Municipal engineering construction quality monitoring method and system based on dual-carbon target

Abstract

The invention relates to the technical field of engineering construction intelligent management, and discloses a municipal engineering construction quality monitoring method and system based on a dual-carbon target, and the method comprises the following steps: collecting the carbon emission data and construction quality parameters of a municipal engineering construction site in real time through an Internet of Things sensor network; the carbon emission data comprises construction machinery energy consumption data, material transportation carbon emission data and on-site construction carbon emission data, and the construction quality parameters comprise structural strength data, material ratio data and construction process parameters; and the acquired carbon emission data and construction quality parameters are transmitted to a cloud data processing center for data cleaning and standardization processing. By constructing an Internet of Things real-time acquisition network and a carbon efficiency quality correlation evaluation model, the limitation that the Internet of Things real-time acquisition network and the carbon efficiency quality correlation evaluation model are mutually independent in traditional management is broken through, and on the premise of ensuring that core quality indexes such as structural strength and material performance reach the standard, a high-carbon emission link and a low-efficiency construction process can be identified, and an optimization decision scheme giving consideration to an emission reduction target is dynamically generated.

Description

Technical Field

[0001] This invention relates to the field of intelligent management technology for engineering construction, specifically to a method and system for monitoring the construction quality of municipal engineering projects based on dual-carbon targets. Background Technology

[0002] Currently, the quality monitoring of municipal engineering construction mainly relies on manual sampling and offline laboratory analysis. Although some automated monitoring systems based on IoT sensors have emerged, they are still limited to the independent collection of single quality parameters such as structural strength and material ratio, lacking the ability to coordinate real-time monitoring with carbon emission data during the construction process. In terms of carbon emission management, existing technologies mostly focus on macro-regional carbon accounting or energy consumption monitoring during the building operation and maintenance phase. Dynamic carbon emission monitoring for complex processes at municipal engineering construction sites is still in its infancy, generally suffering from low data collection frequency, limited coverage, and insufficient accuracy, making it difficult to meet the requirements for refined carbon control during the construction process under dual carbon objectives. Although cloud computing and big data technologies have been applied in the field of engineering management, carbon emission data and quality parameters are still disconnected, and no effective correlation analysis model has been established, resulting in the inability to identify the inherent coupling relationship between carbon emission anomalies and quality fluctuations, and even more so, the inability to achieve early warning and coordinated response to exceed limits. Furthermore, the existing system lacks a scientific carbon efficiency quality assessment system, making it difficult to quantify the carbon emission costs and quality benefits under different construction techniques, material selections, and machinery configurations. This results in a lack of data support for carbon emission reduction decisions, easily leading to a dilemma of sacrificing quality for emission reduction or ignoring carbon emissions and engaging in high-energy-consuming construction. In terms of traceability, the carbon footprint records of building materials often lack on-site construction links due to information gaps in the supply chain. Moreover, the credibility and tamper-proof capabilities of carbon emission monitoring data, quality testing data, and decision-making processes are insufficient, making it difficult to support the authoritative requirements of carbon audits, carbon trading, and project acceptance.

[0003] To address these issues, those skilled in the art have proposed a method and system for monitoring the construction quality of municipal engineering projects based on dual carbon targets. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for monitoring the construction quality of municipal engineering projects based on dual carbon targets, thus solving the problems mentioned in the background section.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for monitoring the construction quality of municipal engineering projects based on dual carbon targets, comprising the following steps: The carbon emission data and construction quality parameters of municipal engineering construction sites are collected in real time through the Internet of Things sensor network. The carbon emission data includes construction machinery energy consumption data, material transportation carbon emission data, and on-site construction carbon emission data. The construction quality parameters include structural strength data, material ratio data, and construction process parameters. The collected carbon emission data and construction quality parameters are transmitted to the cloud data processing center for data cleaning and standardization, and a carbon emission benchmark model is constructed. This model is based on historical municipal engineering data, the current carbon emission factor library, and carbon emission standards for construction processes. Real-time carbon emission data is compared and analyzed with carbon emission benchmark models to calculate carbon emission deviation values. When the carbon emission deviation value exceeds a preset threshold, a carbon emission warning is triggered, and the fluctuation of construction quality parameters during the corresponding period is analyzed in conjunction with the warning. Establish a correlation assessment model between carbon emissions and construction quality, analyze the nonlinear relationship between carbon emission data and construction quality parameters through machine learning algorithms, and generate a carbon efficiency quality assessment index; Based on the carbon efficiency quality assessment index, the output includes carbon emission reduction decision schemes that include construction process adjustment plans, material replacement suggestions, and construction machinery scheduling optimization.

[0006] Preferably, the step of establishing a correlation assessment model between carbon emissions and construction quality includes: establishing a carbon emission factor library for municipal engineering sub-projects, which covers carbon emission benchmark values ​​for concrete pouring, steel bar processing, earthwork excavation, and road paving operations; dividing the municipal engineering project into multiple construction stages according to the project schedule, and setting dynamic carbon emission quotas for each construction stage; regionally correcting the carbon emission benchmark values ​​by combining geographic information data and climate environment data, and using time series analysis to predict the trend of historical municipal engineering carbon emission data to generate a dynamic adjustment curve for the carbon emission benchmark.

[0007] Preferably, the carbon emission early warning triggering includes: setting a three-level carbon emission early warning threshold dynamically calculated based on the historical data mean and standard deviation, namely a level one warning threshold, a level two exceeding threshold, and a level three severe exceeding threshold, wherein the level one warning threshold is set to 105% of the benchmark value, the level two exceeding threshold is set to 110% of the benchmark value, and the level three severe exceeding threshold is set to 120% of the benchmark value; when the carbon emission deviation value reaches the level one warning threshold, a prompt warning message is generated and abnormal data is recorded, and a high-frequency data acquisition mode is activated, increasing the monitoring frequency from minutes to seconds; when the carbon emission deviation value reaches the level two exceeding threshold, a quality parameter linkage verification is initiated, comparing whether the concrete strength test data and compaction test data within the same time period are synchronously abnormal, and calling the correlation evaluation model to calculate the probability of quality-carbon emission coupling anomaly; when the carbon emission deviation value reaches the level three severe exceeding threshold, a construction suspension command is triggered, which is automatically sent to the key construction machinery controller through the construction site's industrial control network, and an emergency response plan is pushed to the project management terminal. The emergency response plan includes alternative construction process parameters, an emergency material allocation list, and rapid carbon emission control measures.

[0008] Preferably, the processing of carbon emission data includes source tracing and tracking steps: Each batch of building materials entering the site is assigned a unique carbon identification code. This code is automatically bound to the materials upon arrival at the site via an Internet of Things sensor network and uploaded to the cloud data processing center to record the carbon emission data of the building materials throughout the entire process from production and transportation to construction and installation. A carbon footprint distribution map of municipal engineering projects is constructed. This map is automatically generated based on the division of projects in the carbon emission benchmark model and identifies key contributors to carbon emissions. Establish a mapping database between quality defects and carbon emission anomalies. This database uses the carbon efficiency quality assessment index as the association key. When a quality defect is found, trace the carbon emission exceedance record corresponding to the defect location to generate a traceable carbon emission and quality correlation file. This file is used for project acceptance and carbon audit.

[0009] Preferably, the carbon emission reduction strategy optimization step includes: Based on the carbon efficiency quality assessment index, a multi-objective optimization algorithm is used to solve the minimum carbon emission scheme under construction quality constraints. Generate an optimization report that includes a list of low-carbon building material replacements, an intelligent scheduling table for construction machinery, and suggestions for adjusting construction process parameters; Simulate the impact of different emission reduction strategies on engineering quality, and select low-carbon solutions with less impact on quality than the preset value; link the optimized solution with the BIM construction progress model to realize the time-sequential and visual deployment of carbon emission reduction measures.

[0010] A municipal engineering construction quality monitoring system based on dual carbon targets, comprising: A carbon emission data acquisition terminal network, consisting of energy consumption monitoring meters, fuel consumption sensors, building material weighing sensors, and GPS positioning devices for transport vehicles deployed at construction sites, is used to collect mechanical energy consumption data, material usage data, and transportation mileage data in real time. A construction quality parameter monitoring sensor array is constructed, which includes a concrete temperature sensor, a compaction tester, strain gauges, and displacement gauges to collect structural construction quality data. The cloud-based data processing unit includes a data receiving module, a data cleaning module, and a data standardization module, which are used to preprocess the received carbon emission data and quality parameters. Carbon emission benchmark database, which stores carbon emission benchmark values ​​for typical sub-projects of municipal engineering, historical project carbon emission data, and carbon emission factor database; The assessment and analysis unit includes a carbon emission deviation calculation module, a carbon efficiency quality correlation analysis module, and a machine learning prediction module, which are used to calculate carbon emission deviation values ​​and generate carbon efficiency quality assessment indices. The decision support unit generates construction process adjustment plans, material scheduling suggestions, and machinery configuration optimization plans based on the output results of the evaluation and analysis unit. The early warning notification unit sends tiered early warning information to the project management platform when the carbon emission deviation exceeds a preset threshold.

[0011] Preferably, the carbon emission benchmark database includes: A carbon emission factor database covering the entire life cycle of municipal engineering projects, encompassing carbon emission accounting factors for the planning, design, construction, and operation and maintenance phases. The dynamic carbon emission quota management module automatically adjusts the carbon emission limit values ​​for each construction stage based on the actual progress of the project. The regional correction sub-library stores carbon emission correction coefficients for different climate zones and terrain conditions; the carbon emission baseline dynamic update module regularly optimizes the carbon emission baseline model parameters based on the latest monitoring data.

[0012] Preferably, the system includes a blockchain evidence storage module, which performs hash operations on the following data and uploads them to the blockchain network: raw carbon emission monitoring data, carbon efficiency quality assessment index, early warning processing records, carbon emission reduction decision-making schemes, and engineering quality acceptance data.

[0013] This invention provides a method and system for monitoring the construction quality of municipal engineering projects based on dual carbon targets. It has the following beneficial effects: 1. This invention achieves deep integration and collaborative optimization of carbon emission control and construction quality monitoring in municipal engineering projects. By constructing an IoT real-time data acquisition network and a carbon efficiency quality correlation assessment model, it breaks through the limitation of the two being independent in traditional management. Under the premise of ensuring that core quality indicators such as structural strength and material performance meet the standards, it can accurately identify high-carbon emission links and inefficient construction processes, dynamically generate optimized decision-making schemes that take into account both emission reduction targets and quality requirements, effectively avoid quality risks or carbon emission runaway problems caused by a single target orientation, and form a scientific construction quality control system under dual carbon constraints.

[0014] 2. This invention establishes a tiered, interconnected early warning and full-process precise traceability mechanism. By setting three levels of carbon emission threshold early warning and linking them to verify fluctuations in quality parameters during the same period, it achieves tiered control from anomaly alerts to emergency responses, significantly improving the timeliness of problem detection and the targeted nature of handling. Combining unique carbon identification codes for building materials with blockchain evidence storage technology, it constructs an immutable carbon-quality correlation archive covering the entire process of production, transportation, and construction. This not only supports precise tracing and responsibility determination of quality defects and carbon emission anomalies, but also provides highly reliable data support for engineering carbon audits, carbon trading verification, and acceptance, significantly enhancing the transparency and long-term regulatory capabilities of municipal engineering construction management.

[0015] 3. This invention enhances the intelligent decision-making level and data security of green construction in municipal engineering. By mining the nonlinear relationship between carbon emissions and quality parameters through machine learning algorithms, it autonomously generates multi-objective solutions for low-carbon building material replacement, intelligent machinery scheduling, and process parameter optimization. It also links with the BIM progress model to achieve time-series simulation and visual deployment of emission reduction measures, greatly improving the scientificity and operability of carbon emission reduction strategies. The blockchain module hashes carbon monitoring data, assessment indices, early warning records, and decision-making schemes onto the blockchain, ensuring the tamper-proof and verifiable nature of key information. This provides reliable technical support for establishing a long-term carbon efficiency and quality management system for municipal engineering, promoting the industry's digital transformation, and achieving dual-carbon goals. Attached Figure Description

[0016] Figure 1 This is the main flowchart of the collaborative monitoring of carbon emissions and quality in municipal engineering projects according to the present invention; Figure 2 This is a flowchart illustrating the generation and optimization process of carbon emission reduction decision-making schemes in this invention. Detailed Implementation

[0017] The technical solutions in 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 some embodiments of the present invention, and not all embodiments. 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.

[0018] Please see the appendix Figure 1 - Appendix Figure 2 This invention provides a method for monitoring the construction quality of municipal engineering projects based on dual carbon targets, comprising the following steps: The carbon emission data and construction quality parameters of municipal engineering construction sites are collected in real time through the Internet of Things sensor network. The carbon emission data includes construction machinery energy consumption data, material transportation carbon emission data, and on-site construction carbon emission data. The construction quality parameters include structural strength data, material ratio data, and construction process parameters. The collected carbon emission data and construction quality parameters are transmitted to the cloud data processing center for data cleaning and standardization, and a carbon emission benchmark model is constructed. This model is based on historical municipal engineering data, the current carbon emission factor library, and carbon emission standards for construction processes. Specifically, this process is based on the principles of ubiquitous IoT sensing and cloud-based collaborative computing: a heterogeneous sensor network deployed at the construction site captures multi-source carbon emission data in real time, such as energy consumption of construction machinery, material transportation mileage, and on-site operating conditions, as well as quality parameters such as concrete strain, asphalt temperature, and compaction degree, through protocols such as Modbus, Zigbee, and 4G / 5G, forming a spatiotemporally synchronized raw data stream; the cloud data processing center uses a streaming computing framework to filter noise, imputate missing values, and normalize the dimensions of massive heterogeneous data, eliminating outliers caused by sensor drift and communication interference, and then constructs a time series baseline based on historical municipal engineering carbon emission big data, coupled with a national or industry-released carbon emission factor library (covering unit carbon emission coefficients for each stage of building material production, transportation, and construction), and combined with specific construction process parameters (such as concrete curing cycle and paving temperature threshold) to establish a dynamic carbon emission benchmark model for sub-projects. This model is continuously iterated and optimized through machine learning algorithms to achieve adaptive correction of carbon emission benchmarks under different geological, climatic, and engineering types, thereby supporting subsequent real-time carbon efficiency deviation calculation and quality correlation analysis.

[0019] Real-time carbon emission data is compared and analyzed with a carbon emission benchmark model to calculate carbon emission deviation values. When the carbon emission deviation value exceeds a preset threshold, a carbon emission warning is triggered, and the fluctuation of construction quality parameters during the corresponding period is analyzed in conjunction with the warning. Triggering a carbon emission warning includes setting three levels of carbon emission warning thresholds dynamically calculated based on the historical data mean and standard deviation: Level 1 warning threshold, Level 2 exceedance threshold, and Level 3 severe exceedance threshold. The Level 1 warning threshold is set to 105% of the benchmark value, the Level 2 exceedance threshold to 110% of the benchmark value, and the Level 3 severe exceedance threshold to 120% of the benchmark value. When the carbon emission deviation value reaches the Level 1 warning threshold, a prompt warning message is generated and recorded. Abnormal data will trigger a high-frequency data acquisition mode, increasing the monitoring frequency from minutes to seconds. When the carbon emission deviation reaches the level 2 exceedance threshold, a quality parameter linkage check will be initiated, comparing the concrete strength test data and compaction test data within the same time period to see if they are synchronously abnormal, and calling the correlation evaluation model to calculate the probability of quality-carbon emission coupling anomaly. When the carbon emission deviation reaches the level 3 severe exceedance threshold, a construction suspension command will be triggered. This suspension command will be automatically sent to the controllers of key construction machinery through the construction site's industrial control network, and an emergency response plan will be pushed to the project management terminal. The emergency response plan includes alternative construction process parameters, an emergency material allocation list, and rapid carbon emission control measures.

[0020] Specifically, this mechanism is based on the principles of statistical process control and hierarchical response closed loop: the system compares real-time carbon emission data streams with dynamic benchmark models online, uses a sliding time window algorithm to calculate the deviation rate between the current value and the benchmark, and adaptively generates three levels of statistical thresholds based on the historical data mean and standard deviation to form a hierarchical early warning system; when the deviation rate reaches the first-level threshold, the system activates a high-frequency sampling mode based on an event-driven architecture, uses a sensor queue buffer mechanism to increase data density to capture instantaneous anomalies, and triggers log chain recording to ensure traceability; if the deviation continues to expand to the second-level threshold, the quality-carbon emission coupling analysis engine is activated, uses a time-series alignment algorithm to retrieve quality sensor data within the same window period, calls a pre-trained correlation evaluation model to calculate the probability of co-occurrence of the two anomalies, and identifies root causes such as process runaway or material deterioration; when the deviation exceeds the third-level threshold, the system sends a signed emergency stop command to the construction machinery PLC controller based on the industrial control protocol to achieve automatic shutdown at the physical layer, and generates an emergency plan based on the rule engine and pushes it to the project management terminal through a message queue, forming a fully automatic protection closed loop from monitoring, analysis to execution, avoiding the dual risks of quality accidents and carbon emission runaway.

[0021] A correlation assessment model between carbon emissions and construction quality is established. Machine learning algorithms are used to analyze the nonlinear relationship between carbon emission data and construction quality parameters, generating a carbon efficiency quality assessment index. The steps for establishing this model include: creating a carbon emission factor library for various municipal engineering projects, covering carbon emission benchmark values ​​for concrete pouring, steel reinforcement processing, earthwork excavation, and road paving; dividing the municipal engineering project into multiple construction phases according to the project schedule and setting dynamic carbon emission quotas for each phase; regionally adjusting the carbon emission benchmark values ​​using geographic information data and climate data; and using time series analysis to predict trends in historical municipal engineering carbon emission data, generating a dynamic adjustment curve for the carbon emission benchmark.

[0022] Specifically, firstly, a carbon emission factor database for municipal engineering sub-items is constructed, standardizing the carbon emission intensity of work units such as concrete pouring and steel bar processing into calculable benchmark parameters. Then, based on the project schedule network plan, the total carbon target is decomposed into dynamic quotas for each construction stage to achieve process-based constraints. By coupling the spatial interpolation algorithm of the geographic information system with the climate environment compensation model, the benchmark value is regionally corrected to eliminate the impact of geological conditions and temperature and humidity differences on energy consumption and material weather resistance. At the same time, a time series prediction model is used to mine the trend and periodic characteristics of historical carbon emission data to generate a self-learning benchmark dynamic adjustment curve, ensuring that the benchmark is continuously optimized with technological progress and policy evolution. Finally, using this multi-dimensional benchmark model as a reference, a machine learning nonlinear regression algorithm is used to jointly train real-time carbon emission data and construction quality parameters, extracting process coupling characteristics and spatiotemporal cross-characteristics to construct a carbon efficiency quality assessment index. This index is essentially a Pareto optimal quantitative expression of quality utility and carbon emission economy, supporting subsequent early warning linkage and emission reduction decisions.

[0023] Based on the carbon efficiency quality assessment index, the output includes carbon emission reduction decision schemes that include construction process adjustment plans, material replacement suggestions, and construction machinery scheduling optimization.

[0024] Processing carbon emission data includes source tracing and tracking steps: Each batch of building materials entering the site is assigned a unique carbon identification code. This code is automatically bound to the materials upon arrival at the site via an Internet of Things sensor network and uploaded to the cloud data processing center to record the carbon emission data of the building materials throughout the entire process from production and transportation to construction and installation. A carbon footprint distribution map of municipal engineering projects is constructed. This map is automatically generated based on the division of projects in the carbon emission benchmark model and identifies key contributors to carbon emissions. Establish a mapping database between quality defects and carbon emission anomalies. This database uses the carbon efficiency quality assessment index as the association key. When a quality defect is found, trace the carbon emission exceedance record corresponding to the defect location to generate a traceable carbon emission and quality correlation file. This file is used for project acceptance and carbon audit.

[0025] The steps for optimizing carbon emission reduction strategies include: Based on the carbon efficiency quality assessment index, a multi-objective optimization algorithm is used to solve the minimum carbon emission scheme under construction quality constraints. Generate an optimization report that includes a list of low-carbon building material replacements, an intelligent scheduling table for construction machinery, and suggestions for adjusting construction process parameters; Simulate the impact of different emission reduction strategies on engineering quality, and select low-carbon solutions with less impact on quality than the preset value; link the optimized solution with the BIM construction progress model to realize the time-sequential and visual deployment of carbon emission reduction measures.

[0026] Specifically, aiming at Pareto frontier optimization, within the quality-carbon emission coupled constraint space constructed by the carbon efficiency quality assessment index, a non-dominated sorting genetic algorithm or particle swarm optimization algorithm is used to traverse the low-carbon building material replacement combinations, the spatiotemporal matrix of machinery scheduling, and the feasible region of process parameters to solve for the minimum carbon emission configuration scheme that satisfies the threshold values ​​of quality indicators such as strength, compaction, and durability. Then, a surrogate model is used to quickly simulate the quality impact of each candidate strategy, screen out the set of low-carbon schemes with quality degradation less than the preset tolerance, and generate an executable material replacement list, machinery scheduling schedule, and process adjustment suggestions. Finally, the optimized scheme is mapped in four dimensions with the BIM construction progress model. Utilizing the parameterized driving capability of BIM, emission reduction measures are decomposed into time-stamped task nodes, realizing the visualization and dynamic deployment of emission reduction actions, resource allocation, and schedule plans during construction, ensuring that carbon emission reduction strategies are accurately implemented in the spatiotemporal dimension without affecting the timely delivery of the project.

[0027] A municipal engineering construction quality monitoring system based on dual carbon targets, comprising: A carbon emission data acquisition terminal network, consisting of energy consumption monitoring meters, fuel consumption sensors, building material weighing sensors, and GPS positioning devices for transport vehicles deployed at construction sites, is used to collect real-time data on mechanical energy consumption, material usage, and transportation mileage. The carbon emission benchmark database includes: A carbon emission factor database covering the entire life cycle of municipal engineering projects, encompassing carbon emission accounting factors for the planning, design, construction, and operation and maintenance phases. The dynamic carbon emission quota management module automatically adjusts the carbon emission limit values ​​for each construction stage based on the actual progress of the project. The regional correction sub-library stores carbon emission correction coefficients for different climate zones and terrain conditions; the carbon emission baseline dynamic update module regularly optimizes the carbon emission baseline model parameters based on the latest monitoring data.

[0028] A construction quality parameter monitoring sensor array is constructed, which includes a concrete temperature sensor, a compaction tester, strain gauges, and displacement gauges to collect structural construction quality data. The cloud-based data processing unit includes a data receiving module, a data cleaning module, and a data standardization module, which are used to preprocess the received carbon emission data and quality parameters. Carbon emission benchmark database, which stores carbon emission benchmark values ​​for typical sub-projects of municipal engineering, historical project carbon emission data, and carbon emission factor database; The assessment and analysis unit includes a carbon emission deviation calculation module, a carbon efficiency quality correlation analysis module, and a machine learning prediction module, which are used to calculate carbon emission deviation values ​​and generate carbon efficiency quality assessment indices. The decision support unit generates construction process adjustment plans, material scheduling suggestions, and machinery configuration optimization plans based on the output results of the evaluation and analysis unit. The early warning notification unit sends tiered early warning information to the project management platform when the carbon emission deviation exceeds a preset threshold.

[0029] The system includes a blockchain evidence storage module, which performs hash calculations on the following data and uploads them to the blockchain network: raw carbon emission monitoring data, carbon efficiency quality assessment index, early warning processing records, carbon emission reduction decision-making schemes, and engineering quality acceptance data.

[0030] Specifically, based on the principles of cryptographic hashing and the immutability of distributed ledgers, the system achieves trusted evidence storage: it performs SHA-256 hashing on key information such as original carbon emission monitoring data, carbon efficiency quality assessment index, early warning processing records, carbon emission reduction decision-making schemes, and engineering quality acceptance data to generate unique data fingerprints. These fingerprints, along with corresponding timestamps and data source identifiers, are packaged into transactions via smart contracts and uploaded to consortium blockchains or public blockchain networks. Utilizing the chain structure, consensus mechanism, and multi-node distributed storage characteristics of blockchain, the system ensures that no party can unilaterally tamper with historical records. This constructs a full-process, cross-entity trusted evidence chain from carbon emission collection, quality assessment, anomaly early warning to decision-making and acceptance, providing legally valid non-repudiable data support for carbon trading verification, engineering quality responsibility tracing, and environmental regulatory audits.

[0031] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for monitoring the construction quality of municipal engineering projects based on dual carbon targets, characterized in that, Includes the following steps: The carbon emission data and construction quality parameters of municipal engineering construction sites are collected in real time through the Internet of Things sensor network. The carbon emission data includes construction machinery energy consumption data, material transportation carbon emission data, and on-site construction carbon emission data. The construction quality parameters include structural strength data, material ratio data, and construction process parameters. The collected carbon emission data and construction quality parameters are transmitted to the cloud data processing center for data cleaning and standardization, and a carbon emission benchmark model is constructed. This model is based on historical municipal engineering data, the current carbon emission factor library, and carbon emission standards for construction processes. Real-time carbon emission data is compared and analyzed with carbon emission benchmark models to calculate carbon emission deviation values. When the carbon emission deviation value exceeds a preset threshold, a carbon emission warning is triggered, and the fluctuation of construction quality parameters during the corresponding period is analyzed in conjunction with the warning. Establish a correlation assessment model between carbon emissions and construction quality, analyze the nonlinear relationship between carbon emission data and construction quality parameters through machine learning algorithms, and generate a carbon efficiency quality assessment index; Based on the carbon efficiency quality assessment index, the output includes carbon emission reduction decision schemes that include construction process adjustment plans, material replacement suggestions, and construction machinery scheduling optimization.

2. The method for monitoring the construction quality of municipal engineering projects based on dual carbon targets according to claim 1, characterized in that, The steps for establishing a correlation assessment model between carbon emissions and construction quality include: establishing a carbon emission factor library for municipal engineering sub-projects, which covers carbon emission benchmark values ​​for concrete pouring, steel bar processing, earthwork excavation, and road paving operations; dividing municipal engineering into multiple construction stages according to the project schedule and setting dynamic carbon emission quotas for each construction stage; regionally correcting the carbon emission benchmark values ​​by combining geographic information data and climate environmental data; using time series analysis to predict the trend of historical municipal engineering carbon emission data and generating a dynamic adjustment curve for the carbon emission benchmark.

3. The method for monitoring the construction quality of municipal engineering projects based on dual carbon targets according to claim 1, characterized in that, The carbon emission early warning triggering mechanism includes: setting three levels of carbon emission early warning thresholds dynamically calculated based on historical data mean and standard deviation, namely, a Level 1 warning threshold, a Level 2 exceeding threshold, and a Level 3 severe exceeding threshold. The Level 1 warning threshold is set to 105% of the benchmark value, the Level 2 exceeding threshold is set to 110% of the benchmark value, and the Level 3 severe exceeding threshold is set to 120% of the benchmark value. When the carbon emission deviation value reaches the Level 1 warning threshold, a prompt warning message is generated and abnormal data is recorded. At the same time, a high-frequency data acquisition mode is activated, increasing the monitoring frequency from minutes to seconds. When the carbon emission deviation value reaches the Level 2 exceeding threshold, a quality parameter linkage verification is initiated, comparing whether the concrete strength test data and compaction test data within the same time period are synchronously abnormal, and calling the correlation evaluation model to calculate the probability of quality-carbon emission coupling anomaly. When the carbon emission deviation value reaches the Level 3 severe exceeding threshold, a construction suspension command is triggered. This suspension command is automatically sent to the key construction machinery controllers through the construction site's industrial control network, and an emergency response plan is pushed to the project management terminal. The emergency response plan includes alternative construction process parameters, an emergency material allocation list, and rapid carbon emission control measures.

4. The method for monitoring the construction quality of municipal engineering projects based on dual carbon targets according to claim 1, characterized in that, Processing carbon emission data includes source tracing and tracking steps: Each batch of building materials entering the site is assigned a unique carbon identification code. This code is automatically bound to the materials upon arrival at the site via an Internet of Things sensor network and uploaded to the cloud data processing center to record the carbon emission data of the building materials throughout the entire process from production and transportation to construction and installation. A carbon footprint distribution map of municipal engineering projects is constructed. This map is automatically generated based on the division of projects in the carbon emission benchmark model and identifies key contributors to carbon emissions. Establish a mapping database between quality defects and carbon emission anomalies. This database uses the carbon efficiency quality assessment index as the association key. When a quality defect is found, trace the carbon emission exceedance record corresponding to the defect location to generate a traceable carbon emission and quality correlation file. This file is used for project acceptance and carbon audit.

5. The method for monitoring the construction quality of municipal engineering projects based on dual carbon targets according to claim 1, characterized in that, The carbon emission reduction strategy optimization steps include: Based on the carbon efficiency quality assessment index, a multi-objective optimization algorithm is used to solve the minimum carbon emission scheme under construction quality constraints. Generate an optimization report that includes a list of low-carbon building material replacements, an intelligent scheduling table for construction machinery, and suggestions for adjusting construction process parameters; Simulate the impact of different emission reduction strategies on engineering quality, and select low-carbon solutions with less impact on quality than the preset value; link the optimized solution with the BIM construction progress model to realize the time-sequential and visual deployment of carbon emission reduction measures.

6. A municipal engineering construction quality monitoring system based on dual carbon targets, comprising the municipal engineering construction quality monitoring method based on dual carbon targets according to any one of claims 1-5, characterized in that, include: A carbon emission data acquisition terminal network, consisting of energy consumption monitoring meters, fuel consumption sensors, building material weighing sensors, and GPS positioning devices for transport vehicles deployed at construction sites, is used to collect mechanical energy consumption data, material usage data, and transportation mileage data in real time. A construction quality parameter monitoring sensor array is constructed, which includes a concrete temperature sensor, a compaction tester, strain gauges, and displacement gauges to collect structural construction quality data. The cloud-based data processing unit includes a data receiving module, a data cleaning module, and a data standardization module, which are used to preprocess the received carbon emission data and quality parameters. Carbon emission benchmark database, which stores carbon emission benchmark values ​​for typical sub-projects of municipal engineering, historical project carbon emission data, and a carbon emission factor database; The assessment and analysis unit includes a carbon emission deviation calculation module, a carbon efficiency quality correlation analysis module, and a machine learning prediction module, which are used to calculate carbon emission deviation values ​​and generate carbon efficiency quality assessment indices. The decision support unit generates construction process adjustment plans, material scheduling suggestions, and machinery configuration optimization plans based on the output results of the evaluation and analysis unit. The early warning notification unit sends tiered early warning information to the project management platform when the carbon emission deviation exceeds a preset threshold.

7. The municipal engineering construction quality monitoring system based on dual carbon targets according to claim 6, characterized in that, The carbon emission benchmark database contains: A carbon emission factor database covering the entire life cycle of municipal engineering projects, encompassing carbon emission accounting factors for the planning, design, construction, and operation and maintenance phases. The dynamic carbon emission quota management module automatically adjusts the carbon emission limit values ​​for each construction stage based on the actual progress of the project. A regional correction sub-database stores carbon emission correction coefficients under different climate zones and topographic conditions; The carbon emission benchmark dynamic update module regularly optimizes the carbon emission benchmark model parameters based on the latest monitoring data.

8. The municipal engineering construction quality monitoring system based on dual carbon targets according to claim 6, characterized in that, The system includes a blockchain evidence storage module, which performs hash operations on the following data and uploads them to the blockchain network: raw carbon emission monitoring data, carbon efficiency quality assessment index, early warning processing records, carbon emission reduction decision-making schemes, and engineering quality acceptance data.

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