Carbon emission dynamic prediction method and device based on performance degradation and process evolution

Abstract

The invention relates to a performance degradation and process evolution-based carbon emission dynamic prediction method and device. The method comprises the following steps of 1, acquiring carbon emission related data and corresponding carbon emission data of an infrastructure in each stage in real time; 2, combining the degradation data and the corresponding carbon emission data to obtain a performance degradation analysis result; 3, combining the optimization data with the corresponding carbon emission data to obtain a process evolution analysis result; 4, training to obtain a carbon emission dynamic prediction model; 5, inputting actual carbon emission related data into the carbon emission dynamic prediction model to obtain a carbon emission dynamic prediction result; and 6, optimizing the carbon emission based on the dynamic prediction result of the carbon emission. Compared with the prior art, the method has the advantages of improving the accuracy and comprehensiveness of carbon emission prediction 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 device for dynamically predicting carbon emissions based on performance degradation and process evolution. Background Art

[0002] As the global climate change problem becomes increasingly serious, the reduction of carbon emissions has become a global consensus and goal. As an important source of carbon emissions, the infrastructure sector is particularly important for carbon emission management and optimization control. Traditional carbon emission assessment methods usually rely on static data and fixed models, and cannot effectively consider the dynamic changes of infrastructure at different life cycle stages. In particular, as the infrastructure is operated, its performance will deteriorate, and with the application of new technologies and new materials, the impact of process evolution on carbon emissions is constantly changing. Therefore, how to accurately predict carbon emissions throughout the life cycle of infrastructure has become a challenge for current carbon emission management.

[0003] Existing carbon emission prediction technologies mostly focus on static calculations or short-term predictions, and lack dynamic analysis methods for performance degradation and process evolution. In addition, there is a lack of effective methods for long-term carbon emission trend prediction and optimization that combine performance degradation with process advancement, resulting in inaccurate and incomplete prediction results. Summary of the invention

[0004] The purpose of the present invention is to provide a method and device for dynamic prediction of carbon emissions based on performance degradation and process evolution in order to improve the accuracy and comprehensiveness of carbon emission prediction.

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

[0006] A method for dynamic prediction of carbon emissions based on performance degradation and process evolution, the method comprising the following steps:

[0007] Step 1: Collect carbon emission-related data and corresponding carbon emission data of infrastructure at each stage in real time. The carbon emission-related data at each stage includes historical data and real-time data;

[0008] Step 2: Combine the degradation data and corresponding carbon emission data in historical data and real-time data to analyze the performance degradation of the infrastructure during long-term use, evaluate the impact of performance degradation on carbon emissions, and obtain the performance degradation analysis results;

[0009] Step 3: Combine the optimization data and corresponding carbon emission data in historical data and real-time data to evaluate the impact of the use of new technologies, new materials and new processes on carbon emissions and obtain the process evolution analysis results;

[0010] Step 4: A carbon emission dynamic prediction model is obtained by training based on the performance degradation analysis results, process evolution analysis results, carbon emission related data and corresponding carbon emission data;

[0011] Step 5: Input the actual carbon emission related data into the carbon emission dynamic prediction model to obtain the carbon emission dynamic prediction results;

[0012] Step 6: Optimize carbon emissions based on the dynamic carbon emissions prediction results.

[0013] Furthermore, the degradation data includes equipment failure rate, energy consumption change and material degradation data.

[0014] Furthermore, the optimization data includes data on the updating of technical processes, the introduction of energy-saving and emission-reduction technologies, and the application of new materials.

[0015] Furthermore, the performance degradation analysis results are the data with the greatest impact on carbon emissions screened out from the degradation data; the process evolution analysis results are the data with the greatest impact on carbon emissions screened out from the optimization data. The performance degradation analysis results and the process evolution analysis results are used as training sets for the model.

[0016] Furthermore, the carbon emission dynamic prediction model is constructed based on regression analysis or neural network or support vector machine.

[0017] In another aspect of the present invention, a carbon emission dynamic prediction device based on performance degradation and process evolution is proposed, the device comprising a data acquisition module, a performance degradation analysis module, a process evolution analysis module, a carbon emission prediction module and an optimization suggestion module;

[0018] The device includes a data collection module for collecting carbon emission related data and corresponding carbon emission data of the infrastructure in each stage in real time, and the carbon emission related data in each stage includes historical data and real-time data;

[0019] The performance degradation analysis module is used to combine the degradation data in historical data and real-time data with the corresponding carbon emission data to analyze the performance degradation of infrastructure during long-term use, evaluate the impact of performance degradation on carbon emissions, and obtain performance degradation analysis results;

[0020] The process evolution analysis module is used to combine the optimization data and corresponding carbon emission data in historical data and real-time data to evaluate the impact of the use of new technologies, new materials and new processes on carbon emissions and obtain the process evolution analysis results;

[0021] The carbon emission prediction module is used to train a carbon emission dynamic prediction model based on performance degradation analysis results, process evolution analysis results, carbon emission related data and corresponding carbon emission data; input actual carbon emission related data into the carbon emission dynamic prediction model to obtain carbon emission dynamic prediction results;

[0022] The optimization suggestion module is used to optimize carbon emissions based on the dynamic prediction results of carbon emissions.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] The present invention combines historical data with real-time monitoring data to analyze the performance degradation of infrastructure during long-term use and evaluate the impact of degradation on carbon emissions. At the same time, by analyzing the evolution of technical processes during infrastructure construction and operation, the invention evaluates the impact of the use of new technologies, new processes and new materials on carbon emissions. The analysis results of performance degradation and process evolution are considered at the same time, and the analysis results are used as input to construct a dynamic prediction model for carbon emissions, realizing multi-scale evaluation and enabling long-term prediction of carbon emissions based on real-time data. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a flow chart of the present invention;

[0026] Figure 2 It is a module diagram corresponding to the present invention. DETAILED DESCRIPTION

[0027] 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.

[0028] The present invention proposes a method for dynamic prediction of carbon emissions based on performance degradation and process evolution, aiming to achieve accurate prediction and optimization of carbon emissions throughout the life cycle of infrastructure by combining performance degradation, process evolution and machine learning algorithms. The method collects carbon emission-related data at each stage (design, construction, operation and maintenance) in real time, analyzes the impact of equipment performance degradation and process evolution on carbon emissions, constructs a dynamic prediction model for carbon emissions based on machine learning, and provides optimization suggestions based on the prediction results, such as adjusting the process, selecting low-carbon materials, and improving energy efficiency. The method and device can be widely used in carbon emission management in infrastructure fields such as roads, waterways, and buildings, providing a scientific basis for low-carbon decision-making and promoting the construction and operation of low-carbon infrastructure. The method flow chart is shown below. Figure 1 As shown, the method comprises the following steps:

[0029] Step 1: Collect carbon emission-related data and corresponding carbon emission data of infrastructure at each stage in real time. The carbon emission-related data at each stage includes historical data and real-time data;

[0030] Step 2: Combine the degradation data and corresponding carbon emission data in historical data and real-time data to analyze the performance degradation of the infrastructure during long-term use, evaluate the impact of performance degradation on carbon emissions, and obtain the performance degradation analysis results;

[0031] Step 3: Combine the optimization data and corresponding carbon emission data in historical data and real-time data to evaluate the impact of the use of new technologies, new materials and new processes on carbon emissions and obtain the process evolution analysis results;

[0032] Step 4: A carbon emission dynamic prediction model is obtained by training based on the performance degradation analysis results, process evolution analysis results, carbon emission related data and corresponding carbon emission data;

[0033] Step 5: Input the actual carbon emission related data into the carbon emission dynamic prediction model to obtain the carbon emission dynamic prediction results;

[0034] Step 6: Optimize carbon emissions based on the dynamic carbon emissions prediction results.

[0035] Performance degradation data includes data such as equipment failure rate, changes in energy consumption, and material degradation, which are used to assess the increase in carbon emissions during infrastructure operation.

[0036] Process evolution data includes updates on technological processes, the introduction of energy-saving and emission-reduction technologies, the application of new materials, etc., and are used to evaluate the impact of different processes on carbon emissions.

[0037] The carbon emission dynamic prediction model is based on machine learning algorithms, which include but are not limited to regression analysis, neural networks, support vector machines (SVM), etc., and is used to make high-precision predictions of carbon emissions by combining performance degradation and process evolution data.

[0038] The carbon emission dynamic prediction model also combines the life cycle assessment (LCA) method to comprehensively consider the contribution of each stage of infrastructure to carbon emissions.

[0039] The carbon emission prediction results include indicators such as total carbon emissions, carbon emissions per unit product, and carbon emissions at different stages, which can provide data support for low-carbon design and operation.

[0040] Corresponding to the above method, the present invention also proposes a carbon emission dynamic prediction device based on performance degradation and process evolution, the structure diagram of which is as follows: Figure 2 As shown, the device includes a data acquisition module, a performance degradation analysis module, a process evolution analysis module, a carbon emission prediction module and an optimization suggestion module;

[0041] The device includes a data collection module for collecting carbon emission related data and corresponding carbon emission data of the infrastructure in each stage in real time, and the carbon emission related data in each stage includes historical data and real-time data;

[0042] The performance degradation analysis module is used to combine the degradation data in historical data and real-time data with the corresponding carbon emission data to analyze the performance degradation of infrastructure during long-term use, evaluate the impact of performance degradation on carbon emissions, and obtain performance degradation analysis results;

[0043] The process evolution analysis module is used to combine the optimization data and corresponding carbon emission data in historical data and real-time data to evaluate the impact of the use of new technologies, new materials and new processes on carbon emissions and obtain the process evolution analysis results;

[0044] The carbon emission prediction module is used to train a carbon emission dynamic prediction model based on performance degradation analysis results, process evolution analysis results, carbon emission related data and corresponding carbon emission data; input actual carbon emission related data into the carbon emission dynamic prediction model to obtain carbon emission dynamic prediction results;

[0045] The optimization suggestion module is used to optimize carbon emissions based on the dynamic prediction results of carbon emissions.

[0046] The data acquisition module is connected to equipment, materials, transportation and other links through sensors and monitoring systems to obtain various carbon emission data in real time.

[0047] The carbon emission prediction module uses a machine learning algorithm for predictive analysis, which can automatically adjust input data and weights to optimize prediction accuracy.

[0048] The optimization suggestion module automatically generates low-carbon optimization measures by combining real-time data and forecast results, and provides operational suggestions and improvement plans through the user interface.

[0049] The key steps of the present invention are:

[0050] 1. Data Collection

[0051] Through sensors and monitoring equipment, real-time carbon emission data of infrastructure in the design, construction, operation and maintenance stages are collected, including data on equipment energy consumption, material use, transportation emissions, etc., to ensure the comprehensiveness and accuracy of data collection.

[0052] 2. Performance degradation analysis

[0053] Combine historical data with real-time monitoring data to analyze the performance degradation of infrastructure during long-term use and assess the impact of degradation on carbon emissions. For example, how factors such as equipment aging and material degradation lead to increased carbon emissions.

[0054] 3. Process evolution analysis

[0055] By analyzing the evolution of technology and processes during infrastructure construction and operation, we can assess the impact of the use of new technologies, processes and materials on carbon emissions. For example, the use of new energy-saving processes or low-carbon materials may reduce long-term carbon emissions.

[0056] 4. Establishment of carbon emission prediction model

[0057] Based on the analysis results of performance degradation and process evolution, a dynamic prediction model for carbon emissions is constructed. This model combines machine learning algorithms with physical models to make long-term predictions of carbon emissions based on real-time data.

[0058] 5. Optimization plan generation

[0059] Based on the prediction results, optimization suggestions are generated, including measures such as improving processes, selecting low-carbon materials, and improving equipment energy efficiency to help reduce carbon emissions.

[0060] 6. Decision support

[0061] Provide low-carbon design and operation decision support through the optimization suggestion module. The system can provide real-time operation suggestions based on carbon emission forecasts and support dynamic adjustment of carbon emission management;

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] 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;

[0064] 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;

[0065] 3. Refined management: Through accurate quantification and optimized control of carbon emissions, it can effectively reduce infrastructure carbon emissions, promote the application of low-carbon technologies, and help achieve green development goals;

[0066] 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.

[0067] The present invention is used in the dynamic prediction application of carbon emissions of highway infrastructure to monitor and optimize carbon emissions in the entire process of highway construction and operation through real-time data collection, performance degradation analysis, process evolution evaluation and dynamic prediction model. The specific steps are as follows:

[0068] Step 1: Data Collection

[0069] During the construction and operation of highway infrastructure, sensor networks are used to collect carbon emission-related data, including:

[0070] Design stage: record the types, quantities and carbon emission factors of building materials in the design plan. The data comes from the carbon emission factor database;

[0071] Construction phase: real-time monitoring of fuel consumption of construction equipment, equipment operation time, and mileage and fuel consumption of vehicles during transportation;

[0072] Operational phase: collecting highway traffic flow, vehicle emission factors (based on vehicle type and fuel type), and monitoring energy consumption during routine maintenance;

[0073] Step 2: Performance degradation analysis

[0074] Combine historical data with real-time monitoring data to assess the impact of performance degradation of highway infrastructure during use on carbon emissions:

[0075] Equipment performance: By monitoring the decline in fuel efficiency of construction equipment and maintenance equipment, the carbon emission trend can be assessed;

[0076] Material degradation analyzes the deterioration of road pavement materials (such as asphalt and concrete) and assesses the increased maintenance frequency and carbon emissions caused by the reduction in their durability;

[0077] Step 3: Process Evolution Assessment

[0078] Analyze the impact of process improvements on carbon emissions in highway construction and operation:

[0079] Energy-saving technology: Evaluate the application effects of low-energy construction equipment and new road materials (such as permeable concrete);

[0080] Intelligent Transportation Systems: Analyze the contribution of intelligent traffic management to reducing traffic congestion and optimizing vehicle carbon emissions;

[0081] Step 4: Construction of carbon emission prediction model

[0082] Combining data collection, performance degradation analysis and process evolution evaluation results, a dynamic prediction model for carbon emissions is constructed:

[0083] Use machine learning algorithms (such as random forests or support vector machines) combined with physical models to achieve long-term trend forecasts of carbon emissions; conduct phased forecasts of carbon emissions during the design, construction, and operation phases, and verify model accuracy through historical data and simulations;

[0084] Step 5: Generate optimization plan

[0085] Based on the prediction results, the system proposes a carbon emission optimization plan, including:

[0086] Use low-carbon materials (such as high-efficiency asphalt, green concrete) and energy-efficient construction equipment during the construction phase;

[0087] During the operation phase, new energy vehicles are used and the highway traffic management system is optimized to reduce carbon emissions during peak traffic flow; the optimization plan is displayed in a visual way and comes with operational suggestions.

[0088] Applied to the dynamic prediction of carbon emissions of waterway infrastructure (such as ports and waterways), the present invention is used to monitor carbon emissions during the entire process of construction, shipping and operation, and optimize and control carbon emissions in combination with dynamic models; the specific steps are:

[0089] Step 1: Data Collection

[0090] During the construction and operation of waterway infrastructure, carbon emissions data is collected in real time, including:

[0091] Construction phase: monitoring the energy consumption of construction equipment during port construction and collecting transportation emissions of construction materials (such as steel and concrete);

[0092] Shipping stage: shipboard sensors are used to record the fuel consumption, speed and distance of the ship, and to collect emission factors of different ship types;

[0093] Port operation stage: collecting electricity consumption and carbon emissions during maintenance of port equipment (such as cranes and conveyor belts);

[0094] Step 2: Performance degradation analysis

[0095] Analyze the performance degradation of waterway infrastructure during long-term operation:

[0096] Ship performance degradation: monitor the decline in ship fuel efficiency and assess its impact on carbon emissions;

[0097] Port equipment degradation: Analyzing the impact of reduced electrical efficiency of cranes and conveying equipment on operational carbon emissions.

[0098] Step 3: Process Evolution Assessment

[0099] Assess the impact of new technologies, new materials and new processes on carbon emissions:

[0100] Green energy application: Analyze the emission reduction effect of ports using solar energy and wind energy to replace traditional electricity;

[0101] Waterway optimization: Assess the contribution of improvements in waterway design (such as shortening voyage distances) to shipping carbon emissions.

[0102] Step 4: Construction of carbon emission prediction model

[0103] Based on performance degradation and process evolution data, a dynamic prediction model for carbon emissions is constructed. The life cycle assessment (LCA) method is used to combine multi-stage data to build a dynamic prediction model; deep learning algorithms (such as neural networks) are used to analyze the trend of carbon emissions over time and predict carbon emission levels under different scenarios;

[0104] Step 5: Generate optimization plan

[0105] Provide optimization suggestions based on the prediction results. Use new energy ships and energy-saving equipment to reduce carbon emissions from shipping and port operations; optimize port operation processes through intelligent scheduling systems to reduce energy waste; propose channel optimization plans for ship navigation routes to reduce navigation time and fuel consumption; the optimization plan is displayed in real time through the user interface, and users can adjust the optimization strategy according to specific needs and dynamically update the prediction results.

[0106] 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 method for dynamic prediction of carbon emissions based on performance degradation and process evolution, characterized in that: The method comprises the following steps: Step 1: Collect carbon emission-related data and corresponding carbon emission data of infrastructure at each stage in real time. The carbon emission-related data at each stage includes historical data and real-time data; Step 2: Combine the degradation data and corresponding carbon emission data in historical data and real-time data to analyze the performance degradation of the infrastructure during long-term use, evaluate the impact of performance degradation on carbon emissions, and obtain the performance degradation analysis results; Step 3: Combine the optimization data and corresponding carbon emission data in historical data and real-time data to evaluate the impact of the use of new technologies, new materials and new processes on carbon emissions and obtain the process evolution analysis results; Step 4: A carbon emission dynamic prediction model is obtained by training based on the performance degradation analysis results, process evolution analysis results, carbon emission related data and corresponding carbon emission data; Step 5: Input the actual carbon emission related data into the carbon emission dynamic prediction model to obtain the carbon emission dynamic prediction results; Step 6: Optimize carbon emissions based on the dynamic carbon emissions prediction results.

2. According to claim 1, a method for dynamic prediction of carbon emissions based on performance degradation and process evolution is characterized in that: The degradation data includes equipment failure rate, energy consumption change and material degradation data.

3. According to claim 1, a method for dynamic prediction of carbon emissions based on performance degradation and process evolution is characterized in that: The optimization data include the updating of technical processes, the introduction of energy-saving and emission-reduction technologies and the application data of new materials.

4. According to claim 1, a method for dynamic prediction of carbon emissions based on performance degradation and process evolution is characterized in that: The performance degradation analysis results are the data with the greatest impact on carbon emissions screened out from the degradation data; the process evolution analysis results are the data with the greatest impact on carbon emissions screened out from the optimization data. The performance degradation analysis results and the process evolution analysis results are used as training sets for the model.

5. According to claim 1, a method for dynamic prediction of carbon emissions based on performance degradation and process evolution is characterized in that: The dynamic prediction model of carbon emissions is built based on regression analysis or neural network or support vector machine.

6. A carbon emission dynamic prediction device based on performance degradation and process evolution, characterized in that: The device includes a data acquisition module, a performance degradation analysis module, a process evolution analysis module, a carbon emission prediction module and an optimization suggestion module; The device includes a data collection module for collecting carbon emission related data and corresponding carbon emission data of the infrastructure in each stage in real time, and the carbon emission related data in each stage includes historical data and real-time data; The performance degradation analysis module is used to combine the degradation data in historical data and real-time data with the corresponding carbon emission data to analyze the performance degradation of infrastructure during long-term use, evaluate the impact of performance degradation on carbon emissions, and obtain performance degradation analysis results; The process evolution analysis module is used to combine the optimization data and corresponding carbon emission data in historical data and real-time data to evaluate the impact of the use of new technologies, new materials and new processes on carbon emissions and obtain the process evolution analysis results; The carbon emission prediction module is used to train a carbon emission dynamic prediction model based on performance degradation analysis results, process evolution analysis results, carbon emission related data and corresponding carbon emission data; input actual carbon emission related data into the carbon emission dynamic prediction model to obtain carbon emission dynamic prediction results; The optimization suggestion module is used to optimize carbon emissions based on the dynamic prediction results of carbon emissions.

7. The carbon emission dynamic prediction device based on performance degradation and process evolution according to claim 6 is characterized in that: The degradation data includes equipment failure rate, energy consumption change and material degradation data.

8. The carbon emission dynamic prediction device based on performance degradation and process evolution according to claim 6 is characterized in that: The optimization data include the updating of technical processes, the introduction of energy-saving and emission-reduction technologies and the application data of new materials.

9. The carbon emission dynamic prediction device based on performance degradation and process evolution according to claim 6 is characterized in that: The performance degradation analysis results are the data with the greatest impact on carbon emissions screened out from the degradation data; the process evolution analysis results are the data with the greatest impact on carbon emissions screened out from the optimization data. The performance degradation analysis results and the process evolution analysis results are used as training sets for the model.

10. The carbon emission dynamic prediction device based on performance degradation and process evolution according to claim 6, characterized in that: The dynamic prediction model of carbon emissions is built based on regression analysis or neural network or support vector machine.

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