Multi-scale fusion method and system for dynamic assessment of infrastructure carbon emission

Abstract

The invention relates to a multi-scale fusion method and system for dynamic evaluation of infrastructure carbon emission. The method comprises the following steps: step 1, collecting multi-scale carbon emission related data and corresponding multi-scale carbon emission data of an infrastructure; step 2, carrying out pretreatment; 3, constructing a multi-scale carbon emission evaluation model based on the data in the step 2, and obtaining quantized multi-scale carbon emission related data and quantized multi-scale carbon emission data; 4, obtaining a multi-scale carbon emission prediction model based on quantized data training, and obtaining a multi-scale carbon emission prediction result; and step 5, optimizing the carbon emission of each stage of the infrastructure based on the multi-scale carbon emission prediction result. Compared with the prior art, the method has the advantages of performing full-period and full-time-domain accurate evaluation on the carbon emission of the infrastructure and the like.

Description

Technical Field

[0001] The present invention relates to the field of infrastructure carbon emissions, and in particular to a method and system for dynamic assessment of infrastructure carbon emissions over the entire life cycle based on multi-scale fusion. Background Art

[0002] As the global climate change problem becomes increasingly serious, attention to greenhouse gas emissions continues to increase. The construction and operation of infrastructure runs through the entire process of urbanization and industrialization. Its carbon emissions throughout its life cycle are an important part of greenhouse gas emissions, which has a profound impact on environmental protection and the realization of sustainable development goals. Therefore, the accurate assessment and effective control of infrastructure carbon emissions have become one of the important issues in the global response to climate change.

[0003] Although existing carbon emission assessment technologies and methods can provide carbon emission data to a certain extent, they still have significant shortcomings. First, current assessment methods are usually limited to a single scale, such as analysis of a specific project, a region or a time period, which makes it difficult to achieve a full-cycle, full-time assessment from design, construction to operation. Secondly, the traditional carbon emission assessment has limited accuracy and cannot fully reflect the carbon emission characteristics of infrastructure in different periods, different processes and different material selections, resulting in a reduced reference value of the assessment results in practical applications. Summary of the invention

[0004] The purpose of the present invention is to provide a multi-scale fusion method and system for dynamic assessment of infrastructure carbon emissions in order to accurately assess the carbon emissions of infrastructure over the entire cycle and time domain.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A multi-scale fusion method for dynamic assessment of infrastructure carbon emissions, the method comprising the following steps:

[0007] Step 1: Collect multi-scale carbon emission-related data and corresponding multi-scale carbon emission data at each stage of infrastructure;

[0008] Step 2: Preprocess the multi-scale carbon emission related data and the corresponding multi-scale carbon emission data;

[0009] Step 3: construct a multi-scale carbon emission assessment model based on the pre-processed data obtained in step 2 to obtain quantified multi-scale carbon emission related data and quantified multi-scale carbon emission data;

[0010] Step 4: A multi-scale carbon emission prediction model is obtained based on the quantified data training, and a multi-scale carbon emission prediction result is obtained based on the prediction model;

[0011] Step 5: Optimize the carbon emissions of each stage of infrastructure based on the multi-scale carbon emissions prediction results.

[0012] Furthermore, the multi-scale carbon emission related data and corresponding multi-scale carbon emission data of each stage include multi-scale carbon emission related data and carbon emission data of the design stage, the construction stage and the operation stage.

[0013] Furthermore, the multi-scale carbon emission related data include material type, energy consumption, transportation method and construction technology, and the material type, energy consumption, transportation method and construction technology in the multi-scale carbon emission related data correspond to the carbon emission factor.

[0014] Furthermore, the multiscale carbon emission assessment model includes a project layer, a regional layer, a time layer and an indicator layer. The multiscale carbon emission assessment model divides the multiscale carbon emission related data and the corresponding multiscale carbon emission data into the project layer, the regional layer, the time layer and the indicator layer, and obtains quantified multiscale carbon emission related data and quantified multiscale carbon emission data through mathematical models and carbon emission factors.

[0015] Furthermore, the multi-scale carbon emission prediction model is constructed based on a support vector machine or a decision tree or a deep neural network.

[0016] In another aspect of the present invention, a multi-scale fusion system for dynamic assessment of carbon emissions from infrastructure is also proposed, the system comprising a data acquisition module, a data processing module, a carbon emission assessment module and a decision support module;

[0017] Among them, the data acquisition module is used to collect multi-scale carbon emission related data and corresponding multi-scale carbon emission data at various stages of infrastructure;

[0018] The data processing module is used to preprocess the multi-scale carbon emission related data and the corresponding multi-scale carbon emission data;

[0019] The carbon emission assessment module is used to construct a multi-scale carbon emission assessment model based on the pre-processed data, obtain quantified multi-scale carbon emission related data and quantified multi-scale carbon emission data, obtain a multi-scale carbon emission prediction model based on the quantified data training, and obtain a multi-scale carbon emission prediction result based on the prediction model;

[0020] The decision support module is used to optimize the carbon emissions of each stage of infrastructure based on the multi-scale carbon emissions prediction results.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] The present invention obtains the carbon emission data generated at each stage of the infrastructure, conducts a multi-scale full life cycle assessment based on a multi-scale carbon emission assessment model, and conducts a refined analysis of the carbon emissions at each stage of the design, construction, and operation and maintenance of the infrastructure, ensuring the comprehensiveness and accuracy of carbon emission monitoring, and achieving accurate full-cycle and full-time assessment of the carbon emissions of the infrastructure, thereby effectively reducing the carbon emissions of the infrastructure and promoting the application of low-carbon technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a flow chart of the method of the present invention;

[0024] Figure 2 It is a system structure diagram of the present invention. DETAILED DESCRIPTION

[0025] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0026] In order to solve the problems of the existing technology, it is necessary to develop a method and system that can comprehensively consider the multi-scale characteristics of the entire life cycle of infrastructure and dynamically predict carbon emissions in combination with real-time data. This will provide strong technical support for the accurate quantification, optimization control and low-carbon decision-making of carbon emissions. Therefore, the present invention proposes a multi-scale fusion method for dynamic assessment of infrastructure carbon emissions. The flow chart is as follows: Figure 1 The method comprises the following steps:

[0027] Step 1: Collect multi-scale carbon emission-related data and corresponding multi-scale carbon emission data at each stage of infrastructure;

[0028] Step 2: Preprocess the multi-scale carbon emission related data and the corresponding multi-scale carbon emission data;

[0029] Step 3: construct a multi-scale carbon emission assessment model based on the pre-processed data obtained in step 2 to obtain quantified multi-scale carbon emission related data and quantified multi-scale carbon emission data;

[0030] Step 4: A multi-scale carbon emission prediction model is obtained based on the quantified data training, and a multi-scale carbon emission prediction result is obtained based on the prediction model;

[0031] Step 5: Optimize the carbon emissions of each stage of infrastructure based on the multi-scale carbon emissions prediction results.

[0032] In step 1, multi-scale data collection is carried out, and carbon emission-related data, including material type, energy consumption, transportation method, construction technology, etc., are collected in real time through sensor networks during the design, construction, and operation and maintenance stages of infrastructure;

[0033] In step 2, multi-scale data preprocessing is performed to clean, normalize, and standardize the collected raw data to ensure the consistency and comparability of the data;

[0034] In step 3, a multi-scale carbon emission model is established. Based on the data preprocessing results, carbon emission assessment models of different scales are constructed, covering the project level, regional level, time level and indicator level, and dynamic calculations are performed through mathematical models and carbon emission factor libraries;

[0035] In step 4, carbon emission assessment and analysis are carried out to evaluate the carbon emission data at each stage, and dynamic prediction and trend analysis are carried out by combining physical models with machine learning algorithms;

[0036] In step 5, carbon emission optimization and decision support are carried out. Based on the evaluation and analysis results, carbon emission control and optimization plans are proposed to provide decision support for the low-carbon design and operation of infrastructure.

[0037] The carbon emission assessment model is based on the life cycle assessment (LCA) method, taking into account environmental impact factors at each stage, covering the design stage, construction stage and operation and maintenance stage.

[0038] Machine learning algorithms include but are not limited to support vector machines (SVM), decision trees (DT) and deep neural networks (DNN), which are used to dynamically predict future carbon emissions based on historical carbon emission data.

[0039] Carbon emission data includes but is not limited to energy consumption data, material procurement data, transportation emission data, construction equipment emission data and energy efficiency data during operation and maintenance.

[0040] Carbon emission control and optimization plans include reducing carbon emissions by adjusting design plans, optimizing construction processes, selecting low-carbon materials, improving energy efficiency and optimizing transportation routes.

[0041] The key steps of the present invention are:

[0042] 1. Multi-scale data collection: Through sensors and data collection equipment, carbon emission-related data such as energy consumption, material use, and transportation emissions are collected during the design, construction, and operation and maintenance stages of infrastructure;

[0043] 2. Data preprocessing and integration: Clean, standardize and normalize the collected raw data to ensure the consistency and comparability of data from different sources and in different formats;

[0044] 3. Construction of multi-scale carbon emission assessment model: Combined with the life cycle assessment (LCA) method, a refined carbon emission assessment model is constructed from multiple dimensions such as project level, regional level, and time level to quantify carbon emissions at each stage;

[0045] 4. Dynamic prediction and optimization analysis: Using physical models and machine learning algorithms, combined with real-time collected data, long-term carbon emission prediction is carried out, and optimized control solutions are output to support low-carbon decision-making;

[0046] 5. Assessment and decision support: Generate carbon emission assessment reports, provide optimization suggestions, and provide decision-making basis for low-carbon design, operation management and carbon emission reduction measures.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] 1. Multi-scale full life cycle assessment: This invention uses a multi-scale assessment model to conduct a detailed analysis of carbon emissions at each stage of infrastructure design, construction, and operation and maintenance to ensure the comprehensiveness and accuracy of carbon emissions monitoring;

[0049] 2. Dynamic prediction and optimization: Combining real-time data with prediction models, the present invention can predict future carbon emission trends and provide timely emission reduction suggestions, providing data support for low-carbon decision-making;

[0050] 3. Refined management: Through accurate quantification and optimized control of carbon emissions, infrastructure carbon emissions can be effectively reduced, the application of low-carbon technologies can be promoted, and the realization of green development goals can be facilitated;

[0051] 4. Strong scalability: The assessment system of the present invention is applicable to various types of infrastructure, has broad application prospects, and can provide quantitative carbon emission assessment and optimization solutions for different types of infrastructure.

[0052] The present invention also proposes a multi-scale fusion system for dynamic assessment of infrastructure carbon emissions. The system structure diagram is shown in Figure 2 As shown, the system includes a data acquisition module, a data processing module, a carbon emission assessment module and a decision support module;

[0053] Among them, the data acquisition module is used to collect multi-scale carbon emission related data and corresponding multi-scale carbon emission data at various stages of infrastructure;

[0054] The data processing module is used to preprocess the multi-scale carbon emission related data and the corresponding multi-scale carbon emission data;

[0055] The carbon emission assessment module is used to construct a multi-scale carbon emission assessment model based on the pre-processed data, obtain quantified multi-scale carbon emission related data and quantified multi-scale carbon emission data, obtain a multi-scale carbon emission prediction model based on the quantified data training, and obtain a multi-scale carbon emission prediction result based on the prediction model;

[0056] The decision support module is used to optimize the carbon emissions of each stage of infrastructure based on the multi-scale carbon emissions prediction results.

[0057] The present invention can be applied to the dynamic assessment of carbon emissions of highway infrastructure. Through the multi-scale carbon emission assessment system, carbon emissions can be comprehensively monitored and optimized from the design stage to the operation and maintenance stage;

[0058] Step 1: Carbon emission data collection

[0059] During the entire process of highway construction and operation, the system installs a variety of sensors to collect carbon emission-related data in real time, including:

[0060] Design stage: Collect carbon emission factors of various materials, including production and transportation data of cement, steel bars and other materials;

[0061] Construction phase: monitor the fuel consumption of construction equipment (such as excavators, rollers, etc.), and record the operating time and energy efficiency of construction equipment;

[0062] Operational stage: Obtain data on highway traffic flow, vehicle speed, transportation energy efficiency, etc. through on-board sensors and traffic management systems;

[0063] Step 2: Data preprocessing and analysis

[0064] The system cleans, standardizes and normalizes the data collected at each stage to ensure data consistency. By comparing and analyzing the carbon emission characteristics of different materials, construction equipment, transportation methods and traffic flow, the carbon emission contribution of each link is evaluated;

[0065] Step 3: Construction of multi-scale carbon emission assessment model

[0066] The system builds a multi-scale assessment model based on carbon emission data from the design, construction and operation stages. Different carbon emission factors and calculation methods are used in each stage, as follows:

[0067] Design stage: Based on the LCA (Life Cycle Assessment) method, carbon emissions assessment of building materials and construction plans is carried out;

[0068] Construction phase: Carbon emissions are calculated using fuel consumption data of construction equipment combined with factors such as transportation distance and mode of transportation;

[0069] Operational stage: Calculate carbon emissions during traffic operation through traffic flow data, vehicle fuel consumption and emission standards;

[0070] Step 4: Dynamic prediction and optimization analysis

[0071] Combined with historical carbon emission data, machine learning algorithms (such as support vector machines (SVM)) are used to dynamically predict highway carbon emissions, identify the main factors affecting carbon emissions, and predict carbon emissions in the next few years. By analyzing the prediction results, carbon emission reduction measures in highway reconstruction, reinforcement, energy efficiency optimization, etc. are provided;

[0072] Step 5: Optimization plan generation and decision support

[0073] Based on the dynamic evaluation results, the system generates optimization plans, such as: using low-carbon materials (such as low-carbon cement, recycled steel, etc.); optimizing construction processes to reduce energy consumption during the construction phase; adopting new energy or energy-efficient transportation methods, such as electric trucks, hybrid vehicles, etc.; improving traffic management to reduce traffic congestion and carbon emission peak hours; the system presents the optimization suggestions in a visual manner and provides operational guidelines to help decision makers take appropriate carbon emission mitigation measures during the planning, construction and operation stages.

[0074] The present invention can be applied to the dynamic assessment of carbon emissions from waterway infrastructure (such as ports and waterways). Through dynamic monitoring and predictive analysis, the carbon emission management of waterway infrastructure can be optimized;

[0075] Step 1: Carbon emission data collection

[0076] During the design, construction and operation of waterway infrastructure, the system collects relevant carbon emission data through a sensor network:

[0077] Construction phase: Collect carbon emission data during the construction process, mainly from fuel consumption of material transportation (such as cement, steel, etc.) and construction equipment (such as cranes and hoists);

[0078] Shipping stage: monitor the ship's fuel consumption, speed, distance and other data, and collect ship operation information through GPS and ship-borne sensors;

[0079] Port operation stage: monitoring port electricity consumption, energy consumption of logistics equipment (such as cranes, conveyor belts), and recording carbon emissions of transported goods;

[0080] Step 2: Data cleaning and integration

[0081] The system standardizes and normalizes the carbon emission data collected at each stage. All data from different sources will be converted into a unified carbon emission unit (such as tons of CO2) to ensure data consistency and quantify carbon emissions at each stage;

[0082] Step 3: Construction of multi-scale carbon emission assessment model

[0083] The system builds a multi-scale assessment model based on the LCA method and carbon emission factor library, combined with the carbon emission characteristics of the design, construction and operation stages:

[0084] Construction phase: Calculate the carbon emissions from material transportation, stacking and other links during port construction through the material carbon emission factor;

[0085] Shipping stage: Evaluate carbon emissions based on ship fuel consumption data, voyage, speed, and carbon emission factors of different ship types;

[0086] Operational stage: Calculate the port's operational carbon emissions through factors such as power consumption and equipment usage;

[0087] Step 4: Dynamic prediction and optimization analysis

[0088] Combine historical data with real-time sensor information and use machine learning models (such as decision tree DT, random forest RF, etc.) for dynamic prediction. The system predicts future carbon emission trends by comparing different carbon emission patterns and historical data, and makes optimization suggestions, such as: improving ship fuel efficiency and reducing fuel consumption; using green energy (such as solar energy and wind energy) to replace traditional power supply; optimizing waterway management and reducing shipping carbon emissions;

[0089] Step 5: Optimization plan generation and decision support

[0090] Based on the evaluation results, the system generates carbon emission optimization plans for ports and shipping to help decision makers take feasible emission reduction measures, such as: using environmentally friendly ships to reduce carbon emissions; improving the energy efficiency of port facilities and reducing electricity consumption; optimizing waterway design and reducing the sailing distance of ships; the system provides the optimization plan to decision makers, and adjusts and updates the plan based on real-time data to ensure continuous optimization of carbon emissions during operations.

[0091] Existing technologies mainly conduct static analysis based on historical data and lack dynamic prediction capabilities. They are unable to provide forward-looking guidance for future carbon emission control, which limits the effective implementation of infrastructure carbon emission reduction measures.

[0092] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A multi-scale fusion method for dynamic assessment of infrastructure carbon emissions, characterized in that: The method comprises the following steps: Step 1: Collect multi-scale carbon emission-related data and corresponding multi-scale carbon emission data at each stage of infrastructure; Step 2: Preprocess the multi-scale carbon emission related data and the corresponding multi-scale carbon emission data; Step 3: construct a multi-scale carbon emission assessment model based on the pre-processed data obtained in step 2 to obtain quantified multi-scale carbon emission related data and quantified multi-scale carbon emission data; Step 4: A multi-scale carbon emission prediction model is obtained based on the quantified data training, and a multi-scale carbon emission prediction result is obtained based on the prediction model; Step 5: Optimize the carbon emissions of each stage of infrastructure based on the multi-scale carbon emissions prediction results.

2. A multi-scale fusion method for dynamic assessment of infrastructure carbon emissions according to claim 1, characterized in that: The multi-scale carbon emission related data and corresponding multi-scale carbon emission data of each stage include multi-scale carbon emission related data and carbon emission data of the design stage, the construction stage and the operation stage.

3. A multi-scale fusion method for dynamic assessment of infrastructure carbon emissions according to claim 1, characterized in that: The multi-scale carbon emission related data include material type, energy consumption, transportation method and construction technology. The material type, energy consumption, transportation method and construction technology in the multi-scale carbon emission related data correspond to carbon emission factors.

4. A multi-scale fusion method for dynamic assessment of infrastructure carbon emissions according to claim 3, characterized in that: The multiscale carbon emission assessment model includes a project layer, a regional layer, a time layer and an indicator layer. The multiscale carbon emission assessment model divides the multiscale carbon emission related data and the corresponding multiscale carbon emission data into the project layer, the regional layer, the time layer and the indicator layer, and obtains quantified multiscale carbon emission related data and quantified multiscale carbon emission data through mathematical models and carbon emission factors.

5. The multi-scale fusion method for dynamic assessment of infrastructure carbon emissions according to claim 1 is characterized in that: The multi-scale carbon emission prediction model is constructed based on a support vector machine or a decision tree or a deep neural network.

6. A multi-scale fusion system for dynamic assessment of infrastructure carbon emissions, characterized in that: The system includes data acquisition module, data processing module, carbon emission assessment module and decision support module; Among them, the data acquisition module is used to collect multi-scale carbon emission related data and corresponding multi-scale carbon emission data at various stages of infrastructure; The data processing module is used to preprocess the multi-scale carbon emission related data and the corresponding multi-scale carbon emission data; The carbon emission assessment module is used to construct a multi-scale carbon emission assessment model based on the pre-processed data, obtain quantified multi-scale carbon emission related data and quantified multi-scale carbon emission data, obtain a multi-scale carbon emission prediction model based on the quantified data training, and obtain a multi-scale carbon emission prediction result based on the prediction model; The decision support module is used to optimize the carbon emissions of each stage of infrastructure based on the multi-scale carbon emissions prediction results.

7. A multi-scale fusion system for dynamic assessment of infrastructure carbon emissions according to claim 6, characterized in that: The multi-scale carbon emission related data and corresponding multi-scale carbon emission data of each stage include multi-scale carbon emission related data and carbon emission data of the design stage, the construction stage and the operation stage.

8. A multi-scale fusion system for dynamic assessment of infrastructure carbon emissions according to claim 6, characterized in that: The multi-scale carbon emission related data include material type, energy consumption, transportation method and construction technology. The material type, energy consumption, transportation method and construction technology in the multi-scale carbon emission related data correspond to carbon emission factors.

9. A multi-scale fusion system for dynamic assessment of infrastructure carbon emissions according to claim 8, characterized in that: The multiscale carbon emission assessment model includes a project layer, a regional layer, a time layer and an indicator layer. The multiscale carbon emission assessment model divides the multiscale carbon emission related data and the corresponding multiscale carbon emission data into the project layer, the regional layer, the time layer and the indicator layer, and obtains quantified multiscale carbon emission related data and quantified multiscale carbon emission data through mathematical models and carbon emission factors.

10. A multi-scale fusion system for dynamic assessment of infrastructure carbon emissions according to claim 6, characterized in that: The multi-scale carbon emission prediction model is constructed based on a support vector machine or a decision tree or a deep neural network.

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