Dynamic regulation and control method for construction disturbance threshold value in ecological sensitive area

Abstract

The invention discloses an ecological sensitive area construction disturbance threshold value dynamic regulation and control method, which comprises the following steps of S1, acquiring basic environment data of an ecological sensitive area, and performing preprocessing; s2, according to the preprocessed basic environment data, constructing an initial construction disturbance threshold value system in combination with an environmental protection threshold value database, and determining ecological disturbance control references of different construction links; s3, multi-dimensional disturbance indexes in the construction process are monitored in real time based on the intelligent construction technology, the initial construction disturbance threshold value is dynamically corrected, and a construction disturbance threshold value dynamic regulation and control model is constructed; the invention relates to the technical field of ecological protection and engineering construction management. According to the dynamic regulation and control method for the construction disturbance threshold value of the ecological sensitive area, the disturbance index in the construction process is monitored in real time, the initial construction disturbance threshold value is dynamically corrected in combination with the ecological model, dynamic regulation and control of the construction disturbance threshold value are achieved, and the changing ecological environment condition in the construction process can be flexibly coped with.

Description

A method for dynamic control of construction disturbance threshold in ecologically sensitive areas Technical Field

[0001] This invention relates to the field of ecological protection and engineering construction management, and more specifically, to a method for dynamically controlling the construction disturbance threshold in ecologically sensitive areas. Background Technology

[0002] When construction activities are carried out in ecologically sensitive areas, various disturbances generated during the construction process, such as vibration, noise, dust emissions, and changes in water quality, can seriously affect ecological and environmental elements such as topography, vegetation cover, soil type, and hydrological conditions, thereby damaging biodiversity and threatening the stability and sustainability of the ecosystem.

[0003] Traditional construction disturbance control methods often employ fixed environmental thresholds, which are typically established based on general ecological studies and lack specific consideration for the environmental conditions and construction scenarios of ecologically sensitive areas. In actual construction, due to the complexity of ecologically sensitive areas and the dynamic nature of construction activities, fixed environmental thresholds fail to accurately reflect the actual impact of construction disturbances on the ecological environment. This results in either overly strict controls, affecting construction efficiency and costs, or insufficient controls, causing irreversible damage to the ecological environment.

[0004] Furthermore, existing methods for monitoring construction disturbances are mostly limited in scope, making it difficult to comprehensively and in real-time acquire multi-dimensional disturbance index data during construction. They also lack effective dynamic control mechanisms, failing to adjust disturbance thresholds promptly based on actual conditions during construction, thus hindering the balance between ecological protection and engineering construction. Therefore, there is an urgent need for a method capable of dynamically controlling construction disturbance thresholds to adapt to the complex needs of construction in ecologically sensitive areas and effectively protect the ecological environment. Summary of the Invention

[0005] The purpose of this invention is to provide a method for dynamic control of construction disturbance threshold in ecologically sensitive areas. This method addresses the shortcomings of existing construction disturbance monitoring methods, which are mostly limited in scope and cannot comprehensively and in real time obtain multi-dimensional disturbance index data during construction. Furthermore, these methods lack effective dynamic control mechanisms and cannot adjust the disturbance threshold in a timely manner according to the actual situation during construction.

[0006] This invention achieves the above objectives through the following technical solution: a method for dynamic control of construction disturbance thresholds in ecologically sensitive areas, comprising the following steps: S1, acquiring basic environmental data of the ecologically sensitive area and preprocessing it; S2, constructing an initial construction disturbance threshold system based on the preprocessed basic environmental data and an environmental protection threshold database, and determining the ecological disturbance control benchmarks for different construction stages; S3, monitoring multi-dimensional disturbance indicators in the construction process in real time based on intelligent construction technology, dynamically correcting the initial construction disturbance threshold, and constructing a dynamic control model for construction disturbance thresholds; S4, sending the dynamic control model for construction disturbance thresholds to a construction management cloud platform to execute dynamic control of construction disturbance thresholds in ecologically sensitive areas.

[0007] Furthermore, step S1 includes the following steps: integrating multiple types of environmental data using multi-source data fusion technology to obtain basic environmental data for ecologically sensitive areas, wherein the basic environmental data covers at least one of topography, vegetation cover, soil type and hydrological conditions; extracting key ecological elements, processing outliers and normalizing the obtained environmental data to obtain preprocessed basic environmental data.

[0008] Furthermore, step S2 includes the following steps: conducting in-depth analysis of the preprocessed basic environmental data, using ecological models to simulate the potential responses of ecologically sensitive areas under different construction scenarios, and identifying key ecological factors and their sensitivity; combining the environmental protection threshold database, and formulating preliminary disturbance threshold ranges for each key ecological factor based on ecological protection goals and construction types; and refining and improving the preliminary disturbance threshold ranges for the duration and spatial range of different construction stages to obtain unique ecological disturbance control benchmarks for each stage.

[0009] Furthermore, the key ecological factors include soil erosion modulus and biodiversity index; the sensitivity is measured by a sensitivity coefficient, which is calculated as the ratio of the rate of change of ecological factors to the rate of change of construction disturbance intensity.

[0010] Furthermore, step S3 includes the following steps: deploying sensors across the construction area to collect multi-dimensional disturbance index data, specifically including vibration, noise, dust concentration, and water quality; using edge computing devices to perform preliminary analysis and feature extraction on the collected data, quickly determining whether an initial construction disturbance threshold warning has been triggered, and if so, immediately marking and uploading detailed data to the cloud; using deep learning algorithms in the cloud, combined with historical construction data and ecological models, analyzing the impact of the current disturbance index on the ecologically sensitive area, and dynamically correcting the initial construction disturbance threshold; based on the corrected threshold, constructing a dynamic control model for the construction disturbance threshold that includes data feedback, analysis and decision-making, and instruction issuance mechanisms.

[0011] Furthermore, the initial construction disturbance threshold is dynamically corrected using the following formula: in, The corrected threshold. As the initial threshold, This represents the degree of impact of the current disturbance index on the ecologically sensitive area (calculated using an ecological model, ranging from 0 to 1). And β are weighting coefficients, and +β=1, determined by fitting historical data.

[0012] Furthermore, between steps S3 and S4, the following steps are also included: S3.5, Constructing a multi-subject collaborative prediction-decision system for construction disturbance.

[0013] Furthermore, step S3.5 specifically includes the following steps: constructing a multi-source heterogeneous data fusion hub to integrate real-time construction monitoring data, ecological expert knowledge base, environmental regulatory rules, and public feedback information to form a full-element data pool; developing a deep learning-based disturbance propagation prediction model, combining geospatial analysis and ecological mechanisms to simulate the dynamic impact path of construction activities on sensitive areas and potential areas exceeding threshold risks; building a multi-agent reinforcement learning decision engine, inputting the interests of multiple parties and real-time prediction results, and generating a set of dynamic control schemes that take into account ecological compliance, engineering efficiency, and cost through a multi-objective optimization algorithm; and establishing a digital twin verification platform to virtually simulate and evaluate the effects of the decision schemes, select the optimal scheme, and push it to the construction terminal.

[0014] Furthermore, a closed-loop feedback mechanism is set up to continuously correct the prediction model and decision-making rules through on-site sensors and public participation data, so as to achieve adaptive iterative optimization of the system.

[0015] Furthermore, the method for dynamically controlling the construction disturbance threshold in ecologically sensitive areas also includes: S5, using digital pre-assembly technology to reduce on-site construction procedures and reduce the direct disturbance of construction to ecologically sensitive areas; S6, using ecological restoration technology to repair the ecologically disturbed areas in real time in response to the environmental damage caused by construction; S7, continuously tracking and monitoring the environmental restoration of ecologically sensitive areas until construction is completed and the ecological environment is restored to the preset standard.

[0016] The beneficial effects of this invention are as follows: 1. By real-time monitoring of disturbance indicators during the construction process, such as vibration, noise, and dust concentration, and combining them with an ecological model to dynamically correct the initial construction disturbance threshold, dynamic control of the construction disturbance threshold is achieved, which can flexibly respond to changes in the ecological environment during the construction process.

[0017] 2. By employing edge computing and deep learning algorithms, the technology can quickly analyze multi-dimensional data collected by on-site sensors and upload it to the cloud platform in real time for in-depth analysis, effectively improving the efficiency and accuracy of environmental monitoring during construction.

[0018] 3. By integrating environmental basic data such as topography, vegetation, and hydrology with real-time monitoring data from the construction site through multi-source data fusion technology, comprehensive data support is provided for the dynamic correction of construction disturbance thresholds, making decision-making more scientific and accurate.

[0019] 4. Through a multi-subject collaborative prediction-decision system and deep learning algorithms, it is possible to simulate the dynamic impact path and potential risks of construction activities on ecologically sensitive areas, providing a reliable basis for construction management decisions, and optimizing construction plans and adjusting disturbance thresholds according to the needs of different construction stages.

[0020] 5. This invention also provides ecological restoration technology and a continuous tracking and monitoring mechanism, which can promptly repair the ecological disturbances that have occurred during the construction process, and ensure that the ecological environment can be restored to the preset standard after the construction is completed, so as to achieve sustainable construction and ecological environmental protection in parallel. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of this application and constitute a part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 is a flowchart of the method of the present invention; Figure 2 is a flowchart of the dynamic control algorithm of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Example 1: Please refer to Figures 1-2. This invention provides a technical solution: a method for dynamic control of construction disturbance thresholds in ecologically sensitive areas. The method includes the following steps: S1. Acquire basic environmental data of ecologically sensitive areas and perform preprocessing; wherein, ecologically sensitive areas refer to areas with important ecological functions or special ecological value, including wetlands, nature reserves, and habitats of rare species, which require strict environmental control; basic environmental data include geological data, ecological data, and meteorological data; S2. Based on the preprocessed basic environmental data, and combined with an environmental protection threshold database, construct an initial construction disturbance threshold system to determine the ecological disturbance control benchmarks for different construction stages; wherein, the environmental protection threshold database is a static threshold, such as the daytime ≤70dB specified in the "Emission Standard for Environmental Noise at Construction Site Boundaries" (GB12523-2011); the dynamic threshold is based on species sensitivity weighting; the construction disturbance control benchmark is: suspended particulate matter: PM10 concentration ≤150μg / m³. 3 (Refer to the "Integrated Emission Standard for Air Pollutants"); S3. Based on intelligent construction technology, monitor multi-dimensional disturbance indicators in real time during construction, dynamically correct the initial construction disturbance threshold, and construct a dynamic control model for construction disturbance thresholds; where multi-dimensional disturbance indicators refer to time and space dimensions; S4. Send the dynamic control model for construction disturbance thresholds to the construction management cloud platform to execute dynamic control of construction disturbance thresholds in ecologically sensitive areas; where dynamic control execution includes control strategies, adopting graded early warning: Level 1 (orange) adjusts construction intensity, Level 2 (red) triggers work stoppage; it should be noted that during operation, preprocessing (S1) standardizes basic data, eliminates errors, and ensures the reliability of subsequent analysis; Threshold system (S2): Combines environmental protection standards and the characteristics of sensitive areas to formulate differentiated control benchmarks to improve targeting; Dynamic correction (S3): Monitors multi-dimensional indicators in real time and uses algorithms to dynamically adjust thresholds to avoid the lag of traditional fixed thresholds; Cloud platform control (S4): Centralized management model to achieve collaboration among multiple entities (construction party, regulatory department), improve response efficiency, and the overall design takes into account ecological protection and engineering efficiency, reducing the risk of construction disturbance to sensitive areas.

[0024] In one embodiment, step S1 includes the following steps: using multi-source data fusion technology to integrate satellite remote sensing imagery, UAV low-altitude aerial photography data, and real-time monitoring data from ground sensors to obtain basic environmental data for ecologically sensitive areas. This basic environmental data includes topography, vegetation cover, soil type, and hydrological conditions. Image recognition and semantic segmentation algorithms are used to process the remote sensing imagery and aerial photography data, extracting key ecological elements within the ecologically sensitive areas and marking their spatial locations. Outlier detection and filtering are performed on the data collected by ground sensors to eliminate noise. The preprocessed basic environmental data is then normalized using the Z-score standardization method to ensure a mean of 0 and a standard deviation of 1, eliminating the influence of dimensions.

[0025] In one embodiment, step S2 includes the following steps: performing in-depth analysis on the preprocessed basic environmental data, using the InVEST model to simulate the potential response of ecologically sensitive areas under different construction scenarios, and identifying key ecological factors and their sensitivities; wherein, key ecological factors include: soil erosion modulus and biodiversity index; the sensitivity calculation formula is as follows: sensitivity coefficient = rate of change of ecological factor / rate of change of construction disturbance intensity; combining the environmental protection threshold database, for each key ecological factor, based on ecological protection goals and construction type, a preliminary disturbance threshold range is formulated; for example, for noise, the daytime construction threshold range is 55-70 dB(A), and the nighttime construction threshold range is 45-55 dB(A).

[0026] For different construction stages with varying durations and spatial ranges, the initial disturbance threshold range is modified and refined to obtain unique ecological disturbance control benchmarks for each stage. For example, for short-duration (≤1 hour) high-intensity construction (such as blasting), the vibration threshold can be appropriately relaxed to a peak particle velocity ≤5cm / s; for long-duration (>1 hour) continuous construction (such as earthwork excavation), the vibration threshold should be strictly controlled at a peak particle velocity ≤2cm / s.

[0027] In one embodiment, step S3 includes the following steps: Deploying sensors across the entire construction area to collect multi-dimensional disturbance index data, including vibration, noise, dust concentration, and water quality; using edge computing devices to perform preliminary analysis and feature extraction on the collected data to quickly determine whether an initial construction disturbance threshold warning has been triggered; if triggered, immediately marking and uploading detailed data to the cloud; and using the LSTM neural network algorithm in deep learning algorithms on the cloud, combined with historical construction data and an ecological model, analyzing the impact of the current disturbance index on the ecologically sensitive area, and dynamically correcting the initial construction disturbance threshold. The correction formula can be weighted and adjusted based on historical data and the current impact level, and the calculation formula is as follows: in, The corrected threshold. As the initial threshold, This represents the degree of impact of the current disturbance index on the ecologically sensitive area (calculated using an ecological model, ranging from 0 to 1). And β are weighting coefficients, and +β=1, determined by fitting historical data; based on the corrected threshold, a dynamic control model for construction disturbance thresholds is constructed, which includes data feedback, analysis and decision-making, and instruction issuance mechanisms, to achieve precise ecological control of the construction process.

[0028] In one embodiment, between step S3 and step S4, the following step is also included: S3.5, constructing a multi-subject collaborative prediction-decision system for construction disturbance.

[0029] Specifically, this includes the following steps: integrating real-time construction monitoring data (data update frequency ≥ 1 time / minute), an ecological expert knowledge base (containing at least 50 ecological protection rules), environmental protection regulatory rules (compliant with relevant national and local environmental protection regulations), and public feedback information (collected through online questionnaires, hotlines, etc., with a monthly collection volume ≥ 100 pieces) to form a comprehensive data pool; developing a deep learning-based disturbance propagation prediction model, specifically employing a convolutional neural network-long short-term memory network hybrid model, combining geospatial analysis and ecological mechanisms to simulate the dynamic impact path of construction activities on sensitive areas and potential areas exceeding threshold risks; wherein, the impact path simulation uses a diffusion equation, and the calculation formula is as follows: in, The concentration of the disturbance index. For time, Where is the diffusion coefficient. This refers to wind speed or water flow speed. The attenuation coefficient is used to construct a multi-agent reinforcement learning decision engine. Inputting the interests of multiple parties and real-time prediction results, the engine generates a set of dynamic control schemes that balance ecological compliance (ecological violation rate ≤ 5%), engineering efficiency (construction progress deviation ≤ 10%), and cost (cost increase rate ≤ 15%) through the NSGA-II algorithm, a multi-objective optimization algorithm. The interests of the multiple parties include: the construction party pursuing engineering efficiency, the environmental protection party focusing on ecological protection, and the public concerned about quality of life. A digital twin verification platform is established to virtually simulate and evaluate the decision schemes, select the optimal scheme, and push it to the construction terminal. The evaluation indicators include an ecological impact index (calculated based on changes in key ecological factors, ranging from 0-100), an engineering efficiency index (calculated based on construction progress completion, ranging from 0-100), and a cost index (calculated based on the ratio of actual cost to budgeted cost, ranging from 0-100). The optimal scheme should satisfy an ecological impact index ≥ 80, an engineering efficiency index ≥ 85, and a cost index ≤ 90.

[0030] In one embodiment, a closed-loop feedback mechanism is designed to continuously correct the prediction model and decision rules through on-site sensors and public participation data, so as to achieve adaptive iterative optimization of the system. The correction cycle is once a month. The system is evaluated based on the prediction accuracy of the model before and after correction (accuracy improvement ≥5%) and the effectiveness of the decision plan (ecological violation rate reduction ≥10% after implementation) to ensure continuous optimization of the system.

[0031] In one embodiment, the method further includes: S5, using digital pre-assembly technology to reduce on-site construction procedures and reduce direct disturbance to ecologically sensitive areas; wherein, the digital pre-assembly technology uses 3D modeling software (such as Revit, Tekla, etc.) to simulate component pre-assembly, with a simulation accuracy error ≤1mm. Through pre-assembly, on-site construction procedures can be reduced by 30%-50%, and direct disturbance to ecologically sensitive areas can be reduced, such as reducing earthwork excavation by 20%-40% and reducing vegetation damage area by 15%-30%; S6, for environmental damage caused by construction, ecological restoration technology is used to repair the ecologically disturbed areas in real time; wherein, the ecological restoration technology includes vegetation restoration (using local suitable plants, with a survival rate ≥90%), soil improvement (adding organic fertilizer, improving soil fertility by ≥20%), and water purification (using technologies such as biological filters and constructed wetlands, with a water quality compliance rate ≥95%). The restoration process should develop a detailed restoration plan based on the degree of environmental damage. The restoration cycle will be determined according to specific circumstances, generally 1-3 years for vegetation restoration, 0.5-1 year for soil improvement, and 3-6 months for water purification. S7. Continuously monitor the environmental restoration of ecologically sensitive areas until construction is completed and the ecological environment has recovered to the preset standards. Continuous monitoring will combine regular monitoring (at least once a month) with random checks (determined based on actual conditions). Monitoring indicators include vegetation coverage, soil quality, and water quality. Preset standards will be formulated based on the original environmental data and ecological protection goals of the ecologically sensitive areas, such as restoring vegetation coverage to over 90% of pre-construction levels and restoring the soil erosion modulus to ≤500t / (km²). 2 •a) The water quality meets the standards for the corresponding functional area.

[0032] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0033] The above embodiments provide a detailed description of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for dynamically controlling the construction disturbance threshold in ecologically sensitive areas, characterized in that, The method includes the following steps: S1, acquiring basic environmental data of ecologically sensitive areas and preprocessing it; S2, constructing an initial construction disturbance threshold system based on the preprocessed basic environmental data and an environmental protection threshold database, and determining the ecological disturbance control benchmark for different construction stages; S3, monitoring multi-dimensional disturbance indicators in the construction process in real time based on intelligent construction technology, dynamically correcting the initial construction disturbance threshold, and constructing a dynamic control model for construction disturbance thresholds; S4, sending the dynamic control model for construction disturbance thresholds to the construction management cloud platform to execute dynamic control of construction disturbance thresholds in ecologically sensitive areas.

2. The method for dynamically controlling the construction disturbance threshold in ecologically sensitive areas according to claim 1, characterized in that, Step S1 includes the following steps: using multi-source data fusion technology to integrate multiple types of environmental data to obtain basic environmental data of ecologically sensitive areas, wherein the basic environmental data covers at least one of topography, vegetation cover, soil type and hydrological conditions; extracting key ecological elements, processing outliers and normalizing the obtained environmental data to obtain preprocessed basic environmental data.

3. The method for dynamically controlling the construction disturbance threshold in ecologically sensitive areas according to claim 1, characterized in that, Step S2 includes the following steps: conducting in-depth analysis of the preprocessed basic environmental data, using ecological models to simulate the potential responses of ecologically sensitive areas under different construction scenarios, and identifying key ecological factors and their sensitivity; combining the environmental protection threshold database, formulating preliminary disturbance threshold ranges for each key ecological factor based on ecological protection goals and construction types; and refining and improving the preliminary disturbance threshold ranges for the duration and spatial range of different construction stages to obtain unique ecological disturbance control benchmarks for each stage.

4. The method for dynamically controlling the construction disturbance threshold in ecologically sensitive areas according to claim 3, characterized in that: The key ecological factors include soil erosion modulus and biodiversity index; the sensitivity is measured by a sensitivity coefficient, which is calculated as the ratio of the rate of change of the ecological factor to the rate of change of the intensity of construction disturbance.

5. The method for dynamically controlling the construction disturbance threshold in ecologically sensitive areas according to claim 1, characterized in that, Step S3 includes the following steps: Deploying sensors across the entire construction area to collect multi-dimensional disturbance index data, specifically including vibration, noise, dust concentration, and water quality; using edge computing devices to perform preliminary analysis and feature extraction on the collected data to quickly determine whether an initial construction disturbance threshold warning has been triggered; if triggered, immediately marking and uploading detailed data to the cloud; using deep learning algorithms in the cloud, combined with historical construction data and ecological models, analyzing the impact of current disturbance indicators on ecologically sensitive areas, and dynamically correcting the initial construction disturbance threshold; based on the corrected threshold, constructing a dynamic control model for the construction disturbance threshold that includes data feedback, analysis and decision-making, and instruction issuance mechanisms.

6. The method for dynamically controlling the construction disturbance threshold in ecologically sensitive areas according to claim 5, characterized in that, The initial construction disturbance threshold is dynamically corrected using the following formula: in, The corrected threshold. As the initial threshold, This represents the degree of impact of the current disturbance index on the ecologically sensitive area (calculated using an ecological model, ranging from 0 to 1). And β are weighting coefficients, and +β=1, determined by fitting historical data.

7. The method for dynamically controlling the construction disturbance threshold in ecologically sensitive areas according to claim 1, characterized in that, Between step S3 and step S4, the following step is also included: S3.5, Constructing a multi-subject collaborative prediction-decision system for construction disturbance.

8. The method for dynamically controlling the construction disturbance threshold in ecologically sensitive areas according to claim 7, characterized in that, Step S3.5 specifically includes the following steps: constructing a multi-source heterogeneous data fusion hub to integrate real-time construction monitoring data, ecological expert knowledge base, environmental regulatory rules, and public feedback information to form a full-element data pool; developing a deep learning-based disturbance propagation prediction model, combining geospatial analysis and ecological mechanisms to simulate the dynamic impact path of construction activities on sensitive areas and potential areas exceeding threshold risks; building a multi-agent reinforcement learning decision engine, inputting the interests of multiple parties and real-time prediction results, and generating a set of dynamic control schemes that take into account ecological compliance, engineering efficiency, and cost through a multi-objective optimization algorithm; and establishing a digital twin verification platform to virtually simulate and evaluate the effects of the decision schemes, select the optimal scheme, and push it to the construction terminal.

9. The method for dynamically controlling the construction disturbance threshold in ecologically sensitive areas according to claim 8, characterized in that: It also has a closed-loop feedback mechanism that continuously corrects the prediction model and decision-making rules through on-site sensors and public participation data, so as to achieve adaptive iterative optimization of the system.

10. The method for dynamically controlling the construction disturbance threshold in ecologically sensitive areas according to claim 1, characterized in that, The method for dynamic control of construction disturbance threshold in ecologically sensitive areas also includes: S5, using digital pre-assembly technology to reduce on-site construction procedures and reduce direct disturbance to ecologically sensitive areas; S6, using ecological restoration technology to repair the ecologically disturbed areas in real time in response to environmental damage caused by construction; S7, continuously tracking and monitoring the environmental restoration of ecologically sensitive areas until construction is completed and the ecological environment is restored to the preset standard.

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