Intelligent Remote Irrigation Control Device in Forest Cultivation

Abstract

The present invention discloses an intelligent remote irrigation control device for forest cultivation, relating to the field of forest irrigation technology. The device comprises: connecting to a remote sensing platform to obtain image data of a forest area; performing soil feature analysis to identify soil irrigation needs; performing control node mandatory evaluation and setting mandatory coefficients for each control node; extracting mandatory constraint nodes based on the mandatory coefficients of the control nodes; obtaining irrigation execution targets using the maximum cumulative mandatory coefficient of the irrigation time series as an evaluation condition; and performing irrigation strategy analysis to obtain irrigation control information. This device solves the technical problem in the prior art of relying on set irrigation times and frequencies, failing to accurately reflect soil moisture conditions, climate changes, and the growth needs of forest plants, resulting in inaccurate irrigation control, delayed response, and wasted water resources. The device achieves the technical effects of improving the accuracy and real-time performance of irrigation control, increasing irrigation efficiency, and conserving water resources.

Description

Technical Field

[0001] The present application relates to the technical field related to forest irrigation, and in particular to an intelligent remote irrigation control device in forest cultivation. Background Art

[0002] Forest irrigation technology, as an important tool for ecological and environmental protection and forest resource management, has been widely used worldwide. Traditional forest irrigation control methods rely primarily on manual experience and timing control. Their purpose is to ensure the healthy growth of forest vegetation, especially seedlings, and adequate water supply. However, these traditional methods have many limitations. For example, traditional irrigation control methods often rely on manually set irrigation times and frequencies, lacking the ability to dynamically adjust irrigation based on actual soil conditions, meteorological changes, and plant needs. Experience-based irrigation methods often fail to accurately reflect soil moisture conditions, changing climate conditions, and the growth needs of forest plants, leading to water waste or insufficient irrigation. In some cases, excessive irrigation frequency can lead to excess water, causing soil compaction or root rot; while insufficient irrigation can lead to slow plant growth or even death. Irrigation based on a fixed, periodic schedule lacks a mechanism for real-time monitoring of environmental changes. During droughts, it cannot respond quickly to changes in soil dryness, resulting in water shortages. Failure to reduce irrigation volumes promptly during heavy rains can lead to water waste and other ecological problems.

[0003] Therefore, the current relevant technologies rely on set irrigation time and frequency, which cannot accurately reflect the soil moisture conditions, changes in climate conditions and the growth needs of forest plants, resulting in inaccurate irrigation control, delayed response and waste of water resources. Summary of the Invention

[0004] This application solves the technical problems in the existing technology that rely on set irrigation time and frequency, cannot accurately reflect the soil moisture conditions, changes in climatic conditions and the growth needs of forest plants, and thus leads to inaccurate irrigation control, delayed response and waste of water resources by providing an intelligent remote irrigation control device for forest cultivation. It achieves the technical effect of improving the accuracy and real-time performance of irrigation control, improving irrigation efficiency and saving water resources.

[0005] The present application provides an intelligent remote irrigation control device for forest cultivation, the device comprising: a remote sensing platform connection module for connecting to a remote sensing platform to obtain image data of a forest area; a soil feature analysis module for performing soil feature analysis based on the image data, exploring soil irrigation requirements, and constructing a seedling cultivation requirement sequence in combination with seedling growth characteristics and meteorological data; a mandatory coefficient setting module for performing mandatory evaluation of control nodes based on the seedling cultivation requirement sequence, and setting a mandatory coefficient of each control node based on the mandatory evaluation result, wherein the mandatory coefficient is used to reflect the urgency of the seedling cultivation needing irrigation; a mandatory constraint node extraction module for extracting mandatory constraint nodes based on the mandatory coefficients of the control nodes, wherein the mandatory constraint nodes are control nodes whose mandatory coefficients reach an irrigation threshold; an irrigation execution target acquisition module for performing irrigation time sequence search using the mandatory constraint nodes and a single maximum irrigation amount as constraint conditions, and obtaining an irrigation execution target using the maximum cumulative amount of the mandatory coefficients of the irrigation time sequence as an evaluation condition, wherein the irrigation execution target includes an irrigation target area and its irrigation water amount; an irrigation strategy analysis module for performing irrigation strategy analysis based on the irrigation target area and its irrigation water amount to obtain irrigation control information.

[0006] In a possible implementation, the soil feature analysis module further performs the following processing: the image data includes a hyperspectral image and a thermal infrared image, and the hyperspectral image and the thermal infrared image are spatially aligned; soil feature analysis is performed on the hyperspectral image and the thermal infrared image respectively to obtain spectral soil analysis results and thermal infrared soil analysis results; based on the spatial alignment relationship, the spectral soil analysis results and the thermal infrared soil analysis results are fused to obtain fused soil features; and irrigation demand analysis is performed based on the fused soil features to obtain the soil irrigation demand.

[0007] In a possible implementation, the soil feature analysis module also performs the following processing: performing hierarchical segmentation based on the spectral soil analysis results and the thermal infrared soil analysis results, establishing associated feature pairs according to the equivalence relationship of the segmentation levels, and the associated feature pairs are spectral features and thermal infrared features of the same segmentation level; hierarchically dividing the associated feature pairs according to the sorting relationship of the segmentation levels to construct a multi-feature hierarchy, each feature hierarchy corresponds to a segmentation level, and has built-in spectral features and thermal infrared features of the corresponding segmentation level; based on the multi-feature hierarchy, performing first-order feature interaction in the same feature hierarchy to obtain first-order feature interaction results; performing second-order interaction in adjacent feature hierarchies to obtain second-order feature interaction results; merging the first-order feature interaction results with the second-order feature interaction results to obtain the fused soil feature.

[0008] In a possible implementation, the soil feature analysis module further performs the following processing: identifying the contribution of the spectral feature and the thermal infrared feature to the soil moisture feature of the current segmentation level, and obtaining key spectral features and key thermal infrared features;

[0009] The key spectral features and key thermal infrared features are used to analyze soil moisture features to obtain spectral moisture features and thermal infrared moisture features; the spectral moisture features and thermal infrared moisture features are averaged and fused to obtain the first-order interaction results of the features.

[0010] In a possible implementation, the soil feature analysis module also performs the following processing: obtaining the change gradient relationship between the key spectral features, key thermal infrared features and neighborhood features in the same feature level, and fitting the correlation feature relationship; fusing the change coefficients of the correlation feature relationship of the spectral features and the correlation feature relationship of the thermal infrared features to obtain the feature change relationship; obtaining the feature change relationship of adjacent feature levels, performing inter-level aggregation, and obtaining the feature second-order interaction result.

[0011] In a possible implementation, the soil characteristic analysis module further performs the following processing: evaluating the current soil moisture content based on the fused soil characteristics; using the evaporation model constructed using historical forest soil monitoring data to estimate water loss based on the monitored soil characteristics of the current period to obtain a water loss rate; using the forest soil water content threshold, performing a time-series compensation analysis on the current soil moisture content according to the water loss rate to obtain the soil irrigation demand, which represents the time-series demand for soil water replenishment when the forest soil water content threshold is reached.

[0012] In a possible implementation, the soil characteristic analysis module further performs the following processing: sorting the soil irrigation demand characteristics according to the time series relationship to construct a soil demand characteristic time series diagram; obtaining the seedling soil moisture threshold according to the seedling growth characteristics, and correcting the soil demand characteristic time series diagram using the deviation between the seedling soil moisture threshold and the forest soil water content threshold; analyzing the irrigation demand of the growth time period according to the seedling growth characteristics to construct a growth irrigation constraint characteristic time series diagram; extracting humidity impact values according to the meteorological data, the humidity impact values including positive impact and negative impact, sorting the humidity impact values according to time information to construct a meteorological impact characteristic time series diagram; aligning the soil demand characteristic time series diagram, the growth irrigation constraint characteristic time series diagram, and the meteorological impact characteristic time series diagram according to the time series relationship, superimposing features according to the time series alignment relationship to construct a seedling irrigation demand time series diagram, the seedling irrigation demand time series diagram having a seedling soil moisture threshold mark; obtaining the seedling demand sequence based on the seedling irrigation demand time series diagram.

[0013] In a possible implementation, the irrigation execution target acquisition module also performs the following processing: setting the irrigation penalty coefficient for the cultivated seedlings; taking the product of the standard deviation of the forced coefficient and the irrigation penalty coefficient as a correction factor; and adding the correction factor and its irrigation penalty coefficient to the evaluation conditions.

[0014] In a possible implementation, the irrigation execution target obtaining module further performs the following processing: the expression of the evaluation condition is: ,in, , To evaluate the results, is the mandatory coefficient of the i-th seedling in the selected area, is the irrigation requirement of the i-th seedling in the region, i is the number of seedlings in the selected irrigation area, j is the number of seedlings in the unselected area, is the correction factor for the unselected j-th seedling, is the irrigation penalty coefficient for not selecting the jth seedling, is the mandatory coefficient for the jth seedling not being selected.

[0015] This application proposes an intelligent remote irrigation control device for forest cultivation, which connects to a remote sensing platform to obtain image data of forest areas; conducts soil feature analysis to identify soil irrigation needs; conducts mandatory evaluation of control nodes and sets mandatory coefficients for each control node; extracts mandatory constraint nodes based on the mandatory coefficients of each control node; obtains irrigation execution targets using the maximum cumulative mandatory coefficient of the irrigation time series as the evaluation criterion; and analyzes irrigation strategies to obtain irrigation control information. This device addresses the technical issues of existing technologies, which rely on set irrigation times and frequencies, fail to accurately reflect soil moisture conditions, climate changes, and the growth needs of forest plants, leading to inaccurate irrigation control, delayed response, and water resource waste. It achieves the technical effects of improving the accuracy and real-time nature of irrigation control, enhancing irrigation efficiency, and conserving water resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the accompanying drawings of the embodiments of the present disclosure are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the apparatus according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0017] Figure 1 A schematic diagram of the structure of an intelligent remote irrigation control device for forest cultivation provided in an embodiment of the present application;

[0018] Figure 2 This is a schematic diagram of the execution flow of the soil characteristic analysis module in the intelligent remote irrigation control device for forest cultivation provided in an embodiment of the present application.

[0019] Explanation of the reference numerals: remote sensing platform connection module 10 , soil characteristic analysis module 20 , forced coefficient setting module 30 , forced constraint node extraction module 40 , irrigation execution target acquisition module 50 , irrigation strategy analysis module 60 . DETAILED DESCRIPTION

[0020] 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.

[0021] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0022] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms “first\second” involved are merely to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, device, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0023] The embodiment of the present application provides an intelligent remote irrigation control device for forest cultivation, such as Figure 1 As shown, the device includes:

[0024] The remote sensing platform connection module 10 is used to connect to the remote sensing platform to obtain image data of the forest area.

[0025] Preferably, image data of forest areas are obtained in real time through a remote sensing platform, which usually comes from satellite remote sensing, UAV remote sensing, ground sensors and other equipment, and can provide detailed information about forest areas, such as soil moisture, vegetation cover, climate change, vegetation health, etc. Specifically, a remote sensing platform refers to a platform that uses satellites, UAVs or other high-altitude / ground equipment to collect images. It usually collects spectral data of different bands through sensors to generate image data, which can cover a wide area and is particularly suitable for monitoring large-scale forest areas; image data of forest areas, that is, image information obtained from remote sensing platforms, may include satellite images, such as ground images taken by satellites such as Landsat and MODIS; UAV images, high-definition images taken by UAVs equipped with high-resolution cameras; thermal infrared imaging, which uses thermal imaging technology to obtain the temperature distribution of forest areas for analysis of water evaporation, soil moisture, etc.; multispectral / hyperspectral images, which analyze images of different spectral bands (visible light, infrared light, etc.) to provide information on the health status of soil and vegetation.

[0026] The soil characteristic analysis module 20 is used to perform soil characteristic analysis based on the image data, explore soil irrigation requirements, and construct a seedling cultivation requirement sequence in combination with seedling growth characteristics and meteorological data.

[0027] Preferably, by analyzing the image data provided by the remote sensing platform, characteristics related to soil, seedling growth and meteorology are extracted, thereby intelligently evaluating irrigation needs and constructing a seedling demand sequence in combination with seedling growth characteristics and meteorological data. Specifically, soil characteristic analysis refers to the analysis of image data obtained through the remote sensing platform (such as satellite images, images taken by drones, etc.), which may include evaluating information such as soil moisture, soil type, soil temperature, etc. in the forest area to generate soil characteristic analysis results. For example, through multispectral images, remote sensing technology can identify changes in soil moisture, evaluate whether the soil is dry or needs irrigation, and then analyze the current soil moisture status based on the soil characteristic analysis results to determine whether the moisture level required for seedling growth is met, thereby generating soil irrigation needs. Specifically, by analyzing the soil moisture information in the remote sensing data and combining it with historical meteorological data (such as precipitation, temperature, etc.), it can be inferred when the soil needs irrigation to achieve the effect of precise irrigation. The soil moisture demand needs to be combined with the growth needs of the plant. For example, seedlings in forest cultivation will have different requirements for moisture during their growth process.

[0028] Preferably, a seedling demand sequence is constructed in combination with the growth characteristics of the cultivated seedlings and meteorological data. Specifically, each plant has different water requirements at different growth stages. For example, seedlings have a higher water demand in the early growth stage, but may require less water in the mature stage. The growth status of the seedlings is monitored through remote sensing technology, sensors, etc., and real-time growth data of the seedlings (such as leaf area index, tree height, root development, etc.) is obtained. Then, real-time meteorological data (such as temperature, humidity, precipitation, wind speed, etc.) is obtained to predict future climate changes and adjust the irrigation plan. For example, if insufficient precipitation is expected in the future, the irrigation demand will increase; conversely, the irrigation amount may be reduced. Then, the meteorological data, current soil irrigation demand and seedling growth characteristics are combined to dynamically adjust the irrigation strategy and establish an irrigation demand sequence, i.e., a seedling demand sequence, which represents the irrigation amount and irrigation priority required at different time points in the future (such as a week, a month, etc.). Through this sequence, irrigation time, irrigation amount and priority can be planned in advance to ensure timely water supply during the growth of the seedlings and ensure the timeliness and scientificity of irrigation.

[0029] The mandatory coefficient setting module 30 is used to perform mandatory evaluation of the control nodes according to the seedling cultivation demand sequence, and set the mandatory coefficient of each control node based on the mandatory evaluation result. The mandatory coefficient is used to reflect the urgency of the irrigation required for the seedling cultivation.

[0030] Preferably, the urgency of the irrigation control nodes is evaluated according to the irrigation demand sequence of the cultivated seedlings, and the irrigation priority of each irrigation node is dynamically adjusted by setting a mandatory coefficient. Specifically, by analyzing the demand sequence of the cultivated seedlings, it is determined in which periods the seedlings have a higher demand for water and in which periods the demand is lower. Control nodes usually refer to points that can control water flow or irrigation volume, such as switches, sprinkler controllers, valves, etc. of irrigation pipes, which are distributed in different positions of the irrigation area and control the irrigation process of different areas. Mandatory evaluation of control nodes refers to judging the irrigation urgency of each control node according to the demand sequence of the cultivated seedlings. For example, if the soil in a certain area is too dry, or the seedlings in the area are in urgent need of water within a certain period of time, then the control nodes in the area will be evaluated as high-mandatory nodes, which means that priority irrigation is required; then The mandatory coefficient of the control node is set according to the mandatory assessment results to quantify the urgency of each control node, that is, the degree to which the node should be executed first in the irrigation system. For example, a node with a higher mandatory coefficient means that the soil drought in the area is more serious, or the irrigation demand of the seedlings is more urgent and requires immediate irrigation; a node with a lower mandatory coefficient indicates that the moisture condition in the area is better and no water replenishment is required for the time being. The basis for setting the mandatory coefficient usually includes soil moisture (if the soil moisture is too low, the mandatory coefficient will be higher), the seedling growth stage and meteorological conditions (under climatic conditions such as drought and high temperature, the urgency of irrigation will increase). The mandatory coefficient is used to reflect the urgency of irrigation for seedling cultivation. That is, the level of the mandatory coefficient determines the operation priority of each irrigation node, which can help the irrigation system make reasonable scheduling according to the needs of different regions.

[0031] The mandatory constraint node extraction module 40 is configured to extract mandatory constraint nodes according to the mandatory coefficients of the control nodes, wherein the mandatory constraint nodes are control nodes whose mandatory coefficients reach an irrigation threshold.

[0032] Preferably, based on the forcing coefficient of each irrigation control node, it is determined which control nodes have urgent irrigation needs, so that irrigation operations are given priority. The irrigation threshold is a critical value preset based on historical data and actual needs to determine which control nodes have urgent irrigation needs and must be irrigated immediately. For example, it can be set that when the forcing coefficient reaches or exceeds a certain value (such as 0.8 or 1.0), it means that the irrigation demand of the control node is already very urgent and action needs to be taken as soon as possible. Specifically, when the forcing coefficient of the control node reaches the irrigation threshold, that is, the irrigation demand of these nodes has reached or exceeded the set threshold, indicating that the irrigation demand of these areas is already very urgent and irrigation needs to be given priority. The control nodes that need to be irrigated immediately among all control nodes are screened as forced constraint nodes for priority irrigation scheduling, so as to accurately and effectively respond to the water needs of different areas, avoid waste of water resources and ensure the rapid recovery of seedlings and soil moisture.

[0033] The irrigation execution target acquisition module 50 is used to perform irrigation time sequence search using the mandatory constraint node and the single maximum irrigation amount as constraint conditions, and obtain the irrigation execution target using the maximum cumulative amount of the mandatory coefficient of the irrigation time sequence as the evaluation condition. The irrigation execution target includes the irrigation target area and its irrigation water amount.

[0034] Preferably, the optimal irrigation time and amount are determined according to the urgent needs of the irrigation control nodes (mandatory constraint nodes) and the limitation of the single maximum irrigation amount. Finally, the optimal irrigation plan is selected by evaluating the effects of different irrigation time sequences, and the final irrigation execution target, that is, the irrigation target area and its required water volume, is determined. Specifically, the mandatory constraint node represents the area with the most urgent irrigation demand, and the single maximum irrigation volume refers to the maximum water limit that the irrigation system can apply at each irrigation. Irrigation time sequence search refers to simulating and exploring different irrigation time sequences with mandatory constraint nodes and the single maximum irrigation volume as constraints, that is, by evaluating each time sequence plan, finding the most suitable irrigation time sequence, including the optimal irrigation time and water distribution method, and then calculating the cumulative amount of mandatory coefficients for each time sequence plan, that is, calculating the maximum amount of water that can be applied at each irrigation. The sum of the forced coefficients of all control nodes within the time period. Among multiple irrigation time sequence plans, the irrigation plan with the largest cumulative forced coefficient is taken as the optimal irrigation plan, indicating that this plan can most effectively meet the irrigation needs of all forced constraint nodes, ensuring that water is replenished in time to where it is most needed, and thus obtaining the irrigation execution target. Among them, the irrigation execution target includes the irrigation target area and its irrigation water volume. The irrigation target area refers to the specific geographical area that needs to be irrigated, which is usually the area with the most urgent irrigation demand determined by the selection of forced constraint nodes; the irrigation water volume refers to the amount of water that needs to be replenished in each target area during each irrigation period. For example, the water replenishment amount of the forced constraint node area is determined based on factors such as soil moisture conditions, climatic conditions, and plant growth requirements, and does not exceed the maximum irrigation amount, thereby ensuring efficient and reasonable allocation of water resources.

[0035] The irrigation strategy analysis module 60 is used to analyze the irrigation strategy according to the irrigation target area and the irrigation water volume to obtain irrigation control information.

[0036] Preferably, after determining the target irrigation area and the amount of water required for irrigation, the irrigation strategy analysis is carried out and specific irrigation control information is generated to guide irrigation operations, ensure that the irrigation system can accurately and effectively perform irrigation tasks, and maximize the efficiency of water resource utilization. Specifically, the irrigation strategy analysis is to formulate a reasonable irrigation strategy based on the target area and irrigation water volume, which usually includes comprehensive consideration of weather forecasts (such as precipitation, temperature, wind speed, etc.), soil type, seedling requirements, etc., to determine the irrigation time window for each target area, to ensure that irrigation can be carried out at the most appropriate time, and to avoid water evaporation loss; to determine the irrigation intensity based on the permeability and water absorption capacity of the soil, that is, to reasonably allocate the amount of water for each irrigation, to ensure that the water can be Effective absorption; select the most suitable irrigation method (such as drip irrigation, sprinkler irrigation, infiltration irrigation, etc.); determine the irrigation cycle frequency according to the changes in soil moisture and seedling needs in different time periods; and then generate specific irrigation control information, including starting irrigation equipment (such as water pumps, sprinklers, drip irrigation systems, etc.) according to the requirements of the irrigation target area, irrigation period and irrigation intensity. According to the irrigation strategy, determine which irrigation equipment should be started and when. For example, some areas may need to turn on multiple irrigation equipment for simultaneous irrigation; and water flow control information of each irrigation node (flow rate, irrigation duration, etc.). The irrigation system performs specific irrigation operations according to the irrigation control information and ensures the accuracy, timeliness and rational use of resources of the irrigation process.

[0037] The intelligent remote irrigation control device for forest cultivation, according to an embodiment of the present invention, addresses the technical problem of prior art relying on set irrigation times and frequencies, failing to accurately reflect soil moisture conditions, climate changes, and the growth needs of forest plants. This leads to inaccurate irrigation control, delayed response, and water resource waste. This device achieves the technical effects of improving the accuracy and real-time nature of irrigation control, enhancing irrigation efficiency, and conserving water resources. The intelligent remote irrigation control device for forest cultivation includes a remote sensing platform connection module 10, a soil characteristic analysis module 20, a forced coefficient setting module 30, a forced constraint node extraction module 40, an irrigation execution target acquisition module 50, and an irrigation strategy analysis module 60.

[0038] The specific configuration of the soil characteristic analysis module 20 will be described in detail below. Figure 2As shown, the soil feature analysis module 20 may further include: the image data includes a hyperspectral image and a thermal infrared image, and the hyperspectral image and the thermal infrared image are spatially aligned; soil feature analysis is performed on the hyperspectral image and the thermal infrared image respectively to obtain spectral soil analysis results and thermal infrared soil analysis results; based on the spatial alignment relationship, the spectral soil analysis results and the thermal infrared soil analysis results are fused to obtain fused soil features; irrigation demand analysis is performed according to the fused soil features to obtain the soil irrigation demand.

[0039] Preferably, hyperspectral images collect information in different spectral bands through hyperspectral imaging technology, providing more detailed reflectance information of soil, vegetation and other objects than ordinary color images, so as to identify the chemical composition, moisture, mineral distribution and other characteristics of the soil. Thermal infrared images detect thermal radiation emitted by the surface through thermal infrared imaging technology to obtain detailed information about surface temperature and heat changes, and help analyze soil temperature, evaporation rate and soil moisture, etc. The hyperspectral image and the thermal infrared image are then spatially registered, that is, the two different types of image data are aligned so that they correspond to the same position in space, to ensure that data from different image sources can be combined and analyzed in the same geographic coordinate system, thereby maintaining the geometric consistency between the two images.

[0040] Preferably, performing soil feature analysis on hyperspectral images means identifying different material components in the soil (such as organic matter, minerals, etc.), as well as soil moisture, pH value and other properties through spectral analysis technology (such as analysis of spectral reflectance curves), wherein the reflectance of different bands can reflect the moisture, salinity, mineral composition and other conditions of the soil, thereby judging the irrigation needs of the soil; performing soil feature analysis on thermal infrared images means that thermal infrared images obtain the moisture condition and evaporation condition of the soil by measuring the temperature distribution of the soil. The temperature change of the soil can indirectly reflect the wetness of the soil, because when the soil is rich in moisture, the evaporation rate is low and the temperature is relatively low; conversely, when the soil is short of water, the evaporation rate increases and the soil temperature rises. The temperature of dry soil changes quickly, while the temperature of moist soil changes slowly, which appears as a higher temperature on the thermal infrared image.

[0041] Preferably, the spectral analysis results provided by the hyperspectral image and the thermal analysis results provided by the thermal infrared image are fused according to the spatial registration relationship, that is, data fusion methods such as weighted averaging and principal component analysis (PCA) are used to combine the advantages of the two images, thereby providing more comprehensive and accurate soil characteristic data. For example, the spectral image provides chemical and moisture information of the soil, while the thermal infrared image provides temperature and evaporation information of the soil. The fused soil characteristics include multi-dimensional characteristics such as spectral information, temperature information, moisture status, evaporation rate, etc. of the soil, which can more comprehensively and accurately analyze the moisture status and irrigation needs of the soil; then, irrigation demand analysis is performed based on the fused soil characteristics, that is, whether the soil is in a drought state (needs to be supplemented with water). For example, if the soil temperature is too high and the humidity is low, it is recommended to increase the irrigation amount; if the soil moisture is moderate, the irrigation amount can be reduced to avoid waste of water resources, thereby accurately predicting the irrigation needs of the soil and ensuring the accuracy and efficiency of irrigation.

[0042] The specific configuration of the soil feature analysis module 20 will be described in detail below. The soil feature analysis module 20 may further include: performing hierarchical segmentation based on the spectral soil analysis results and the thermal infrared soil analysis results, establishing associated feature pairs according to the equivalence relationship of the segmentation levels, the associated feature pairs being spectral features and thermal infrared features of the same segmentation level; hierarchically dividing the associated feature pairs according to the sorting relationship of the segmentation levels to construct a multi-feature hierarchy, each feature hierarchy corresponding to a segmentation level, and having built-in spectral features and thermal infrared features of the corresponding segmentation level; based on the multi-feature hierarchy, performing first-order feature interaction in the same feature hierarchy to obtain first-order feature interaction results; performing second-order interaction in adjacent feature hierarchies to obtain second-order feature interaction results; merging the first-order feature interaction results with the second-order feature interaction results to obtain the fused soil feature.

[0043] Preferably, the analysis results of hyperspectral and thermal infrared images are divided into levels, and through the interaction of features at different levels, a comprehensive soil feature is generated by combining multi-dimensional information, thereby improving the accuracy of irrigation decisions. Specifically, the spectral analysis results and thermal infrared analysis results are divided into levels, usually according to certain characteristics of the soil (such as moisture, temperature, mineral content, etc.). For example, soil moisture may be divided into different levels such as dry, moderate, and moist, and soil temperature may also be divided into different levels. According to the equivalence relationship of the segmentation levels, an associated feature pair is established, that is, in the spectral features and thermal infrared features, each The spectral features and thermal infrared features in each segmentation level correspond one to one, and the soil features of each level are defined by the spectral data and thermal infrared data to ensure the consistency of the two types of data; then the associated feature pairs are divided into levels according to the level segmentation, and each feature level corresponds to a segmentation level. Each level contains the spectral features and thermal infrared features of the level. For example, level 1 may correspond to the spectral features and thermal infrared features of "dry" soil, level 2 corresponds to the features of "moderate" soil, and so on. A multi-feature hierarchy is established to organize and store soil features of different segmentation levels, and the feature information of each level can be classified and processed.

[0044] Preferably, according to the established multi-feature hierarchy, first-order feature interaction is performed in the same feature hierarchy, that is, spectral data and thermal infrared data are combined by mathematical methods (such as weighted summation, correlation analysis, etc.) to perform first-order interaction analysis to extract comprehensive information of spectral and thermal infrared features in the hierarchy as the first-order interaction result (feature combination at the hierarchy), reflecting the comprehensive characteristics of the soil at the segmentation level; then second-order interaction is performed in adjacent feature hierarchies, that is, the first-order interaction results in the two hierarchies are combined to perform higher-level feature interaction, such as using cross analysis or weighted summation methods to fuse information from different hierarchies to produce more comprehensive soil characteristics as the second-order interaction result to capture the relationship between different segmentation levels; the first-order interaction results and the second-order interaction results are then merged to obtain the final fused soil characteristics, which include comprehensive information of spectral and thermal infrared data, and can reflect information in multiple dimensions such as soil moisture, temperature, and humidity, providing accurate input for subsequent irrigation demand analysis.

[0045] The specific configuration of the soil feature analysis module 20 will be described in detail below. The soil feature analysis module 20 may further include: identifying the contribution of the spectral features and thermal infrared features to the soil moisture features at the current segmentation level to obtain key spectral features and key thermal infrared features; performing soil moisture feature analysis using the key spectral features and key thermal infrared features to obtain spectral moisture features and thermal infrared moisture features; and averaging the spectral moisture features and thermal infrared moisture features to obtain a first-order interaction result of the features.

[0046] Preferably, by identifying key features in spectral and thermal infrared data, combining these features to analyze soil moisture, and finally obtaining a comprehensive moisture analysis result through feature fusion, which is then used for irrigation decision-making. Specifically, the contribution of spectral features to soil moisture features is identified. Hyperspectral images contain information on multiple spectral bands. The reflectivity of different bands corresponds to different soil and vegetation characteristics. The reflectivity of some spectral bands is more sensitive to humidity, while other bands may be less sensitive to humidity changes. By analyzing the data of these bands, key spectral features that are highly correlated with soil moisture are identified; thermal infrared images can provide information on soil surface temperature. Soil moisture usually affects the temperature distribution of soil. By analyzing the changes in soil temperature and thermal radiation in thermal infrared images, key thermal infrared features closely related to soil moisture can be identified. For example, moist soil usually has a lower temperature because the evaporation of water can absorb a certain amount of heat, while dry soil has a higher temperature.

[0047] Preferably, soil moisture feature analysis is performed based on key spectral features, that is, the changes in soil moisture are analyzed by extracting key features from spectral data. For example, certain spectral bands (such as near-infrared bands) are very sensitive to water reflection and can reflect the high and low soil moisture, thereby obtaining spectral moisture features, that is, the manifestation of soil moisture in spectral features; soil moisture feature analysis is performed based on key thermal infrared features, that is, the influence of soil moisture on temperature is analyzed by extracting key features from thermal infrared images. For example, higher humidity usually means lower soil temperature, thereby obtaining thermal infrared moisture features, that is, the manifestation of soil moisture in thermal infrared data; the spectral moisture features and thermal infrared moisture features are averaged and fused to obtain a more accurate soil moisture feature, which, as a first-order interaction result of features, represents the joint analysis and comprehensive reflection of soil moisture by spectral features and thermal infrared features at the same level, thereby enhancing the reliability and accuracy of soil moisture estimation, thereby optimizing irrigation management and saving water resources.

[0048] The specific configuration of the soil feature analysis module 20 will be described in detail below. The soil feature analysis module 20 may further include: obtaining the change gradient relationship between the key spectral features, key thermal infrared features, and neighboring features in the same feature level, and fitting the correlation feature relationship; fusing the correlation feature relationship of the spectral features with the correlation feature relationship of the thermal infrared features by the change coefficient to obtain the feature change relationship; obtaining the feature change relationship of adjacent feature levels, performing inter-level aggregation, and obtaining the feature second-order interaction result.

[0049] Preferably, by analyzing the changing relationship between spectral features and thermal infrared features, using gradient information to fit the correlation between features, and integrating feature relationships at different levels, a more comprehensive description of soil features is obtained, thereby optimizing irrigation decisions. Specifically, at the same feature level, each spectral feature and thermal infrared feature is not only associated with other features, but may also have a changing relationship with its neighboring features. By analyzing the changing gradients of these neighborhood features (i.e., the rate of change of the feature value with space or time), the local variation law of soil features can be captured. For example, the change of soil moisture at different locations may be affected by the soil conditions of the neighborhood. Then, by calculating the gradient changes of spectral features and thermal infrared features, the trend and amplitude of the change of these features in the local range are identified to determine the importance of certain features to soil moisture or other soil properties. For example, the spectral features of certain bands may show stronger gradient changes in areas with greater soil moisture changes, indicating that the features of these bands have a strong correlation with soil moisture. Then, the changing gradient relationships of spectral features, thermal infrared features and neighborhood features are fitted to establish correlation feature relationships. For example, the features of certain spectral bands may have a linear relationship with soil moisture, while other bands may show a nonlinear relationship.

[0050] Preferably, the correlation feature relationship of the spectral features and the correlation feature relationship of the thermal infrared features are fused by the variation coefficient, that is, the variation coefficient (sensitivity or weight of feature change) is introduced to fuse the correlation feature relationship of the spectral features and the thermal infrared features, and balance the role of the two in the change of features such as soil moisture. For example, the spectral feature change of a certain band has a greater impact on soil moisture, while the change of the thermal infrared feature is smaller, then the variation coefficient of the spectral feature may be given a higher weight, and vice versa, the thermal infrared feature is given a higher weight, and then a comprehensive feature change relationship is obtained, reflecting the relative importance and role of the two data sources in soil feature analysis; and then the feature change relationships of adjacent feature levels are fused according to the variation coefficient and correlation degree of each level by weighted averaging, summation, etc., to capture the feature interactions and correlations between different levels, so as to obtain a more comprehensive description of soil features, that is, to obtain the second-order interaction results of features, and more accurately mine soil features and describe soil states.

[0051] The specific configuration of the soil characteristic analysis module 20 will be described in detail below. The soil characteristic analysis module 20 may further include: evaluating the current soil moisture content based on the fused soil characteristics; utilizing an evapotranspiration model constructed using historical forest soil monitoring data to estimate water loss based on the monitored soil characteristics of the current period to obtain a water loss rate; and performing a time-series compensation analysis of the current soil moisture content according to the water loss rate based on a forest soil moisture threshold to obtain the soil irrigation demand, which represents the time-series soil water replenishment requirement when the forest soil moisture threshold is reached.

[0052] Preferably, soil moisture is estimated based on the fused soil characteristics, and the water loss rate is evaluated in combination with the evapotranspiration model, and ultimately the amount and time of irrigation water replenishment required to meet soil moisture demand are calculated. The evapotranspiration model is established based on historical data and climatic conditions and is used to estimate soil moisture loss. The evapotranspiration model generally considers the impact of meteorological factors (such as temperature, humidity, wind speed, solar radiation, etc.) on water loss. The evaporation and plant transpiration processes (i.e., "evapotranspiration") of soil moisture under different climatic conditions are analyzed through historical forest soil monitoring data. Specifically, by analyzing the fused soil characteristics and combining them with the soil moisture model (such as the relationship between moisture and spectral characteristics), the current soil moisture content is estimated, reflecting the current wetness of the soil, usually expressed as a percentage (the proportion of water in the soil to the soil). The evapotranspiration model is then used to estimate the water loss of the monitored soil characteristics during the current monitoring period, thereby obtaining the water loss rate, which reflects the amount of water lost per unit time under current climatic conditions. This is usually affected by meteorological conditions (such as temperature, precipitation, wind speed, etc.) and soil properties (such as soil type, structure, humidity, etc.).

[0053] Preferably, a time-series compensation analysis is performed on the current soil moisture content according to the water loss rate based on the forest soil water content threshold, that is, based on the current soil moisture level and the future water loss rate, the amount of water that needs to be supplemented in certain future periods in order to maintain the soil moisture above the threshold is calculated. The time-series compensation analysis can be adjusted according to the water loss rate, climate change and soil characteristics in different time periods. The soil water content threshold refers to the minimum soil moisture level required for forest plant growth. For example, when the soil moisture content is lower than a certain threshold, the growth of the plant will be affected, which may lead to excessive water stress and affect its normal development. Ultimately, the required soil irrigation demand is obtained, which characterizes the amount of water required to be supplemented and the timing of irrigation in order to make the soil moisture reach the set forest soil water content threshold, that is, the time period for irrigation and the amount of water for each irrigation. This serves as the soil water replenishment timing demand, providing a scientific basis for precise forest irrigation management.

[0054] The specific configuration of the soil characteristic analysis module 20 will be described in detail below. The soil characteristic analysis module 20 may further include: sorting the soil irrigation demand characteristics according to a time series relationship to construct a soil demand characteristic time series diagram; obtaining a seedling soil moisture threshold based on the seedling growth characteristics, and correcting the soil demand characteristic time series diagram using the deviation between the seedling soil moisture threshold and the forest soil water content threshold; analyzing the irrigation demand of the growth period according to the seedling growth characteristics to construct a growth irrigation constraint characteristic time series diagram; extracting humidity impact values according to the meteorological data, wherein the humidity impact values include positive and negative impacts, sorting the humidity impact values according to time information, and constructing a meteorological impact characteristic time series diagram; aligning the soil demand characteristic time series diagram, the growth irrigation constraint characteristic time series diagram, and the meteorological impact characteristic time series diagram according to the time series relationship, and superimposing features according to the time series alignment relationship to construct a seedling irrigation demand time series diagram, wherein the seedling irrigation demand time series diagram has a seedling soil moisture threshold marker; and obtaining the seedling demand sequence based on the seedling irrigation demand time series diagram.

[0055] Preferably, the soil irrigation demand in each irrigation period is sorted in chronological order, that is, the demand in different time periods is sorted according to factors such as the urgency of the irrigation demand, soil moisture and meteorological changes, and a soil demand characteristic time series diagram is compiled to show the soil demand for different areas at different time points, as well as the changes in soil moisture and irrigation demand in each time period. Each seedling has its own specific soil moisture demand during the growth process. According to the growth characteristics of the cultivated seedlings, the soil moisture threshold of the cultivated seedlings is obtained, and then the soil demand characteristic time series diagram is corrected by the deviation between the soil moisture threshold of the cultivated seedlings and the forest soil water content threshold, so that the irrigation plan is more in line with the growth characteristics of the seedlings. Then, according to the growth time cycle of the seedlings, the irrigation demand at different growth stages is analyzed, and a growth irrigation constraint characteristic time series diagram is constructed to describe the water demand and irrigation timing of the seedlings at different growth stages.

[0056] Preferably, the humidity impact value is obtained by analyzing the meteorological data and classified into positive and negative impacts. Positive impact usually refers to beneficial water replenishment such as precipitation, while negative impact refers to increased temperature or wind speed leading to increased soil moisture evaporation. The humidity impact values are sorted according to time information to form a meteorological impact characteristic time series diagram to show the specific impact of meteorological conditions on soil moisture and irrigation demand in different time periods; then the soil demand characteristic time series diagram, the growth irrigation constraint characteristic time series diagram, and the meteorological impact characteristic time series diagram are time-series aligned, that is, aligned in chronological order to ensure that all demand characteristics, meteorological impacts and growth stage information can correspond to each other on the time axis, and the characteristics of each time series diagram are aligned. The data are superimposed to form a time series diagram of seedling irrigation demand, which integrates factors such as soil moisture demand, meteorological influence, and seedling growth characteristics to make more accurate irrigation decisions. Among them, the time series diagram of seedling irrigation demand will mark the soil moisture threshold of the seedling to clarify which periods need to increase irrigation and which periods can reduce irrigation. Finally, based on the time series diagram of seedling irrigation demand, a detailed seedling demand sequence is extracted, which not only reflects the irrigation demand of seedlings in different time periods, but also takes into account soil moisture threshold, meteorological changes, seedling growth stage and other factors, providing an accurate irrigation scheduling plan for the irrigation system to ensure that seedlings can get the required water supply at different growth stages to achieve the best growth effect.

[0057] The specific configuration of the irrigation execution target acquisition module 50 will be described in detail below. The irrigation execution target acquisition module 50 may further include: setting an irrigation penalty coefficient for the seedling cultivation; using the product of the standard deviation of the mandatory coefficient and the irrigation penalty coefficient as a correction factor; and adding the correction factor and the irrigation penalty coefficient to the evaluation condition.

[0058] Preferably, the accuracy and rationality of irrigation decisions are adjusted by introducing irrigation penalty coefficients and correction factors, thereby ensuring that the execution of irrigation plans is more in line with demand and avoiding improper irrigation. Specifically, the irrigation penalty coefficient is used to represent the penalty or cost of irrigation behavior. For example, for different cultivated seedlings, when irrigating, not only whether it is an irrigation demand node is considered, but also the growth characteristics of the cultivated seedlings (high price, high irrigation requirements, high growth environment requirements, etc.) are considered. The irrigation penalty coefficient is set based on these characteristics, specifically including defining a penalty function for each cultivated seedling, which is used to calculate the penalty value based on factors such as the irrigation demand, growth characteristics, and economic value of the cultivated seedlings. For selected cultivated seedlings, if the irrigation operation meets the needs of the cultivated seedlings, the penalty value can be low; if not, the penalty value can be high; for unselected cultivated seedlings, their penalty values are calculated based on their irrigation needs and the potential impact of the current irrigation strategy, and the penalties for selected and unselected cultivated seedlings are comprehensively considered, such as The penalty value for selected seedlings is combined with the penalty value for unselected seedlings to form a comprehensive penalty value; then the standard deviation of the forced coefficient (the degree of dispersion of the forced coefficient values of all control nodes) is multiplied by the determined irrigation penalty coefficient to obtain a correction factor, which is used to fine-tune irrigation decisions. If the irrigation demand in a certain area fluctuates greatly (that is, the standard deviation is large) and the irrigation penalty coefficient is high, the correction factor will be larger, thereby increasing the irrigation priority for that area and avoiding excessive or insufficient irrigation; finally, the correction factor and the irrigation penalty coefficient are added to the evaluation conditions, where the evaluation conditions are used to measure the pros and cons of different irrigation schemes, usually including soil moisture, irrigation demand, meteorological factors, etc. By adding the correction factor and the irrigation penalty coefficient to the evaluation conditions, it is possible to more accurately balance the irrigation demand and potential negative impacts of each area when making irrigation decisions, thereby effectively balancing the needs of precision irrigation and water conservation, and improving overall irrigation efficiency, ensuring efficient use of water resources and healthy growth of seedlings.

[0059] The specific configuration of the irrigation execution target acquisition module 50 will be described in detail below. The irrigation execution target acquisition module 50 may further include: the expression of the evaluation condition is: ,in, , To evaluate the results, is the mandatory coefficient of the i-th seedling in the selected area, is the irrigation requirement of the i-th seedling in the region, i is the number of seedlings in the selected irrigation area, j is the number of seedlings in the unselected area, is the correction factor for the unselected j-th seedling, is the irrigation penalty coefficient for not selecting the jth seedling, is the mandatory coefficient for the jth seedling not being selected.

[0060] Although the present application makes various references to certain modules in the apparatus according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0061] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. Intelligent remote irrigation control device for forest cultivation, characterized in that: The device comprises: Remote sensing platform connection module, used to connect to the remote sensing platform to obtain image data of the forest area; A soil feature analysis module is used to analyze soil features based on the image data, explore soil irrigation requirements, and construct a seedling cultivation requirement sequence based on seedling growth characteristics and meteorological data; A mandatory coefficient setting module is used to perform mandatory evaluation of control nodes according to the seedling cultivation demand sequence, and set the mandatory coefficient of each control node based on the mandatory evaluation result, wherein the mandatory coefficient is used to reflect the urgency of the seedling cultivation needing irrigation; A mandatory constraint node extraction module is used to extract mandatory constraint nodes according to the mandatory coefficient of the control node, wherein the mandatory constraint node is a control node whose mandatory coefficient reaches an irrigation threshold; an irrigation execution target acquisition module, configured to use the mandatory constraint node and the single maximum irrigation amount as constraint conditions, perform irrigation time series search, and use the maximum cumulative amount of mandatory coefficients of the irrigation time series as an evaluation condition to obtain the irrigation execution target, wherein the irrigation execution target includes the irrigation target area and the irrigation water amount; An irrigation strategy analysis module is used to analyze the irrigation strategy according to the irrigation target area and the irrigation water volume to obtain irrigation control information; The soil characteristic analysis module performs the following steps: The image data includes a hyperspectral image and a thermal infrared image, and the hyperspectral image and the thermal infrared image are spatially registered; Performing soil feature analysis on the hyperspectral image and the thermal infrared image respectively to obtain spectral soil analysis results and thermal infrared soil analysis results; Based on the spatial registration relationship, the spectral soil analysis results and the thermal infrared soil analysis results are fused to obtain fused soil features; Performing irrigation demand analysis based on the fused soil characteristics to obtain the soil irrigation demand; The soil characteristic analysis module performs the following steps: Performing hierarchical segmentation based on the spectral soil analysis results and the thermal infrared soil analysis results, and establishing associated feature pairs according to the equivalence relationship of the segmentation levels, wherein the associated feature pairs are spectral features and thermal infrared features of the same segmentation level; According to the sorting relationship of the segmentation levels, the associated feature pairs are hierarchically divided to construct a multi-feature hierarchy, where each feature hierarchy corresponds to a segmentation level, and the spectral features and thermal infrared features corresponding to the segmentation level are built in; Based on the multiple feature levels, performing first-order feature interaction in the same feature level to obtain a first-order feature interaction result; Conduct second-order interactions in adjacent feature levels to obtain feature second-order interaction results; The first-order interaction result of the features is combined with the second-order interaction result of the features to obtain the fused soil feature.

2. The intelligent remote irrigation control device for forest cultivation according to claim 1, characterized in that: The soil characteristic analysis module performs the following steps: Identify the contribution of the spectral features and thermal infrared features to the soil moisture features of the current segmentation level, and obtain key spectral features and key thermal infrared features; Utilizing the key spectral features and the key thermal infrared features to analyze soil moisture characteristics, and obtaining spectral moisture characteristics and thermal infrared moisture characteristics; The spectral humidity feature and the thermal infrared humidity feature are averaged and fused to obtain the first-order interaction result of the feature.

3. The intelligent remote irrigation control device for forest cultivation according to claim 2, characterized in that: The soil characteristic analysis module performs the following steps: Obtaining the change gradient relationship between the key spectral features, the key thermal infrared features and the neighborhood features in the same feature level, and fitting the correlation feature relationship; The correlation characteristic relationship of the spectral feature and the correlation characteristic relationship of the thermal infrared feature are fused with the variation coefficient to obtain the feature variation relationship; The feature change relationship between adjacent feature levels is obtained, and inter-level aggregation is performed to obtain the second-order interaction result of the features.

4. The intelligent remote irrigation control device for forest cultivation according to claim 1, characterized in that: The soil characteristic analysis module performs the following steps: evaluating current soil moisture content based on the fused soil characteristics; Using the evapotranspiration model constructed using historical forest soil monitoring data, water loss was estimated based on the monitored soil characteristics of the current period to obtain the water loss rate; Based on the forest soil water content threshold, the current soil water content is subjected to a time series compensation analysis according to the water loss rate to obtain the soil irrigation demand, which represents the soil water replenishment time series demand when the forest soil water content threshold is reached.

5. The intelligent remote irrigation control device for forest cultivation according to claim 4, characterized in that: The soil characteristic analysis module performs the following steps: Sort the soil irrigation demand characteristics according to the time series relationship and construct a soil demand characteristic time series diagram; Obtaining a soil moisture threshold for the cultivated seedlings based on the seedling growth characteristics, and using a deviation between the soil moisture threshold for the cultivated seedlings and the forest soil water content threshold to correct the soil demand characteristic time series diagram; Performing irrigation demand analysis during the growth period according to the growth characteristics of the cultivated seedlings, and constructing a time series diagram of growth irrigation constraint characteristics; Extracting humidity impact values based on the meteorological data, wherein the humidity impact values include positive impact and negative impact, sorting the humidity impact values according to time information, and constructing a meteorological impact feature time series diagram; Aligning the soil demand feature time series diagram, the growth irrigation constraint feature time series diagram, and the meteorological influence feature time series diagram according to a time series relationship, superimposing features according to the time series alignment relationship, and constructing a seedling cultivation irrigation demand time series diagram, wherein the seedling cultivation irrigation demand time series diagram has a seedling cultivation soil moisture threshold mark; Based on the seedling cultivation irrigation demand timing diagram, the seedling cultivation demand sequence is obtained.

6. The intelligent remote irrigation control device for forest cultivation according to claim 1, characterized in that: The irrigation execution target acquisition module performs the following steps: Setting an irrigation penalty coefficient for the cultivated seedlings; The product of the standard deviation of the mandatory coefficient and the irrigation penalty coefficient is used as a correction factor; The correction factor and the irrigation penalty coefficient thereof are added to the evaluation conditions.

7. The intelligent remote irrigation control device for forest cultivation according to claim 6, characterized in that: The expression of the evaluation condition is: ,in, , To evaluate the results, is the mandatory coefficient of the i-th seedling in the selected area, is the irrigation requirement of the i-th seedling in the region, i is the number of seedlings in the selected irrigation area, j is the number of seedlings in the unselected area, is the correction factor for the unselected j-th seedling, is the irrigation penalty coefficient for not selecting the jth seedling, is the mandatory coefficient for the jth seedling not being selected.

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