Seagrass Bed Ecological Restoration Assessment and Monitoring Platform Integrated with Data Analysis

Abstract

The present application provides a seagrass bed ecological restoration evaluation and monitoring platform combined with data analysis, which relates to the technical field of ecological restoration evaluation and monitoring, and includes: receiving the restoration request information of the seagrass bed ecological restoration area; analyzing the plant quantity according to the proportion of the vegetation distribution area, the proportion of the vegetation distribution area and the area of the seagrass bed ecological restoration area; making sowing and transplanting configurations according to the target restoration quantity of the vegetation and the restoration target time zone; obtaining the environmental configuration information of the restoration target time zone to analyze the growth trends of sowing and transplanting; and monitoring the seagrass bed ecological restoration according to the sowing growth trend time sequence diagram and the transplanting growth trend time sequence diagram. Through the present application, the technical problem in the prior art that the required time period of the habitat restoration method is long and the effect is unstable, affecting the restoration efficiency and the sustainability of ecological restoration can be solved, and the technical effect of achieving the rapid restoration of the seagrass bed in a short time, improving the restoration success rate and ensuring the healthy restoration of the ecosystem can be achieved.

Description

Technical Field

[0001] This application relates to the technical field of ecological restoration assessment and monitoring, and particularly to a seagrass bed ecological restoration assessment and monitoring platform combined with data analysis. Background Art

[0002] As an important part of the marine ecological environment, the seagrass bed ecosystem plays an important role in protecting biodiversity, improving water quality, and reducing coastal erosion. However, due to the influence of human activities, such as overfishing, pollution, and climate change, the area of seagrass beds has gradually decreased, and the ecological functions have been damaged. Therefore, ecological restoration has become the key means to restore the seagrass bed ecosystem. However, there are some defects in the existing seagrass bed restoration technologies, which restrict the improvement of their effects.

[0003] Currently, most of the existing restoration methods rely on traditional artificial seeding and transplanting technologies. Although these methods can restore the vegetation coverage of seagrass beds to a certain extent, they often cannot guarantee the growth and survival rate of plants. For example, traditional seeding methods often cannot accurately control the seeding depth and density, resulting in plants not reaching the optimal growth state during the restoration process. In addition, traditional transplanting methods have high requirements for plants to adapt to the environment, and the transplanted plants are easily affected by environmental changes, such as water temperature, light, and flow rate, resulting in slow growth or high mortality. Therefore, the restoration effects of these methods are often not as expected, and the restoration period is relatively long.

[0004] In summary, there is a technical problem in the prior art that due to the long time period required for the habitat restoration method and the unstable effect, the restoration process is long and the uncertainty is large, further affecting the restoration efficiency and the sustainability of ecological restoration. Summary of the Invention

[0005] The purpose of this application is to provide a seagrass bed ecological restoration assessment and monitoring platform combined with data analysis to solve the technical problem in the prior art that due to the long time period required for the habitat restoration method and the unstable effect, the restoration process is long and the uncertainty is large, further affecting the restoration efficiency and the sustainability of ecological restoration.

[0006] In view of the above problems, the present application provides a seagrass bed ecological restoration assessment and monitoring platform combined with data analysis, including: a restoration request information receiving module, which is used to receive the restoration request information of the seagrass bed ecological restoration area, wherein the restoration request information includes the target vegetation type of the seagrass bed, the proportion of the vegetation distribution area, and the restoration target time zone; a plant quantity analysis module, which is used to analyze the plant quantity according to the proportion of the vegetation distribution area, the proportion of the vegetation distribution area, and the area of the seagrass bed ecological restoration area to obtain the target restoration quantity of the vegetation; a sowing and transplanting configuration module, which is used to perform sowing and transplanting configuration according to the target restoration quantity of the vegetation and the restoration target time zone to obtain the target sowing quantity and the target transplanting quantity; a growth trend analysis module, which is used to obtain the environmental configuration information of the restoration target time zone for sowing and transplanting growth trend analysis to obtain a sowing growth trend time series diagram and a transplanting growth trend time series diagram; a restoration monitoring module, which is used to monitor the seagrass bed ecological restoration according to the sowing growth trend time series diagram and the transplanting growth trend time series diagram.

[0007] The technical solution provided in the present application has at least the following technical effects or advantages: Through the restoration request information receiving module, which is used to receive the restoration request information of the seagrass bed ecological restoration area, wherein the restoration request information includes the target vegetation type of the seagrass bed, the proportion of the vegetation distribution area, and the restoration target time zone; a plant quantity analysis module, which is used to analyze the plant quantity according to the proportion of the vegetation distribution area, the proportion of the vegetation distribution area, and the area of the seagrass bed ecological restoration area to obtain the target restoration quantity of the vegetation; a sowing and transplanting configuration module, which is used to perform sowing and transplanting configuration according to the target restoration quantity of the vegetation and the restoration target time zone to obtain the target sowing quantity and the target transplanting quantity; a growth trend analysis module, which is used to obtain the environmental configuration information of the restoration target time zone for sowing and transplanting growth trend analysis to obtain a sowing growth trend time series diagram and a transplanting growth trend time series diagram; a restoration monitoring module, which is used to monitor the seagrass bed ecological restoration according to the sowing growth trend time series diagram and the transplanting growth trend time series diagram. That is to say, by achieving the technical goal of shortening the seagrass bed ecological restoration cycle, improving the restoration efficiency and stability, the technical effect of achieving the rapid restoration of the seagrass bed in a short time, ensuring the sustainability and stability of the restoration effect, improving the restoration success rate, and ensuring the healthy restoration of the ecosystem is achieved.

[0008] The above description is only an overview of the technical solution of this application. In order to understand the technical means of this application more clearly, it can be implemented in accordance with the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the following specifically illustrates the specific implementation manners of this application. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of this application, nor is it used to limit the scope of this application. Other features of this application will become easily understandable through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in this application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings described below are only exemplary. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0010] Figure 1 Structural schematic diagram of the seagrass bed ecological restoration assessment and monitoring platform of this application combined with data analysis;

[0011] Figure 2 Flow schematic diagram of obtaining the target restoration quantity of vegetation in the seagrass bed ecological restoration assessment and monitoring platform of this application combined with data analysis.

[0012] Description of reference numerals:

[0013] Restoration request information receiving module 11, plant quantity analysis module 12, sowing and transplanting configuration module 13, growth trend analysis module 14, restoration monitoring module 15. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0014] By providing a seagrass bed ecological restoration assessment and monitoring platform combined with data analysis, this application solves the technical problem in the prior art that due to the long time cycle required for the habitat restoration method and the unstable effect, the restoration process is long and the uncertainty is large, further affecting the restoration efficiency and the sustainability of ecological restoration. It realizes the technical goal of shortening the seagrass bed ecological restoration cycle, improving the restoration efficiency and stability, achieving the technical effect of rapidly restoring the seagrass bed in a short time, ensuring the sustainability and stability of the restoration effect, improving the restoration success rate, and ensuring the healthy restoration of the ecosystem.

[0015] Next, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited by the example embodiments described here. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application. Additionally, it should be noted that for the sake of convenience of description, only the parts related to the present application are shown in the drawings rather than all of them.

[0016] Please refer to the attached Figure 1 , the present application provides a seagrass bed ecological restoration assessment and monitoring platform combined with data analysis, specifically including:

[0017] A restoration request information receiving module 11, which is used to receive the restoration request information of the seagrass bed ecological restoration area. Among them, the restoration request information includes the target vegetation type of the seagrass bed, the proportion of the vegetation distribution area, and the restoration target time zone.

[0018] Specifically, receive the restoration request information of the seagrass bed ecological restoration area. The restoration request information includes the target vegetation type of the seagrass bed, the proportion of the vegetation distribution area, and the restoration target time zone. The target vegetation type indicates the type of seagrass to be planted or restored in the restoration area. For example, if the target vegetation type is "Padina", it means that the restoration goal of this area is to restore Padina. The proportion of the vegetation distribution area refers to the distribution ratio of each vegetation type during the restoration process, expressed as the percentage of this type of vegetation in the total restoration area. For example, if the Padina planned to be restored in the target area accounts for 50% of the total restoration area, then the proportion of the vegetation distribution area is 50%. The restoration target time zone refers to the time range or period for ecological restoration, which helps to determine the timing and progress of restoration. For example, if the restoration target time zone is "from spring to autumn", then the restoration will start in spring and be completed before autumn. Through the restoration request information, the restoration tasks can be effectively planned and executed to ensure that the ecological restoration process meets the predetermined goals.

[0019] A plant quantity analysis module 12, which is used to perform plant quantity analysis based on the proportion of the vegetation distribution area, the proportion of the vegetation distribution area, and the area of the seagrass bed ecological restoration area to obtain the target restoration quantity of the vegetation.

[0020] Specifically, perform plant quantity analysis based on the proportion of the vegetation distribution area and the area of the seagrass bed ecological restoration area, that is, multiply the area proportion of the target vegetation type in the restoration area by the total area of the seagrass bed that needs to be ecologically restored to determine the area where plants need to be distributed in a specific restoration area, and then estimate through the unit area requirement of the plants to calculate the required number of plants and obtain the target restoration quantity of the vegetation.

[0021] The seeding and transplanting configuration module 13 is used to perform seeding and transplanting configuration according to the target vegetation restoration quantity and the restoration target time zone, so as to obtain the target seeding quantity and the target transplanting quantity.

[0022] Specifically, the target vegetation restoration quantity represents the total number of plants that need to be planted or transplanted in the entire restoration area. For example, assuming that the target vegetation restoration quantity is 3,000 plants, it means that 3,000 plants are needed in the restoration area to complete the restoration task. Then, according to the restoration target time zone, the entire restoration process can be allocated to different time nodes for seeding and transplanting at appropriate times. By reasonably dividing the time zone, the number of plants that need to be seeded or transplanted in each stage can be determined. For example, if the seeding stage is planned to be completed in spring and the transplanting stage is planned to be carried out in summer, then according to the requirements of different stages, the 3,000 plants can be reasonably allocated to seeding and transplanting. Assuming that 2,000 plants are needed in the seeding stage, then the target seeding quantity is 2,000 plants, and the remaining 1,000 plants are the target transplanting quantity. Therefore, the target seeding quantity and the target transplanting quantity are 2,000 plants and 1,000 plants respectively.

[0023] The growth trend analysis module 14 is used to obtain the environmental configuration information of the restoration target time zone for seeding and transplanting growth trend analysis, so as to obtain the seeding growth trend time series diagram and the transplanting growth trend time series diagram.

[0024] Specifically, according to the environmental conditions in the set restoration time zone, the growth changes of plants during seeding and transplanting are analyzed. The restoration target time zone refers to the time period for ecological restoration, such as set from April to September. The environmental configuration information of the restoration target time zone includes factors such as temperature, humidity, and light, all of which can affect the growth process of plants. For example, in spring, the temperature is relatively low and the humidity is relatively high, while in summer it is warm and dry. The environmental conditions will directly affect the growth rate and health status of plants. According to the environmental configuration information, seeding and transplanting growth trend analysis is carried out to obtain the changes of plants in different growth stages. The seeding growth trend time series diagram is graphical data recording the health status of plants during the entire growth process after seeding, while the transplanting growth trend time series diagram records the growth changes of plants after transplanting. For example, the plants may grow rapidly in the first month after seeding and the growth rate slows down in the third month, and after transplanting, they may go through an adaptation period until the plants grow stably. Through the seeding growth trend time series diagram and the transplanting growth trend time series diagram, the growth dynamics of plants at different time nodes can be clearly seen, providing data support for subsequent restoration work.

[0025] The restoration monitoring module 15 is used to monitor the seagrass bed ecological restoration according to the seeding growth trend time series diagram and the transplanting growth trend time series diagram.

[0026] Specifically, the seagrass bed ecological restoration monitoring is carried out according to the sowing growth trend time series diagram and the transplanting growth trend time series diagram, that is, the growth changes of the vegetation during the ecological restoration process are tracked in real time, so as to monitor the restoration progress and effect. The sowing growth trend time series diagram records the growth conditions of the plants from the start of sowing to a certain period of time, including key indicators such as growth rate and health status. When it is found in the time series diagram that the plant growth is slow or abnormal conditions occur, such as growth stagnation or withering, the environmental conditions or restoration strategies can be adjusted in time, such as increasing irrigation or adjusting soil nutrients. The transplanting growth trend time series diagram records the growth conditions of the transplanted plants, including the process of the plants adapting to the new environment and the health recovery situation. For example, in the initial stage of transplantation, the plants may experience an adaptation period with a slow growth rate, but over time, the plants will gradually recover their health. If it is found through time series diagram analysis that the transplanted plants have an overly long adaptation period and unsatisfactory growth, corresponding measures can be taken during the restoration process, such as adjusting the transplanting time or selecting more suitable plant varieties. Through the comprehensive monitoring of the sowing growth trend time series diagram and the transplanting growth trend time series diagram, the health status of the vegetation during the ecological restoration process can be comprehensively understood, ensuring the smooth completion of the restoration goal, and improving the efficiency and effect of the ecological restoration.

[0027] The seagrass bed ecological restoration evaluation and monitoring platform combined with data analysis can achieve the technical goals of shortening the seagrass bed ecological restoration cycle, improving the restoration efficiency and stability, achieving the rapid restoration of the seagrass bed in a relatively short time, ensuring the sustainability and stability of the restoration effect, increasing the restoration success rate, and ensuring the healthy restoration of the ecosystem.

[0028] Furthermore, as Figure 2 shown, this application also includes: calculating the product of the vegetation distribution area ratio and the area of the seagrass bed ecological restoration area to obtain the target vegetation distribution area; obtaining the target plant mature period distribution area record data set; performing a mode calculation on the target plant mature period distribution area record data set to obtain the target plant single plant distribution area; calculating the target vegetation restoration quantity according to the target vegetation distribution area and the target plant single plant distribution area.

[0029] Specifically, multiply the required area ratio of the vegetation by the total area of the seagrass bed ecological restoration area to calculate the actual vegetation area that needs to be restored.

[0030] Next, collect the distribution data of the target plants during their mature period to reflect the actual growth conditions of different plants during the ecological restoration process, help determine the distribution law of the plants during the mature period, and provide a basis for subsequent restoration calculations.

[0031] Then, calculate the mode of the dataset recording the distribution area of target plants at the mature stage to obtain the distribution area of a single target plant, and find the value with the highest frequency in the dataset for analyzing the distribution trend of mature plants in different regions, so as to obtain the average area occupied by a single plant in the target restoration area.

[0032] Finally, based on the target distribution area of the vegetation and the distribution area of a single target plant, calculate the target restoration quantity of the vegetation to determine the number of planted plants required to meet the set target distribution area of the vegetation.

[0033] By first calculating the target distribution area of the vegetation, then combining the distribution of target plants at the mature stage, and finally determining the number of plants required for restoration according to the distribution area of a single plant, it provides data support for the implementation of precise seagrass bed ecological restoration and ensures the smooth progress of the restoration work.

[0034] Furthermore, this application also includes: performing cluster analysis on the recorded data of the distribution area of target plants at the mature stage according to the distribution area deviation threshold to obtain multiple clusters of recorded data of the distribution area of target plants at the mature stage; deleting the clusters in which the number of the distribution area of target plants at the mature stage within the cluster is less than or equal to the high-frequency cluster quantity threshold to obtain the recorded data of the distribution area of target plants at the mature stage for the mode; extracting the mean value of the recorded data of the distribution area of target plants at the mature stage for the mode and setting it as the distribution area of a single target plant.

[0035] Specifically, perform cluster analysis on the recorded data of the distribution area of target plants at the mature stage according to the distribution area deviation threshold to ensure that each type of data has a certain similarity in the distribution area. By setting the distribution area deviation threshold, plant groups with different distribution characteristics are identified. For example, if the set distribution area deviation threshold is 5 square decimeters, then all data within the deviation range of the distribution area will be classified into the same category for further analysis. Among them, the distribution area deviation threshold is obtained by those skilled in the art through custom setting according to the actual situation.

[0036] Next, delete the clusters in which the number of the distribution area of target plants at the mature stage within the cluster is less than or equal to the high-frequency cluster quantity threshold to remove the clusters lacking representativeness in the distribution area and ensure that the retained data is more statistically significant. By setting the high-frequency cluster quantity threshold, such as ten plants, when the number of plants within the cluster is less than or equal to ten, it will be deleted, thereby eliminating unrepresentative data and making the subsequent analysis more accurate. Among them, the high-frequency cluster quantity threshold is obtained by those skilled in the art through custom setting according to the actual situation.

[0037] Then, obtain the recorded data of the distribution area of target plants at the mature stage for the mode. By calculating the data within the retained clusters, find the distribution area value that appears most frequently, that is, the mode, to represent the main distribution characteristics of the cluster.

[0038] Finally, extract the mean of the recorded data on the distribution area of the modal target plants at the mature stage, and set it as the distribution area per single target plant to obtain a more balanced and accurate distribution area per single plant.

[0039] Through cluster analysis, deleting unrepresentative clusters, calculating the mode, and extracting the mean, the most representative distribution area of the target plants at the mature stage is gradually screened out, thereby obtaining a more accurate distribution area per single plant, ensuring that the distribution area of the target plants better meets the actual ecological restoration requirements, and thus providing reliable data support for subsequent restoration work.

[0040] Furthermore, this application also includes: setting the environmental time series information of the restoration target time zone; calibrating based on the maturity duration identification table according to the vegetation type to obtain the sowing maturity duration; and performing sowing and transplanting configuration according to the sowing maturity duration, the target number of vegetation to be restored, and the restoration target time zone to obtain the target sowing number and the target transplanting number.

[0041] Specifically, setting the environmental time series information of the restoration target time zone means that during the ecological restoration process, the specific time period of the restoration work is set according to the time interval. For example, the restoration target time zone may be set as "spring to autumn", and the environmental time series information includes factors such as the climate conditions, light intensity, and temperature changes during this period, which are used to dynamically adjust and optimize the restoration work according to environmental changes.

[0042] Then, different vegetation types vary in the time required for maturity. By referring to the maturity duration identification table, the maturity cycle of each type of vegetation is identified. For example, if the target vegetation type is "Padina", according to the identification table, it may be found that the time required for its maturity is four months, which is then used to reasonably plan the sowing and transplanting time arrangements to ensure that the plants can grow healthily during the restoration process.

[0043] Then, perform sowing and transplanting configuration according to the sowing maturity duration, the target number of vegetation to be restored, and the restoration target time zone to obtain the target sowing number and the target transplanting number, which are used to reasonably arrange the sowing and transplanting work. For example, if the target number of vegetation to be restored is 1000 plants and the sowing maturity duration is four months, then sowing can be carried out as planned within the restoration target time zone to ensure that the plants can mature and be successfully transplanted within the appropriate time period.

[0044] By setting the environmental time series information, calibrating the maturity duration of the vegetation, and combining with the restoration target time zone to plan the time and quantity of sowing and transplanting, it provides comprehensive time and quantity scheduling support for the ecological restoration work, ensuring that the restoration work can proceed smoothly according to the plan and maximizing the restoration effect.

[0045] Further, the present application also includes: aligning the sowing maturity duration with the initial moment of the restoration target time zone to obtain the sowing maturity time zone; obtaining the sowing time zone proportion according to the ratio of the sowing maturity time zone to the restoration target time zone; calculating the product of the sowing time zone proportion and the target number of vegetation restorations to obtain the target sowing number; subtracting the target sowing number from the target number of vegetation restorations to obtain the target transplant number.

[0046] Specifically, by aligning the sowing maturity duration with the initial moment of the restoration target time zone to obtain the sowing maturity time zone, the time arrangement of sowing is ensured to match the time zone of the restoration target. By aligning the sowing maturity duration with the initial moment of the restoration target time zone, the optimal sowing start time can be accurately calculated. For example, if the sowing maturity duration is three months and the restoration target time zone starts from April, then the sowing maturity time zone will start from January.

[0047] Next, obtaining the sowing time zone proportion according to the ratio of the sowing maturity time zone to the restoration target time zone, to obtain the importance and time allocation of the sowing process in the restoration cycle. For example, if the sowing maturity time zone is three months and the restoration target time zone is six months, then the sowing time zone proportion is 50%.

[0048] Then, calculating the product of the sowing time zone proportion and the target number of vegetation restorations to obtain the target sowing number, to determine the number of plants to be sown. For example, if the sowing time zone proportion is 50% and the target number of vegetation restorations is 1000 plants, then the target sowing number will be 500 plants.

[0049] Finally, subtracting the target sowing number from the target number of vegetation restorations to obtain the target transplant number, calculating the number of plants to be transplanted, that is, by deducting the determined sowing number from the target number of vegetation restorations, the remaining number of plants is the number of plants that need to be restored by transplantation. For example, if the target number of vegetation restorations is 1000 plants and the target sowing number is 500 plants, then the target transplant number will be 500 plants.

[0050] By planning the time and the number of plants for ecological restoration, a reasonable time arrangement and plant configuration are provided for the ecological restoration work, ensuring the efficient implementation of the restoration task as planned.

[0051] Furthermore, this application also includes: segmenting the restoration target time zone according to a preset step size to obtain a time series of growth trend time diagrams; collecting a sowing healthy growth trend time diagram record data set and a transplanting healthy growth trend time diagram record data set with the environmental configuration information and the time series of growth trend time diagrams as constraints; performing high-frequency time diagram sorting on the sowing healthy growth trend time diagram record data set to obtain the sowing growth trend time diagram; and performing high-frequency time diagram sorting on the transplanting healthy growth trend time diagram record data set to obtain the transplanting growth trend time diagram.

[0052] Specifically, the restoration target time zone is segmented according to a preset step size to more finely monitor the growth changes during the ecological restoration process and obtain a time series of growth trend time diagrams. The preset step size refers to the length of a time period and is used to determine the recording frequency of the growth trend. For example, if the step size is set to one month, then a recording point will be generated for each month within the restoration target time zone, reflecting the growth of the vegetation, thereby forming a time series of growth trend time diagrams.

[0053] Next, the environmental configuration information may include conditions such as temperature, humidity, and light, while the time series of growth trend time diagrams provides time nodes for collecting healthy growth data. By using the environmental configuration information and the time series of growth trend time diagrams as constraints, collecting the sowing healthy growth trend time diagram record data set and the transplanting healthy growth trend time diagram record data set can respectively collect the healthy growth conditions of the plants during the sowing stage and the transplanting stage.

[0054] Then, high-frequency time diagram sorting is performed on the sowing healthy growth trend time diagram record data set to remove irrelevant or low-frequency parts, and finally retain the high-frequency data that can accurately reflect the plant growth process to obtain the sowing growth trend time diagram, thereby being able to accurately capture the changes and form the sowing growth trend time diagram.

[0055] Similarly, high-frequency time diagram sorting is performed on the transplanting healthy growth trend time diagram record data set to retain the growth dynamics of the plants during the transplanting stage and obtain the transplanting growth trend time diagram. The transplanted plants will go through an adaptation period, so the high-frequency data can help monitor whether they adapt to the new environment and whether they maintain a healthy growth trend.

[0056] By time-segmenting the restoration target time zone, combining the environmental configuration information, collecting the healthy growth data during the sowing and transplanting stages, and using the high-frequency time diagram sorting technology, it is ensured that the monitored growth data is true and accurate, providing systematic data support for the dynamic analysis of the plant growth during the ecological restoration process and helping to further optimize the restoration strategy.

[0057] Furthermore, this application also includes: evaluating the pairwise sequence similarity of the recorded data set of the sowing healthy growth trend time series diagram to obtain a set of growth trend time series diagram similarities; performing LOF outlier analysis on the recorded data set of the sowing healthy growth trend time series diagram according to the set of growth trend time series diagram similarities to obtain a set of LOF outlier factors; extracting the minimum value of the sowing healthy growth trend time series diagram recording data in the set of LOF outlier factors and setting it as the sowing growth trend time series diagram.

[0058] Specifically, the pairwise sequence similarity of the recorded data set of the sowing healthy growth trend time series diagram is evaluated to obtain a set of growth trend time series diagram similarities. By comparing the similarity of each pair of time series data in the recorded data set of the sowing healthy growth trend time series diagram, the similarity is evaluated. For example, it is calculated through Euclidean distance or cosine similarity to identify the time points when the growth trends are similar.

[0059] Next, local outlier factor outlier analysis is performed on the recorded data set of the sowing healthy growth trend time series diagram according to the set of growth trend time series diagram similarities to obtain a set of LOF outlier factors. LOF (Local Outlier Factor) analysis is a density-based outlier detection method. By calculating the density of each data point in its neighborhood, it is determined whether the data point is significantly different from the surrounding points. Through LOF outlier analysis, data points with abnormal growth trend changes can be identified. For example, if the LOF value of a certain data point is large, it indicates that this point is abnormal in the overall growth trend and may be due to external environmental changes or other factors leading to abnormal growth.

[0060] Then, the minimum value of the sowing healthy growth trend time series diagram recording data in the set of LOF outlier factors is extracted and set as the sowing growth trend time series diagram, that is, the data point with the smallest outlier factor value is selected as the final sowing growth trend time series diagram recording data, indicating the one that can best represent the normal or stable growth trend, and further ensuring that the final time series diagram reflects a typical and normal growth process of the plant.

[0061] Through pairwise sequence similarity evaluation, similar time series in the data set are identified, and then abnormal data is excluded through LOF outlier analysis. Finally, the record that can best represent the normal growth trend is extracted to ensure that the final sowing growth trend time series diagram can accurately reflect the healthy growth of the plant, which helps to filter out abnormal growth patterns and can also provide more accurate and reliable data support for subsequent repair work.

[0062] Furthermore, this application also includes: obtaining the first sowing healthy growth trend time series diagram record data and the second sowing healthy growth trend time series diagram record data of the sowing healthy growth trend time series diagram record dataset; calculating the image similarity of the same moment of the first sowing healthy growth trend time series diagram record data and the second sowing healthy growth trend time series diagram record data to obtain image similarity time series information; calculating the proportion of the moments in the image similarity time series information where the image similarity is greater than or equal to the image similarity threshold, denoted as the growth trend time series diagram similarity, and adding it to the growth trend time series diagram similarity set.

[0063] Specifically, the first sowing healthy growth trend time series diagram record data and the second sowing healthy growth trend time series diagram record data are randomly extracted from the sowing healthy growth trend time series diagram record dataset. For example, the first sowing healthy growth trend time series diagram record data and the second sowing healthy growth trend time series diagram record data may represent different time stages, used to analyze the change in the health status of the plant at two time points and understand its growth situation.

[0064] Next, by comparing the growth trend images at two time points or time periods, such as comparing the pixel differences or features in the images, and then calculating the image similarity of the same moment of the first sowing healthy growth trend time series diagram record data and the second sowing healthy growth trend time series diagram record data, sorting according to the time sequence, obtaining the sequence of image similarity as the image similarity time series information, and further measuring the growth consistency or change of the plant at different time points. For example, if the image similarity between two time points is higher, it means that the growth trend of the plant changes less over a period of time, and vice versa.

[0065] Then, calculate the proportion of the moments in the image similarity time series information where the image similarity is greater than or equal to the image similarity threshold, find the moments where the similarity is greater than or equal to the preset threshold, and calculate the proportion in all moments, denoted as the growth trend time series diagram similarity, and add it to the growth trend time series diagram similarity set. The image similarity threshold is a standard value used to distinguish whether the growth state is stable. Among them, the image similarity threshold is obtained by those skilled in the art through custom settings according to the actual situation.

[0066] By comparing the image data of the sowing healthy growth trend, calculating the similarity, and screening out the moments with a stable growth state according to the preset threshold, it provides specific data support for the monitoring of the growth trend, can evaluate the growth status of the plant in real time, and provides a more accurate basis for subsequent repair work.

[0067] In summary, the seagrass bed ecological restoration assessment and monitoring platform combined with data analysis provided by this application has the following technical effects: through the restoration request information receiving module, which is used to receive the restoration request information of the seagrass bed ecological restoration area, where the restoration request information includes the target vegetation type of the seagrass bed, the proportion of the vegetation distribution area, and the restoration target time zone; the plant quantity analysis module, which is used to analyze the plant quantity according to the proportion of the vegetation distribution area, the proportion of the vegetation distribution area, and the area of the seagrass bed ecological restoration area to obtain the target restoration quantity of the vegetation; the sowing and transplanting configuration module, which is used to perform sowing and transplanting configuration according to the target restoration quantity of the vegetation and the restoration target time zone to obtain the target sowing quantity and the target transplanting quantity; the growth trend analysis module, which is used to obtain the environmental configuration information of the restoration target time zone for sowing and transplanting growth trend analysis to obtain the sowing growth trend time series diagram and the transplanting growth trend time series diagram; the restoration monitoring module, which is used to monitor the seagrass bed ecological restoration according to the sowing growth trend time series diagram and the transplanting growth trend time series diagram. That is to say, by achieving the technical goal of shortening the seagrass bed ecological restoration cycle, improving the restoration efficiency and stability, the technical effect of achieving the rapid restoration of the seagrass bed in a relatively short time, ensuring the sustainability and stability of the restoration effect, improving the restoration success rate, and ensuring the healthy restoration of the ecosystem is achieved.

[0068] The above description of the disclosed embodiments enables those skilled in the art to implement or use this application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0069] Obviously, those skilled in the art can make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of this application and its equivalent technologies, this application is also intended to include these changes and modifications.

Claims

1. A seagrass bed ecological restoration assessment and monitoring platform combined with data analysis, characterized in that, Including: A repair request information receiving module, which is used to receive the repair request information of the seagrass bed ecological restoration area. Among them, the repair request information includes the target vegetation type of the seagrass bed, the proportion of the vegetation distribution area, and the restoration target time zone; A plant quantity analysis module, which is used to analyze the plant quantity according to the proportion of the vegetation distribution area, the proportion of the vegetation distribution area, and the area of the seagrass bed ecological restoration area to obtain the target restoration quantity of the vegetation; A sowing and transplanting configuration module, which is used to configure sowing and transplanting according to the target restoration quantity of the vegetation and the restoration target time zone to obtain the target sowing quantity and the target transplanting quantity; A growth trend analysis module, which is used to obtain the environmental configuration information of the restoration target time zone for sowing and transplanting growth trend analysis, and obtain the sowing growth trend time series diagram and the transplanting growth trend time series diagram; A restoration monitoring module, which is used to monitor the seagrass bed ecological restoration according to the sowing growth trend time series diagram and the transplanting growth trend time series diagram; Analyzing the plant quantity according to the proportion of the vegetation distribution area, the proportion of the vegetation distribution area, and the area of the seagrass bed ecological restoration area to obtain the target restoration quantity of the vegetation, including: Calculating the product of the proportion of the vegetation distribution area and the area of the seagrass bed ecological restoration area to obtain the target distribution area of the vegetation; Obtaining the target plant mature period distribution area record data set; Calculating the mode of the target plant mature period distribution area record data set to obtain the single-plant distribution area of the target plant; Calculating the target restoration quantity of the vegetation according to the target distribution area of the vegetation and the single-plant distribution area of the target plant; Obtaining the environmental configuration information of the restoration target time zone for sowing and transplanting growth trend analysis, and obtaining the sowing growth trend time series diagram and the transplanting growth trend time series diagram, including: Dividing the restoration target time zone according to a preset step size to obtain the growth trend time series diagram time sequence; Constrained by the environmental configuration information and the growth trend time series diagram time sequence, collecting the sowing healthy growth trend time series diagram record data set and the transplanting healthy growth trend time series diagram record data set; Performing high-frequency time series diagram sorting on the sowing healthy growth trend time series diagram record data set to obtain the sowing growth trend time series diagram; Performing high-frequency time series diagram sorting on the transplanting healthy growth trend time series diagram record data set to obtain the transplanting growth trend time series diagram.

2. The seagrass bed ecological restoration evaluation and monitoring platform combined with data analysis according to claim 1, characterized in that, Calculating the mode of the target plant mature period distribution area record data set to obtain the single-plant distribution area of the target plant, including: Performing cluster analysis on the target plant mature period distribution area record data according to the distribution area deviation threshold to obtain multiple clusters of target plant mature period distribution area record data; Deleting the clusters in which the number of target plant mature period distribution areas within the cluster is less than or equal to the high-frequency cluster internal quantity threshold to obtain the mode target plant mature period distribution area record data; Extracting the mean value of the mode target plant mature period distribution area record data and setting it as the single-plant distribution area of the target plant.

3. The seagrass bed ecological restoration assessment and monitoring platform integrated with data analysis according to claim 1, characterized in that, Perform seeding and transplanting configuration according to the target vegetation restoration quantity and the restoration target time zone to obtain the target seeding quantity and the target transplanting quantity, including: Set the environmental time series information of the restoration target time zone; Calibrate according to the vegetation type based on the mature duration identification table to obtain the seeding mature duration; Perform seeding and transplanting configuration according to the seeding mature duration, the target vegetation restoration quantity and the restoration target time zone to obtain the target seeding quantity and the target transplanting quantity.

4. The seagrass bed ecological restoration evaluation and monitoring platform combined with data analysis according to claim 3, characterized in that, Perform seeding and transplanting configuration according to the seeding mature duration, the target vegetation restoration quantity and the restoration target time zone to obtain the target seeding quantity and the target transplanting quantity, including: Align the seeding mature duration with the initial moment of the restoration target time zone to obtain the seeding mature time zone; Obtain the seeding time zone proportion according to the ratio of the seeding mature time zone to the restoration target time zone; Calculate the product of the seeding time zone proportion and the target vegetation restoration quantity to obtain the target seeding quantity; Subtract the target seeding quantity from the target vegetation restoration quantity to obtain the target transplanting quantity.

5. The seagrass bed ecological restoration assessment and monitoring platform integrated with data analysis according to claim 1, characterized in that Perform high-frequency time series graph sorting on the seeding healthy growth trend time series graph record data set to obtain the seeding growth trend time series graph, including: Perform pairwise sequence similarity evaluation on the seeding healthy growth trend time series graph record data set to obtain a set of growth trend time series graph similarities; Perform LOF outlier analysis on the seeding healthy growth trend time series graph record data set according to the set of growth trend time series graph similarities to obtain a set of LOF outlier factors; Extract the minimum seeding healthy growth trend time series graph record data of the set of LOF outlier factors and set it as the seeding growth trend time series graph.

6. The seagrass bed ecological restoration assessment and monitoring platform integrated with data analysis according to claim 5, characterized in that Perform pairwise sequence similarity evaluation on the seeding healthy growth trend time series graph record data set to obtain a set of growth trend time series graph similarities, including: Obtain the first seeding healthy growth trend time series graph record data and the second seeding healthy growth trend time series graph record data of the seeding healthy growth trend time series graph record data set; Calculate the similarity of the images at the same moment of the first seeding healthy growth trend time series graph record data and the second seeding healthy growth trend time series graph record data to obtain image similarity time series information; Calculate the proportion of the moments in the image similarity time series information where the image similarity is greater than or equal to the image similarity threshold, set it as the growth trend time series graph similarity, and add it to the set of growth trend time series graph similarities.

Images