Slope vegetation restoration system and restoration method thereof

Abstract

The invention discloses a slope vegetation restoration system and a restoration method thereof, and relates to the field of slope vegetation restoration. Microenvironment data are obtained, timed collection of microenvironment data and abnormal threshold values is set, slope vegetation type data are obtained, parameterization and standardization processing are carried out, and a demand matching state report is generated; s200, performing regional growth environment setting based on the demand matching state report obtained in the S200, setting a feedback mechanism based on the setting of the growth environment in the S300, regulating and controlling setting operation based on the feedback mechanism, generating a feedback regulation and control report, and performing dynamic regulation and control based on the growth demand parameters of the plants by collecting microenvironment data in real time. The slope area can better adapt to environmental changes, temperature, humidity and illumination conditions are accurately adjusted by utilizing a feedback mechanism and demand matching analysis, so that a continuous and suitable growth environment is provided for vegetation in different growth stages, and meanwhile, support is provided for long-term stable recovery of the slope vegetation through regular restoration effect evaluation and datamation recording.

Description

Technical Field

[0001] The invention relates to the field of slope vegetation restoration, and in particular to a slope vegetation restoration system and a restoration method thereof. Background Art

[0002] With the continuous changes in the natural environment, slope ecosystems are facing increasingly severe challenges. Factors such as weathering, rainfall erosion and human activities have aggravated the instability of slopes and vegetation damage, affecting the balance and long-term effectiveness of slope ecosystems. Traditional slope vegetation restoration methods usually rely on simple artificial covering or irrigation methods, lacking systematic support for vegetation demand and microenvironment regulation, and cannot effectively achieve precise ecological restoration.

[0003] There is still room for improvement in the existing slope vegetation restoration methods in terms of refined management and long-term effects. First, traditional methods rely more on fixed-frequency manual maintenance in terms of microenvironment regulation, and it is difficult to dynamically respond to environmental changes in the slope area. Second, the growth requirements of different types of vegetation vary greatly, and existing methods pay less attention to the requirements of different growth stages and may not be able to fully meet the diverse ecological restoration needs. In addition, in the evaluation of restoration effects, existing technologies often lack systematic monitoring and data analysis methods, resulting in insufficient quantitative evaluation of restoration effects, which may affect the accuracy of continuous optimization. Summary of the invention

[0004] The purpose of the present invention is to address the shortcomings of the prior art and provide a slope vegetation restoration system and a restoration method thereof, aiming to achieve an adaptive ecological restoration process through dynamic collection of microenvironmental data, feedback regulation and restoration effect evaluation. First, the temperature, humidity and light conditions of the slope area are identified by using the microenvironmental data collected in real time, and dynamic adjustments are made based on the growth needs of the plants to adapt to environmental changes and reduce the frequency of manual maintenance. Secondly, through precise analysis and regulation and optimization of growth demand parameters, different types of vegetation are provided with environmental support that is more in line with their growth cycles, thereby improving the survival rate and restoration effect of vegetation. Finally, in the restoration effect evaluation step, the restoration process is analyzed in a digitized manner, the restoration effect can be quantified, and the overall stability and long-term recovery capacity of the slope ecology are improved.

[0005] In a first aspect, the present application provides a slope vegetation restoration method, comprising the following steps:

[0006] Step S100, obtaining microenvironment data, and setting a timed collection of microenvironment data and an abnormal threshold;

[0007] Step S200, obtaining slope vegetation type data, parameterizing and standardizing it, and generating a demand matching status report;

[0008] Step S300, setting a regional growth environment based on the demand matching status report obtained in step S200;

[0009] Step S400, setting a feedback mechanism based on the setting of the growth environment in step S300, and regulating the setting operation based on the feedback mechanism, and generating a feedback regulation report;

[0010] Step S500: Analyze and adjust based on the feedback control report in step S400, and set control priority and adjustment optimization;

[0011] Step S600: Collect vegetation growth data, calculate the restoration effect of each area based on the comparison between the collected vegetation growth data and the growth requirement parameters, and generate a restoration effect evaluation report.

[0012] In some specific implementations, obtaining microenvironment data and setting a timed collection of microenvironment data and an abnormal threshold specifically include:

[0013] Step S100.1, arrange microenvironment data acquisition devices in different areas of the slope to obtain microenvironment data.

[0014] Among them, the sensor layout density is 1 sensor per 100 square meters.

[0015] Among them, the micro-environment data acquisition device includes: temperature and humidity sensors, light sensors and soil moisture sensors.

[0016] Step S100.1.1, transmitting the obtained microenvironment data to a central data processing unit via wireless.

[0017] Step S100.2: Set the central data processing unit to start collection at a fixed time.

[0018] The central data processing unit is set to collect numerical data of air temperature, air humidity, light intensity and soil moisture once every hour.

[0019] Among them, at the turn of the dry season and the rainy season or during special weather periods, the collection frequency automatically increases to once every 30 minutes to record the impact of abnormal weather on the slope microenvironment.

[0020] Step S100.2.1: When the central data processing unit detects that a specified abnormal threshold is exceeded at a sensor point, an automatic alarm is sent to remind of the abnormal situation and the abnormal data is automatically marked.

[0021] Among them, the abnormal thresholds are set as: temperature>35℃ and soil moisture<15%.

[0022] If a sensor fails or loses data during data collection, the central data processing unit will automatically send a fault alarm within 30 seconds and record the ID and timestamp of the faulty sensor to facilitate subsequent maintenance and data supplementation.

[0023] In some specific implementations, the slope vegetation type data is obtained, parameterized and standardized, and a demand matching status report is generated, specifically including:

[0024] Step S200.1, obtain slope vegetation type data and divide it into different growth stages.

[0025] Among them, different growth stages include: germination stage, seedling stage and maturity stage.

[0026] Step S200.1.1: Based on the slope vegetation type data, the requirements for microenvironment data at each growth stage are parameterized.

[0027] Among them, demand parameterization includes:

[0028] The temperature requirement during the germination period is greater than 0:20-25℃.

[0029] The air humidity requirement during the seedling stage is greater than 0:60-70%.

[0030] The soil moisture requirement during maturity is greater than 0:20-30%.

[0031] Step S200.1.2: uniformly convert the demand parameters of each stage into growth demand parameter standards.

[0032] Step S200.1.3: Arrange and classify the microenvironmental requirement parameters based on the vegetation growth stage, and form a preliminary requirement parameter list.

[0033] Among them, the preliminary list of required parameters includes: temperature, air humidity, light intensity and soil moisture indicators.

[0034] Step S200.2: Based on the obtained preliminary demand parameter list, standardize the demand parameters of different vegetation at each growth stage, and generate a complete growth demand parameter standard set.

[0035] Among them, standardization processing is to conduct statistical analysis of various parameters based on the actual adaptability of vegetation to micro-environmental data such as temperature, humidity, and light.

[0036] The analysis means that if the suitable temperature range for a certain plant is 20-30℃, it will be standardized to 25℃±5℃.

[0037] Among them, the standard set of growth requirement parameters covers the range of requirements for microenvironment data at different growth stages.

[0038] Step S200.3: compare the obtained microenvironment data with the growth requirement parameter standard set one by one.

[0039] Among them, the comparison is to match and analyze the microenvironment data of each area with the needs of the plants at each growth stage.

[0040] Among them, matching analysis means that if the temperature of a certain area exceeds the growth requirement by ±5°C, the area is marked as a temperature control priority area; if the soil moisture is lower than 10% of the growth requirement, it is marked as a humidity control priority area.

[0041] Step S200.4: Based on the comparison results, generate a demand matching status report for each area.

[0042] Among other things, the report content indicates whether the region’s microenvironmental data meets or does not meet growth requirements.

[0043] In some specific implementations, the regional growth environment control setting is performed based on the demand matching status report obtained in step S200, specifically including:

[0044] Step S300.1: Based on the obtained demand matching status report, identify the areas that do not meet the growth demand parameters, and form a list of areas for demand priority regulation.

[0045] Among them, the area list setting refers to the division of areas into temperature control priority areas and humidity control priority areas.

[0046] Step S300.2: In the temperature control priority area, the temperature is adjusted by heating or cooling measures based on the actual deviation of the temperature amplitude.

[0047] Among them, when the regional temperature is lower than the lower limit of the growth requirement parameter, the controllable heating device is started to raise the temperature to the required range.

[0048] Among them, if the regional temperature is higher than the upper limit of growth requirements, ventilation equipment is used to cool down to control the regional temperature.

[0049] Step S300.3: In the humidity control priority area, the soil humidity and air humidity are adjusted based on the humidity deviation degree in the demand matching status report.

[0050] Among them, if the soil moisture is lower than the lower limit of the required parameter, the sprinkler system will be started to provide the required moisture to the soil.

[0051] Among them, if the air humidity is too low, an atomization device should be used to increase the air humidity to meet the growth needs of vegetation.

[0052] Among them, when the light intensity does not meet the growth requirements of an area, shading measures or enhanced light are used to regulate the light intensity.

[0053] Among them, if the light intensity in the area is too high, a movable sunshade is used to reduce the impact of direct light and control the light intensity to an appropriate range.

[0054] If there is insufficient light, reflectors are used to increase the light to ensure that the vegetation can obtain the required amount of light.

[0055] In some specific implementations, a feedback mechanism is set based on the setting of the growth environment in step S300, and the setting operation is regulated based on the feedback mechanism to generate a feedback regulation report, which specifically includes:

[0056] Step S400.1: Set up a monitoring mechanism in the area after temperature, humidity and light are regulated.

[0057] Among them, the monitoring mechanism monitors targets including: temperature, humidity, light intensity and soil moisture.

[0058] Among them, the monitoring data transmission refers to the central data processing unit.

[0059] Step S400.2: Compare each parameter with the growth demand parameter standard set based on the monitoring data received by the central data processing unit.

[0060] Among them, when the microenvironment data of a certain area deviates from the growth requirement parameter standard, the feedback mechanism is triggered, the area is marked and a real-time feedback record is generated.

[0061] In this process, based on the feedback records, the corresponding control devices are activated through the central data processing unit to implement dynamic control of the microenvironment.

[0062] The feedback mechanism is as follows:

[0063] F adj =α·|P obs -P std |+β·T+γ·ΔR

[0064] Where: F adj It is a feedback control parameter used to determine whether it is necessary to start the control and its intensity. adj Exceeding the preset threshold F thresh When the control is automatically triggered, P obs is the actual value of the currently collected microenvironment data, which includes: current temperature, humidity and light intensity, P stdis the corresponding standard value of the growth requirement parameter, that is, the target parameter value of the plant requirement. T is the regulation time factor, that is, the time since the last regulation, which is used to measure the regulation interval. If the time interval is short, it means that the area needs frequent regulation. ΔR is the recovery deviation rate, that is, the percentage of the current collected data compared to the change after the last regulation, which is used to judge the efficiency of microenvironment recovery.

[0065] Here, it should be noted that if the humidity demand is 30%, the humidity reaches 28% after the last adjustment, and the current collection value is 24%, then the recovery deviation rate is **-14%%**.

[0066] Here, it is necessary to explain that the weight parameters are: α, β, and γ are the weights of microenvironment deviation, regulation interval time, and recovery deviation rate, respectively, which control the influence of each factor on feedback regulation.

[0067] Among them, the initial value of the weight parameter is:

[0068] α=1.2,β=0.8,γ=1.0

[0069] Here, we need to explain the influence of microenvironment deviation: In the formula, |P obs -P std |It reflects the difference between the current microenvironment data and the growth requirement parameters. The larger the difference, the more urgent the need for growth adjustment.

[0070] Here, it is necessary to explain the control time factor: T is used to measure the time interval from the last control to the current time. If the interval is short, the control frequency is too high, and it may be necessary to adjust the adaptability parameters or control methods of the region.

[0071] Here, it is necessary to explain the recovery deviation rate: ΔR reflects the effect of microenvironment recovery after regulation. A negative deviation rate indicates that the regulation failed to achieve the expected effect, and a positive deviation rate indicates that the regulation is effective.

[0072] Among them, dynamic control includes: temperature control, humidity control and light control.

[0073] Among them, temperature control means that when the monitoring data detects that the temperature is lower or higher than the required parameters, the heating or cooling device is automatically started to adjust the temperature.

[0074] Among them, humidity control means that when the air humidity or soil humidity is not within the required range, the water spray or atomization device is automatically started to adjust the humidity.

[0075] Among them, for light control, when the light intensity does not meet the required parameters, the shading or reflective device will be automatically activated to adjust the light.

[0076] Among them, when the microenvironment data returns to the growth requirement parameter range, the control device will automatically stop.

[0077] Step S400.3: After the control is completed, the central data processing unit records the control process of each area, including the changes in microenvironment data before and after the control and the execution time of the control device, and generates a feedback control report.

[0078] Among them, the feedback and control report includes: the deviation of microenvironment data in each region, control measures and control effects.

[0079] In some specific implementations, analysis and adjustment are performed based on the feedback control report in step S400, and control priority and adjustment optimization are set, specifically including:

[0080] Step S500.1: Analyze the control data of each region based on the generated feedback control report.

[0081] Among them, the frequency of regulation and the number of deviations from growth demand parameters in each region are counted, and areas that require frequent regulation or long-term deviations are identified as key areas for optimization.

[0082] Among them, the analysis includes: regulation frequency, deviation amplitude and microenvironment recovery effect.

[0083] Among them, the analysis was to use weighted average algorithm and variance analysis to identify the regulation pattern and stability of regional microenvironment data.

[0084] The weighted average algorithm is specifically:

[0085]

[0086] Where: W avg is the weighted average regulation intensity, which is used to judge the intensity and priority of regulation needs in each region. i is the number of times the ith region is regulated, that is, the total number of times the region is regulated within a specified time, d i : The deviation amplitude of the ith region, that is, the absolute value of the deviation between the microenvironment data of the region and the growth requirement parameters, r i is the recovery effect of the ith region, that is, the degree to which the microenvironment data of each region recovers to the growth requirement parameters after regulation. The higher the recovery effect, the better the regulation effect. f ,w d ,w r It is the weight coefficient of the number of controls, deviation amplitude and recovery effect, which is used to adjust the impact of each factor on the intensity of control.

[0087] Among them, variance analysis refers to the analysis of the control data of each region according to the control frequency, deviation amplitude and recovery The effect is grouped and the data of each area is set as a group and recorded as X i, where each group contains multiple observations.

[0088] Among them, the group means of each region or group in variance analysis are calculated as follows:

[0089]

[0090] Where: is the mean of the ith region, n i is the number of data points in the region, X ij represents the jth data point in the i-th region.

[0091]

[0092] Where: is the grand mean of all regions, N is the total number of data points, and k is the number of regions.

[0093] The variance between groups in variance analysis is calculated as:

[0094]

[0095] Where: represents the between-group variance, reflecting the difference in means between different regions.

[0096] The variance within the group is calculated in the variance analysis as:

[0097]

[0098] In the formula, Represents the within-group variance, reflecting the degree of dispersion of data within the region.

[0099] The calculation of F statistics is as follows:

[0100]

[0101] In the formula: F value is used to test whether the difference between groups is significant.

[0102] The significance test is to compare the calculated F value with the critical value F critical Compare, look up from the F distribution table, determine according to the significance level α and degrees of freedom, if F>F critical, , it is believed that there are significant differences in the regulatory needs of different regions and further optimization is needed.

[0103] Among them, if the F value is significantly higher than the critical value, the relevant area will be set as high priority and the regulation frequency will be increased.

[0104] Step S500.1.1: Generate priority regulation labels for each region based on the analysis results.

[0105] Step S500.2: Based on the priority control label, filter out areas where the control frequency exceeds 2 times per hour.

[0106] Step S500.2.1: loosen or tighten the growth requirement parameter range based on the screened areas.

[0107] Among them, if the temperature in a certain area is frequently adjusted, the temperature requirement standard can be adjusted from the original 25℃±5℃ to 25℃±7℃.

[0108] Among them, a linear regression model is used, with historical control data as independent variables, to calculate the adaptive parameter range.

[0109] Here, it should be noted that the linear regression model is a prior art well known to those skilled in the art and will not be described in detail here.

[0110] Step S500.2.2: Based on the adjusted parameter range, a dynamic adjustment algorithm is used to automatically optimize the growth requirement parameters based on regional seasonal changes and environmental trends.

[0111] The dynamic adjustment algorithm is specifically as follows:

[0112] D adj =η·(E obs -E target )+λ·ΔT+μ·ΔR

[0113] Where: D adj is a dynamic adjustment parameter, which indicates the adjustment value of the control parameter. The adjusted control parameter is P new =P original +D adj , E obs is the observed real-time microenvironment data, such as current temperature, humidity or light intensity, E target is the target value of growth requirements, that is, the standard value of the microenvironment required by the plant. ΔT is the time interval since the last regulation, expressed in hours, which is used to measure the frequency of regulation. ΔR is the recovery rate of the microenvironment after regulation, which is defined as the speed at which the environment recovers to the target value after regulation, expressed in percentage.

[0114] Among them, the weight parameters in the formula are described as follows: η is the regulation intensity coefficient, which is used to adjust the impact of the difference between the observed value and the target value on the result. The recommended initial value is 1.5; λ is the time interval coefficient, which is used to control the impact of the regulation time interval. The recommended initial value is 0.7. The longer the time interval, the greater the regulation demand; μ is the recovery rate coefficient, which reflects the efficiency of environmental recovery after regulation. The recommended initial value is 0.5. The lower the recovery rate, the limited regulation effect and the need to increase the regulation intensity.

[0115] Step S500.3: Optimize the control frequency of each area based on the historical data of the feedback mechanism.

[0116] Among them, the regulation frequency is automatically adjusted based on the recovery deviation and recovery time of different areas. If the microenvironment can recover quickly after regulation, the regulation frequency can be reduced. If the recovery is slow, the regulation priority will be increased.

[0117] Step S500.3.1: Based on the priority regulation of each region, set seasonal regulation priority rules.

[0118] Priority rules include: high priority area and low priority area.

[0119] Among them, the high priority areas are: critical growth periods, and high priority regulation is set. The critical growth periods include: germination period and seedling period.

[0120] Among them, the low priority area is: the mature area with higher stability, which is set to have a lower priority.

[0121] In some specific implementations, vegetation growth data is collected, and based on the comparison between the collected vegetation growth data and the growth requirement parameters, the restoration effect of each area is calculated, and a restoration effect evaluation report is generated, which specifically includes:

[0122] Step S600.1: Based on the microenvironment control status of each area, collect vegetation growth data of each area.

[0123] Among them, vegetation growth data include: plant height, leaf area, root distribution and vegetation coverage.

[0124] Step S600.2: Compare the data collected in steps S100 to S400 and the adjusted microenvironment data with the plant growth requirement parameter standard set one by one, and determine whether the current microenvironment meets the vegetation growth requirements.

[0125] Among them, the standard sets of growth demand parameters are compared one by one and calculated using a matching analysis algorithm.

[0126] Among them, the matching condition is that if the temperature, humidity and light parameters are all within the required standard range, it is determined that the requirements are met; if any parameter is not met, it is determined that the requirements are not met.

[0127] Step S600.3: Use the weighted restoration index formula to calculate the vegetation restoration effect of each area and use it to measure the comprehensive restoration effect.

[0128] Among them, let the repair effect index be R eff , specifically:

[0129] R eff =w h·H+w a ·A+w r ·D+w c ·C

[0130] Where: H is the average plant height, reflecting the overall growth height of the vegetation; A is the average leaf area, reflecting the photosynthesis capacity of the vegetation; D is the root distribution density, indicating the soil stability of the vegetation; C is the vegetation coverage rate, indicating the ecological coverage of the region; w h 、w a 、w r 、w c are the weights of plant height, leaf area, root distribution and coverage rate, respectively.

[0131] The weights of plant height, leaf area, root distribution and coverage are initially set as: h =0.3, w a =0.2, w r =0.3, w c =0.2.

[0132] Among them, the calculation of the repair effect index R eff Finally, the restoration effect is counted by region, a restoration effect analysis table is generated, and it is determined which areas have reached the ideal restoration state.

[0133] Step S600.4: Based on the repair effect analysis table, the central data processing unit generates a repair effect evaluation report.

[0134] Among them, the restoration effect evaluation report records include: vegetation growth conditions, microenvironment matching conditions and restoration effect indicators in each area.

[0135] Among them, the restoration effect evaluation report also includes: the restoration effect index R of each area eff values, how well regional needs are being met, areas where restoration is progressing faster, and areas that need additional regulatory support.

[0136] In a second aspect, the present application further provides a slope vegetation restoration system, comprising the following modules:

[0137] The data acquisition module obtains microenvironment data and sets the timed collection of microenvironment data and abnormal thresholds.

[0138] The demand analysis module obtains slope vegetation type data, parameterizes and standardizes it, and generates a demand matching status report.

[0139] The environment control module sets the regional growth environment based on the demand matching status report obtained in step S200.

[0140] The feedback control module sets a feedback mechanism based on the setting of the growth environment in S300, and controls the setting operation based on the feedback mechanism to generate a feedback control report.

[0141] The control optimization module analyzes and adjusts based on the feedback control report in S400, and sets control priorities and adjustment optimization.

[0142] The restoration evaluation module collects vegetation growth data, calculates the restoration effect of each area based on the comparison between the collected vegetation growth data and the growth demand parameters, and generates a restoration effect evaluation report.

[0143] In summary, the present application provides a slope vegetation restoration system and a restoration method thereof, which obtains microenvironment data, sets a timed collection of microenvironment data and anomaly thresholds, obtains slope vegetation type data, and performs parameterized and standardized processing, generates a demand matching status report, sets a regional growth environment based on the demand matching status report obtained in S200, sets a feedback mechanism based on the setting of the growth environment in S300, and regulates the setting operation based on the feedback mechanism, generates a feedback regulation report, analyzes and adjusts based on the feedback regulation report in S400, sets regulation priority and adjustment optimization, collects vegetation growth data, and performs a regional growth environment setting based on the collected vegetation growth status report. The long-term data is compared with the growth requirement parameters, the restoration effect of each area is calculated, and a restoration effect evaluation report is generated. By collecting microenvironment data in real time and dynamically adjusting based on the growth requirement parameters of the plants, the slope area can better adapt to environmental changes. The intelligent feedback mechanism and demand matching analysis are used to accurately adjust the temperature, humidity and light conditions, thereby providing a continuous and suitable growth environment for vegetation at different growth stages. At the same time, through regular restoration effect evaluation and digital recording, this method realizes the quantification of restoration effects, provides support for the long-term and stable recovery of slope vegetation, and greatly improves the survival rate of vegetation and the stability of the overall ecosystem. BRIEF DESCRIPTION OF THE DRAWINGS

[0144] Figure 1 It is an overall flow chart of a slope vegetation restoration method provided in an embodiment of the present application.

[0145] Figure 2 It is an overall flow chart of a slope vegetation restoration system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0146] Please refer to Figure 1 and Figure 2 , which shows the process of an embodiment of a slope vegetation restoration system and a restoration method thereof according to the present disclosure.

[0147] like Figure 1 As shown, a slope vegetation restoration method comprises the following steps:

[0148] Step S100, obtaining microenvironment data, and setting a timed collection of microenvironment data and an abnormal threshold;

[0149] Step S200, obtaining slope vegetation type data, parameterizing and standardizing it, and generating a demand matching status report;

[0150] Step S300, setting a regional growth environment based on the demand matching status report obtained in step S200;

[0151] Step S400, setting a feedback mechanism based on the setting of the growth environment in step S300, and regulating the setting operation based on the feedback mechanism, and generating a feedback regulation report;

[0152] Step S500: Analyze and adjust based on the feedback control report in step S400, and set control priority and adjustment optimization;

[0153] Step S600: Collect vegetation growth data, calculate the restoration effect of each area based on the comparison between the collected vegetation growth data and the growth requirement parameters, and generate a restoration effect evaluation report.

[0154] In some specific implementations, obtaining microenvironment data and setting a timed collection of microenvironment data and an abnormal threshold specifically include:

[0155] Step S100.1, arrange microenvironment data acquisition devices in different areas of the slope to obtain microenvironment data.

[0156] Among them, the sensor layout density is 1 sensor per 100 square meters.

[0157] Among them, the micro-environment data acquisition device includes: temperature and humidity sensors, light sensors and soil moisture sensors.

[0158] Step S100.1.1, transmitting the obtained microenvironment data to a central data processing unit via wireless.

[0159] Step S100.2: Set the central data processing unit to start collection at a fixed time.

[0160] The central data processing unit is set to collect numerical data of air temperature, air humidity, light intensity and soil moisture once every hour.

[0161] Among them, at the turn of the dry season and the rainy season or during special weather periods, the collection frequency automatically increases to once every 30 minutes to record the impact of abnormal weather on the slope microenvironment.

[0162] Step S100.2.1: When the central data processing unit detects that a specified abnormal threshold is exceeded at a sensor point, an automatic alarm is sent to remind of the abnormal situation and the abnormal data is automatically marked.

[0163] Among them, the abnormal thresholds are set as: temperature>35℃ and soil moisture<15%.

[0164] If a sensor fails or loses data during data collection, the central data processing unit will automatically send a fault alarm within 30 seconds and record the ID and timestamp of the faulty sensor to facilitate subsequent maintenance and data supplementation.

[0165] In some specific implementations, the slope vegetation type data is obtained, parameterized and standardized, and a demand matching status report is generated, specifically including:

[0166] Step S200.1, obtain slope vegetation type data and divide it into different growth stages.

[0167] Among them, different growth stages include: germination stage, seedling stage and maturity stage.

[0168] Step S200.1.1: Based on the slope vegetation type data, the requirements for microenvironment data at each growth stage are parameterized.

[0169] Among them, demand parameterization includes:

[0170] The temperature requirement during the germination period is greater than 0:20-25℃.

[0171] The air humidity requirement during the seedling stage is greater than 0:60-70%.

[0172] The soil moisture requirement during maturity is greater than 0:20-30%.

[0173] Step S200.1.2: uniformly convert the demand parameters of each stage into growth demand parameter standards.

[0174] Step S200.1.3: Arrange and classify the microenvironmental requirement parameters based on the vegetation growth stage, and form a preliminary requirement parameter list.

[0175] Among them, the preliminary list of required parameters includes: temperature, air humidity, light intensity and soil moisture indicators.

[0176] Step S200.2: Based on the obtained preliminary demand parameter list, standardize the demand parameters of different vegetation at each growth stage, and generate a complete growth demand parameter standard set.

[0177] Among them, standardization processing is to conduct statistical analysis of various parameters based on the actual adaptability of vegetation to micro-environmental data such as temperature, humidity, and light.

[0178] The analysis means that if the suitable temperature range for a certain plant is 20-30℃, it will be standardized to 25℃±5℃.

[0179] Among them, the standard set of growth requirement parameters covers the range of requirements for microenvironment data at different growth stages.

[0180] Step S200.3: compare the obtained microenvironment data with the growth requirement parameter standard set one by one.

[0181] Among them, the comparison is to match and analyze the microenvironment data of each area with the needs of the plants at each growth stage.

[0182] Among them, matching analysis means that if the temperature of a certain area exceeds the growth requirement by ±5°C, the area is marked as a temperature control priority area; if the soil moisture is lower than 10% of the growth requirement, it is marked as a humidity control priority area.

[0183] Step S200.4: Based on the comparison results, generate a demand matching status report for each area.

[0184] Among other things, the report content indicates whether the region’s microenvironmental data meets or does not meet growth requirements.

[0185] In some specific implementations, the regional growth environment control setting is performed based on the demand matching status report obtained in step S200, specifically including:

[0186] Step S300.1: Based on the obtained demand matching status report, identify the areas that do not meet the growth demand parameters, and form a list of areas for demand priority regulation.

[0187] Among them, the area list setting refers to the division of areas into temperature control priority areas and humidity control priority areas.

[0188] Step S300.2: In the temperature control priority area, the temperature is adjusted by heating or cooling measures based on the actual deviation of the temperature amplitude.

[0189] Among them, when the regional temperature is lower than the lower limit of the growth requirement parameter, the controllable heating device is started to raise the temperature to the required range.

[0190] Among them, if the regional temperature is higher than the upper limit of growth requirements, ventilation equipment is used to cool down to control the regional temperature.

[0191] Step S300.3: In the humidity control priority area, the soil humidity and air humidity are adjusted based on the humidity deviation degree in the demand matching status report.

[0192] Among them, if the soil moisture is lower than the lower limit of the required parameter, the sprinkler system will be started to provide the required moisture to the soil.

[0193] Among them, if the air humidity is too low, an atomization device should be used to increase the air humidity to meet the growth needs of vegetation.

[0194] Among them, when the light intensity does not meet the growth requirements of an area, shading measures or enhanced light are used to regulate the light intensity.

[0195] Among them, if the light intensity in the area is too high, a movable sunshade is used to reduce the impact of direct light and control the light intensity to an appropriate range.

[0196] If there is insufficient light, reflectors are used to increase the light to ensure that the vegetation can obtain the required amount of light.

[0197] In some specific implementations, a feedback mechanism is set based on the setting of the growth environment in step S300, and the setting operation is regulated based on the feedback mechanism to generate a feedback regulation report, which specifically includes:

[0198] Step S400.1: Set up a monitoring mechanism in the area after temperature, humidity and light are regulated.

[0199] Among them, the monitoring mechanism monitors targets including: temperature, humidity, light intensity and soil moisture.

[0200] Among them, the monitoring data transmission refers to the central data processing unit.

[0201] Step S400.2: Compare each parameter with the growth demand parameter standard set based on the monitoring data received by the central data processing unit.

[0202] Among them, when the microenvironment data of a certain area deviates from the growth requirement parameter standard, the feedback mechanism is triggered, the area is marked and a real-time feedback record is generated.

[0203] In this process, based on the feedback records, the corresponding control devices are activated through the central data processing unit to implement dynamic control of the microenvironment.

[0204] The feedback mechanism is as follows:

[0205] F adj =α·|P obs -P std |+β·T+γ·ΔR

[0206] Where: F adj It is a feedback control parameter used to determine whether it is necessary to start the control and its intensity. adj Exceeding the preset threshold F thresh When the control is automatically triggered, Pobs is the actual value of the currently collected microenvironment data, which includes: current temperature, humidity and light intensity, P std is the corresponding standard value of the growth requirement parameter, that is, the target parameter value of the plant requirement. T is the regulation time factor, that is, the time since the last regulation, which is used to measure the regulation interval. If the time interval is short, it means that the area needs frequent regulation. ΔR is the recovery deviation rate, that is, the percentage of the current collected data compared to the change after the last regulation, which is used to judge the efficiency of microenvironment recovery.

[0207] Here, it should be noted that if the humidity demand is 30%, the humidity reaches 28% after the last adjustment, and the current collection value is 24%, then the recovery deviation rate is **-14%%**.

[0208] Here, it is necessary to explain that the weight parameters are: α, β, and γ are the weights of microenvironment deviation, regulation interval time, and recovery deviation rate, respectively, which control the influence of each factor on feedback regulation.

[0209] Among them, the initial value of the weight parameter is:

[0210] α=1.2,β=0.8,γ=1.0

[0211] Here, we need to explain the influence of microenvironment deviation: In the formula, |P obs -P std |It reflects the difference between the current microenvironment data and the growth requirement parameters. The larger the difference, the more urgent the need for growth adjustment.

[0212] Here, it is necessary to explain the control time factor: T is used to measure the time interval from the last control to the current time. If the interval is short, the control frequency is too high, and it may be necessary to adjust the adaptability parameters or control methods of the region.

[0213] Here, it is necessary to explain the recovery deviation rate: ΔR reflects the effect of microenvironment recovery after regulation. A negative deviation rate indicates that the regulation failed to achieve the expected effect, and a positive deviation rate indicates that the regulation is effective.

[0214] Among them, dynamic control includes: temperature control, humidity control and light control.

[0215] Among them, temperature control means that when the monitoring data detects that the temperature is lower or higher than the required parameters, the heating or cooling device is automatically started to adjust the temperature.

[0216] Among them, humidity control means that when the air humidity or soil humidity is not within the required range, the water spray or atomization device is automatically started to adjust the humidity.

[0217] Among them, for light control, when the light intensity does not meet the required parameters, the shading or reflective device will be automatically activated to adjust the light.

[0218] Among them, when the microenvironment data returns to the growth requirement parameter range, the control device will automatically stop.

[0219] Step S400.3: After the control is completed, the central data processing unit records the control process of each area, including the changes in microenvironment data before and after the control and the execution time of the control device, and generates a feedback control report.

[0220] Among them, the feedback and control report includes: the deviation of microenvironment data in each region, control measures and control effects.

[0221] In some specific implementations, analysis and adjustment are performed based on the feedback control report in step S400, and control priority and adjustment optimization are set, specifically including:

[0222] Step S500.1: Analyze the control data of each region based on the generated feedback control report.

[0223] Among them, the frequency of regulation and the number of deviations from growth demand parameters in each region are counted, and areas that require frequent regulation or long-term deviations are identified as key areas for optimization.

[0224] Among them, the analysis includes: regulation frequency, deviation amplitude and microenvironment recovery effect.

[0225] Among them, the analysis was to use weighted average algorithm and variance analysis to identify the regulation pattern and stability of regional microenvironment data.

[0226] The weighted average algorithm is specifically:

[0227]

[0228] Where: W avg is the weighted average regulation intensity, which is used to judge the intensity and priority of regulation needs in each region. i is the number of times the ith region is regulated, that is, the total number of times the region is regulated within a specified time, d i : The deviation amplitude of the ith region, that is, the absolute value of the deviation between the microenvironment data of the region and the growth requirement parameters, r i is the recovery effect of the ith region, that is, the degree to which the microenvironment data of each region recovers to the growth requirement parameters after regulation. The higher the recovery effect, the better the regulation effect. f ,w d ,w r It is the weight coefficient of the number of controls, deviation amplitude and recovery effect, which is used to adjust the impact of each factor on the intensity of control.

[0229] Among them, variance analysis refers to the analysis of the control data of each region according to the control frequency, deviation amplitude and recovery The effect is grouped and the data of each area is set as a group and recorded as X i , where each group contains multiple observations.

[0230] Among them, the group means of each region or group in variance analysis are calculated as follows:

[0231]

[0232] Where: is the mean of the ith region, n i is the number of data points in the region, X ij represents the jth data point in the i-th region.

[0233]

[0234] Where: is the grand mean of all regions, N is the total number of data points, and k is the number of regions.

[0235] The variance between groups in variance analysis is calculated as:

[0236]

[0237] Where: represents the between-group variance, reflecting the difference in means between different regions.

[0238] The variance within the group is calculated in the variance analysis as:

[0239]

[0240] In the formula, Represents the within-group variance, reflecting the degree of dispersion of data within the region.

[0241] The calculation of F statistics is as follows:

[0242]

[0243] In the formula: F value is used to test whether the difference between groups is significant.

[0244] The significance test is to compare the calculated F value with the critical value F critical Compare, look up from the F distribution table, determine according to the significance level α and degrees of freedom, if F>F critical, , it is believed that there are significant differences in the regulatory needs of different regions and further optimization is needed.

[0245] Among them, if the F value is significantly higher than the critical value, the relevant area will be set as high priority and the regulation frequency will be increased.

[0246] Step S500.1.1: Generate priority regulation labels for each region based on the analysis results.

[0247] Step S500.2: Based on the priority control label, filter out areas where the control frequency exceeds 2 times per hour.

[0248] Step S500.2.1: loosen or tighten the growth requirement parameter range based on the screened areas.

[0249] Among them, if the temperature in a certain area is frequently adjusted, the temperature requirement standard can be adjusted from the original 25℃±5℃ to 25℃±7℃.

[0250] Among them, a linear regression model is used, with historical control data as independent variables, to calculate the adaptive parameter range.

[0251] Here, it should be noted that the linear regression model is a prior art well known to those skilled in the art and will not be described in detail here.

[0252] Step S500.2.2: Based on the adjusted parameter range, a dynamic adjustment algorithm is used to automatically optimize the growth requirement parameters based on regional seasonal changes and environmental trends.

[0253] The dynamic adjustment algorithm is specifically as follows:

[0254] D adj =η·(E obs -E target )+λ·ΔT+μ·ΔR

[0255] Where: D adj is a dynamic adjustment parameter, which indicates the adjustment value of the control parameter. The adjusted control parameter is P new =P original +D abj , E obs is the observed real-time microenvironment data, such as current temperature, humidity or light intensity, E target is the target value of growth requirements, that is, the standard value of the microenvironment required by the plant. ΔT is the time interval since the last regulation, expressed in hours, which is used to measure the frequency of regulation. ΔR is the recovery rate of the microenvironment after regulation, which is defined as the speed at which the environment recovers to the target value after regulation, expressed in percentage.

[0256] Among them, the weight parameters in the formula are described as follows: η is the regulation intensity coefficient, which is used to adjust the impact of the difference between the observed value and the target value on the result. The recommended initial value is 1.5; λ is the time interval coefficient, which is used to control the impact of the regulation time interval. The recommended initial value is 0.7. The longer the time interval, the greater the regulation demand; μ is the recovery rate coefficient, which reflects the efficiency of environmental recovery after regulation. The recommended initial value is 0.5. The lower the recovery rate, the limited regulation effect and the need to increase the regulation intensity.

[0257] Step S500.3: Optimize the control frequency of each area based on the historical data of the feedback mechanism.

[0258] Among them, the regulation frequency is automatically adjusted based on the recovery deviation and recovery time of different areas. If the microenvironment can recover quickly after regulation, the regulation frequency can be reduced. If the recovery is slow, the regulation priority will be increased.

[0259] Step S500.3.1: Based on the priority regulation of each region, set seasonal regulation priority rules.

[0260] Priority rules include: high priority area and low priority area.

[0261] Among them, the high priority areas are: critical growth periods, and high priority regulation is set. The critical growth periods include: germination period and seedling period.

[0262] Among them, the low priority area is: the mature area with higher stability, which is set to have a lower priority.

[0263] In some specific implementations, vegetation growth data is collected, and based on the comparison between the collected vegetation growth data and the growth requirement parameters, the restoration effect of each area is calculated, and a restoration effect evaluation report is generated, which specifically includes:

[0264] Step S600.1: Based on the microenvironment control status of each area, collect vegetation growth data of each area.

[0265] Among them, vegetation growth data include: plant height, leaf area, root distribution and vegetation coverage.

[0266] Step S600.2: Compare the data collected in steps S100 to S400 and the adjusted microenvironment data with the plant growth requirement parameter standard set one by one, and determine whether the current microenvironment meets the vegetation growth requirements.

[0267] Among them, the standard sets of growth demand parameters are compared one by one and calculated using a matching analysis algorithm.

[0268] Among them, the matching condition is that if the temperature, humidity and light parameters are all within the required standard range, it is determined that the requirements are met; if any parameter is not met, it is determined that the requirements are not met.

[0269] Step S600.3: Use the weighted restoration index formula to calculate the vegetation restoration effect of each area and use it to measure the comprehensive restoration effect.

[0270] Among them, let the repair effect index be R eff , specifically:

[0271] R eff =w h ·H+w a ·A+w r ·D+w c ·C

[0272] Where: H is the average plant height, reflecting the overall growth height of the vegetation; A is the average leaf area, reflecting the photosynthesis capacity of the vegetation; D is the root distribution density, indicating the soil stability of the vegetation; C is the vegetation coverage rate, indicating the ecological coverage of the region; w h 、w a 、w r 、w c are the weights of plant height, leaf area, root distribution and coverage rate, respectively.

[0273] The weights of plant height, leaf area, root distribution and coverage are initially set as: h =0.3, w a =0.2, w r =0.3, w c =0.2.

[0274] Among them, the calculation of the repair effect index R eff Finally, the restoration effect is counted by region, a restoration effect analysis table is generated, and it is determined which areas have reached the ideal restoration state.

[0275] Step S600.4: Based on the repair effect analysis table, the central data processing unit generates a repair effect evaluation report.

[0276] Among them, the restoration effect evaluation report records include: vegetation growth conditions, microenvironment matching conditions and restoration effect indicators in each area.

[0277] Among them, the restoration effect evaluation report also includes: the restoration effect index R of each area eff values, how well regional needs are being met, areas where restoration is progressing faster, and areas that need additional regulatory support.

[0278] like Figure 2As shown, the present application further provides a slope vegetation restoration system, comprising the following modules:

[0279] The data acquisition module obtains microenvironment data and sets the timed collection of microenvironment data and abnormal thresholds.

[0280] The demand analysis module obtains slope vegetation type data, parameterizes and standardizes it, and generates a demand matching status report.

[0281] The environment control module sets the regional growth environment based on the demand matching status report obtained in step S200.

[0282] The feedback control module sets a feedback mechanism based on the setting of the growth environment in S300, and controls the setting operation based on the feedback mechanism to generate a feedback control report.

[0283] The control optimization module analyzes and adjusts based on the feedback control report in S400, and sets control priorities and adjustment optimization.

[0284] The restoration evaluation module collects vegetation growth data, calculates the restoration effect of each area based on the comparison between the collected vegetation growth data and the growth demand parameters, and generates a restoration effect evaluation report.

[0285] In the above content, in actual application, first, micro-environment data acquisition devices are deployed in various areas of the slope to collect micro-environment data such as ambient temperature, air humidity, light intensity and soil moisture. The acquisition devices are set to collect data once an hour and transmit all micro-environment data to the central data processing unit. Each acquisition device includes a temperature and humidity sensor, a light sensor and a soil moisture sensor. When the temperature is outside the specific threshold range of 35°C and the soil moisture is outside 15%, the central data processing unit will record the abnormal situation and issue an alarm to ensure the integrity of the monitoring data.

[0286] Based on the type and growth stage of slope vegetation, its growth requirements are parameterized. Taking the germination period, seedling period and maturity period as examples, the temperature requirement in the germination period is 20-25°C, the air humidity requirement in the seedling period is 60-70%, and the soil humidity requirement in the maturity period is 20-30%. After standardizing these demand data, a standard set of growth demand parameters is formed. The central data processing unit compares the collected microenvironment data with this standard set of parameters one by one to identify and record whether each area meets the growth requirements of the plants.

[0287] Based on the above comparison results, the central data processing unit generates a list of control areas. If the temperature in a certain area is lower than the standard lower limit of the growth requirements, the heating device is started. If the temperature exceeds the upper limit, the ventilation equipment is started to lower the temperature. If the soil humidity in the humidity control priority area is lower than the required standard, the water spray system will be automatically started to adjust the soil humidity. If the air humidity is too low, the atomization device will be started to increase the air humidity. For areas where the light intensity does not meet the growth requirements, if the light is too high, the movable sunshade device will be started. If the light is insufficient, the reflector will be used to increase the light to ensure that the plants can obtain the required light conditions.

[0288] During the implementation of regulation, the microenvironment data of each area is monitored in real time. The central data processing unit automatically determines whether to initiate further regulation based on the feedback mechanism. When the monitored microenvironment data deviates from the growth requirement standard, the regulation device is automatically triggered to make adjustments. When the temperature, humidity, or light returns to the required range, the regulation is automatically stopped to save resources. During the regulation process, the feedback data and regulation effects of each area will generate feedback regulation records for subsequent analysis.

[0289] By analyzing the feedback control records, we can identify areas where control is frequent or recovery effects are poor, apply weighted average algorithm and dynamic adjustment algorithm, adjust the growth demand parameter standards of the corresponding areas, and dynamically optimize the control priority. The central data processing unit generates an optimization strategy report based on the optimization results to provide support for subsequent control and ensure that the control strategy can be flexibly adjusted according to actual conditions.

[0290] Vegetation growth data including plant height, leaf area, root distribution and vegetation were collected every two weeks. Coverage rate, forming the repair effect index, and calculating the repair effect index R eff =w h ·H+w a ·A+w r ·D+w c ·C, where H, A, D, and C represent plant height, leaf area, root distribution density, and coverage, respectively. h 、w a 、w r 、w c The weights of each item are set as follows: initially set to 0.3, 0.2, 0.3, 0.2. The central data processing unit generates a restoration effect evaluation report based on the restoration effect indicators, pointing out the restoration progress of each area to ensure the continuity and effectiveness of vegetation restoration work.

Claims

1. A slope vegetation restoration method, characterized in that: The steps include: S100, obtaining microenvironment data, and setting a timed collection of microenvironment data and anomaly thresholds; S200, obtaining slope vegetation type data, parameterizing and standardizing it, and generating a demand matching status report; S300, setting a regional growth environment based on the demand matching status report obtained in S200; S400, setting a feedback mechanism based on the setting of the growth environment in S300, and regulating the setting operation based on the feedback mechanism, and generating a feedback regulation report; S500: Analyze and adjust based on the feedback control report in S400, and set control priority and adjustment optimization; S600, collecting vegetation growth data, calculating the restoration effect of each area based on the comparison between the collected vegetation growth data and the growth requirement parameters, and generating a restoration effect evaluation report.

2. A slope vegetation restoration method according to claim 1, characterized in that: Obtain microenvironment data and set up timed collection of microenvironment data and abnormal thresholds, including: S100.

1. Arrange microenvironment data acquisition devices in different areas of the slope to obtain microenvironment data; S100.1.1, transmitting the obtained microenvironment data to a central data processing unit via wireless; S100.2, setting the central data processing unit to start collection at a scheduled time; S100.2.

1. When the central data processing unit detects that a specified abnormal threshold is exceeded at a sensor point, an automatic alarm will be sent to warn of the abnormal situation and the abnormal data will be automatically marked; Among them, the abnormal thresholds are all set to be greater than 0.

3. A slope vegetation restoration method according to claim 1, characterized in that: Obtain slope vegetation type data, parameterize and standardize it, and generate a demand matching status report, including: S200.

1. Obtain slope vegetation type data and divide it into different growth stages; S200.1.

1. Based on the slope vegetation type data, parameterize the demand for microenvironmental data at each growth stage; S200.1.

2. Convert the demand parameters of each stage into growth demand parameter standards; S200.1.

3. Organize and classify the microenvironmental demand parameters based on the vegetation growth stage and form a preliminary list of demand parameters; S200.

2. Based on the obtained preliminary demand parameter list, standardize the demand parameters of different vegetation at each growth stage and generate a complete standard set of growth demand parameters; S200.3, comparing the obtained microenvironment data with the growth requirement parameter standard set one by one; S200.

4. Based on the comparison results, generate a demand matching status report for each area.

4. A slope vegetation restoration method according to claim 1, characterized in that: Based on the demand matching status report obtained in S200, the regional growth environment regulation and setting is performed, specifically including: S300.

1. Based on the obtained demand matching status report, identify the areas that do not meet the growth demand parameters and form a list of areas with priority regulation of demand; Among them, the area list setting refers to the division of areas into temperature control priority areas and humidity control priority areas; S300.2, temperature control priority area, based on the actual deviation of the temperature amplitude, the temperature is adjusted by heating or cooling measures; S300.3, humidity control priority area, adjust soil humidity and air humidity based on the humidity deviation degree in the demand matching status report; Among them, when the light intensity does not meet the growth requirements of an area, shading measures or enhanced light are used to regulate the light intensity.

5. A slope vegetation restoration method according to claim 1, characterized in that: After the growth environment is set in S300, a feedback mechanism is set, and the setting operation is regulated based on the feedback mechanism to generate a feedback regulation report, which specifically includes: S400.

1. Set up a monitoring mechanism in the area after temperature, humidity and light control; wherein the monitoring data transmission refers to the central data processing unit; S400.

2. Compare each parameter with the growth requirement parameter standard set based on the monitoring data received by the central data processing unit; When the microenvironment data of a certain area deviates from the growth requirement parameter standard, the feedback mechanism is triggered, marking the area and generating real-time feedback records; Among them, based on the feedback records, the corresponding control devices are activated through the central data processing unit to implement dynamic control of the microenvironment; The feedback mechanism is as follows: F adj =α·|P obs -P std |+β·T+γ·ΔR Where: F adj It is a feedback control parameter used to determine whether it is necessary to start the control and its intensity. adj Exceeding the preset threshold F thresh When the control is automatically triggered, P obs is the actual value of the currently collected microenvironment data, which includes: current temperature, humidity and light intensity, P std is the corresponding standard value of the growth requirement parameter, that is, the target parameter value of the plant requirement, T is the regulation time factor, that is, the time since the last regulation, which is used to measure the regulation interval. If the time interval is short, it means that the area needs frequent regulation, ΔR is the recovery deviation rate, that is, the percentage of the current collected data compared to the change after the last regulation, which is used to judge the efficiency of microenvironment recovery; Among them, dynamic control includes: temperature control, humidity control and light control; Among them, temperature control means that when the monitoring data detects that the temperature is lower or higher than the required parameters, the heating or cooling device is automatically started to adjust the temperature; Among them, humidity control means that when the air humidity or soil humidity is not within the required range, the water spray or atomization device is automatically started to adjust the humidity; Among them, light control, when the light intensity does not meet the required parameters, the sunshade or reflective device is automatically activated to adjust the light; S400.

3. After the control is completed, the central data processing unit records the control process of each area, including the changes in microenvironment data before and after the control and the execution time of the control device, and generates a feedback control report.

6. A slope vegetation restoration method according to claim 1, characterized in that: Analyze and adjust based on the feedback control report in S400, and set control priorities and adjustment optimization, including: S500.

1. Analyze the control data of each region based on the generated feedback control report; Among them, the analysis was to use weighted average algorithm and variance analysis to identify the regulatory pattern and stability of regional microenvironment data; The weighted average algorithm is specifically: Where: W avg is the weighted average regulation intensity, which is used to determine the intensity and priority of each region’s regulation needs. i is the number of regulation times of the ith region, that is, the total number of regulation times of the region within a specified time, d i : The deviation amplitude of the ith region, that is, the absolute value of the deviation between the microenvironment data of the region and the growth requirement parameters, r i is the recovery effect of the ith region, that is, the degree to which the microenvironment data of each region recovers to the growth requirement parameters after regulation. The higher the recovery effect, the better the regulation effect. f ,w d ,w r is the weight coefficient of the number of regulation times, deviation amplitude and recovery effect, which is used to adjust the impact of each factor on the regulation intensity; Among them, variance analysis refers to the analysis of the control data of each region according to the control frequency, deviation amplitude and recovery The effect is grouped and the data of each area is set as a group and recorded as X i , where each group contains multiple observations; S500.1.

1. Based on the analysis results, generate priority regulation labels for each region; S500.

2. Based on the priority control label, filter out areas where the control frequency exceeds 2 times per hour; S500.2.

1. Relax or tighten the growth requirement parameter range based on the selected areas; Among them, a linear regression model is used to calculate the adaptive parameter interval with historical regulation data as the independent variable; S500.2.2, based on the adjusted parameter range, adopt a dynamic adjustment algorithm and automatically optimize the growth demand parameters based on regional seasonal changes and environmental trends; The dynamic adjustment algorithm is specifically as follows: D adj =η·(E obs -E target )+λ·ΔT+μ·ΔR Where: D adj is a dynamic adjustment parameter, which indicates the adjustment value of the control parameter. The adjusted control parameter is P new =P ori g inal +D adj , E obs is the observed real-time microenvironment data, such as current temperature, humidity or light intensity, E target is the target value of growth demand, that is, the standard value of the microenvironment required by the plant, ΔT is the time interval since the last regulation, in hours, which is used to measure the frequency of regulation, ΔR is the recovery rate of the microenvironment after regulation, which is defined as the speed at which the environment recovers to the target value after regulation, in percentage; S500.

3. Optimize the control frequency of each area based on the historical data of the feedback mechanism; Among them, the control frequency is automatically adjusted based on the recovery deviation and recovery time of different areas. If the microenvironment can recover quickly after control, the control frequency can be reduced. If the recovery is slow, the control priority is increased. S500.3.

1. Based on the priority regulation of each region, set seasonal regulation priority rules.

7. A slope vegetation restoration method according to claim 1, characterized in that: Collect vegetation growth data, compare the collected vegetation growth data with growth demand parameters, calculate the restoration effect of each area, and generate a restoration effect evaluation report, including: S600.

1. Collect vegetation growth data of each area based on the microenvironment regulation status of each area; S600.2, comparing the data collected in S100 to S400 and the adjusted microenvironment data with the plant growth requirement parameter standard set one by one, and determining whether the current microenvironment meets the vegetation growth requirements; S600.

3. Use the weighted restoration index formula to calculate the vegetation restoration effect of each area and use it to measure the comprehensive effect of restoration; Among them, let the repair effect index be R eff , specifically: R eff =w h ·H+w a ·A+w r ·D+w c ·C Where: H is the average plant height, reflecting the overall growth height of the vegetation; A is the average leaf area, reflecting the photosynthesis capacity of the vegetation; D is the root distribution density, indicating the soil stability of the vegetation; C is the vegetation coverage rate, indicating the ecological coverage of the region; w h 、w a 、w r 、w c are the weights of plant height, leaf area, root distribution and coverage, respectively; Among them, the calculation of the repair effect index R eff Finally, the restoration effect is counted by region, a restoration effect analysis table is generated, and it is determined which areas have reached the ideal restoration state; S600.

4. Based on the repair effect analysis table, the central data processing unit generates a repair effect evaluation report.

8. A slope vegetation restoration system, characterized in that: Includes the following modules: The data acquisition module obtains microenvironment data and sets the timed collection of microenvironment data and abnormal thresholds; Demand analysis module, which obtains slope vegetation type data, performs parameterization and standardization processing, and generates demand matching status report; The environment control module sets the regional growth environment based on the demand matching status report obtained by S200; A feedback control module sets a feedback mechanism based on the setting of the growth environment in S300, and controls the setting operation based on the feedback mechanism to generate a feedback control report; The control optimization module analyzes and adjusts based on the feedback control report in S400, and sets control priorities and adjustment optimization; The restoration evaluation module collects vegetation growth data, calculates the restoration effect of each area based on the comparison between the collected vegetation growth data and the growth demand parameters, and generates a restoration effect evaluation report.

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