Vegetation water and soil ecological restoration optimization method and system based on multi-source data fusion

Abstract

The invention relates to the technical field of ecological restoration, in particular to a vegetation water and soil ecological restoration optimization method and system based on multi-source data fusion, and the method comprises the following steps: obtaining monthly-scale evapotranspiration data, extracting extreme values to construct an interval sequence, screening adaptive vegetation to generate a list, and calculating soil nutrient release and vegetation absorption amount. Determining the maximum planting density and the target planting density, judging the critical point of root layer moisture, and setting a maintenance water replenishing period. According to the method, by matching the evapotranspiration water supply capacity section with the water consumption characteristics of the plants, high coupling of vegetation allocation and regional hydrological conditions is ensured, and the resource bearing reasonability of vegetation allocation is enhanced in combination with a density allocation mechanism of the sustainable nutrient release capacity of the soil in unit area and the plant absorption capacity; the recovery speed and the resource utilization efficiency are effectively balanced, the root layer moisture critical point is calculated according to the soil structure parameters and the rainfall erosion data after information fusion, and the maintenance water supplementing rhythm is optimized to reduce the resource waste and the vegetation stress occurrence probability.

Description

Technical Field

[0001] The present invention relates to the field of ecological restoration technology, and in particular to a vegetation and soil and water ecological restoration optimization method and system based on multi-source data fusion. Background Art

[0002] The field of ecological restoration technology focuses on restoring the functions of damaged or degraded ecosystems through human intervention or assisted natural restoration. This field encompasses a variety of ecosystem types, including forests, wetlands, grasslands, deserts, and mines, focusing on ecological structure reconstruction, hydrological regulation, biodiversity restoration, and the enhancement of ecological services. Core technical approaches include soil restoration, vegetation restoration, water management and reconstruction, microbial community regulation, and ecological monitoring and assessment. The goal is to restore the self-sustaining and successional capacity of ecosystems, thereby achieving regional ecosystem stability, connectivity, and sustainability.

[0003] Among them, the vegetation and soil and water ecological restoration optimization method aims to achieve the improvement of vegetation coverage, soil erosion control and ecological function restoration in degraded areas by optimizing vegetation configuration structure, species selection, soil and water conservation project layout and ecological process regulation mechanism. The method is widely used in scenarios such as mine reclamation, soil and water erosion area management, mountain ecological barrier construction and river and lake buffer zone restoration. The purpose is to improve the stability and adaptability of plant communities, while enhancing soil and water conservation capacity and ecosystem service functions, and achieving continuous improvement of regional ecological environment quality.

[0004] Traditional restoration methods mainly rely on static vegetation configuration experience and conventional hydrogeological indicators for species selection and density design, and fail to dynamically match the actual differences in regional water supply and demand, resulting in high plant mortality and slow cover recovery in areas of drought or concentrated precipitation fluctuations. Traditional methods ignore the coupling judgment between soil nutrient release dynamics and vegetation nutrient needs, resulting in high planting density leading to soil infertility or low planting density leading to resource waste. In terms of maintenance and management, they mostly rely on fixed-cycle water replenishment, fail to fully consider the root zone moisture fluctuations caused by rainfall erosion, and respond slowly to sudden droughts, affecting ecological restoration effects and resource utilization efficiency. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the existing technology and propose a vegetation and soil and water ecological restoration optimization method and system based on multi-source data fusion.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a vegetation and soil ecological restoration optimization method based on multi-source data fusion, comprising the following steps:

[0007] S1: Obtain evapotranspiration data for the restoration area, extract the interval range consisting of the maximum and minimum evapotranspiration values ​​within a month, construct the regional water supply capacity segment throughout the year by comparing the maximum difference, and generate the evapotranspiration water supply capacity segment sequence;

[0008] S2: calling the evapotranspiration water supply capacity segment sequence, determining the overlap duration of the water consumption characteristic curve and the evapotranspiration segment and the corresponding evapotranspiration difference; if the duration of the difference exceeds a set evapotranspiration imbalance threshold, excluding the corresponding vegetation species, and obtaining a list of vegetation that can adapt to the water conditions;

[0009] S3: Calling the vegetation list that can adapt to the water conditions, calculating the maximum vegetation planting density based on the total sustainable nutrient release of the soil per unit area and the annual absorption of a single plant, and obtaining the maximum planting density value that can be sustainably supported;

[0010] S4: Call the maximum planting density value that can be sustainably supported, calculate the average annual plant density requirement based on the coverage target value and the restoration period, make an intersection judgment with the maximum planting density, and select the median value of the intersection as the standard planting configuration density to obtain a target planting density benchmark that is adapted to the coverage rate.

[0011] The present invention has the following improvements: the evaporation water supply capacity segment sequence includes the monthly average potential evaporation extreme difference segment, the annual evaporation upper and lower limit alternating pattern and the water supply stability bandwidth characteristics; the vegetation list that can adapt to moisture conditions includes transpiration intensity compatible plant species, species with maximum water supply overlap ratio and evaporation differential response controllable communities; the maximum sustainable supported planting density value includes the soil nutrient release capacity limit per unit area, the annual plant absorption load capacity and the annual nutrient balance critical value; the target planting density benchmark for coverage adaptation includes the minimum annual average plant number requirement, the restoration year and density function value and the median of coverage density control.

[0012] The present invention is improved in that the step of obtaining the evaporation water supply capacity segment sequence is specifically as follows:

[0013] S111: Obtain evapotranspiration data for the restoration area, calculate the maximum and minimum potential evapotranspiration values ​​based on the daily evapotranspiration records in the monthly data, extract the maximum and minimum evapotranspiration values, construct the two types of maximum and minimum values ​​into upper and lower boundary interval sets, and generate monthly evapotranspiration boundary interval values;

[0014] S112: Based on the monthly evapotranspiration boundary interval values, respectively calculating the difference between the potential evapotranspiration boundary and the actual evapotranspiration boundary for each month throughout the year, dividing the monthly potential difference and the actual difference into sets, and extracting the maximum interval span in the two sets to obtain the annual evapotranspiration range interval width value;

[0015] S113: Calling the annual evapotranspiration range width value, performing segment stability judgment on the boundary difference sequence of each month throughout the year, constructing a segment sequence of annual water supply capacity based on the difference fluctuation amplitude, and establishing an evapotranspiration water supply capacity segment sequence.

[0016] The present invention is improved in that the steps for obtaining the vegetation list that can adapt to moisture conditions are specifically as follows:

[0017] S211: Calling the evapotranspiration water supply capacity segment sequence to obtain the transpiration rate per unit leaf area and the soil moisture content in the root zone of the candidate vegetation, integrating the two data into the total water consumption data of each plant on a monthly basis according to the time series, and drawing a water resource consumption change sequence curve for each vegetation on a monthly basis to generate a vegetation water consumption characteristic change trend value;

[0018] S212: Based on the vegetation water consumption characteristic change trend value, the water consumption change curve of the candidate vegetation is compared with the monthly segments in the evapotranspiration water supply capacity segment sequence, the duration and extent of the water consumption value exceeding the evapotranspiration upper limit in each month are calculated, and the longest time period of the difference in each month is calculated to obtain the evapotranspiration supply and demand imbalance duration period value;

[0019] S213: Based on the evapotranspiration supply and demand imbalance duration value and the set evapotranspiration imbalance threshold, a judgment is made item by item, and the vegetation species with a difference duration greater than the threshold are marked as unadaptable groups, and the remaining species are formed into a screening list to obtain a list of vegetation that can adapt to the moisture conditions.

[0020] The present invention is improved in that the step of obtaining the maximum sustainable planting density value is specifically as follows:

[0021] S311: Retrieving the list of vegetation that can adapt to the water conditions, extracting the maximum effective root depth, nitrogen absorption rate per unit root length, phosphorus absorption rate per unit root length, potassium absorption rate per unit root length, and saturated nutrient requirement of the remaining vegetation, calculating the total absorption of each indicator per unit time on a monthly basis and converting it into an annual absorption value to generate the total annual nutrient absorption value of the vegetation;

[0022] S312: Based on the total annual nutrient uptake of vegetation, soil particle composition parameters, organic matter mineralization rate, and available nutrient release period of the target restoration area are collected. Based on the nutrient release potential corresponding to the particle ratio and mineralization rate, the total amount of nitrogen, phosphorus, and potassium that can be released per unit area on an annual scale is calculated, and the maximum supportable planting density per unit area is calculated.

[0023] S313: Based on the maximum supportable planting density per unit area, proportional conversion is performed with the land area and planting type of the target area, configurations exceeding the density upper limit are eliminated, and the upper and lower limits of the adapted planting density range are determined to obtain the maximum planting density value that can be sustainably supported.

[0024] The present invention is improved in that the steps for obtaining the target planting density benchmark for coverage adaptation are specifically as follows:

[0025] S411: Calling the maximum sustainable planting density value, setting the vegetation coverage years and coverage rate target values ​​of the target restoration area, calculating the annual average plant density requirement using the land area, coverage rate target value and coverage years, and generating the annual average coverage density requirement value;

[0026] S412: Based on the annual average coverage density requirement value, a numerical intersection judgment is performed with the maximum planting density value that can be sustainably supported, an intersection interval between the two is calculated, and a determination is made as to whether there are continuous sections in the intersection interval with positive differences between the upper and lower limits. A median derived value of the intersection interval is obtained by calculation to generate a median intersection density judgment result;

[0027] S413: Based on the median judgment result of the intersection density, a density point close to the median position in the intersection interval is selected as a planting configuration reference benchmark value to generate a target planting density benchmark for coverage adaptation.

[0028] The present invention is improved in that the method further comprises the following steps:

[0029] S5: calling the target planting density benchmark adapted to the coverage rate, determining the intersection of the clay migration rate caused by rainfall erosion intensity and the water content decrease slope, marking the time node as the critical point of root layer water supply, setting the maintenance and water replenishment adjustment cycle according to the minimum time interval between nodes, and obtaining the recommended cycle for the adjustable maintenance interval section;

[0030] The recommended period for the adjustable maintenance interval section specifically includes the water decay starting node, the minimum water replenishment interval length, and the root layer water imbalance warning daily threshold.

[0031] The present invention is improved in that the steps for obtaining the recommended period for the adjustable maintenance interval section are specifically as follows:

[0032] S511: calling the target planting density benchmark adapted to the coverage ratio, extracting the root zone soil moisture change data of the corresponding vegetation, calculating the moisture content decline slope in a continuous period based on the data in a time series, obtaining the maximum single-day decline rate and constructing a change trend, and generating a root layer moisture decline trend value;

[0033] S512: Based on the root zone moisture decline trend value, collect soil clay mass ratio data with a particle size less than a set particle size threshold and daily rainfall intensity sequence data within the restoration area, calculate the clay loss rate curve and compare the trend with the root zone moisture decline trend value, identify the intersection sequence with equal slopes, and perform amplitude conversion based on the cumulative offset of rainfall intensity and the clay instability factor within the intersection segment. Calculate and obtain the dynamic time span of moisture stability between the intersections as a reference value for the adjustment period duration, and obtain the stability duration value of the intersection segment;

[0034] S513: Based on the stability duration value of the intersection section, the shortest time period is selected as the lower limit of the cycle, and the cycle mapping is performed in combination with the rainfall frequency trend per unit time and the transpiration rate recovery capacity of the vegetation in the restoration zone. The cycle is converted into a recommended interval for the maintenance operation cycle length, and the recommended cycle for the adjustable maintenance interval section is obtained.

[0035] A vegetation and soil and water ecological restoration optimization system based on multi-source data fusion, which is used to implement the above-mentioned vegetation and soil and water ecological restoration optimization method based on multi-source data fusion, includes:

[0036] The evapotranspiration water supply analysis module obtains the evapotranspiration data of the restoration area, extracts the interval range consisting of the maximum and minimum evapotranspiration values ​​within a month, constructs the regional water supply capacity segment throughout the year by comparing the maximum difference, and generates the evapotranspiration water supply capacity segment sequence;

[0037] The adaptive vegetation screening module calls the evapotranspiration water supply capacity segment sequence, determines the overlap duration of the water consumption characteristic curve and the evapotranspiration segment and the corresponding evapotranspiration difference, and excludes the corresponding vegetation species if the difference duration exceeds the set evapotranspiration imbalance threshold, thereby obtaining a list of vegetation that can adapt to the water conditions;

[0038] The maximum density analysis module calls the vegetation list that can adapt to the moisture conditions, calculates the maximum vegetation planting density based on the total sustainable nutrient release of the soil per unit area and the annual absorption of a single plant, and obtains the maximum planting density value that can be sustainably supported;

[0039] The planting density benchmark identification module calls the maximum sustainable planting density value, calculates the average annual plant density required based on the coverage target value and the restoration years, makes an intersection judgment with the maximum planting density, and selects the average value of the intersection as the planting configuration standard density to obtain the target planting density benchmark for coverage adaptation;

[0040] The maintenance cycle adjustment module calls the target planting density benchmark adapted to the coverage rate, and by judging the intersection of the clay migration rate caused by rainfall erosion intensity and the slope of water content decrease, marks the time node as the critical point of root layer water supply, and sets the maintenance and water replenishment adjustment cycle according to the minimum time interval between nodes to obtain the recommended cycle for the adjustable maintenance interval section.

[0041] Compared with the prior art, the advantages and positive effects of the present invention are:

[0042] In the present invention, by matching the evapotranspiration water supply capacity section with the water consumption characteristics of plants, species with poor water adaptability can be excluded, ensuring a high degree of coupling between vegetation configuration and regional hydrological conditions, and combining the density allocation mechanism of the sustainable nutrient release capacity of soil per unit area and the absorption capacity of plants to enhance the resource carrying rationality of vegetation configuration. The standard planting density calculated according to the coverage rate and the restoration period effectively balances the recovery speed and resource utilization efficiency. The root layer moisture critical point is calculated based on the soil structure parameters and rainfall erosion data after information fusion, and the maintenance and water replenishment rhythm is optimized to reduce resource waste and the probability of vegetation stress. The adaptability, sustainability and soil and water conservation effect of the restoration process are improved as a whole, and the construction of stable vegetation communities and the enhancement of ecological functions are achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a flow chart of the method of the present invention;

[0044] Figure 2 A flow chart of the sequence of sections for obtaining evapotranspiration water supply capacity according to the present invention;

[0045] Figure 3 A flow chart for obtaining a list of vegetation that can adapt to moisture conditions for the present invention;

[0046] Figure 4 A flow chart for obtaining a maximum sustainable planting density value supported by the present invention;

[0047] Figure 5 A flow chart for obtaining a target planting density benchmark for coverage adaptation for the present invention;

[0048] Figure 6 The present invention is a flowchart for obtaining a recommended cycle for an adjustable maintenance interval section. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0050] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings and are only for the convenience of describing the present invention and simplifying the description. They do not indicate or imply that the devices or elements referred to must have a specific direction, be constructed and operate in a specific direction, and therefore should not be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0051] See also Figure 1 The present invention provides a technical solution: a vegetation and soil ecological restoration optimization method based on multi-source data fusion, comprising the following steps:

[0052] S1: Obtain evapotranspiration data for the restoration area, extract the interval range consisting of the maximum and minimum evapotranspiration values ​​within a month, and construct the regional water supply capacity segment throughout the year by comparing the maximum difference based on the set of monthly intervals throughout the year, thus generating a sequence of evapotranspiration water supply capacity segments;

[0053] S2: Call the evapotranspiration water supply capacity segment sequence to obtain the transpiration rate per unit leaf area and the changing trend of the root zone soil moisture content of the candidate vegetation. Based on the monthly data, a monthly water consumption characteristic curve is constructed. The overlap duration of the water consumption characteristic curve and the evapotranspiration segment and the corresponding evapotranspiration difference are determined. If the difference duration exceeds the set evapotranspiration imbalance threshold, the corresponding vegetation species is excluded, and a list of vegetation that can adapt to the water conditions is obtained.

[0054] S3: Call the list of vegetation that can adapt to water conditions, extract the maximum effective root depth of the remaining plants, the nitrogen, phosphorus and potassium absorption rate per unit root length, and the saturated nutrient requirement. Combined with the soil particle composition, organic matter mineralization rate and available nutrient release cycle of the restoration area, the maximum vegetation planting density is calculated based on the total sustainable nutrient release of the soil per unit area and the annual absorption of a single plant, to obtain the maximum planting density value that can be sustainably supported;

[0055] S4: Call the maximum sustainable planting density value, set the target vegetation coverage years and the coverage target value required for regional restoration, calculate the average annual plant density required based on the coverage target value and the restoration years, perform an intersection judgment with the maximum planting density, and select the average value of the intersection as the standard planting configuration density to obtain the target planting density benchmark for coverage adaptation;

[0056] S5: Call the target planting density benchmark adapted to the coverage rate, extract the slope of the soil moisture content decrease in the root zone of the corresponding vegetation, collect the proportion of clay particles with a particle size smaller than the target particle size and the daily rainfall intensity sequence in the restoration area, and mark the time node as the critical point of root layer water supply by judging the intersection position of the clay particle migration rate caused by rainfall erosion intensity and the slope of the moisture content decrease. Set the maintenance and water replenishment adjustment cycle according to the minimum time interval between nodes to obtain the recommended cycle for the adjustable maintenance interval section.

[0057] The evapotranspiration water supply capacity segment sequence includes the monthly average potential evapotranspiration extreme range segment, the annual evapotranspiration upper and lower limit alternating pattern and the water supply stability bandwidth characteristics. The vegetation list that can adapt to moisture conditions includes plant species compatible with transpiration intensity, species with the maximum water supply overlap ratio and communities with controllable evapotranspiration differential response. The maximum sustainable supported planting density value includes the limit of soil nutrient release capacity per unit area, the annual plant absorption load capacity and the annual nutrient balance critical value. The target planting density benchmark for coverage adaptation includes the minimum annual average plant number requirement, the restoration year and density function value and the median of coverage density control. The recommended period for the adjustable maintenance interval segment is specifically the water attenuation starting node, the minimum water replenishment interval length and the root layer water imbalance warning daily threshold.

[0058] See also Figure 2 The specific steps for obtaining the evapotranspiration water supply capacity segment sequence are as follows:

[0059] S111: Obtain evapotranspiration data for the restoration area, calculate the maximum and minimum potential evapotranspiration values ​​based on the daily evapotranspiration records in the monthly data, extract the maximum and minimum evapotranspiration values, construct the two types of maximum and minimum values ​​into upper and lower boundary interval sets, and generate monthly evapotranspiration boundary interval values;

[0060] To obtain a monthly potential evapotranspiration (ET) and actual ET data series for the restoration area, the geographic boundaries of the monitoring area and the frequency of meteorological parameter acquisition must first be set. For example, a drought restoration zone within the range of 103.7° to 104.1° east longitude and 34.5° to 34.8° north latitude is selected. Daily potential ET data (unit: mm / d) and actual ET data are collected monthly through regional meteorological stations or remote sensing systems. The daily values ​​are sorted to extract the maximum and minimum potential ET values ​​within each month. For example, if the highest value in a month is 7.3 mm / d and the lowest value is 2.1 mm / d, the potential ET boundary for that month is 2.1 to 7.3 mm / d. Similarly, the upper and lower limits of the actual ET are extracted using the same method. For example, if the highest value is 6.8 mm / d and the lowest value is 2.0 mm / d, the actual ET boundary range is 2.0 to 6. 8mm / d; the above process needs to call the daily scale original sequence data during execution, extract the boundaries by filtering the maximum / minimum functions within the month, and construct a boundary interval set; taking March 2022 as an example, set the daily potential evapotranspiration array as follows: [3.1,3.7,2.9,6.4,5.2,7.3,4.5,…], then the minimum value is 2.9mm / d and the maximum value is 7.3mm / d. The actual evapotranspiration array is [2.8,3.0,2.5,6.1,4.9,6.8,4.1,…], with a minimum value of 2.5mm / d and a maximum value of 6.8mm / d, thus forming the monthly upper and lower limit intervals. Repeat the above operation to calculate the 12 months of the year, and classify them into the potential evapotranspiration boundary set and the actual evapotranspiration boundary set, providing the boundary data basis for the subsequent difference calculation and generating the monthly evapotranspiration boundary interval value.

[0061] S112: Based on the monthly evapotranspiration boundary interval values, the difference between the potential evapotranspiration boundary and the actual evapotranspiration boundary for each month of the year is calculated, the monthly potential difference and the actual difference are divided into sets, and the maximum interval span in the two sets is extracted to obtain the annual evapotranspiration range interval width value;

[0062] Based on the monthly evapotranspiration boundary interval values, the difference between the upper and lower limits of potential evapotranspiration and actual evapotranspiration needs to be calculated respectively. The difference is defined as the difference between the maximum and minimum values ​​of the month. For example, the potential evapotranspiration difference in March is 7.3-2.9=4.4mm, and the actual evapotranspiration difference is 6.8-2.5=4.3mm. After completing the calculations for 12 months of the year, a potential difference sequence and an actual difference sequence are formed. For example, the potential difference array is: [4.4, 5.2, 3.9, ...], and the actual difference array is: [4.3, 4.9, 3.6, ...], so as to construct a difference set. Next, the set needs to be sorted into The time axis is divided, and the groups can be set according to seasons, climate characteristics, terrain and other attributes. For example, it can be divided into four seasons: spring, summer, autumn and winter. The maximum internal difference item of each season is counted as the seasonal segment extreme value, and then the spans of the four segments are compared respectively, and the maximum value is extracted as the representative of the annual extreme value. For example, if the corresponding potential difference maximum values ​​of spring, summer, autumn and winter are 4.4mm, 5.9mm, 3.7mm and 4.2mm, the width of the annual extreme value interval is 5.9mm. This value represents the widest evapotranspiration fluctuation segment throughout the year. As a quantitative indicator of the extreme changes in hydrological conditions throughout the year, the width of the annual evapotranspiration range interval is obtained.

[0063] S113: Calling the annual evapotranspiration range width value, performing a segment stability judgment on the boundary difference sequence of each month of the year, constructing a segment sequence of the annual water supply capacity based on the difference fluctuation amplitude, and establishing a segment sequence of the evapotranspiration water supply capacity;

[0064] The width of the evapotranspiration range for the whole year is called, and the time series modeling of the difference between the upper and lower limits of evapotranspiration for each month of the year is performed to analyze the stability characteristics of its variation range. That is, it is necessary to judge whether the difference of each month is continuously in a stable section within the range of the whole year. If the difference changes for three consecutive months fluctuate within the range width of ±10%, it is regarded as a stable section; the ±10% difference tolerance band is a preset benchmark value, which is set based on the measured results that the interannual change rate of evapotranspiration fluctuations in the semi-arid areas of northern China is usually less than 12%. Therefore, 10% is selected as a representative interval for ease of application. For example, the range width is 5.9mm, and the ±10% range is set to 5.3mm to 6.5mm. If the consecutive differences from June to August are 5.4mm, 5.6mm and 6.8mm respectively, mm and 5.9 mm, then the segment meets the stability conditions; at the same time, a steady-state judgment threshold is set that the monthly difference change rate shall not exceed ±0.5 mm. This threshold is the reference line for the sliding average judgment of the difference in conventional hydrological projects and is often used for daily-monthly data fluctuation control. It belongs to the parameter limit setting. The setting basis is that the maximum monthly difference fluctuation value throughout the year does not exceed 1.2 mm, eliminating short-term disturbances and identifying continuous fluctuation trends; based on the above judgment, a segment sequence of water supply capacity throughout the year is constructed, and a time segment label is constructed with each group of stable segments as a water bandwidth unit, such as segment 1 for March-May and segment 2 for June-August. Each segment is marked as a category such as strong, weak, or very weak water bandwidth, forming a sequence output, and establishing a segment sequence of evapotranspiration water supply capacity.

[0065] As shown in Table 1, the upper and lower limits, differences, and difference classifications of potential and actual evapotranspiration for each month in 2022 are listed:

[0066] Table 1 Statistics of evapotranspiration difference sections

[0067]

[0068] Table 1 lists the changes in evapotranspiration boundaries and differences for typical months. This can be used to subsequently extract stable segments and classify moisture bandwidths, providing a basis for establishing a sequence of evapotranspiration water supply capacity segments. This sequence output will be directly linked to the subsequent assessment and screening of vegetation water adaptability and serve as an important input for evapotranspiration bandwidth control strategies.

[0069] See also Figure 3 , the steps to obtain the vegetation list that can adapt to water conditions are as follows:

[0070] S211: Calling the evapotranspiration water supply capacity segment sequence to obtain the transpiration rate per unit leaf area and the soil moisture content in the root zone of the candidate vegetation, integrating the two data into the total water consumption data of each plant on a monthly basis according to the time series, and drawing the water resource consumption change sequence curve of each vegetation on a monthly basis to generate the vegetation water consumption characteristic change trend value;

[0071] Call the evapotranspiration water supply capacity segment sequence to obtain the transpiration rate per unit leaf area of ​​each candidate vegetation and the change trend of its corresponding root zone soil moisture content. First, it is necessary to identify the vegetation type and its distribution segment. For example, select drought-tolerant plant A and shallow-rooted plant B respectively and monitor them continuously from March to August. The transpiration rate unit is mm 3 / cm 2 / h, soil moisture content is in m 3 / m 3 , are collected on an hourly scale and integrated into monthly scale cumulative water consumption through time series. For example, the average transpiration rate of plant A in a certain month is 2.5 mm 3 / cm 2 / h, and the daily exposure time is 10 hours, then the monthly cumulative transpiration is: 2.5×10×30=750mm 3 / cm 2 / month, after being converted into transpiration per plant, combined with its leaf area (e.g. 120cm2), the water consumption is 750×120=90,000mm 3 / month=90cm 3 / month, i.e. 0.09L / month; at the same time, record the daily average change sequence of soil moisture content in the root zone, average it by month, and combine the change rate of moisture content in the first and second ten days to form a trend value; for example, the moisture content in a certain month changes from 0.27m 3 / m 3 Down to 0.22m 3 / m 3 , then the monthly descent rate is 0.05m 3 / m 3 , which can be used as a reflection of vegetation water extraction; the above data are integrated by time and plotted into a continuous curve from March to August as a dynamic reflection of the monthly water consumption of each vegetation under different water supply environments, generating a trend value of the change of vegetation water consumption characteristics.

[0072] S212: Based on the trend value of vegetation water consumption characteristics, the water consumption change curve of the candidate vegetation is compared with the monthly segments in the evapotranspiration water supply capacity segment sequence, and the duration and extent of the water consumption value exceeding the evapotranspiration upper limit in each month are calculated. The longest period of time during which the difference lasts in each month is calculated to obtain the duration period of the evapotranspiration supply and demand imbalance;

[0073] Based on the trend value of vegetation water consumption characteristics, the water consumption curve constructed for each vegetation is compared with the evapotranspiration water supply capacity segment sequence on a monthly basis. The focus is on extracting whether the actual water consumption value of each month exceeds the upper limit of the evapotranspiration bandwidth of that month, and calculating the cumulative number of hours of the exceeded period and the average daily excess amplitude. For example, in mid-May, the evapotranspiration upper limit is 6.5 mm / d, and the water consumption value of plant A is higher than this value in 5 days, namely 7.1, 6.9, 6.7, 7.4, and 6.8 mm / d, then the cumulative number of days of excess is 5 days, and the excess amplitude is the daily value minus the upper limit. For example, the total excess value over 5 days is 0.6+0.4+0.2+0.9+0.3=2.4mm, with an average of 0.48mm / d. The statistical data of each month are aggregated and sorted by vegetation classification to identify the months with the largest continuous excess values ​​for each vegetation, and the longest duration period of evapotranspiration imbalance within the annual cycle is constructed. If plant B exceeds the value continuously in April, May, and June, and the duration days in each month are 8, 10, and 7 days, the corresponding longest time period is 3 months, recorded as 90 days, and the duration period of evapotranspiration supply and demand imbalance is obtained.

[0074] S213: Based on the duration of the evapotranspiration supply-demand imbalance and the set evapotranspiration-transpiration imbalance threshold, a judgment is made item by item, and the vegetation species with a difference duration greater than the threshold are marked as unsuitable groups. The remaining species are then included in a screening list to obtain a list of vegetation that can adapt to the water conditions.

[0075] The imbalance threshold is set to 45 days, which is based on the stability limit of plant water deficit during the restoration of degraded vegetation in the Loess Hilly Region. Referring to the Chinese Ecological Restoration Monitoring Standards, if the evapotranspiration imbalance lasts for more than 50 days, root growth will be inhibited. 45 days is taken as the adaptation limit. Based on this, vegetation with a cycle value greater than 45 days is removed and recorded as unadapted species, and the remaining vegetation is retained to form a list of water adaptability screening results. For example, if the cycle value of plant A is 30 days, it will be retained if it is less than the threshold, and plant C with a cycle value of 70 days will be removed. The screening results are summarized annually to form a target restoration list, and a list of vegetation that can adapt to water conditions is output.

[0076] Table 2 shows the comparison data of evapotranspiration and evapotranspiration for typical candidate vegetation in May, which provides the basis for calculating the duration and magnitude of the deviation:

[0077] Table 2 Statistics of water consumption and evapotranspiration differences of candidate vegetation in May

[0078]

[0079] As shown in Table 2, both candidate vegetation types A and B experienced excess transpiration in May, but A's excess was more stable and lasted for a shorter period, making it a candidate for inclusion in the screening list and retention for ecological restoration planting configurations under drought or fluctuating hydrological conditions. Finally, the screening was completed using thresholds and the results were output.

[0080] See also Figure 4 The specific steps for obtaining the maximum sustainable planting density value are as follows:

[0081] S311: Calling a list of vegetation that can adapt to water conditions, extracting the maximum effective root depth, nitrogen absorption rate per unit root length, phosphorus absorption rate per unit root length, potassium absorption rate per unit root length, and saturated nutrient requirement of the remaining vegetation, calculating the total absorption of each indicator per unit time on a monthly basis and converting it into an annual absorption value to generate the total annual nutrient absorption value of the vegetation;

[0082] Call the list of vegetation that can adapt to water conditions, and extract the maximum effective root depth, nitrogen absorption rate per unit root length, phosphorus absorption rate per unit root length, potassium absorption rate per unit root length, and plant saturated nutrient requirement for each remaining species in the list. These data must be collected uniformly based on the same monitoring period, such as root depth in cm, nutrient element absorption rate in mg / cm / month, and plant saturated nutrient requirement in mg; taking vegetation A as an example, the maximum effective root depth is 80 cm, the nitrogen absorption rate per unit root length is 0.35 mg / cm / month, and the total annual nitrogen absorption is: 0 .35×80×12=336mg / year. Similarly, the phosphorus absorption is calculated as 0.12×80×12=115.2mg / year, and the potassium absorption is calculated as 0.28×80×12=268.8mg / year. If the saturated demand value of the plant is nitrogen: 350mg, phosphorus: 120mg, and potassium: 270mg, then the above absorption values ​​are all within the demand range, indicating that the nutrient absorption meets the standard. Similarly, the monthly absorption of the three types of nutrients for each vegetation is summarized according to the above formula, and uniformly converted into annual scale data as the basis for analysis to generate the total annual nutrient absorption value of the vegetation.

[0083] Collect soil particle composition parameters in the target area, such as 60% sand, 25% silt, and 15% clay. Then monitor the organic matter mineralization rate and the release cycle of available nutrients. For example, if the mineralization rate is 0.025g / kg / month and the release cycle is 10 months, a nutrient release calculation model is established based on the particle ratio and mineralization rate. The annual release per unit area is calculated according to the formula. The total annual nutrient absorption value of vegetation is then used to make a supply and demand ratio. The formula is:

[0084]

[0085] In it, enter the parameter: Qj =720mg, C j =0.15, R j =950mg / m 2 、S j =720mg / m 2 , α=0.85, β=30, we get

[0086]

[0087] The maximum allowable planting density per unit area is 0.5229 plants / m 2 , generate the controllable vegetation density limit. Parameter and symbol description:

[0088] In this formula structure, the numerator reflects the total intensity of the overall planting system's demand for soil resources by weighted accumulation of the nutrient requirements of each species and the root layer water and soil structure (through the logarithmic transformation of the clay ratio); the denominator is based on the difference between soil release and plant absorption, and takes into account the loss compensation coefficient, reflecting the true performance of the sustainable supply capacity of soil resources; the square root operation is used to converge numerical fluctuations and improve the stability of the planting density evaluation results under diverse vegetation input conditions.

[0089] The innovation of the formula is that it introduces the normalized soil clay ratio parameter C j Its logarithmic transformation, combined with plant demand Q j A composite weighted term is formed, thereby improving the differential expression of responses of different vegetation under different soil layer water retention capacities; at the same time, by adding the absorption attenuation adjustment coefficient α and the loss compensation constant β, a stable characterization mechanism of the supply and demand relationship under a variety of actual deviation factors is established, so that the formula can make a nonlinear, dynamic, and differential comprehensive evaluation of the regional soil resource carrying capacity of vegetation, overcoming the problem of distortion or failure of traditional linear density assessment models in extreme supply and demand imbalance scenarios.

[0090] In the above example, the maximum allowable planting density per unit area D is calculated. a =0.5229 plants / m 2 This value is within the reasonable planting density standard range (the general recommended planting density is 0.4 to 0.6 plants / m 2 ), indicating that the amount of nutrients released by the soil at this density level can basically cover the total annual absorption of vegetation, reflecting that the system is in the sustainable planting configuration range; this result is the specific calculation output of the controllable vegetation density limit value, which can be directly used for the screening of subsequent planting configuration plans and the determination of the upper and lower limits of configuration, providing a clear calculation basis and parameter support for the maximum planting density value that can be sustainably supported.

[0091] S312: Based on the total annual nutrient absorption of vegetation, collect soil particle composition parameters, organic matter mineralization rate values, and available nutrient release cycle values ​​of the target restoration area. Based on the nutrient release potential corresponding to the particle ratio and mineralization rate, calculate the total amount of nitrogen, phosphorus, and potassium that can be released per unit area on an annual scale using the formula:

[0092]

[0093] Calculate and obtain the maximum planting density per unit area;

[0094] Among them, D a Indicates the maximum planting density per unit area, Q i represents the total annual nutrient requirement of the i-th plant species, C i represents the normalized value of the clay mass ratio of the i-th plant species in the soil layer corresponding to the root depth, R i It represents the total amount of nutrients that can be released per unit area of ​​soil, S i represents the total annual absorption of the i-th plant species, α represents the absorption attenuation adjustment coefficient of plant root length, and β represents the loss compensation constant during the release of soil nutrients. It represents the summation operation of the total nutrient requirements of n candidate vegetation types multiplied by the root layer water storage carrying factor, where n represents the total number of candidate vegetation types, ln(C i +1) represents the logarithmic conversion value of soil clay water storage contribution of species i, |R i -S i α| represents the absolute value of the net nutrient surplus of soil per unit area;

[0095] According to the controllable vegetation density limit, the area unit conversion and proportion conversion are carried out in combination with the land area of ​​the target area and the planned planting type. If the target area is 5000m 2 , the total number of plants allowed to be planted is 0.5229×5000=2614.5, which is rounded down to 2614 plants. If the planting plan is configured as 3 plants per row and 1.2m row spacing, the planting amount per unit row spacing under this configuration is 3 / 1.2=2.5 plants / m. If the density exceeds the upper limit, the plants need to be removed and readjusted to 2 plants per row, that is, 2 / 1.2≈1.67 plants / m, which meets the density limit requirement. The final calculated minimum density configuration is 0.35 plants / m 2 , the maximum configuration is 0.52 plants / m 2 , thereby obtaining the maximum planting density value that can be sustainably supported.

[0096] S313: Based on the maximum supportable planting density per unit area, the land area and planting type of the target area are proportionally converted, configurations exceeding the density upper limit are eliminated, and the upper and lower limits of the adapted planting density range are determined to obtain the maximum sustainable supportable planting density value;

[0097] See also Figure 5 The specific steps for obtaining the target planting density benchmark for coverage adaptation are as follows:

[0098] S411: Call the maximum sustainable planting density value, set the vegetation coverage years and coverage rate target values ​​for the target restoration area, calculate the annual average plant density requirement using the land area, coverage rate target value, and coverage years, and generate the annual average coverage density requirement value;

[0099] Call the maximum sustainable supported planting density value, set the vegetation coverage years and coverage rate target value of the target restoration area, multiply the land area by the coverage rate target value and then divide it by the coverage years, calculate the average annual plant density requirement, and generate the average annual coverage density requirement value. After calling the maximum sustainable supported planting density value, set the target restoration area to 12000m 2 The target coverage rate is 85%, and the coverage period is set to 5 years. The calculated target coverage area is 12000·0.85=10200m 2 Considering that the average area occupied by a single plant is 4.9m 2 , the corresponding annual average number of plants required is Plants / year, convert this value into plant density per unit area Plant / m 2 , set the maximum sustainable planting density to 0.5229 plants / m 2 Based on this, the coverage demand density value of the region at the annual scale per unit area is constructed, that is, the annual average coverage density demand value.

[0100] S412: Based on the annual average coverage density requirement value and the maximum sustainable planting density value, a numerical intersection judgment is performed, the intersection interval between the two is calculated, and it is determined whether there are continuous sections with positive upper and lower limit differences in the intersection interval, using the formula:

[0101]

[0102] The median derivation value of the intersection interval is obtained by operation, and the median judgment result of the intersection density is generated;

[0103] Among them, M d represents the median of the intersection density interval, D max Denotes the maximum sustainable planting density, D Z Indicates the annual average coverage density requirement value, V represents the sum of vegetation demand intensity of the j-th candidate area within the set period, j represents the unit vegetation demand value of the jth region, γ j represents the coverage response factor of the jth region, T represents the vegetation coverage years, |D max -DZ | represents the absolute value of the difference between the upper and lower limits of coverage density, 2 is the calculation factor for taking the median, and m represents the total number of regions;

[0104] Based on the numerical intersection judgment of the annual average coverage density requirement value and the maximum planting density value supported by sustainability, the intersection interval between the two is calculated, and it is determined whether there is a continuous segment with a positive difference between the upper and lower limits in the intersection interval. max =0.5229 plants / m 2 、D Z =0.0347 plants / m 2 The intersection difference is |0.5229-0.0347|=0.4882 plants / m 2 , collect m = 3 regional unit demand values ​​V j and the response factor γ j , set V1 = 0.021 plants / m 2 、V2=0.018 plants / m 2 、V3=0.016 plants / m 2 , the response factors are set as γ1 = 1.1, γ2 = 1.0, γ3 = 0.9, the coverage period T = 5, and the formula is substituted into the calculation:

[0105]

[0106] Generate the intersection density median judgment result.

[0107] Parameter description and operation logic: Among them, D max Indicates the maximum sustainable planting density (plants / m 2 ), D Z Indicates the annual average coverage density requirement (plants / m 2 ), |D max -D Z | is the absolute value of the difference between the two, It represents the weighted sum of the unit demand value and the response factor of each region, and T is the coverage period. The formula reflects the comprehensive performance of the actual resource carrying capacity and regional regulation capacity by superimposing the density difference and the total response intensity and taking the average. The benefit of the formula is that it integrates two types of structural participation items, namely, coverage pressure difference and regional response attributes. It not only considers the static coverage threshold problem, but also reflects the regulatory effect of the dynamic regional absorption capacity on density arrangement. The results show that the median density is 0.2496 plants / m 2 It is located within the executable planting density range, providing a benchmark reference value for subsequent density configuration. It is positioned between the upper and lower limits and incorporates regional response weights to improve configuration reliability and representativeness.

[0108] S413: Based on the intersection density median judgment result, a density point close to the median position in the intersection interval is selected as a planting configuration reference benchmark value to generate a target planting density benchmark for coverage adaptation;

[0109] According to the median judgment result of the intersection density, the density point close to the median position in the intersection interval is selected as the reference value of the planting configuration, and the target planting density benchmark for coverage adaptation is generated. The median value of 0.2496 plants / m 2 Compared with the preset density configuration points 0.1, 0.2, 0.25, 0.35, 0.5 (unit: plant / m 2 ) were used to compare the deviations, and the obtained deviations were 0.1496, 0.0496, 0.0004, 0.1004, and 0.2504, respectively. The minimum deviation value corresponded to the density point of 0.25 plants / m 2 Therefore, it was selected as the target planting density benchmark for coverage adaptation, and the subsequent plant community structure construction and regional nutrient supply configuration plan were arranged accordingly.

[0110] See also Figure 6 The specific steps for obtaining the recommended cycle for the adjustable maintenance interval section are as follows:

[0111] S511: Calling the target planting density benchmark for coverage adaptation, extracting the root zone soil moisture change data of the corresponding vegetation, calculating the moisture content decline slope in a continuous period based on the data in a time series, obtaining the maximum single-day decline rate and constructing a change trend, and generating a root layer moisture decline trend value;

[0112] Call the target planting density benchmark adapted to the coverage rate, extract the root zone soil moisture change data of the corresponding vegetation, calculate the moisture content decline slope in the continuous period based on the data in time series, obtain the maximum single-day decline rate and construct the change trend, generate the root layer moisture decline trend value, and set the target density to 0.25 plants / m 2 The vegetation is sandy shrub type. The root layer soil moisture content data for 60 consecutive days were obtained from the monitoring equipment. The starting moisture content was set at 18% and the minimum value was 6.5%. A set of data was recorded every day. The sliding difference method was used to calculate the moisture content change per unit time and fit the downward trend curve. A concentrated drought period occurred from the 24th to the 30th day, and the moisture content dropped rapidly from 13% to 8.1%. This period was the maximum decline interval, and the maximum single-day decline was 1.2%. This was used as the highest slope point in the root layer moisture decline trend curve, and a trend line was constructed with the daily moisture content change in the entire period. The curve slope stability area was determined by linear regression, and the root layer moisture decline trend value under this vegetation density was obtained. This trend value will be used as the basic indicator for subsequent soil stability assessment and comparison operations.

[0113] S512: Based on the downward trend value of the root layer water content, collect the mass ratio data of soil clay particles with a particle size smaller than the set particle size threshold and the daily rainfall intensity series data in the restoration area, calculate the clay loss rate curve and compare it with the downward trend value of the root layer water content, identify the intersection sequence with equal slopes, and convert the amplitude based on the cumulative offset of rainfall intensity and the clay instability factor in the intersection section using the formula:

[0114]

[0115] The dynamic time span of moisture stability between intersection points is obtained by calculation, which is used as a reference value for the adjustment period and the stability duration value of the intersection section is obtained;

[0116] Among them, T s Indicates the stability time span of the intersection segment, which is used to evaluate the maintenance period of the water retention capacity in the segment. represents the sum of the weighted product of rainfall intensity and clay fraction from day k = 1 to day k = p, p represents the total number of days covered by the rainfall and clay response calculation, is the segment length of the daily time series, and δ k represents the normalized value of the mass ratio of clay particles with particle size smaller than the target particle size in the soil on day k, I k represents the rainfall intensity value on the kth day, in millimeters per hour, θ represents the difference between the maximum slope value and the minimum slope value in the downward trend curve of the soil moisture content in the root zone, ω represents the difference between the maximum loss rate and the minimum loss rate in the clay migration trend curve, dW represents the change value of the root layer soil moisture content per unit time, dL represents the change value of the soil clay mass ratio per unit time, dt represents the time unit interval, usually 1 hour or 1 day, It indicates the rate of change of root layer water content per unit time, in volume percentage per hour. It represents the rate of change of clay content per unit time, in mass percentage per hour. μ represents the compensation constant for moisture fluctuation under the interference of non-rainfall factors, which is often used to adjust the comparison error of actual observation values.

[0117] Collect the mass ratio data of soil clay particles with particle size smaller than the set particle size threshold and the daily rainfall intensity series data in the restoration area, calculate the clay loss rate curve and compare it with the trend value of the root layer water content decline, identify the intersection sequence with equal slopes, and convert the amplitude based on the cumulative offset of rainfall intensity and the clay instability factor in the intersection section using the formula:

[0118]

[0119] The dynamic time span of moisture stability between intersections is obtained by calculation as a reference value for the adjustment period. The stability duration value of the intersection section is obtained. The particle size threshold is set to 2 μm. Rainfall and soil sample data are collected within 5 days. Some monitoring data are shown in Table 3:

[0120] Table 3 Soil clay and rainfall intensity monitoring data

[0121] Number of days k <![CDATA[Clay ratio δ k (dimensionless)]]> <![CDATA[Rainfall intensity I k (mm / h)]]> 1 0.32 4.6 2 0.27 3.2 3 0.30 2.8 4 0.22 5.0 5 0.25 4.4

[0122] As shown in Table 1, we get

[0123]

[0124] θ=1.2,ω=0.9, μ=0.1%,substitute into the formula to get:

[0125]

[0126] The generated intersection segment stability duration value is 8.973 days.

[0127] Formula parameters and logic description: T s is the intersection stability time span, is the weighted total offset intensity of the rainfall-to-clay ratio, θ-ω is the stability conflict value reflected by the difference in slopes of the two types of curves, the numerator as a whole constitutes the spatial amplitude factor, and the denominator is the difference in the dynamic change rates of water and clay, and μ represents the error compensation constant for other factors. Overall, this formula reflects the water stability span at the intersection of the fluctuation trends. The formula is beneficial because it integrates the synchronous change indicators of the dual trend curves and considers the cumulative disturbance and fluctuation differences after the offset response, effectively improving the sensitivity and accuracy of water stability identification. The results indicate that water retention at the intersection can maintain a relatively stable state for approximately 9 days, providing a periodic boundary basis for maintenance decisions.

[0128] S513: Based on the stability duration of the intersection section, the shortest time period is selected as the lower limit of the cycle. The rainfall frequency trend per unit time and the transpiration rate recovery capacity of the restoration zone vegetation are combined to perform cycle mapping, which is converted into a recommended maintenance operation cycle length interval to obtain the recommended cycle for the adjustable maintenance interval section.

[0129] According to the stability duration of the intersection section, the shortest time period was selected as the lower limit of the cycle. The cycle mapping was performed based on the rainfall frequency trend per unit time and the transpiration rate recovery capacity of the vegetation in the restoration zone. The cycle was converted into a recommended interval for the maintenance operation cycle length, and the recommended cycle for the adjustable maintenance interval section was obtained. The 12 slope intersection sections monitored throughout the year in the region were analyzed. The shortest was 8.973 days and the longest was 15.2 days. The rainfall triggering frequency in each section showed a monthly decreasing trend, and the transpiration recovery lag value in the region was 3.5 days. Based on this, the lower limit of the cycle was established as 8 days, the upper limit was set as 15 days, and the recommended cycle segment was constructed in the form of a natural integer value as [8,15] days.

[0130] The vegetation and soil and water ecological restoration optimization system based on multi-source data fusion is used to implement the vegetation and soil and water ecological restoration optimization method based on multi-source data fusion. The system includes:

[0131] The evapotranspiration water supply analysis module obtains the evapotranspiration data of the restoration area, extracts the interval range consisting of the maximum and minimum evapotranspiration values ​​within a month, constructs the regional water supply capacity segment throughout the year by comparing the maximum difference, and generates the evapotranspiration water supply capacity segment sequence;

[0132] The adaptive vegetation screening module calls the evapotranspiration water supply capacity segment sequence to determine the overlap duration of the water consumption characteristic curve and the evapotranspiration segment and the corresponding evapotranspiration difference. If the difference duration exceeds the set evapotranspiration imbalance threshold, the corresponding vegetation species is excluded to obtain a list of vegetation that can adapt to the water conditions.

[0133] The maximum density analysis module calls a list of vegetation that can adapt to moisture conditions, calculates the maximum vegetation planting density based on the total sustainable nutrient release of the soil per unit area and the annual absorption of a single plant, and obtains the maximum planting density value that can be sustainably supported;

[0134] The plant density benchmark identification module calls the maximum sustainable planting density value, calculates the average annual plant density required based on the coverage target value and the restoration years, makes an intersection judgment with the maximum planting density, and selects the average value of the intersection as the planting configuration standard density to obtain the target planting density benchmark for coverage adaptation;

[0135] The maintenance cycle adjustment module calls the target planting density benchmark for coverage adaptation, and by judging the intersection of the clay migration rate caused by rainfall erosion intensity and the slope of water content decrease, marks the time node as the critical point of root layer water supply, and sets the maintenance and water replenishment adjustment cycle according to the minimum time interval between nodes, and obtains the recommended cycle for the adjustable maintenance interval section.

[0136] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. The vegetation and soil ecological restoration optimization method based on multi-source data fusion is characterized by: The following steps are involved: S1: Obtain evapotranspiration data for the restoration area, extract the interval range consisting of the maximum and minimum evapotranspiration values ​​within a month, construct the regional water supply capacity segment throughout the year by comparing the maximum difference, and generate the evapotranspiration water supply capacity segment sequence; S2: calling the evapotranspiration water supply capacity segment sequence, determining the overlap duration of the water consumption characteristic curve and the evapotranspiration segment and the corresponding evapotranspiration difference; if the duration of the difference exceeds a set evapotranspiration imbalance threshold, excluding the corresponding vegetation species, and obtaining a list of vegetation that can adapt to the water conditions; S3: Calling the vegetation list that can adapt to the water conditions, calculating the maximum vegetation planting density based on the total sustainable nutrient release of the soil per unit area and the annual absorption of a single plant, and obtaining the maximum planting density value that can be sustainably supported; S4: Call the maximum planting density value that can be sustainably supported, calculate the average annual plant density requirement based on the coverage target value and the restoration period, make an intersection judgment with the maximum planting density, and select the median value of the intersection as the standard planting configuration density to obtain a target planting density benchmark that is adapted to the coverage rate.

2. The vegetation and soil ecological restoration optimization method based on multi-source data fusion according to claim 1 is characterized in that: The evapotranspiration water supply capacity segment sequence includes the monthly average potential evapotranspiration extreme range segment, the annual evapotranspiration upper and lower limit alternating pattern and the water supply stability bandwidth characteristics; the vegetation list that can adapt to moisture conditions includes plant species compatible with transpiration intensity, species with maximum water supply overlap ratio and controllable evapotranspiration differential response communities; the maximum sustainable supported planting density value includes the limit of soil nutrient release capacity per unit area, the annual plant absorption load capacity and the annual nutrient balance critical value; the target planting density benchmark for coverage adaptation includes the minimum annual average plant number requirement, the restoration year and density function value and the median of coverage density control.

3. The vegetation and soil ecological restoration optimization method based on multi-source data fusion according to claim 2 is characterized in that: The steps for obtaining the evapotranspiration water supply capacity segment sequence are specifically as follows: S111: Obtain evapotranspiration data for the restoration area, calculate the maximum and minimum potential evapotranspiration values ​​based on the daily evapotranspiration records in the monthly data, extract the maximum and minimum evapotranspiration values, construct the two types of maximum and minimum values ​​into upper and lower boundary interval sets, and generate monthly evapotranspiration boundary interval values; S112: Based on the monthly evapotranspiration boundary interval values, respectively calculating the difference between the potential evapotranspiration boundary and the actual evapotranspiration boundary for each month throughout the year, dividing the monthly potential difference and the actual difference into sets, and extracting the maximum interval span in the two sets to obtain the annual evapotranspiration range interval width value; S113: Calling the annual evapotranspiration range width value, performing segment stability judgment on the boundary difference sequence of each month throughout the year, constructing a segment sequence of annual water supply capacity based on the difference fluctuation amplitude, and establishing an evapotranspiration water supply capacity segment sequence.

4. The vegetation and soil ecological restoration optimization method based on multi-source data fusion according to claim 3 is characterized in that: The steps for obtaining the vegetation list that can adapt to moisture conditions are as follows: S211: Calling the evapotranspiration water supply capacity segment sequence to obtain the transpiration rate per unit leaf area and the soil moisture content in the root zone of the candidate vegetation, integrating the two data into the total water consumption data of each plant on a monthly basis according to the time series, and drawing a water resource consumption change sequence curve for each vegetation on a monthly basis to generate a vegetation water consumption characteristic change trend value; S212: Based on the vegetation water consumption characteristic change trend value, the water consumption change curve of the candidate vegetation is compared with the monthly segments in the evapotranspiration water supply capacity segment sequence, the duration and extent of the water consumption value exceeding the evapotranspiration upper limit in each month are calculated, and the longest time period of the difference in each month is calculated to obtain the evapotranspiration supply and demand imbalance duration period value; S213: Based on the evapotranspiration supply and demand imbalance duration value and the set evapotranspiration imbalance threshold, a judgment is made item by item, and the vegetation species with a difference duration greater than the threshold are marked as unadaptable groups, and the remaining species are formed into a screening list to obtain a list of vegetation that can adapt to the moisture conditions.

5. The vegetation and soil ecological restoration optimization method based on multi-source data fusion according to claim 4 is characterized in that: The specific steps for obtaining the maximum sustainable planting density value are as follows: S311: Retrieving the list of vegetation that can adapt to the water conditions, extracting the maximum effective root depth, nitrogen absorption rate per unit root length, phosphorus absorption rate per unit root length, potassium absorption rate per unit root length, and saturated nutrient requirement of the remaining vegetation, calculating the total absorption of each indicator per unit time on a monthly basis and converting it into an annual absorption value to generate the total annual nutrient absorption value of the vegetation; S312: Based on the total annual nutrient uptake of vegetation, soil particle composition parameters, organic matter mineralization rate, and available nutrient release period of the target restoration area are collected. Based on the nutrient release potential corresponding to the particle ratio and mineralization rate, the total amount of nitrogen, phosphorus, and potassium that can be released per unit area on an annual scale is calculated, and the maximum supportable planting density per unit area is calculated. S313: Based on the maximum supportable planting density per unit area, proportional conversion is performed with the land area and planting type of the target area, configurations exceeding the density upper limit are eliminated, and the upper and lower limits of the adapted planting density range are determined to obtain the maximum planting density value that can be sustainably supported.

6. The vegetation and soil ecological restoration optimization method based on multi-source data fusion according to claim 5 is characterized in that: The steps for obtaining the target planting density benchmark for coverage adaptation are specifically as follows: S411: Calling the maximum sustainable planting density value, setting the vegetation coverage years and coverage rate target values ​​of the target restoration area, calculating the annual average plant density requirement using the land area, coverage rate target value and coverage years, and generating the annual average coverage density requirement value; S412: Based on the annual average coverage density requirement value, a numerical intersection judgment is performed with the maximum planting density value that can be sustainably supported, an intersection interval between the two is calculated, and a determination is made as to whether there are continuous sections in the intersection interval with positive differences between the upper and lower limits. A median derived value of the intersection interval is obtained by calculation to generate a median intersection density judgment result; S413: Based on the median judgment result of the intersection density, a density point close to the median position in the intersection interval is selected as a planting configuration reference benchmark value to generate a target planting density benchmark for coverage adaptation.

7. The vegetation and soil ecological restoration optimization method based on multi-source data fusion according to claim 6 is characterized in that: The method further comprises the following steps: S5: calling the target planting density benchmark adapted to the coverage rate, determining the intersection of the clay migration rate caused by rainfall erosion intensity and the water content decrease slope, marking the time node as the critical point of root layer water supply, setting the maintenance and water replenishment adjustment cycle according to the minimum time interval between nodes, and obtaining the recommended cycle for the adjustable maintenance interval section; The recommended period for the adjustable maintenance interval section specifically includes the water decay starting node, the minimum water replenishment interval length, and the root layer water imbalance warning daily threshold.

8. The vegetation and soil ecological restoration optimization method based on multi-source data fusion according to claim 7 is characterized in that: The steps for obtaining the recommended period for the adjustable maintenance interval section are as follows: S511: calling the target planting density benchmark adapted to the coverage ratio, extracting the root zone soil moisture change data of the corresponding vegetation, calculating the moisture content decline slope in a continuous period based on the data in a time series, obtaining the maximum single-day decline rate and constructing a change trend, and generating a root layer moisture decline trend value; S512: Based on the root zone moisture decline trend value, collect soil clay mass ratio data with a particle size less than a set particle size threshold and daily rainfall intensity sequence data within the restoration area, calculate the clay loss rate curve and compare the trend with the root zone moisture decline trend value, identify the intersection sequence with equal slopes, and perform amplitude conversion based on the cumulative offset of rainfall intensity and the clay instability factor within the intersection segment. Calculate and obtain the dynamic time span of moisture stability between the intersections as a reference value for the adjustment period duration, and obtain the stability duration value of the intersection segment; S513: Based on the stability duration value of the intersection section, the shortest time period is selected as the lower limit of the cycle, and the cycle mapping is performed in combination with the rainfall frequency trend per unit time and the transpiration rate recovery capacity of the vegetation in the restoration zone. The cycle is converted into a recommended interval for the maintenance operation cycle length, and the recommended cycle for the adjustable maintenance interval section is obtained.

9. The vegetation and soil ecological restoration optimization system based on multi-source data fusion is characterized by: The system is used to implement the vegetation and soil ecological restoration optimization method based on multi-source data fusion according to any one of claims 1 to 8, and the system includes: The evapotranspiration water supply analysis module obtains the evapotranspiration data of the restoration area, extracts the interval range consisting of the maximum and minimum evapotranspiration values ​​within a month, constructs the regional water supply capacity segment throughout the year by comparing the maximum difference, and generates the evapotranspiration water supply capacity segment sequence; The adaptive vegetation screening module calls the evapotranspiration water supply capacity segment sequence, determines the overlap duration of the water consumption characteristic curve and the evapotranspiration segment and the corresponding evapotranspiration difference, and excludes the corresponding vegetation species if the difference duration exceeds the set evapotranspiration imbalance threshold, thereby obtaining a list of vegetation that can adapt to the water conditions; The maximum density analysis module calls the vegetation list that can adapt to the moisture conditions, calculates the maximum vegetation planting density based on the total sustainable nutrient release of the soil per unit area and the annual absorption of a single plant, and obtains the maximum planting density value that can be sustainably supported; The planting density benchmark identification module calls the maximum sustainable planting density value, calculates the average annual plant density required based on the coverage target value and the restoration years, makes an intersection judgment with the maximum planting density, and selects the average value of the intersection as the planting configuration standard density to obtain the target planting density benchmark for coverage adaptation; The maintenance cycle adjustment module calls the target planting density benchmark adapted to the coverage rate, and by judging the intersection of the clay migration rate caused by rainfall erosion intensity and the slope of water content decrease, marks the time node as the critical point of root layer water supply, and sets the maintenance and water replenishment adjustment cycle according to the minimum time interval between nodes to obtain the recommended cycle for the adjustable maintenance interval section.

Images