Deep well reinforcing and filling method based on construction waste

Abstract

The invention provides a deep well reinforcing and filling method based on construction waste, which comprises the following steps: collecting and coding the construction waste through automatic classification and secondary inspection by professionals, establishing a whole-course tracking database, pretreating the waste, designing a mixture formula according to physical characteristics of the waste, and adding an adhesive to adapt to different filling requirements. Finite element analysis is adopted to simulate stress distribution and deformation in the filling process, a rheological model is constructed to evaluate the influence on an underground water system, filling materials are conveyed mechanically during construction, sensors are arranged on the well wall to monitor pressure, temperature, displacement and compactness, and a self-adaptive control system is used for adjusting construction parameters. A prestressed reinforcement cage is inserted into a large-stress area to enhance the structural stability, in the grouting process, the grouting speed and pressure are monitored in real time and automatically adjusted, a long-term monitoring mechanism is established, the deep well state prediction trend is regularly checked, and abnormal changes are found and responded in time.

Description

Technical Field

[0001] This invention relates to the field of deep well construction and treatment technology, and in particular to a method for reinforcing and filling deep wells based on construction waste. Background Technology

[0002] In modern urban construction, the disposal of construction waste is an increasingly serious environmental problem. With the acceleration of urbanization and the large-scale development of old city renovation projects, a large amount of construction waste has been generated. The traditional disposal methods are mainly landfill and simple dumping. These methods not only occupy a large amount of valuable land resources, but also pollute the soil, water and air, and waste reusable building materials. Traditional landfills may also cause environmental geological problems such as groundwater pollution and land subsidence due to improper site selection or poor management.

[0003] In recent years, in order to improve resource utilization and reduce environmental pollution, both domestic and foreign countries have begun to pay attention to the recycling and reuse of construction waste. However, existing construction waste recycling technologies are mostly focused on surface treatment, such as simple sorting, crushing and screening. There is insufficient research on the deep processing and high-value-added applications of construction waste. Existing technologies lack intelligence and automation in the processing, resulting in low efficiency and difficulty in ensuring the accuracy of sorting and the consistency of material quality. In addition, due to the lack of an effective full-process tracking mechanism, it is difficult to ensure the legality of the waste source and the transparency of the processing.

[0004] Deep well reinforcement and filling is one of the key technologies in underground engineering construction. Its purpose is to enhance the stability of the well wall, prevent collapse, and ensure construction safety. Traditional methods usually use natural sand or cement mortar as filling materials. These materials are expensive, inconvenient to transport, and have limited adaptability to specific geological conditions. In addition, traditional filling methods are relatively rough in operation, and cannot accurately control the filling quality and uniformity, which can easily cause stress concentration, thereby affecting the safety and durability of the well wall structure. Summary of the Invention

[0005] The purpose of this invention is to provide a method for reinforcing and filling deep wells based on construction waste.

[0006] The problem this invention aims to solve is the low efficiency, resource waste, and environmental pollution associated with traditional construction waste treatment methods. It achieves efficient waste utilization through automated sorting and full-process tracking, improves material performance through pretreatment and precise proportioning of filling materials, optimizes the filling process using finite element analysis and rheological models to reduce negative impacts on groundwater systems, ensures construction safety and quality through intelligent monitoring and adaptive control systems, prevents potential risks through long-term monitoring mechanisms, and achieves stability and durability of deep well structures. This invention also promotes the recycling of construction waste and the development of green construction technologies.

[0007] A method for reinforcing and filling deep wells based on construction waste, the technical solution of which is as follows: S1: Set up construction waste collection points at construction sites, automatically classify the collected construction waste on the sorting production line, equip it with corresponding sorting containers, use professional personnel to conduct secondary inspections to assign codes to each batch of waste, record its source, type and quantity information, and establish a database for full-process tracking. S2: Pre-treatment of construction waste includes crushing, washing, drying, and screening. The pre-treated construction waste is classified and its chemical composition is analyzed. Its physical properties, including density, compressibility, compressive strength, and permeability, are calculated. Based on the calculated physical properties, a mixture formula is designed, including crushed stone, concrete fragments, and brick powder. A binder, including cement and lime, is added. The proportion of filling materials is dynamically adjusted through experiments. S3: Based on finite element analysis, simulate the stress distribution and deformation of deep well walls under different filling conditions. Combine the fluidity, settlement characteristics and hardening time of the filling material to construct a rheological model and evaluate the filling activities and the impact of the filling material on the surrounding environment, especially the groundwater system. S4: Construction machinery is used to transport the filling material to the designated location and distribute it evenly. Sensors are installed on the well wall to monitor pressure changes, temperature fluctuations and well wall movement in real time during the filling process. A layer-by-layer filling method is adopted, and each layer is compacted after filling. For areas with high stress, prestressed steel cages are inserted to work together with the filling material. An adaptive compaction control system is developed, which combines sensors to monitor the compaction degree of each layer of material in real time and automatically adjusts the working parameters of the construction machinery based on feedback. S5: Drill grouting holes and determine the hole diameter and depth according to the geological conditions and structural requirements of the deep well. Monitor the pressure, flow rate and temperature in real time during the grouting process through IoT technology and sensors. Automatically adjust the grouting speed and pressure according to the real-time data. Record and analyze the data of each grouting to provide a reference for subsequent maintenance. S6: Establish a long-term monitoring mechanism to regularly check the status of deep wells, including well wall integrity, changes in the performance of filling materials, groundwater level, and soil pressure data. Predict future trends and promptly detect abnormal changes. When an abnormality is detected, an alarm will be automatically issued to notify relevant personnel to take emergency measures to avoid accidents.

[0008] Furthermore, in S1, the collected construction waste is automatically sorted on the sorting line, and professional personnel conduct secondary inspections to assign codes to each batch of waste, establishing a database for full-process tracking, including: Different types of construction waste are identified by images captured by cameras, and the opening and closing of sorting containers are automatically controlled. IoT sensors are installed in each sorting container to monitor the filling level, weight, and waste type information of each container in real time. Each batch of waste is coded with a QR code, and the data is uploaded to a cloud database using blockchain technology.

[0009] Furthermore, the pretreatment of construction waste in S2 includes crushing, washing, drying, and screening; calculating its physical properties including density, compressibility, compressive strength, and permeability; and dynamically adjusting the mix ratio of the filling material through experiments, including: S21: Adjust the crushing parameters according to the type of construction waste, set up multi-stage cleaning stations, use a circulating water system to clean the construction waste, remove surface dirt and impurities, remove moisture through drying equipment, and equip a vibrating screen with screens of various apertures for fine screening. S22: Perform chemical composition analysis on the pretreated construction waste materials, including the content of calcium, silicon and iron. The analysis results are fed back to the data analysis platform, which integrates data from various pretreatment stages, including size distribution, chemical composition and moisture content. S23: Conduct physical property tests on construction waste after chemical composition analysis. Based on the obtained physical property data, simulate the physical properties of different mixture formulations in a digital environment to predict the performance of the mixtures under different conditions. Combine machine learning algorithms to dynamically adjust the mixture formulations based on historical data and current test results.

[0010] Furthermore, in S3, a rheological model is constructed by combining the fluidity, settlement characteristics, and hardening time of the filling material to evaluate the filling activity and the impact of the filling material on the surrounding environment, especially the groundwater system, including: S31: The finite element analysis tool, which combines multi-physics coupling with environmental factors such as temperature and humidity, simulates the gradual filling and compaction process during actual construction, evaluates the stress distribution and deformation at different times, and dynamically updates the finite element model. S32: A rheological model is constructed based on the updated finite element model, combined with the fluidity, settling characteristics, and hardening time of the filling material, using a multi-scale modeling method. ,in It is shear stress. It is the shear rate. It is time. It's temperature. It is the initial yield stress. It is a temperature-dependent hardening rate constant. It is the temperature sensitivity coefficient. This is a reference temperature. It is a temperature-dependent consistency coefficient. It is the power-law exponent; S33: Using the constructed rheological model combined with a geographic information system and a hydrogeological model, assess the impact of infilling activities on groundwater level, water quality and flow path, and simulate different infilling schemes for scheme selection.

[0011] Furthermore, sensors are installed on the well wall in S4. For areas with high stress, prestressed steel cages are inserted to work together with the filling material to develop an adaptive compaction control system. This system automatically adjusts the operating parameters of the construction machinery based on feedback, including: S41: Install pressure sensors at different heights and positions on the well wall to monitor pressure changes during the filling process; install temperature sensors on the well wall surface to monitor temperature fluctuations; install displacement sensors to monitor the movement of the well wall; and install compaction sensors to monitor compaction. S42: Establish an adaptive compaction control model based on pressure, temperature, displacement, and compaction degree data. ,in It is the pressure force of the construction machinery. It's the initial pressure. It is the pressure sensitivity coefficient. It is a real-time pressure change. It is the temperature sensitivity coefficient. It is a real-time temperature change. It is the displacement sensitivity coefficient. It is a real-time displacement change. It is the compaction sensitivity coefficient. It involves real-time changes in compaction degree. The sensor network collects pressure, temperature, displacement, and compaction degree data in real time and transmits them to the control system. The control system adjusts the compaction force of the construction machinery based on the real-time data through an adaptive compaction control model. S43: For areas with high stress, inserting a prestressed steel cage and infill material works together. ,in This is a prestressed steel cage insertion indicator; 1 indicates insertion, and 0 indicates no insertion. It is real-time stress. It is a preset stress threshold. When the real-time stress exceeds the preset threshold, the prestressed steel cage is automatically inserted. S44: Automatically adjusts the operating parameters of construction machinery based on feedback. ,in It is the pressure adjustment amount in the next moment. It is feedback gain. It is the target compaction degree. It is real-time compaction. After each layer is filled, the compaction sensor detects the actual compaction and dynamically adjusts the compaction of the next layer according to the feedback adjustment mechanism to gradually approach the target value.

[0012] Furthermore, in step S5, the pressure, flow rate, and temperature during the grouting process are monitored in real time using IoT technology and sensors, and the grouting speed and pressure are automatically adjusted based on the real-time data, including: S51: Conduct a geological survey of the environment surrounding the deep well to determine the geological conditions and structural requirements of the deep well, and design the diameter and depth of the grouting holes to ensure uniform distribution of grouting material based on the geological survey results; S52: Dynamically adjust grouting speed and pressure based on real-time data. , ,in and It is an adaptive gain for grouting speed that varies with time. and It is an adaptive gain of grouting pressure that varies with time. and It refers to the target traffic and target pressure. and It is a moment Traffic and real-time pressure, It is the real-time temperature. and yes The grouting speed and pressure are adjusted at any given time.

[0013] Furthermore, S6 establishes a long-term monitoring mechanism to periodically check the status of the deep well, predict future trends, and promptly detect abnormal changes. When an abnormality is detected, an alarm is automatically issued, including: S61: Set the normal range for each parameter. When the sensor data exceeds the threshold, trigger an abnormal alarm. Use statistical process control methods to monitor the changing trend of parameters and identify potential abnormal situations. S62: When one of the parameters approaches the threshold, the system issues a Level 1 warning to remind relevant personnel to pay attention to the changing trend of that parameter. When any parameter exceeds the threshold, the system issues a level-two warning, notifying relevant personnel to conduct further checks and assessments. When multiple parameters are abnormal at the same time or a certain parameter is seriously out of range, the system will issue a level three warning, automatically activate the emergency plan, and notify relevant personnel to take emergency measures. S63: Upon receiving the warning information, relevant personnel shall take corresponding measures based on the actual situation, including adjusting construction parameters, reinforcing the well wall, and repairing the filling material. After the emergency response is completed, a detailed accident report shall be generated.

[0014] The beneficial effects of this invention are: by classifying, pre-treating and optimizing the proportions, building materials that might otherwise be discarded can be reused, thereby maximizing the use of resources and reducing the demand for natural resources; Reducing the landfill or incineration of construction waste lowers the risk of pollution to soil, water and air. At the same time, using treated construction waste as filler material reduces the use of traditional materials such as cement, thereby reducing carbon emissions. Compared with traditional deep well reinforcement and filling methods, it reduces material procurement costs, and at the same time, due to the application of automation and intelligent technologies, it improves construction efficiency and reduces labor costs; Finite element analysis, rheological model construction, and real-time monitoring system are used to ensure the safety and controllability of the filling process and effectively prevent potential safety accidents. By employing IoT technology and an adaptive control system, the construction process can be managed and dynamically adjusted in a refined manner, thereby improving construction quality and providing data support for subsequent maintenance. Establishing a long-term monitoring mechanism allows for the timely detection and response to any anomalies, ensuring the long-term stability and safety of deep well structures. It also provides valuable experience and data for future similar projects. Attached Figure Description

[0015] Figure 1 This is a flowchart of a deep well reinforcement and filling method based on construction waste. Detailed Implementation

[0016] The present invention will be further described clearly and completely below, but the scope of protection of the present invention is not limited thereto.

[0017] A method for reinforcing and filling deep wells based on construction waste, the technical solution of which is as follows: S1: Set up construction waste collection points at construction sites, automatically classify the collected construction waste on the sorting production line, equip it with corresponding sorting containers, use professional personnel to conduct secondary inspections to assign codes to each batch of waste, record its source, type and quantity information, and establish a database for full-process tracking. S2: Pre-treatment of construction waste includes crushing, washing, drying, and screening. The pre-treated construction waste is classified and its chemical composition is analyzed. Its physical properties, including density, compressibility, compressive strength, and permeability, are calculated. Based on the calculated physical properties, a mixture formula is designed, including crushed stone, concrete fragments, and brick powder. A binder, including cement and lime, is added. The proportion of filling materials is dynamically adjusted through experiments. S3: Based on finite element analysis, simulate the stress distribution and deformation of deep well walls under different filling conditions. Combine the fluidity, settlement characteristics and hardening time of the filling material to construct a rheological model and evaluate the filling activities and the impact of the filling material on the surrounding environment, especially the groundwater system. S4: Construction machinery is used to transport the filling material to the designated location and distribute it evenly. Sensors are installed on the well wall to monitor pressure changes, temperature fluctuations and well wall movement in real time during the filling process. A layer-by-layer filling method is adopted, and each layer is compacted after filling. For areas with high stress, prestressed steel cages are inserted to work together with the filling material. An adaptive compaction control system is developed, which combines sensors to monitor the compaction degree of each layer of material in real time and automatically adjusts the working parameters of the construction machinery based on feedback. S5: Drill grouting holes and determine the hole diameter and depth according to the geological conditions and structural requirements of the deep well. Monitor the pressure, flow rate and temperature in real time during the grouting process through IoT technology and sensors. Automatically adjust the grouting speed and pressure according to the real-time data. Record and analyze the data of each grouting to provide a reference for subsequent maintenance. S6: Establish a long-term monitoring mechanism to regularly check the status of deep wells, including well wall integrity, changes in the performance of filling materials, groundwater level, and soil pressure data. Predict future trends and promptly detect abnormal changes. When an abnormality is detected, an alarm will be automatically issued to notify relevant personnel to take emergency measures to avoid accidents.

[0018] refer to Figure 1 The diagram shown is a flowchart of a deep well reinforcement and filling method based on construction waste.

[0019] Furthermore, in S1, the collected construction waste is automatically sorted on the sorting line, and professional personnel conduct secondary inspections to assign codes to each batch of waste, establishing a database for full-process tracking, including: Different types of construction waste are identified by images captured by cameras, and the opening and closing of sorting containers are automatically controlled. IoT sensors are installed in each sorting container to monitor the filling level, weight, and waste type information of each container in real time. Each batch of waste is coded with a QR code, and the data is uploaded to a cloud database using blockchain technology.

[0020] Furthermore, the pretreatment of construction waste in S2 includes crushing, washing, drying, and screening; calculating its physical properties including density, compressibility, compressive strength, and permeability; and dynamically adjusting the mix ratio of the filling material through experiments, including: S21: Adjust the crushing parameters according to the type of construction waste, set up multi-stage cleaning stations, use a circulating water system to clean the construction waste, remove surface dirt and impurities, remove moisture through drying equipment, and equip a vibrating screen with screens of various apertures for fine screening. S22: Perform chemical composition analysis on the pretreated construction waste materials, including the content of calcium, silicon and iron. The analysis results are fed back to the data analysis platform, which integrates data from various pretreatment stages, including size distribution, chemical composition and moisture content. S23: Conduct physical property tests on construction waste after chemical composition analysis. Based on the obtained physical property data, simulate the physical properties of different mixture formulations in a digital environment to predict the performance of the mixtures under different conditions. Combine machine learning algorithms to dynamically adjust the mixture formulations based on historical data and current test results.

[0021] Furthermore, in S3, a rheological model is constructed by combining the fluidity, settlement characteristics, and hardening time of the filling material to evaluate the filling activity and the impact of the filling material on the surrounding environment, especially the groundwater system, including: S31: The finite element analysis tool, which combines multi-physics coupling with environmental factors such as temperature and humidity, simulates the gradual filling and compaction process during actual construction, evaluates the stress distribution and deformation at different times, and dynamically updates the finite element model. S32: A rheological model is constructed based on the updated finite element model, combined with the fluidity, settling characteristics, and hardening time of the filling material, using a multi-scale modeling method. ,in It is shear stress. It is the shear rate. It is time. It's temperature. It is the initial yield stress. It is a temperature-dependent hardening rate constant. It is the temperature sensitivity coefficient. This is a reference temperature. It is a temperature-dependent consistency coefficient. It is the power-law exponent; S33: Using the constructed rheological model combined with a geographic information system and a hydrogeological model, assess the impact of infilling activities on groundwater level, water quality and flow path, and simulate different infilling schemes for scheme selection.

[0022] Furthermore, sensors are installed on the well wall in S4. For areas with high stress, prestressed steel cages are inserted to work together with the filling material to develop an adaptive compaction control system. This system automatically adjusts the operating parameters of the construction machinery based on feedback, including: S41: Install pressure sensors at different heights and positions on the well wall to monitor pressure changes during the filling process; install temperature sensors on the well wall surface to monitor temperature fluctuations; install displacement sensors to monitor the movement of the well wall; and install compaction sensors to monitor compaction. S42: Establish an adaptive compaction control model based on pressure, temperature, displacement, and compaction degree data. ,in It is the pressure force of the construction machinery. It's the initial pressure. It is the pressure sensitivity coefficient. It is a real-time pressure change. It is the temperature sensitivity coefficient. It is a real-time temperature change. It is the displacement sensitivity coefficient. It is a real-time displacement change. It is the compaction sensitivity coefficient. It involves real-time changes in compaction degree. The sensor network collects pressure, temperature, displacement, and compaction degree data in real time and transmits them to the control system. The control system adjusts the compaction force of the construction machinery based on the real-time data through an adaptive compaction control model. S43: For areas with high stress, inserting a prestressed steel cage and infill material works together. ,in This is a prestressed steel cage insertion indicator; 1 indicates insertion, and 0 indicates no insertion. It is real-time stress. It is a preset stress threshold. When the real-time stress exceeds the preset threshold, the prestressed steel cage is automatically inserted. S44: Automatically adjusts the operating parameters of construction machinery based on feedback. ,in It is the pressure adjustment amount in the next moment. It is feedback gain. It is the target compaction degree. It is real-time compaction. After each layer is filled, the compaction sensor detects the actual compaction and dynamically adjusts the compaction of the next layer according to the feedback adjustment mechanism to gradually approach the target value.

[0023] Furthermore, in step S5, the pressure, flow rate, and temperature during the grouting process are monitored in real time using IoT technology and sensors, and the grouting speed and pressure are automatically adjusted based on the real-time data, including: S51: Conduct a geological survey of the environment surrounding the deep well to determine the geological conditions and structural requirements of the deep well, and design the diameter and depth of the grouting holes to ensure uniform distribution of grouting material based on the geological survey results; S52: Dynamically adjust grouting speed and pressure based on real-time data. , ,in and It is an adaptive gain for grouting speed that varies with time. and It is an adaptive gain of grouting pressure that varies with time. and It refers to the target traffic and target pressure. and It is a moment Traffic and real-time pressure, It is the real-time temperature. and yes The grouting speed and pressure are adjusted at any given time.

[0024] Furthermore, S6 establishes a long-term monitoring mechanism to periodically check the status of the deep well, predict future trends, and promptly detect abnormal changes. When an abnormality is detected, an alarm is automatically issued, including: S61: Set the normal range for each parameter. When the sensor data exceeds the threshold, trigger an abnormal alarm. Use statistical process control methods to monitor the changing trend of parameters and identify potential abnormal situations. S62: When one of the parameters approaches the threshold, the system issues a Level 1 warning to remind relevant personnel to pay attention to the changing trend of that parameter. When any parameter exceeds the threshold, the system issues a level-two warning, notifying relevant personnel to conduct further checks and assessments. When multiple parameters are abnormal at the same time or a certain parameter is seriously out of range, the system will issue a level three warning, automatically activate the emergency plan, and notify relevant personnel to take emergency measures. S63: Upon receiving the warning information, relevant personnel shall take corresponding measures based on the actual situation, including adjusting construction parameters, reinforcing the well wall, and repairing the filling material. After the emergency response is completed, a detailed accident report shall be generated.

[0025] This invention provides a method for deep well reinforcement and filling based on construction waste. Construction waste is collected and coded through automated sorting and secondary inspection by professionals, establishing a full-process tracking database. The waste is pre-treated, and a mixture formula is designed based on its physical properties. Adhesives are added to adapt to different filling requirements. Finite element analysis is used to simulate stress distribution and deformation during the filling process, and a rheological model is constructed to assess the impact on the groundwater system. During construction, mechanical conveying of filling materials is used, and sensors are installed on the well wall to monitor pressure, temperature, displacement, and compaction. An adaptive control system is used to adjust construction parameters. For areas with high stress, prestressed steel cages are inserted to enhance structural stability. During grouting, the grouting speed and pressure are monitored in real time and automatically adjusted. A long-term monitoring mechanism is established to periodically check the deep well status and predict trends, promptly detecting and responding to abnormal changes.

Claims

1. A method for reinforcing and filling deep wells based on construction waste, characterized in that, include: S1: Set up construction waste collection points at construction sites, automatically classify the collected construction waste on the sorting production line, equip it with corresponding sorting containers, use professional personnel to conduct secondary inspections to assign codes to each batch of waste, record its source, type and quantity information, and establish a database for full-process tracking. S2: Pre-treatment of construction waste includes crushing, washing, drying, and screening. The pre-treated construction waste is classified and its chemical composition is analyzed. Its physical properties, including density, compressibility, compressive strength, and permeability, are calculated. Based on the calculated physical properties, a mixture formula is designed, including crushed stone, concrete fragments, and brick powder. A binder, including cement and lime, is added. The proportion of filling materials is dynamically adjusted through experiments. S3: Based on finite element analysis, simulate the stress distribution and deformation of deep well walls under different filling conditions. Combine the fluidity, settlement characteristics and hardening time of the filling material to construct a rheological model and evaluate the filling activities and the impact of the filling material on the surrounding environment, especially the groundwater system. S4: Construction machinery is used to transport the filling material to the designated location and distribute it evenly. Sensors are installed on the well wall to monitor pressure changes, temperature fluctuations and well wall movement in real time during the filling process. A layer-by-layer filling method is adopted, and each layer is compacted after filling. For areas with high stress, prestressed steel cages are inserted to work together with the filling material. An adaptive compaction control system is developed, which combines sensors to monitor the compaction degree of each layer of material in real time and automatically adjusts the working parameters of the construction machinery based on feedback. S5: Drill grouting holes and determine the hole diameter and depth according to the geological conditions and structural requirements of the deep well. Monitor the pressure, flow rate and temperature in real time during the grouting process through IoT technology and sensors. Automatically adjust the grouting speed and pressure according to the real-time data. Record and analyze the data of each grouting to provide a reference for subsequent maintenance. S6: Establish a long-term monitoring mechanism to regularly check the status of deep wells, including well wall integrity, changes in the performance of filling materials, groundwater level, and soil pressure data. Predict future trends and promptly detect abnormal changes. When an abnormality is detected, an alarm will be automatically issued to notify relevant personnel to take emergency measures to avoid accidents.

2. The method for deep well reinforcement and filling based on construction waste as described in claim 1, characterized in that, In step S1, the collected construction waste is automatically sorted on the sorting line, and professional personnel conduct a secondary inspection to assign a code to each batch of waste. A database is established for full-process tracking, including: Different types of construction waste are identified by images captured by cameras, and the opening and closing of sorting containers are automatically controlled. IoT sensors are installed in each sorting container to monitor the filling level, weight, and waste type information of each container in real time. Each batch of waste is coded with a QR code, and the data is uploaded to a cloud database using blockchain technology.

3. The method for reinforcing and filling deep wells based on construction waste as described in claim 1, characterized in that, The pretreatment of construction waste in S2 includes crushing, washing, drying, and screening; calculation of its physical properties including density, compressibility, compressive strength, and permeability; and dynamic adjustment of the mix proportions for the filling material through experiments, including: S21: Adjust the crushing parameters according to the type of construction waste, set up multi-stage cleaning stations, use a circulating water system to clean the construction waste, remove surface dirt and impurities, remove moisture through drying equipment, and equip a vibrating screen with screens of various apertures for fine screening. S22: Perform chemical composition analysis on the pretreated construction waste materials, including the content of calcium, silicon and iron. The analysis results are fed back to the data analysis platform, which integrates data from various pretreatment stages, including size distribution, chemical composition and moisture content. S23: Conduct physical property tests on construction waste after chemical composition analysis. Based on the obtained physical property data, simulate the physical properties of different mixture formulations in a digital environment to predict the performance of the mixtures under different conditions. Combine machine learning algorithms to dynamically adjust the mixture formulations based on historical data and current test results.

4. The method for reinforcing and filling deep wells based on construction waste as described in claim 1, characterized in that, In S3, a rheological model is constructed by combining the fluidity, settlement characteristics, and hardening time of the filling material to evaluate the filling activities and the impact of the filling material on the surrounding environment, especially the groundwater system, including: S31: The finite element analysis tool, which combines multi-physics coupling with environmental factors such as temperature and humidity, simulates the gradual filling and compaction process during actual construction, evaluates the stress distribution and deformation at different times, and dynamically updates the finite element model. S32: A rheological model is constructed based on the updated finite element model, combined with the fluidity, settling characteristics, and hardening time of the filling material, using a multi-scale modeling method. ,in It is shear stress. It is the shear rate. It is time. It's temperature. It is the initial yield stress. It is a temperature-dependent hardening rate constant. It is the temperature sensitivity coefficient. This is a reference temperature. It is a temperature-dependent consistency coefficient. It is the power-law exponent; S33: Using the constructed rheological model combined with a geographic information system and a hydrogeological model, assess the impact of infilling activities on groundwater level, water quality and flow path, and simulate different infilling schemes for scheme selection.

5. A method for reinforcing and filling deep wells based on construction waste as described in claim 1, characterized in that, Sensors are installed on the well wall in S4. For areas with high stress, prestressed steel cages are inserted to work in conjunction with the filling material to develop an adaptive compaction control system. This system automatically adjusts the operating parameters of the construction machinery based on feedback, including: S41: Install pressure sensors at different heights and positions on the well wall to monitor pressure changes during the filling process; install temperature sensors on the well wall surface to monitor temperature fluctuations; install displacement sensors to monitor the movement of the well wall; and install compaction sensors to monitor compaction. S42: Establish an adaptive compaction control model based on pressure, temperature, displacement, and compaction degree data. ,in It is the pressure force of the construction machinery. It's the initial pressure. It is the pressure sensitivity coefficient. It is a real-time pressure change. It is the temperature sensitivity coefficient. It is a real-time temperature change. It is the displacement sensitivity coefficient. It is a real-time displacement change. It is the compaction sensitivity coefficient. It involves real-time changes in compaction degree. The sensor network collects pressure, temperature, displacement, and compaction degree data in real time and transmits them to the control system. The control system adjusts the compaction force of the construction machinery based on the real-time data through an adaptive compaction control model. S43: For areas with high stress, inserting a prestressed steel cage and infill material works together. ,in This is a prestressed steel cage insertion indicator; 1 indicates insertion, and 0 indicates no insertion. It is real-time stress. It is a preset stress threshold. When the real-time stress exceeds the preset threshold, the prestressed steel cage is automatically inserted. S44: Automatically adjusts the operating parameters of construction machinery based on feedback. ,in It is the pressure adjustment amount in the next moment. It is feedback gain. It is the target compaction degree. It is real-time compaction. After each layer is filled, the compaction sensor detects the actual compaction and dynamically adjusts the compaction of the next layer according to the feedback adjustment mechanism to gradually approach the target value.

6. The method for reinforcing and filling deep wells based on construction waste as described in claim 1, characterized in that, The S5 process uses IoT technology and sensors to monitor the pressure, flow rate, and temperature during the grouting process in real time, and automatically adjusts the grouting speed and pressure based on the real-time data, including: S51: Conduct a geological survey of the environment surrounding the deep well to determine the geological conditions and structural requirements of the deep well, and design the diameter and depth of the grouting holes to ensure uniform distribution of grouting material based on the geological survey results; S52: Dynamically adjust grouting speed and pressure based on real-time data. , ,in and It is an adaptive gain for grouting speed that varies with time. and It is an adaptive gain of grouting pressure that varies with time. and It refers to the target traffic and target pressure. and It is a moment Traffic and real-time pressure, It is the real-time temperature. and yes The grouting speed and pressure are adjusted at any given time.

7. A method for reinforcing and filling deep wells based on construction waste as described in claim 1, characterized in that, The S6 establishes a long-term monitoring mechanism to periodically check the status of the deep well, predict future trends, and promptly detect abnormal changes. When an abnormality is detected, an alarm is automatically issued, including: S61: Set the normal range for each parameter. When the sensor data exceeds the threshold, trigger an abnormal alarm. Use statistical process control methods to monitor the changing trend of parameters and identify potential abnormal situations. S62: When one of the parameters approaches the threshold, the system issues a Level 1 warning to remind relevant personnel to pay attention to the changing trend of that parameter. When any parameter exceeds the threshold, the system issues a level-two warning, notifying relevant personnel to conduct further checks and assessments. When multiple parameters are abnormal at the same time or a certain parameter is seriously out of range, the system will issue a level three warning, automatically activate the emergency plan, and notify relevant personnel to take emergency measures. S63: Upon receiving the warning information, relevant personnel shall take corresponding measures based on the actual situation, including adjusting construction parameters, reinforcing the well wall, and repairing the filling material. After the emergency response is completed, a detailed accident report shall be generated.

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