value of the acquired soil temperature and humidity monitoring data is compared with a preset accuracy value reference threshold based on historical data. If the accuracy value of the soil temperature and humidity monitoring data is greater than or equal to the preset accuracy value reference threshold, it indicates that the accuracy of the soil temperature and humidity monitoring data is high. In this case, a normal data signal is generated, and the soil temperature and humidity monitoring data is classified as accuracy monitoring data. If the accuracy value of the soil temperature and humidity monitoring data is less than the preset accuracy value reference threshold, it indicates that the accuracy of the soil temperature and humidity monitoring data is low. In this case, an abnormal data signal is generated, and the soil temperature and humidity monitoring data is classified as inaccuracy monitoring data.

[0018] Preferably, in S4, if the soil temperature and humidity monitoring data is highly accurate, it is directly input into the operation decision system to dynamically adjust the operation time and depth of straw return and deep tillage, specifically:

[0019] According to the real-time collected soil temperature and humidity data and weather forecast data, the operation time T adjust and operating depth D adjust Dynamic adjustment is performed, and the expression is: T adjust =T base +k1·ΔT soil +k2·ΔH soil +k3·ΔQ; where T adjust is the adjusted operation time; T base is the preset operation time; ΔT soil is the difference between the current soil temperature and the preset temperature; ΔH soil is the difference between the current soil moisture and the preset moisture; ΔQ is the difference between the accuracy value of the soil temperature and humidity monitoring data and the accuracy threshold, which affects the fine-tuning of the operation time; k1, k2, k3 are adjustment coefficients;

[0020] The working depth is adjusted according to the changes in soil moisture and soil temperature. The adjustment formula is: D adjust =D base +k4·ΔT soil +k5·ΔH soil +k6·ΔQ; where D adjust is the adjusted working depth, D base is the preset operating depth; ΔT soil and ΔH soil is the difference between soil temperature and humidity and the preset value; k4, k5, k6 are adjustment coefficients; the adjusted operation time and operation depth are input into the operation decision system, and dynamically adjusted according to actual farmland conditions and tillage machinery.

[0021] Preferably, if the accuracy of the soil temperature and humidity monitoring data is low, that is, the accuracy value of the soil temperature and humidity monitoring data is less than the preset accuracy value reference threshold, the severity of the deviation of the monitoring data is judged, and the expression is: S severity =|QQ threshold |+A·ΔT soil +B·ΔH soil Where S severity is the severity of the deviation, Q is the accuracy value of the current soil temperature and humidity monitoring data; Q threshold is the preset accuracy value reference threshold; ΔT soil is the difference between soil temperature and preset temperature; ΔH soil is the difference between soil moisture and preset moisture; A and B are adjustment coefficients;

[0022] According to the calculated deviation severity S severity , if its value exceeds the reference threshold S of the set deviation severity severity, then the deviation is considered serious and the data collection frequency of the soil temperature and humidity sensor needs to be increased. The expression is: iF severity threshold iF severity ≥S threshold ;f adjust is the adjusted data collection frequency, f base is the preset basic data collection frequency, γ is the adjustment coefficient, which is used to control the impact of the severity of the deviation on the collection frequency; S threshold is the reference threshold for the severity of the deviation.

[0023] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0024] 1. The present invention collects soil temperature and humidity and meteorological data in real time by deploying multiple soil temperature and humidity sensors and meteorological sensors in the field, and generates soil temperature and humidity anomaly index and meteorological data deviation index by using data processing and feature extraction methods. On this basis, the present invention evaluates the accuracy of soil temperature and humidity monitoring data through a data prediction model, and dynamically adjusts the tillage timing and depth according to the accuracy value, ensuring that the operation is adapted to the soil and meteorological conditions, and improving the accuracy of tillage decisions.

[0025] 2. The present invention analyzes the severity of the deviation of the monitoring data and automatically increases the data collection frequency of the soil temperature and humidity sensor when the monitoring data accuracy is low, thereby improving the monitoring accuracy. This dynamic control mechanism based on real-time data feedback effectively avoids farming errors caused by misjudgment of soil temperature and humidity, optimizes farming operations, improves the growth environment and yield of crops, and improves soil quality, providing more intelligent and reliable technical support for precision agriculture. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0027] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0028] ​In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0029] For examples, see Figure 1 As shown, the straw returning to the field deep loosening and tillage control method based on soil temperature and humidity described in this embodiment includes the following steps:

[0030] S1: Several soil temperature and humidity sensors are deployed in different areas of the field to collect soil temperature and humidity data in real time over multiple time periods, and different types of meteorological sensors are deployed to collect soil meteorological environment data in real time;

[0031] S2: preprocessing the collected soil temperature and humidity data and soil meteorological environment data, and extracting features from the preprocessed soil temperature and humidity data and soil meteorological environment data, respectively extracting soil temperature and humidity mutation features and soil meteorological prediction deviation features;

[0032] S3: Based on the extracted soil temperature and humidity mutation characteristics and soil meteorological forecast deviation characteristics, a data prediction model is constructed, and the accuracy of the soil temperature and humidity monitoring data is determined based on the output results of the data prediction model;

[0033] S4: If the accuracy of the soil temperature and humidity monitoring data is high, it will be directly input into the operation decision-making system to dynamically adjust the operation time and depth of straw return and deep tillage. If the accuracy of the soil temperature and humidity monitoring data is low, the severity of the deviation of the soil temperature and humidity monitoring data will be judged. If the severity is high, the data collection frequency of the soil temperature and humidity sensor will be increased.

[0034] Among them, in S1, several soil temperature and humidity sensors are deployed in different areas of the field to collect soil temperature and humidity data in real time in multiple time periods, and different types of meteorological sensors are deployed to collect soil meteorological environment data in real time, specifically:

[0035] Multiple soil temperature and humidity sensors are deployed in different areas of the field to obtain more comprehensive and accurate soil temperature and humidity data. The number of sensors deployed may vary depending on the area of ​​the field, soil type and crop type. Usually, dozens to hundreds of sensors are deployed to ensure the representativeness and coverage of the data.

[0036] Sensors need to be placed at different locations and depths in the field. These locations should be representative, covering different soil types, crop varieties, growth stages, etc. Sensors can be placed in different directions of the field (such as southeast, northwest, etc.) to collect soil data under different environmental conditions.

[0037] Shallow sensors: installed on the soil surface (0-20 cm), they mainly monitor the temperature and humidity of the surface soil, which plays an important role in the initial growth of crops and the evaluation of the effect of returning straw to the field.

[0038] Middle layer sensor: It is placed in the soil layer at a depth of 20-40 cm to monitor the moisture and temperature of the middle layer of soil. This depth is crucial for the water supply to the crop roots and the effect of deep tillage.

[0039] Deep sensors: They are placed in the soil layer of 40-80 cm or deeper, mainly monitoring the moisture and temperature of the deep soil. These data help analyze the distribution of moisture in the deep layer and determine the necessity and timing of deep tillage.

[0040] Types of soil temperature and humidity sensors include: Capacitive sensor: obtains soil moisture by measuring the dielectric constant of the soil, usually with high accuracy and stability, and is suitable for long-term monitoring. Resistive sensor: calculates soil moisture by measuring changes in resistance, usually at a lower price, but is easily affected by soil salinity and may not be suitable for certain special soil types. TDR (time domain reflectometry) sensor: uses the reflective properties of the soil to measure moisture content, has very high accuracy and reliability, and is suitable for application scenarios with high precision requirements. Thermal conductivity sensor: measures changes in the thermal conductivity of the soil to infer humidity and temperature, suitable for high-precision long-term monitoring.

[0041] The sensor needs to have high-precision temperature and humidity measurement capabilities and be able to capture details in tiny changes in soil temperature and humidity. The sensor should have strong anti-interference capabilities and be able to resist the impact of external environmental changes (such as wind, rain, etc.) on data collection. Due to the complex soil environment and the need for sensors to be buried in the soil for a long time, they need to have strong durability and corrosion resistance. In order to facilitate the real-time transmission of field data, sensors usually need to have wireless communication functions, such as LoRa, NB-IoT, Wi-Fi, etc., to ensure that data can be uploaded to the central processing system in a timely manner.

[0042] Meteorological sensors are used to monitor soil meteorological environmental data in real time. These sensors collect meteorological data related to soil status and further assist in analyzing soil moisture and temperature changes. The purpose of deploying meteorological sensors is to obtain external meteorological factors that affect soil moisture and temperature, such as precipitation, temperature, humidity, wind speed, etc. Common types of meteorological sensors include: Temperature sensor: used to monitor air temperature in real time. Air temperature has a direct impact on soil temperature and humidity, so this data is crucial. Relative humidity sensor: used to monitor air humidity in real time. Air humidity has a significant impact on soil evaporation rate, especially under high temperature or drought conditions, the evaporation rate of soil moisture will accelerate. Precipitation sensor: used to monitor precipitation. The direct impact of precipitation on soil moisture is particularly important. By monitoring precipitation, it can be assessed whether the soil is in a moist state and whether it is suitable for cultivation. Wind speed sensor: monitors wind speed. Wind force has an impact on the evaporation rate of soil moisture, temperature fluctuations, etc., especially in arid areas, strong winds will accelerate the evaporation of soil moisture. Atmospheric pressure sensor: monitors changes in atmospheric pressure. Changes in air pressure are often related to meteorological changes and can provide auxiliary information for the prediction of meteorological models.

[0043] The combination of meteorological data and soil data is very important because meteorological conditions directly affect the moisture status and temperature of the soil. For example, when there is heavy precipitation, soil moisture tends to increase. At this time, early or deep deep tillage should be avoided to avoid soil compaction. In dry or hot weather conditions, soil moisture evaporates quickly. At this time, timely adjustment of irrigation or tillage depth can effectively improve water retention capacity and crop growth conditions. Therefore, the combination of meteorological data and soil temperature and humidity data can improve the accuracy of tillage, and help farmers better grasp the timing of tillage and avoid unsuitable tillage conditions. The data collected by all deployed soil temperature and humidity sensors and meteorological sensors usually need to be transmitted to the central data management platform in real time through wireless communication technologies (such as LoRa, NB-IoT, 4G / 5G, Wi-Fi, etc.).

[0044] S2: Preprocess the collected soil temperature and humidity data and soil meteorological environment data, and perform feature extraction on the preprocessed soil temperature and humidity data and soil meteorological environment data, respectively extracting soil temperature and humidity mutation characteristics and soil meteorological prediction deviation characteristics.

[0045] Data preprocessing is the process of organizing, cleaning and standardizing the original collected data to ensure the accuracy and effectiveness of subsequent analysis. The main purpose of preprocessing is to remove noise, fill in missing data, smooth data, handle outliers, etc., to ensure that the data is more accurate and reliable.

[0046] Since sensors may be affected by environmental interference, hardware failure, or data transmission errors, abnormal values ​​(such as extremely high or low values) may appear in the collected soil temperature and humidity data or meteorological data. By setting a reasonable threshold range, data that is obviously inconsistent with the actual situation can be removed. Some missing values ​​may appear during the data collection process, such as the failure to collect temperature and humidity data for a certain period of time. Common methods include interpolation (such as linear interpolation, spline interpolation, etc.) or using historical data for filling.

[0047] In order to remove high-frequency noise in the data, smoothing algorithms such as sliding average and weighted average are usually used to reduce the interference of short-term fluctuations on the analysis results. Through low-pass filters or other filtering techniques, frequent changes or noise in the data are removed to make the data more consistent with the actual trend. Ensure that the units of all sensor data are unified. For example, soil temperature should be uniformly expressed in degrees Celsius and humidity should be uniformly expressed in percentage form. In order to eliminate the deviation caused by different dimensions between different sensors, it is usually necessary to standardize the data (such as Z-score standardization) or normalize (such as Min-Max normalization) to convert all data to the same scale for subsequent processing.

[0048] The mutation characteristics of soil temperature and humidity refer to the large changes in soil temperature and humidity within a certain period of time. This mutation may be caused by changes in external meteorological conditions (such as precipitation, temperature changes) or human operations (such as irrigation, tillage, etc.). The extraction of these mutation characteristics can help the system capture the drastic changes in soil conditions in a timely manner and adjust the tillage strategy. The mutation points of soil temperature and humidity are detected using the difference method or threshold method. For example, when the soil temperature or humidity changes by more than a certain threshold (such as ±5%), it is considered a mutation. The moment with a large change amplitude can be found by the difference method (the difference between the current moment and the previous moment) or the analysis based on the sliding window. Analyze the change trend of soil temperature and humidity over time, capture whether the rising or falling trend of temperature and humidity continues, and then identify short-term fluctuations and long-term change trends. The intensity of the mutation characteristics can be quantified by calculating the rate and amplitude of data change. For example, the rate of change of soil temperature in a short period of time or the rate of increase and decrease of humidity can be calculated, which helps to evaluate the potential impact of mutations on crop growth.

[0049] The soil meteorological forecast deviation characteristics refer to the extraction of forecast deviation characteristics by comparing the differences between the actual collected meteorological data and the meteorological forecast model or historical data. These deviation characteristics can reveal the inaccuracy of meteorological forecasts and their impact on soil temperature and humidity. Compare the real-time meteorological data (such as temperature, precipitation, humidity, etc.) with the meteorological forecast data to calculate the deviation between the two. For example, calculate the error between the temperature forecast and the actual temperature, or the difference between the precipitation forecast and the actual precipitation. Extract features such as deviation amplitude (difference between predicted value and actual value), deviation change rate (change of forecast error over time), and deviation persistence (whether the error continues to increase or decrease), which can be used as the basis for soil meteorological impact analysis. Data with large deviations may mean that the operation plan needs to be adjusted or more flexible measures need to be taken. Based on historical meteorological data and forecast errors, a deviation correction model (such as a model based on regression analysis) is constructed so that the meteorological forecast data can be corrected in the system, thereby improving the accuracy of subsequent analysis and decision-making.

[0050] The soil temperature and humidity anomaly index is generated by analyzing the extracted soil temperature and humidity change trend characteristics over time. The soil temperature and humidity anomaly index is obtained as follows:

[0051] For each soil temperature and moisture data point, its neighborhood is defined, and a parameter K is selected to represent the neighborhood size of each data point (i.e., the neighborhood contains K nearest neighbors).

[0052] Calculate the distance from each point in the data set to all other points (for example, using Euclidean distance). For a data point p, calculate the distance d(p,q) to all other points q, expressed as: Where p=(p1,p2,…,p n ) and q=(q1,q2,…,q n ) is the characteristic vector of the soil temperature and humidity data point. For the data point p and the point q in the neighborhood, the reachable distance r is defined k (p,q) is: r k (p,q)=max(d(p,q),k dist (q)); where k dist (q) is the distance from point q to its K-neighbors, that is, the distance from point q to its K-th nearest neighbor. Local reachability density LRD k (p) represents the density of data point p in its neighborhood, which is the inverse of the reachable distance of the point. The calculation formula is: Among them, N k (p) is the K neighbors of data point p, r k (p,q) is the reachable distance between the data point p and its neighboring point q. The local outlier factor is calculated, that is, the soil temperature and humidity anomaly index is calculated. The calculation formula is: Where QSD is the soil temperature and humidity anomaly index.

[0053] The larger the value of the soil temperature and humidity anomaly index, the greater the difference between the soil temperature and humidity data at that point and its neighboring data, that is, the data point may be an outlier. Specifically, when the soil temperature and humidity change dramatically, or the temperature and humidity data in a certain area are significantly inconsistent with the surrounding environment, the LOF algorithm will calculate a higher anomaly index. This situation may indicate that the soil temperature and humidity sensor has failed, the data collection is abnormal, or the soil itself has undergone sudden atypical changes (such as a sudden increase in humidity or a sharp increase in temperature in a local area), resulting in inaccurate monitoring data at that point. Therefore, a larger anomaly index usually indicates that there are errors in the monitoring data, and the accuracy of the data needs to be further verified and corrected.

[0054] On the contrary, the smaller the soil temperature and humidity anomaly index is, the more consistent the soil temperature and humidity data at this point is with the surrounding data, that is, the data at this point is in line with the overall trend of its environment. A smaller anomaly index indicates that the soil temperature and humidity sensor is working properly, the data is more accurate, and there is no significant deviation. Therefore, a smaller anomaly index usually means that the soil temperature and humidity monitoring data is more accurate and can better reflect the actual temperature and humidity conditions of the soil. In this case, the data can be directly used in the farming decision-making system to optimize the operation time and depth, thereby improving agricultural production efficiency.

[0055] The deviation characteristics between the extracted real-time meteorological data and the meteorological forecast data are analyzed to generate the meteorological data deviation index. The method for obtaining the meteorological data deviation index is as follows:

[0056] Let x t represents the real-time meteorological data at time t, and Represents the weather forecast data at the same time t, and the difference is the error: Among them, e t is the absolute error at the tth moment, and the error e at each moment t It will be multiplied by a weight w t , the weight can be set according to the importance of different time periods. For example, the critical period of crop growth or the period of time when meteorological conditions change drastically can be given a larger weight. t Is a non-negative value that indicates the relative importance of each moment. The weighted error is: For all collected meteorological data points, the meteorological data deviation index is calculated, and the expression is: Where N is the total number of data points and WMAE is the meteorological data deviation index.

[0057] The larger the meteorological data deviation index is, the greater the difference between the real-time meteorological data and the meteorological forecast data is, which usually means that the meteorological forecast is more inaccurate. For the soil temperature and humidity monitoring system based on meteorological data, the increase in the meteorological data deviation index will lead to a decrease in the accuracy of the soil temperature and humidity monitoring data. This is because the changes in soil temperature and humidity are greatly affected by meteorological conditions (such as precipitation, temperature, humidity, etc.). When the meteorological data deviation is large, the prediction and regulation of soil temperature and humidity will also produce errors, thereby affecting the accuracy of farming decisions. Therefore, when the meteorological data deviation index is large, the credibility of the monitoring data is low, which may lead to inappropriate tillage timing and depth selection, thereby affecting crop growth and soil quality.

[0058] On the contrary, the smaller the meteorological data deviation index is, the smaller the difference between the real-time meteorological data and the meteorological forecast data is, and the higher the accuracy of the prediction model is. At this time, the accuracy of the soil temperature and humidity monitoring data based on meteorological data is also high, because the impact of meteorological factors on soil is more precise. A lower deviation index indicates that the meteorological forecast is more accurate, thereby improving the reliability of the soil temperature and humidity prediction model, and can better control the optimal time and depth of straw return and deep tillage. This can not only effectively improve soil quality, but also improve agricultural production efficiency and ensure that crops grow in a suitable environment.

[0059] S3: Based on the extracted soil temperature and humidity mutation characteristics and soil meteorological forecast deviation characteristics, a data prediction model is constructed, and the accuracy of the soil temperature and humidity monitoring data is determined based on the output results of the data prediction model.

[0060] The soil temperature and humidity anomaly index and the meteorological data deviation index are normalized, and the accuracy value of the soil temperature and humidity monitoring data is calculated by the normalized soil temperature and humidity anomaly index and the meteorological data deviation index.

[0061] For example, the present invention can use the following formula to calculate the accuracy value of soil temperature and humidity monitoring data, and the calculation expression is: Where Q is the accuracy value of soil temperature and humidity monitoring data, QSD is the soil temperature and humidity anomaly index, WMAE is the meteorological data deviation index, a1 and a2 are the proportional coefficients of the soil temperature and humidity anomaly index and the meteorological data deviation index, and a2>a1>0.

[0062] The accuracy value of the acquired soil temperature and humidity monitoring data is compared with the accuracy value reference threshold preset according to historical data. If the accuracy value of the soil temperature and humidity monitoring data is greater than or equal to the preset accuracy value reference threshold, it means that the accuracy of the soil temperature and humidity monitoring data is high. At this time, a normal data signal is generated, and the soil temperature and humidity monitoring data is classified as accuracy monitoring data; if the accuracy value of the soil temperature and humidity monitoring data is less than the preset accuracy value reference threshold, it means that the accuracy of the soil temperature and humidity monitoring data is low. At this time, a data abnormality signal is generated, and the soil temperature and humidity monitoring data is classified as inaccuracy monitoring data.

[0063] S4: If the accuracy of the soil temperature and humidity monitoring data is high, it will be directly input into the operation decision-making system to dynamically adjust the operation time and depth of straw return and deep tillage. If the accuracy of the soil temperature and humidity monitoring data is low, the severity of the deviation of the soil temperature and humidity monitoring data will be judged. If the severity is high, the data collection frequency of the soil temperature and humidity sensor will be increased.

[0064] By deploying soil temperature and humidity sensors and meteorological sensors, soil temperature and humidity data and meteorological environment data are collected in real time. The accuracy value Q of the soil temperature and humidity monitoring data is calculated and compared with the preset accuracy reference threshold Q. If the accuracy value of the soil temperature and humidity monitoring data is greater than or equal to the preset accuracy value reference threshold, the data is considered accurate and can proceed to the next step; if it is inaccurate, the system will mark it as data abnormality and suspend farming.

[0065] According to the real-time collected soil temperature and humidity data and weather forecast data, the operation time T adjust and operating depth D adjust Dynamic adjustment is performed, and the expression is: T adjust =T base +k1·ΔT soil +k2·ΔH soil +k3·ΔQ; where T adjust is the adjusted operation time; T base is the preset operation time (for example, the conventional operation time or the best operation time in historical data); ΔT soil is the difference between the current soil temperature and the preset temperature (which can be obtained by real-time monitoring of the soil temperature sensor); ΔH soil is the difference between the current soil moisture and the preset humidity (also obtained in real time through the humidity sensor); ΔQ is the difference between the accuracy value of the soil temperature and humidity monitoring data and the accuracy threshold, which affects the fine-tuning of the operation time. The higher the accuracy value, the smaller the adjustment range of the operation time; k1, k2, k3 are adjustment coefficients, which are used to adjust the sensitivity of the operation time according to specific farmland, climate conditions and other factors.

[0066] The operation depth (i.e. the depth of subsoiling) is adjusted according to the changes in soil moisture and soil temperature. Wet soil is suitable for shallow tillage, while dry soil is suitable for deep tillage. The adjustment formula is: D adjust =D base +k4·ΔT soil +k5·ΔH soil +k6·ΔQ; where D adjust is the adjusted working depth, D base is the preset operating depth (which can be determined through historical data or experience); ΔT soil and ΔH soil is the difference between soil temperature and humidity and the preset value, which is similar to the operation time adjustment formula; k4, k5, k6 are adjustment coefficients used to adjust the operation depth according to actual soil conditions, deep tillage goals and other factors.

[0067] The adjusted operation time and operation depth are input into the operation decision system, and dynamic adjustments are made according to the actual farmland conditions and tillage machinery to ensure the best results of straw return and deep tillage. During the operation, if the adjustment effect of the operation time or depth is found to be unsatisfactory, the system can make optimization adjustments through further monitoring data feedback.

[0068] If the accuracy of the soil temperature and humidity monitoring data is low, that is, the accuracy value of the soil temperature and humidity monitoring data is less than the preset accuracy value reference threshold, the severity of the deviation of the monitoring data is judged, and the expression is: S severity =|QQ threshold |+A·ΔT soil +B·ΔH soil Where S severity is the severity of the deviation, the larger the value, the more serious the deviation; Q is the accuracy value of the current soil temperature and humidity monitoring data; Q threshold is the preset accuracy value reference threshold; ΔT soil is the difference between soil temperature and preset temperature; ΔH soil is the difference between soil moisture and preset humidity; A and B are adjustment coefficients used to control the effects of temperature and humidity on the severity of deviation.

[0069] According to the calculated deviation severity S severity , if its value exceeds the reference threshold S of the set deviation severity severity , then the deviation is considered serious and the data collection frequency of the soil temperature and humidity sensor needs to be increased. The expression is: iF severity threshold iF severity ≥S threshold ;f​adjust is the adjusted data collection frequency, f base is the preset basic data collection frequency (for example, the initial collection frequency is once an hour); γ is the adjustment coefficient, which is used to control the impact of the severity of the deviation on the collection frequency; S threshold The reference threshold for the severity of the deviation. The purpose of increasing the frequency of data collection is to obtain more accurate monitoring data and improve the accuracy of operational decisions. After increasing the frequency of data collection, the system continues to monitor data changes to ensure that the deviation is improved. If the deviation gradually decreases, the system can gradually return to the initial data collection frequency.

[0070] In this embodiment, first, multiple soil temperature and humidity sensors are arranged in different areas of the field, and meteorological sensors are deployed to collect soil temperature and humidity and meteorological environment data in real time. Then, the collected data is preprocessed, and the mutation characteristics of soil temperature and humidity and the prediction deviation characteristics of meteorological data are extracted. Next, a data prediction model is constructed based on the extracted features, and the accuracy of the soil temperature and humidity monitoring data is evaluated according to the model results. If the data accuracy is high, it will be directly input into the operation decision system for dynamic adjustment of the operation time and depth of straw return and deep tillage; if the data accuracy is low, the severity of the deviation is evaluated. If it is serious, the data collection frequency of the sensor is increased to ensure the accuracy of subsequent data and optimize operation regulation.

[0071] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0072] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0073] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the objects associated with each other are in an "or" relationship, but it may also indicate an "and / or" relationship, which can be understood by referring to the context. A person of ordinary skill in the art can appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this article can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0074] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A straw return and deep loosening tillage control method based on soil temperature and humidity, characterized in that: The following steps are involved: S1: Several soil temperature and humidity sensors are deployed in different areas of the field to collect soil temperature and humidity data in real time over multiple time periods, and different types of meteorological sensors are deployed to collect soil meteorological environment data in real time; S2: preprocessing the collected soil temperature and humidity data and soil meteorological environment data, and extracting features from the preprocessed soil temperature and humidity data and soil meteorological environment data, respectively extracting soil temperature and humidity mutation features and soil meteorological prediction deviation features; S3: Based on the extracted soil temperature and humidity mutation characteristics and soil meteorological forecast deviation characteristics, a data prediction model is constructed, and the accuracy of the soil temperature and humidity monitoring data is determined based on the output results of the data prediction model; S4: If the accuracy of the soil temperature and humidity monitoring data is high, it will be directly input into the operation decision-making system to dynamically adjust the operation time and depth of straw return and deep tillage. If the accuracy of the soil temperature and humidity monitoring data is low, the severity of the deviation of the soil temperature and humidity monitoring data will be judged. If the severity is high, the data collection frequency of the soil temperature and humidity sensor will be increased.

2. The straw return and deep loosening tillage control method based on soil temperature and humidity according to claim 1 is characterized by: In S2, the soil temperature and humidity anomaly index is generated after analyzing the extracted soil temperature and humidity change trend characteristics over time. The soil temperature and humidity anomaly index is obtained as follows: For each soil temperature and humidity data point, define its neighborhood and select a parameter K to represent the neighborhood size of each data point. For a data point p, calculate the distance d(p,q) to point q. The expression is: Where p=(p1,p2,…,p n ) and q=(q1,q2,…,q n ) is the characteristic vector of the soil temperature and humidity data point. For the data point p and the point q in the neighborhood, the reachable distance r is defined k (p,q) is: r k (p,q)=max(d(p,q),k dist (q)); where k dist (q) is the distance from point q to its K-neighbors, that is, the distance from point q to its K-th nearest neighbor, the local reachability density LRD k (p) represents the density of data point p in its neighborhood, and the calculation formula is: Among them, N k (p) is the K neighbors of data point p, r k (p,q) is the reachable distance between the data point p and its neighboring point q. The local outlier factor is calculated, that is, the soil temperature and humidity anomaly index is calculated. The calculation formula is: Where QSD is the soil temperature and humidity anomaly index.

3. The method for controlling straw return to field deep loosening and tillage based on soil temperature and humidity according to claim 2, characterized in that: In S2, the deviation characteristics between the extracted real-time meteorological data and the meteorological forecast data are analyzed to generate a meteorological data deviation index. The method for obtaining the meteorological data deviation index is as follows: Let x t represents the real-time meteorological data at time t, and Represents the weather forecast data at the same time t, and the difference is the error: Among them, e t is the absolute error at the tth moment, and the error e at each moment t It will be multiplied by a weight w t , the weighted error is: For all collected meteorological data points, the meteorological data deviation index is calculated, and the expression is: Where N is the total number of data points and WMAE is the meteorological data deviation index.

4. The method for controlling straw return to field and deep loosening cultivation based on soil temperature and humidity according to claim 3, characterized in that: In S3, a data prediction model is constructed based on the extracted soil temperature and humidity mutation characteristics and soil meteorological forecast deviation characteristics, and the accuracy of the soil temperature and humidity monitoring data is determined based on the output results of the data prediction model, specifically: The soil temperature and humidity anomaly index and the meteorological data deviation index are normalized, and the accuracy value of the soil temperature and humidity monitoring data is calculated by the normalized soil temperature and humidity anomaly index and the meteorological data deviation index.

5. The method for controlling straw return to field and deep loosening cultivation based on soil temperature and humidity according to claim 4, characterized in that: The accuracy value of the acquired soil temperature and humidity monitoring data is compared with the accuracy value reference threshold preset according to historical data. If the accuracy value of the soil temperature and humidity monitoring data is greater than or equal to the preset accuracy value reference threshold, it means that the accuracy of the soil temperature and humidity monitoring data is high. At this time, a normal data signal is generated, and the soil temperature and humidity monitoring data is classified as accuracy monitoring data; if the accuracy value of the soil temperature and humidity monitoring data is less than the preset accuracy value reference threshold, it means that the accuracy of the soil temperature and humidity monitoring data is low. At this time, a data abnormality signal is generated, and the soil temperature and humidity monitoring data is classified as inaccuracy monitoring data.

6. The method for controlling straw return to field deep loosening and tillage based on soil temperature and humidity according to claim 1, characterized in that: In S4, if the soil temperature and humidity monitoring data are highly accurate, they are directly input into the operation decision system to dynamically adjust the operation time and depth of straw return and deep tillage, specifically: According to the real-time collected soil temperature and humidity data and weather forecast data, the operation time T adjust and operating depth D adjust Dynamic adjustment is performed, and the expression is: T adjust =T base +k1·ΔT soil +k2·ΔH soil +k3·ΔQ; where T adjust is the adjusted operation time; T base The preset operation time; ΔT soil is the difference between the current soil temperature and the preset temperature; ΔH soil is the difference between the current soil moisture and the preset moisture; ΔQ is the difference between the accuracy value of the soil temperature and humidity monitoring data and the accuracy threshold, which affects the fine-tuning of the operation time; k1, k2, k3 are adjustment coefficients; The working depth is adjusted according to the changes in soil moisture and soil temperature. The adjustment formula is: D adjust =D base +k4·ΔT soil +k5·ΔH soil +k6·ΔQ; where D adjust is the adjusted working depth, D base is the preset operating depth; ΔT soil and ΔH soil is the difference between soil temperature and humidity and the preset value; k4, k5, k6 are adjustment coefficients; the adjusted operation time and operation depth are input into the operation decision system, and dynamically adjusted according to actual farmland conditions and tillage machinery.

7. The method for controlling straw return to field deep loosening and tillage based on soil temperature and humidity according to claim 6, characterized in that: If the accuracy of the soil temperature and humidity monitoring data is low, that is, the accuracy value of the soil temperature and humidity monitoring data is less than the preset accuracy value reference threshold, the severity of the deviation of the monitoring data is judged, and the expression is: S severity =|QQ threshold |+A·ΔT soil +B·ΔH soil Where S severity is the severity of the deviation, Q is the accuracy value of the current soil temperature and humidity monitoring data; Q threshold is the preset accuracy value reference threshold; ΔT soil is the difference between soil temperature and preset temperature; ΔH soil is the difference between soil moisture and preset moisture; A and B are adjustment coefficients; According to the calculated deviation severity S severity , if its value exceeds the reference threshold S of the set deviation severity severity , then the deviation is considered serious and the data collection frequency of the soil temperature and humidity sensor needs to be increased. The expression is: f adjust is the adjusted data collection frequency, f base is the preset basic data collection frequency, γ is the adjustment coefficient, which is used to control the impact of the severity of the deviation on the collection frequency; S threshold is the reference threshold for the severity of the deviation.

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