Construction material carbon emission real-time intelligent monitoring platform and monitoring method

Abstract

The invention discloses a construction material carbon emission real-time intelligent monitoring platform and method. The platform is composed of a material data acquisition module, a carbon emission real-time monitoring module, an intelligent algorithm analysis module, a carbon emission alarm module and a data storage and visualization module. The material data acquisition module can acquire information of construction materials in real time; the carbon emission real-time monitoring module is used for calculating the carbon emission generated in the transportation, stacking and construction processes of each material in real time; the intelligent algorithm analysis module predicts a carbon emission trend through a machine learning algorithm based on historical data and real-time data flow, and generates a carbon emission reduction optimization scheme; the carbon emission alarm module automatically triggers an alarm when the carbon emission exceeds a preset threshold value; and the data storage and visualization module uploads the data to a cloud end and displays the carbon emission condition in a chart form. According to the scheme, real-time intelligent management of construction carbon emission can be achieved, and carbon emission in the construction process is effectively reduced.

Description

Technical Field

[0001] The present invention relates to the field of carbon emissions from construction, and in particular to a real-time monitoring and management platform for carbon emissions from construction materials. Background Art

[0002] As the world pays more attention to environmental protection and sustainable development, the construction industry, as one of the important sources of carbon emissions, has received increasing attention for its carbon emissions during construction. In construction, the use, transportation and operation of a large number of materials and construction equipment will cause significant carbon emissions, which not only has a negative impact on the environment, but may also cause companies to face policy pressure and social responsibility challenges.

[0003] At present, traditional construction carbon emission monitoring solutions mainly rely on manual records and empirical estimates, lacking real-time and accuracy, and are difficult to fully reflect the true situation of carbon emissions during the construction process. Construction companies are usually unable to effectively grasp the carbon emissions generated by various materials during transportation, stacking and construction, resulting in inefficient implementation of carbon emission control measures. In addition, existing monitoring methods often fail to combine with modern information technology methods, lack intelligent and automated management platforms, and cannot meet the industry's needs for low-carbon construction management.

[0004] In recent years, with the rapid development of new technologies such as the Internet of Things, cloud computing and big data, intelligent monitoring and management systems have been widely used in all walks of life. However, intelligent monitoring platforms for carbon emissions in construction are still relatively scarce, especially in real-time data collection, carbon emission calculation and dynamic monitoring. This has led to major challenges for construction units in achieving low-carbon construction goals, responding to environmental regulations and enhancing corporate social responsibility.

[0005] Therefore, there is an urgent need for a real-time intelligent monitoring platform for carbon emissions of construction materials based on modern information technology. Through automated data collection, real-time monitoring, intelligent analysis and visual display, it can help construction units to fully and accurately grasp the carbon emissions situation, thereby formulating practical emission reduction measures, improving the green and environmental protection level of construction management, and helping to achieve sustainable development goals. Summary of the invention

[0006] In view of the problem that the construction carbon emission monitoring scheme in the prior art relies on manual records and empirical estimates, the purpose of the present invention is to provide a real-time intelligent monitoring scheme for carbon emissions of construction materials. This scheme innovatively realizes real-time monitoring and optimized management of carbon emissions at construction sites based on Internet of Things sensors, real-time data analysis and intelligent feedback means, thereby overcoming the problems existing in the prior art.

[0007] In order to achieve the above object, the present invention provides a real-time intelligent monitoring platform for carbon emissions of construction materials, the platform comprising:

[0008] A material data collection module, wherein the material data collection module is configured to collect the type, quantity, transportation method and carbon emission factor data of the materials at the construction site in real time, and record the transportation path and energy consumption information of the materials;

[0009] A carbon emission real-time monitoring module, which is configured to interact with the material data collection module and can calculate the carbon emission of each material based on the carbon emission factor database and the data such as the type, quantity, transportation method, and transportation distance of the material collected by the material data collection module;

[0010] An intelligent algorithm analysis module, wherein the intelligent algorithm analysis module is configured to interact with the carbon emission real-time monitoring module and the data storage and visualization module, and can predict the carbon emission trend through a machine learning algorithm based on the real-time data calculated by the carbon emission real-time monitoring module and the historical data stored in the data storage and visualization module, and generate a carbon emission reduction optimization plan;

[0011] A carbon emission alarm module, which is configured to interact with the carbon emission real-time monitoring module data, and can automatically trigger an alarm and notify the on-site management personnel when the carbon emission calculated by the carbon emission real-time monitoring module exceeds a preset threshold;

[0012] The data storage and visualization module is configured to interact with the carbon emission real-time monitoring module data, and can store and visualize the carbon emission monitoring data calculated and generated by the carbon emission real-time monitoring module.

[0013] In some embodiments of the present invention, the material data acquisition module includes multiple sensors, RFID identification devices and GPS monitoring devices, and the multiple sensors, RFID identification devices and GPS monitoring devices are deployed in coordination in a mode that can collect real-time status data of materials and record transportation trajectories.

[0014] In some embodiments of the present invention, the carbon emission real-time monitoring module includes a carbon emission calculation unit and a display unit. The carbon emission calculation unit calculates the carbon emissions of materials during transportation, stacking and use stages according to a preset carbon emission factor database, and displays the monitoring data in real time through the display unit.

[0015] In some embodiments of the present invention, the carbon emission factor database includes basic data such as material carbon emission factor table, transportation tool carbon emission factor table, energy carbon emission factor table, etc., covering the carbon emission factor information of materials in the three stages of transportation, stacking and use.

[0016] In some embodiments of the present invention, the intelligent algorithm analysis module is configured to first clean and extract features from historical carbon emission data and real-time data, and use a neural network model to train and learn the relationship between multiple factors and carbon emissions; then, based on the trained neural network model, new data is input in real time to generate predicted values ​​and trend curves.

[0017] In some embodiments of the present invention, the data storage and visualization module includes a cloud database and a data visualization interface, which is used to periodically generate carbon emission analysis reports and support interactive operations for data display.

[0018] In order to achieve the above object, the present invention provides a real-time intelligent monitoring method for carbon emissions of construction materials, comprising:

[0019] (1) Collect data on the types, quantities, transportation methods, and carbon emission factors of materials at the construction site in real time, and record the transportation routes and energy consumption information of the materials;

[0020] (2) Based on the carbon emission factor database, calculate the real-time carbon emission of each material according to the data used in step (1);

[0021] (3) Based on the real-time carbon emission data of each material calculated in step (2) and the stored historical carbon emission data of each material, the carbon emission trend is predicted through a machine learning algorithm, and a carbon emission reduction optimization plan is generated;

[0022] (4) The carbon emissions of each material are monitored and an alarm is generated when the carbon emissions exceed a preset threshold.

[0023] In some embodiments of the present invention, when performing real-time carbon emissions monitoring, the monitoring method calculates the carbon emissions of materials during transportation, stacking and use stages according to a preset carbon emission factor database, and displays the monitoring data in real time through the display unit.

[0024] In some embodiments of the present invention, the monitoring method stores the carbon emission factor data of each material, transportation tool and energy type in the three stages of transportation, stacking and use through a preset carbon emission factor database; and in the real-time monitoring process, by collecting the material type, quantity, transportation route and energy consumption information at the construction site, the carbon emissions of the material in each stage are calculated according to the carbon emission factors.

[0025] In some embodiments of the present invention, when the monitoring method analyzes carbon emission trends and predicts carbon emissions in step (3), the historical data and real-time data of carbon emissions are first cleaned and features are extracted, and a neural network model is trained to learn the relationship between multiple factors and carbon emissions; then, based on the trained neural network model, new data is input in real time to generate predicted values ​​and trend curves; at the same time, carbon emission anomalies are detected and alarms are triggered through an anomaly detection algorithm, and targeted carbon emission reduction optimization suggestions are provided in combination with the analysis results.

[0026] The real-time intelligent monitoring solution for carbon emissions of construction materials provided by the present invention can realize real-time intelligent management of construction carbon emissions, effectively reduce carbon emissions during the construction process, and realize automatic monitoring, calculation and management of carbon emissions of materials on the construction site.

[0027] The real-time intelligent monitoring solution for carbon emissions of construction materials provided by the present invention is specifically composed of multi-stage carbon emission calculation, intelligent real-time monitoring and prediction, dynamic update function, automated precise management and visual display. Through the carbon emission factor database, the system supports carbon emission calculation in multiple stages such as transportation, stacking and use, ensuring accurate monitoring of the entire construction process; combined with machine learning algorithms, it can analyze historical and real-time data, predict carbon emission trends and issue early warnings in a timely manner; further based on the dynamic update of the carbon emission factor database, it ensures that the system performs calculations based on the latest industry standards and policies; in addition, automated carbon emission calculations reduce manual intervention, improve management efficiency, and through the visual display module, it is convenient for construction units to grasp carbon emission data in real time, providing a basis for decision-making.

[0028] The real-time intelligent monitoring scheme for carbon emissions of construction materials provided by the present invention can significantly improve the efficiency of construction carbon emission management, reduce manual intervention and improve the accuracy of calculations when applied in specific applications. The scheme of the present invention can help construction units to discover the risk of exceeding carbon emissions in advance, optimize the construction process, reduce unnecessary carbon emissions, and achieve green and low-carbon construction goals through real-time monitoring and intelligent prediction; the scheme of the present invention can enhance the compliance of construction companies with environmental protection laws and regulations and avoid the risk of violations through precise calculation and reporting functions; the scheme of the present invention can ensure that construction units optimize resource allocation and energy use by providing scientific data support, promote the construction industry to develop in a sustainable and environmentally friendly direction, and enhance the social responsibility and market competitiveness of enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments.

[0030] Figure 1 This is a schematic diagram of the construction material carbon emission real-time intelligent monitoring platform in the present invention;

[0031] Figure 2This is a flow chart of the real-time intelligent monitoring of carbon emissions from construction materials in the present invention. DETAILED DESCRIPTION

[0032] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further described below with reference to specific diagrams.

[0033] The main problem faced in real-time monitoring of carbon emissions generated by the use, transportation and operation of large amounts of materials and construction equipment in the construction industry is data diversity and complexity.

[0034] Among them, the materials, transportation methods, construction equipment and other factors involved in the construction site are different. The carbon emission factor of each factor is different, and the carbon emissions are greatly affected by environmental conditions. How to accurately monitor these changing data is one of the main problems that need to be overcome.

[0035] Furthermore, the on-site construction environment changes dynamically, and materials, transportation routes, and construction progress will change over time. How to adjust carbon emission calculations and update data in real time requires efficient system support. In addition, the carbon emissions of the construction process are divided into multiple stages. How to accurately calculate and integrate data at different stages, and how to achieve real-time monitoring and alarming in the case of huge amounts of data are one of the main problems that need to be overcome.

[0036] The solution of the present invention effectively overcomes the above technical difficulties by organically cooperating with technical means such as real-time collection and fusion of multiple data sources, dynamic carbon emission factor database and intelligent calculation model.

[0037] The solution of the present invention firstly utilizes sensors and IoT devices to collect data in real time, integrating multi-dimensional data such as materials, transportation, and energy consumption to ensure accurate monitoring of carbon emissions; secondly, it constructs a dynamically updated carbon emission factor database, and combines it with machine learning algorithms to perform intelligent calculation and prediction of carbon emissions, so that the carbon emission factor can be updated in real time and accurately reflect changes at the construction site.

[0038] On this basis, in order to cope with the problem of multi-stage carbon emission calculation, the present invention processes carbon emissions at different stages through an innovative intelligent calculation model to ensure refined management.

[0039] Finally, through efficient data processing technology and real-time feedback mechanism, the system can trigger alarms in time and provide optimization suggestions when carbon emissions exceed the standard, helping construction units optimize the construction process and achieve green and low-carbon construction.

[0040] In this regard, the present invention provides a real-time intelligent monitoring platform for carbon emissions of construction materials. The intelligent monitoring platform is based on the organic integration of Internet of Things sensors, real-time data analysis and intelligent feedback technology, so as to realize real-time monitoring and optimized management of carbon emissions at construction sites.

[0041] See also Figure 1 , which shows an example of the structure of the real-time intelligent monitoring platform for carbon emissions of construction materials provided by the present invention.

[0042] Based on the diagram, the real-time intelligent monitoring platform 100 for construction material carbon emissions is specifically composed of a material data collection module 110 , a carbon emission real-time monitoring module 120 , an intelligent algorithm analysis module 130 , a carbon emission alarm module 140 and a data storage and visualization module 150 , which cooperate with each other.

[0043] The material data collection module 110 in this platform is configured to collect information of construction materials in real time.

[0044] Specifically, the material data collection module 110 can be deployed at the construction site through corresponding sensors and RFID identification equipment, and collect the type, quantity, transportation method and carbon emission factor data of the materials at the construction site, and record the transportation route and energy consumption information of the materials.

[0045] The carbon emission factor data here specifically include the carbon emission factor of materials, the carbon emission factor of transportation methods, and the carbon emission factor of construction equipment energy consumption.

[0046] Among them, the material carbon emission factor refers to the carbon emissions per unit of material during production, transportation, stacking and use;

[0047] The carbon emission factor of transport mode reflects the carbon emissions per unit distance generated by different modes of transport (such as trucks, ships, etc.);

[0048] The carbon emission factor of construction equipment energy consumption indicates the amount of carbon dioxide emissions corresponding to each unit of energy consumption (such as electricity or fuel).

[0049] When monitoring and collecting these carbon emission factor data, real-time data is first collected through sensors and RFID identification equipment deployed at the construction site. The sensors deployed at the construction site monitor the energy consumption of the construction equipment, and the RFID equipment deployed at the construction site automatically identifies the type, quantity and transportation method of the materials. Then all monitoring data are matched with the corresponding factors in the carbon emission factor database, so as to realize real-time calculation and monitoring of the carbon emissions of construction materials.

[0050] The carbon emission real-time monitoring module 120 in this platform is configured to interact with the material data collection module 110, and can obtain various on-site monitoring data collected in real time by the material data collection module 110, and can further use the carbon emission factor database and monitoring data to calculate in real time the carbon emissions generated by each material during transportation, stacking and construction.

[0051] Specifically, the carbon emission real-time monitoring module 120 calculates the carbon emission of each material based on a preset carbon emission factor database and further combines the real-time monitoring data such as material type, transportation method, and transportation distance obtained from the material data acquisition module 110 to calculate the carbon emission of each material.

[0052] The carbon emission factor database here is used to store and manage carbon emission factors of materials and activities during the construction process.

[0053] Specifically, the carbon emission factor database contains preset basic data such as material carbon emission factor table, transportation tool carbon emission factor table, energy carbon emission factor table, etc., covering the carbon emission factor information of materials in the three stages of transportation, stacking and use.

[0054] Furthermore, the carbon emission factor database is configured to support rapid retrieval of relevant carbon emission factors based on material type, transportation method, energy type and construction activity, and provide accurate data support for the carbon emission calculation unit through preset calculation rules.

[0055] Furthermore, the carbon emission factor database is also configured to support dynamic updating of carbon emission factor data, and can receive the latest carbon emission factor data required by industry standards or government regulations to ensure the real-time and accuracy of the data.

[0056] The intelligent algorithm analysis module 130 in this platform is configured to interact with the carbon emission real-time monitoring module 120 and the data storage and visualization module 150. It can obtain the real-time carbon emission data calculated by the carbon emission real-time monitoring module 120 and the carbon emission historical data stored in the data storage and visualization module 150, and predict the carbon emission trend through a machine learning algorithm based on this. At the same time, it can further generate a carbon emission reduction optimization plan.

[0057] The carbon emission alarm module 140 in this platform is configured to interact with the carbon emission real-time monitoring module 120 in data, and can automatically trigger an alarm and notify the on-site management personnel when the carbon emissions calculated by the carbon emission real-time monitoring module exceed a preset threshold.

[0058] The data storage and visualization module 150 in this platform is configured to interact with the material data collection module 110, the carbon emission real-time monitoring module 120, the intelligent algorithm analysis module 130, and the carbon emission alarm module 140. It can upload and store various carbon emission monitoring data to the cloud, and display the carbon emission situation in the form of charts to provide reference for construction parties and regulatory units.

[0059] With respect to the real-time intelligent monitoring platform for carbon emissions of construction materials provided in the present invention, its specific implementation scheme and corresponding technical features are described below.

[0060] In some embodiments of the present invention, the material data acquisition module 110 in the platform specifically includes multiple sensors, RFID identification devices and GPS monitoring devices, and these multiple sensors, RFID identification devices and GPS monitoring devices are deployed in coordination in a mode that can collect real-time status data of materials and record transportation trajectories.

[0061] Specifically, the sensors in the material data collection module 110 are used to monitor the energy consumption and operating status of the construction equipment in real time, capture the energy consumption data of each equipment, and calculate it in combination with the carbon emission factor of the equipment.

[0062] The RFID identification device in the material data collection module 110 automatically identifies the type and quantity of the material through the RFID tag embedded in the material packaging, and matches it with the carbon emission factor database to ensure accurate recording of the carbon emission information of each material.

[0063] The GPS monitoring device in the material data collection module 110 is used to track the transportation path of the material in real time, record the transportation route and time, and further accurately calculate the carbon emissions generated during the transportation process.

[0064] The material data collection module 110 ensures the real-time collection and recording of material transportation methods, carbon emission factors, energy consumption information and path data through the collaborative work between multiple sensors, RFID identification devices and GPS monitoring devices, providing comprehensive and accurate data support for the carbon emission monitoring platform.

[0065] In some embodiments of the present invention, the carbon emission real-time monitoring module 120 in the platform is specifically composed of a carbon emission calculation unit and a display unit. The carbon emission calculation unit is configured to calculate the carbon emissions of materials during transportation, stacking and use according to a preset carbon emission factor database, and display the monitoring data in real time through the display unit.

[0066] Specifically, the carbon emission calculation unit in the real-time carbon emission monitoring module 120 calculates the carbon emissions of the materials in the transportation, stacking and use stages in sequence according to the preset carbon emission factor database and the real-time collected material data.

[0067] As a further explanation, during the transportation stage, the carbon emission calculation unit calculates carbon emissions based on the material transportation method, distance and carbon emission factors of related transportation tools; during the stacking stage, the carbon emission calculation unit calculates carbon emissions based on the material storage method, time and related energy consumption parameters affecting carbon emissions; during the use stage, the carbon emission calculation unit calculates based on the material consumption, energy consumption of construction equipment and carbon emission factors.

[0068] Finally, the carbon emission calculation unit adds up the carbon emissions of each stage to obtain the total carbon emissions of the material, and displays the monitoring results in the form of real-time charts or data tables through the display unit to help managers fully understand the carbon emission status of the construction site.

[0069] In some embodiments of the present invention, the intelligent algorithm analysis module 130 in the platform uses a machine learning algorithm to analyze carbon emission trends and predict carbon emissions.

[0070] Specifically, the intelligent algorithm analysis module 130 here uses machine learning algorithms including linear regression, decision tree and other algorithms.

[0071] Furthermore, the intelligent algorithm analysis module 130 is specifically configured to first clean and extract features from historical carbon emission data and real-time data, and use neural network model training to learn the relationship between multiple factors and carbon emissions, where the factors include material type, transportation distance, energy consumption, etc.

[0072] Next, based on the trained neural network model, new data is input in real time to generate predicted values ​​and trend curves.

[0073] Furthermore, the intelligent algorithm analysis module 130 is also provided with an anomaly detection algorithm, and is configured to perform abnormal data detection on the real-time data generated by the carbon emission real-time monitoring module 120 based on historical data through the anomaly detection algorithm, and provide targeted carbon emission reduction optimization suggestions based on the analysis results when carbon emission anomalies are found and alarms are triggered.

[0074] Furthermore, the intelligent algorithm analysis module 130 is also configured with an algorithm capable of real-time dynamic prediction and self-learning, as well as a visualization display unit, to provide precise support for construction companies to achieve green and low-carbon management.

[0075] In some embodiments of the present invention, the intelligent algorithm analysis module 130 in the platform can generate a carbon emission reduction optimization plan based on the prediction results after analyzing the carbon emission trend. The specific implementation process is as follows:

[0076] The intelligent algorithm analysis module 130 first identifies key sources of carbon emissions during the construction process (such as high-energy-consuming equipment, large amounts of transported materials, etc.) through machine learning algorithms; then combines carbon emission prediction data and optimization algorithms to provide specific measures to reduce carbon emissions. For example, the system may recommend adjusting material transportation routes, choosing low-carbon emission transportation methods, optimizing the energy efficiency of equipment, or choosing more environmentally friendly building materials.

[0077] The optimization plan generated by the intelligent algorithm analysis module 130 will be fed back to the construction unit through the system to help it take effective carbon emission reduction measures during the construction process.

[0078] This intelligent algorithm analysis module 130 can automatically generate feasible emission reduction strategies based on real-time data and prediction results, reduce manual intervention, improve the intelligence and accuracy of carbon emission management, and provide construction units with specific and actionable emission reduction solutions.

[0079] In some embodiments of the present invention, the carbon emission alarm module 140 in the platform obtains the real-time carbon emissions of each material produced by the carbon emission real-time monitoring module 120, and compares the real-time carbon emissions of each material with the preset threshold value. When the carbon emissions exceed the preset threshold value, an alarm is automatically triggered and the alarm information is sent to the management terminal via SMS, APP push, etc.

[0080] In some embodiments of the present invention, the data storage and visualization module 150 in the platform includes a cloud database and a data visualization interface, which is used to periodically generate carbon emission analysis reports and support interactive operations for data display.

[0081] Specifically, the cloud database in the data storage and visualization module 150 is configured to store historical carbon emission data, real-time monitoring data, and predictive analysis results of the construction site, supporting the storage and rapid retrieval of large-scale data while ensuring data security and reliability.

[0082] The data visualization interface in the data storage and visualization module 150 is configured to maintain a real-time data connection with the cloud database, and can intuitively present the key data of carbon emissions stored in the cloud database in the form of charts, trend curves, and dynamic dashboards, and support user interactive operations such as data filtering, zooming, and category queries.

[0083] Furthermore, when the data storage and visualization module 150 periodically generates a carbon emission analysis report, the data visualization interface regularly calls the historical data and predictive analysis results in the cloud database, summarizes and analyzes the data through preset templates and algorithms, and generates a report containing carbon emission trends, key emission sources and optimization suggestions, which is presented in the form of a combination of graphics and text on the visualization interface, so that users can fully understand the carbon emission status and make scientific decisions.

[0084] The data storage and visualization module 150 effectively realizes the organic integration of data availability and ease of use by integrating big data storage capabilities and real-time interactive experience.

[0085] In some embodiments of the present invention, the real-time intelligent monitoring platform for carbon emissions of construction materials is further configured to support an API interface, and can be integrated with other construction management systems through the API interface to achieve data sharing and unified management.

[0086] The real-time intelligent monitoring platform for carbon emissions of construction materials formed based on the above solution can realize real-time intelligent management of construction carbon emissions when implemented, and effectively reduce carbon emissions during the construction process.

[0087] Here we will explain in detail the process of real-time intelligent monitoring of carbon emissions of construction materials based on this real-time intelligent monitoring platform for carbon emissions of construction materials.

[0088] See also Figure 2 The process of real-time intelligent monitoring of carbon emissions of construction materials based on this platform is as follows:

[0089] (1) Collect data on the type, quantity, transportation method, and carbon emission factor of materials at the construction site in real time, and record the transportation route and energy consumption information of the materials.

[0090] (2) Based on the carbon emission factor database, the real-time carbon emission of each material is calculated according to the data used in step (1).

[0091] Specifically, this step calculates the carbon emissions of materials during transportation, stacking and use stages based on the preset carbon emission factor database, and displays the monitoring data in real time.

[0092] Specifically, the function of the carbon emission factor database here is to store and provide carbon emission factors of materials, transportation methods and equipment energy consumption related to construction, which are used to cooperate with the calculation of real-time carbon emissions. Based on the carbon emission factor database, this step collects the types, quantities, transportation methods and energy consumption information of materials at the construction site in real time, matches these data with the corresponding factors in the carbon emission factor database, and calculates the carbon emissions of materials in the transportation, stacking and use stages respectively; finally, the real-time total carbon emissions are obtained by summing up the data, and the results are visualized in the display unit.

[0093] (3) Based on the real-time carbon emission data of each material calculated in step (2) and the stored historical carbon emission data of each material, the carbon emission trend is predicted through a machine learning algorithm, and a carbon emission reduction optimization plan is generated.

[0094] This step specifically uses algorithms including linear regression and decision trees to analyze carbon emission trends and predict carbon emissions.

[0095] Furthermore, in this step, when analyzing carbon emission trends and predicting carbon emissions, first, the carbon emission-related data of construction materials (such as material type, quantity, transportation method, energy consumption information, etc.) are extracted from the historical database, and the extracted data are cleaned and standardized;

[0096] Next, the linear regression algorithm is used to analyze the linear relationship between carbon emissions and key influencing factors to predict the overall trend of carbon emissions. At the same time, the decision tree algorithm is used to explore the nonlinear impact of multi-dimensional variables in complex scenarios (such as different construction stages, equipment load conditions, etc.) on carbon emissions and generate detailed prediction results.

[0097] In this way, this step can identify potential high emission risks and provide early warnings. At the same time, this step is based on the organic integration of multiple algorithms, which can take into account both global trends and local complexity, ensure the accuracy and applicability of the prediction results, and provide a scientific basis for carbon emission management during the construction process.

[0098] (4) The carbon emissions of each material are monitored and an alarm is generated when the carbon emissions exceed a preset threshold.

[0099] With respect to the real-time intelligent monitoring platform solution for carbon emissions of construction materials provided by the present invention, the application implementation process of the real-time intelligent monitoring platform for carbon emissions of construction materials is further illustrated below through specific application examples.

[0100] Combination Figure 1 As shown, when the solution of the real-time intelligent monitoring platform for carbon emissions of construction materials provided by the present invention is implemented and applied, a corresponding real-time intelligent monitoring platform for carbon emissions of construction materials is constructed based on the solution provided by the present invention.

[0101] The entire platform includes a front-end data collection part deployed at the construction site, a data analysis and processing part deployed at the back end, and a data storage part deployed in the cloud.

[0102] Specifically, the entire platform 100 is mainly composed of a material data collection module 110, a carbon emission real-time monitoring module 120, an intelligent algorithm analysis module 130, a carbon emission alarm module 140, a data storage and visualization module 150, and a system integration and communication module 160.

[0103] The material data collection module 110 in the platform is a front-end data collection module, which is specifically composed of multiple IoT sensors, RFID identification devices, and GPS location monitoring devices. Multiple IoT sensors and RFID identification devices are deployed at the construction site, and GPS location monitoring devices are deployed on corresponding transport vehicles.

[0104] In this way, the carbon emission data of construction materials (including but not limited to cement, steel, sand and gravel, etc.) are collected in real time through the IoT sensors deployed at the construction site. When each material entering the construction site is loaded on the transport vehicle, the material information (including material type, quantity, weight, etc.) is automatically registered through the RFID device, and the transportation trajectory and energy consumption are monitored in real time through the GPS device during transportation. At the same time, after all data collection is completed, it is transmitted to the data processing unit.

[0105] Furthermore, in this example, the material data collection module 110 performs data collection process as follows:

[0106] (1) When materials enter the construction site, the RFID device automatically reads the tag information, including material name, batch, manufacturer, etc.

[0107] (2) IoT sensors deployed at construction sites collect carbon emission data of construction materials (including but not limited to cement, steel, sand and gravel, etc.) in real time;

[0108] (3) GPS location monitoring equipment monitors the vehicle location and operation path in real time, and records the mileage and time of material transportation.

[0109] (4) The collected data is pre-processed by the data processing unit and transmitted to the data analysis and processing server deployed at the back end to automatically calculate the carbon emissions generated during the transportation process and store the information in the cloud database.

[0110] The carbon emission real-time monitoring module 120, the intelligent algorithm analysis module 130, and the carbon emission alarm module 140 in the platform are deployed in coordination with each other and run in the data analysis and processing server deployed at the back end.

[0111] The carbon emission real-time monitoring module 120 is specifically composed of a material carbon emission calculation unit and a real-time monitoring display unit. The material carbon emission calculation unit calculates the carbon emission of each material according to the material type, transportation method, transportation distance and other information through a preset carbon emission factor database.

[0112] As a further explanation, the material carbon emission calculation unit in the real-time carbon emission monitoring module 120 integrates a corresponding carbon emission calculation model. The carbon emission calculation model is specifically composed of the following calculation formula:

[0113] C=C SC +C YS (1)

[0114] C SC =M×F (2)

[0115] C YS =M×D×F (3)

[0116] in:

[0117] C represents the carbon emissions during the material production and transportation stages;

[0118] C SC Indicates the carbon emissions during the material production stage;

[0119] C YS Indicates the carbon emissions during the material transportation stage;

[0120] M represents the quality of the material;

[0121] F is the carbon emission factor, which depends on the type of material and the mode of transportation;

[0122] D is the transportation distance.

[0123] Accordingly, the real-time carbon emission monitoring module 120 can calculate the carbon emissions of materials during transportation, stacking and use according to the preset carbon emission factor database, and display the calculation results in real time at the construction site through the display unit, and dynamically update them at the management terminal.

[0124] The intelligent algorithm analysis module 130 specifically combines machine learning algorithms and construction schedules to analyze carbon emission trends at each stage; on this basis, the intelligent algorithm analysis module 130 can also access historical data and data streams from on-site sensors to make predictions using algorithms such as linear regression and decision trees.

[0125] Specifically, the intelligent algorithm analysis module 130 is mainly composed of a data analysis unit and an intelligent feedback unit.

[0126] The data analysis unit here is configured to analyze the real-time collected carbon emission data based on big data and machine learning algorithms, calculate the total carbon emissions, and predict future carbon emission trends based on historical data.

[0127] Accordingly, the data analysis unit in the intelligent algorithm analysis module 130 first extracts the carbon emission-related data of construction materials (such as material type, quantity, transportation method, energy consumption information, etc.) from the historical database, and cleans and standardizes the extracted data; secondly, the data analysis unit uses a linear regression algorithm to analyze the linear relationship between carbon emissions and key influencing factors, and predicts the overall trend of carbon emissions; at the same time, the data analysis unit simultaneously uses a decision tree algorithm to explore the nonlinear influence of multi-dimensional variables in complex scenarios (such as different construction stages, equipment load conditions, etc.) on carbon emissions, and generates detailed prediction results.

[0128] Based on the analysis and prediction of the data analysis unit, the intelligent algorithm analysis module 130 can identify potential high emission risks and provide early warnings; at the same time, it takes into account global trends and local complexities to ensure the accuracy and applicability of the prediction results, and provide a scientific basis for carbon emission management during the construction process.

[0129] Accordingly, the intelligent feedback module in the intelligent algorithm analysis module 130 is configured to remind construction management personnel of high-carbon emission materials or processes in real time based on the data analysis results of the data analysis unit, and to propose low-carbon alternatives or optimization suggestions.

[0130] The intelligent feedback module here locates materials or processes with high carbon emissions through the analysis and prediction results of the data analysis unit, and provides alternatives or optimization suggestions based on the low-carbon option library.

[0131] Specifically, this intelligent feedback module first identifies high-emission sources (such as high-energy consumption equipment, long-distance transportation of materials, etc.), and then combines alternative materials, low-carbon transportation methods or optimized construction processes to evaluate and generate feasible optimization solutions from the dimensions of cost, feasibility and carbon emission reduction benefits. For example, it recommends environmentally friendly materials with lower carbon emission factors, more efficient equipment or adjusting operating hours to reduce energy consumption. Finally, the module provides suggestions to managers through text messages or APP push, and supports data visualization and interactive feedback to ensure the accuracy and practical operability of low-carbon solutions, thereby facilitating low-carbon management of the construction process.

[0132] As a further illustration, when the intelligent algorithm analysis module 130 is implemented, the process of predicting carbon emission trends and generating carbon emission reduction optimization solutions is as follows:

[0133] (1) Collect historical construction data, material information, ambient temperature and humidity, equipment operating time and other parameters;

[0134] (2) Predict future carbon emission trends through machine learning models and issue alerts when data is abnormal. Among them, the machine learning model can select appropriate time series prediction models or regression models according to the prediction target and data characteristics, such as ARIMA, LSTM (long short-term memory network) and other models suitable for time series data, and linear regression, support vector regression (SVR), decision tree regression and other models suitable for regression data;

[0135] (3) Based on the predicted future carbon emission trends, generate a variety of carbon emission reduction plans, such as equipment energy efficiency optimization, rational resource allocation, etc., and provide suggestions to construction personnel.

[0136] The carbon emission alarm module 140 uses intelligent threshold preset technology to set an allowable range for the carbon emission of each material calculated and determined in the carbon emission real-time monitoring module 120. When the monitoring value exceeds the preset threshold, the system will issue an alarm and notify the on-site management personnel through SMS, APP push, etc.

[0137] Specifically, the carbon emission alarm module 140 performs an alarm response process configuration as follows:

[0138] (1) When carbon emissions exceed the threshold, the system immediately triggers an alarm and sends it to the management terminal.

[0139] (2) The system will also automatically record the alarm information and require the construction site to take necessary emission reduction measures.

[0140] (3) If the alarm persists and remains unresolved, the system will upgrade the alarm level and notify the relevant regulatory authorities.

[0141] The data storage and visualization module 150 in the platform adopts a cloud computing architecture and cooperates with the corresponding data transmission module to realize real-time transmission of all data to the cloud database, and supports visual chart display so that the construction party and the regulatory department can intuitively view the carbon emissions.

[0142] Specifically, the cloud database in the data storage and visualization module 150 is configured with a carbon emission reporting unit, which can generate real-time carbon emission reports, making it convenient for construction companies to view and archive them regularly.

[0143] Furthermore, the visualization content of the data storage and visualization module 150 is configured as follows:

[0144] (1) Carbon emission trend chart of each material, comparison chart of total construction carbon emissions, and carbon emission analysis report generated on a monthly / quarterly / annual basis.

[0145] (2) Multi-dimensional data analysis interface, where you can view historical data on carbon emissions by dragging the slider or clicking on each chart node.

[0146] The system integration and communication module 160 in the platform is configured to support integration with other construction management systems through an API interface, thereby facilitating data sharing and unified management.

[0147] Specifically, the system integration and communication module 160 supports multiple communication protocols: using MQTT, HTTP and other protocols to ensure low-latency and high-real-time remote data transmission.

[0148] The system integration and communication module 160 supports multiple interface settings, and each module has a standardized interface and can exchange data with other monitoring platforms and intelligent terminal devices.

[0149] The real-time intelligent monitoring platform for carbon emissions of construction materials formed based on this will have the following functions:

[0150] (1) Real-time monitoring: Through sensors and data acquisition modules, real-time monitoring of carbon emissions of different construction materials can be achieved.

[0151] (2) Intelligent analysis and feedback: Through the data analysis module, high-carbon emission materials or construction stages can be intelligently identified, and optimization suggestions can be given based on preset standards.

[0152] (3) Remote monitoring and early warning: Construction managers can remotely view real-time data at any time through mobile devices or computers, and receive early warnings when carbon emissions exceed the standard.

[0153] (4) Low-carbon decision support: The platform can provide data support for construction companies to formulate low-carbon construction strategies based on historical carbon emission data.

[0154] (5) Application scenarios: Applicable to construction projects, infrastructure projects, road construction and other construction scenarios where carbon emissions need to be controlled.

[0155] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A real-time intelligent monitoring platform for carbon emissions from construction materials, characterized in that: The platform includes: A material data collection module, wherein the material data collection module is configured to collect the type, quantity, transportation method and carbon emission factor data of the materials at the construction site in real time, and record the transportation path and energy consumption information of the materials; A carbon emission real-time monitoring module, which is configured to interact with the material data collection module and can calculate the carbon emission of each material based on the carbon emission factor database and the data such as the type, quantity, transportation method, and transportation distance of the material collected by the material data collection module; An intelligent algorithm analysis module, wherein the intelligent algorithm analysis module is configured to interact with the carbon emission real-time monitoring module and the data storage and visualization module, and can predict the carbon emission trend through a machine learning algorithm based on the real-time data calculated by the carbon emission real-time monitoring module and the historical data stored in the data storage and visualization module, and generate a carbon emission reduction optimization plan; A carbon emission alarm module, which is configured to interact with the carbon emission real-time monitoring module data, and can automatically trigger an alarm and notify the on-site management personnel when the carbon emission calculated by the carbon emission real-time monitoring module exceeds a preset threshold; The data storage and visualization module is configured to interact with the carbon emission real-time monitoring module data, and can store and visualize the carbon emission monitoring data calculated and generated by the carbon emission real-time monitoring module.

2. The real-time intelligent monitoring platform for carbon emissions from construction materials according to claim 1 is characterized in that: The material data collection module includes multiple sensors, RFID identification devices and GPS monitoring devices, and the multiple sensors, RFID identification devices and GPS monitoring devices are deployed in coordination in a mode that can collect real-time status data of materials and record transportation tracks.

3. The real-time intelligent monitoring platform for carbon emissions from construction materials according to claim 1 is characterized in that: The carbon emission real-time monitoring module includes a carbon emission calculation unit and a display unit. The carbon emission calculation unit calculates the carbon emissions of materials during transportation, stacking and use stages according to a preset carbon emission factor database, and displays the monitoring data in real time through the display unit.

4. The real-time intelligent monitoring platform for carbon emissions from construction materials according to claim 1 is characterized in that: The carbon emission factor database includes a material carbon emission factor table, a transportation tool carbon emission factor table, and an energy carbon emission factor table.

5. The real-time intelligent monitoring platform for carbon emissions from construction materials according to claim 1 is characterized in that: The intelligent algorithm analysis module is configured to first clean and extract features from historical and real-time carbon emission data, and use a neural network model to train and learn the relationship between multiple factors and carbon emissions; then, based on the trained neural network model, new data is input in real time to generate predicted values ​​and trend curves.

6. The real-time intelligent monitoring platform for carbon emissions from construction materials according to claim 1 is characterized in that: The data storage and visualization module includes a cloud database and a data visualization interface, which is used to periodically generate carbon emission analysis reports and support interactive operations for data display.

7. A real-time intelligent monitoring method for carbon emissions of construction materials, characterized in that: include: (1) Collect data on the types, quantities, transportation methods, and carbon emission factors of materials at the construction site in real time, and record the transportation routes and energy consumption information of the materials; (2) Based on the carbon emission factor database, calculate the real-time carbon emission of each material according to the data used in step (1); (3) Based on the real-time carbon emission data of each material calculated in step (2) and the stored historical carbon emission data of each material, the carbon emission trend is predicted through a machine learning algorithm, and a carbon emission reduction optimization plan is generated; (4) The carbon emissions of each material are monitored and an alarm is generated when the carbon emissions exceed a preset threshold.

8. The method for real-time intelligent monitoring of carbon emissions from construction materials according to claim 7 is characterized in that: When performing real-time carbon emission monitoring, the monitoring method calculates the carbon emissions of materials during transportation, stacking and use stages according to a preset carbon emission factor database, and displays the monitoring data in real time through the display unit.

9. The method for real-time intelligent monitoring of carbon emissions from construction materials according to claim 7, characterized in that: The monitoring method stores the carbon emission factor data of each material, transportation tool and energy type in the three stages of transportation, stacking and use through a preset carbon emission factor database; and in the real-time monitoring process, by collecting the material type, quantity, transportation route and energy consumption information at the construction site, calculates the carbon emissions of the material in each stage according to the carbon emission factor.

10. The method for real-time intelligent monitoring of carbon emissions from construction materials according to claim 7, characterized in that: When the monitoring method analyzes the carbon emission trend and predicts the carbon emission in step (3), the historical data and real-time data of carbon emissions are first cleaned and features are extracted, and a neural network model is trained to learn the relationship between multiple factors and carbon emissions; then, based on the trained neural network model, new data is input in real time to generate predicted values ​​and trend curves; at the same time, an anomaly detection algorithm is used to detect carbon emission anomalies and trigger an alarm, and a targeted carbon emission reduction optimization suggestion plan is provided in combination with the analysis results.

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