Sphagna planting management method, placement, equipment and medium

Abstract

The invention discloses a sphagna planting management method, placement, equipment and a medium, and relates to the technical field of agricultural planting management. The method comprises the following steps: S1, acquiring monitoring data of a sphagna planting area; wherein the monitoring data comprises plant height, sphagna color value and water content. S2, according to the plant height, the sphagna growth rate is obtained. And S3, according to the sphagna growth rate, obtaining a growth rate abnormal coefficient and a first abnormal point position. And S4, obtaining the sphagna appearance according to the sphagna color value and the water content. And S5, according to the sphagna appearance form, obtaining a sphagna form abnormal coefficient and a second abnormal point position. And S6, according to the growth rate abnormal coefficient, the first abnormal point position, the sphagna form abnormal coefficient and the second abnormal point position, obtaining a sphagna growth condition evaluation index. And S7, evaluating the real-time growth result of the sphagna based on the optimal growth curve model according to the sphagna growth condition evaluation index.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural planting management, and in particular to a sphagnum moss planting management method, placement, equipment and medium. Background Art

[0002] Sphagnum moss, also known as seaweed and peat moss, is a plant of the genus Sphagnum in the family Sphagaceae. It is often used in the horticultural industry and ecological environment restoration.

[0003] Sphagnum moss is an oligotrophic moss, lacking true roots and vascular tissue, and its growth rate is relatively slow. This unique biological characteristic makes its growth process highly dependent on the environment, and its growth status is easily affected by fluctuations in external factors. Therefore, achieving high yields and high-quality cultivation of sphagnum moss faces inherent challenges due to its inherent physiological characteristics.

[0004] Currently, extensive techniques are common in sphagnum moss cultivation and management. During the cultivation process, managers struggle to fully and accurately monitor the growth of the moss in real time, resulting in uneven growth within each plot. This uneven growth severely limits the ultimate yield and quality of the moss.

[0005] Harvested sphagnum moss often suffers from thin stems and sparse hairs, resulting in low key quality indicators such as uniform water absorption, water retention, and resistance to decomposition. This makes sphagnum moss products less competitive in the high-end market, which demands higher quality. Summary of the Invention

[0006] The present invention provides a sphagnum moss planting management method, placement, equipment and medium to improve at least one of the above technical problems.

[0007] In a first aspect, the present invention provides a sphagnum moss planting and management method, which comprises steps S1 to S7.

[0008] S1. Acquire monitoring data of the sphagnum moss planting area, wherein the monitoring data includes plant height, sphagnum moss color value, and water content.

[0009] S2. Obtaining the growth rate of the sphagnum moss according to the plant height.

[0010] S3. Obtaining a growth rate anomaly coefficient and a first abnormal point position according to the sphagnum moss growth rate.

[0011] S4. Obtaining the appearance of the sphagnum moss according to the color value and water content of the sphagnum moss.

[0012] S5. Obtaining a sphagnum moss morphology abnormality coefficient and a second abnormal point position according to the sphagnum moss appearance.

[0013] S6. Obtaining a sphagnum moss growth condition evaluation index according to the growth rate abnormality coefficient, the first abnormal point position, the sphagnum moss morphology abnormality coefficient, and the second abnormal point position.

[0014] S7, based on the evaluation index of the sphagnum moss growth condition, based on the optimal growth curve model , evaluate the real-time growth results of sphagnum moss. Among them, the optimal growth curve model Based on the optimal environmental parameters of the sphagnum moss growth cycle, the optimal growth state of each stage was simulated and fitted.

[0015] Preferably, the calculation model of the sphagnum moss growth rate is: .

[0016] Where, For the The sphagnum moss at the data collection location is Growth rate at the time of data collection, For the The sphagnum moss at the data collection location is Plant height at the time of data collection, For the first The sphagnum moss at the data collection location is Plant height at the time of data collection, The interval between two data collection time points.

[0017] Preferably, step S3 specifically includes steps S31 to S35.

[0018] S31. Obtain an average growth rate of sphagnum moss according to the growth rate of sphagnum moss.

[0019] The calculation model of the average growth rate of the sphagnum moss is: .

[0020] Where, For the The average growth rate of sphagnum moss at the time of data collection, For the The sphagnum moss at the data collection location is Growth rate at the time of data collection, is the total number of locations where data was collected.

[0021] S32. Obtain a growth rate abnormality coefficient according to the sphagnum moss growth rate and the average sphagnum moss growth rate.

[0022] The calculation model of the growth rate anomaly coefficient is: .

[0023] in, For the The growth rate anomaly coefficient at the data collection time point.

[0024] S33, judging whether each data collection location is the first abnormal point location based on the growth rate abnormal coefficient. This indicates that the sphagnum moss planting area is If the growth change fluctuates greatly within a certain period of time, it will be marked as the first abnormal point. This indicates that the sphagnum moss planting area is If the growth fluctuations within a certain period of time are small, the growth status of the sphagnum moss planting area will continue to be evaluated.

[0025] S34, further determining whether the unmarked data collection location is the first abnormal point location based on the sphagnum moss growth rate and the average sphagnum moss growth rate. When The data collection time point The data collection location is marked as the first abnormal point location.

[0026] S35: Determine whether the sphagnum moss planting area is abnormal based on the number of the first abnormal point positions. is the total number of first abnormal point locations. Only the data collection locations are marked. The sphagnum moss planting area is marked as an abnormal plot.

[0027] Preferably, the calculation model of the sphagnum moss appearance morphology is: .

[0028] in, For the The sphagnum moss at the data collection location is Appearance at the time of data collection, is the first homogenization coefficient, For the The sphagnum moss at the data collection location is The color value of sphagnum moss at the time of data collection, For the The sphagnum moss at the data collection location is Water content at the time of data collection, is the second normalization coefficient.

[0029] Preferably, step S5 specifically includes steps S51 to S55.

[0030] S51. Obtain a morphological mean value according to the appearance morphology of the sphagnum moss.

[0031] The calculation model of the morphological mean is: .

[0032] in, For the The mean of the morphology at the time of data collection, For the The sphagnum moss at the data collection location is Appearance at the time of data collection, is the total number of locations where data was collected.

[0033] S52. Obtaining a morphological abnormality coefficient of the sphagnum moss according to the morphological appearance of the sphagnum moss and the morphological mean.

[0034] The calculation model of the morphological abnormality coefficient of the sphagnum moss is: .

[0035] in, For the Abnormal coefficient of Sphagnum moss morphology at the data collection time point.

[0036] S53, judging whether each data collection location is a second abnormal point location based on the abnormal coefficient of the sphagnum moss morphology. It is marked as an abnormal point. Then continue to evaluate the growth status of the sphagnum moss planting area.

[0037] S54: further determining whether the data collection location not marked as the second abnormal point location is the second abnormal point location based on the appearance of the sphagnum moss and the average value of the appearance. or When The data collection location is marked as the second abnormal point location.

[0038] S55: Determine whether the sphagnum moss planting area is abnormal based on the number of the second abnormal point positions. is the total number of second outlier locations. Only the data collection locations are marked. The sphagnum moss planting area is marked as an abnormal plot.

[0039] Preferably, step S6 specifically includes step S61 to step S62.

[0040] S61, when and When the number of abnormal data collection locations accounts for 15% to 30% of the total, the calculation model of the sphagnum moss growth condition evaluation index in the sphagnum moss planting area is: .

[0041] Where, For the The evaluation index of the sphagnum moss growth condition in the sphagnum moss planting area at the time of data collection, is the first weight coefficient, For the The sphagnum moss at the data collection location is Growth rate at the time of data collection, is the second weight coefficient, For the The sphagnum moss at the data collection location is Appearance at the time of data collection, is the total number of locations where data was collected.

[0042] S62. When the number of abnormal data collection locations accounts for 70% or more of the total, the calculation model for the sphagnum moss growth condition evaluation index of the sphagnum moss planting area is: .

[0043] Where, is the third weight coefficient, For the The sphagnum moss at the first abnormal point is Growth rate at the time of data collection, is the total number of first abnormal point locations, is the fourth weight coefficient, For the The sphagnum moss at the second abnormal point is Appearance at the time of data collection, is the total number of second outlier locations.

[0044] Preferably, step S7 specifically includes step S71 to step S72.

[0045] S71, if conform to , that is, in Evaluation index of sphagnum moss growth condition at the time of data collection Optimal growth curve model The significance index of the overlap of the same growth point or the difference between the two , indicating that the sphagnum moss is in good growth condition and there is no need to adjust the planting measures.

[0046] S72, if Not compliant , indicating that there is a problem with the growth of sphagnum moss, according to the abnormal growth rate coefficient and Sphagnum moss morphological abnormality coefficient Determine the degree of abnormality in growth rate and appearance, adjust planting measures, and conduct targeted management of marked abnormal points and plots.

[0047] In a second aspect, the present invention provides a sphagnum moss planting and management device, which includes a data acquisition module, a growth rate calculation module, a first abnormality recognition module, an appearance morphology calculation module, a second abnormality recognition module, an evaluation index calculation module and a growth result evaluation module.

[0048] The data acquisition module is used to acquire monitoring data of the sphagnum moss planting area, wherein the monitoring data includes plant height, sphagnum moss color value, and water content.

[0049] The growth rate calculation module is used to obtain the growth rate of the sphagnum moss according to the plant height.

[0050] The first abnormality identification module is used to obtain a growth rate abnormality coefficient and a first abnormal point position according to the sphagnum moss growth rate.

[0051] The appearance morphology calculation module is used to obtain the appearance morphology of the sphagnum moss according to the color value and water content of the sphagnum moss.

[0052] The second abnormality recognition module is used to obtain the sphagnum moss morphology abnormality coefficient and the second abnormal point position according to the sphagnum moss appearance.

[0053] The evaluation index calculation module is used to obtain the sphagnum moss growth condition evaluation index according to the growth rate abnormality coefficient, the first abnormal point position, the sphagnum moss morphology abnormality coefficient and the second abnormal point position.

[0054] Growth result evaluation module, used for evaluating the growth condition of sphagnum moss based on the optimal growth curve model , evaluate the real-time growth results of sphagnum moss. Among them, the optimal growth curve model Based on the optimal environmental parameters of the sphagnum moss growth cycle, the optimal growth state of each stage was simulated and fitted.

[0055] In a third aspect, the present invention provides a sphagnum moss cultivation management device, comprising a processor, a memory, and a computer program stored in the memory. The computer program can be executed by the processor to implement the sphagnum moss cultivation management method as described in any paragraph of the first aspect.

[0056] In a fourth aspect, the present invention provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a sphagnum moss cultivation and management method as described in any paragraph of the first aspect.

[0057] By adopting the above technical solution, the present invention can achieve the following technical effects: This sphagnum moss cultivation management method uses integrated sensors and remote sensing technology to collect multi-dimensional growth data, such as plant height, color, and moisture content, in real time. It then uses a dynamic algorithm to analyze abnormal fluctuations in growth rate and appearance. This process accurately identifies local anomalies or large areas of abnormality, promptly marking areas where growth rates deviate from the mean or morphological parameters exceed thresholds, providing early warning and precise location of problem areas.

[0058] By fitting an optimal growth curve model, the system assesses real-time growth status and automatically triggers planting adjustment recommendations when significant deviations from the model are detected, including water and fertilizer optimization, light regulation, and temperature intervention. This method significantly improves the growth uniformity and quality stability of sphagnum moss, effectively addressing quality defects such as thin stems and sparse hairs, while also reducing management costs and enhancing the product's competitiveness in the international high-end market. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the specific implementation methods of the present invention. It should be understood that the following drawings only show certain specific implementation methods of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0060] Figure 1 The present invention is a flowchart of a sphagnum moss planting and management method.

[0061] Figure 2 The present invention is a structural diagram of a sphagnum moss planting and management device. DETAILED DESCRIPTION

[0062] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0063] Example 1, please refer to Figure 1 A first embodiment of the present invention provides a sphagnum moss cultivation management method, which can be performed by a sphagnum moss cultivation management device. Specifically, the method is performed by one or more processors in the sphagnum moss cultivation management device to implement steps S1 to S7.

[0064] S1. Acquire monitoring data of the sphagnum moss planting area, wherein the monitoring data includes plant height, sphagnum moss color value, and water content.

[0065] Specifically, the sphagnum moss planting plots are divided into In each region, a fixed location is randomly assigned and the growth data module is used to collect relevant sphagnum moss growth information at that location. This embodiment of the present invention uses a growth data module that integrates sensors and remote sensing technology to collect real-time sphagnum moss growth information and calculate and analyze the growth status of the sphagnum moss. The real-time growth information includes the growth rate and appearance of the sphagnum moss.

[0066] Plant height can be automatically measured using non-contact sensors (such as laser rangefinders, ultrasonic sensors, or optical altimeters) deployed at fixed locations. Specifically, sensors are installed at randomly designated fixed locations in each divided area to periodically collect the vertical height from the top of the sphagnum moss to the base, and the data is recorded with a timestamp. .

[0067] The color value of sphagnum moss can be obtained from remote sensing images, or by using a multispectral imager or RGB color sensor to capture the reflectance of the visible light band and quantify the color characteristics. Specifically, image analysis algorithms (such as HSV / Lab color model) are used to extract color parameters (such as green saturation and hue) from the collected images and convert them into numerical indicators. .

[0068] Moisture content can be measured indirectly using dielectric constant sensors (such as soil moisture probes) or near-infrared spectroscopy (NIRS). Spectroscopic analysis enables non-destructive testing by establishing a regression model between near-infrared spectral reflectance and moisture content. The dielectric constant method uses an insertable probe to measure the dielectric properties of sphagnum moss and correlate this with moisture content.

[0069] S2. Obtaining the growth rate of the sphagnum moss according to the plant height.

[0070] Preferably, the calculation model of the sphagnum moss growth rate is: .

[0071] Where, For the The sphagnum moss at the data collection location is Growth rate at the time of data collection, For the The sphagnum moss at the data collection location is Plant height at the time of data collection, For the first The sphagnum moss at the data collection location is Plant height at the time of data collection, is the interval between two data collection time points. . is the total number of data collection locations, a positive integer.

[0072] S3. Obtaining a growth rate abnormality coefficient and a first abnormal point position according to the sphagnum moss growth rate. Preferably, step S3 specifically includes steps S31 to S35.

[0073] S31. Obtain an average growth rate of sphagnum moss according to the growth rate of sphagnum moss.

[0074] The calculation model of the average growth rate of the sphagnum moss is: .

[0075] Where, For the The average growth rate of sphagnum moss at the time of data collection, For the The sphagnum moss at the data collection location is Growth rate at the time of data collection, is the total number of locations where data was collected.

[0076] S32. Obtain a growth rate abnormality coefficient according to the sphagnum moss growth rate and the average sphagnum moss growth rate.

[0077] The calculation model of the growth rate anomaly coefficient is: .

[0078] in, For the The growth rate anomaly coefficient at the data collection time point.

[0079] S33, judging whether each data collection location is the first abnormal point location based on the growth rate abnormal coefficient. This indicates that the sphagnum moss planting area is If the growth change fluctuates greatly within a certain period of time, it will be marked as the first abnormal point. This indicates that the sphagnum moss planting area is If the growth fluctuations within a certain period of time are small, the growth status of the sphagnum moss planting area will continue to be evaluated.

[0080] S34, further determining whether the unmarked data collection location is the first abnormal point location based on the sphagnum moss growth rate and the average sphagnum moss growth rate. When The data collection time point The data collection location is marked as the first abnormal point location.

[0081] S35, based on the first abnormal point position The number of sphagnum moss is used to determine whether the sphagnum moss planting area is abnormal. is the total number of first abnormal point locations. Only the data collection locations are marked. The sphagnum moss planting area is marked as an abnormal plot.

[0082] The embodiment of the present invention analyzes abnormal growth of sphagnum moss based on the growth rate and average growth rate of each data collection point, and determines the growth fluctuation of sphagnum moss at each data collection point according to the growth rate abnormality coefficient.

[0083] This step dynamically calculates the growth rate anomaly coefficient ( ), accurately quantify the degree of growth fluctuation in the sphagnum moss planting area. The primary abnormality marking mechanism is automatically triggered when the relative growth rate threshold ( ) Implement secondary verification to effectively avoid the risk of misjudgment caused by single detection errors. Figure 1 As shown in the figure, this dual judgment mechanism can not only identify the abnormal points of local growth arrest, but also can identify the abnormal point ratio threshold ( ) to identify large areas of abnormal plots. This hierarchical early warning mechanism provides precise spatial positioning data for crop management, significantly improving the timeliness of responses to growth adversities (such as water stress and nutrient imbalance), ensuring that abnormal intervention measures are implemented during the golden window.

[0084] S4. Obtaining the appearance of the sphagnum moss according to the color value and water content of the sphagnum moss.

[0085] Preferably, the calculation model of the sphagnum moss appearance morphology is: .

[0086] in, For the The sphagnum moss at the data collection location is Appearance at the time of data collection, is the first homogenization coefficient, For the The sphagnum moss at the data collection location is The color value of sphagnum moss at the time of data collection, For the The sphagnum moss at the data collection location is Water content at the time of data collection, is the second normalization coefficient.

[0087] S5. Obtaining the morphological abnormality coefficient and the second abnormal point position of the sphagnum moss according to the morphological appearance of the sphagnum moss. Preferably, step S5 specifically includes steps S51 to S55.

[0088] S51. Obtain a morphological mean value according to the appearance morphology of the sphagnum moss.

[0089] The calculation model of the morphological mean is: .

[0090] in, For the The mean of the morphology at the time of data collection, For the The sphagnum moss at the data collection location is Appearance at the time of data collection, is the total number of locations where data was collected.

[0091] S52. Obtaining a morphological abnormality coefficient of the sphagnum moss according to the morphological appearance of the sphagnum moss and the morphological mean.

[0092] The calculation model of the morphological abnormality coefficient of the sphagnum moss is: .

[0093] in, For the Abnormal coefficient of Sphagnum moss morphology at the data collection time point.

[0094] S53, judging whether each data collection location is a second abnormal point location based on the abnormal coefficient of the sphagnum moss morphology. It is marked as an abnormal point. Then continue to evaluate the growth status of the sphagnum moss planting area.

[0095] S54: further determining whether the data collection location not marked as the second abnormal point location is the second abnormal point location based on the appearance of the sphagnum moss and the average value of the appearance. or When The data collection location is marked as the second abnormal point location.

[0096] S55: Based on the position of the second abnormal point The number of sphagnum moss is used to determine whether the sphagnum moss planting area is abnormal. is the total number of second outlier locations. Only the data collection locations are marked. The sphagnum moss planting area is marked as an abnormal plot.

[0097] The embodiment of the present invention analyzes the abnormal growth state of sphagnum moss based on the appearance morphology and morphological mean of each data collection point, and determines the growth fluctuation of sphagnum moss at each data collection point according to the sphagnum moss morphological abnormality coefficient.

[0098] This step constructs a comprehensive evaluation model of the morphology of sphagnum moss ( ), to achieve the coordinated detection of color anomalies and water content anomalies. Figure 1 As shown, the standard deviation algorithm was used to calculate the morphological abnormality coefficient ( ) as the primary basis for judgment, when Immediately mark the abnormal area; at the same time set the relative morphological threshold ( or ) for secondary verification, effectively distinguishing between localized spot-like abnormalities (such as fading caused by disease) and large-scale continuous abnormalities (such as water imbalance caused by uneven irrigation). This dual judgment mechanism significantly improves detection sensitivity, avoids misjudgments that may occur when testing with a single parameter, and is particularly capable of specifically identifying early mildew (color value mutation) and root rot (abnormal water content). By using the abnormal point ratio threshold ( ) Determine plot-level anomalies, provide precise spatial positioning for differentiated irrigation and disease control, and advance the quality control window by 7-10 days.

[0099] S6. Obtaining a sphagnum moss growth condition evaluation index based on the growth rate abnormality coefficient, the first abnormal point location, the sphagnum moss morphology abnormality coefficient, and the second abnormal point location. Preferably, step S6 specifically includes steps S61 to S62.

[0100] S61, when and When the number of abnormal data collection locations accounts for 15% to 30% of the total, the calculation model of the sphagnum moss growth condition evaluation index in the sphagnum moss planting area is: .

[0101] Where, For the The evaluation index of the sphagnum moss growth condition in the sphagnum moss planting area at the time of data collection, is the first weight coefficient, For the The sphagnum moss at the data collection location is Growth rate at the time of data collection, is the second weight coefficient, For the The sphagnum moss at the data collection location is Appearance at the time of data collection, is the total number of locations where data was collected.

[0102] S62. When the number of abnormal data collection locations accounts for 70% or more of the total, the calculation model for the sphagnum moss growth condition evaluation index of the sphagnum moss planting area is: .

[0103] Where, is the third weight coefficient, For the The sphagnum moss at the first abnormal point is Growth rate at the time of data collection, is the total number of first abnormal point locations, is the fourth weight coefficient, For the The sphagnum moss at the second abnormal point is Appearance at the time of data collection, is the total number of second outlier locations.

[0104] This step is achieved by integrating the growth rate anomaly coefficient ( ) and Sphagnum moss morphological abnormality coefficient ( ) real-time data, combined with the spatial distribution of the first abnormal point location and the second abnormal point location, to build an adaptive evaluation model, which significantly improves the accuracy and robustness of the sphagnum moss growth assessment. Figure 1 As shown in the figure, the model dynamically switches calculation rules according to different abnormality levels: when there are few abnormal points, a weighted average algorithm is used to ensure the representativeness of the overall trend; when the abnormal points are concentrated, the data in the abnormal area is focused to avoid the dilution of abnormal signals by normal data, thereby achieving specific quantification of growth adversity (such as local diseases or environmental stress). This grading mechanism provides a basis for subsequent optimal growth curve model. The assessment provides a reliable input basis, effectively supports precise planting decisions (such as fixed-point irrigation or fertilization), improves management response time by more than 20%, and reduces the misjudgment rate to below 5%.

[0105] S7, based on the evaluation index of the sphagnum moss growth condition, based on the optimal growth curve model , evaluate the real-time growth results of sphagnum moss. Among them, the optimal growth curve model Based on the optimal environmental parameters of the sphagnum moss growth cycle, the optimal growth state of each stage is simulated and fitted. Preferably, step S7 specifically includes steps S71 to S72.

[0106] S71, if conform to , that is, in Evaluation index of sphagnum moss growth condition at the time of data collection Optimal growth curve model The significance index of the overlap of the same growth point or the difference between the two , indicating that the sphagnum moss is in good growth condition and there is no need to adjust the planting measures.

[0107] S72, if Not compliant , indicating that there is a problem with the growth of sphagnum moss, according to the abnormal growth rate coefficient and Sphagnum moss morphological abnormality coefficient Determine the degree of abnormality in growth rate and appearance, adjust planting measures, and conduct targeted management of marked abnormal points and plots.

[0108] This step is to evaluate the growth status of sphagnum moss by ) and the preset optimal growth curve model Conduct dynamic comparison to achieve scientific evaluation of growth results. Figure 1 As shown, when With model The difference significance index of the corresponding growth point Or completely overlap, the system confirms that the sphagnum moss growth meets the optimal state and automatically maintains the existing planting plan; Significant deviation from the model , an abnormal alarm is triggered immediately. This is based on statistical significance ( ) judgment mechanism, effectively avoiding subjective experience-based misjudgments and ensuring that the evaluation results have biological credibility.

[0109] By correlating the growth rate anomaly coefficient ( ) and morphological abnormality coefficient ( ), this method can accurately locate the root cause of the anomaly - if Abnormally high values ​​indicate uneven growth rates and require adjustment of water and fertilizer supply. If the threshold is exceeded, it indicates that the appearance of the disease is affected and the lighting or humidity control needs to be optimized. Figure 2 The management device structure shown in the figure uses a hierarchical diagnostic mechanism to drive the system to implement targeted irrigation, shading, or warming interventions at identified abnormal points. This also supports the reconstruction of overall environmental parameters for large areas with abnormalities (with an abnormality ratio of ≥ 70%), significantly shortening the quality correction cycle. This ultimately increases velvet density by 15%, improves stem thickness by 20%, and reduces resource waste by 30%, fundamentally resolving the uneven quality of sphagnum moss discussed in the previous article.

[0110] Based on the above embodiment, in an optional embodiment of the present invention, the sphagnum moss planting and management method further includes step S8.

[0111] S8. Obtaining planting adjustment suggestions based on the real-time growth results of the sphagnum moss. Preferably, the planting adjustment suggestions include: improving water and fertilizer management, adjusting lighting, and improving ambient temperature.

[0112] According to the evaluation index of sphagnum moss growth condition and the optimal growth curve model When the deviation is detected and When the system triggers targeted planting adjustment suggestions. For example, if the color value of sphagnum moss in a certain area is Significantly reduced (e.g. 30% below the mean) and water content If the light intensity is abnormally high, the system recommends deploying dimmable LED lighting in that area, increasing daily light intensity from 8,000 lux to 12,000 lux. It also recommends reducing irrigation frequency (from twice daily to every other day) to simultaneously improve photosynthesis efficiency and root aeration. This dynamic regulation mechanism precisely matches the needs of the sphagnum moss growth cycle, simultaneously improving stem thickness and hair density through optimized environmental parameters.

[0113] To overcome the shortcomings of existing technologies, the present invention uses big data to collect sphagnum moss growth and environmental parameters, analyze the moss's growth status in real time, and rapidly address and resolve the problems encountered in the aforementioned background technology. The sphagnum moss cultivation and management method of the present invention uses integrated sensors and remote sensing technology to collect multi-dimensional growth data, such as plant height, color, and moisture content, in real time. It then uses a dynamic algorithm to analyze abnormal fluctuations in growth rate and appearance. This process accurately identifies localized abnormal points or large-scale abnormal plots, promptly marking areas where growth rates deviate from the mean or where morphological parameters exceed thresholds, enabling early warning and precise location of problem areas.

[0114] By fitting an optimal growth curve model, the system assesses real-time growth status and automatically triggers planting adjustment recommendations when significant deviations from the model are detected, including water and fertilizer optimization, light regulation, and temperature intervention. This method significantly improves the growth uniformity and quality stability of sphagnum moss, effectively addressing quality defects such as thin stems and sparse hairs, while also reducing management costs and enhancing the product's competitiveness in the international high-end market.

[0115] It is understandable that the sphagnum moss planting and management device can be an electronic device with computing capabilities, such as a portable notebook computer, a desktop computer, a server, a smart phone, or a tablet computer.

[0116] Embodiment 2: The present invention provides a sphagnum moss planting and management device, which includes a data acquisition module, a growth rate calculation module, a first abnormality recognition module, an appearance morphology calculation module, a second abnormality recognition module, an evaluation index calculation module and a growth result evaluation module.

[0117] The data acquisition module is used to acquire monitoring data of the sphagnum moss planting area, wherein the monitoring data includes plant height, sphagnum moss color value, and water content.

[0118] The growth rate calculation module is used to obtain the growth rate of the sphagnum moss according to the plant height.

[0119] The first abnormality identification module is used to obtain a growth rate abnormality coefficient and a first abnormal point position according to the sphagnum moss growth rate.

[0120] The appearance morphology calculation module is used to obtain the appearance morphology of the sphagnum moss according to the color value and water content of the sphagnum moss.

[0121] The second abnormality recognition module is used to obtain the sphagnum moss morphology abnormality coefficient and the second abnormal point position according to the sphagnum moss appearance.

[0122] The evaluation index calculation module is used to obtain the sphagnum moss growth condition evaluation index according to the growth rate abnormality coefficient, the first abnormal point position, the sphagnum moss morphology abnormality coefficient and the second abnormal point position.

[0123] Growth result evaluation module, used for evaluating the growth condition of sphagnum moss based on the optimal growth curve model , evaluate the real-time growth results of sphagnum moss. Among them, the optimal growth curve model Based on the optimal environmental parameters of the sphagnum moss growth cycle, the optimal growth state of each stage was simulated and fitted.

[0124] In a third embodiment, the present invention provides a sphagnum moss cultivation management device, comprising a processor, a memory, and a computer program stored in the memory. The computer program can be executed by the processor to implement a sphagnum moss cultivation management method as described in any one of the first embodiment.

[0125] Embodiment 4: The present invention provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a sphagnum moss cultivation management method as described in any paragraph of Embodiment 1.

[0126] Obviously, the embodiments described above are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0127] In the several embodiments provided in the embodiments of the present invention, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device and method embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions, and operations of the devices, methods, and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or part of the code, which contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the boxes can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, as well as the combination of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified functions or actions, or can be implemented using a combination of dedicated hardware and computer instructions.

[0128] In addition, the functional modules in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0129] If the functions are implemented as software modules and sold or used as standalone products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for causing a computer device (which can be a personal computer, electronic device, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, removable hard drives, read-only memories, random access memories, magnetic disks, or optical disks. It should be noted that, as used herein, the terms "comprise," "include," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. Without further limitation, the phrase "comprises a..." does not preclude the presence of other identical elements in the process, method, article, or device comprising the elements.

[0130] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The singular forms "a", "an", "the" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise.

[0131] It should be understood that the term "and / or" as used herein is merely a description of the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0132] The word "if," as used herein, may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0133] The references to "first" and "second" in the embodiments merely distinguish similar objects and do not represent a specific ordering of the objects. It is understood that the specific order or precedence of "first" and "second" can be interchanged where appropriate. It should be understood that the objects distinguished by "first" and "second" can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein.

[0134] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for planting and managing sphagnum moss, characterized in that: Include: Acquire monitoring data of the sphagnum moss planting area; wherein the monitoring data includes plant height, sphagnum moss color value, and water content; obtaining a growth rate of sphagnum moss according to the plant height; According to the sphagnum moss growth rate, obtaining a growth rate anomaly coefficient and a first abnormal point position; Obtaining the appearance of the sphagnum moss according to the color value and water content of the sphagnum moss; According to the appearance of the sphagnum moss, an abnormal coefficient of the sphagnum moss morphology and a second abnormal point position are obtained; Obtaining a sphagnum moss growth condition evaluation index according to the growth rate abnormality coefficient, the first abnormal point position, the sphagnum moss morphology abnormality coefficient, and the second abnormal point position; According to the evaluation index of sphagnum moss growth condition, based on the optimal growth curve model , evaluate the real-time growth results of sphagnum moss; wherein, the optimal growth curve model Based on the optimal environmental parameters of the sphagnum moss growth cycle, the optimal growth state of each stage was simulated and fitted.

2. A sphagnum moss planting and management method according to claim 1, characterized in that: The calculation model of the sphagnum moss growth rate is: Where, For the The sphagnum moss at the data collection location is Growth rate at the time of data collection, For the The sphagnum moss at the data collection location is Plant height at the time of data collection, For the first The sphagnum moss at the data collection location is Plant height at the time of data collection, The interval between two data collection time points.

3. The method for planting and managing sphagnum moss according to claim 1, wherein: According to the sphagnum moss growth rate, obtaining a growth rate anomaly coefficient and a first abnormal point position specifically includes: According to the sphagnum moss growth rate, the average growth rate of sphagnum moss is obtained; the calculation model of the average growth rate of sphagnum moss is: Where, For the The average growth rate of sphagnum moss at the time of data collection, For the The sphagnum moss at the data collection location is Growth rate at the time of data collection, is the total number of data collection locations; According to the sphagnum moss growth rate and the average sphagnum moss growth rate, a growth rate anomaly coefficient is obtained; the calculation model of the growth rate anomaly coefficient is: ;in, For the Growth rate anomaly coefficient at the data collection time point; According to the growth rate anomaly coefficient, determine whether each data collection position is the first anomaly point position; wherein, if This indicates that the sphagnum moss planting area is If the growth change fluctuates greatly during the time, it is marked as the first abnormal point position; if This indicates that the sphagnum moss planting area is If the growth fluctuations within a certain period are small, continue to evaluate the growth status of the sphagnum moss planting area; According to the sphagnum moss growth rate and the average sphagnum moss growth rate, further determining whether the unmarked data collection position is the first abnormal point position; wherein, when When The data collection time point The data collection location is marked as the first abnormal point location; According to the number of the first abnormal point positions, it is determined whether the sphagnum moss planting area is abnormal; wherein, is the total number of first abnormal point positions; if Only the data collection location is marked; if The sphagnum moss planting area is marked as an abnormal plot.

4. The method for planting and managing sphagnum moss according to claim 1, wherein: The calculation model of the sphagnum moss appearance is: ;in, For the The sphagnum moss at the data collection location is Appearance at the time of data collection, is the first homogenization coefficient, For the The sphagnum moss at the data collection location is The color value of sphagnum moss at the time of data collection, For the The sphagnum moss at the data collection location is Water content at the time of data collection, is the second normalization coefficient.

5. The method for planting and managing sphagnum moss according to claim 1, characterized in that: According to the appearance of the sphagnum moss, obtaining the sphagnum moss morphology abnormality coefficient and the second abnormal point position specifically includes: According to the appearance of the sphagnum moss, the morphological mean is obtained; the calculation model of the morphological mean is: ;in, For the The mean of the morphology at the time of data collection, For the The sphagnum moss at the data collection location is Appearance at the time of data collection, is the total number of data collection locations; According to the sphagnum moss appearance and the morphological mean, the sphagnum moss morphological abnormality coefficient is obtained; the calculation model of the sphagnum moss morphological abnormality coefficient is: ;in, For the Sphagnum moss morphological abnormality coefficient at the data collection time point; According to the abnormal coefficient of morphology of sphagnum moss, it is judged whether each data collection position is the second abnormal point position; wherein, if It is marked as an abnormal point; if Then continue to evaluate the growth status of the sphagnum moss planting area; According to the appearance morphology of the sphagnum moss and the morphological mean, it is further determined whether the data collection position not marked as the second abnormal point position is the second abnormal point position; wherein, when or When The data collection location is marked as the second abnormal point location; According to the number of the second abnormal point positions, it is determined whether the sphagnum moss planting area is abnormal; wherein, is the total number of second abnormal point positions; if Only the data collection location is marked; if The sphagnum moss planting area is marked as an abnormal plot.

6. The method for planting and managing sphagnum moss according to claim 1, characterized in that: Obtaining a sphagnum moss growth condition evaluation index according to the growth rate abnormality coefficient, the first abnormal point position, the sphagnum moss morphology abnormality coefficient, and the second abnormal point position specifically includes: when and When the number of abnormal data collection locations accounts for 15% to 30% of the total, the calculation model of the sphagnum moss growth condition evaluation index in the sphagnum moss planting area is: Where, For the The evaluation index of the sphagnum moss growth condition in the sphagnum moss planting area at the time of data collection, is the first weight coefficient, For the The sphagnum moss at the data collection location is Growth rate at the time of data collection, is the second weight coefficient, For the The sphagnum moss at the data collection location is Appearance at the time of data collection, is the total number of data collection locations; When the number of abnormal data collection locations accounts for 70% or more of the total, the calculation model of the sphagnum moss growth condition evaluation index in the sphagnum moss planting area is: Where, is the third weight coefficient, For the The sphagnum moss at the first abnormal point is Growth rate at the time of data collection, is the total number of first abnormal point locations, is the fourth weight coefficient, For the The sphagnum moss at the second abnormal point is Appearance at the time of data collection, is the total number of second outlier locations.

7. The method for planting and managing sphagnum moss according to claim 1, characterized in that: According to the evaluation index of sphagnum moss growth condition, based on the optimal growth curve model , evaluate the real-time growth results of sphagnum moss, including: like conform to , that is, in Evaluation index of sphagnum moss growth condition at the time of data collection Optimal growth curve model The significance index of the overlap of the same growth point or the difference between the two , indicating that the sphagnum moss is growing well and there is no need to adjust the planting measures; like Not compliant , indicating that there is a problem with the growth of sphagnum moss, according to the abnormal growth rate coefficient and Sphagnum moss morphological abnormality coefficient Determine the degree of abnormality in growth rate and appearance, adjust planting measures, and conduct targeted management of marked abnormal points and plots.

8. A sphagnum moss planting and management device, characterized in that: Include: A data acquisition module is used to acquire monitoring data of the sphagnum moss planting area; wherein the monitoring data includes plant height, sphagnum moss color value, and water content; A growth rate calculation module, used for obtaining the growth rate of sphagnum moss according to the plant height; a first abnormality identification module, configured to obtain a growth rate abnormality coefficient and a first abnormal point position according to the sphagnum moss growth rate; an appearance morphology calculation module, configured to obtain the appearance morphology of the sphagnum moss according to the color value and water content of the sphagnum moss; A second abnormality recognition module is used to obtain a morphological abnormality coefficient of the sphagnum moss and a second abnormal point position according to the appearance of the sphagnum moss; an evaluation index calculation module, configured to obtain an evaluation index of sphagnum moss growth condition according to the growth rate abnormality coefficient, the first abnormal point position, the sphagnum moss morphology abnormality coefficient, and the second abnormal point position; Growth result evaluation module, used to evaluate the growth condition of the sphagnum moss based on the optimal growth curve model , evaluate the real-time growth results of sphagnum moss; wherein, the optimal growth curve model Based on the optimal environmental parameters of the sphagnum moss growth cycle, the optimal growth state of each stage was simulated and fitted.

9. A sphagnum moss planting and management device, characterized in that: It comprises a processor, a memory, and a computer program stored in the memory; the computer program can be executed by the processor to implement a sphagnum moss planting and management method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the sphagnum moss cultivation and management method according to any one of claims 1 to 7.

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