Brown soil planting management method driven by climate data

Abstract

The present application provides a brown soil planting management method driven by climate data, which relates to the field of agricultural information technology. By sampling and detecting the distribution area of ​​brown soil, information on the proportion of brown soil components, brown soil pH value, soil particle ratio and soil type sub-class ratio are obtained; the interactive user end receives crop variety information and crop growth stage; through the climate data association component, target climate data is configured for the crop variety information and crop growth stage based on the sampling and detection information; climate monitoring data is collected and compared with the target climate data to obtain the climate data deviation index; when the climate data deviation index is greater than or equal to the deviation index threshold, a climate data interference signal is generated and sent to the user end. The present application solves the technical problem that the traditional method lacks quantitative climate environment assessment standards, resulting in the inability to achieve precise and batch brown soil planting management, and improves the scientificity, accuracy and universality of brown soil planting management.

Description

Technical Field

[0001] The present application relates to the field of agricultural information technology, and in particular to a brown soil planting management method driven by climate data. Background Art

[0002] Brown soil is a widely distributed soil type with unique physical and chemical properties. It is rich in organic matter and suitable for the growth of a variety of crops, but due to its special composition and structure, it is particularly sensitive to climatic conditions. Climate factors, such as temperature, humidity, precipitation and light, have a direct and profound impact on the growth and development of crops in brown soil. Suitable climatic conditions can promote the healthy growth of crops, while unfavorable climate conditions may lead to growth stunting and even reduced yields. Therefore, controlling and managing the climate environment of brown soil cultivation has become the key to improving crop yield and quality.

[0003] In traditional planting management, farmers and agricultural experts mainly rely on experience and intuitive judgment to decide when to irrigate, fertilize or take other intervention measures. However, this approach is often too general and cannot make the best decision for the specific conditions of each piece of land. Due to the lack of detailed analysis of soil and climate data, planting management cannot be accurate enough to meet the needs of specific crops and soil conditions. In addition, relying on personal experience is also susceptible to subjective factors, and the judgments of different people may vary greatly, resulting in unstable planting management results. More importantly, this approach cannot handle large-scale planting scenarios, limiting the development trend of large-scale and intensive modern agriculture. Summary of the invention

[0004] This application provides a brown soil planting management method driven by climate data, which solves the technical problem that traditional planting methods are difficult to quantify and evaluate the impact of climate and environment, resulting in limited accuracy of brown soil planting management and inability to implement it in batches, thereby improving the scientificity, accuracy and universality of brown soil planting management.

[0005] In view of the above problems, the present application provides a brown soil planting management method driven by climate data, including: sampling and detecting the brown soil distribution area to obtain brown soil component ratio information, brown soil pH value information, soil particle ratio and soil type sub-category ratio information; interactive user end, receiving crop variety information and crop growth stage; through the climate data association component, based on the brown soil component ratio information, the brown soil pH value information, the soil particle ratio and the soil type sub-category ratio information, configuring target climate data for the crop variety information and the crop growth stage; through the meteorological monitoring component, collecting climate monitoring data; comparing the climate monitoring data with the target climate data to obtain a climate data deviation index; when the climate data deviation index is greater than or equal to the deviation index threshold, generating a climate data interference signal and sending it to the user end.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] Sampling and testing of the brown soil distribution area to obtain brown soil component ratio information, brown soil pH information, soil particle ratio and soil type sub-class ratio information, provide basic data for subsequent analysis, ensure a full understanding of soil conditions, and provide personalized management for crop growth. The interactive user end receives crop variety information and crop growth stage to customize climate management strategies according to crop types and growth stages, ensure that climate data configuration meets the actual needs of specific crops, and improve the pertinence of management. Through the climate data association component, based on the brown soil component ratio information, the brown soil pH information, the soil particle ratio and the soil type sub-class ratio information, configure target climate data for the crop variety information and the crop growth stage, combine soil characteristics with crop needs, determine ideal or optimal climate conditions, create a quantitative target climate benchmark, guide crop growth, and optimize the growth environment. Through the meteorological monitoring component, collect climate monitoring data, track and record climate conditions in real time, ensure the timeliness and accuracy of data, provide feedback on actual climate conditions, and enable managers to understand the environmental status in a timely manner. Compare the climate monitoring data with the target climate data to obtain the climate data deviation index, evaluate the difference between the actual climate and the target climate, quantify whether the climate conditions are suitable for crop growth, and provide a basis for necessary intervention. When the climate data deviation index is greater than or equal to the deviation index threshold, a climate data interference signal is generated and sent to the user end, warning the user when the climate conditions do not meet the standards, prompting the user to take action, prevent potential growth problems, ensure healthy growth of crops, and reduce losses.

[0008] In summary, this application achieves precise soil and climate environment management through continuous monitoring and analysis of quantitative data and target configuration; and improves the adaptability to climate change and optimizes crop growth conditions through real-time monitoring and deviation analysis, thereby realizing scientific, precise and batch-based brown soil planting management, significantly improving the stability and efficiency of agricultural production.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic diagram of a brown soil planting management method driven by climate data provided in an embodiment of the present application;

[0011] Figure 2A schematic diagram of a process for obtaining and retrieving a climate data set in a brown soil planting management method driven by climate data provided in an embodiment of the present application;

[0012] Figure 3 A schematic diagram of a process for obtaining a high-frequency retrieval climate data set in a brown soil planting management method driven by climate data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0013] The embodiment of the present application provides a brown soil planting management method driven by climate data, which solves the technical problem that traditional planting methods are difficult to quantify and evaluate the impact of climate and environment, resulting in limited accuracy of brown soil planting management and inability to implement it in batches, thereby improving the scientificity, accuracy and universality of brown soil planting management.

[0014] like Figure 1 As shown, the embodiment of the present application provides a brown soil planting management method driven by climate data, the method comprising:

[0015] Step S1: sampling and testing the brown soil distribution area to obtain brown soil component ratio information, brown soil pH value information, soil particle ratio and soil type sub-class ratio information.

[0016] Specifically, the brown soil composition ratio information refers to the relative content of different chemical elements and minerals in the brown soil. Different elements and minerals have different effects on the growth of crops. For example, nitrogen, phosphorus, and potassium are the main nutrients for plant growth, and their content directly affects the health and yield of crops. The brown soil pH value information refers to the acidity and alkalinity of the brown soil. The pH value affects the availability of nutrients in the soil and the growth and health of crops. The soil particle ratio refers to the ratio of particles of different sizes in the soil, such as sand, clay, and silt. The ratio of different particles determines the physical properties of the soil, such as drainage, water retention, and aeration. For example, soil with a high clay content has strong water retention capacity but poor drainage; sandy soil has good drainage but poor water retention capacity. The soil type sub-class ratio information refers to the classification and sub-classification information of the soil, such as loam, clay, etc. and their respective proportions. Soils of different sub-classes have different characteristics. Understanding soil sub-classes helps to formulate planting plans and soil management strategies more accurately.

[0017] Multiple sampling points are selected in the area where brown soil is distributed to ensure the representativeness of the samples. Next, soil sampling tools such as drills or shovels are used to take soil samples from different depths and send them to the laboratory for analysis to determine various key properties of the soil, including component ratio, pH value, soil particle ratio and soil type sub-category ratio information. In the laboratory, spectral analysis, chemical analysis and other methods are used to determine the component ratio of the soil, which can help determine the content of nutrients in the soil. The pH value can be determined by pH test paper or pH meter, which is a commonly used method to determine the acidity and alkalinity of the soil. The soil particle ratio is usually determined by screening or specific gravity, while the soil type sub-category ratio is determined by observing the texture and structure of the soil. This step provides key information about the condition of the soil, which is an integral part of making planting strategies and management decisions. By accurately understanding the characteristics of the soil, it is possible to better create a suitable growth environment for crops, thereby improving crop yields and quality.

[0018] Step S2: The interactive user terminal receives crop variety information and crop growth stage.

[0019] Specifically, crop variety information refers to the specific variety of crops planted, and different varieties have different requirements for climate and soil conditions. Crop growth stages refer to the different growth stages of crops from planting to maturity, such as germination, vegetative growth, flowering, and maturity. Crop variety information and crop growth stages are received through an interactive user terminal. The user enters the type and growth stage of the crop through the interface, such as entering "corn" as the crop variety and "flowering" as the growth stage. This information is very important for determining the current and future needs of crops. Different crops may have very different requirements for environmental conditions at the same growth stage. Collecting this information helps to formulate management plans based on the specific needs of the crops.

[0020] Step S3: configuring target climate data for the crop variety information and the crop growth stage based on the brown soil component ratio information, the brown soil pH value information, the soil particle ratio and the soil type subclass ratio information through a climate data association component.

[0021] Specifically, the climate data association component is a software system whose main function is to analyze and process the crop variety information and growth stage data received from the user end, combined with the soil property data obtained from soil sampling and testing, to determine the most suitable climate conditions for the growth of specific crops. Target climate data refers to ideal climate conditions such as temperature and humidity configured based on soil information and crop requirements, which are used to guide planting management. Using the climate data association component, the target climate data is configured according to the brown soil information obtained in step S1 and the crop information provided in step S2. This component will integrate information such as soil composition and pH value to generate ideal climate conditions that meet crop needs, such as temperature range and humidity level. For example, if corn requires a temperature of 25 to 30°C and 60% humidity during the flowering period, the system will configure these target climate data.

[0022] Step S4: Collect climate monitoring data through the meteorological monitoring component.

[0023] Furthermore, the climate monitoring data described in step S4 of the embodiment of the present application at least includes ambient temperature, precipitation, light intensity, CO2 concentration, and air humidity.

[0024] Specifically, the meteorological monitoring component is a combination of hardware and software for collecting and transmitting data about current climate conditions in real time. It mainly includes: a sensor network composed of sensors deployed in fields or greenhouses, such as thermometers, rain gauges, light sensors, and CO2 monitors, data loggers that collect data from sensors, communication equipment that transmits data, and software platforms that analyze and visualize climate data. Climate monitoring data refers to climate condition data collected in real time, including ambient temperature, precipitation, light intensity, CO2 concentration, air humidity, etc. The meteorological monitoring component collects climate data in the environment in real time, including ambient temperature, precipitation, light intensity, CO2 concentration, and air humidity. These data are the basis for evaluating current climate conditions and ensure that managers can grasp the latest environmental status.

[0025] Step S5: Compare the climate monitoring data with the target climate data to obtain a climate data deviation index.

[0026] Specifically, the climate data deviation index is a measure of the difference between climate monitoring data and target climate data, which is used to determine whether the actual climate conditions meet crop requirements. The collected climate monitoring data is compared with the target climate data, and the difference between the two is calculated to obtain a quantitative indicator, namely the climate data deviation index. This deviation index is used to quantify the difference between the actual climate conditions and the target conditions, helping to determine whether the climate conditions match the crop requirements. The calculation of the deviation index can be a simple difference or a more complex functional relationship, depending on the specific crop and soil conditions. For example, if the target temperature is 22 degrees Celsius and the actual temperature is 28 degrees Celsius, the deviation index may be 6, indicating that the temperature is too high. Similarly, if the target humidity is 70% and the actual humidity is 50%, the deviation index will be 20, indicating that the humidity is low.

[0027] Step S6: When the climate data deviation index is greater than or equal to the deviation index threshold, a climate data interference signal is generated and sent to the user end.

[0028] Specifically, the climate data interference signal is an alarm signal generated when the climate data deviation exceeds the threshold, which is used to notify the user to take measures to adjust the planting management. When the climate data deviation index is greater than or equal to the set deviation index threshold, it means that the monitored climate conditions have significantly deviated from the target conditions. The system generates a climate data interference signal and sends it to the user end through an alarm or other notification method, reminding the user to take corresponding measures, such as irrigation, shading or ventilation, to maintain an ideal environment for crop growth. For example, if the temperature deviation is too large, the system will issue an alarm and advise the user to irrigate or adjust the shading facilities to ensure that the crops grow in an ideal climate environment.

[0029] For example, farmers are planting wheat, and wheat is in a rapid growth period. According to the collected soil samples, the soil is acidic and rich in clay, which means that higher humidity and moderate temperature are needed to support the growth of wheat. Through the meteorological monitoring component, it is found that the actual temperature is higher than the target temperature and the precipitation is lower than expected. In this case, the deviation index calculated by the system will indicate that the climatic conditions do not meet the needs of wheat growth. Once the deviation index exceeds the preset threshold, the system will send an interference signal to the farmer, indicating that measures such as watering may be needed to reduce the temperature and increase the humidity.

[0030] A dynamic, data-driven management process is created through steps S1 to S6 to monitor and adjust the planting environment in real time to suit the needs of the crops, thereby improving crop yield and quality.

[0031] Furthermore, step S3 of the embodiment of the present application also includes:

[0032] Step S31: Perform multi-level sorting of positive sample climate data on the crop variety information and the crop growth stage through the brown soil component ratio information, the brown soil pH value information, the soil particle ratio and the soil type sub-class ratio information to obtain a retrieval climate data set.

[0033] Step S32: performing multi-attribute joint sorting on the retrieved climate data set to obtain a high-frequency retrieved climate data set.

[0034] Step S33: configuring attribute intervals according to the high-frequency retrieval climate data set to generate the target climate data.

[0035] Specifically, positive sample climate data refers to a set of climate data samples that meet crop needs. The retrieval climate data set is a set of climate data obtained through multi-level sorting of positive sample climate data for further analysis. The high-frequency retrieval climate data set is a climate data set after multi-attribute joint sorting, representing the climate data that best meets crop needs. Attribute interval configuration is to set the ideal climate condition interval based on the attribute range of the high-frequency retrieval climate data set. Target climate data refers to the specific climate condition data generated based on the attribute interval configuration, which is used to guide actual management.

[0036] First, the climate data association component uses data mining technology to analyze historical successful planting cases and extract the corresponding climate data as positive samples. These positive samples contain the climate conditions that lead to good crop growth. Then, these positive samples are sorted at multiple levels, that is, they are classified according to crop varieties and growth stages to find climate data suitable for specific crops and growth stages, and obtain the retrieved climate data set.

[0037] After obtaining the retrieval climate data set, it is sorted by multiple attributes to obtain a high-frequency retrieval climate data set. That is, it is sorted comprehensively according to multiple attributes, including temperature, humidity, precipitation, etc., to screen out the climate data that meets the conditions.

[0038] Based on the high-frequency retrieval climate data set, the climate data association component sets a reasonable range for each climate parameter. This range is the expected climate conditions to configure the attribute interval to generate target climate data. The attribute interval configuration process includes setting the ideal climate condition range. For example, if the data shows that crops grow best when the temperature is 20 to 25 degrees Celsius, the system sets 20 to 25 degrees Celsius as the target temperature interval. The same method also applies to other climate parameters, such as humidity, light, etc. This process ensures that the generated target climate data can provide the best growth environment for crops.

[0039] Preferably, Figure 2 As shown, step S31 of the embodiment of the present application also includes:

[0040] The brown soil component ratio information, the brown soil pH value information, the soil particle ratio and the soil type subclass ratio information are traversed to configure a fault tolerance threshold set.

[0041] Step 1: extract k sorting feature values ​​from the brown soil component ratio information, the brown soil pH value information, the soil particle ratio and the soil type subclass ratio information, the total number of fault tolerance thresholds ≥k≥1, and the initial value of k is equal to 1.

[0042] Step 2: Based on the fault tolerance threshold set and the k-item sorting feature values, the crop variety information and the crop growth stage are sorted into a k-1 level retrieval climate data set to obtain a k-level retrieval climate data set.

[0043] When k is equal to the total number of error tolerance thresholds, the k-level retrieval climate data set is set as the retrieval climate data set.

[0044] When k is less than the total number of fault tolerance thresholds, k is increased by one, and steps 1 to 2 are repeated.

[0045] Specifically, the fault-tolerant threshold set is a threshold set used to handle data uncertainty and error to tolerate a certain degree of error. The total number of fault-tolerant thresholds refers to the number of eigenvalues ​​allowed in the fault-tolerant threshold set, and the total number of fault-tolerant thresholds ≥ k ≥ 1. The k-item sorting eigenvalues ​​refer to the k most important soil characteristics and crop requirement characteristics selected for screening climate data. The k-level retrieval climate data set refers to the climate data set that has been screened based on k eigenvalues, which are considered to be the most suitable for the current crop and growth stage.

[0046] First, the climate data association component traverses all data from the brown soil composition ratio, pH value, soil particle ratio, and soil type subclass ratio information, and configures a set of tolerance thresholds, that is, defining a range or threshold for each feature value to handle errors or changes in the data. For example, the pH tolerance threshold is set to ±0.5 to allow for natural fluctuations in soil acidity and alkalinity.

[0047] The climate data association component extracts k key eigenvalues ​​from soil information, which are used for further sorting. Using these k eigenvalues ​​and the set of fault tolerance thresholds, the crop variety information and growth stage are sorted for k-1 level retrieval climate data sets. The specific process includes: the initial value of k is set to 1, that is, first extract a eigenvalue for screening. The extracted eigenvalue is applied to climate data filtering to obtain the climate data set that best matches the target crop. For example, when k=1, the pH value is selected for screening. The appropriate climate data set is screened out based on the pH value. Then compare k and the total number of fault tolerance thresholds. If k is less than the total number of fault tolerance thresholds, k is increased by one, and steps one to two are repeated, that is, a new eigenvalue is added for screening. For example, when k=2, the pH value and the soil particle ratio may be considered at the same time for screening to obtain a more accurate climate data set. By repeating steps one to two, more eigenvalues ​​can be gradually introduced to refine the retrieval climate data set and improve the accuracy of the retrieval climate data set. When k is equal to the total number of fault tolerance thresholds, it means that all necessary sorting levels have been completed, and the current k-level retrieval climate data set is the final retrieval climate data set.

[0048] Through the above process, the climate data association component can extract and apply key feature values ​​step by step, configure a set of fault-tolerant thresholds, and optimize the climate data sorting process to ensure that the final target climate data best meets the actual needs of crops and soil conditions, making climate data-driven planting management more targeted and reliable.

[0049] Preferably, Figure 3 As shown, step S32 of the embodiment of the present application also includes:

[0050] The retrieval climate data set includes first retrieval climate data, second retrieval climate data, and up to the Mth retrieval climate data; pairwise distance analysis is performed on the first retrieval climate data, the second retrieval climate data, and up to the Mth retrieval climate data to obtain a number of retrieval climate data distances; based on the several retrieval climate data distances, the first retrieval climate data, the second retrieval climate data, and up to the Mth retrieval climate data are calibrated at the centroid to obtain the centroid retrieval climate data, which is set as the high-frequency retrieval climate data set.

[0051] Specifically, the first search climate data, the second search climate data, and the Mth search climate data refer to several groups of climate data in the search climate data set screened out in step S31, and each search climate data represents a climate condition or data set. The search climate data distance refers to the distance value obtained in the pairwise distance analysis, which indicates the degree of difference between different climate data. The centroid search climate data refers to the climate data obtained based on the centroid calibration. The high-frequency search climate data set contains a comprehensive representation of multiple search climate data. These high-frequency data are the most representative climate data and are used to formulate target climate data.

[0052] For several groups of climate data in the retrieved climate data set, the distance between each pair of climate data is calculated, which can be Euclidean distance, Manhattan distance, etc. This distance measures the difference between two climate data sets. Based on all calculated retrieval climate data distances, the center point of these data sets, i.e., the centroid, is found. The centroid calculation considers the average of the attribute values ​​of all climate data to determine the center of the climate data. The data point obtained by centroid calibration is set as the final climate data set, i.e., the high-frequency retrieval climate data. These data represent the central positions of multiple retrieval climate data, i.e., the most representative climate conditions. Step S32 ensures that the generated target climate data has high representativeness and accuracy, and provides a reliable basis for further climate management and adjustment.

[0053] Optionally, step S32 of the embodiment of the present application further includes:

[0054] According to the climate data attribute set, a climate data attribute consistency deviation is configured; and the first retrieved climate data, the second retrieved climate data, and the Mth retrieved climate data are subjected to a pairwise statistical analysis of the attribute proportions that do not satisfy the climate data attribute consistency deviation, to generate the plurality of retrieved climate data distances.

[0055] Specifically, the climate data attribute set refers to a set of various attributes of climate data, including temperature, humidity, precipitation, light, etc. The climate data attribute consistency deviation is the deviation value between a certain attribute in the climate data and the expected standard. The climate data retrieval distance refers to the distance value obtained by counting the proportion of attributes that do not meet the consistency deviation, which refers to the difference between the climate data attributes and reflects the similarity of the climate data.

[0056] First, define the attribute set of climate data, including temperature, humidity, precipitation, light, etc. Each attribute has a preset range and standard. Set the allowed deviation range of each climate data attribute and the consistency deviation of climate data attributes. Then, use statistical analysis tools to calculate the inconsistency of the same attribute between each pair of climate data. For example, calculate temperature deviation, humidity deviation, etc., and calculate the proportion of attributes with the same attribute deviation greater than the consistency deviation of two climate data as the retrieval climate data distance of the two climate data.

[0057] Optionally, step S32 of the embodiment of the present application further includes:

[0058] Based on the distances of the several retrieved climate data, the inverse of the average distance between the first retrieved climate data and other retrieved climate data is calculated and set as the first centroid coefficient; until based on the distances of the several retrieved climate data, the inverse of the average distance between the Mth retrieved climate data and other retrieved climate data is calculated and set as the Mth centroid coefficient; the first centroid coefficient is extracted until the retrieved climate data in the Mth centroid coefficient that is greater than or equal to the centroid coefficient threshold are configured with the same attribute interval to generate the centroid retrieval climate data.

[0059] Specifically, the centroid coefficient is a coefficient used to measure the representativeness of a data point. The centroid coefficient refers to the reciprocal of the mean distance between a data point and other data points. The smaller the distance mean, the larger the centroid coefficient, indicating that the data point is closer to other data points and more representative. The centroid coefficient threshold is a pre-defined value used to distinguish which data points have a high enough centroid coefficient to meet the conditions of becoming centroid data. For example, if the threshold is set to 0.5, a centroid coefficient greater than or equal to 0.5 is considered to be highly representative.

[0060] For each retrieval climate data in the retrieval climate data set, calculate the mean of the attribute distance between it and all other retrieval climate data, and take the reciprocal to obtain the corresponding centroid coefficient. That is, calculate the reciprocal of the mean of the retrieval climate data distance between the first retrieval climate data and other retrieval climate data, and set it as the first centroid coefficient; until the reciprocal of the mean of the retrieval climate data distance between the Mth retrieval climate data and other retrieval climate data is calculated, and set it as the Mth centroid coefficient. Compare the calculated first to Mth centroid coefficients with the centroid coefficient threshold, extract data points greater than or equal to the centroid coefficient threshold, configure them with the same attribute interval, obtain the centroid retrieval climate data and set it as the high-frequency retrieval climate data set.

[0061] Step S32 calculates the centroid coefficient of each data by performing pairwise attribute consistency deviation statistics on the climate data, and extracts the retrieval climate data that meets the centroid coefficient threshold. It comprehensively considers the attribute consistency of the data to ensure that the generated centroid retrieval climate data can accurately represent the core characteristics of the climate data, thereby improving the scientificity and accuracy of data selection and providing a reliable basis for the generation of target climate data.

[0062] Furthermore, step S5 of the embodiment of the present application further includes:

[0063] Calculate the climate indicator deviation vector array between the climate monitoring data and the target climate data; use the climate indicator deviation vector array as a constraint to search the agricultural database for the crop variety information and the crop growth stage to obtain crop growth status record data, wherein the agricultural database is a multi-party shared database; and count the proportion of crop abnormal data in the crop growth status record data, which is set as the climate data deviation index.

[0064] Specifically, the climate indicator deviation vector array refers to an array composed of deviation vectors between climate monitoring data and target climate data. Each vector represents the difference between the actual value and the target value of a certain climate indicator. The agricultural database is a database containing information on crop growth status, climate conditions, management measures, etc., which is shared and maintained by multiple institutions. For example, a data system shared by agricultural research institutions, meteorological departments and farmers. Crop growth status record data is a data set that records the status information of crops at different growth stages, including growth status, health problems, yield, etc. The proportion of crop abnormal data refers to the proportion of abnormal data recorded in the crop growth status record data, such as growth retardation, pests and diseases, etc. The climate data deviation index is an indicator that quantifies the degree of deviation between climate monitoring data and target climate data, which is reflected by counting the proportion of crop abnormal data. A higher deviation index indicates that the climate data deviates greatly from the target climate data, resulting in a higher proportion of crop abnormal data.

[0065] First, mathematical calculation tools and data analysis software are used to compare climate monitoring data with target climate data, calculate the deviation of each climate indicator, and obtain a climate indicator deviation vector array. Then, the calculated climate indicator deviation vector array is used as a constraint to retrieve records related to crop varieties and growth stages from a multi-party shared agricultural database. For example, query the growth record of a crop under the current climate deviation conditions to obtain relevant crop growth status data. Then, the retrieved crop growth status record data is analyzed, and the proportion of records marked as abnormal is counted as a climate data deviation index. For example, if 80 of the 500 retrieved records show crop abnormalities, such as poor growth, pests and diseases, etc., the proportion of crop abnormal data is 16%. The above process can help quantify the impact of climate deviation on crop growth, help managers take corresponding measures against climate change, and improve the accuracy and efficiency of crop management.

[0066] In summary, the brown soil planting management method based on climate data driven provided by the embodiment of the present application has the following technical effects:

[0067] Sampling and testing of the brown soil distribution area to obtain brown soil component ratio information, brown soil pH information, soil particle ratio and soil type sub-class ratio information, provide basic data for subsequent analysis, ensure a full understanding of soil conditions, and provide personalized management for crop growth. The interactive user end receives crop variety information and crop growth stage to customize climate management strategies according to crop types and growth stages, ensure that climate data configuration meets the actual needs of specific crops, and improve the pertinence of management. Through the climate data association component, based on the brown soil component ratio information, the brown soil pH information, the soil particle ratio and the soil type sub-class ratio information, configure target climate data for the crop variety information and the crop growth stage, combine soil characteristics with crop needs, determine ideal or optimal climate conditions, create a quantitative target climate benchmark, guide crop growth, and optimize the growth environment. Through the meteorological monitoring component, collect climate monitoring data, track and record climate conditions in real time, ensure the timeliness and accuracy of data, provide feedback on actual climate conditions, and enable managers to understand the environmental status in a timely manner. Compare the climate monitoring data with the target climate data to obtain the climate data deviation index, evaluate the difference between the actual climate and the target climate, quantify whether the climate conditions are suitable for crop growth, and provide a basis for necessary intervention. When the climate data deviation index is greater than or equal to the deviation index threshold, a climate data interference signal is generated and sent to the user end, warning the user when the climate conditions do not meet the standards, prompting the user to take action, prevent potential growth problems, ensure healthy growth of crops, and reduce losses.

[0068] Overall, the embodiments of the present application achieve precise soil and climate environment management through continuous monitoring and analysis of quantitative data and target configuration; and improve the adaptability to climate change and optimize crop growth conditions through real-time monitoring and deviation analysis, thereby realizing scientific, precise and batch brown soil planting management, and significantly improving the stability and efficiency of agricultural production.

[0069] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A brown soil planting management method driven by climate data, characterized in that: The method comprises: Sampling and testing of brown soil distribution areas to obtain information on brown soil composition ratio, brown soil pH value, soil particle ratio and soil type sub-class ratio; Interactive client, receiving crop variety information and crop growth stage; By means of a climate data association component, target climate data is configured for the crop variety information and the crop growth stage based on the brown soil component ratio information, the brown soil pH value information, the soil particle ratio and the soil type subclass ratio information; Collect climate monitoring data through meteorological monitoring components; Comparing the climate monitoring data with the target climate data to obtain a climate data deviation index; When the climate data deviation index is greater than or equal to the deviation index threshold, a climate data interference signal is generated and sent to the user end; By means of a climate data association component, target climate data is configured for the crop variety information and the crop growth stage based on the brown soil component ratio information, the brown soil pH value information, the soil particle ratio and the soil type subclass ratio information, including: The positive sample climate data is multi-level sorted for the crop variety information and the crop growth stage through the brown soil component ratio information, the brown soil pH value information, the soil particle ratio and the soil type sub-class ratio information to obtain a retrieval climate data set; Performing multi-attribute joint sorting on the retrieved climate data set to obtain a high-frequency retrieved climate data set; Perform attribute interval configuration according to the high-frequency retrieval climate data set to generate the target climate data; The positive sample climate data is multi-level sorted for the crop variety information and the crop growth stage through the brown soil component ratio information, the brown soil pH value information, the soil particle ratio and the soil type sub-class ratio information to obtain a retrieval climate data set, including: Traversing the brown soil component ratio information, the brown soil pH value information, the soil particle ratio and the soil type sub-class ratio information, and configuring a fault tolerance threshold set; Step 1: extracting k sorting feature values ​​from the brown soil component ratio information, the brown soil pH value information, the soil particle ratio and the soil type subclass ratio information, the total number of fault tolerance thresholds ≥ k ≥ 1, and the initial value of k is equal to 1; Step 2: Based on the fault tolerance threshold set and the k-item sorting feature values, sort the crop variety information and the crop growth stage into a k-1 level retrieval climate data set to obtain a k-level retrieval climate data set; When k is equal to the total number of fault tolerance thresholds, setting the k-level retrieval climate data set as the retrieval climate data set; When k is less than the total number of fault tolerance thresholds, k is increased by one, and steps 1 to 2 are repeated.

2. The method according to claim 1, characterized in that Performing multi-attribute joint sorting on the retrieved climate data set to obtain a high-frequency retrieved climate data set includes: The retrieved climate data set includes first retrieved climate data, second retrieved climate data, and up to Mth retrieved climate data; Performing pairwise distance analysis on the first searched climate data, the second searched climate data, and up to the Mth searched climate data to obtain a plurality of searched climate data distances; According to the distances of the plurality of retrieval climate data, centroid calibration is performed on the first retrieval climate data, the second retrieval climate data, and the Mth retrieval climate data to obtain centroid retrieval climate data, which is set as the high-frequency retrieval climate data set.

3. The method according to claim 2, characterized in that Performing pairwise distance analysis on the first searched climate data, the second searched climate data, and up to the Mth searched climate data to obtain a plurality of searched climate data distances, including: According to the climate data attribute set, configure the climate data attribute consistency deviation; The first searched climate data, the second searched climate data, and up to the Mth searched climate data are subjected to statistics on the proportion of attributes that do not satisfy the climate data attribute consistency deviation in pairs, to generate the plurality of searched climate data distances.

4. The method according to claim 2, characterized in that According to the plurality of retrieval climate data distances, centroid calibration is performed on the first retrieval climate data, the second retrieval climate data, and up to the Mth retrieval climate data to obtain centroid retrieval climate data, including: Based on the plurality of retrieval climate data distances, calculating the inverse of the average of the retrieval climate data distances between the first retrieval climate data and other retrieval climate data, and setting the inverse of the average of the retrieval climate data distances between the first retrieval climate data and other retrieval climate data as the first centroid coefficient; Until, based on the distances of the plurality of retrieved climate data, the inverse of the average of the distances between the Mth retrieved climate data and other retrieved climate data is calculated and set as the Mth centroid coefficient; Extract the first centroid coefficient until the Mth centroid coefficient, and perform the same attribute interval configuration on the search climate data that is greater than or equal to the centroid coefficient threshold, to generate the centroid search climate data.

5. The method according to claim 1, characterized in that Comparing the climate monitoring data with the target climate data to obtain a climate data deviation index includes: Calculate a climate indicator deviation vector array between the climate monitoring data and the target climate data; Taking the climate index deviation vector array as a constraint, searching the agricultural database for the crop variety information and the crop growth stage to obtain crop growth status record data, wherein the agricultural database is a multi-party shared database; The proportion of abnormal crop data in the crop growth status record data is counted and set as the climate data deviation index.

6. The method according to claim 1, characterized in that The climate monitoring data at least includes ambient temperature, precipitation, light intensity, CO2 concentration, and air humidity.

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