Distributed forestry seedling raising spraying mechanism intelligent management and control system

Abstract

The invention belongs to the field of seedling culture spraying control, relates to a data analysis technology, and aims to solve the problem that the growth stage of a nursery cannot be identified and spraying parameters cannot be adaptively adjusted according to the growth stage in the prior art, in particular to a distributed forestry seedling culture spraying mechanism intelligent management and control system which comprises an intelligent management and control platform. The intelligent management and control platform is in communication connection with a growth identification module, a spraying control module, a growth evaluation module, a fault detection module and a database, and the growth identification module is used for identifying and analyzing the growth state of the forestry nursery; the spraying control module is used for automatically controlling a spraying mechanism for forestry seedling raising according to the irrigation parameters; partitioned precise irrigation of the seedling raising area is achieved, and the water resource utilization rate is effectively increased; the growth uniformity of the nursery stock is ensured through dynamic matching of the growth state and the irrigation parameters; a real-time fault detection mechanism significantly improves the operation reliability of the system, and reduces the nursery stock loss caused by equipment abnormality.

Description

Technical Field

[0001] This invention belongs to the field of seedling spray control and involves data analysis technology, specifically an intelligent management and control system for spray mechanisms in distributed forestry seedling cultivation. Background Technology

[0002] The Intelligent Control System for Distributed Forestry Seedling Sprinkler Mechanisms is a modern forestry seedling management solution based on the Internet of Things, big data, and artificial intelligence technologies. It aims to achieve precise and intelligent sprinkler management of distributed seedling bases.

[0003] The invention patent with publication number CN109874576B discloses a fully automatic sprinkler control system for sprout cultivation rooms. This sprinkler control system can not only automatically control the spraying time and duration for soaking, germination and seedling raising; but also form a forward and downward fan-shaped micro-spray to achieve uniform spraying of water. However, the system cannot identify the growth stage of sprouts and adaptively adjust the spraying parameters according to their growth stage, and it cannot dynamically optimize the spraying parameters according to the real-time monitored growth status, resulting in the inability to guarantee the cultivation effect of sprouts.

[0004] To address the aforementioned technical problems, this application proposes a solution. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent control system for the spraying mechanism of distributed forestry seedling cultivation, which solves the problem that existing technologies cannot identify the growth stage of the nursery and adaptively adjust the spraying parameters according to its growth stage.

[0006] The technical problem to be solved by this invention is: how to provide an intelligent control system for a distributed forestry seedling spraying mechanism that can identify the growth stage of the nursery and adaptively adjust the spraying parameters according to the growth stage.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] A distributed forestry seedling spraying mechanism intelligent control system includes an intelligent control platform, which is communicatively connected to a growth recognition module, a spraying control module, a growth evaluation module, a fault detection module, and a database.

[0009] The growth recognition module is used to identify and analyze the growth status of forestry nurseries: the coverage area of ​​forestry seedling nodes is divided into several analysis areas, the root and stem growth deviation coefficient and leaf growth deviation coefficient of the analysis area are obtained, and the uniformity of nursery growth in the analysis area is judged by the root and stem growth deviation coefficient and leaf growth deviation coefficient; if the requirements are met, the irrigation parameters of the analysis area are retrieved from the database.

[0010] The sprinkler control module is used to automatically control the sprinkler mechanism for forestry seedling cultivation according to irrigation parameters;

[0011] The growth assessment module is used to assess and analyze the growth status of forestry seedlings: obtain the growth analysis coefficient of the analysis area, and determine whether the growth status of the forestry nursery in the analysis area meets the requirements through the growth analysis coefficient;

[0012] The fault detection module is used to perform fault detection and analysis on the spraying mechanism.

[0013] Furthermore, the process of obtaining the root and stem growth deviation coefficient and the leaf growth deviation coefficient includes: periodically taking pictures of the forestry nursery in the analysis area, enlarging the pictures into pixel grid images, performing grayscale transformation and marking them as analysis images, segmenting the root and stem regions and leaf regions in the analysis images using the binary method and extracting the length value of a single root and the area value of a single leaf, calculating the variance of all root and stem length values ​​to obtain the root and stem growth deviation coefficient, and calculating the variance of all leaf area values ​​to obtain the leaf growth deviation coefficient.

[0014] Furthermore, the specific process for determining whether the nursery growth uniformity of the analysis area meets the requirements includes: retrieving the root and stem growth deviation threshold and the leaf growth deviation threshold from the database, and comparing the root and stem growth deviation coefficient and the leaf growth deviation coefficient of the analysis area with the root and stem growth deviation threshold and the leaf growth deviation threshold, respectively; if the root and stem growth deviation coefficient is less than the root and stem growth deviation threshold and the leaf growth deviation coefficient is less than the leaf growth deviation threshold, then the nursery growth uniformity of the analysis area is determined to meet the requirements; otherwise, the nursery growth uniformity of the analysis area is determined to not meet the requirements, a spraying optimization signal is generated and sent to the mobile terminal of the management personnel.

[0015] Furthermore, the process of retrieving irrigation parameters includes: summing and averaging the length values ​​of all roots and stems to obtain the root and stem growth performance value; summing and averaging the area values ​​of all leaves to obtain the leaf growth performance value; and retrieving the corresponding irrigation parameters from the database using the root and stem growth performance value and the leaf growth performance value. The irrigation parameters include soil moisture threshold, single irrigation duration, irrigation water pressure, and fertilizer concentration.

[0016] Furthermore, the specific process of the sprinkler control module automatically controlling the sprinkler mechanism for forestry seedling cultivation includes: acquiring the soil moisture value in the analysis area in real time, comparing the soil moisture value with the soil moisture threshold in the irrigation parameters corresponding to the analysis area; if the soil moisture value is greater than the soil moisture threshold, it is determined that the analysis area does not have sprinkler characteristics; if the soil moisture value is less than or equal to the soil moisture threshold, it is determined that the analysis area has sprinkler characteristics, and controlling the sprinkler mechanism by the single irrigation duration, irrigation water pressure, and fertilizer concentration.

[0017] Furthermore, the process of obtaining the growth analysis coefficient includes: extracting the chlorophyll content of the leaf regions in the analysis image; summing and averaging the chlorophyll content of all leaf regions in the analysis image to obtain the growth assessment value of the analysis region; after the forestry nursery in the analysis region has completed planting, establishing a rectangular coordinate system with time as the X-axis and growth assessment value as the Y-axis; marking the analysis points in the rectangular coordinate system with the acquisition time of the analysis image as the x-axis and the corresponding growth assessment value of the analysis image as the y-axis; connecting all the analysis points from left to right to obtain the analysis curve; retrieving the standard curve of the forestry nursery from the database and translating it into the rectangular coordinate system of the analysis curve; marking the area values ​​of all closed regions formed by the analysis curve and the standard curve as growth analysis values; drawing a perpendicular line from the rightmost endpoint of the standard curve to the X-axis to obtain the interception line segment; marking the area value of the closed region formed by the standard curve, the X-axis, the Y-axis, and the interception line segment as the growth measurement value; and marking the ratio of the growth analysis value to the growth measurement value as the growth analysis coefficient.

[0018] Furthermore, the specific process for determining whether the growth status of the forestry nursery in the analysis area meets the requirements includes: obtaining the growth analysis threshold from the database, comparing the growth analysis coefficient with the growth analysis threshold; if the growth analysis coefficient is less than the growth analysis threshold, the growth status of the forestry nursery in the analysis area is determined to meet the requirements; if the growth analysis coefficient is greater than or equal to the growth analysis threshold, the growth status of the forestry nursery in the analysis area is determined to not meet the requirements, generating a parameter optimization signal and sending the parameter optimization signal to the mobile terminal of the management personnel.

[0019] Furthermore, the specific process of fault detection analysis of the sprinkler mechanism by the fault detection module includes: marking the sprinkler process of the sprinkler mechanism as the detection process, dividing the detection process into several detection periods, obtaining the flow fluctuation value and water pressure fluctuation value of the detection period, obtaining the flow fluctuation threshold and water pressure fluctuation threshold through the database, and comparing the flow fluctuation value and water pressure fluctuation value with the flow fluctuation threshold and water pressure fluctuation threshold respectively: if the flow fluctuation value is less than the flow fluctuation threshold and the water pressure fluctuation value is less than the water pressure fluctuation threshold, it is determined that the sprinkler head does not have fault characteristics; otherwise, it is determined that the sprinkler head has fault characteristics, generating a fault processing signal and sending the fault processing signal to the mobile terminal of the management personnel.

[0020] Furthermore, the process of obtaining the flow rate fluctuation value and the water pressure fluctuation value includes: obtaining the spray liquid flow rate and water pressure of the nozzle during the detection period and marking them as flow rate value and pressure value respectively; marking the absolute value of the difference between the flow rate value of the detection period and the flow rate value of the previous detection period as the flow rate fluctuation value; and marking the difference between the maximum and minimum pressure values ​​during the detection period as the water pressure fluctuation value.

[0021] The present invention has the following beneficial effects:

[0022] 1. This application enables precise irrigation of seedling areas by zone, effectively improving water resource utilization; through dynamic matching of growth status and irrigation parameters, it ensures the uniformity of seedling growth; and the real-time fault detection mechanism significantly improves the reliability of system operation and reduces seedling losses caused by equipment malfunctions.

[0023] 2. This application can accurately identify the degree of growth difference between roots and leaves in the nursery area, and promptly trigger the spray optimization command to avoid over- or under-irrigation in local areas due to uneven growth. Managers can quickly locate the problem area and adjust the irrigation strategy based on the spray optimization signal, thereby improving the seedling survival rate and growth consistency.

[0024] 3. This application achieves dynamic optimization of irrigation parameters based on the actual growth status of the plants. Through dual quantitative analysis of root and leaf growth data, it ensures that irrigation intensity, frequency, and nutrient supply are precisely matched with the current developmental stage of the seedlings. This data-driven parameter retrieval method effectively solves the problem of rigid parameter settings in traditional systems, reducing the ineffective consumption of water and fertilizer while ensuring irrigation effectiveness.

[0025] 4. This application solves the problem of lack of dynamic basis for adjusting spray parameters in the prior art, and realizes a quantitative evaluation mechanism based on growth curve comparison. For example, if an abnormal increase in the growth analysis coefficient of a certain area is found in the middle of the seedling stage, the irrigation water pressure or fertilizer solution ratio of that area can be adjusted in time to avoid the extension of the seedling cycle due to growth lag, and effectively ensure the overall growth uniformity of the nursery. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0028] Figure 2 This is a flowchart of the method in Embodiment 2 of the present invention. Detailed Implementation

[0029] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] In existing technologies, traditional forestry seedling sprinkler systems mostly adopt a fixed program control mode, which cannot dynamically adjust irrigation parameters according to the seedling growth stage. Although the patent with publication number CN109874576B achieves automated sprinkler control, it lacks the ability to identify the real-time growth status of plants, resulting in insufficient matching between the sprinkler volume and actual growth needs. In large-scale seedling production scenarios, seedlings in different areas often exhibit uneven growth due to differences in microenvironment, and existing systems cannot effectively identify these differences and make targeted adjustments, easily leading to water waste or localized insufficient irrigation.

[0031] To address these issues, researchers discovered that the dynamic correlation between growth status and irrigation demand is key to precise regulation. Analysis revealed that the uniformity of root and leaf growth serves as a core indicator for irrigation decisions. Based on this, a method was proposed to quantify growth deviations using image processing technology and establish a multi-module collaborative closed-loop management system. The system first segments the nursery area for local state analysis, then combines real-time soil data to implement differentiated irrigation, and finally evaluates the regulatory effect through growth curve comparison.

[0032] Example 1: As Figure 1 As shown, the intelligent control system for the spraying mechanism of distributed forestry seedling cultivation includes an intelligent control platform, which is connected to a growth recognition module, a spraying control module, a growth assessment module, a fault detection module, and a database.

[0033] The growth recognition module is used to identify and analyze the growth status of forestry nurseries. It divides the coverage area of ​​forestry seedling nodes into several analysis regions, periodically captures images of the nurseries within these regions, enlarges the captured images into pixel-level images, performs grayscale transformation, and labels them as analysis images. Using a binary method, it segments the root and leaf regions in the analysis images, extracting the length value of a single root and the area value of a single leaf. It calculates the variance of all root length values ​​to obtain the root growth deviation coefficient, and calculates the variance of all leaf area values ​​to obtain the leaf growth deviation coefficient. Finally, it retrieves root and leaf growth deviation thresholds from the database and compares the root and leaf growth deviation coefficients of the analysis regions with... The root and stem growth deviation thresholds and leaf growth deviation thresholds are compared: if the root and stem growth deviation coefficient is less than the root and stem growth deviation threshold and the leaf growth deviation coefficient is less than the leaf growth deviation threshold, then the nursery growth uniformity of the analysis area is determined to meet the requirements. The length values ​​of all roots and stems are summed and averaged to obtain the root and stem growth performance value, and the area values ​​of all leaves are summed and averaged to obtain the leaf growth performance value. The corresponding irrigation parameters are retrieved from the database using the root and stem growth performance value and the leaf growth performance value. The irrigation parameters include soil moisture threshold, single irrigation duration, irrigation water pressure, and fertilizer concentration. Otherwise, the nursery growth uniformity of the analysis area is determined to not meet the requirements, a sprinkler optimization signal is generated, and the sprinkler optimization signal is sent to the mobile terminal of the management personnel.

[0034] Among these, pixel-grid image refers to a gridded image where the original image is enlarged to the actual physical size of each pixel. This can be achieved using image interpolation algorithms, such as bilinear or bicubic interpolation, to improve image detail resolution. Grayscale transformation refers to the process of converting a color image to a grayscale image. Specifically, a weighted average method can be used to convert RGB channel values ​​into a single grayscale value, eliminating color interference and simplifying subsequent segmentation operations. Binary methods refer to segmentation methods that convert grayscale images into black-and-white binary images. These can be achieved using fixed thresholding or adaptive thresholding methods, such as the Otsu algorithm, to distinguish between rootstocks, leaves, and background areas. Variance calculation refers to the quantitative analysis of the dispersion of multiple sample data. Specifically, the variance formula in statistics can be used to measure the growth consistency of rootstock length and leaf area within a region of the nursery.

[0035] Specifically, growth data for forestry nurseries within the analysis area was obtained through periodic image acquisition. After magnification and grayscale processing, the captured images were transformed into analytical images. Binary segmentation techniques were used to extract the morphological features of roots, stems, and leaves. The length of each root and the area of ​​each leaf were measured independently, and the root growth deviation coefficient and leaf growth deviation coefficient were calculated by determining the population variance. These two coefficients reflect the dispersion of root length distribution and the uniformity of leaf area distribution within the area, respectively, providing a quantitative basis for subsequent assessment of growth uniformity. For example, a large variance in root length in a certain analysis area indicates significant differences in root development among seedlings in that area, necessitating targeted adjustments to irrigation strategies.

[0036] The root and stem growth deviation coefficient is a value obtained by calculating the variance of all root and stem lengths. Specifically, it can be calculated using image segmentation technology to extract root and stem lengths and then using the variance formula. This coefficient quantifies the dispersion of root and stem growth. The leaf growth deviation coefficient is a value obtained by calculating the variance of all leaf areas. Specifically, it can be calculated using image grayscale transformation to segment leaf regions and then using the variance formula. This coefficient quantifies the uniformity of leaf growth. The sprinkler optimization signal is a control command triggered when growth uniformity is not up to standard. Specifically, it can be implemented by sending alarm information to a preset terminal via a mobile communication module, prompting manual intervention to adjust the irrigation strategy.

[0037] Specifically, the system pre-stores root and stem growth deviation thresholds and leaf growth deviation thresholds for different nursery varieties in a database. During the judgment process, the root and stem growth deviation coefficients calculated in real time are compared with the corresponding thresholds. If both are below the thresholds, the growth uniformity is deemed to meet the standard; if either coefficient exceeds the threshold, a spray optimization signal is triggered. This signal is transmitted to the management personnel's terminal via a mobile communication network, which can be set up as an SMS or application push notification, thereby achieving immediate feedback on abnormal states.

[0038] The root and stem growth performance value is a quantitative indicator obtained by calculating the arithmetic mean of the length values ​​of all root and stems within the analyzed area. Specifically, this can be achieved by extracting the root and stem region using image segmentation technology, measuring its pixel length, and converting it to actual length units. This value reflects the average growth level of the root and stems within the region. The leaf growth performance value is a quantitative indicator obtained by calculating the arithmetic mean of the area values ​​of all leaves within the analyzed area. Specifically, this can be achieved by segmenting the leaf region using image processing algorithms, calculating its pixel area, and converting it to actual area units. This value reflects the average growth status of the leaves within the region. Irrigation parameters refer to a set of preset control parameters stored in a database. Specifically, this can be achieved by establishing a mapping relationship between historical growth data and irrigation effects, and by matching the current growth performance value with the historical optimal parameter combination to achieve precise control.

[0039] Specifically, when the uniformity of nursery growth in the analysis area meets the requirements, the system automatically retrieves irrigation parameters. First, it performs an arithmetic mean calculation on the collected root and stem length dataset. For example, it adds the root and stem lengths of 50 seedlings in the area and divides by 50 to obtain a value representing the overall root and stem development level. Simultaneously, the same processing is performed on the leaf area dataset; for example, the projected area of ​​each leaf is converted to square centimeters and then averaged. These two growth performance values ​​are input into the database query module, which pre-stores the optimal combination of irrigation parameters corresponding to different growth stages. For example, when the root and stem growth performance value is in the 10-15 cm range and the leaf growth performance value is in the 20-25 square centimeter range, the system automatically matches the corresponding soil moisture threshold, irrigation duration, and other parameter sets.

[0040] The sprinkler control module is used to automatically control the sprinkler mechanism for forestry seedling cultivation: it acquires the soil moisture value in the analysis area in real time and compares the soil moisture value with the soil moisture threshold in the irrigation parameters corresponding to the analysis area: if the soil moisture value is greater than the soil moisture threshold, it is determined that the analysis area does not have sprinkler characteristics; if the soil moisture value is less than or equal to the soil moisture threshold, it is determined that the analysis area has sprinkler characteristics. The sprinkler mechanism is controlled by the single irrigation duration, irrigation water pressure, and fertilizer concentration.

[0041] The soil moisture threshold refers to a pre-set critical humidity value used to determine whether spraying needs to be activated. This is achieved by dynamically comparing real-time data collected by a soil moisture sensor with the preset threshold, which is set based on the growth requirements of different seedling varieties. The single irrigation duration refers to the length of each spraying operation, controlled by a timing module and a solenoid valve for precise water volume regulation. Irrigation water pressure refers to the pressure of the liquid flowing through the pipes during spraying, adjusted by a pressure sensor linked to a variable frequency pump to ensure uniform spray coverage. Fertilizer concentration refers to the proportion of nutrients added to the irrigation water, dynamically adjusted by a proportional valve and fertilizer mixing device to meet the nutritional needs of different growth stages.

[0042] Specifically, when the soil moisture sensor detects that the real-time humidity in a certain area is lower than a preset threshold, the system automatically triggers a spraying command, controlling the spraying duration, water pressure, and fertilizer solution ratio according to the corresponding irrigation parameters for that area. For example, when insufficient humidity is detected, the controller activates the solenoid valve and simultaneously adjusts the output pressure of the variable frequency water pump, while also coordinating with the fertilizer solution mixing device to inject nutrients according to a set ratio until the preset irrigation duration is reached, after which it automatically shuts off. The entire process requires no manual intervention, achieving precise irrigation control.

[0043] The growth assessment module is used to evaluate and analyze the growth status of forestry seedlings. It extracts chlorophyll content from leaf areas in the analysis image, sums and averages the chlorophyll content of all leaf areas in the analysis image to obtain the growth assessment value for the analysis area. After planting is completed in the forestry nursery of the analysis area, a rectangular coordinate system is established with time as the X-axis and the growth assessment value as the Y-axis. Analysis points are marked in the rectangular coordinate system with the image acquisition time as the x-axis and the corresponding growth assessment value as the y-axis. All analysis points are connected sequentially from left to right to obtain the analysis curve. The standard curve of the forestry nursery is retrieved from the database and translated into the rectangular coordinate system of the analysis curve. All curves formed by the analysis curve and the standard curve are then compared. The area value of the closed region is marked as the growth analysis value. A perpendicular line is drawn from the rightmost endpoint of the standard curve to the X-axis to obtain the interception line segment. The area value of the closed region formed by the standard curve, X-axis, Y-axis, and interception line segment is marked as the growth measurement value. The ratio of the growth analysis value to the growth measurement value is marked as the growth analysis coefficient. The growth analysis threshold is obtained from the database. The growth analysis coefficient is compared with the growth analysis threshold: if the growth analysis coefficient is less than the growth analysis threshold, the growth status of the forestry nursery in the analysis area is determined to meet the requirements; if the growth analysis coefficient is greater than or equal to the growth analysis threshold, the growth status of the forestry nursery in the analysis area is determined to not meet the requirements. A parameter optimization signal is generated and sent to the mobile terminal of the management personnel.

[0044] Among these metrics, chlorophyll content refers to the concentration of photosynthetic pigments in leaves. This can be achieved by measuring the reflectance of leaf regions using spectral analysis techniques and converting it into a chlorophyll index, used to quantify the plant's photosynthetic capacity. The growth assessment value is an indicator reflecting the overall growth status of the nursery within a region, expressed as the average chlorophyll content. This can be achieved by automatically calculating chlorophyll distribution data and obtaining the mean value using image processing algorithms, used to establish a dynamic growth trend model. The standard curve is a pre-stored benchmark curve reflecting the change of the assessment value over time under ideal growth conditions. This can be achieved by fitting historical planting data to generate a polynomial function curve, used to provide a reference system for comparing growth states. The area of ​​the enclosed region refers to the area of ​​the overlapping region formed by the analysis curve and the standard curve in the coordinate system. This can be achieved by calculating the geometric area of ​​the region enclosed between the curves using integral algorithms or grid segmentation methods, used to quantify the deviation between actual growth and the ideal state.

[0045] Specifically, after the nursery planting is completed, leaf images are periodically collected and chlorophyll content data is extracted. The average chlorophyll content at each time point is used as the growth assessment value to construct a time-series curve. This curve is spatially superimposed on a pre-stored standard growth curve in the database. The area of ​​the difference region between the two curves is calculated, and then proportionally converted to the total area covered by the standard curve to obtain a quantitative coefficient characterizing the quality of growth. For example, when the actual growth curve perfectly matches the standard curve, the difference area is zero, and the growth analysis coefficient reaches its optimal value; when the actual growth lags behind or exceeds the standard curve, the difference area increases, leading to an increase in the coefficient, triggering the parameter optimization mechanism.

[0046] The growth analysis coefficient is the ratio of the area of ​​all closed regions formed by the analysis curve and the standard curve to the growth measurement value. Specifically, it can be calculated by extracting chlorophyll content from leaf regions using image processing algorithms, constructing an analysis curve based on time-series growth assessment values, and then comparing the area with the standard curve. This coefficient quantifies the deviation between the actual growth state and the expected standard.

[0047] The growth analysis threshold is a pre-set critical value for determining whether the growth status meets the standard. This threshold can be set through historical data statistics or expert experience and stored in a database. It serves as the benchmark for determining whether spray parameters need adjustment.

[0048] The parameter optimization signal is an instruction that triggers managers to dynamically adjust irrigation parameters. Specifically, this signal can be pushed to a mobile terminal via a mobile communication network. This signal is used to promptly report abnormal growth information, prompting manual intervention for optimization.

[0049] Specifically, after planting is completed in the forestry nursery within the analysis area, chlorophyll content data of leaves is collected periodically to establish an analytical curve showing the change of growth assessment values ​​over time. This analytical curve is then overlaid and compared with a pre-stored standard curve in the database. The ratio of the area of ​​the closed region formed by the two curves to the baseline area of ​​the standard curve is calculated to obtain the growth analysis coefficient. When this coefficient exceeds a preset threshold, it indicates that the current growth rate or health status deviates from the expected target. The system automatically generates a parameter optimization signal and pushes it to the management personnel. This achieves a closed-loop feedback mechanism based on dynamic monitoring of growth status, avoiding continuous growth deviations caused by environmental changes or equipment errors.

[0050] The fault detection module is used to perform fault detection and analysis on the sprinkler system: the sprinkler process is marked as the detection process, and the detection process is divided into several detection periods. During the detection period, the spray liquid flow rate and water pressure of the sprinkler head are acquired and marked as flow rate value and pressure value, respectively. The absolute value of the difference between the flow rate value of the detection period and the flow rate value of the previous detection period is marked as the flow rate fluctuation value. The difference between the maximum and minimum pressure values ​​during the detection period is marked as the water pressure fluctuation value. The flow rate fluctuation threshold and water pressure fluctuation threshold are obtained from the database. The flow rate fluctuation value and water pressure fluctuation value are compared with the flow rate fluctuation threshold and water pressure fluctuation threshold, respectively. If the flow rate fluctuation value is less than the flow rate fluctuation threshold and the water pressure fluctuation value is less than the water pressure fluctuation threshold, the sprinkler head is determined to have no fault characteristics; otherwise, the sprinkler head is determined to have fault characteristics, a fault processing signal is generated, and the fault processing signal is sent to the mobile terminal of the management personnel.

[0051] The detection period refers to the independent monitoring intervals that divide the spraying process according to the time dimension. Specifically, it can be implemented using a timer or a periodic sampler to achieve dynamic segmented monitoring of the spraying process.

[0052] The flow fluctuation value refers to the absolute value of the change in the flow rate of the spray liquid between adjacent detection periods. Specifically, it can be achieved by using a flow sensor to collect data in real time and using a difference calculation module to reflect the stability of the spray liquid supply.

[0053] Water pressure fluctuation value refers to the difference between the maximum and minimum water pressure of the sprinkler head within a single detection period. Specifically, it can be achieved by using a pressure sensor in conjunction with an extreme value recorder to characterize the pressure stability of the sprinkler system.

[0054] The flow fluctuation threshold and water pressure fluctuation threshold are benchmark parameters that are pre-stored in the database. They can be obtained through statistical analysis of historical operating data and are used as quantitative standards to judge the operating status of the sprinkler system.

[0055] Specifically, the spraying process is divided into multiple consecutive monitoring periods, during which the flow rate and pressure data of the sprinkler heads are collected in real time. The flow rate fluctuation value is obtained by calculating the absolute value of the difference in flow rate between adjacent periods, and the extreme pressure difference within the current period is extracted as the water pressure fluctuation value. These two parameters are compared with preset thresholds in the database. When either parameter exceeds the corresponding threshold, it is determined that the sprinkler head has fault characteristics such as blockage or pipeline leakage. After a fault handling signal is generated, it is automatically pushed to the management terminal via a wireless communication module, triggering the maintenance response mechanism.

[0056] Example 2: Figure 2 As shown, the intelligent control method for the sprinkler system in distributed forestry seedling cultivation includes the following steps:

[0057] Step 1: Identify and analyze the growth status of forestry nurseries: Divide the coverage area of ​​forestry seedling nodes into several analysis areas, periodically obtain the root and stem growth deviation coefficient and leaf growth deviation coefficient of the analysis areas, and determine whether the nursery growth uniformity in the analysis areas meets the requirements based on the root and stem growth deviation coefficient and leaf growth deviation coefficient.

[0058] Step 2: Automatic control of the sprinkler system for forestry seedling cultivation: Real-time acquisition of soil moisture values ​​within the analysis area, and control of the sprinkler system through irrigation parameters when the analysis area exhibits sprinkler characteristics;

[0059] Step 3: Evaluate and analyze the growth status of forestry seedlings: Obtain the growth analysis coefficient of the analysis area, and determine whether the growth status of the forestry nursery in the analysis area meets the requirements through the growth analysis coefficient;

[0060] Step 4: Perform fault detection and analysis on the sprinkler system: Obtain the flow rate fluctuation value and water pressure fluctuation value during the detection period, and determine whether the sprinkler head has fault characteristics based on the flow rate fluctuation value and water pressure fluctuation value.

[0061] The intelligent control system for the sprinkler system in distributed forestry seedling cultivation divides the coverage area of ​​the seedling cultivation nodes into several analysis zones during operation. It periodically acquires the root and stem growth deviation coefficients and leaf growth deviation coefficients of these zones, and uses these coefficients to determine whether the uniformity of seedling growth within the analysis zone meets requirements. It also acquires real-time soil moisture values ​​within the analysis zone and controls the sprinkler system using irrigation parameters when the zone exhibits sprinkler characteristics. Furthermore, it acquires growth analysis coefficients for each analysis zone, using these coefficients to determine whether the growth status of the forestry seedlings within the analysis zone meets requirements. Finally, it acquires flow rate and water pressure fluctuation values ​​during the detection period, and uses these fluctuations to determine whether the sprinkler heads exhibit malfunction characteristics.

[0062] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

[0063] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0064] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to specific implementations. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. An intelligent control system for a distributed forestry seedling spraying mechanism, characterized in that, It includes an intelligent management and control platform, which is communicatively connected to a growth identification module, a spray control module, a growth assessment module, a fault detection module, and a database; The growth recognition module is used to identify and analyze the growth status of forestry nurseries: the coverage area of ​​forestry seedling nodes is divided into several analysis areas, the root and stem growth deviation coefficient and leaf growth deviation coefficient of the analysis area are obtained, and the uniformity of nursery growth in the analysis area is judged by the root and stem growth deviation coefficient and leaf growth deviation coefficient; if the requirements are met, the irrigation parameters of the analysis area are retrieved from the database. The sprinkler control module is used to automatically control the sprinkler mechanism for forestry seedling cultivation according to irrigation parameters; The growth assessment module is used to assess and analyze the growth status of forestry seedlings: obtain the growth analysis coefficient of the analysis area, and determine whether the growth status of the forestry nursery in the analysis area meets the requirements through the growth analysis coefficient; The fault detection module is used to perform fault detection and analysis on the spraying mechanism.

2. The intelligent control system for the spray mechanism of distributed forestry seedling cultivation according to claim 1, characterized in that, The process of obtaining the root and stem growth deviation coefficient and the leaf growth deviation coefficient includes: periodically taking pictures of forestry nurseries within the analysis area, enlarging the pictures into pixel grid images, performing grayscale transformation, and marking them as analysis images, segmenting the root and stem regions and leaf regions in the analysis images using the binary method, and extracting the length value of a single root and the area value of a single leaf, calculating the variance of all root and stem length values ​​to obtain the root and stem growth deviation coefficient, and calculating the variance of all leaf area values ​​to obtain the leaf growth deviation coefficient.

3. The intelligent control system for the spraying mechanism of distributed forestry seedling cultivation according to claim 2, characterized in that, The specific process for determining whether the uniformity of nursery growth in the analysis area meets the requirements includes: retrieving the root and stem growth deviation threshold and the leaf growth deviation threshold from the database, and comparing the root and stem growth deviation coefficient and the leaf growth deviation coefficient of the analysis area with the root and stem growth deviation threshold and the leaf growth deviation threshold, respectively; if the root and stem growth deviation coefficient is less than the root and stem growth deviation threshold and the leaf growth deviation coefficient is less than the leaf growth deviation threshold, then the uniformity of nursery growth in the analysis area is determined to meet the requirements; otherwise, the uniformity of nursery growth in the analysis area is determined to not meet the requirements, a spraying optimization signal is generated and sent to the mobile terminal of the management personnel.

4. The intelligent control system for the spray mechanism of distributed forestry seedling cultivation according to claim 3, characterized in that, The process of retrieving irrigation parameters includes: summing and averaging the length values ​​of all roots and stems to obtain the root and stem growth performance value; summing and averaging the area values ​​of all leaves to obtain the leaf growth performance value; and retrieving the corresponding irrigation parameters from the database using the root and stem growth performance value and the leaf growth performance value. The irrigation parameters include soil moisture threshold, single irrigation duration, irrigation water pressure, and fertilizer concentration.

5. The intelligent control system for the spray mechanism of distributed forestry seedling cultivation according to claim 4, characterized in that, The specific process of the sprinkler control module automatically controlling the sprinkler mechanism for forestry seedling cultivation includes: acquiring the soil moisture value in the analysis area in real time, comparing the soil moisture value with the soil moisture threshold in the irrigation parameters corresponding to the analysis area; if the soil moisture value is greater than the soil moisture threshold, it is determined that the analysis area does not have sprinkler characteristics; if the soil moisture value is less than or equal to the soil moisture threshold, it is determined that the analysis area has sprinkler characteristics, and controlling the sprinkler mechanism by the single irrigation duration, irrigation water pressure, and fertilizer concentration.

6. The intelligent control system for the spraying mechanism of distributed forestry seedling cultivation according to claim 5, characterized in that, The process of obtaining the growth analysis coefficient includes: extracting the chlorophyll content of the leaf areas in the analysis image; summing and averaging the chlorophyll content of all leaf areas in the analysis image to obtain the growth assessment value of the analysis area; after the forestry nursery in the analysis area has completed planting, establishing a rectangular coordinate system with time as the X-axis and growth assessment value as the Y-axis; marking the analysis points in the rectangular coordinate system with the acquisition time of the analysis image as the x-axis and the corresponding growth assessment value of the analysis image as the y-axis; connecting all the analysis points from left to right to obtain the analysis curve; retrieving the standard curve of the forestry nursery from the database and translating it into the rectangular coordinate system of the analysis curve; marking the area values ​​of all closed regions formed by the analysis curve and the standard curve as growth analysis values; drawing a perpendicular line from the rightmost endpoint of the standard curve to the X-axis to obtain the interception line segment; marking the area value of the closed region formed by the standard curve, the X-axis, the Y-axis, and the interception line segment as the growth measurement value; and marking the ratio of the growth analysis value to the growth measurement value as the growth analysis coefficient.

7. The intelligent control system for the spray mechanism of distributed forestry seedling cultivation according to claim 6, characterized in that, The specific process for determining whether the growth status of the forestry nursery in the analysis area meets the requirements includes: obtaining the growth analysis threshold from the database, comparing the growth analysis coefficient with the growth analysis threshold; if the growth analysis coefficient is less than the growth analysis threshold, the growth status of the forestry nursery in the analysis area is determined to meet the requirements; if the growth analysis coefficient is greater than or equal to the growth analysis threshold, the growth status of the forestry nursery in the analysis area is determined to not meet the requirements, generating a parameter optimization signal and sending the parameter optimization signal to the mobile terminal of the management personnel.

8. The intelligent control system for the spraying mechanism of distributed forestry seedling cultivation according to claim 7, characterized in that, The specific process of fault detection and analysis of the sprinkler mechanism by the fault detection module includes: marking the sprinkler process of the sprinkler mechanism as the detection process, dividing the detection process into several detection time periods, obtaining the flow fluctuation value and water pressure fluctuation value of the detection time period, obtaining the flow fluctuation threshold and water pressure fluctuation threshold through the database, and comparing the flow fluctuation value and water pressure fluctuation value with the flow fluctuation threshold and water pressure fluctuation threshold respectively: if the flow fluctuation value is less than the flow fluctuation threshold and the water pressure fluctuation value is less than the water pressure fluctuation threshold, it is determined that the sprinkler head does not have fault characteristics; otherwise, it is determined that the sprinkler head has fault characteristics, generating a fault processing signal and sending the fault processing signal to the mobile terminal of the management personnel.

9. The intelligent control system for the spray mechanism of distributed forestry seedling cultivation according to claim 8, characterized in that, The process of obtaining flow fluctuation value and water pressure fluctuation value includes: obtaining the spray liquid flow rate and water pressure of the nozzle during the detection period and marking them as flow rate value and pressure value respectively; marking the absolute value of the difference between the flow rate value of the detection period and the flow rate value of the previous detection period as the flow fluctuation value; and marking the difference between the maximum and minimum pressure values ​​during the detection period as the water pressure fluctuation value.

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