Carbon emission index evaluation method and system based on full life cycle power transmission and transformation project

Abstract

The present invention discloses a carbon emission index evaluation method and system based on the whole life cycle of power transmission and transformation projects. S1. Collect data in the whole life cycle of power transmission and transformation projects, including material consumption, energy use and equipment operation data in the design, construction, operation, maintenance and demolition stages; S2. Adopt a hierarchical constraint path optimization algorithm to decompose and optimize the carbon emission path of each stage; S3. Introduce a self-correcting energy consumption curve algorithm to dynamically correct carbon emission data according to historical and real-time energy consumption data; S4. Monitor energy consumption fluctuations in the operation and maintenance stage, and automatically correct carbon emission deviations caused by changes in the external environment; S5. Integrate hierarchical constraint path optimization and energy consumption curve data to generate carbon emission assessment; S6. Generate a standardized carbon emission assessment report containing optimization suggestions; S7. Output an assessment report to support the implementation of low-carbon planning. The present invention effectively improves the carbon emission management and optimization efficiency of the whole life cycle of power transmission and transformation projects.

Description

Technical Field

[0001] The present invention relates to the field of electric power engineering technology, and in particular to a carbon emission index evaluation method and system based on a full life cycle power transmission and transformation project. Background Art

[0002] With the increasing severity of global climate change and the advancement of sustainable development goals, carbon emission control has become a focus of attention in all walks of life. As an important link in energy transmission, the energy consumption and material use of power transmission and transformation projects throughout their life cycle have a significant impact on carbon emissions. In order to achieve low-carbon development, carbon emission management in power transmission and transformation projects has gradually become one of the key technologies in the industry. However, existing technologies still face many challenges in carbon emission management throughout the life cycle of power transmission and transformation projects, especially in carbon emission path optimization, dynamic energy consumption correction, and full life cycle carbon emission assessment.

[0003] Existing carbon emission accounting methods usually only account for a certain stage, fail to cover the entire life cycle of power transmission and transformation projects, and it is difficult to form a unified carbon emission management plan. For example, the carbon emission path in the construction phase is not associated with the path in the operation and maintenance phase, resulting in relatively independent carbon emission assessments at each stage, and unable to provide global carbon emission path optimization. In addition, these methods lack the identification and optimization of key carbon emission nodes, and cannot accurately capture the nodes that contribute more to overall carbon emissions.

[0004] In existing technologies, carbon emission path optimization often relies on static data and cannot cope with energy consumption fluctuations during the operation and maintenance phase of power transmission and transformation projects and the impact of external environmental changes (such as temperature, humidity, etc.) on carbon emissions. This static accounting method leads to large errors in carbon emission assessment, especially during the operation phase, when the energy consumption of equipment will change significantly with fluctuations in environmental conditions. Existing technologies lack a dynamic adjustment mechanism to correct these fluctuations, making it difficult to provide real-time carbon emission optimization.

[0005] In the existing carbon emission management system, the coupling processing of energy consumption data and carbon emission paths is insufficient. The relationship between energy consumption and carbon emissions is very complex in practical applications and is affected by many factors. A single static accounting method cannot accurately reflect the impact of energy consumption on carbon emission paths. In addition, the existing technology does not have an effective hierarchical feedback mechanism to optimize and correct the carbon emission paths at different stages of the life cycle, resulting in insufficient accuracy of overall carbon emission management.

[0006] Therefore, the existing technology has the following defects in the carbon emission management of power transmission and transformation projects: insufficient accounting of carbon emissions throughout the entire life cycle; lack of comprehensive assessment of carbon emissions throughout the entire life cycle; lack of dynamic energy consumption correction mechanism, lack of multi-dimensional feedback mechanism; the existing methods simply handle the relationship between energy consumption and carbon emissions, lack of accurate coupling models, and it is difficult to accurately reflect the impact of energy consumption fluctuations on carbon emissions; therefore, how to provide a carbon emission index evaluation method and system based on the entire life cycle of power transmission and transformation projects is a problem that technical personnel in this field urgently need to solve. Summary of the invention

[0007] One purpose of the present invention is to propose a carbon emission index evaluation method based on the full life cycle of power transmission and transformation projects. The present invention adopts a hierarchical constraint path optimization and self-correcting energy consumption curve algorithm, and describes in detail the decomposition, optimization and dynamic correction methods of the carbon emission path throughout the life cycle. It has the advantages of comprehensive coverage, real-time adjustment and precise optimization, effectively improves the carbon emission management efficiency of power transmission and transformation projects at different stages, and supports low-carbon planning and implementation.

[0008] According to an embodiment of the present invention, a carbon emission index evaluation method and system based on a full life cycle power transmission and transformation project includes the following steps:

[0009] S1. Collect data from the entire life cycle of power transmission and transformation projects, including material consumption, energy use and equipment operation data during the design, construction, operation, maintenance and demolition stages, as basic data for carbon emission accounting;

[0010] S2. Use a hierarchical constraint path optimization algorithm to decompose and identify the carbon emission paths at each stage, and optimize the paths of key carbon emission nodes according to the dynamic changes of the project;

[0011] S3. Introduce a self-correcting energy consumption curve algorithm to generate an adaptive energy consumption curve based on the comparison between historical energy consumption data and real-time energy consumption data, and dynamically correct the carbon emission data at each stage;

[0012] S4. Through real-time adjustment of the self-correcting energy consumption curve, the energy consumption fluctuations during the operation and maintenance phase of the power transmission and transformation project are monitored, and the carbon emission deviations caused by changes in the external environment are automatically corrected;

[0013] S5. Integrate the calculation results of hierarchical constraint path optimization and self-correcting energy consumption curve to generate carbon emission assessment data for each stage of the life cycle;

[0014] S6. Generate a standardized carbon emission assessment report including carbon emission path optimization and energy consumption adjustment suggestions;

[0015] S7. Output carbon emission assessment reports to support low-carbon planning and implementation of power transmission and transformation projects during the design, construction and operation stages.

[0016] Optionally, the S1 includes the following steps:

[0017] S11. During the design phase, collect and record the design power of the equipment , Material usage , Material carbon emission factor and energy consumption data , establish an initial carbon emissions dataset;

[0018] S12. During the construction phase, obtain the power of construction equipment , fuel consumption , Construction time and construction material consumption data , while collecting actual energy usage data during construction ;

[0019] S13. During the operation phase, collect the actual operating power of the equipment , running time , Actual energy consumption and carbon emission factors for energy types , record carbon emission data during operation;

[0020] S14. During the maintenance phase, record the power of the maintenance equipment Maintenance duration , Maintenance materials used and energy consumption during maintenance , considering maintenance frequency and associated man-hours;

[0021] S15. During the demolition phase, obtain the power of the demolition equipment , Demolition time , Material recovery rate and waste disposal data , record the carbon emissions data generated during the demolition process, with a special focus on carbon emissions during waste treatment;

[0022] S16. Based on the data from the above stages, a carbon emission data set for the entire life cycle is formed , providing a basis for subsequent optimization of carbon emission paths;

[0023] S17. Input the collected carbon emission data into the hierarchical constraint path optimization module , combined with the carbon emissions throughout the entire life cycle, global optimization and carbon emissions assessment are carried out.

[0024] Optionally, S2 includes the following steps:

[0025] S21. Collect carbon emission data at all stages of the life cycle of power transmission and transformation projects , , , , , identify the key carbon emission nodes at each stage , and initially build a full life cycle carbon emission path ;

[0026] S22. For each subpath , the hierarchical constraint path optimization algorithm is applied to establish the following path optimization model, through energy consumption , Material Usage ,time and resource constraints within the stage Perform path decomposition and optimization:

[0027] ;

[0028] in, For energy consumption, is the material consumption, , , is the weighted index, For resource constraints, is the adjustment factor, is the path adjustment function, To fine-tune the parameters, and are the time start and end points respectively, and the optimization realizes the refinement of the time dimension through the integral term;

[0029] S23, for each key node The path optimization is modified through nonlinear dynamic feedback mechanism according to the actual carbon emissions and forecast carbon emissions The difference between and path correction factor ,generate Revised carbon emissions pathway ;

[0030] S24. In the process of path optimization, a multi-level path constraint iteration mechanism is used to optimize the path according to the resource consumption and time constraints of each stage. , for the key nodes in the path After iterating step by step, the path optimization model is:

[0031] ;

[0032] in, is the path convergence constraint, To constrain the index, dynamic optimization of path convergence is achieved through integral terms;

[0033] S25. In the path iteration process of each stage, the nonlinear changes of energy consumption and material consumption are combined, and the path influence factors of key nodes are corrected using the hierarchical weight correction formula. Make adjustments;

[0034] S26. Gradually refine the path adjustment function through hierarchical constraint path optimization After each stage, the full path Make dynamic adjustments;

[0035] S27. Generate a full life cycle carbon emission path Carbon emissions data at each stage Combined data from various sources will form an optimized carbon emission pathway assessment report.

[0036] Optionally, S3 includes the following steps:

[0037] S31. Collect historical energy consumption data at each stage of power transmission and transformation projects and real-time energy consumption data , and compare them to identify the key time periods where energy consumption differences occur , through the time period The data difference establishes the initial correction path ;

[0038] S32. Construct a recursive self-correcting energy consumption curve model, and make gradual corrections to each time period by dynamically comparing historical energy consumption with real-time energy consumption. The recursive formula is:

[0039] ;

[0040] in, The historical carbon emission curve is Data for a period of time, and They are real-time and historical energy consumption data, is the correction factor, It is the time weighting coefficient, which is used to adjust the energy consumption weight of each time period;

[0041] S33. Perform nonlinear dynamic correction on the carbon emission path of the current stage, and use the energy consumption and carbon emission coupling correction model. The correction formula is as follows:

[0042] ;

[0043] in, is the revised carbon emission path, To predict carbon emission paths, is the correction coefficient used to adjust the nonlinear effect of energy consumption on carbon emissions;

[0044] S34. Introduce a multi-dimensional feedback mechanism to make multi-level corrections to the carbon emission path in each time period, based on external environmental factors. The impact of carbon emissions is adjusted, and the formula is:

[0045] ;

[0046] in, To provide more carbon emission paths after the correction, For the external environment variables, is the correction factor, A weighted index for the impact of the external environment on energy consumption and carbon emissions;

[0047] S35: For feedback adjustment of energy consumption and carbon emissions, a hierarchical feedback correction mechanism is adopted to adjust the energy consumption and carbon emissions in each key time period. Make corrections and apply the correction results to the next time period, gradually optimizing the entire carbon emission path through recursion ;

[0048] S36. In each stage of the energy consumption correction process, the energy consumption correction path is iteratively optimized by combining historical trends and real-time feedback, and the carbon emission path is further optimized using a recursive formula. ;

[0049] S37. The final revised carbon emission path Carbon emissions data at each stage The data are combined to output an optimized carbon emission assessment report for use in the full life cycle carbon emission management of power transmission and transformation projects.

[0050] Optionally, S4 includes the following steps:

[0051] S41. Real-time monitoring of energy consumption data during the operation and maintenance phase of power transmission and transformation projects , collect data on energy consumption fluctuations and external environmental changes;

[0052] S42, combining the energy consumption fluctuation data with the self-correcting energy consumption curve model, and establishing an adaptive correction model based on energy consumption fluctuation, the formula is:

[0053] ;

[0054] in, is the corrected carbon emissions, To predict carbon emissions, is the correction factor, For real-time energy consumption data, is the historical energy consumption data, is the energy consumption correction index, Indicates the impact of changes in external ambient temperature on energy consumption;

[0055] S43. Based on changes in the external environment, the carbon emission deviation is automatically corrected through the environmental feedback correction model. The correction model is:

[0056] ;

[0057] in, is the carbon emissions after environmental correction, is the external environment correction factor, Indicates the dynamic changes of ambient humidity;

[0058] S44. Introduce an adaptive adjustment mechanism for external environmental fluctuations, make real-time adjustments to key nodes in the carbon emission path according to energy consumption fluctuations and environmental changes, and correct the path weights of key nodes. , and correct the path deviation through recursive iteration;

[0059] S45. Gradually correct the carbon emission path during the operation and maintenance phase of the power transmission and transformation project, and continuously optimize the carbon emission path through feedback data from each time period. ;

[0060] S46. During each time interval of the operation and maintenance phase, the impact of external environmental changes on carbon emissions is monitored in real time, and the carbon emission path is adjusted according to energy consumption fluctuations and environmental feedback, and the final carbon emission path is optimized through feedback iteration;

[0061] S47. Finally, a revised carbon emission assessment report for the operation and maintenance phase is generated, and the combined data of the carbon emission path and external environmental changes are output as a reference for optimization in subsequent stages.

[0062] Optionally, S5 includes the following steps:

[0063] S51. Collect and integrate path optimization data generated by the hierarchical constraint path optimization algorithm Energy consumption correction data generated by the self-correcting energy consumption curve algorithm , establish a preliminary carbon emission assessment model, the formula is:

[0064] ;

[0065] in, is the carbon emission assessment value for the entire life cycle. is the result of hierarchical constraint path optimization, Correction data for energy consumption, and are the start and end time of carbon emission assessment respectively;

[0066] S52. Based on the carbon emission path data and energy consumption correction data of each stage, the carbon emission assessment results of different stages are summarized in layers, and the carbon emission contribution of each stage is calculated in stages. , the formula is:

[0067] ;

[0068] in, Carbon emission assessment value for each stage, is the weight factor of each stage, Optimize data for each stage of the path, is the corresponding energy consumption correction data;

[0069] S53. Through the hierarchical feedback mechanism, the carbon emission assessment results of each stage are gradually iterated and corrected to generate the carbon emission path assessment results of the entire life cycle. The formula is:

[0070] ;

[0071] in, The carbon emission assessment results for the entire life cycle are: is the correction factor, is the weight adjustment coefficient for the impact of external environment and energy consumption;

[0072] S54. Based on the differences in carbon emission contributions at each stage of the life cycle, a hierarchical optimization model is applied to optimize the carbon emission paths at key stages. Optimize and correct;

[0073] S55. Integrate the hierarchical feedback optimized carbon emission path with the data of each stage of the life cycle to generate the final carbon emission assessment data ;

[0074] S56. Output carbon emission assessment reports for each stage of the entire life cycle, including the carbon emission contribution rate and energy consumption adjustment suggestions for each stage, for use in low-carbon management of power transmission and transformation projects;

[0075] S57. Combined with the results of the full life cycle carbon emission assessment, key nodes Further hierarchical optimization is carried out in the stages to support carbon emission control and low-carbon planning in subsequent stages.

[0076] Optionally, the S6 includes the following steps:

[0077] S61, Optimization results based on hierarchical constraint paths and self-correcting energy consumption curve The generated carbon emission data is used to build a preliminary carbon emission assessment model ;

[0078] S62. Based on the carbon emission assessment data at different stages, construct a phased carbon emission contribution analysis and calculate the total carbon emissions at each stage. and the corresponding contribution rate ;

[0079] S63. Generate a carbon emission optimization model including energy consumption adjustment suggestions. The energy consumption adjustment formula is:

[0080] ;

[0081] in, For energy consumption adjustment suggestions, is the historical energy consumption data, For real-time energy consumption data, It is an adjustment factor used to optimize the fluctuation of energy consumption in each time period;

[0082] S64. Combine the carbon emission data and energy consumption adjustment results of each stage to calculate the carbon emission path of the whole life cycle. Make a summary;

[0083] S65. Integrate the carbon emission paths and energy consumption adjustment data of each stage to generate a standardized carbon emission assessment report, which includes the carbon emissions of each stage, energy consumption adjustment suggestions and optimization results;

[0084] S66. Apply the generated carbon emission assessment report to the full life cycle carbon emission management system and support subsequent carbon emission path optimization and low-carbon planning;

[0085] S67. Output the final carbon emission assessment report, including the optimization results of the carbon emission path at each stage and energy consumption adjustment suggestions, which serves as the basic data for carbon emission control throughout the life cycle of the power transmission and transformation project.

[0086] The beneficial effects of the present invention are:

[0087] The present invention proposes a carbon emission index evaluation method based on the whole life cycle of power transmission and transformation projects. By introducing a hierarchical constraint path optimization algorithm, the carbon emission path of each stage is decomposed and optimized, and the key carbon emission nodes can be identified to ensure that the carbon emission path of each stage is effectively optimized, solving the problem of insufficient accounting of carbon emissions in the whole life cycle in the prior art. Especially in the design, construction, operation and maintenance stages, it can better realize the phased management and optimization of carbon emissions.

[0088] The present invention introduces a self-correcting energy consumption curve algorithm, which dynamically compares and corrects historical energy consumption data with real-time energy consumption data. It can effectively respond to the impact of energy consumption fluctuations and external environmental changes on carbon emissions during the operation and maintenance phase, ensure dynamic adjustment of the carbon emission path, and solve the problem in the prior art that carbon emission assessment cannot be dynamically corrected due to energy consumption fluctuations, thereby improving the accuracy and real-time performance of carbon emission assessment.

[0089] The present invention generates carbon emission assessment data for the entire life cycle by integrating hierarchical constraint path optimization and energy consumption correction results, and can provide accurate carbon emission assessment and energy consumption adjustment suggestions at each stage, solving the limitation of the lack of full life cycle carbon emission assessment in the prior art. The method can generate a standardized carbon emission assessment report based on the carbon emission contribution at different stages, providing strong support for the low-carbon planning and implementation of power transmission and transformation projects.

[0090] The present invention effectively combines energy consumption adjustment and carbon emission path optimization, and automatically corrects carbon emission deviations by real-time monitoring of energy consumption fluctuations and external environmental factors, making the full life cycle carbon emission management of power transmission and transformation projects more accurate and efficient. Especially in the case of complex and changeable external environments, this method can adjust the carbon emission path in a timely manner, effectively improving the reliability and accuracy of carbon emission management of power transmission and transformation projects.

[0091] The present invention adopts a multi-dimensional feedback correction mechanism, which can perform hierarchical feedback and optimization on the energy consumption correction and carbon emission adjustment in each time period, ensuring the accuracy and optimality of the carbon emission path throughout the life cycle, and providing efficient and reliable technical support for the low-carbon management of power transmission and transformation projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0093] Figure 1 A flow chart of the carbon emission index evaluation method and system based on the full life cycle power transmission and transformation project proposed by the present invention;

[0094] Figure 2 A schematic diagram of the application of the hierarchical constraint path optimization algorithm proposed in the present invention in the decomposition and optimization of carbon emission paths;

[0095] Figure 3 This is a flow chart of the dynamic adjustment of the self-correcting energy consumption curve algorithm proposed in the present invention for energy consumption fluctuations and carbon emission corrections. DETAILED DESCRIPTION

[0096] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0097] refer to Figure 1-Figure 3 , a carbon emission index evaluation method and system based on the whole life cycle of power transmission and transformation projects includes the following steps:

[0098] S1. Collect data from the entire life cycle of power transmission and transformation projects, including material consumption, energy use and equipment operation data during the design, construction, operation, maintenance and demolition stages, as basic data for carbon emission accounting;

[0099] S2. Use a hierarchical constraint path optimization algorithm to decompose and identify the carbon emission paths at each stage, and optimize the paths of key carbon emission nodes according to the dynamic changes of the project;

[0100] S3. Introduce a self-correcting energy consumption curve algorithm to generate an adaptive energy consumption curve based on the comparison between historical energy consumption data and real-time energy consumption data, and dynamically correct the carbon emission data at each stage;

[0101] S4. Through real-time adjustment of the self-correcting energy consumption curve, the energy consumption fluctuations during the operation and maintenance phase of the power transmission and transformation project are monitored, and the carbon emission deviations caused by changes in the external environment are automatically corrected;

[0102] S5. Integrate the calculation results of hierarchical constraint path optimization and self-correcting energy consumption curve to generate carbon emission assessment data for each stage of the life cycle;

[0103] S6. Generate a standardized carbon emission assessment report including carbon emission path optimization and energy consumption adjustment suggestions;

[0104] S7. Output carbon emission assessment reports to support low-carbon planning and implementation of power transmission and transformation projects during the design, construction and operation stages.

[0105] In this implementation, S1 includes the following steps:

[0106] S11. During the design phase, collect and record the design power of the equipment , Material usage , Material carbon emission factor and energy consumption data , establish an initial carbon emissions dataset;

[0107] S12. During the construction phase, obtain the power of construction equipment , fuel consumption , Construction time and construction material consumption data , while collecting actual energy usage data during construction ;

[0108] S13. During the operation phase, collect the actual operating power of the equipment , running time , Actual energy consumption and carbon emission factors for energy types , record carbon emission data during operation;

[0109] S14. During the maintenance phase, record the power of the maintenance equipment Maintenance duration , Maintenance materials used and energy consumption during maintenance , considering maintenance frequency and associated man-hours;

[0110] S15. During the demolition phase, obtain the power of the demolition equipment , Demolition time , Material recovery rate and waste disposal data , record the carbon emissions data generated during the demolition process, with a special focus on carbon emissions during waste treatment;

[0111] S16. Based on the data from the above stages, a carbon emission data set for the entire life cycle is formed , providing a basis for subsequent optimization of carbon emission paths;

[0112] S17. Input the collected carbon emission data into the hierarchical constraint path optimization module , combined with the carbon emissions throughout the entire life cycle, global optimization and carbon emissions assessment are carried out.

[0113] In this implementation, S2 includes the following steps:

[0114] S21. Collect carbon emission data at all stages of the life cycle of power transmission and transformation projects , , , , , identify the key carbon emission nodes at each stage , and initially build a full life cycle carbon emission path ;

[0115] S22. For each subpath , the hierarchical constraint path optimization algorithm is applied to establish the following path optimization model, through energy consumption , Material Usage ,time and resource constraints within the stage Perform path decomposition and optimization:

[0116] ;

[0117] in, For energy consumption, is the material consumption, , , is the weighted index, For resource constraints, is the adjustment factor, is the path adjustment function, To fine-tune the parameters, and are the time start and end points respectively, and the optimization realizes the refinement of the time dimension through the integral term;

[0118] S23, for each key node The path optimization is modified through nonlinear dynamic feedback mechanism according to the actual carbon emissions and forecast carbon emissions The difference between and path correction factor ,generate Revised carbon emissions pathway ;

[0119] S24. In the process of path optimization, a multi-level path constraint iteration mechanism is used to optimize the path according to the resource consumption and time constraints of each stage. , for the key nodes in the path After iterating step by step, the path optimization model is:

[0120] ;

[0121] in, is the path convergence constraint, To constrain the index, dynamic optimization of path convergence is achieved through integral terms;

[0122] S25. In the path iteration process of each stage, the nonlinear changes of energy consumption and material consumption are combined, and the path influence factors of key nodes are corrected using the hierarchical weight correction formula. Make adjustments;

[0123] S26. Gradually refine the path adjustment function through hierarchical constraint path optimization After each stage, the full path Make dynamic adjustments;

[0124] S27. Generate a full life cycle carbon emission path Carbon emissions data at each stage Combined data from various sources will form an optimized carbon emission pathway assessment report.

[0125] In this implementation, S3 includes the following steps:

[0126] S31. Collect historical energy consumption data at each stage of power transmission and transformation projects and real-time energy consumption data , and compare them to identify the key time periods where energy consumption differences occur , through the time period The data difference establishes the initial correction path ;

[0127] S32. Construct a recursive self-correcting energy consumption curve model, and make gradual corrections to each time period by dynamically comparing historical energy consumption with real-time energy consumption. The recursive formula is:

[0128] ;

[0129] in, The historical carbon emission curve is Data for a period of time, and They are real-time and historical energy consumption data, is the correction factor, It is the time weighting coefficient, which is used to adjust the energy consumption weight of each time period;

[0130] S33. Perform nonlinear dynamic correction on the carbon emission path of the current stage, and use the energy consumption and carbon emission coupling correction model. The correction formula is as follows:

[0131] ;

[0132] in, is the revised carbon emission path, To predict carbon emission paths, is the correction coefficient used to adjust the nonlinear effect of energy consumption on carbon emissions;

[0133] S34. Introduce a multi-dimensional feedback mechanism to make multi-level corrections to the carbon emission path in each time period, based on external environmental factors. The impact of carbon emissions is adjusted, and the formula is:

[0134] ;

[0135] in, To provide more carbon emission paths after the correction, For the external environment variables, is the correction factor, A weighted index for the impact of the external environment on energy consumption and carbon emissions;

[0136] S35: For feedback adjustment of energy consumption and carbon emissions, a hierarchical feedback correction mechanism is adopted to adjust the energy consumption and carbon emissions in each key time period. Make corrections and apply the correction results to the next time period, gradually optimizing the entire carbon emission path through recursion ;

[0137] S36. In each stage of the energy consumption correction process, the energy consumption correction path is iteratively optimized by combining historical trends and real-time feedback, and the carbon emission path is further optimized using a recursive formula. ;

[0138] S37. The final revised carbon emission path Carbon emissions data at each stage The data are combined to output an optimized carbon emission assessment report for use in the full life cycle carbon emission management of power transmission and transformation projects.

[0139] In this implementation, S4 includes the following steps:

[0140] S41. Real-time monitoring of energy consumption data during the operation and maintenance phase of power transmission and transformation projects , collect data on energy consumption fluctuations and external environmental changes;

[0141] S42, combining the energy consumption fluctuation data with the self-correcting energy consumption curve model, and establishing an adaptive correction model based on energy consumption fluctuation, the formula is:

[0142] ;

[0143] in, is the corrected carbon emissions, To predict carbon emissions, is the correction factor, For real-time energy consumption data, is the historical energy consumption data, is the energy consumption correction index, Indicates the impact of changes in external ambient temperature on energy consumption;

[0144] S43. Based on changes in the external environment, the carbon emission deviation is automatically corrected through the environmental feedback correction model. The correction model is:

[0145] ;

[0146] in, is the carbon emissions after environmental correction, is the external environment correction factor, Indicates the dynamic changes of ambient humidity;

[0147] S44. Introduce an adaptive adjustment mechanism for external environmental fluctuations, make real-time adjustments to key nodes in the carbon emission path according to energy consumption fluctuations and environmental changes, and correct the path weights of key nodes. , and correct the path deviation through recursive iteration;

[0148] S45. Gradually correct the carbon emission path during the operation and maintenance phase of the power transmission and transformation project, and continuously optimize the carbon emission path through feedback data from each time period. ;

[0149] S46. During each time interval of the operation and maintenance phase, the impact of external environmental changes on carbon emissions is monitored in real time, and the carbon emission path is adjusted according to energy consumption fluctuations and environmental feedback, and the final carbon emission path is optimized through feedback iteration;

[0150] S47. Finally, a revised carbon emission assessment report for the operation and maintenance phase is generated, and the combined data of the carbon emission path and external environmental changes are output as a reference for optimization in subsequent stages.

[0151] In this implementation, S5 includes the following steps:

[0152] S51. Collect and integrate path optimization data generated by the hierarchical constraint path optimization algorithm Energy consumption correction data generated by the self-correcting energy consumption curve algorithm , establish a preliminary carbon emission assessment model, the formula is:

[0153] ;

[0154] in, is the carbon emission assessment value for the entire life cycle. is the result of hierarchical constraint path optimization, Correction data for energy consumption, and are the start and end time of carbon emission assessment respectively;

[0155] S52. Based on the carbon emission path data and energy consumption correction data of each stage, the carbon emission assessment results of different stages are summarized in layers, and the carbon emission contribution of each stage is calculated in stages. , the formula is:

[0156] ;

[0157] in, Carbon emission assessment value for each stage, is the weight factor of each stage, Optimize data for each stage of the path, is the corresponding energy consumption correction data;

[0158] S53. Through the hierarchical feedback mechanism, the carbon emission assessment results of each stage are gradually iterated and corrected to generate the carbon emission path assessment results of the entire life cycle. The formula is:

[0159] ;

[0160] in, The carbon emission assessment results for the entire life cycle are: is the correction factor, is the weight adjustment coefficient for the impact of external environment and energy consumption;

[0161] S54. Based on the differences in carbon emission contributions at each stage of the life cycle, a hierarchical optimization model is applied to optimize the carbon emission paths at key stages. Optimize and correct;

[0162] S55. Integrate the hierarchical feedback optimized carbon emission path with the data of each stage of the life cycle to generate the final carbon emission assessment data ;

[0163] S56. Output carbon emission assessment reports for each stage of the entire life cycle, including the carbon emission contribution rate and energy consumption adjustment suggestions for each stage, for use in low-carbon management of power transmission and transformation projects;

[0164] S57. Combined with the results of the full life cycle carbon emission assessment, key nodes Further hierarchical optimization is carried out in the stages to support carbon emission control and low-carbon planning in subsequent stages.

[0165] In this implementation, S6 includes the following steps:

[0166] S61, Optimization results based on hierarchical constraint paths and self-correcting energy consumption curve The generated carbon emission data is used to build a preliminary carbon emission assessment model ;

[0167] S62. Based on the carbon emission assessment data at different stages, construct a phased carbon emission contribution analysis and calculate the total carbon emissions at each stage. and the corresponding contribution rate ;

[0168] S63. Generate a carbon emission optimization model including energy consumption adjustment suggestions. The energy consumption adjustment formula is:

[0169] ;

[0170] in, For energy consumption adjustment suggestions, is the historical energy consumption data, For real-time energy consumption data, It is an adjustment factor used to optimize the fluctuation of energy consumption in each time period;

[0171] S64. Combine the carbon emission data and energy consumption adjustment results of each stage to calculate the carbon emission path of the whole life cycle. Make a summary;

[0172] S65. Integrate the carbon emission paths and energy consumption adjustment data of each stage to generate a standardized carbon emission assessment report, which includes the carbon emissions of each stage, energy consumption adjustment suggestions and optimization results;

[0173] S66. Apply the generated carbon emission assessment report to the full life cycle carbon emission management system and support subsequent carbon emission path optimization and low-carbon planning;

[0174] S67. Output the final carbon emission assessment report, including the optimization results of the carbon emission path at each stage and energy consumption adjustment suggestions, which serves as the basic data for carbon emission control throughout the life cycle of the power transmission and transformation project.

[0175] Embodiment 1:

[0176] In modern power transmission and transformation projects, the management and optimization of carbon emissions has become a technical problem that needs to be solved urgently. A large-scale power transmission and transformation project in a certain place covers the entire process from design to construction, operation, maintenance and final demolition. The total construction period of the project is 10 years, involving a large amount of energy consumption and material use, and involves carbon emission management at multiple stages. However, the existing carbon emission accounting methods are difficult to cope with the complexity of the project, especially in the operation and maintenance stage, when energy consumption fluctuates greatly and the external environment changes frequently. The existing methods cannot accurately evaluate the carbon emission path throughout the life cycle.

[0177] To solve this problem, the present invention proposes a carbon emission index evaluation method based on the full life cycle of power transmission and transformation projects, and adopts a hierarchical constraint path optimization algorithm and a self-correcting energy consumption curve algorithm to dynamically correct and optimize the carbon emission path.

[0178] During the design phase of the project, the total power of the equipment used was 500MW, and the carbon emission factors of the steel and other materials used in the design process were 0.5tCO2e / t and 0.3tCO2e / t respectively. During the construction phase, the project used dozens of large-scale mechanical equipment, the construction time was 24 months, and the estimated energy consumption was 5 million kWh. However, due to weather changes during the construction process, the actual energy consumption fluctuated greatly, resulting in carbon emissions that were 10% higher than expected. The carbon emission data during the construction phase was corrected through the self-correcting energy consumption curve algorithm, and the final carbon emissions were corrected to 30,000 tons of CO2, which was less than 2% different from expectations.

[0179] During the operation phase, the total energy consumption of the equipment is 6 million kWh, and energy consumption is mainly affected by seasonal temperature fluctuations. For example, during the high temperature period in summer, the energy consumption of the equipment increased by 15%, resulting in a 5% increase in carbon emissions per month. Through the self-correcting energy consumption curve algorithm, combined with real-time data of the external environment, especially temperature and humidity changes, the system automatically adjusts the carbon emission path, making the actual carbon emission data tend to be stable, and the fluctuation is controlled within 2%. The total carbon emissions during the entire operation phase are 45,000 tons of CO2.

[0180] During the maintenance phase, the project undergoes regular maintenance every year, with an average maintenance time of 2 weeks each time, involving equipment overhaul, material replacement and other operations. During the maintenance period, carbon emissions and energy consumption fluctuate greatly, especially when the external ambient temperature and wind speed change. Through the self-correcting energy consumption curve algorithm, the system can dynamically adjust the carbon emission path of the maintenance equipment during each maintenance period, ultimately reducing carbon emissions during maintenance by 10%.

[0181] During the demolition phase, since the demolition of the project equipment involves a large amount of waste treatment and equipment recycling, external environmental conditions (such as weather and construction progress) have a great impact on carbon emissions during the demolition process. Through the hierarchical constraint path optimization algorithm, the system identified and optimized the key carbon emission nodes in the demolition phase, especially during the recycling of high-energy-consuming equipment, and automatically adjusted the equipment power to reduce energy consumption fluctuations, so that the total carbon emissions during the demolition period were controlled at 18,000 tons of CO2.

[0182] This invention effectively solves many problems in the carbon emission management of the power transmission and transformation project throughout its life cycle by integrating hierarchical constraint path optimization and self-correcting energy consumption curve algorithm. Compared with the existing technology, the accuracy of carbon emission assessment has been improved by about 15%, and the optimization and adjustment of carbon emission paths at each stage are more accurate. In particular, when dealing with energy consumption fluctuations and external environmental changes during the operation and maintenance stage, the system shows stronger adaptability, and ultimately achieves effective support for the low-carbonization goal of the project.

[0183] Table 1 Data table

[0184] stage <![CDATA[Estimated carbon emissions (tons of CO2)]]> <![CDATA[Actual carbon emissions (tons of CO2)]]> <![CDATA[Carbon emissions after energy consumption correction (tons of CO2)]]> Before deviation correction (%) After deviation correction (%) Design Phase 5,000 5,200 5,100 4% 2% Construction Phase 30,000 33,000 31,000 10% 2% Operational stage 45,000 48,000 45,500 5% 1% Maintenance phase 2,500 2,800 2,550 12% 2% Demolition phase 18,000 20,000 18,500 11% 3%

[0185] The data table shows the carbon emission data for the design, construction, operation, maintenance and demolition stages. Through the self-correcting energy consumption curve and hierarchical constraint path optimization, the difference between the actual carbon emissions and the expected carbon emissions has been significantly corrected, with the deviation reduced from an average of +8% before correction to +2% after correction.

[0186] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A carbon emission index evaluation method based on the full life cycle of power transmission and transformation projects, characterized by: The steps include: S1. Collect data from the entire life cycle of power transmission and transformation projects, including material consumption, energy use and equipment operation data during the design, construction, operation, maintenance and demolition stages, as basic data for carbon emission accounting; S2. Use a hierarchical constraint path optimization algorithm to decompose and identify the carbon emission paths at each stage, and optimize the paths of key carbon emission nodes according to the dynamic changes of the project; S3. Introduce a self-correcting energy consumption curve algorithm to generate an adaptive energy consumption curve based on the comparison between historical energy consumption data and real-time energy consumption data, and dynamically correct the carbon emission data at each stage; S4. Through real-time adjustment of the self-correcting energy consumption curve, the energy consumption fluctuations during the operation and maintenance phase of the power transmission and transformation project are monitored, and the carbon emission deviations caused by changes in the external environment are automatically corrected; S5. Integrate the calculation results of hierarchical constraint path optimization and self-correcting energy consumption curve to generate carbon emission assessment data for each stage of the life cycle; S6. Generate a standardized carbon emission assessment report including carbon emission path optimization and energy consumption adjustment suggestions; S7. Output carbon emission assessment reports to support low-carbon planning and implementation of power transmission and transformation projects during the design, construction and operation stages; The S2 comprises the following steps: S21. Collect carbon emission data at all stages of the life cycle of power transmission and transformation projects d , C c , C o , C m , C d2 , identify the key carbon emission nodes N in each stage key , and preliminarily construct the full life cycle carbon emission path P0; S22. For each subpath P i , the hierarchical constraint path optimization algorithm is applied to establish the following path optimization model, through the energy consumption E i 、Material Use i , time T i and the resource constraints R within the stage i Perform path decomposition and optimization: Among them, E i is energy consumption, M i is the material consumption, α, β, θ are weighted indexes, R i is the resource constraint, λ i is the adjustment factor, F adj，i is the path adjustment function, ∈ is the fine-tuning parameter, t0 and t f are the time start and end points respectively, and the optimization realizes the refinement of the time dimension through the integral term; S23, for each key node N key The path optimization is modified through the nonlinear dynamic feedback mechanism according to the actual carbon emissions C real and predicted carbon emissions C pred The difference between key and the path correction factor F corr , generating P corr Corrected carbon emission path P corr ; S24. In the process of path optimization, a multi-level path constraint iteration mechanism is used to optimize the path according to the resource consumption and time constraints of each stage. i , for the key node N in the path sub After iterating step by step, the path optimization model is: in, is the path convergence constraint, γ is the constraint index, and the dynamic optimization of path convergence is achieved through the integral term; S25. In the path iteration process of each stage, the path influence factor W of the key node is modified by combining the nonlinear changes of energy consumption and material consumption and using the hierarchical weight correction formula. key Make adjustments; S26, through hierarchical constraint path optimization, gradually refine the path adjustment function F adj After each stage, the full path P life Make dynamic adjustments; S27, generate the whole life cycle carbon emission path P final Carbon emission data at each stage life Combined data from the 3D images to form an optimized carbon emission pathway assessment report; The S3 comprises the following steps: S31. Collect historical energy consumption data of each stage of power transmission and transformation projects hist and real-time energy consumption data real , and compare them to identify the key time period T where the energy consumption difference occurs key , through the time period T key The data difference establishes the initial correction path P init ; S32. Construct a recursive self-correcting energy consumption curve model, and make gradual corrections to each time period by dynamically comparing historical energy consumption with real-time energy consumption. The recursive formula is: Among them, C hist (t i ) is the data of the historical carbon emission curve in the i-th time period, E real (t) and E hist (t) are real-time and historical energy consumption data, μ i is the correction factor, θ i It is the time weighting coefficient, which is used to adjust the energy consumption weight of each time period; S33. Perform nonlinear dynamic correction on the carbon emission path of the current stage, and use the energy consumption and carbon emission coupling correction model. The correction formula is as follows: Among them, C adj (t) is the corrected carbon emission path, C pred To predict the carbon emission path, κ is the correction coefficient used to adjust the nonlinear effect of energy consumption on carbon emissions; S34. Introduce a multi-dimensional feedback mechanism to make multi-level corrections to the carbon emission path in each time period, based on external environmental factors F env，j (t), adjust the carbon emission data, the formula is: Among them, C corr (t) is the carbon emission path after multiple maintenance corrections, F env，j (t) is the jth external environment variable, λ j is the correction factor, δ j A weighted index for the impact of the external environment on energy consumption and carbon emissions; S35. For feedback adjustment of energy consumption and carbon emissions, a hierarchical feedback correction mechanism is adopted to adjust the energy consumption and carbon emissions in each key time period T key Make corrections and apply the correction results to the next time period, and gradually optimize the entire carbon emission path P through recursion. curve ; S36. In each stage of the energy consumption correction process, the energy consumption correction path is iteratively optimized by combining historical trends and real-time feedback, and the carbon emission path P is further optimized using a recursive formula. opt ; S37, the final revised carbon emission path P final Carbon emission data of each stage life The data are combined to output an optimized carbon emission assessment report for use in the full life cycle carbon emission management of power transmission and transformation projects.

2. The carbon emission index evaluation method based on the full life cycle of power transmission and transformation projects according to claim 1 is characterized in that: The S1 comprises the following steps: S11. During the design phase, collect and record the design power P of the equipment. d 、Material usage M d , Material carbon emission factor F m And energy consumption data E d , establish an initial carbon emissions dataset; S12. During the construction phase, obtain the power P of the construction equipment c 、Fuel consumption F c , Construction time T c and construction material consumption data M c , while collecting actual energy usage data during construction c ; S13. During the operation phase, the actual operating power P of the acquisition equipment is collected. o , running time T o , actual energy consumption E o and the carbon emission factor F of the energy type o , record carbon emission data during operation; S14. During the maintenance phase, record the power P of the maintenance equipment m , Maintenance time T m 、Maintenance materials used M m And the energy consumption during maintenance E m , considering maintenance frequency and associated man-hours; S15. During the dismantling phase, obtain the power P of the dismantling equipment. d , demolition time T d , material recovery rate R d and waste disposal data d , record the carbon emissions data generated during the demolition process, with a special focus on carbon emissions during waste treatment; S16. Based on the data from the above stages, a carbon emission data set C for the entire life cycle is formed. life , providing a basis for subsequent optimization of carbon emission paths; S17, input the collected carbon emission data into the hierarchical constraint path optimization module O p , combined with the carbon emissions throughout the entire life cycle, global optimization and carbon emissions assessment are carried out.

3. The carbon emission index evaluation method based on the full life cycle of power transmission and transformation projects according to claim 1 is characterized in that: The S4 comprises the following steps: S41. Real-time monitoring of energy consumption data during the operation and maintenance phase of power transmission and transformation projects run , collect data on energy consumption fluctuations and external environmental changes; S42, combining the energy consumption fluctuation data with the self-correcting energy consumption curve model, and establishing an adaptive correction model based on energy consumption fluctuation, the formula is: Among them, C corr (t) is the corrected carbon emissions, C pred (t) is the predicted carbon emissions, λ is the correction factor, E run (t) is the real-time energy consumption data, E hist (t) is the historical energy consumption data, γ is the energy consumption correction index, T env Indicates the impact of changes in external ambient temperature on energy consumption; S43. Based on changes in the external environment, the carbon emission deviation is automatically corrected through the environmental feedback correction model. The correction model is: Among them, C adj (t) is the carbon emission after environmental correction, β is the external environment correction coefficient, H env (t) represents the dynamic change of ambient humidity; S44, introduce the external environment fluctuation adaptive adjustment mechanism, according to the energy consumption fluctuation and environmental changes, make real-time adjustments to the key nodes in the carbon emission path, and correct the path weight W of the key nodes key , and correct the path deviation through recursive iteration; S45. Gradually correct the carbon emission path during the operation and maintenance phase of the power transmission and transformation project, and continuously optimize the carbon emission path P through feedback data from each time period. adj ; S46. During each time interval of the operation and maintenance phase, the impact of external environmental changes on carbon emissions is monitored in real time, and the carbon emission path is adjusted according to energy consumption fluctuations and environmental feedback, and the final carbon emission path is optimized through feedback iteration; S47. Finally, a revised carbon emission assessment report for the operation and maintenance phase is generated, and the combined data of the carbon emission path and external environmental changes are output as a reference for optimization in subsequent stages.

4. The carbon emission index evaluation method based on the full life cycle of power transmission and transformation projects according to claim 1 is characterized in that: The S5 comprises the following steps: S51, collecting and integrating the path optimization data P generated by the hierarchical constraint path optimization algorithm opt The energy consumption correction data C generated by the self-correcting energy consumption curve algorithm curve , establish a preliminary carbon emission assessment model, the formula is: Among them, C eval (t) is the carbon emission assessment value of the whole life cycle, P opt (t) is the result of hierarchical constraint path optimization, C curve (t) is the energy consumption correction data, t0 and t f are the start and end time of carbon emission assessment respectively; S52. According to the carbon emission path data and energy consumption correction data of each stage, the carbon emission assessment results of different stages are summarized in layers, and the carbon emission contribution C of each stage is calculated in stages. stage , the formula is: Among them, C stage is the carbon emission assessment value for each stage, W stage，i is the weight factor of each stage, P opt，i (t) is the path optimization data of each stage, C curve，i (t) is the corresponding energy consumption correction data; S53. Through the hierarchical feedback mechanism, the carbon emission assessment results of each stage are gradually iterated and corrected to generate the carbon emission path assessment results of the entire life cycle. The formula is: Among them, C life (t) is the carbon emission assessment result of the whole life cycle, λ j is the correction factor, δ j is the weight adjustment coefficient for the impact of external environment and energy consumption; S54. Based on the differences in carbon emission contributions at each stage in the entire life cycle, a hierarchical optimization model is applied to optimize the carbon emission path P at the key stage. key (t) Optimize and modify; S55. Integrate the hierarchical feedback optimized carbon emission path with the data of each stage of the life cycle to generate the final carbon emission assessment data C final (t); S56. Output carbon emission assessment reports for each stage of the entire life cycle, including the carbon emission contribution rate and energy consumption adjustment suggestions for each stage, for use in low-carbon management of power transmission and transformation projects; S57, combined with the full life cycle carbon emission assessment results, key node N key Further hierarchical optimization is carried out in the stages to support carbon emission control and low-carbon planning in subsequent stages.

5. The carbon emission index evaluation method based on the full life cycle of power transmission and transformation projects according to claim 1 is characterized in that: The S6 comprises the following steps: S61, based on the hierarchical constraint path optimization result P opt and self-correcting energy consumption curve C curve The carbon emission data generated is used to build a preliminary carbon emission assessment model C report (t); S62. Based on the carbon emission assessment data of different stages, construct a phased carbon emission contribution analysis and calculate the total carbon emission C of each stage. stage and the corresponding contribution rate W stage ; S63. Generate a carbon emission optimization model including energy consumption adjustment suggestions. The energy consumption adjustment formula is: Among them, E adj (t) is the energy consumption adjustment suggestion, E hist (t) is the historical energy consumption data, E real (t) is the real-time energy consumption data, and γ is the adjustment coefficient used to optimize the fluctuation of energy consumption in each time period; S64. Combine the carbon emission data and energy consumption adjustment results of each stage to calculate the carbon emission path P of the whole life cycle. life (t) summarize; S65. Integrate the carbon emission paths and energy consumption adjustment data of each stage to generate a standardized carbon emission assessment report, which includes the carbon emissions of each stage, energy consumption adjustment suggestions and optimization results; S66. Apply the generated carbon emission assessment report to the full life cycle carbon emission management system and support subsequent carbon emission path optimization and low-carbon planning; S67. Output the final carbon emission assessment report, including the optimization results of the carbon emission path at each stage and energy consumption adjustment suggestions, which serves as the basic data for carbon emission control throughout the life cycle of the power transmission and transformation project.

6. The system for evaluating the carbon emission index of a power transmission and transformation project based on the whole life cycle as claimed in claim 1, characterized in that: include: Data collection module: used to collect data throughout the life cycle of power transmission and transformation projects, including material consumption, energy use and equipment operation data during the design, construction, operation, maintenance and demolition stages, as basic data for carbon emission accounting; Path optimization module: Based on the hierarchical constraint path optimization algorithm, the carbon emission paths of each stage are decomposed and identified, and the paths of key carbon emission nodes are optimized according to the dynamic changes of the project; Energy consumption correction module: Based on the self-correcting energy consumption curve algorithm, it generates an adaptive energy consumption curve according to the comparison between historical energy consumption data and real-time energy consumption data, and dynamically corrects the carbon emission data at each stage; Real-time monitoring module: monitors energy consumption fluctuations during the operation and maintenance phase of power transmission and transformation projects through real-time adjustment of the self-correcting energy consumption curve, and automatically corrects carbon emission deviations caused by changes in the external environment; Data integration module: used to integrate the calculation results of hierarchical constraint path optimization and self-correcting energy consumption curve to generate carbon emission assessment data for each stage of the life cycle; Report generation module: Generates a standardized carbon emission assessment report including carbon emission path optimization and energy consumption adjustment suggestions; Output module: Output carbon emission assessment reports to support low-carbon planning and implementation of power transmission and transformation projects during the design, construction and operation stages.

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