Plant growth evaluation method based on ecological data analysis

Abstract

The invention relates to the technical field of ecological data processing, in particular to a plant growth evaluation method based on ecological data analysis, and the method comprises the steps: collecting data; determining a temporary grid; soil type identification; identifying an abnormal grid; adjusting a grid; and evaluating the plant growth degree. According to the method, the accuracy of plant growth monitoring is improved through dynamic monitoring and intelligent adjustment, bad growth conditions and abnormal plants can be recognized in time, then targeted intervention measures are provided, the vulnerability of an ecological system is reduced, and meanwhile, the high efficiency and accuracy of data acquisition are ensured through dynamic adjustment of grid spacing and optimization of a monitoring strategy. The method also provides a scientific basis for soil quality and plant health, is helpful for realizing ecological balance, promoting reasonable utilization of resources, improving the stability of an ecological system, and effectively solves the problems of insufficient evaluation comprehensiveness and reduced data acquisition timeliness caused by dependence on a fixed trigger strategy and single data source.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological data processing, and in particular to a plant growth evaluation method based on ecological data analysis. Background Art

[0002] With the intensification of global climate change and environmental pressure, plant growth faces many challenges, especially in arid areas and ecologically fragile areas. The particle size of the soil has an important impact on plant growth. Coarse-textured soils usually have good drainage but poor water retention, while fine-textured soils have good water retention but poor air permeability, which can easily lead to root hypoxia. Therefore, it is crucial to accurately assess soil types and their effects on plant growth, especially drought resistance. Traditional assessment methods often focus on a single factor, lack real-time response and comprehensive analysis of environmental changes, and are difficult to accurately identify potential problems in plant growth. Therefore, there is an urgent need for a comprehensive assessment method based on ecological data analysis to cope with complex ecological environments, ensure healthy plant growth, and optimize resource allocation.

[0003] The patent document of Chinese patent application publication number CN116796925A discloses a plant growth evaluation system based on multivariate information, which includes: a plurality of resident modules, which are arranged in the trees to be monitored, for collecting the growth information and position information of the trees to be monitored, and transmitting feedback signals according to a preset trigger strategy; a plurality of broadcast modules, which are distributed in the monitoring area at preset intervals, for broadcasting confirmation signals with a first preset duration as the broadcast cycle; a plurality of receiving modules, which are arranged in the monitoring area in a preset distribution manner, for collecting the feedback signals; a collection module, which can communicate with each resident module over a short distance, for collecting the growth information with a second preset duration as the collection cycle; an evaluation module, which is connected to each receiving module and the collection module, for rating each tree to be detected according to a preset rating strategy; a writing module, It is connected to the evaluation module to generate a rating result from the rating and write it into the corresponding resident module; wherein the monitoring area is the growth area of ​​each tree to be monitored, the preset trigger strategy is that each resident module receives the confirmation signal, the confirmation signal is a fixed signal sent in a broadcast manner, and the feedback signal is grouped with the resident module; the preset rating strategy is to determine the first rating information through the growth information of a single tree to be tested, to determine the second rating information through the position information of the tree to be tested, and to combine the first rating information with the second rating information in a preset integrated manner to generate a rating result for a single tree to be tested; wherein the fixed signal is a signal emitted at a preset frequency and a preset wavelength, and the preset integration method is to combine the first rating information and the second rating information.

[0004] It can be seen that the system mainly relies on the resident module to collect growth information, ignoring other important environmental factors such as soil quality, climate change, etc., thus limiting the comprehensiveness of the assessment; the system relies on preset trigger strategies and cannot adapt to dynamically changing environments or emergencies, thus affecting the timeliness and accuracy of data collection. Summary of the invention

[0005] To this end, the present invention provides a plant growth assessment method based on ecological data analysis to overcome the problems in the prior art of insufficient assessment comprehensiveness and reduced timeliness of data collection due to reliance on fixed trigger strategies and a single data source.

[0006] To achieve the above object, the present invention provides a plant growth assessment method based on ecological data analysis, comprising: Collecting the growth data and photosynthesis rate of the plants in each grid in the observation area constructed based on the preset grid spacing, as well as the particle size and humidity of the soil at a preset period; Determine a plurality of first temporary grids according to the humidity, the preset standard humidity, the growth data and the preset growth standard range; determining a soil type according to the particle size of each of the first temporary grids; Determine a number of non-drought-tolerant grids according to the granularity and the soil type of each of the first temporary grids within a preset determination time period; Determine the change consistency according to the granularity and the photosynthesis rate of any two adjacent non-drought-tolerant grids, and determine a number of abnormal grids according to the change consistency; Adjusting the preset grid spacing according to the number of all the abnormal grids within the preset adjustment time and the preset standard quantity fluctuation value until the quantity fluctuation value determined based on the number is less than or equal to the preset standard quantity fluctuation value; Determine a number of normal grids according to the abnormal grids, determine the plant growth degree according to the growth data of the plants in the normal grids and output it; The growth data include plant height, root length and root biomass.

[0007] Further, the determining of a plurality of first temporary grids according to the humidity, the preset standard humidity, the growth data and the preset growth standard range comprises: When the humidity is lower than the preset standard humidity, and the plant height, root length and root biomass are not within the preset growth standard range, the current grid is determined to be a first temporary grid, and a plurality of first temporary grids are formed.

[0008] Further, determining the soil type according to the particle size of each temporary grid includes: When the particle size is greater than a preset standard particle size, the soil type is determined to be coarse-textured soil.

[0009] Further, the determining of a number of non-drought-tolerant grids according to the granularity and the soil type of each of the first temporary grids within a preset determination time period includes: The standard deviation of the particle size is calculated to form a particle fluctuation degree. When the particle fluctuation degree is greater than a preset standard fluctuation degree and the soil type is the coarse-textured soil, the first temporary grid is determined to be a non-drought-tolerant grid, and a plurality of non-drought-tolerant grids are formed.

[0010] Further, the determining of the change consistency according to the granularity and the photosynthesis rate of any two adjacent non-drought-tolerant grids includes: Respectively calculating the relative deviations of the particle size and the photosynthesis rate of any two adjacent non-drought-tolerant grids to obtain a drought-tolerant particle deviation and a drought-tolerant photosynthesis deviation; The relative deviation of the drought-tolerant particle deviation and the drought-tolerant photosynthetic deviation is calculated to obtain the consistency of change.

[0011] Further, determining a number of abnormal grids according to the change consistency includes: When the change consistency is greater than a preset standard change consistency, it is determined that an abnormal situation exists between the two adjacent non-drought-tolerant grids, and a plurality of second temporary grids are obtained; The abnormal consistency is determined according to the granularity of the adjacent preset comparison time lengths of each of the second temporary grids and the photosynthesis rate, and a number of abnormal grids are determined according to the abnormal consistency.

[0012] Furthermore, the determining of the abnormal consistency according to the granularity of the adjacent preset comparison time lengths of each second temporary grid and the photosynthesis rate includes: Relative deviations of the particle size and the photosynthesis rate of adjacent preset comparison time periods are calculated respectively to obtain abnormal particle deviation and abnormal photosynthesis deviation; The relative deviation of the abnormal particle deviation and the abnormal photosynthetic deviation is calculated to obtain the abnormal consistency.

[0013] Further, determining a number of abnormal grids according to the abnormal consistency includes: When the abnormal consistency is greater than a preset standard abnormal consistency, the second temporary grid is determined to be an abnormal grid, and a plurality of abnormal grids are formed.

[0014] Further, adjusting the preset grid spacing according to the number of all the abnormal grids within the preset adjustment time and the preset standard number fluctuation value includes: Calculating the standard deviation of the quantity to obtain a quantity fluctuation value; When the quantity fluctuation value is greater than the preset standard quantity fluctuation value, the preset grid spacing is adjusted according to the relative deviation between the quantity fluctuation value and the preset standard quantity fluctuation value and a preset adjustment coefficient.

[0015] Further, determining a number of normal grids according to the abnormal grid comprises: Eliminate abnormal grids from all grids and obtain several normal grids.

[0016] Compared with the prior art, the beneficial effect of the present invention is that the accuracy of plant growth monitoring is improved through dynamic monitoring and intelligent adjustment, and poor growth conditions and abnormal plants can be identified in time, thereby providing targeted intervention measures to reduce the vulnerability of the ecosystem. At the same time, the efficiency and accuracy of data collection are ensured by dynamically adjusting the grid spacing and optimizing the monitoring strategy. This method also provides a scientific basis for soil quality and plant health, helps to achieve ecological balance, promotes the rational use of resources, improves the stability of the ecosystem, and effectively solves the problem of insufficient comprehensiveness of evaluation and reduced timeliness of data collection due to reliance on fixed trigger strategies and a single data source.

[0017] Furthermore, by monitoring the real-time data within each grid and comparing it with the preset growth standard range, areas of poor growth can be quickly located, laying the foundation for subsequent analysis.

[0018] Furthermore, by introducing a preset standard particle size, the physical properties of the soil can be effectively identified, providing basic data for subsequent growth evaluation.

[0019] Furthermore, by calculating the size of particle fluctuations and specific soil types, areas that are not suitable for the growth of certain plants under drought conditions can be effectively identified, and the potential drought risk of coarse-textured soils can be effectively identified.

[0020] Furthermore, by quantifying the consistency of changes between adjacent grids, potential growth problems or environmental stresses can be quickly discovered.

[0021] Furthermore, by introducing the consistency of preset standard changes, it is possible to effectively monitor abnormal situations that may occur during plant growth and locate problem grids in a timely manner.

[0022] Furthermore, by calculating the anomaly consistency, it helps to identify grids that show significant changes in the time dimension.

[0023] Furthermore, by introducing a preset standard abnormality consistency, areas where significant abnormalities occur during plant growth can be identified in a timely manner.

[0024] Furthermore, through dynamic adjustment, the grid spacing can be flexibly adjusted according to actual data changes, making data collection more accurate and efficient, thereby improving the accuracy of plant growth assessment.

[0025] Furthermore, by determining the normal grid, the health status of plants in the area and the suitability of the growth environment can be more accurately reflected, which helps to ensure the reliability of the data and improve the accuracy of subsequent evaluations. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a flow chart of a plant growth assessment method based on ecological data analysis according to an embodiment of the present invention; Figure 2 A decision logic diagram for determining a first temporary grid according to an embodiment of the present invention; Figure 3 A decision logic diagram for determining soil type according to an embodiment of the present invention; Figure 4 This is a decision logic diagram for determining a non-drought-tolerant grid according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0028] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0029] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0030] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0031] See also Figure 1As shown, it is a flow chart of the plant growth assessment method based on ecological data analysis in this embodiment; The embodiment of the present invention provides a plant growth assessment method based on ecological data analysis, comprising: The growth data and photosynthesis rate of the plants in each grid in the observation area constructed based on the preset grid spacing, as well as the particle size and humidity of the soil are collected at a preset period, wherein the particle size of the soil is collected by a soil probe, and after measuring the size and distribution of the particles in the soil sample, the data of multiple soil collection points are statistically analyzed to obtain the average particle size of the grid and form the particle size; Determine a plurality of first temporary grids according to the humidity, the preset standard humidity, the growth data and the preset growth standard range; determining a soil type according to the particle size of each of the first temporary grids; Determine a number of non-drought-tolerant grids according to the granularity and the soil type of each of the first temporary grids within a preset determination time period; Determine a change consistency according to the granularity and the photosynthesis rate of any two adjacent non-drought-tolerant grids, and determine a number of abnormal grids and a number of normal grids according to the change consistency; Adjusting the preset grid spacing according to the number of all the abnormal grids within the preset adjustment time and the preset standard quantity fluctuation value until the quantity fluctuation value determined based on the number is less than or equal to the preset standard quantity fluctuation value; Determine a number of normal grids based on the abnormal grids, input the growth data of the plants in the normal grids into a Logistic growth curve model, calculate the plant growth degree and form an evaluation report output; The growth data includes plant height, root length and root biomass, wherein the plant height is collected using a laser ranging sensor, and the root length and root biomass are collected using a soil probe.

[0032] The preset period refers to the time interval for each collection of observation data, which mainly depends on the growth characteristics of the plant, the speed of environmental changes, the requirements for data analysis accuracy and resource limitations. It is usually set between 1 day and 5 days. This embodiment is set to 3 days, which can capture long-term growth trends while reducing waste of resources.

[0033] The preset grid spacing refers to the distance at which the grids are divided in the observation area, which depends on the plant density and growth characteristics and is usually set between 0.2 meters and 1 meter. In this embodiment, it is set to 0.5 meters, which can accurately monitor the growth of plants and improve the accuracy and reliability of the data.

[0034] The preset growth standard range refers to the standard value range of plant height, root length and root biomass, which is usually determined according to the growth characteristics and growth stages of different plants. The plant height standard range is set between ±1 cm and ±5 cm, the root length standard range is set between ±1 cm and ±3 cm, and the root biomass standard range is set between ±5 g and ±10 g. In this embodiment, the plant height standard range is set to ±2 cm, the root length standard range is set to ±1.5 cm, and the root biomass standard range is set to ±5 g to improve the accuracy of the assessment, making the judgment of the plant growth status more scientific and effective. In this way, growth abnormalities can be discovered in time and corresponding management measures can be taken to ensure the healthy growth of plants.

[0035] The preset determination time refers to the time window used to identify growth data, which depends on the growth cycle and is usually set between 2 days and 7 days. In this embodiment, it is set to 3 days, which can quickly capture the growth change trend.

[0036] The preset adjustment time refers to the time period for adjusting the grid spacing when an abnormal situation is found. It depends on the monitoring frequency and is usually set between 3 days and 7 days. In this embodiment, it is set to 4 days to be able to respond to environmental changes in a timely manner.

[0037] The preset standard quantity fluctuation value refers to the threshold used to determine the change in the number of abnormal grids, which is usually set based on historical data and is usually set between 2% and 10%. In this embodiment, it is set to 5%, which can more sensitively detect growth anomalies and facilitate corresponding management measures.

[0038] By setting the preset grid spacing to construct the observation area, the growth data and photosynthesis rate of the plants in each grid are collected, and the particle size and humidity information of the soil are obtained. First, several first temporary grids are determined based on humidity, standard humidity, growth data and growth standard range. Next, the soil type is determined by particle size analysis, and non-drought-tolerant grids are identified. Then, the consistency of changes is calculated based on the particle size and photosynthesis rate of adjacent non-drought-tolerant grids to distinguish abnormal grids from normal grids. The grid spacing is adjusted according to the number fluctuation value of the abnormal grids to ensure that the normal grids are finally identified. Finally, the growth data in the normal grid is input into the Logistic growth curve model, the plant growth is calculated, and an evaluation report output is formed.

[0039] The accuracy of plant growth monitoring is improved through dynamic monitoring and intelligent adjustment, which can timely identify adverse growth conditions and abnormal plants, and then provide targeted intervention measures to reduce the vulnerability of the ecosystem. At the same time, by dynamically adjusting the grid spacing and optimizing the monitoring strategy, the efficiency and accuracy of data collection are ensured. This method also provides a scientific basis for soil quality and plant health, helps to achieve ecological balance, promote the rational use of resources, and improve the stability of the ecosystem. It effectively solves the problem of insufficient comprehensiveness of evaluation and reduced timeliness of data collection due to reliance on fixed trigger strategies and a single data source.

[0040] Please continue reading Figure 2 As shown, it is a decision logic diagram for determining the first temporary grid according to an embodiment of the present invention; Specifically, determining a plurality of first temporary grids according to the humidity, the preset standard humidity, the growth data, and the preset growth standard range includes: When the humidity is lower than the preset standard humidity, and the plant height, root length and root biomass are not within the preset growth standard range, the current grid is determined to be a first temporary grid, and a plurality of first temporary grids are formed.

[0041] The preset standard humidity refers to the ideal humidity value set according to the plant growth requirements and environmental conditions, which is used to evaluate whether the actual humidity meets the growth standard. It depends on factors such as plant species, soil type and growth stage, and is usually set between 40% and 70%. In this embodiment, it is set to 60%, which can meet the optimal growth conditions of most plants and help avoid the risks of drought and over-humidity.

[0042] By monitoring the difference between humidity and preset standard humidity, and comparing plant height, root length and root biomass with preset growth standard ranges, grids with insufficient humidity and unqualified growth data are identified and marked as the first temporary grids.

[0043] By monitoring the real-time data within each grid and comparing it with the preset growth standard range, areas of poor growth can be quickly located, laying the foundation for subsequent analysis.

[0044] Please continue reading Figure 3 As shown, it is a decision logic diagram for determining soil type in this embodiment; Specifically, determining the soil type according to the particle size of each temporary grid includes: When the particle size is greater than a preset standard particle size, the soil type is determined to be coarse-textured soil; When the particle size is less than or equal to the preset standard particle size, the soil type is determined to be fine-textured soil.

[0045] The preset standard particle size refers to the ideal particle size value for classifying soil types, which depends on the water and nutrient retention capacity required for plant growth and the aeration of the soil. It is usually set between 0.2 mm and 2 mm. In this embodiment, it is set to 1 mm, which can effectively distinguish between coarse and fine texture soils and help evaluate the drainage and nutrient supply capacity of the soil.

[0046] In the process of determining the soil type, the particle size data of each temporary grid is monitored. When the particle size is greater than the preset standard particle size, the grid is determined to be coarse soil.

[0047] By introducing preset standard particle sizes, the physical properties of the soil can be effectively identified, providing basic data for subsequent growth evaluation.

[0048] Please continue reading Figure 4 As shown, it is a decision logic diagram for determining a non-drought-tolerant grid in this embodiment; Specifically, the determining of a number of non-drought-tolerant grids according to the granularity and the soil type of each of the first temporary grids within a preset determination time period includes: The standard deviation of the particle size is calculated to form a particle fluctuation degree. When the particle fluctuation degree is greater than a preset standard fluctuation degree and the soil type is the coarse-textured soil, the first temporary grid is determined to be a non-drought-tolerant grid, and a plurality of non-drought-tolerant grids are formed.

[0049] The preset standard fluctuation refers to the fluctuation range set for determining soil stability when evaluating soil particle size, which depends on the growth requirements of different plant species and the characteristics of soil types. It is usually set between 0.5 and 1.0, and is set to 0.8 in this embodiment to ensure a sensitive response to soil fluctuations.

[0050] The particle fluctuation is obtained by calculating the standard deviation of the particle size in the first temporary grid to judge the stability of the soil. When the particle fluctuation is greater than the preset standard fluctuation and the soil type is coarse soil, the grid is determined to be a non-drought tolerant grid.

[0051] By calculating the size of particle fluctuations and specific soil types, areas that are not suitable for the growth of certain plants under drought conditions can be effectively identified, and the potential drought risk of coarse-textured soils can be effectively identified.

[0052] Specifically, determining the change consistency according to the granularity and the photosynthesis rate of any two adjacent non-drought-tolerant grids includes: Respectively calculating the relative deviations of the particle size and the photosynthesis rate of any two adjacent non-drought-tolerant grids to obtain a drought-tolerant particle deviation and a drought-tolerant photosynthesis deviation; The relative deviation of the drought-tolerant particle deviation and the drought-tolerant photosynthetic deviation is calculated to obtain the consistency of change.

[0053] By calculating the relative deviation of the particle size and photosynthesis rate of any two adjacent non-drought-tolerant grids, the drought-tolerant particle deviation and drought-tolerant photosynthesis deviation are obtained, and then the relative deviation of the two is further calculated to obtain the consistency of change. This process can quantify the differences in growth conditions between different grids and help identify whether there are abnormal changes caused by environmental factors.

[0054] By quantifying the consistency of changes between adjacent grids, potential growth problems or environmental stresses can be quickly identified.

[0055] Specifically, determining a number of abnormal grids according to the change consistency includes: When the change consistency is greater than a preset standard change consistency, it is determined that an abnormal situation exists between the two adjacent non-drought-tolerant grids, and a plurality of second temporary grids are obtained; The abnormal consistency is determined according to the granularity of the adjacent preset comparison time lengths of each of the second temporary grids and the photosynthesis rate, and a number of abnormal grids are determined according to the abnormal consistency.

[0056] The adjacent preset comparison time refers to the time interval used to analyze the fluctuation and consistency of data in adjacent time periods. It is used to detect the changes between two consecutive data collection cycles. It depends on the growth rate of the plant and the frequency of changes in environmental conditions. It is usually set between 1 day and 3 days. In this embodiment, it is set to 2 days, which can capture daily changes without being too frequent, avoiding the burden of data duplication and excessive analysis.

[0057] The preset standard change consistency is a threshold used to judge the consistency of growth status between adjacent grids, which depends on the stability of the plant growth environment and the expected growth uniformity. It is usually set between 0.1 and 0.3, and is set to 0.2 in this embodiment, which helps to sensitively identify grids with large changes and discover potential growth problems in time.

[0058] When the change consistency is greater than the preset standard change consistency, it is determined that there is an abnormality in the two adjacent non-drought-tolerant grids, and several second temporary grids are formed. Then, by comparing the granularity and photosynthesis rate of each second temporary grid during the adjacent preset comparison time, the abnormal consistency is calculated, and several abnormal grids are further determined, which helps to accurately identify the problem areas in the growth environment.

[0059] By introducing the consistency of preset standard changes, it is possible to effectively monitor abnormal situations that may occur during plant growth and locate problem grids in a timely manner.

[0060] Specifically, determining the abnormal consistency according to the granularity of the adjacent preset comparison time lengths of each second temporary grid and the photosynthesis rate includes: Relative deviations of the particle size and the photosynthesis rate of adjacent preset comparison time periods are calculated respectively to obtain abnormal particle deviation and abnormal photosynthesis deviation; The relative deviation of the abnormal particle deviation and the abnormal photosynthetic deviation is calculated to obtain the abnormal consistency.

[0061] First, the relative deviations of the particle size and photosynthesis rate of adjacent preset comparison time are calculated to form abnormal particle deviation and abnormal photosynthesis deviation. Then, the relative deviation of these two deviations is calculated to finally obtain the abnormal consistency.

[0062] Calculating anomaly consistency helps identify grids that show significant changes in the time dimension.

[0063] Specifically, determining a number of abnormal grids according to abnormal consistency includes: When the abnormal consistency is greater than a preset standard abnormal consistency, the second temporary grid is determined to be an abnormal grid, and a plurality of abnormal grids are formed.

[0064] The preset standard abnormal consistency refers to a threshold value set when evaluating the consistency of changes in the plant growth process, which is used to determine whether there are abnormalities between adjacent grids. It depends on factors such as soil type, plant species, and growth environment, and is usually set between 0.1 and 0.3. In this embodiment, it is set to 0.2, which can sensitively respond to environmental changes and fluctuations in plant growth status, and detect potential problems in a timely manner.

[0065] When the calculated abnormal consistency exceeds the preset standard abnormal consistency, the corresponding second temporary grid is determined to be an abnormal grid, thereby forming a plurality of abnormal grids.

[0066] By introducing a preset standard anomaly consistency, areas where significant anomalies occur during plant growth can be identified in a timely manner.

[0067] Specifically, adjusting the preset grid spacing according to the number of all abnormal grids within the preset adjustment time and the preset standard number fluctuation value includes: Calculating the standard deviation of the quantity to obtain a quantity fluctuation value; When the quantity fluctuation value is greater than the preset standard quantity fluctuation value, the preset grid spacing is adjusted according to the relative deviation between the quantity fluctuation value and the preset standard quantity fluctuation value and a preset adjustment coefficient.

[0068] The new grid spacing is equal to the preset grid spacing multiplied by an adjustment factor, which is determined by the preset adjustment coefficient and the relative deviation of the quantity fluctuation value from the preset standard quantity fluctuation value. The adjustment factor is calculated by subtracting the product of the relative deviation and the preset adjustment coefficient from 1; The preset adjustment coefficient refers to a parameter used to quantify the adjustment range when adjusting the grid spacing. It depends on the growth characteristics of the specific plant species and the rate of change of the growth environment. It is usually set between 0.1 and 0.5. In this embodiment, it is set to 0.2 to achieve more precise adjustments and ensure adaptability and effectiveness in dynamic environments.

[0069] The quantity fluctuation value is obtained by calculating the standard deviation of the number of abnormal grids within the preset adjustment time. If the quantity fluctuation value exceeds the preset standard quantity fluctuation value, the preset grid spacing is adjusted accordingly according to the relative deviation between the two and the preset adjustment coefficient to optimize the grid division.

[0070] Through dynamic adjustment, the grid spacing can be flexibly adjusted according to actual data changes, making data collection more accurate and efficient, thereby improving the accuracy of plant growth assessment.

[0071] Specifically, determining a number of normal grids according to the abnormal grid includes: Eliminate abnormal grids from all grids and obtain several normal grids.

[0072] By determining the normal grid, the health status of plants in the area and the suitability of the growing environment can be more accurately reflected, which helps to ensure the reliability of the data and improve the accuracy of subsequent evaluations.

[0073] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0074] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A plant growth assessment method based on ecological data analysis, characterized in that: include: Collecting the growth data and photosynthesis rate of the plants in each grid in the observation area constructed based on the preset grid spacing, as well as the particle size and humidity of the soil at a preset period; Determine a plurality of first temporary grids according to the humidity, the preset standard humidity, the growth data and the preset growth standard range; determining a soil type according to the particle size of each of the first temporary grids; Determine a number of non-drought-tolerant grids according to the granularity and the soil type of each of the first temporary grids within a preset determination time period; Determine the change consistency according to the granularity and the photosynthesis rate of any two adjacent non-drought-tolerant grids, and determine a number of abnormal grids according to the change consistency; Adjusting the preset grid spacing according to the number of all the abnormal grids within the preset adjustment time and the preset standard quantity fluctuation value until the quantity fluctuation value determined based on the number is less than or equal to the preset standard quantity fluctuation value; Determine a number of normal grids according to the abnormal grids, determine the plant growth degree according to the growth data of the plants in the normal grids and output it; The growth data include plant height, root length and root biomass.

2. The plant growth assessment method based on ecological data analysis according to claim 1, characterized in that: Determining a plurality of first temporary grids according to the humidity, the preset standard humidity, the growth data and the preset growth standard range comprises: When the humidity is lower than the preset standard humidity, and the plant height, root length and root biomass are not within the preset growth standard range, the current grid is determined to be a first temporary grid, and a plurality of first temporary grids are formed.

3. The plant growth assessment method based on ecological data analysis according to claim 2, characterized in that: Determining the soil type according to the particle size of each temporary grid includes: When the particle size is greater than a preset standard particle size, the soil type is determined to be coarse-textured soil.

4. The plant growth assessment method based on ecological data analysis according to claim 3, characterized in that: The determining of a number of non-drought-tolerant grids according to the granularity and the soil type of each of the first temporary grids within a preset determination time period includes: The standard deviation of the particle size is calculated to form a particle fluctuation degree. When the particle fluctuation degree is greater than a preset standard fluctuation degree and the soil type is the coarse-textured soil, the first temporary grid is determined to be a non-drought-tolerant grid, and a plurality of non-drought-tolerant grids are formed.

5. The plant growth assessment method based on ecological data analysis according to claim 4, characterized in that: Determining the consistency of change according to the granularity and the photosynthesis rate of any two adjacent non-drought-tolerant grids includes: Respectively calculating the relative deviations of the particle size and the photosynthesis rate of any two adjacent non-drought-tolerant grids to obtain a drought-tolerant particle deviation and a drought-tolerant photosynthesis deviation; The relative deviation of the drought-tolerant particle deviation and the drought-tolerant photosynthetic deviation is calculated to obtain the consistency of change.

6. The plant growth assessment method based on ecological data analysis according to claim 5, characterized in that: Determining a number of abnormal grids according to the change consistency comprises: When the change consistency is greater than a preset standard change consistency, it is determined that an abnormal situation exists between the two adjacent non-drought-tolerant grids, and a plurality of second temporary grids are obtained; The abnormal consistency is determined according to the granularity of the adjacent preset comparison time lengths of each of the second temporary grids and the photosynthesis rate, and a number of abnormal grids are determined according to the abnormal consistency.

7. The plant growth assessment method based on ecological data analysis according to claim 6, characterized in that: The determining of the abnormal consistency according to the granularity of the adjacent preset comparison time lengths of each second temporary grid and the photosynthesis rate comprises: Relative deviations of the particle size and the photosynthesis rate of adjacent preset comparison time periods are calculated respectively to obtain abnormal particle deviation and abnormal photosynthesis deviation; The relative deviation of the abnormal particle deviation and the abnormal photosynthetic deviation is calculated to obtain the abnormal consistency.

8. The plant growth assessment method based on ecological data analysis according to claim 7, characterized in that: Determining a number of abnormal grids according to abnormal consistency includes: When the abnormal consistency is greater than a preset standard abnormal consistency, the second temporary grid is determined to be an abnormal grid, and a plurality of abnormal grids are formed.

9. The plant growth assessment method based on ecological data analysis according to claim 8, characterized in that: The adjusting the preset grid spacing according to the number of all the abnormal grids within the preset adjustment time and the preset standard number fluctuation value comprises: Calculating the standard deviation of the quantity to obtain a quantity fluctuation value; When the quantity fluctuation value is greater than the preset standard quantity fluctuation value, the preset grid spacing is adjusted according to the relative deviation between the quantity fluctuation value and the preset standard quantity fluctuation value and a preset adjustment coefficient.

10. The plant growth assessment method based on ecological data analysis according to claim 9, characterized in that: Determining a number of normal grids according to the abnormal grid comprises: Eliminate abnormal grids from all grids and obtain several normal grids.

Images