Regional ecological restoration potential assessment method based on multi-dimensional data

Abstract

The invention relates to the technical field of ecological assessment, in particular to a regional ecological restoration potential assessment method based on multi-dimensional data, which comprises the following steps: collecting pH value, moisture and nutrients; calculating microcosmic abnormal indexes and types, and determining microcosmic sub-regions; collecting the vegetation index and the coverage degree of the microscopic sub-region; calculating a mesoscopic anomaly index, and determining a mesoscopic sub-region; collecting rainfall and temperature of the mesoscopic sub-region; calculating a macroscopic adaptation index, and determining an index mode; calculating a potential index, and determining a macroscopic abnormal sub-region; calculating the aggregation degree of the macroscopic sub-regions, and adjusting the weight; and generating a recovery potential evaluation report. According to the invention, through step-by-step acquisition and analysis of multi-dimensional data, microscopic environment indexes, vegetation conditions and meteorological conditions are effectively integrated, a hierarchical evaluation system from microcosmic to macroscopic is formed, and the problem of low accuracy of evaluation of regional vegetation recovery potential caused by dependence on a single static index and a rough model is effectively solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ecological assessment, and particularly relates to a regional ecological restoration potential assessment method based on multi-dimensional data. BACKGROUND

[0002] With the continuous change of global ecological environment and the continuous intensification of human activities, the stability and restoration capacity of regional ecosystems are seriously affected, and the problem of ecological degradation is increasingly prominent. In the face of complex natural conditions and environmental disturbances, how to scientifically and systematically assess the ecological restoration potential of different regions has become a key challenge in the process of ecological governance and sustainable development.

[0003] Chinese patent application publication No. CN120297548A discloses a power transmission corridor vegetation carbon sink restoration potential assessment method and system. The method comprises: establishing a base database of a target region to be constructed of the power transmission corridor; obtaining vector space data and statistical data of the target region, including topographic and geomorphic vector maps, power infrastructure distribution data, meteorological data, and vegetation distribution data; according to the operation safety requirements of the power transmission line and the vegetation classification system, marking the target region as a plurality of sub-regions, and associating voltage levels, line corridor lengths, tower base numbers, and minimum vertical distance parameters between conductors and trees; combining land use to generate recommended vegetation types, vegetation height limits, carbon densities, and power grid safety correction factor data for each sub-region; dividing land units and matching vegetation schemes: based on the change of land use before and after the construction of the power transmission corridor, each sub-region is divided into land units; at least one recommended vegetation type scheme is matched for each land unit, and the corresponding carbon sink accounting factor is associated; accounting for carbon sink restoration potential: based on the carbon sink measurement method, the carbon sink loss amount of each land unit due to the destruction of the original vegetation by the construction of the power transmission corridor is calculated; combining the power grid safety correction factor, the total amount of new carbon sinks of each land unit under the recommended vegetation scheme is calculated; the overall vegetation carbon sink restoration potential of the power transmission corridor is evaluated by a net carbon sink amount formula.

[0004] It can be seen that the power transmission corridor vegetation carbon sink restoration potential assessment method has the following problems: the data acquisition and processing of the method are complex, involving multi-source spatial data, statistical data, and vegetation classification information, and the data integration and updating are difficult, which may lead to lagging or inaccurate evaluation results; in the sub-region division and land unit matching process, only static land use and recommended vegetation types are relied on, and the dynamic response capability to environmental factor changes (such as climate, moisture conditions, and soil nutrients) is lacking, which cannot accurately reflect the vegetation growth suitability; the carbon sink accounting process is based on static carbon density and correction factors, and lacks comprehensive consideration of micro and meso ecological conditions, which cannot capture the differences in vegetation restoration potential at different scales; the application of the power grid safety correction factor is relatively rough, and the influence of different terrains, line corridor characteristics, and tower base surrounding environment on vegetation growth and carbon sink restoration is not fully considered. SUMMARY

[0005] To this end, the present application provides a method for assessing regional ecological restoration potential based on multi-dimensional data, which overcomes the problem of low accuracy in assessing regional vegetation restoration potential due to the reliance on single static indicators and rough models in the prior art by comprehensive collection and dynamic analysis of micro, meso and macro ecological parameters.

[0006] To achieve the above-mentioned purpose, in one aspect, the present application provides a method for assessing regional ecological restoration potential based on multi-dimensional data, comprising: collecting pH value, water content and nutrient content of each to-be-observed sub-region in the observation region in real time; determining micro-abnormality index and micro-abnormality type according to the pH value, the water content and the nutrient content, and determining a plurality of micro-abnormality sub-regions according to the micro-abnormality index; collecting normalized vegetation index and vegetation coverage of each micro-abnormality sub-region in real time; determining meso-abnormality index according to the normalized vegetation index and the vegetation coverage, and determining a plurality of meso-abnormality sub-regions according to the meso-abnormality index and the micro-abnormality type; collecting precipitation and temperature in each meso-abnormality sub-region in real time; determining macro-adaptation index according to the precipitation and the temperature, and determining index calculation mode according to the meso-abnormality index and the micro-abnormality index; determining potential index according to the index calculation mode, the macro-adaptation index, the meso-abnormality index, the micro-abnormality index and a preset potential weight set, and determining a plurality of macro-abnormality sub-regions according to the potential index; determining aggregation degree according to the distribution position of the macro-abnormality sub-region, and adjusting the preset potential weight set according to the aggregation degree and the index calculation mode; generating a restoration potential assessment report according to the potential index determined after adjusting the preset potential weight set.

[0007] Further, the process of determining micro-abnormality index and micro-abnormality type according to the pH value, the water content and the nutrient content comprises: comparing the pH value, the water content, the nutrient content of each to-be-observed sub-region with a respective preset micro-threshold value to obtain a plurality of corresponding micro-deviation degrees; calculating the micro-abnormality index according to each micro-deviation degree and a preset micro-weight set; determining the micro-abnormality type according to the highest value among all the micro-deviation degrees.

[0008] Further, the process of determining a plurality of micro-abnormal sub-regions according to the micro-abnormal index comprises: When the micro-abnormal index is greater than a preset micro-abnormal threshold, the sub-region to be observed is determined as the micro-abnormal sub-region.

[0009] Further, the process of determining a meso-abnormal index according to the normalized vegetation index and the vegetation coverage comprises: The normalized vegetation index and the vegetation coverage of each micro-abnormal sub-region are compared with corresponding preset meso-thresholds, respectively, to obtain a plurality of corresponding meso-deviations; The meso-abnormal index is calculated according to each meso-deviation and a preset meso-weighting group.

[0010] Further, the process of determining a plurality of meso-abnormal sub-regions according to the meso-abnormal index and the micro-abnormal type comprises: When the meso-abnormal index is greater than a preset meso-abnormal threshold, the abnormal cause type of the micro-abnormal sub-region is determined according to the corresponding micro-abnormal type; A plurality of meso-abnormal sub-regions are determined according to the abnormal cause types of other micro-abnormal sub-regions within a preset determination range centered on each micro-abnormal sub-region.

[0011] Further, the process of determining a plurality of meso-abnormal sub-regions according to the abnormal cause types of other micro-abnormal sub-regions within a preset determination range centered on each micro-abnormal sub-region comprises: The micro-abnormal index of the micro-abnormal sub-region is sequentially taken as a center region from high to low; When the center region and the abnormal cause types of other micro-abnormal sub-regions within the preset determination range are the same, the center region and the other micro-abnormal sub-regions are merged to generate the meso-abnormal sub-region; When the spatial overlap rate of the newly generated meso-abnormal sub-region and the existing meso-abnormal sub-region is greater than a preset overlap threshold, they are merged into one meso-abnormal sub-region; After merging into one meso-abnormal sub-region, the meso-abnormal index of the merged meso-abnormal sub-region is updated based on the weighted average of the areas and meso-abnormal indexes of each meso-abnormal sub-region before merging; Wherein, the merged micro-abnormal sub-region no longer participates in subsequent merging.

[0012] Further, the process of determining a macro-adaptation index according to the precipitation and the temperature, and determining an index calculation mode according to the meso-abnormal index and the micro-abnormal index comprises: comparing the precipitation and the temperature of each meso-anomaly sub-region with corresponding preset macro threshold values to obtain a plurality of corresponding macro deviation degrees; calculating the macro adaptation index according to each macro deviation degree and a preset macro weight set; determining that the index calculation mode is a meso-macro mode when the average of all the meso-anomaly indexes is greater than a preset meso-average threshold value and the average of all the micro-anomaly indexes is within a preset micro-average threshold value range; determining that the index calculation mode is a micro-macro mode when the average of all the micro-anomaly indexes is greater than a maximum value of the preset micro-average threshold value range and the average of all the meso-anomaly indexes is less than or equal to the preset meso-average threshold value; determining that the index calculation mode is a micro-meso mode when the average of all the meso-anomaly indexes is greater than the preset meso-average threshold value and the average of all the micro-anomaly indexes is greater than the maximum value of the preset micro-average threshold value range.

[0013] Further, the process of determining a potential index according to the index calculation mode, the macro adaptation index, the meso-anomaly index, the micro-anomaly index, and a preset potential weight set, and determining a plurality of macro-anomaly sub-regions according to the potential index includes: when the index calculation mode is the meso-macro mode, calculating the potential index according to the macro adaptation index, the meso-anomaly index, and the preset potential weight set; when the index calculation mode is the micro-macro mode, calculating the potential index according to the macro adaptation index, the micro-anomaly index, and the preset potential weight set; when the index calculation mode is the micro-meso mode, calculating the potential index according to the meso-anomaly index, the micro-anomaly index, and the preset potential weight set; determining that the meso-anomaly sub-region is the macro-anomaly sub-region when the potential index is greater than a preset potential index threshold value.

[0014] Further, the process of determining a concentration according to the distribution position of the macro-anomaly sub-region and adjusting the preset potential weight set according to the concentration and the index calculation mode includes: calculating the Euclidean distance from the center of each macro-anomaly sub-region to a preset reference point to obtain a plurality of distribution distances; calculating the reciprocal of the standard deviation of all the distribution distances to obtain the concentration; when the concentration is greater than a preset concentration threshold value, counting the number of potential indexes calculated according to the meso-macro mode, the micro-macro mode, and the micro-meso mode to obtain a meso-macro number, a micro-macro number, and a micro-meso number; adjust the preset potential weight group according to the maximum value, the aggregation degree and the preset aggregation threshold of the macro quantity, the micro quantity and the micro-macro quantity.

[0015] In another aspect, the present application also provides a regional ecological restoration potential evaluation system based on multi-dimensional data, comprising: The first acquisition module is configured to acquire the pH value, water content and nutrient content of each to-be-observed sub-region in the observation region in real time. The micro-determination module is connected with the first acquisition module and configured to determine a micro abnormality index and a micro abnormality type according to the pH value, the water content and the nutrient content, and determine a plurality of micro abnormality sub-regions according to the micro abnormality index. The second acquisition module is connected with the micro-determination module and configured to acquire the normalized vegetation index and the vegetation coverage of each micro abnormality sub-region in real time. The meso-determination module is connected with the second acquisition module and configured to determine a meso abnormality index according to the normalized vegetation index and the vegetation coverage, and determine a plurality of meso abnormality sub-regions according to the meso abnormality index and the micro abnormality type. The third acquisition module is connected with the meso-determination module and configured to acquire the precipitation and the temperature in each meso abnormality sub-region in real time. The mode-determination module is connected with the third acquisition module, the micro-determination module and the meso-determination module respectively, and configured to determine a macro adaptation index according to the precipitation and the temperature, and determine an index calculation mode according to the macro adaptation index, the meso abnormality index and the micro abnormality index. The macro-determination module is connected with the mode-determination module and configured to determine a potential index according to the index calculation mode, the macro adaptation index, the meso abnormality index, the micro abnormality index and a preset potential weight group, and determine a plurality of macro abnormality sub-regions according to the potential index. The adjustment module is connected with the macro-determination module and configured to determine an aggregation degree according to the distribution position of the macro abnormality sub-regions, and adjust the preset potential weight group according to the aggregation degree and the index calculation mode. The generation module is connected with the macro-determination module and configured to generate a restoration potential evaluation report according to the potential index determined after the preset potential weight group is adjusted.

[0016] Compared with the prior art, the beneficial effects of the present application are that, by hierarchical collection and analysis of multi-dimensional data, the microenvironment indicators, vegetation conditions and meteorological conditions are effectively integrated to form a hierarchical evaluation system from micro to macro. At the micro level, the potential stress and abnormal characteristics of soil and vegetation are revealed by using pH, water content and nutrient content; at the meso level, the spatial aggregation and health status of ecological response are reflected by using normalized difference vegetation index and coverage; at the macro level, the adaptability of the region to the climate condition is considered by combining precipitation and temperature. Through dynamic selection of index calculation mode and weight adjustment driven by aggregation degree, fine calculation of potential index is realized, so that the interaction between each layer index can fully reflect the contribution and constraint relationship of different ecological factors, and the final output of the restoration potential evaluation result can accurately reflect the possibility and priority repair area of regional ecological restoration, providing quantitative basis for scientific formulation of ecological management and restoration strategy, and effectively solving the problem of low accuracy of vegetation restoration potential evaluation of the evaluation region due to relying on single static index and rough model.

[0017] Further, by comparing the pH, water content and nutrient content of each sub-region with the preset threshold, the deviation degree of the micro environment is quantified, and the micro abnormal index is calculated by combining the weight, which can accurately reflect the local stress and potential abnormal situation of the soil environment; at the same time, by selecting the highest value of the deviation degree to determine the micro abnormal type, the most critical environmental limiting factor can be identified, which provides a scientific basis for subsequent meso and macro level restoration potential evaluation, and realizes effective connection and quantitative analysis from micro environmental conditions to ecological restoration potential.

[0018] Further, by setting the micro abnormal threshold and comparing the micro abnormal index with the micro abnormal threshold, the embodiment can effectively identify the sub-regions where the key factors such as soil pH, water content or nutrients deviate significantly from the suitable range, mark these regions as micro abnormal sub-regions, and reflect the mutual influence and constraint relationship between each environmental parameter, for example, insufficient soil moisture may exacerbate nutrient absorption limitation, and acid-base imbalance may affect plant root growth, thereby accurately identifying the key areas limited by ecological restoration at the micro level, providing a reliable basis for subsequent meso and macro level comprehensive analysis, and realizing fine division of potential evaluation.

[0019] Further, by comparing the normalized vegetation index and vegetation coverage of the micro abnormal sub-region with the preset meso threshold, the deviation degree of the vegetation growth condition of each region from the ideal growth condition can be quantified; by combining the meso abnormal index calculated by the preset meso weight group, the contribution of different vegetation indicators to the ecological restoration potential can be comprehensively reflected, the vegetation growth deficiency or abnormal region can be accurately identified, and quantitative basis can be provided for subsequent meso and macro level ecological evaluation, so that the correlation between vegetation condition, coverage and soil, water and other factors can be reasonably reflected.

[0020] Further, by combining the mesoscale anomaly index with the microscale anomaly type and aggregating the anomaly cause types of adjacent sub-regions centered on the microscale anomaly sub-region, the embodiment can identify continuous regions with the same ecological restriction reason at the mesoscale, thereby reflecting the cumulative effect of microscale anomaly factors on ecological restoration at a larger spatial scale, and achieving accurate division of potential obstacle regions, providing a reliable spatial basis for subsequent macro-level restoration potential assessment.

[0021] Further, by sequentially merging adjacent microscale anomaly sub-regions with the same anomaly cause type centered on the microscale anomaly sub-region with a higher microscale anomaly index, a continuous region with clear ecological anomaly characteristics can be formed at the mesoscale, and the mesoscale anomaly index is updated by area-weighted average for spatially overlapping parts, so that the generated mesoscale anomaly sub-region can truly reflect the strength and distribution of local ecological pressure, taking into account anomaly intensity and spatial continuity, providing more accurate and stable basic data for subsequent potential assessment.

[0022] Further, by converting precipitation and temperature deviation into a macro-adaptation index and combining the average of mesoscale and microscale anomaly indexes for hierarchical judgment, dynamic adaptation of multi-scale environmental factors is achieved. When the mesoscale anomaly characteristics are significant and the microscale fluctuations are within a reasonable range, the medium-macro mode is entered to highlight the dominant role of regional overall environment on macro-adaptation; when the microscale anomaly is significant and the mesoscale change is weak, the micro-macro mode is entered to emphasize the influence of local details on macro-performance; when both mesoscale and microscale anomalies are significant, the micro-mesoscale mode is entered to reflect the superimposed effect of multi-level factors. This method enables flexible switching of judgment logic through mutual constraint and weighted combination of parameters, ensuring that the index calculation results can reflect both overall trends and local anomalies, thereby improving the accuracy and robustness of adaptability assessment.

[0023] Further, by introducing macro-adaptation index, mesoscale anomaly index, microscale anomaly index and preset potential weight set for combined operation under different index calculation modes, the dynamic balance of cause-effect relationship and hierarchical progression between multi-level indicators can be achieved, so that the potential index can reflect the adaptability of macro-environmental conditions and integrate mesoscale and microscale anomaly fluctuation characteristics, thereby accurately locking the macro-anomaly sub-region when the potential index exceeds the preset threshold, avoiding deviation caused by single parameter dominance, and improving the accuracy and stability of regional anomaly identification.

[0024] Further, the aggregation degree is obtained by calculating the reciprocal of the distribution distance and the standard deviation of the macroscopic abnormal sub-region relative to the preset reference point, which can intuitively reflect the spatial concentration degree of the abnormal region; when the aggregation degree exceeds the preset threshold, the number of potential indexes under different calculation modes is counted, which is helpful to identify the main factors affecting the ecological restoration potential at the micro, meso and macro levels; based on the aggregation degree, mode distribution and threshold, the preset potential weight set is dynamically adjusted, which can reasonably enhance or weaken the influence of each level index in the calculation of the potential index, so that the potential index can more accurately reflect the actual situation of regional ecological restoration, and ensure that the evaluation result can take into account the spatial distribution characteristics and the comprehensive effect of multi-dimensional indexes.

[0025] Further, through multi-level and multi-dimensional data collection and analysis, key ecological parameters such as soil pH, moisture content, nutrient content, vegetation index, vegetation coverage, precipitation and temperature can be comprehensively processed to realize hierarchical identification and aggregation of micro, meso and macro anomalies; through the calculation of potential index and the adjustment of aggregation degree, the ecological anomaly weights of different scales and types are reasonably distributed, so as to accurately reflect the ecological restoration potential of each region, realize the fine judgment and scientific regulation of potential risk areas, and provide quantitative, dynamic and operable decision basis for regional ecological restoration. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 Flowchart of the regional ecological restoration potential evaluation method based on multi-dimensional data of the present embodiment; Figure 2 Determination logic diagram for determining the micro abnormal sub-region of the present embodiment; Figure 3 Determination logic diagram for determining the abnormal cause type of the present embodiment; Figure 4 Determination logic diagram for determining the meso and macro modes of the present embodiment. DETAILED DESCRIPTION

[0027] In order to make the purpose and advantages of the present application clearer and more apparent, the present application will be further described below in conjunction with the embodiments; it should be understood that the specific embodiments described herein are only used to explain the present application, and do not limit the present application.

[0028] The preferred embodiments of the present application will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application, and are not intended to limit the protection scope of the present application.

[0029] On the one hand, please refer to Figure 1As shown, it is a flowchart of the regional ecological restoration potential evaluation method based on multi-dimensional data of the embodiment. The embodiment provides a regional ecological restoration potential evaluation method based on multi-dimensional data, which comprises the following steps: collecting the pH value, water content and nutrient content of each to-be-observed sub-region in the observation region in real time; determining the micro-abnormal index and micro-abnormal type according to the pH value, the water content and the nutrient content, and determining a plurality of micro-abnormal sub-regions according to the micro-abnormal index; collecting the normalized vegetation index and vegetation coverage of each micro-abnormal sub-region in real time; determining the meso-abnormal index according to the normalized vegetation index and the vegetation coverage, and determining a plurality of meso-abnormal sub-regions according to the meso-abnormal index and the micro-abnormal type; collecting the precipitation and temperature in each meso-abnormal sub-region in real time; determining the macro-adaptation index according to the precipitation and the temperature, and determining an index calculation mode according to the meso-abnormal index and the micro-abnormal index; determining a potential index according to the index calculation mode, the macro-adaptation index, the meso-abnormal index, the micro-abnormal index and a preset potential weight set, and generating a restoration potential evaluation report according to the potential index.

[0030] In the embodiment, the micro-abnormal type refers to the local ecological state abnormal type determined according to the soil parameters such as pH value, water content and nutrient content, the meso-abnormal type refers to the regional ecological abnormal type determined on the basis of the micro-abnormal sub-region in combination with the normalized vegetation index and the vegetation coverage, the macro-abnormal type refers to the overall ecological abnormal type determined on the basis of the meso-abnormal sub-region in combination with the precipitation and the temperature, and the potential index comprehensively reflects the ecological restoration potential of each sub-region by combining the macro-adaptation index, the meso-abnormal index and the micro-abnormal index and combining the preset weight, so as to quantify the restoration priority and potential difference of different regions.

[0031] In the embodiment, the data collection is realized by multi-source sensing and remote sensing technology. Specifically, the soil monitoring device is used to acquire the pH value, water content and nutrient content of each sub-region in real time; for the micro-abnormal sub-region, the normalized vegetation index and vegetation coverage are extracted by calling the unmanned aerial vehicle or satellite remote sensing data; for the meso-abnormal sub-region, the precipitation and temperature are acquired in real time by the meteorological monitoring device, thereby forming multi-dimensional and multi-level dynamic observation data.

[0032] The preset potential weight set includes a macroscopic adaptation index weight, a mesoscopic anomaly index weight, and a microscopic anomaly index weight. The macroscopic adaptation index weight depends on the influence degree of the regional climate adaptation capability, is usually set between 0 and 1, is set as 0.4 in the embodiment, and can reflect the contribution of the climate factor in the potential index calculation; the mesoscopic anomaly index weight depends on the importance of the vegetation coverage and the health status, is usually set between 0 and 1, is set as 0.35 in the embodiment, and can reflect the influence of the mesoscopic ecological response on the recovery potential; and the microscopic anomaly index weight depends on the abnormal degree of the soil environment, is usually set between 0 and 1, is set as 0.25 in the embodiment, and can reflect the contribution of the microscopic environmental pressure to the potential evaluation.

[0033] In the embodiment, by adjusting the macroscopic adaptation index weight, the mesoscopic anomaly index weight, and the microscopic anomaly index weight, the contributions of the indexes at each layer in the potential index calculation are optimized, the potential index calculated again can comprehensively reflect the comprehensive action of the soil environment, the vegetation state, and the climate adaptation capability, and the recovery potential evaluation report finally generated clearly indicates the recovery potential level and the priority repair order of each region, thereby providing a quantitative basis for the ecological management and repair decision.

[0034] By the step-by-step collection and analysis of the multi-dimensional data, the microscopic environmental index, the vegetation state, and the meteorological condition are effectively integrated to form a hierarchical evaluation system from the microscopic to the macroscopic. At the microscopic layer, the potential pressure and the abnormal characteristics of the soil and the vegetation are revealed by the pH value, the water content, and the nutrient content; at the mesoscopic layer, the spatial aggregation and the health status of the ecological response are reflected by the normalized difference vegetation index and the coverage; and at the macroscopic layer, the adaptation capability of the region to the climate condition is considered in combination with the precipitation and the temperature. Through the dynamic selection of the index calculation mode and the weight adjustment driven by the aggregation degree, the fine calculation of the potential index is realized, the interaction between the indexes at each layer can fully reflect the contribution and the constraint relationship of different ecological factors, and the recovery potential evaluation result finally output can accurately reflect the possibility of the regional ecological recovery and the priority repair region, thereby providing a quantitative basis for scientifically formulating the ecological management and repair strategy, and effectively solving the problem of low accuracy of the evaluation of the vegetation recovery potential of the region due to the dependence on only a single static index and a rough model.

[0035] Specifically, the process of determining the microscopic anomaly index and the microscopic anomaly type according to the pH value, the water content, and the nutrient content includes: comparing the pH value, the water content, the nutrient content, and the respective preset microscopic threshold value of each of the to-be-observed sub-regions to obtain a plurality of corresponding microscopic deviation degrees; calculating the microscopic anomaly index according to each of the microscopic deviation degrees and a preset microscopic weight set; and determining the microscopic anomaly type according to the maximum value in all the microscopic deviation degrees.

[0036] In this embodiment, the preset micro threshold values include pH threshold value, water content threshold value, and nutrient content threshold value. The pH threshold value is used to determine the soil acid-base balance condition, is determined according to the suitable range of crop or vegetation growth, is usually set between 5.5 and 7.5, and is set to 6.0 to 7.5 in this embodiment, and can identify abnormal areas of soil acidification or alkalization. The water content threshold value is used to evaluate the soil moisture condition, is determined according to the soil type and precipitation condition, is usually set between 15% and 40%, and is set to 20% to 35% in this embodiment, and can determine the influence of soil drought or excessive moisture on ecological restoration. The nutrient content threshold value is used to measure the soil nutrient condition, is determined according to the soil background value of nitrogen, phosphorus, potassium and the like, is usually set between 10 mg / kg and 50 mg / kg, and is set to 15 mg / kg to 45 mg / kg in this embodiment, and can identify micro abnormal areas of soil nutrient deficiency or excess.

[0037] In this embodiment, the micro deviation degree is obtained by calculating the absolute value of the difference between the pH and the pH threshold value, or the absolute value of the difference between the water content threshold value and the water content threshold value, or the absolute value of the difference between the nutrient content and the nutrient content threshold value. The subsequent deviation degree calculation is not described again.

[0038] In this embodiment, the process of calculating the micro abnormal index according to each micro deviation degree and a preset micro weight group includes: first, linearly normalizing each micro deviation degree to convert the micro deviation degrees of different indexes to a unified dimension range (such as 0 to 1), and then performing weighted summation calculation combined with the preset micro weight group to obtain the micro abnormal index.

[0039] In this embodiment, the micro abnormal type specifically includes three types of pH abnormality, water content abnormality, and nutrient abnormality: when the pH deviation threshold value is the largest, the pH abnormality is determined, reflecting the soil acidification or alkalization condition; when the water content deviation threshold value is the largest, the water content abnormality is determined, reflecting the soil excessive moisture or drought pressure; and when the nutrient content deviation threshold value is the largest, the nutrient abnormality is determined, reflecting the deficiency or excess of nitrogen, phosphorus, potassium and the like. Each type of micro abnormality type can clearly determine the main micro environmental factor limiting the ecological restoration potential of the sub-region, and provide a basis for the meso abnormality determination and potential index calculation.

[0040] By comparing the pH, water content, and nutrient content of each sub-region with the preset threshold value, the deviation degree of the micro environment is quantified, and the micro abnormal index is obtained by combining the weight calculation, which can accurately reflect the local pressure and potential abnormal condition of the soil environment; at the same time, by selecting the highest value of the deviation degree to determine the micro abnormal type, the most critical environmental limiting factor can be identified, which provides a scientific basis for the subsequent meso and macro level restoration potential evaluation, and realizes the effective connection and quantitative analysis from the micro environmental condition to the ecological restoration potential.

[0041] Please refer toFigure 2 As shown, it is a determination logic diagram for determining micro-abnormal sub-regions in the embodiment, and the process of determining a plurality of micro-abnormal sub-regions according to the micro-abnormal index includes: when the micro-abnormal index is greater than a preset micro-abnormal threshold, determining that the to-be-observed sub-region is the micro-abnormal sub-region.

[0042] The preset micro-abnormal threshold is a micro-abnormal index threshold, which depends on the soil type, vegetation type and ecological restoration target, and is usually set between 0.3 and 0.7, and is set to 0.5 in the embodiment, which can accurately distinguish between normal sub-regions and restricted sub-regions, and provide a basis for subsequent meso- and macro-level analysis.

[0043] By setting the micro-abnormal threshold and comparing the micro-abnormal index with it, the embodiment can effectively identify sub-regions where key factors such as soil acidity, water content or nutrients deviate significantly from the appropriate range, and mark these regions as micro-abnormal sub-regions, which can reflect the mutual influence and constraint relationship between environmental parameters, for example, insufficient soil moisture may exacerbate nutrient absorption constraints, and acid-base imbalance may affect plant root growth, thereby accurately identifying key areas of ecological restoration restriction at the micro level, providing a reliable basis for subsequent comprehensive analysis of meso- and macro-levels, and achieving fine division of potential assessment.

[0044] Specifically, the process of determining a meso-abnormal index according to the normalized vegetation index and the vegetation coverage includes: comparing the normalized vegetation index and the vegetation coverage of each micro-abnormal sub-region with the corresponding preset meso-threshold to obtain a plurality of corresponding meso-deviation degrees; and calculating the meso-abnormal index according to each meso-deviation degree and a preset meso-weight group.

[0045] In the embodiment, the preset meso-threshold includes a normalized vegetation index threshold and a vegetation coverage threshold. The normalized vegetation index threshold is used to evaluate vegetation growth vigor, which depends on the vegetation type and seasonal growth characteristics, and is usually set between 0.3 and 0.8, and is set to 0.5 in the embodiment, which can distinguish between healthy growth areas and growth restricted areas; the vegetation coverage threshold is used to measure the spatial proportion of vegetation, which depends on the ecological restoration target and the ground type, and is usually set between 20% and 70%, and is set to 40% in the embodiment, which can identify areas with sparse or degraded vegetation.

[0046] In the embodiment, the process of calculating the meso-abnormal index according to each meso-deviation degree and a preset meso-weight group includes: first, linearly normalizing each meso-deviation degree to convert the meso-deviation degrees of different indicators to a unified dimension range (such as 0 to 1), and then combining a preset meso-weight group for weighted summation calculation to obtain the micro-abnormal index.

[0047] In the embodiment, the preset meso-weight set includes a normalized vegetation index weight and a vegetation coverage weight. The normalized vegetation index weight is used to reflect the contribution of vegetation vigor to the meso-anomaly index, depends on the vegetation type and the ecological restoration sensitivity, and is usually set between 0.4 and 0.6, and is set to 0.5 in the embodiment, which can reasonably reflect the importance of the vegetation growth state to the anomaly determination; the vegetation coverage weight is used to reflect the influence of vegetation spatial coverage on the meso-anomaly index, depends on the restoration target and the surface coverage uniformity, and is usually set between 0.4 and 0.6, and is set to 0.5 in the embodiment, which can balance the contributions of the coverage area and the vegetation health state to the meso-anomaly.

[0048] By comparing the normalized vegetation index and the vegetation coverage of the micro-anomaly sub-region with the preset meso-threshold, the deviation of the vegetation growth condition of each region from the ideal growth condition can be quantified; in combination with the preset meso-weight set to calculate the meso-anomaly index, the contributions of different vegetation indexes to the ecological restoration potential can be comprehensively reflected, the vegetation growth deficiency or anomaly region can be accurately identified, and a quantitative basis for subsequent ecological evaluation at the meso and macro levels can be provided, so that the correlation between the vegetation condition, the coverage and the soil, the water and other factors can be reasonably reflected.

[0049] Referring to FIG. 6, Figure 3 As shown in FIG. 6, which is a determination logic diagram for determining the anomaly cause type in the embodiment, in the embodiment, the process of determining a plurality of meso-anomaly sub-regions according to the meso-anomaly index and the micro-anomaly type includes: when the meso-anomaly index is greater than a preset meso-anomaly threshold, determining the anomaly cause type of the micro-anomaly sub-region according to the corresponding micro-anomaly type; and determining a plurality of meso-anomaly sub-regions according to the anomaly cause types of other micro-anomaly sub-regions in a preset determination range centered on each micro-anomaly sub-region.

[0050] The preset meso-anomaly threshold is a meso-anomaly index threshold, depends on the vegetation type and the restoration sensitivity of the target ecosystem, and is usually set between 0.3 and 0.6, and is set to 0.45 in the embodiment, which can effectively identify the regions with abnormal vegetation growth or insufficient coverage at the meso scale; the preset determination range is a preset radius circular region centered on each micro-anomaly sub-region, which depends on the monitoring resolution, the spatial diffusion degree of the anomaly feature and the determination accuracy requirement, and the radius is usually set between 5 mm and 20 mm, and is set to 10 mm in the embodiment, which can effectively cover the local influence range of the anomaly sub-region and avoid missing the boundary anomaly.

[0051] By combining the mesoscopic anomaly index with the microcosmic anomaly type and aggregating the anomaly cause types of adjacent sub-regions centered on the microcosmic anomaly sub-region, the embodiment can identify continuous regions with the same ecological restriction reason at the mesoscopic scale, thereby reflecting the cumulative effect of microcosmic anomaly factors on ecological restoration at a larger spatial scale, achieving accurate division of potential obstacle regions, and providing a reliable spatial basis for subsequent macro-level restoration potential assessment.

[0052] Specifically, the process of determining a plurality of mesoscopic anomaly sub-regions according to the anomaly cause types of other microcosmic anomaly sub-regions within a preset determination range centered on each microcosmic anomaly sub-region includes: sequentially taking the microcosmic anomaly index of the microcosmic anomaly sub-region from high to low as a center region; when the anomaly cause types of the center region and other microcosmic anomaly sub-regions within the preset determination range are the same, merging the center region and the other microcosmic anomaly sub-regions to generate the mesoscopic anomaly sub-region; when the spatial overlap rate of the newly generated mesoscopic anomaly sub-region and the existing mesoscopic anomaly sub-region is greater than a preset overlap threshold, merging them into one mesoscopic anomaly sub-region; after merging into one mesoscopic anomaly sub-region, performing weighted average based on the area and mesoscopic anomaly index of each mesoscopic anomaly sub-region before merging to update the mesoscopic anomaly index of the merged mesoscopic anomaly sub-region; wherein the merged microcosmic anomaly sub-region no longer participates in subsequent merging.

[0053] The preset overlap threshold is a determination parameter for determining the overlap degree between two groups of data or two structures, which depends on the data sampling accuracy, spatial resolution, and sensitivity requirements of application scenarios, and is usually set between 0.3 and 0.7, and is set to 0.5 in the embodiment, which can ensure the determination accuracy while avoiding misjudgment caused by excessive sensitivity.

[0054] By sequentially merging adjacent microcosmic anomaly sub-regions with the same anomaly cause type centered on the sub-region with a higher microcosmic anomaly index, a continuous region with clear ecological anomaly characteristics can be formed at the mesoscopic scale, and the area weighted average is performed on the spatial overlap part to update the mesoscopic anomaly index, so that the generated mesoscopic anomaly sub-region can truly reflect the strength and distribution of local ecological pressure, taking into account the anomaly intensity and spatial continuity, and providing more accurate and stable basic data for subsequent potential assessment.

[0055] Please refer to Figure 4As shown, it is the determination logic diagram of the meso-macro mode determined in the embodiment. In the embodiment, the process of determining the macro adaptation index according to the precipitation and the temperature and determining the index calculation mode according to the meso abnormal index and the micro abnormal index includes: comparing the precipitation and the temperature of each meso abnormal sub-region with the corresponding preset macro threshold respectively to obtain a plurality of corresponding macro deviation degrees; calculating the macro adaptation index according to each macro deviation degree and a preset macro weight group; when the average value of all the meso abnormal indexes is greater than a preset meso average threshold and the average value of all the micro abnormal indexes is within a preset micro average threshold range, it is determined that the index calculation mode is a meso-macro mode; when the average value of all the micro abnormal indexes is greater than the maximum value of the preset micro average threshold range and the average value of all the meso abnormal indexes is less than or equal to the preset meso average threshold, it is determined that the index calculation mode is a micro-macro mode; when the average value of all the meso abnormal indexes is greater than the preset meso average threshold and the average value of all the micro abnormal indexes is greater than the maximum value of the preset micro average threshold range, it is determined that the index calculation mode is a micro-meso mode.

[0056] The preset macro threshold includes a precipitation threshold and a temperature threshold, which are the adaptability definitions of precipitation and temperature respectively, and depend on regional climate conditions and ecological adaptation needs, and are usually set between 70% and 130% of the historical statistical mean value, and in the embodiment, are set to 100 mm of precipitation and 25℃ of temperature, which can accurately reflect the influence of environmental factor deviation on macro adaptability; the preset macro weight group includes a precipitation weight and a temperature weight, which are respectively used to measure the relative importance of precipitation and temperature in the calculation of the macro adaptation index, and depend on the degree of dependence of the ecological system on water and heat, and are usually set between 0.3 and 0.7, and in the embodiment, are set to 0.6 of the precipitation weight and 0.4 of the temperature weight, which can reasonably balance the contribution degrees of different factors; the preset meso average threshold is the critical value of the meso abnormal index, which depends on the abnormal fluctuation level of the regional scale, and is usually set between 0.4 and 0.7, and in the embodiment, is set to 0.5, which can effectively distinguish between normal fluctuations and abnormal situations at the meso level; the preset micro average threshold range is the upper and lower limits of the micro abnormal index, which depends on the detail fluctuation range of the local scale, and is usually set between 0.2 and 0.6, and in the embodiment, is set to 0.3 to 0.5, which can accurately determine whether the fluctuation at the micro level exceeds the reasonable interval.

[0057] In the embodiment, the process of determining the macro adaptation index according to the precipitation and the temperature includes: first, linearly normalizing each macro deviation degree to convert the macro deviation degrees of different indicators to a unified dimension range (such as 0 to 1), and then combining the preset macro weight group to perform weighted summation calculation to obtain the macro adaptation index.

[0058] By converting the precipitation and temperature deviation into a macro adaptation index, and combining the average of meso and micro abnormal indexes for stratified judgment, dynamic adaptation to multi-scale environmental factors is achieved. When the meso abnormal characteristics are significant and the micro fluctuations are within a reasonable range, the meso-macro mode is entered to highlight the dominant role of the overall regional environment on macro adaptation; when the micro abnormality is significant and the meso change is weak, the micro-macro mode is entered to emphasize the influence of local details on macro performance; when both meso and micro abnormalities are significant, the micro-meso mode is entered to reflect the superimposed effect of multi-level factors. This method enables the determination logic to switch flexibly through the mutual constraint and weighted combination of parameters, ensuring that the index calculation results can reflect both the overall trend and capture local abnormalities, thereby improving the accuracy and robustness of the adaptability evaluation.

[0059] Specifically, the process of determining the potential index according to the index calculation mode, the macro adaptation index, the meso abnormal index, the micro abnormal index, and a preset potential weight set, and determining a number of macro abnormal sub-regions according to the potential index includes: when the index calculation mode is the meso-macro mode, calculating the potential index according to the macro adaptation index, the meso abnormal index, and the preset potential weight set; when the index calculation mode is the micro-macro mode, calculating the potential index according to the macro adaptation index, the micro abnormal index, and the preset potential weight set; when the index calculation mode is the micro-meso mode, calculating the potential index according to the meso abnormal index, the micro abnormal index, and the preset potential weight set; when the potential index is greater than a preset potential index threshold, determining that the meso abnormal sub-region is the macro abnormal sub-region.

[0060] The preset potential weight set includes a macro weight, a meso weight and a micro weight. The macro weight is used to measure the influence degree of precipitation and temperature on the potential index, depends on the regional climate conditions, is usually set between 0.3 and 0.5, and is set to 0.4 in the embodiment, which can ensure reasonable reflection of the macro environmental change on the potential index. The meso weight is used to reflect the role of the vegetation index and coverage in the potential index, depends on the vegetation coverage pattern, is usually set between 0.2 and 0.4, and is set to 0.3 in the embodiment, which can highlight the influence of regional vegetation anomaly on the potential index. The micro weight is used to reflect the contribution of soil pH, water content and nutrients and other indicators in the potential index, depends on the soil ecological characteristics, is usually set between 0.2 and 0.4, and is set to 0.3 in the embodiment, which can enhance the sensitivity of micro environmental change in the potential index determination. The preset potential index threshold is used to determine whether the meso abnormal sub-region is upgraded to a macro abnormal sub-region, depends on the adaptability level of the macro, meso and micro comprehensive influence, is usually set between 0.6 and 0.8, and is set to 0.7 in the embodiment, which can effectively screen out the regions with high potential risk and avoid misjudgment or omission.

[0061] In the embodiment, when the index calculation mode is the “meso-macro mode”, the potential index is calculated by weighting the macro adaptation index and the meso abnormal index, and the weight is the corresponding macro weight and meso weight in the preset potential weight set; when the index calculation mode is the “micro-macro mode”, the potential index is calculated by weighting the macro adaptation index and the micro abnormal index, and the weight is the corresponding macro weight and micro weight in the preset potential weight set; when the index calculation mode is the “micro-meso mode”, the potential index is calculated by weighting the meso abnormal index and the micro abnormal index, and the weight is the corresponding meso weight and micro weight in the preset potential weight set.

[0062] By introducing the macro adaptation index, the meso abnormal index, the micro abnormal index and the preset potential weight set for combined operation in different index calculation modes, the dynamic balance of cause and effect and hierarchical progression between multi-level indicators can be achieved, so that the potential index can reflect the adaptability of the macro environmental conditions and integrate the abnormal fluctuation characteristics of meso and micro scales, thereby accurately locking the macro abnormal sub-region when the potential index exceeds the preset threshold, avoiding the deviation caused by single parameter dominance, and improving the accuracy and stability of regional anomaly identification.

[0063] Specifically, the process of determining the aggregation degree according to the distribution position of the macroscopic abnormal sub-regions and adjusting the preset potential weight set according to the aggregation degree and the index calculation mode comprises: calculating the Euclidean distance from the center of each macroscopic abnormal sub-region to a preset reference point to obtain a plurality of distribution distances; calculating the reciprocal of the standard deviation of all the distribution distances to obtain the aggregation degree; when the aggregation degree is greater than a preset aggregation threshold, counting the number of potential indexes calculated according to the macro-micro mode, the micro-macro mode and the micro-micro mode to obtain a macro-micro number, a micro-macro number and a micro-micro number; and adjusting the preset potential weight set according to the maximum value of the macro-micro number, the micro-macro number and the micro-micro number, the aggregation degree and the preset aggregation threshold.

[0064] In the embodiment, when the maximum value is the macro-micro number, the macroscopic weight, the mesoscopic weight and the microscopic weight are adjusted according to the aggregation degree, the preset aggregation threshold and a preset weight adjustment coefficient, wherein w1'=w1×[1+α×(C-C0) / C0], w2'=w2×[1-α×(C-C0) / C0], w3'=w3×[1+α×(C-C0) / C0], w1''=w1' / (w1'+w2'+w3'), w2''=w2' / (w1'+w2'+w3'), w3''=w3' / (w1'+w2'+w3'), w1' is the macroscopic weight after the first calculation, w1 is the macroscopic weight before the first calculation, w2' is the mesoscopic weight after the first calculation, w2 is the mesoscopic weight before the first calculation, w3' is the microscopic weight after the first calculation, w3 is the microscopic weight before the first calculation, w1'' is the adjusted macroscopic weight, w2'' is the adjusted mesoscopic weight, w3'' is the adjusted microscopic weight, C is the aggregation degree, C0 is the preset aggregation threshold, and a is the preset weight adjustment coefficient. When the maximum value is the micro-macro number, the macroscopic weight, the mesoscopic weight and the microscopic weight are adjusted according to the aggregation degree, the preset aggregation threshold and a preset weight adjustment coefficient, wherein w1'=w1×[1+α×(C-C0) / C0], w2'=w2×[1-α×(C-C0) / C0], w3'=w3×[1+α×(C-C0) / C0], w1''=w1' / (w1'+w2'+w3'), w2''=w2' / (w1'+w2'+w3'), w3''=w3' / (w1'+w2'+w3'). When the maximum value is a micro-quantity, the macroscopic weight, the mesoscopic weight and the microscopic weight are adjusted according to the aggregation degree, the preset aggregation threshold and the preset weight adjustment coefficient, wherein w1'=w1×[1-α×(C-C0) / C0], w2'=w2×[1+α×(C-C0) / C0], w3'=w3×[1+α×(C-C0) / C0], w1''=w1' / (w1'+w2'+w3'), w2''=w2' / (w1'+w2'+w3'), w3''=w3' / (w1'+w2'+w3').

[0065] The preset weight adjustment coefficient depends on the adjustment requirement of the sensitivity of the potential weight, is usually set to be between 0.01 and 0.5, is set to be 0.1 in the embodiment, can moderately adjust the macroscopic weight, the mesoscopic weight and the microscopic weight when the aggregation degree changes, ensures that the weight adjustment is smooth and reflects the spatial distribution characteristics of the abnormal sub-regions; the preset aggregation threshold is a reference value for judging the spatial aggregation degree of the macroscopic abnormal sub-region, depends on the region scale and the ecological restoration target, is usually set to be between 0.1 and 0.5, is set to be 0.3 in the embodiment, can identify the high aggregation of the macroscopic abnormal sub-region; the preset reference point is a reference position for calculating the spatial distribution distance of the macroscopic abnormal sub-region, depends on the geographical center of the observation region or the key region of ecological restoration, is usually set to be the geometric center of the observation region, and is set to be the geometric center of the observation region in the embodiment, can provide a unified distance reference to evaluate the aggregation degree.

[0066] The aggregation degree is obtained by calculating the reciprocal of the distribution distance of the macroscopic abnormal sub-region relative to the preset reference point and the standard deviation, and can directly reflect the spatial aggregation degree of the abnormal region; when the aggregation degree exceeds the preset threshold, the number of potential indexes under different calculation modes is counted, which is helpful to identify the main factors affecting the ecological restoration potential at the microscopic, mesoscopic and macroscopic levels; the preset potential weight group is dynamically adjusted based on the aggregation degree, the mode distribution and the threshold, which can reasonably enhance or weaken the influence of each level index in the calculation of the potential index, so that the potential index more accurately reflects the actual situation of the regional ecological restoration, and ensures that the evaluation result can take into account the comprehensive effect of the spatial distribution characteristics and the multi-dimensional index.

[0067] In another aspect, the embodiment also provides a regional ecological restoration potential evaluation system based on multi-dimensional data, comprising: The first acquisition module is used to acquire the pH value, the water content and the nutrient content of each to-be-observed sub-region in the observation region in real time. The microscopic determination module is connected with the first acquisition module, and is used to determine the microscopic abnormal index and the microscopic abnormal type according to the pH value, the water content and the nutrient content, and to determine a plurality of microscopic abnormal sub-regions according to the microscopic abnormal index. a second acquisition module, connected with the micro-determination module, configured to acquire normalized difference vegetation index and vegetation coverage of each micro-abnormal sub-region in real time; a meso-determination module, connected with the second acquisition module, configured to determine meso-abnormal index according to the normalized difference vegetation index and the vegetation coverage, and determine a plurality of meso-abnormal sub-regions according to the meso-abnormal index and the micro-abnormal type; a third acquisition module, connected with the meso-determination module, configured to acquire precipitation and temperature in each meso-abnormal sub-region in real time; a mode-determination module, connected with the third acquisition module, the micro-determination module and the meso-determination module respectively, configured to determine macro-adaptation index according to the precipitation and the temperature, and determine index calculation mode according to the macro-adaptation index, the meso-abnormal index and the micro-abnormal index; a macro-determination module, connected with the mode-determination module, configured to determine potential index according to the index calculation mode, the macro-adaptation index, the meso-abnormal index, the micro-abnormal index and a preset potential weight set, and determine a plurality of macro-abnormal sub-regions according to the potential index; an adjustment module, connected with the macro-determination module, configured to determine aggregation degree according to the distribution position of the macro-abnormal sub-region, and adjust the preset potential weight set according to the aggregation degree and the index calculation mode; a generation module, connected with the macro-determination module, configured to generate a recovery potential evaluation report according to the potential index determined after the preset potential weight set is adjusted.

[0068] Through multi-level and multi-dimensional data acquisition and analysis, key ecological parameters such as soil pH, water content, nutrient content, vegetation index, vegetation coverage, precipitation and temperature can be comprehensively processed to realize hierarchical identification and aggregation of micro, meso and macro abnormalities; through calculation of potential index and adjustment of aggregation degree, the weight of ecological abnormalities of different scales and types is reasonably distributed, so as to accurately reflect the ecological recovery potential of each region, realize fine judgment and scientific regulation of potential risk areas, and provide quantitative, dynamic and operable decision basis for regional ecological recovery.

[0069] The above only describes the preferred embodiments of the present application and is not used to limit the present application; for those skilled in the art, the present application can have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for assessing regional ecological restoration potential based on multidimensional data, characterized in that, include: Real-time data collection of pH, moisture content, and nutrient content of each sub-region to be observed within the observation area; The microscopic anomaly index and microscopic anomaly type are determined based on the pH, moisture content and nutrient content, and several microscopic anomaly sub-regions are determined based on the microscopic anomaly index. Real-time collection of normalized vegetation index and vegetation cover of each of the aforementioned micro-anomaly sub-regions; The mesoscopic anomaly index is determined based on the normalized vegetation index and the vegetation coverage, and several mesoscopic anomaly sub-regions are determined based on the mesoscopic anomaly index and the microscopic anomaly type. Real-time collection of precipitation and temperature in each of the aforementioned mesoscopic anomaly sub-regions; The macro-adaptation index is determined based on the precipitation and temperature, and the index calculation mode is determined based on the meso-anomaly index and the micro-anomaly index. The potential index is determined based on the index calculation model, the macro-adaptation index, the meso-anomaly index, the micro-anomaly index, and the preset potential weight reorganization, and several macro-anomaly sub-regions are determined based on the potential index. The degree of clustering is determined based on the distribution location of the macro-abnormal sub-regions, and the preset potential weight reorganization is adjusted based on the degree of clustering and the index calculation mode. A recovery potential assessment report is generated based on the potential index that has been redefined after the adjustment of the preset potential weights.

2. The method for assessing regional ecological restoration potential based on multidimensional data according to claim 1, characterized in that, The process of determining the microscopic anomaly index and microscopic anomaly type based on the pH, moisture content, and nutrient content includes: The pH, water content, nutrient content, and corresponding preset microscopic threshold of each of the sub-regions to be observed are compared to obtain several corresponding microscopic deviations. The micro-abnormality index is calculated based on each of the aforementioned micro-deviation degrees and the preset micro-weighted reorganization; The micro-anomaly type is determined based on the highest value among all the micro-deviations.

3. The method for assessing regional ecological restoration potential based on multidimensional data according to claim 2, characterized in that, The process of determining several micro-anomaly sub-regions based on micro-anomaly indices includes: When the micro-anomaly index is greater than a preset micro-anomaly threshold, the sub-region to be observed is determined to be the micro-anomaly sub-region.

4. The method for assessing regional ecological restoration potential based on multidimensional data according to claim 3, characterized in that, The process of determining the mesoscopic anomaly index based on the normalized vegetation index and the vegetation cover includes: The normalized vegetation index and vegetation coverage of each of the micro-anomaly sub-regions are compared with the corresponding preset meso-level thresholds to obtain several corresponding meso-level deviations. The meso-level anomaly index is calculated based on the aforementioned meso-level deviation and the preset meso-level weighted reorganization.

5. The method for assessing regional ecological restoration potential based on multidimensional data according to claim 4, characterized in that, The process of determining several meso-level anomaly sub-regions based on the meso-level anomaly index and the micro-level anomaly type includes: When the meso-level anomaly index is greater than the preset meso-level anomaly threshold, the anomaly cause type of the micro-level anomaly sub-region is determined according to the corresponding micro-level anomaly type. Several meso-level anomalous sub-regions are determined based on the anomalous cause types of other micro-level anomalous sub-regions within a preset determination range centered on each of the aforementioned micro-level anomalous sub-regions.

6. The method for assessing regional ecological restoration potential based on multidimensional data according to claim 5, characterized in that, The process of determining several mesoscopic anomaly sub-regions based on the anomaly cause types of other microscopic anomaly sub-regions within a preset determination range centered on each of the aforementioned microscopic anomaly sub-regions includes: The micro-anomaly sub-regions are designated as central regions in descending order of their micro-anomaly indices. When the anomaly cause type of the central region is the same as that of other micro-anomaly sub-regions within the preset determination range, the central region and the other micro-anomaly sub-regions are merged to generate the meso-anomaly sub-region. When the spatial overlap rate between the newly generated meso-level anomalous sub-region and the existing meso-level anomalous sub-region is greater than a preset overlap threshold, they are merged into one meso-level anomalous sub-region. After merging into one meso-level anomalous sub-region, the meso-level anomalous index of the merged meso-level anomalous sub-region is updated by weighted averaging the area and meso-level anomalous index of each meso-level anomalous sub-region before merging. The micro-anomaly sub-regions that have already been merged will not participate in subsequent merging.

7. The method for assessing regional ecological restoration potential based on multidimensional data according to claim 6, characterized in that, The process of determining the macro-adaptation index based on the precipitation and temperature, and determining the index calculation model based on the meso-anomaly index and the micro-anomaly index, includes: The precipitation and temperature of each of the mesoscopic anomaly sub-regions are compared with the corresponding preset macroscopic thresholds to obtain several corresponding macroscopic deviations. The macroeconomic adaptation index is calculated based on the aforementioned macroeconomic deviation and the preset macroeconomic weighting. When the average value of all the meso-level anomaly indices is greater than a preset meso-level average threshold and the average value of all the micro-level anomaly indices is within the range of a preset micro-level average threshold, the index calculation mode is determined to be the meso-macro mode. When the average value of all the micro-anomaly indices is greater than the maximum value of the preset micro-average threshold range, and the average value of all the meso-anomaly indices is less than or equal to the preset meso-average threshold, the index calculation mode is determined to be micro-macro mode. When the average of all the meso-level anomaly indices is greater than a preset meso-level average threshold, and the average of all the micro-level anomaly indices is greater than the maximum value of the preset micro-level average threshold range, the index calculation mode is determined to be the micro-meso-level mode.

8. The method for assessing regional ecological restoration potential based on multidimensional data according to claim 7, characterized in that, The process of determining a potential index based on the index calculation model, the macro-adaptation index, the meso-anomaly index, the micro-anomaly index, and a preset potential weight reorganization, and then determining several macro-anomaly sub-regions based on the potential index, includes: When the index calculation mode is the macro-meta mode, the potential index is calculated based on the macro-adaptation index, the meso-anomaly index, and the preset potential weight reorganization. When the index calculation mode is the micro-macro mode, the potential index is calculated based on the macro adaptation index, the micro anomaly index, and the preset potential weight reorganization. When the index calculation mode is the micro-medium mode, the potential index is calculated based on the meso-level anomaly index, the micro-level anomaly index, and the preset potential weight reorganization. When the potential index is greater than a preset potential index threshold, the meso-level anomaly sub-region is determined to be the macro-level anomaly sub-region.

9. The method for assessing regional ecological restoration potential based on multidimensional data according to claim 8, characterized in that, The process of determining the clustering degree based on the distribution location of the macro-abnormal sub-regions, and adjusting the preset potential weight reorganization based on the clustering degree and the index calculation mode, includes: Calculate the Euclidean distance from the center of each of the macroscopic anomaly sub-regions to a preset reference point to obtain several distribution distances; The degree of clustering is obtained by calculating the reciprocal of the standard deviation of all the distribution distances. When the aggregation degree is greater than the preset aggregation threshold, the number of potential indices calculated according to the medium-macro mode, the micro-macro mode and the micro-medium mode are counted to obtain the number of medium-macro, micro-macro and micro-medium indices. The preset potential weight reorganization is adjusted based on the maximum value of the number of macro-medium, the number of micro-macro, and the number of micro-medium, the aggregation degree, and the preset aggregation threshold.

10. A regional ecological restoration potential assessment system based on multidimensional data, constructed based on the regional ecological restoration potential assessment method based on multidimensional data according to any one of claims 1-9, characterized in that, include: The first acquisition module is used to collect the pH, water content and nutrient content of each sub-region to be observed in real time within the observation area; A microscopic determination module, connected to the first acquisition module, is used to determine the microscopic anomaly index and microscopic anomaly type based on the pH, the moisture content and the nutrient content, and to determine several microscopic anomaly sub-regions based on the microscopic anomaly index. The second acquisition module is connected to the microscopic determination module and is used to acquire the normalized vegetation index and vegetation coverage of each microscopic anomaly sub-region in real time. The mesoscopic determination module is connected to the second acquisition module and is used to determine the mesoscopic anomaly index based on the normalized vegetation index and the vegetation coverage, and to determine several mesoscopic anomaly sub-regions based on the mesoscopic anomaly index and the microscopic anomaly type. The third acquisition module is connected to the mesoscopic determination module and is used to acquire precipitation and temperature in each of the mesoscopic anomaly sub-regions in real time. The mode determination module is connected to the third acquisition module, the micro-determination module and the meso-determination module respectively, and is used to determine the macro-adaptation index based on the precipitation and the temperature, and to determine the index calculation mode based on the macro-adaptation index, the meso-anomaly index and the micro-anomaly index. A macro-determination module, connected to the pattern determination module, is used to determine a potential index based on the index calculation mode, the macro-adaptation index, the meso-anomaly index, the micro-anomaly index, and a preset potential weight reorganization, and to determine several macro-anomaly sub-regions based on the potential index. An adjustment module, connected to the macro determination module, is used to determine the clustering degree based on the distribution location of the macro abnormal sub-regions, and to adjust the preset potential weight reorganization based on the clustering degree and the index calculation mode. A generation module, connected to the macro-determination module, is used to generate a recovery potential assessment report based on the potential index re-determined after adjusting the preset potential weights.

Images