A slope vegetation restoration system and a restoration method thereof

Abstract

The application discloses a kind of slope vegetation restoration system and its restoration method, it is related to slope vegetation restoration field, obtain microenvironment data, and set timing collection microenvironment data and abnormal threshold, obtain slope vegetation species data, and parameterization and standardization processing, generate demand matching state report, based on the demand matching state report obtained in S200 region growth environment setting, based on the setting of growth environment in S300, setting feedback mechanism is carried out, and based on feedback mechanism regulation setting operation, generate feedback control report, by real-time collection microenvironment data and based on the growth demand parameter of plant dynamic regulation, so that slope area can better adapt to environmental change, utilize feedback mechanism and demand matching analysis, accurately adjust temperature, humidity and illumination condition, to provide sustained suitable growth environment for vegetation in different growth stages, at the same time, through regular repair effect evaluation and data record, support is provided for long-term stable recovery of slope vegetation.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of slope vegetation restoration, and in particular to a slope vegetation restoration system and a restoration method thereof. BACKGROUND

[0002] With the continuous change of natural environment, the slope ecosystem is facing increasingly severe challenges. Weathering, rainfall erosion and human activities have intensified the instability of the slope and the damage of the vegetation, affecting the balance and long-term effectiveness of the slope ecosystem. Traditional slope vegetation restoration methods usually rely on simple manual covering or irrigation methods, lack systematic support for vegetation needs and micro-environment regulation, and cannot effectively achieve precise ecological restoration.

[0003] The existing slope vegetation restoration method still needs to be improved in terms of fine management and long-term effect. First, the traditional method relies on fixed frequency manual maintenance in terms of micro-environment regulation, and cannot dynamically respond to environmental changes in the slope area. Second, the growth needs of different types of vegetation are quite different, and the existing method has less consideration for the needs of different growth stages, which may not fully meet the diversified ecological restoration needs. In addition, in the evaluation of the restoration effect, the existing technology often lacks systematic monitoring and data analysis means, resulting in insufficient quantitative evaluation of the restoration effect, which may affect the precision of continuous optimization. SUMMARY

[0004] The present application aims to overcome the shortcomings of the prior art and provide a slope vegetation restoration system and a restoration method thereof, which aims to realize adaptive ecological restoration process through dynamic collection, feedback regulation and restoration effect evaluation of micro-environment data. First, the real-time collected micro-environment data is used to identify the temperature, humidity and light conditions of the slope area, and dynamic adjustment is made based on the growth needs of plants to adapt to environmental changes and reduce the frequency of manual maintenance. Second, through precise analysis and regulation optimization of growth demand parameters, different types of vegetation are provided with environment support that is more suitable for their growth cycle, improving the survival rate and restoration effect of the vegetation. Finally, the restoration effect evaluation step data analyzes the restoration process, making the restoration effect quantifiable, and overall improving the stability and long-term restoration ability of the slope ecology.

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

[0006] Step S100, obtaining micro-environment data and setting a timing collection of micro-environment data and an abnormal threshold value;

[0007] Step S200, obtaining slope vegetation type data and parameterizing and standardizing processing to generate a demand matching state report;

[0008] Step S300, setting the growth environment based on the demand matching state 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 to generate a feedback regulation report;

[0010] Step S500, analyzing and adjusting based on the feedback regulation report in step S400, and setting the regulation priority and adjustment optimization;

[0011] Step S600, collecting vegetation growth data, comparing the collected vegetation growth data with the growth demand parameters, calculating the repair effect of each region, and generating a repair effect evaluation report.

[0012] In some embodiments, microenvironment data is obtained, and a timing collection of microenvironment data and an abnormal threshold are set, specifically including:

[0013] Step S100.1, arranging microenvironment data collection devices in different regions of the slope to obtain microenvironment data.

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

[0015] Among them, the microenvironment data collection device includes a temperature and humidity sensor, a light sensor, and a soil humidity sensor.

[0016] Step S100.1.1, the obtained microenvironment data is transmitted to the central data processing unit through wireless transmission.

[0017] Step S100.2, set the central data processing unit to collect data at a regular time.

[0018] Among them, the central data processing unit is set to collect data on air temperature, air humidity, light intensity and soil humidity once an hour.

[0019] Among them, when the dry season and the rainy season or during special weather, the collection frequency is automatically increased to once every 30 minutes, and the influence of abnormal weather on the microenvironment of the slope is recorded.

[0020] Step S100.2.1, when the central data processing unit detects that the sensor point exceeds the specified abnormal threshold, an automatic alarm will be sent to remind the abnormal situation and automatically mark the abnormal data.

[0021] Among them, the abnormal threshold is set to: temperature > 35℃ and soil humidity < 15%.

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

[0023] In some embodiments, slope vegetation species data is obtained and parameterized and standardized to generate a demand matching status report, which specifically includes:

[0024] Step S200.1: Obtain slope vegetation species data and divide it into different growth stages.

[0025] Different growth stages include germination, seedling, and maturity.

[0026] Step S200.1.1: Based on slope vegetation species data, parameterize the demand of each growth stage for microenvironment data.

[0027] The demand parameterization includes:

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

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

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

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

[0032] Step S200.1.3: Based on the microenvironment demand parameters of the vegetation growth stage, organize and classify them, and form a preliminary demand parameter list.

[0033] The preliminary demand parameter list includes temperature, air humidity, light intensity, and soil moisture indicators.

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

[0035] The standardization process involves statistical analysis of the actual adaptability of vegetation to temperature, humidity, and light microenvironment data.

[0036] If the optimal temperature range for a certain plant is 20-30℃, it is standardized to 25℃±5℃.

[0037] The growth demand parameter standard set covers the demand range of different growth stages for microenvironment data.

[0038] Step S200.3, based on the obtained microenvironment data, a one-by-one comparison is made with the standard set of growth requirement parameters.

[0039] In which, the comparison is to match and analyze the microenvironment data of each area with the growth stage requirements of the plant.

[0040] In which, the matching analysis refers to marking the area as a temperature regulation priority area if the temperature of the area exceeds the growth requirement by ±5℃, and marking the area as a humidity regulation priority area if the soil humidity is less than 10% of the growth requirement.

[0041] Step S200.4, based on the comparison results, a requirement matching state report is generated for each area.

[0042] In which, the report indicates whether the microenvironment data of the area meets or does not meet the growth requirement.

[0043] In some embodiments, based on the requirement matching state report obtained in step S200, the growth environment regulation setting of the area is set, specifically including:

[0044] Step S300.1, based on the requirement matching state report obtained, the area that does not meet the growth requirement parameter is identified, and a list of areas that require priority regulation is set.

[0045] In which, the area list setting refers to the division of the area into temperature regulation priority areas and humidity regulation priority areas.

[0046] Step S300.2, for the temperature regulation priority area, based on the actual deviation of the temperature amplitude, temperature adjustment is made through heating or cooling measures.

[0047] In which, when the temperature of the area 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 temperature.

[0048] In which, if the temperature of the area is higher than the upper limit of the growth requirement, the cooling device is used to lower the temperature to control the temperature of the area.

[0049] Step S300.3, for the humidity regulation priority area, based on the humidity deviation degree in the requirement matching state report, the soil humidity and air humidity are adjusted.

[0050] In which, if the soil humidity is lower than the lower limit of the requirement parameter, the water spraying system is started to provide the required moisture for the soil.

[0051] In which, if the air humidity is too low, the atomization device is applied to increase the air humidity to meet the growth requirement of the vegetation.

[0052] In which, when the light intensity of the area does not meet the growth requirement, shading measures or enhanced light intensity are used to regulate the light intensity.

[0053] Wherein, if the area light intensity is too high, a movable sun shield is used to reduce the impact of direct light and control the light intensity to an appropriate range.

[0054] Wherein, if the light is insufficient, a mirror is used to increase the light to ensure that the vegetation can obtain the required amount of light.

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

[0056] Step S400.1, based on the temperature, humidity and light regulation of the area, a monitoring mechanism is set.

[0057] Wherein, the monitoring mechanism includes: temperature, humidity, light intensity and soil moisture.

[0058] Wherein, the monitoring data is transmitted to the central data processing unit.

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

[0060] Wherein, when the microenvironment data of a certain area deviates from the growth demand parameter standard, the feedback mechanism is triggered, the area is marked and real-time feedback records are generated.

[0061] Wherein, based on the feedback records, the corresponding regulation device is activated by the central data processing unit to implement dynamic regulation of the microenvironment.

[0062] Wherein, the feedback mechanism is specifically:

[0063]

[0064] In the formula: is the feedback regulation parameter, used to judge whether the regulation needs to be started and its intensity, when exceeds the preset threshold , the regulation is automatically triggered, is the current collected microenvironment data actual value, including: current temperature, humidity and light intensity, is the corresponding growth demand parameter standard value, i.e. the target parameter value required by the plant, is the regulation time factor, i.e. the time from the last regulation, used to measure the regulation interval, if the time interval is short, it means that the area needs frequent regulation, is the recovery deviation rate, i.e. the percentage of the change of the current collected data relative to the last regulation, used to judge the efficiency of the microenvironment recovery.

[0065] Here, it needs to be noted that if the humidity requirement is , the humidity after the last control reaches , the current collection value is , then the recovery deviation rate is .

[0066] Here, it needs to be noted that the weight parameter is: , , The weights of microenvironment deviation, control interval time and recovery deviation rate, respectively, control the influence degree of each factor on feedback control.

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

[0068]

[0069] Here, it needs to be noted that the microenvironment deviation impact: The difference between the current microenvironment data and the growth requirement parameter is reflected in the formula, the larger the difference, the more urgent the lengthening requirement.

[0070] Here, it needs to be noted that the control time factor: is used to measure the time interval from the last control to the current time, if the interval time is short, the control frequency is too high, which may need to adjust the adaptability parameters or control method of the region.

[0071] Here, it needs to be noted that the recovery deviation rate: reflects the effect of microenvironment recovery after control, negative deviation rate means that the control fails to achieve the expected effect, positive deviation rate means that the control is effective.

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

[0073] Among them, temperature control refers to when the monitoring data detects that the temperature is lower or higher than the requirement parameter, then the heating or cooling device is automatically started to adjust the temperature.

[0074] Among them, humidity control refers to when the air humidity or soil humidity is not within the requirement range, then the water spraying or atomization device is automatically started to adjust the humidity.

[0075] Among them, light control, when the light intensity does not meet the requirement parameter, then the sunshade or light reflection device is automatically activated to adjust the light.

[0076] Among them, when the microenvironment data recovers to the growth requirement parameter range, then the control device is automatically stopped.

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

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

[0079] In some specific implementations, analysis and adjustments are made based on the feedback control report in step S400, and control priorities and adjustments are set and optimized, specifically including:

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

[0081] This involves statistically analyzing the frequency of regulation and the number of times the growth requirement parameters deviate from those of each region, identifying regions that require frequent regulation or long-term deviations, and designating these as key areas for optimization.

[0082] The analysis included: control frequency, deviation amplitude, and microenvironment recovery effect.

[0083] The analysis involves using a weighted average algorithm and analysis of variance to identify the regulation patterns and stability of regional microenvironment data.

[0084] The weighted average algorithm is as follows:

[0085]

[0086] In the formula: It is a weighted average of the control intensity, used to determine the intensity and priority of control needs in different regions. It is the first The number of regional adjustments refers to the total number of adjustments made to that region within a specified time period. :No. The deviation of a region is the absolute value of the deviation between the microenvironment data and the growth requirement parameters of that region. It is the first The recovery effect of a region refers to the degree to which the microenvironment data of each region recovers to the parameters required for growth after regulation. The higher the recovery effect, the better the regulation effect. These are the weighting coefficients for the number of adjustments, the magnitude of deviation, and the recovery effect, used to adjust the impact of each factor on the intensity of the adjustment.

[0087] Analysis of variance (ANOVA) involves grouping and organizing the control data from each region according to control frequency, deviation magnitude, and recovery effect, with each region's data forming a group, denoted as . Each group contains multiple observations.

[0088] Wherein, the mean value of each area or group in the variance analysis is calculated as:

[0089]

[0090] In the formula: is the mean value of the first area, is the number of data points in the area, represents the first data point of the first area.

[0091]

[0092] In the formula: is the total mean value of all areas, is the total number of data points, is the number of areas.

[0093] Wherein, the inter-group variance in the variance analysis is calculated as:

[0094]

[0095] In the formula: represents the inter-group variance, reflecting the difference in mean values of different areas.

[0096] Wherein, the intra-group variance in the variance analysis is calculated as:

[0097]

[0098] In the formula, represents the intra-group variance, reflecting the dispersion of data within the area.

[0099] Wherein, the statistic is calculated as:

[0100]

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

[0102] Wherein, the significance test is to compare the calculated value with the critical value , which is found from the F distribution table according to the significance level and degrees of freedom. If , it is considered that there is a significant difference in the regulatory requirements of different areas, which needs to be further optimized.

[0103] Wherein, if the F value is significantly higher than the critical value, the relevant area is set to high priority and the frequency of regulation is increased.

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

[0105] Step S500.2. Screening out regions with regulation frequency exceeding 2 times per hour based on the priority regulation labels.

[0106] Step S500.2.1. Relaxing or tightening the growth requirement parameter range based on the screened regions.

[0107] For example, if the temperature of a region is frequently regulated, the temperature requirement standard can be adjusted from the original 25℃±5℃ to 25℃±7℃.

[0108] The linear regression model is used to calculate the adaptive parameter interval based on historical regulation data as the independent variable.

[0109] It should be noted that the linear regression model is a well-known existing technology in the art and will not be described in detail here.

[0110] Step S500.2.2. Using a dynamic adjustment algorithm to automatically optimize the growth requirement parameters based on the adjusted parameter range and the seasonal changes and environmental trends of the region.

[0111] The dynamic adjustment algorithm is as follows:

[0112]

[0113] In the formula: is the dynamic adjustment parameter, representing the adjustment value of the regulation parameter, and the adjusted regulation parameter is , is the observed real-time microenvironment data, such as current temperature, humidity, or light intensity, is the target value of the growth requirement, i.e., the microenvironment standard value required by the plant, is the time interval since the last regulation, measured in hours, used to measure the regulation frequency, is the recovery rate of the microenvironment after regulation, defined as the speed of the environment returning to the target value after regulation, measured in percentage.

[0114] The weight parameters in the formula are as follows: is the regulation intensity coefficient, used to adjust the influence of the difference between the observed value and the target value on the result, and the recommended initial value is 1.5, is the time interval coefficient, used to control the influence of the regulation time interval, and the recommended initial value is 0.7, the longer the time interval, the greater the regulation requirement, is the recovery rate coefficient, reflecting the efficiency of the environment recovery after regulation, and the recommended initial value is 0.5. The lower the recovery rate, the more limited the regulation effect, and the regulation intensity needs to be increased.

[0115] Step S500.3, based on the historical data of the feedback mechanism, the regulation frequency of each region is optimized.

[0116] Among them, based on the recovery deviation and recovery time of different regions, the regulation frequency is automatically adjusted. If the micro environment can recover quickly after regulation, the regulation frequency is reduced, and if the recovery is slow, the regulation priority is increased.

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

[0118] The priority rules include: high-priority areas and low-priority areas.

[0119] Among them, the high-priority area is: the critical growth period, set high-priority regulation, and the critical growth period includes: germination period and seedling period.

[0120] Among them, the low-priority area is: the mature period area with high stability, and the priority is set to be reduced.

[0121] In some specific embodiments, vegetation growth data is collected, and based on the comparison of the collected vegetation growth data and the growth demand parameter, the repair effect of each region is calculated, and a repair effect evaluation report is generated, which specifically includes:

[0122] Step S600.1, based on the micro environment regulation state of each region, collect the vegetation growth data of each region.

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

[0124] Step S600.2, based on the collected data and adjusted micro environment data in steps S100 to S400 and the growth demand parameter standard set of plants, compare them one by one, and judge whether the current micro environment meets the growth demand of vegetation.

[0125] Among them, the growth demand parameter standard set is compared one by one, and the matching analysis algorithm is used for calculation.

[0126] Among them, the matching condition is: if the temperature, humidity and light parameters are all within the demand standard range, it is determined that the demand is met, and if a certain parameter does not meet the demand, it is determined that the demand is not met.

[0127] Step S600.3, using the weighted repair index formula, calculate the vegetation repair effect of each region, and use it to measure the comprehensive effect of repair.

[0128] wherein, the repair effect index is set as , specifically:

[0129]

[0130] In the formula: is the average plant height, reflecting the overall growth height of the vegetation, is the average leaf area, reflecting the photosynthetic capacity of the vegetation, is the root distribution density, indicating the soil stability of the vegetation, is the vegetation coverage, indicating the ecological coverage of the region, 、 、 、 The plant height, leaf area, root distribution and coverage are respectively weighted.

[0131] The initial weights of the plant height, leaf area, root distribution and coverage are set as: .

[0132] After calculating the repair effect index , the repair effect of each region is calculated to generate a repair effect analysis table, and it is determined which regions have reached the ideal repair state.

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

[0134] The repair effect evaluation report record includes: the vegetation growth condition, microenvironment matching situation and repair effect index of each region.

[0135] The repair effect evaluation report also includes: the repair effect index value of each region, the demand satisfaction of the region, and the region with rapid repair progress and the region that needs additional control support.

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

[0137] The data acquisition module obtains microenvironment data and sets a timing to collect microenvironment data and an abnormal threshold value.

[0138] The demand analysis module obtains slope vegetation species data and parameterizes and standardizes the data to generate a demand matching state report.

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

[0140] The feedback regulation module sets the feedback mechanism based on the setting of the growth environment in S300, regulates the setting operation based on the feedback mechanism, and generates a feedback regulation report.

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

[0142] The repair evaluation module collects vegetation growth data, compares the collected vegetation growth data with the growth demand parameters, calculates the repair effect of each region, and generates a repair effect evaluation report.

[0143] In summary, the slope vegetation repair system and its repair method provided by the present application obtain microenvironment data, set a timed collection of microenvironment data and an abnormal threshold, obtain slope vegetation species data, and parameterize and standardize the processing, generate a demand matching state report, set the growth environment of each region based on the demand matching state report obtained in S200, set the feedback mechanism based on the setting of the growth environment in S300, regulate the setting operation based on the feedback mechanism, generate a feedback regulation report, analyze and adjust based on the feedback regulation report in S400, set the regulation priority and adjustment optimization, collect vegetation growth data, compare the collected vegetation growth data with the growth demand parameters, calculate the repair effect of each region, and generate a repair effect evaluation report. By collecting microenvironment data in real time and dynamically regulating based on the growth demand parameters of plants, the slope region can better adapt to environmental changes. By using intelligent feedback mechanism and demand matching analysis, the temperature, humidity and light conditions are precisely adjusted to provide a continuously suitable growth environment for vegetation in different growth stages. At the same time, through regular repair effect evaluation and data recording, the method realizes the quantification of repair effect, provides support for the long-term stable recovery of slope vegetation, and greatly improves the survival rate of vegetation and the stability of the overall ecological system. BRIEF DESCRIPTION OF DRAWINGS

[0144] Figure 1 is a whole flowchart of a slope vegetation repair method provided by an embodiment of the present application.

[0145] Figure 2 is a whole flowchart of a slope vegetation repair system provided by an embodiment of the present application. DETAILED DESCRIPTION

[0146] Please refer to Figure 1 and Figure 2 which show a flowchart of one embodiment of a slope vegetation repair system and its repair method according to the present disclosure.

[0147] As shown in Figure 1 , a slope vegetation repair method includes the following steps:

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

[0149] Step S200, obtaining slope vegetation species data, and parameterizing and standardizing processing, generating a demand matching state report;

[0150] Step S300, setting a regional growth environment based on the demand matching state 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 and setting operation based on the feedback mechanism, generating a feedback regulation report;

[0152] Step S500, analyzing and adjusting based on the feedback regulation report in step S400, and setting a regulation priority and adjustment optimization;

[0153] Step S600, collecting vegetation growth data, comparing the collected vegetation growth data with growth demand parameters, calculating the repair effect of each region, and generating a repair effect evaluation report.

[0154] In some embodiments, the microenvironment data is obtained, and the timing collection microenvironment data and the abnormal threshold value are set, specifically including:

[0155] Step S100.1, arranging microenvironment data collection devices in different regions of the slope to obtain microenvironment data.

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

[0157] Among them, the microenvironment data collection device includes a temperature and humidity sensor, a light sensor, and a soil moisture sensor.

[0158] Step S100.1.1, the obtained microenvironment data is transmitted to the central data processing unit by wireless transmission.

[0159] Step S100.2, the central data processing unit is set to collect data at a timing.

[0160] Among them, the central data processing unit is set to collect data at a timing: collect air temperature, air humidity, light intensity and soil moisture value data once every hour.

[0161] Among them, when the dry season and the rainy season or during special weather, the collection frequency is automatically increased to once every 30 minutes, and the influence of abnormal weather on the microenvironment of the slope is recorded.

[0162] Step S100.2.1, when the central data processing unit detects that the sensor point exceeds the specified abnormal threshold, an automatic alarm is sent to remind the abnormal situation and automatically mark the abnormal data.

[0163] Wherein, the abnormal threshold is set as: temperature > 35℃ and soil humidity < 15%.

[0164] Wherein, if a sensor fails or loses data during 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, facilitating subsequent maintenance and data supplementation.

[0165] In some embodiments, the slope vegetation species data is obtained, parameterized and standardized, and a demand matching state report is generated, specifically including:

[0166] Step S200.1, obtaining slope vegetation species data and dividing different growth stages.

[0167] Wherein, different growth stages include germination period, seedling period and mature period.

[0168] Step S200.1.1, based on the slope vegetation species data, parameterize the demand of each growth stage for microenvironment data.

[0169] Wherein, the demand parameterization includes:

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

[0171] The air humidity requirement of seedling period is greater than 0: 60-70%.

[0172] The soil humidity requirement of mature period is greater than 0: 20-30%.

[0173] Step S200.1.2, unify the demand parameters of each stage into growth demand parameter standard.

[0174] Step S200.1.3, based on the microenvironment demand parameters of vegetation growth stage, sort and classify, and form a preliminary demand parameter list.

[0175] Wherein, the preliminary demand parameter list includes temperature, air humidity, light intensity and soil humidity indicators.

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

[0177] Wherein, the standardization processing is to statistically analyze each parameter by combining the actual adaptability of vegetation to temperature, humidity, light and other microenvironment data.

[0178] wherein analysis refers to, if the suitable temperature range of a certain plant is 20-30℃, it is standardized to 25℃±5℃.

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

[0180] Step S200.3, based on the obtained microenvironment data and the standard set of growth requirement parameters, one by one.

[0181] wherein, the comparison is to match and analyze the microenvironment data of each area with the requirements of each growth stage of the plant.

[0182] wherein, the matching analysis refers to, if the temperature of a certain area exceeds the growth requirement of ±5℃, the area is marked as a temperature control priority area, and if the soil humidity is less than 10% of the growth requirement, it is marked as a humidity control priority area.

[0183] Step S200.4, based on the comparison results, a requirement matching state report is generated for each area.

[0184] wherein, the report indicates whether the microenvironment data of the area meets or does not meet the growth requirements.

[0185] In some embodiments, based on the requirement matching state report obtained in step S200, the growth environment regulation setting of the area is set, which specifically includes:

[0186] Step S300.1, based on the obtained requirement matching state report, identify the area that does not meet the growth requirement parameters, and form a list of areas that require priority regulation.

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

[0188] Step S300.2, the temperature control priority area, based on the actual deviation of the temperature amplitude, adjusts the temperature through heating or cooling measures.

[0189] wherein, when the temperature of the area is lower than the lower limit of the growth requirement parameters, the controllable heating device is started to make the temperature rise to the required range temperature.

[0190] wherein, if the temperature of the area is higher than the upper limit of the growth requirement, the ventilation equipment is used to cool down to control the temperature of the area.

[0191] Step S300.3, the humidity control priority area, based on the humidity deviation degree in the requirement matching state report, adjusts the soil humidity and air humidity.

[0192] If the soil humidity is lower than the lower limit of the demand parameter, the water spraying system is started to provide the required water for the soil.

[0193] If the air humidity is too low, the atomization device is applied to increase the air humidity to meet the growth needs of the vegetation.

[0194] If the light intensity in the area does not meet the growth needs, shading measures or enhanced light are used to regulate the light intensity.

[0195] If the area light intensity 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 the light is insufficient, a mirror is used to increase the light to ensure that the vegetation can obtain the required amount of light.

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

[0198] Step S400.1, based on the temperature, humidity and light regulation in the area, a monitoring mechanism is set.

[0199] The monitoring mechanism includes temperature, humidity, light intensity and soil humidity.

[0200] The monitoring data is transmitted to the central data processing unit.

[0201] Step S400.2, based on the monitoring data received by the central data processing unit, each parameter is compared with the compliance of the growth demand parameter standard set.

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

[0203] Based on the feedback record, the corresponding regulation device is activated by the central data processing unit to implement dynamic regulation of the microenvironment.

[0204] The feedback mechanism specifically includes:

[0205]

[0206] In the formula: is the feedback regulation parameter, which is used to determine whether the regulation needs to be started and the intensity, when the preset threshold is exceeded, the regulation is automatically triggered, is the current collected micro-environment data actual value, the micro-environment data actual value includes: current temperature, humidity and light intensity, is the corresponding growth demand parameter standard value, that is, the target parameter value required by the plant, is the regulation time factor, that is, the time from the last regulation, used to measure the regulation interval, if the time interval is short, it means that the area needs to be regulated frequently, is the recovery deviation rate, that is, the percentage of change of the current collected data relative to the last regulation, used to judge the efficiency of micro-environment recovery.

[0207] Here, it should be noted that if the humidity demand is , the humidity after the last regulation is , and the current collected value is , then the recovery deviation rate is .

[0208] Here, it should be noted that the weight parameter is: , , The weight of micro-environment deviation, regulation interval time and recovery deviation rate respectively controls the influence degree of each factor on feedback regulation.

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

[0210]

[0211] Here, it should be noted that the micro-environment deviation affects: The difference between the current micro-environment data and the growth demand parameter is reflected in the formula, the larger the difference, the more urgent the regulation demand.

[0212] Here, it should be noted that the regulation time factor: is used to measure the time interval from the last regulation to the present, if the interval time is short, the regulation frequency is too high, and the adaptive parameters or regulation method of the area may need to be adjusted.

[0213] Here, it should be noted that the recovery deviation rate: reflects the effect of micro-environment recovery after regulation, negative deviation rate means that the regulation fails to achieve the expected effect, and positive deviation rate means that the regulation is effective.

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

[0215] Among them, temperature regulation refers to automatically starting heating or cooling devices to adjust the temperature when the monitoring data detects that the temperature is lower or higher than the demand parameter.

[0216] The humidity regulation refers to automatically starting the water spraying or atomizing device to adjust the humidity when the air humidity or soil humidity is not within the required range.

[0217] The light regulation refers to automatically activating the sunshade or light-reflecting device to adjust the light when the light intensity does not meet the required parameters.

[0218] When the microenvironment data returns to the growth requirement parameter range, the regulation device is automatically stopped.

[0219] Step S400.3, based on the end of the regulation, the central data processing unit records the regulation process of each region, including the change of microenvironment data before and after the regulation and the execution time of the regulation device, and generates a feedback regulation report.

[0220] The feedback regulation report includes the deviation of the microenvironment data of each region, the regulation measures and the regulation effect.

[0221] In some embodiments, the feedback regulation report generated in step S400 is analyzed and adjusted, and the regulation priority and optimization are set, specifically including:

[0222] Step S500.1, based on the generated feedback regulation report, the regulation data of each region is analyzed.

[0223] The regulation frequency and the number of deviations from the growth requirement parameters of each region are counted, and the regions that need frequent regulation or long-term deviation are identified as the optimization focus regions.

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

[0225] The analysis is to identify the regulation mode and stability of the microenvironment data of the region by using the weighted average algorithm and variance analysis.

[0226] The weighted average algorithm is specifically:

[0227]

[0228] In the formula: is the weighted average regulation intensity, which is used to judge the intensity and priority of the regulation requirement of each region, is the regulation frequency of the i-th region, i.e., the total number of regulations of the region within a specified time, is the deviation amplitude of the i-th region, i.e., the absolute value of the deviation of the microenvironment data of the region from the growth requirement parameters, is the regulation frequency of the i-th region, i.e., the total number of regulations of the region within a specified time, is the deviation amplitude of the i-th region, i.e., the absolute value of the deviation of the microenvironment data of the region from the growth requirement parameters, is the regulation frequency of the i-th region, i.e., the total number of regulations of the region within a specified time, The recovery effect of the region, i.e. the degree to which the microenvironment data of each region after regulation returns to the growth requirement parameters, the higher the recovery effect, the better the regulation effect, is the weight coefficient of the regulation times, the deviation amplitude and the recovery effect, used to adjust the influence of each factor on the regulation intensity.

[0229] Among them, the variance analysis refers to grouping the regulation data of each region according to the regulation frequency, the deviation amplitude and the recovery effect, setting the data of each region as a group, denoted as , wherein each group contains multiple observation values.

[0230] Among them, the group mean of each region or in the variance analysis is calculated as:

[0231]

[0232] In the formula: is the mean value of the th region, is the data point number of the region, represents the th data point of the th region.

[0233]

[0234] In the formula: is the total mean value of all regions, is the total number of data points, is the number of regions.

[0235] Among them, the inter-group variance in the variance analysis is calculated as:

[0236]

[0237] In the formula: represents the inter-group variance, reflecting the difference between the mean values of different regions.

[0238] Among them, the intra-group variance in the variance analysis is calculated as:

[0239]

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

[0241] Among them, the statistic is calculated as:

[0242]

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

[0244] wherein the significance test is to compare the calculated value with a critical value from a distribution table, determined according to the significance level F and degrees of freedom, if , it is considered that there is a significant difference in the regulatory requirements of different regions, which needs to be further optimized.

[0245] wherein if the value is significantly higher than the critical value, the relevant region is set to high priority, and the regulation frequency is increased. F Step S500.1.1, based on the analysis results, generate a priority regulation label for each region.

[0246] Step S500.2, based on the priority regulation label, filter out the regions with regulation frequency exceeding 2 times per hour.

[0247] Step S500.2.1, based on the filtered regions, relax or tighten the growth requirement parameter range.

[0248] wherein if the temperature of a certain region is frequently regulated, the temperature requirement standard can be adjusted from the original 25℃±5℃ to 25℃±7℃.

[0249] wherein a linear regression model is used to calculate the adaptive parameter interval with historical regulation data as the independent variable.

[0250] Here, it should be noted that the linear regression model belongs to the prior art well known to those skilled in the art, and will not be described in detail here.

[0251] Step S500.2.2, based on the adjusted parameter range, use a dynamic adjustment algorithm and automatically optimize the growth requirement parameters based on seasonal changes in the region and environmental trends.

[0252] wherein the dynamic adjustment algorithm is specifically:

[0253]

[0254]

[0255] wherein: is the dynamic adjustment parameter, representing the adjustment value of the regulation parameter, and the adjusted regulation parameter is , is the observed real-time microenvironment data, such as current temperature, humidity or light intensity, is the target value of the growth requirement, i.e. the microenvironment standard value required by the plant, is the time interval since the last regulation, in hours, used to measure the regulation frequency, ​​is the recovery rate of the microenvironment after regulation, defined as the speed of the microenvironment returning to the target value after regulation, with the unit of percentage.

[0256] wherein the weight parameter in the formula is explained as follows: is the regulation intensity coefficient, used to adjust the influence of the difference between the observation value and the target value on the result, and the recommended initial value is 1.5, is the time interval coefficient, used to control the influence of the regulation time interval, and the recommended initial value is 0.7, the longer the time interval, the greater the regulation demand, is the recovery rate coefficient, reflecting the efficiency of the microenvironment recovery after regulation, and the recommended initial value is 0.5, the lower the recovery rate, the more limited the regulation effect, and the regulation intensity needs to be increased.

[0257] Step S500.3, based on the historical data of the feedback mechanism, the regulation frequency of each region is optimized.

[0258] wherein based on the recovery deviation and recovery time of different regions, the regulation frequency is automatically adjusted, if the microenvironment can quickly recover after regulation, the regulation frequency is reduced, if the recovery is slow, the regulation priority is increased.

[0259] Step S500.3.1, based on the basis of the priority regulation of each region, the seasonal regulation priority rule is set.

[0260] The priority rule includes: high-priority areas and low-priority areas.

[0261] wherein the high-priority area is: the critical growth period, set high-priority regulation, the critical growth period includes: germination period and seedling period.

[0262] wherein the low-priority area is: the mature period area with higher stability, set to reduce the priority.

[0263] In some specific embodiments, vegetation growth data is collected, based on the comparison of the collected vegetation growth data and the growth demand parameters, the repair effect of each region is calculated, and a repair effect evaluation report is generated, which specifically includes:

[0264] Step S600.1, based on the microenvironment regulation state of each region, vegetation growth data of each region is collected.

[0265] wherein the vegetation growth data includes: plant height, leaf area, root distribution and vegetation coverage.

[0266] Step S600.2, based on the comparison of the collected data and the adjusted microenvironment data and the standard set of plant growth demand parameters in steps S100 to S400, it is judged whether the current microenvironment meets the vegetation growth demand.

[0267] The growth demand parameter standard set is compared one by one, and a matching analysis algorithm is used for calculation.

[0268] The matching condition is that if the temperature, humidity and light parameters are all within the demand standard range, it is determined that the demand is met, and if a certain parameter is not met, it is determined that the demand is not met.

[0269] Step S600.3, using a weighted repair index formula, calculate the vegetation repair effect of each region, and used to measure the comprehensive effect of repair.

[0270] Wherein, let the repair effect index be , specifically:

[0271]

[0272] In the formula: is the average plant height, reflecting the overall growth height of the vegetation, is the average leaf area, reflecting the photosynthetic capacity of the vegetation, is the root distribution density, indicating the soil stability of the vegetation, is the vegetation coverage, indicating the ecological coverage of the region, 、 、 、 The plant height, leaf area, root distribution and coverage are the weights of the plant height, leaf area, root distribution and coverage, respectively.

[0273] Wherein, the weights of the plant height, leaf area, root distribution and coverage are initially set as: .

[0274] Wherein, the repair effect index is calculated After that, the repair effect of each region is calculated to generate a repair effect analysis table, and it is determined which regions have reached the ideal repair state.

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

[0276] Wherein, the repair effect evaluation report record includes: the vegetation growth condition of each region, the microenvironment matching condition and the repair effect index.

[0277] Wherein, the repair effect evaluation report also includes: the repair effect index value of each region, the demand satisfaction of the region, and the region that needs additional control support.

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

[0279] A data acquisition module obtains microenvironment data and sets a timing to collect microenvironment data and an abnormal threshold.

[0280] A demand analysis module obtains slope vegetation species data and parameterizes and standardizes the data to generate a demand matching state report.

[0281] An environment regulation module sets a growth environment based on the demand matching state report obtained in step S200.

[0282] A feedback regulation module sets a feedback mechanism based on the setting of the growth environment in S300 and regulates the setting based on the feedback mechanism to generate a feedback regulation report.

[0283] A regulation optimization module analyzes and adjusts based on the feedback regulation report in S400 and sets a regulation priority and adjustment optimization.

[0284] A restoration evaluation module collects vegetation growth data, compares the collected vegetation growth data with growth demand parameters, calculates the restoration effect of each region, and generates a restoration effect evaluation report.

[0285] In the above content, in actual application, first, microenvironment data acquisition devices are arranged in each region of the slope to collect microenvironment data such as environmental temperature, air humidity, light intensity, and soil humidity. The acquisition devices are set to collect data once every hour, and all microenvironment data are transmitted to a central data processing unit. Each acquisition device includes a temperature and humidity sensor, a light sensor, and a soil humidity sensor. When the temperature is outside the threshold range of 35℃ and the soil humidity is outside the threshold range of 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 species and growth stages of the slope vegetation, the growth demand is parameterized. Taking the germination period, seedling period, and mature period as examples, the temperature demand of the germination period is 20-25℃, the air humidity demand of the seedling period is 60-70%, and the soil humidity demand of the mature period is 20-30%. After standardizing these demand data, a growth demand parameter standard set is formed. The central data processing unit compares the collected microenvironment data with this parameter standard set one by one to identify and record whether each region meets the growth demand of the plant.

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

[0288] During the implementation of the regulation, the microenvironment data of each area is monitored in real time, and the central data processing unit automatically determines whether to start further regulation according to the feedback mechanism. When the microenvironment data deviates from the growth requirement standard, the regulation device is automatically triggered to adjust. 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 effect of each area will generate a feedback regulation record for subsequent analysis.

[0289] Through analysis of the feedback regulation record, the area with frequent regulation or poor recovery effect is identified. The weighted average algorithm and dynamic adjustment algorithm are applied to adjust the growth requirement parameter standard of the corresponding area and dynamically optimize the regulation priority. The central data processing unit generates an optimization strategy report according to the optimization result to provide support for subsequent regulation, ensuring that the regulation strategy can be flexibly adjusted according to the actual situation.

[0290] Vegetation growth data is collected every two weeks, including plant height, leaf area, root distribution, and vegetation coverage, to form a restoration effect index, and the restoration effect index is calculated , 、 、 、 represent plant height, leaf area, root distribution density, and coverage, respectively 、 、 、 The weights of each item are set as follows: initially set as , , , The central data processing unit generates a restoration effect evaluation report according to the restoration effect index, indicating the restoration progress of each area, to ensure the sustainability and effectiveness of the vegetation restoration work.

Claims

1. A method for the restoration of vegetation on a slope, characterized in that, The method comprises the following steps: S100, obtaining microenvironment data, and setting a timing collection of microenvironment data and an abnormal threshold value; S200, obtaining slope vegetation species data, and parameterizing and standardizing processing to generate a demand matching state report; S300, setting a region growing environment based on the demand matching state report obtained in S200; S400, setting a feedback mechanism based on the setting of the growing environment in S300, and regulating and setting operation based on the feedback mechanism to generate a feedback regulation report; S500, analyzing and adjusting based on the feedback regulation report in S400, and setting a regulation priority and adjustment optimization; S600, collecting vegetation growth data, calculating the repair effect of each region based on the comparison of the collected vegetation growth data and the growth demand parameters, and generating a repair effect evaluation report; Wherein, based on the feedback regulation report for analysis and adjustment, and setting the regulation priority and adjustment optimization, specifically including: S500.1, based on the generated feedback regulation report, analyzing the regulation data of each region; Wherein, the analysis is to identify the regulation mode and stability of the regional microenvironment data by using weighted average algorithm and variance analysis; Wherein, the weighted average algorithm is specifically: In the formula: It is a weighted average of the control intensity, used to determine the intensity and priority of control needs in different regions. It is the first The number of regional regulations It is the first The absolute value of the deviation between regional microenvironment data and growth requirement parameters. It is the first After regional regulation, the microenvironment data recovered to the level of growth requirement parameters. , , They are respectively , and The corresponding weighting coefficients; The variance analysis refers to grouping and arranging the regulation data of each region according to the regulation frequency, the deviation amplitude and the recovery effect, setting the data of each region as a group, and recording as wherein each group contains multiple observation values; S500.1.1, based on the analysis result, generating a priority regulation label for each region; S500.2, based on the priority regulation label, screening out the regions with a regulation frequency of more than 2 times per hour; S500.2.1, based on the screened regions, relaxing or tightening the range of growth demand parameters; Wherein, 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, using a dynamic adjustment algorithm, and automatically optimizing the growth demand parameters based on the seasonal changes and environmental trends of the region; Wherein, the dynamic adjustment algorithm is specifically: In the formula: is a dynamic adjustment parameter, representing the adjustment value of the regulation parameter, and the adjusted regulation parameter is , is the observed real-time microenvironment data, is the microenvironment standard value required by the plant, is the time interval since the last regulation, in hours, used to measure the regulation frequency, is the microenvironment recovery rate after regulation, defined as the speed of the environment recovering to the target value after regulation, in percentage, is the regulation intensity coefficient, is the time interval coefficient, is the recovery rate coefficient; S500.3, based on the historical data of the feedback mechanism, optimizing the regulation frequency of each region; Wherein, based on the recovery deviation and recovery time of different regions, the regulation frequency is automatically adjusted. If the microenvironment can quickly recover after regulation, the regulation frequency is reduced. If the recovery is slow, the regulation priority is increased; S500.3.1, based on the priority regulation of each region, setting seasonal regulation priority rules.

2. The method for slope vegetation restoration according to claim 1, characterized in that, Obtaining microenvironment data and setting a timing collection of microenvironment data and an abnormal threshold value, specifically including: S100.1, arranging microenvironment data collection devices in different regions of the slope to obtain microenvironment data; S100.1.1, transmitting the obtained microenvironment data to the central data processing unit through wireless transmission; S100.2, setting the central data processing unit to collect data at a timing; S100.2.1, when the central data processing unit detects more than a specified abnormal threshold value at the sensor point, it will send an automatic alarm to remind the abnormal situation and automatically mark the abnormal data; Wherein, the abnormal threshold value is greater than 0.

3. The method of claim 1, wherein, Obtaining slope vegetation species data, and parameterizing and standardizing processing to generate a demand matching state report, specifically including: S200.1, obtaining slope vegetation species data, and dividing different growth stages; S200.1.1, based on the slope vegetation species data, parameterize the demand of each growth stage for microenvironment data; S200.1.2, unify the demand parameters of each stage into growth demand parameter standards; S200.1.3, sort and classify the microenvironment demand parameters based on the growth stages of the vegetation, and form a preliminary demand parameter list; S200.2, based on the preliminary demand parameter list obtained, standardize the demand parameters of different vegetation at each growth stage, and generate a complete set of growth demand parameter standards; S200.3, based on the obtained microenvironment data and the set of growth demand parameter standards, compare them one by one; S200.4, based on the comparison results, generate a demand matching state report for each region.

4. The method of claim 1, wherein, Based on the demand matching state report obtained in S200, set the growth environment control, specifically including: S300.1, based on the demand matching state report obtained, identify the regions that do not meet the growth demand parameters, and form a region list setting for demand priority control; Wherein, the region list setting refers to the division of the region into temperature control priority region and humidity control priority region; S300.2, for the temperature control priority region, based on the actual deviated temperature amplitude, adjust the temperature through heating or cooling measures; S300.3, for the humidity control priority region, based on the humidity deviation degree in the demand matching state report, adjust the soil humidity and air humidity; Wherein, for the region where the light intensity does not meet the growth demand, use shading measures or enhance light to regulate the light intensity.

5. The method of claim 1, wherein, Based on the setting of the growth environment in S300, set the feedback mechanism, and based on the feedback mechanism, generate a feedback control report, specifically including: S400.1, based on the region after temperature, humidity and light control, set up a monitoring mechanism; Wherein, the monitoring data is transmitted to the central data processing unit; S400.2, based on the monitoring data received by the central data processing unit, compare the compliance of each parameter with the growth demand parameter standards; Wherein, when the microenvironment data of a certain region deviates from the growth demand parameter standards, the feedback mechanism is triggered, the region is marked and real-time feedback records are generated; Wherein, based on the feedback records, the corresponding control device is activated by the central data processing unit to implement dynamic control of the microenvironment; Wherein, the feedback mechanism specifically includes: In the formula: is a feedback control parameter, used to determine whether control needs to be started and its strength, when exceeds a preset threshold , the control is automatically triggered, is the actual value of the microenvironment data currently collected, including the current temperature, humidity, and light intensity, is the corresponding growth requirement parameter standard value, i.e., the target parameter value required by the plant, is the control time factor, i.e., the time from the last control, used to measure the control interval. If the time interval is short, it means that the area needs to be frequently controlled, is the recovery deviation rate, i.e., the percentage of the change of the current collected data relative to the last control, used to judge the efficiency of the microenvironment recovery, , , are the weights of the microenvironment deviation, control interval time, and recovery deviation rate, respectively, controlling the influence degree of each factor on the feedback control. Wherein, dynamic control includes temperature control, humidity control and light control; Wherein, temperature control refers to automatically starting the heating or cooling device when the monitoring data detects that the temperature is lower or higher than the demand parameter, and adjusting the temperature; Wherein, humidity control refers to automatically starting the water spraying or atomization device when the air humidity or soil humidity is not within the demand range, and adjusting the humidity; Wherein, light control refers to automatically activating the sunshade or light reflection device when the light intensity does not meet the demand parameter, and adjusting the light; S400.3, based on the end of the control, the central data processing unit records the control process of each region, including the change of microenvironment data before and after the control and the execution time of the control device, and generates a feedback control report.

6. The method of claim 1, wherein, S600.1, collecting vegetation growth data of each region based on the microenvironment regulation state of each region; S600.2, comparing the collected data and adjusted microenvironment data with the growth demand parameter standard set of plants one by one, and judging whether the current microenvironment meets the vegetation growth demand; S600.3, calculating the vegetation repair effect of each region by using the weighted repair index formula, and using it to measure the comprehensive effect of repair; S600.4, the central data processing unit generates a repair effect evaluation report based on the repair effect analysis table. Wherein, set the repair effect index is , Specifically: In the formula: is the average value of plant height, reflecting the overall growth height of vegetation, is the average value of leaf area, reflecting the photosynthetic capacity of vegetation, is the root distribution density, indicating the soil stability of vegetation, is the vegetation coverage, indicating the ecological coverage of the region, , , , are the weights of plant height, leaf area, root distribution and coverage, respectively; Wherein, the repair effect index is calculated After that, the repair effect is counted by area, a repair effect analysis table is generated, and it is judged which areas have reached the ideal repair state. The following modules are included:

7. A system for slope revegetation, performing a method for slope revegetation according to any one of claims 1-6, characterized in that, Data acquisition module, obtain microenvironment data, and set up timing collection of microenvironment data and abnormal threshold; Demand analysis module, obtain slope vegetation species data, and parameterize and standardize the processing, generate demand matching state report; Environment regulation module, based on the demand matching state report obtained in S200, set the growth environment of the region; Feedback regulation module, based on the setting of the growth environment in S300, set the feedback mechanism, and regulate and set the operation based on the feedback mechanism, generate feedback regulation report; Regulation optimization module, based on the feedback regulation report in S400, analyze and adjust, and set the regulation priority and adjustment optimization; Repair evaluation module, collect vegetation growth data, compare the collected vegetation growth data with the growth demand parameters, calculate the repair effect of each region, and generate a repair effect evaluation report. ​

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