Intelligent urban carbon emission reduction project evaluation and fund flow distribution system

Abstract

The invention relates to the technical field of carbon emission reduction, in particular to an intelligent urban carbon emission reduction project evaluation and fund flow distribution system, which comprises a data analysis module, a dynamic evaluation and adaptation module, a comprehensive value evaluation module and a fund flow distribution module, the data analysis module is responsible for collecting and processing environmental, economic and social data related to a carbon emission reduction project; the dynamic evaluation and adaptation module dynamically adjusts project evaluation standards and methods; the comprehensive value evaluation module comprehensively evaluates the environmental benefit, the economic benefit and the social influence of the project; and the fund flow distribution module considers the adjustment suggestions proposed by the dynamic evaluation and adaptation module and intelligently distributes limited fund resources. According to the invention, the project strategy can respond to the changes of the market and the environment in time, the resource distribution is optimized, and the success rate and sustainability of the project are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission reduction, and in particular to a smart city carbon emission reduction project evaluation and capital flow allocation system. Background Art

[0003] Traditional project evaluation and funding allocation methods often rely on historical data and empirical judgments, lacking the ability to analyze real-time data and dynamically adapt to external changes. This static evaluation method is difficult to accurately reflect the current environmental and market conditions of the project, resulting in resource allocation that may not be efficient or timely. In addition, traditional methods often only focus on a single dimension of benefit evaluation, such as economic returns, while ignoring other important factors such as environmental protection and social impact, which is at odds with the goal of maximizing the comprehensive benefits of smart cities. Summary of the invention

[0004] Based on the above objectives, the present invention provides a smart city carbon emission reduction project evaluation and capital flow allocation system.

[0005] The smart city carbon emission reduction project evaluation and capital flow allocation system includes a data analysis module, a dynamic evaluation and adaptation module, a comprehensive value assessment module, and a capital flow allocation module, among which;

[0006] The data analysis module is responsible for collecting and processing environmental, economic and social data related to carbon emission reduction projects;

[0007] The dynamic evaluation and adaptation module dynamically adjusts the project evaluation criteria and methods based on the real-time data and analysis results provided by the data analysis module, uses the improved LSTM model to evaluate the impact of external changes on the project, and makes adjustment suggestions to the project based on the model output to ensure that the realization of the project goals is consistent with the current conditions;

[0008] The comprehensive value assessment module comprehensively utilizes the data obtained from the data analysis module and the adjustment suggestions of the dynamic assessment and adaptation module to comprehensively evaluate the environmental benefits, economic benefits and social impacts of the project, and adopts a multi-dimensional evaluation framework to calculate the comprehensive benefit score of the project and evaluate its contribution to the sustainable development of the smart city;

[0009] The fund flow allocation module intelligently allocates limited fund resources according to the comprehensive project benefit score calculated by the comprehensive value assessment module and the adjustment suggestions put forward by the dynamic assessment and adaptation module.

[0010] Furthermore, the data analysis module includes:

[0011] Data collection: The data analysis module automatically collects data related to the carbon reduction project, including environmental data, economic data and social data. The environmental data includes the carbon emission level, energy consumption data, greenhouse gas emission type and amount in the project area. The economic data includes the initial and operating costs of the project, expected economic benefits, and return on investment. The social data includes community participation, resident satisfaction, and the creation of employment opportunities.

[0012] Data preprocessing: preprocess the collected data to ensure data quality, including data cleaning, data normalization and data integration;

[0013] Data analysis: Analyze pre-processed data to identify key trends, patterns, and insights. Analysis methods include trend analysis, correlation analysis, and predictive analysis;

[0014] Data output: Output the analysis results in the form of reports, charts or indicators to provide real-time data support for the dynamic assessment and adaptation module, including providing comprehensive data analysis reports, highlighting the potential risks and opportunities of the project, and recommending priority directions for project evaluation and funding allocation.

[0015] Furthermore, the trend analysis uses a time series analysis method to reveal the trend of data changes over time, and the calculation formula is:

[0016] Among them, MA t is the moving average at time point t, n is the time window size, X t is the observed value at time point t;

[0017] The association analysis uses the Pearson correlation coefficient to measure the linear relationship between two variables, and the calculation formula is:

[0018] Where r is the correlation coefficient, x and y are the observed values ​​of the two variables, and n is the number of observation points;

[0019] The forecast analysis uses linear regression analysis to predict future project benefits, and the calculation formula is:

[0020] Y=β0+β1X1+β2X2+…+β n X n +, where Y is the project benefit index to be predicted, X1, X2,…, X n are factors that affect project benefits, β0,β1,…,β n are model parameters and is the error term.

[0021] Furthermore, the dynamic evaluation and adaptation module includes:

[0022] Real-time data reception and preprocessing: Real-time reception of analysis results on environmental, economic and social data provided by the data analysis module, and standardization of the received data to ensure data consistency and comparability;

[0023] Apply the Weighted Comprehensive Evaluation Model (WCEM): Determine the indicators and weights for evaluating project benefits based on project goals and the results of real-time data analysis. Calculate the weighted comprehensive evaluation value of the project based on the selected indicators and weights to quantify the overall benefits of the project.

[0024] Evaluate the impact of external changes: Select the improved LSTM model to evaluate the potential impact of external environmental changes on the project. Based on real-time data and trend analysis, run the improved LSTM model to obtain prediction results about the future performance of the project.

[0025] Dynamically adjust project evaluation standards and methods: Analyze the output of the improved LSTM model, identify the impact of changes in the external environment on the project, and dynamically adjust the standards and methods of project evaluation based on the prediction results and weighted comprehensive evaluation values;

[0026] Formulate adjustment suggestions: formulate project adjustment strategies based on the results of comprehensive evaluation and forecast analysis, including resource reallocation and strategy adjustment, and use genetic algorithms (GA) to optimize the adjustment strategies and find the optimal project adjustment plan;

[0027] Implementation and feedback: Apply the optimized adjustment suggestions to project management and implementation, collect feedback on implementation results, and iterate and optimize the evaluation criteria, methods, and adjustment strategies based on actual results.

[0028] Furthermore, the weighted comprehensive evaluation model (WCEM) includes:

[0029] Determine the evaluation index system and its weights: Suppose there are m evaluation indicators, which represent different benefit dimensions of the project. i Assigned with corresponding weight w i ,satisfy

[0030] Calculate the weighted comprehensive evaluation value (V): Among them, E i ′ is the standardized value of the i-th indicator.

[0031] Furthermore, the improved LSTM model includes:

[0032] Multivariate input preparation: Set the time series data of n related factors as the input of the model, and the input X at each time point t t is a vector containing n factor values;

[0033] LSTM unit calculation: For each time step t, the state and output of the LSTM unit are calculated, including the forget gate, input gate, output gate, new memory unit and final output. The calculation formula is:

[0034] Forget gate:f t =σ(W f ·[h t-1 ,X t ]+b f );

[0035] Input gate:i t =σ(W i ·[h t-1 ,X t ]+b i );

[0036] Output gate: o t =σ(W o ·[h t-1 ,X t ]+b o );

[0037] New memory unit: c t =f t *c t-1 +i t *tanh(W c ·[h t-1 ,X t ]+b c );

[0038] Final output: h t =o t *tanh(c t );

[0039] Among them, σ is the sigmoid activation function, tanh is the hyperbolic tangent activation function, [h t-1 ,X t ] means to change the hidden state h of the previous time step t-1 and the input X at the current time step t For splicing, W f , W i , W o , W c and b f 、b i 、b o 、b c They are the weight matrix and bias vector of the forget gate, input gate, output gate and new memory unit respectively;

[0040] Application of attention mechanism: Using the output of all time steps {h1,h2,...,h T} Calculate the attention weight a for each time step t , and get the weighted hidden state The calculation formula is:

[0041] Attention weight: a t =softmax(W a ·h t +b a );

[0042] Weighted hidden state:

[0043] Among them, W a and b a are the weight matrix and bias vector of the attention layer, respectively. The softmax function is used to calculate the attention weights and weighted hidden states of each time step. is the hidden state h at all time steps t The weighted sum after adjustment of attention weights;

[0044] Predicting output: Using weighted hidden states Through another linear layer to predict future benefits, we get the predicted value The calculation formula is:

[0045] predict: Among them, W p and b p is the weight matrix and bias vector of the output layer, is the weighted hidden state at the final time step, used to predict the future benefits of the project

[0046] Further, the genetic algorithm (GA) includes:

[0047] Initialize the population: Randomly generate an initial population P, which consists of multiple individuals, each individual C k Represents a fund allocation plan, expressed as:

[0048] P={C1,C2,...,C N};

[0049] Where N is the population size, and each individual C k is a vector containing the proportion of project funding allocation;

[0050] Fitness function calculation:

[0051] Among them, E i ′(C) represents the standardized value of the i-th benefit indicator under scheme C, w i is the weight of the indicator;

[0052] choose:

[0053] Among them, P sel (C k ) represents individual C k the probability of being selected to carry on to the next generation;

[0054] Crossover: Randomly select a point as the crossover point, exchange the parts of the two parent individuals after this point, and generate a new individual, which is expressed as:

[0055]

[0056] Among them, C a1 ,C b1 is the part of the parent individual before the crossover point, C a2 ,C b2 It is the part after the intersection;

[0057] Variation: C k ′=C k ±Δ;

[0058] Among them, C k ′ is the individual after mutation, Δ is the amount of change dynamically adjusted according to the mutation rate;

[0059] Generation of a new generation of population: A new generation of population is selected based on the fitness of individuals, and individuals with high fitness are retained to form the next generation.

[0060] Furthermore, the comprehensive value assessment module includes:

[0061] Multi-dimensional evaluation: A multi-dimensional evaluation framework is used, which contains predefined benefit indicators, including environmental benefit indicators, economic benefit indicators and social benefit indicators. The environmental benefit indicators include carbon emission reduction, energy efficiency, renewable energy use and environmental quality improvement. The economic benefit indicators include cost savings, return on investment (ROI) and economic growth potential. The social benefit indicators include social participation, public satisfaction, job creation and health and safety improvement.

[0062] Weight allocation: assign weights to each benefit indicator;

[0063] Calculation of comprehensive benefit score: Calculate the comprehensive benefit score of the project based on the weighted score of each project benefit indicator. The calculation formula is: Among them, Z represents the comprehensive benefit score of the project, w r represents the weight of the rth benefit indicator, S r represents the corresponding standardized score, and q represents the total number of benefit indicators;

[0064] Sustainable development contribution assessment: Use the comprehensive benefit score Z to assess the project’s contribution to the sustainable development goals of smart cities to guide the optimal allocation of funding flows.

[0065] Furthermore, the fund flow allocation module includes:

[0066] Comprehensive benefit score utilization: The capital flow allocation module receives the comprehensive benefit score Z of each project calculated by the comprehensive value assessment module. The score Z of each project g reflects its combined contribution to the environment, economy and society;

[0067] Dynamic evaluation considerations: Consider the project adjustment suggestions made by the dynamic evaluation and adaptation module based on real-time data and market changes. g , ensuring that resource allocation can adapt to changes in the external environment and conditions;

[0068] Resource allocation decision: Through the decision-making model, according to the comprehensive benefit score Z of the project g and adjustment suggestions A g , determine the funding allocation ratio F for each project g , the allocation model is expressed as:

[0069] F g =f(Z g ,A g ,R), where F g is the proportion of funds allocated to project g, Z g is the comprehensive benefit score of the project, A g is the adjustment suggestion provided by the dynamic assessment and adaptation module, and R is the total available financial resources;

[0070] Application of optimization algorithms: Apply integer programming models to solve optimization problems in fund allocation;

[0071] Strategy execution: Based on the calculated fund allocation plan F g , allocate limited financial resources to different projects and implement funding flow strategies that are consistent with the smart city’s carbon reduction goals.

[0072] Furthermore, the integer programming model includes:

[0073] Define the decision variables: Let x g represents the number of funding units allocated to the g-th project, where g = 1, 2, …, e, and x g is a non-negative integer;

[0074] Establish objective function: The goal is to maximize the total comprehensive benefit. The objective function is defined as:

[0075] Among them, Zg is the comprehensive benefit score of the g-th project;

[0076] Build constraints:

[0077] Total funding limit: If there is a total funding B for allocation, the total funding allocation for all projects shall not exceed B, that is,

[0078] Project funding allocation range: Set the minimum and maximum funding requirements for each project, i.e. L g ≤x g ≤U g , where L g and U g are the minimum and maximum funding requirements of the g-th project, respectively;

[0079] Solve the optimization problem: find the x that maximizes Z g value that satisfies all constraints.

[0080] Beneficial effects of the present invention:

[0081] The present invention realizes the real-time collection, processing and analysis of environmental, economic and social data through the combination of data analysis module and dynamic evaluation and adaptation module. This process not only ensures that project evaluation and decision-making are based on the latest and most accurate information, but also ensures the adaptability and flexibility of project planning and execution by dynamically adjusting evaluation standards and methods. The application of the improved LSTM model further enhances the ability to predict external changes, so that project strategies can respond to market and environmental changes in a timely manner, optimize resource allocation, and improve the success rate and sustainability of projects.

[0082] The present invention uses a multi-dimensional evaluation framework of a comprehensive value assessment module to comprehensively consider the environmental benefits, economic benefits and social impacts of a project. This module not only considers the quantitative data obtained from the data analysis module, but also integrates the suggestions of the dynamic evaluation and adaptation module. By calculating the comprehensive benefit score of the project, a quantitative basis is provided for the allocation of capital flows. This comprehensive and integrated evaluation mechanism supports more accurate and efficient resource allocation decisions, ensuring that limited financial resources are allocated to projects that can bring the greatest comprehensive benefits to smart cities.

[0083] The present invention, through an integer programming model, combines the comprehensive benefit score of the project and the adjustment suggestions of the dynamic evaluation and adaptation module to intelligently determine the funding allocation ratio of each project. This process not only maximizes the total comprehensive benefit, but also ensures that the decision-making process of funding allocation meets the actual requirements of project funding allocation and can adapt to changes in project progress and external conditions. The application of integer programming algorithm to solve the optimization problem of funding allocation not only improves the scientific nature of funding allocation, but also ensures the effectiveness and sustainability of investment decisions, thereby promoting the realization of the sustainable development goals of smart cities. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0085] Figure 1 A schematic diagram of system function modules according to an embodiment of the present invention;

[0086] Figure 2 Schematic diagram of a dynamic evaluation and adaptation module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0087] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0088] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0089] like Figure 1-2 As shown, the smart city carbon emission reduction project evaluation and capital flow allocation system includes a data analysis module, a dynamic evaluation and adaptation module, a comprehensive value evaluation module and a capital flow allocation module, wherein;

[0090] The data analysis module is responsible for collecting and processing environmental, economic and social data related to carbon reduction projects. The analysis results output by the data analysis module provide real-time data support for the dynamic assessment and adaptation module, so that project assessment can be adjusted based on current environmental and market conditions;

[0091] The dynamic evaluation and adaptation module dynamically adjusts the project evaluation standards and methods based on the real-time data and analysis results provided by the data analysis module, uses the improved LSTM model to evaluate the impact of external changes on the project, and makes adjustment suggestions for the project based on the model output to ensure that the realization of the project goals is consistent with the current conditions. The dynamic adjustment suggestions of the dynamic evaluation and adaptation module further guide the decision-making process of the comprehensive value evaluation module and the capital flow allocation module;

[0092] The comprehensive value assessment module comprehensively utilizes the data obtained from the data analysis module and the adjustment suggestions from the dynamic assessment and adaptation module to comprehensively evaluate the environmental benefits, economic benefits and social impacts of the project. It uses a multi-dimensional evaluation framework to calculate the comprehensive benefit score of the project and evaluate its contribution to the sustainable development of the smart city.

[0093] The fund flow allocation module intelligently allocates limited fund resources based on the comprehensive benefit scores of the projects calculated by the comprehensive value assessment module and the adjustment suggestions put forward by the dynamic assessment and adaptation module. The fund flow allocation module gives priority to supporting projects with high comprehensive benefit scores according to the predetermined fund allocation rules, and adapts to changes in project progress and external conditions through a dynamic adjustment mechanism;

[0094] The collaborative relationship between modules ensures that the system can respond to changes in external conditions in real time, conduct effective project evaluation and optimize resource allocation, which not only improves the accuracy and efficiency of evaluation and funding allocation, but also enhances the adaptability and success rate of the project, supporting smart cities to move towards the goal of low-carbon, efficient and sustainable development.

[0095] Data analysis modules include:

[0096] Data collection: The data analysis module automatically collects data related to carbon emission reduction projects, including environmental data, economic data and social data. Environmental data includes carbon emission levels, energy consumption data, greenhouse gas emission types and amounts in the project area. Economic data includes the initial and operating costs of the project, expected economic benefits, and return on investment. Social data includes community participation, resident satisfaction, and the creation of employment opportunities.

[0097] Data preprocessing: preprocess the collected data to ensure data quality, including data cleaning, data normalization and data integration;

[0098] Data analysis: Analyze pre-processed data to identify key trends, patterns, and insights. Analysis methods include trend analysis, correlation analysis, and predictive analysis;

[0099] Data output: Output the analysis results in the form of reports, charts or indicators to provide real-time data support for the dynamic assessment and adaptation module, including providing comprehensive data analysis reports, highlighting potential risks and opportunities of the project, and recommending priority directions for project assessment and funding allocation;

[0100] The data analysis module provides a solid data foundation for the smart city carbon emission reduction project evaluation and capital flow allocation system, ensuring that project decisions are based on accurate, comprehensive and up-to-date information. It not only improves the efficiency and accuracy of project evaluation, but also enhances the system's adaptability to environmental changes and market dynamics.

[0101] Trend analysis uses time series analysis methods to reveal the trend of data changes over time. The calculation formula is:

[0102] in, is the moving average at time point t, n is the time window size, X t is the observed value at time point t;

[0103] Analyze the changes in carbon emissions, project costs and social participation indicators over time through moving averages to identify long-term project trends and support continuous improvement and adjustment of strategies;

[0104] Correlation analysis uses the Pearson correlation coefficient to measure the linear relationship between two variables. The calculation formula is:

[0105] Where r is the correlation coefficient, x and y are the observed values ​​of the two variables, and n is the number of observation points;

[0106] By calculating the correlation between investment cost and economic return, carbon emission reduction and social satisfaction, the mutual influence between key project factors is revealed, providing a basis for formulating investment strategies and optimizing projects;

[0107] Predictive analysis uses linear regression analysis to predict future project benefits, and the calculation formula is:

[0108] Y=β0+β1X1+β2X2+…+β n X n +, where Y is the project benefit index to be predicted, X1, X2,…, X n are factors that affect project benefits, β0,β1,…,β n are model parameters, and are error terms;

[0109] Use linear regression models to predict the carbon reduction effect and economic returns of projects based on historical data, provide a scientific basis for project selection and funding allocation, and predict social impacts, such as changes in resident satisfaction and employment opportunities, to evaluate the social contribution of projects;

[0110] Through the above methods, the data analysis module can effectively identify key trends, patterns and insights from the pre-processed data, providing strong data support for the evaluation and funding allocation of smart city carbon emission reduction projects, which not only enhances the accuracy and reliability of project decision-making, but also improves the efficiency of resource utilization and supports the sustainable development goals of smart cities.

[0111] The dynamic assessment and adaptation module includes:

[0112] Real-time data reception and preprocessing: Real-time reception of analysis results on environmental, economic and social data provided by the data analysis module, and standardization of the received data to ensure data consistency and comparability;

[0113] Apply the Weighted Comprehensive Evaluation Model (WCEM): Determine the indicators and weights for evaluating project benefits based on project goals and the results of real-time data analysis. Calculate the weighted comprehensive evaluation value of the project based on the selected indicators and weights to quantify the overall benefits of the project.

[0114] Evaluate the impact of external changes: Select the improved LSTM model to evaluate the potential impact of external environmental changes on the project. Based on real-time data and trend analysis, run the improved LSTM model to obtain prediction results about the future performance of the project.

[0115] Dynamically adjust project evaluation standards and methods: Analyze the output of the improved LSTM model, identify the impact that changes in the external environment may have on the project, and dynamically adjust the standards and methods of project evaluation based on the prediction results and weighted comprehensive evaluation values;

[0116] Formulate adjustment suggestions: formulate project adjustment strategies based on the results of comprehensive evaluation and forecast analysis, including resource reallocation and strategy adjustment, and use genetic algorithms (GA) to optimize the adjustment strategies and find the optimal project adjustment plan;

[0117] Implementation and feedback: Apply the optimized adjustment suggestions to project management and implementation, collect feedback on implementation results, and further iterate and optimize the evaluation criteria, methods, and adjustment strategies based on actual results;

[0118] In the smart city carbon emission reduction project evaluation and capital flow allocation system, through the weighted comprehensive evaluation model and genetic algorithm, the dynamic evaluation and adaptation module can reflect the impact of external environmental changes on the project in real time, dynamically adjust the evaluation and decision-making process, and ensure that the project implementation strategy always matches the current best practices and environmental conditions. This method improves the flexibility and adaptability of the system and enhances the project success rate.

[0119] The Weighted Comprehensive Evaluation Model (WCEM) includes:

[0120] Determine the evaluation index system and its weights: There are m evaluation indicators, which represent different benefit dimensions of the project, such as environmental benefit E1, economic benefit E2, and social impact E3. i Assigned with corresponding weight w i ,satisfy

[0121] Calculate the weighted comprehensive evaluation value (V): Among them, E i ′ is the standardized value of the i-th indicator;

[0122] The application of WCEM not only improves the efficiency and effectiveness of smart city carbon emission reduction project evaluation and capital flow allocation, but also provides strong support for the sustainable development of smart cities by ensuring the scientificity, comprehensiveness and adaptability of project decision-making and resource allocation.

[0123] The improved LSTM model includes:

[0124] Multivariate input preparation: Set the time series data of n related factors as the input of the model, and the input X at each time point t t is a vector containing n factor values;

[0125] LSTM unit calculation: For each time step t, the state and output of the LSTM unit are calculated, including the forget gate, input gate, output gate, new memory unit and final output. The calculation formula is:

[0126] Forget gate:f t =σ(W f ·[h t-1 ,X t ]+b f );

[0127] Input gate:i t =σ(W i ·[h t-1 ,X t ]+b i );

[0128] Output gate: o t =σ(Wo ·[h t-1 ,X t ]+b o );

[0129] New memory unit: c t =f t *c t-1 +i t *tanh(W c ·[h t-1 ,X t ]+b c );

[0130] Final output: h t =o t *tanh(c t );

[0131] Among them, σ is the sigmoid activation function, tanh is the hyperbolic tangent activation function, [h t-1 ,X t ] means to change the hidden state h of the previous time step t-1 and the input X at the current time step t For splicing, W f , W i , W o , W c and b f 、b i 、b o 、b c They are the weight matrix and bias vector of the forget gate, input gate, output gate and new memory unit respectively;

[0132] Application of attention mechanism: Using the output of all time steps {h1,h2,...,h T} Calculate the attention weight a for each time step t , and get the weighted hidden state The calculation formula is:

[0133] Attention weight: a t =softmax(W a ·h t +b a );

[0134] Weighted hidden state:

[0135] Among them, W a and b a They are the weight matrix and bias vector of the attention layer. The softmax function is used to calculate the attention weight of each time step, so that the model can pay attention to more important time steps and weighted hidden states. is the hidden state h at all time steps t The weighted sum after adjustment of attention weights;

[0136] Predicting output: Using weighted hidden states Through another linear layer to predict future benefits, we get the predicted value The calculation formula is:

[0137] predict: Among them, W p and b p is the weight matrix and bias vector of the output layer, is the weighted hidden state at the final time step, used to predict the future benefits of the project

[0138] The improved LSTM model provides a powerful prediction tool for smart city carbon emission reduction projects through the attention mechanism and multi-variable input processing. The attention mechanism enables the model to automatically identify and focus on the time periods or factors that have the greatest impact on future project benefits, while the multi-variable input can comprehensively consider various relevant external factors, which not only improves the accuracy of the prediction, but also makes project evaluation and funding flow allocation more in line with actual conditions and future expectations, thereby optimizing resource allocation and enhancing the sustainability and success rate of the project.

[0139] Genetic Algorithms (GA) include:

[0140] Initialize the population: Randomly generate an initial population P, which consists of multiple individuals, each individual C k Represents a fund allocation plan, expressed as:

[0141] P={C1,C2,...,C N};

[0142] Where N is the population size, and each individual C k is a vector containing the proportion of project funding allocation;

[0143] Fitness function calculation:

[0144] Among them, E i ′(C) represents the standardized value of the i-th benefit indicator under scheme C, w i is the weight of the indicator;

[0145] choose:

[0146] Among them, P sel (C k ) represents individual C k the probability of being selected to carry on to the next generation;

[0147] Crossover: Randomly select a point as the crossover point, exchange the parts of the two parent individuals after this point, and generate a new individual, which is expressed as:

[0148]

[0149] Among them, C a1 ,C b1 is the part of the parent individual before the crossover point, C a2 ,C b2 It is the part after the intersection;

[0150] Variation: C k ′=C k ±Δ;

[0151] Among them, C k ′ is the individual after mutation, Δ is the amount of change dynamically adjusted according to the mutation rate;

[0152] Generation of a new generation of population: Select a new generation of population based on the fitness of individuals, and retain individuals with high fitness to form the next generation;

[0153] In the present invention, a genetic algorithm (GA) is used to optimize the funding allocation plan for smart city carbon emission reduction projects. The benefits of each funding allocation plan are comprehensively evaluated through a fitness function, and the optimal allocation strategy is iteratively found through selection, crossover and mutation operations, aiming to maximize the comprehensive benefits of the project. It can effectively improve the scientific nature of funding allocation and the performance of project implementation, while enhancing the system's adaptability and flexibility to environmental changes, and promoting the realization of carbon emission reduction and sustainable development goals.

[0154] The comprehensive value assessment module includes:

[0155] Multi-dimensional evaluation: A multi-dimensional evaluation framework is used, which contains predefined benefit indicators, including environmental benefit indicators, economic benefit indicators and social benefit indicators. Environmental benefit indicators include carbon emission reduction, energy efficiency, renewable energy use and environmental quality improvement. Economic benefit indicators include cost savings, return on investment (ROI) and economic growth potential. Social benefit indicators include social participation, public satisfaction, job creation and health and safety improvement.

[0156] Weight allocation: Assign weights to each benefit indicator, which reflects the relative importance of each indicator in contributing to the sustainable development of smart cities;

[0157] Calculation of comprehensive benefit score: Calculate the comprehensive benefit score of the project based on the weighted score of each project benefit indicator. The calculation formula is: Among them, Z represents the comprehensive benefit score of the project, wr represents the weight of the rth benefit indicator, S r represents the corresponding standardized score, and q represents the total number of benefit indicators;

[0158] Sustainable development contribution assessment: Use the comprehensive benefit score Z to assess the project's contribution to the sustainable development goals of smart cities to guide the optimal allocation of funding flows;

[0159] Through the operation of the comprehensive value assessment module, it can be ensured that the evaluation results of the project fully reflect the environmental, economic and social benefits of the project, and the evaluation can be adjusted in a timely manner according to real-time data and dynamic market conditions. The comprehensive evaluation mechanism supports decision makers to optimize resource allocation and improve the efficiency of fund use, while promoting the environmental sustainability and social well-being of smart cities.

[0160] The fund flow allocation module includes:

[0161] Comprehensive benefit score utilization: The capital flow allocation module receives the comprehensive benefit score Z of each project calculated by the comprehensive value assessment module. The score Z of each project g reflects its combined contribution to the environment, economy and society;

[0162] Dynamic evaluation considerations: Consider the project adjustment suggestions made by the dynamic evaluation and adaptation module based on real-time data and market changes. g , ensuring that resource allocation can adapt to changes in the external environment and conditions;

[0163] Resource allocation decision: Through the decision-making model, according to the comprehensive benefit score Z of the project g and adjustment suggestions A g , determine the funding allocation ratio F for each project g , the allocation model is expressed as:

[0164] F g =f(Z g ,A g ,R), where F g is the proportion of funds allocated to project g, Z g is the comprehensive benefit score of the project, A g is the adjustment suggestion provided by the dynamic assessment and adaptation module, and R is the total available financial resources;

[0165] Application of optimization algorithms: Apply integer programming models to solve optimization problems of capital allocation to maximize overall benefits or meet other strategic goals;

[0166] Strategy execution: Based on the calculated fund allocation plan F g , allocate limited financial resources to different projects and implement funding flow strategies that are consistent with smart city carbon reduction goals;

[0167] The funding flow allocation module ensures that funding allocation takes into account the overall value and adaptability of the project, allowing decision makers to make data-driven, strategic resource allocation decisions under changing conditions, thereby promoting the sustainable development of smart cities.

[0168] Integer programming models include:

[0169] Define the decision variables: Let x g represents the number of funding units allocated to the g-th project, where g = 1, 2, …, e, and x g is a non-negative integer;

[0170] Establish objective function: The goal is to maximize the total comprehensive benefit. The objective function is defined as:

[0171] Among them, Z g is the comprehensive benefit score of the g-th project;

[0172] Build constraints:

[0173] Total funding limit: If there is a total funding B for allocation, the total funding allocation for all projects shall not exceed B, that is,

[0174] Project funding allocation range: Set the minimum and maximum funding requirements for each project, i.e. L g ≤x g ≤U g , where L g and U g are the minimum and maximum funding requirements of the g-th project, respectively;

[0175] Solve the optimization problem: find the x that maximizes Z g The value of satisfies all constraints;

[0176] In the smart city carbon emission reduction project evaluation and funding flow allocation system, integer programming algorithms can be used to intelligently allocate limited financial resources so as to select a project combination that can bring the greatest comprehensive benefits to the city from a group of potential projects. By maximizing the objective function of total comprehensive benefits and considering funding limitations and other strategic constraints, integer programming ensures that the funding allocation decision is optimal and meets the actual requirements of project funding allocation.

[0177] This method helps decision makers to support those projects with the greatest comprehensive benefits in a quantitative and optimized manner within budget constraints, thereby supporting the strategic goal of sustainable development of smart cities.

[0178] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present invention is limited to these examples. Under the concept of the present invention, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the present invention as described above, which are not provided in detail for the sake of simplicity.

[0179] The present invention is intended to cover all such substitutions, modifications and variations that fall within the broad scope of the claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. Smart city carbon emission reduction project evaluation and capital flow allocation system, characterized by: It includes data analysis module, dynamic assessment and adaptation module, comprehensive value assessment module and capital flow allocation module, among which; The data analysis module is responsible for collecting and processing environmental, economic and social data related to carbon emission reduction projects; The dynamic evaluation and adaptation module dynamically adjusts the project evaluation criteria and methods based on the real-time data and analysis results provided by the data analysis module, uses the improved LSTM model to evaluate the impact of external changes on the project, and makes adjustment suggestions to the project based on the model output to ensure that the realization of the project goals is consistent with the current conditions; The comprehensive value assessment module comprehensively utilizes the data obtained from the data analysis module and the adjustment suggestions of the dynamic assessment and adaptation module to comprehensively evaluate the environmental benefits, economic benefits and social impacts of the project, and adopts a multi-dimensional evaluation framework to calculate the comprehensive benefit score of the project and evaluate its contribution to the sustainable development of the smart city; The fund flow allocation module intelligently allocates limited fund resources according to the comprehensive project benefit score calculated by the comprehensive value assessment module and the adjustment suggestions put forward by the dynamic assessment and adaptation module.

2. The smart city carbon emission reduction project evaluation and capital flow allocation system according to claim 1 is characterized in that: The data analysis module includes: Data collection: The data analysis module automatically collects data related to the carbon reduction project, including environmental data, economic data and social data. The environmental data includes the carbon emission level, energy consumption data, greenhouse gas emission type and amount in the project area. The economic data includes the initial and operating costs of the project, expected economic benefits, and return on investment. The social data includes community participation, resident satisfaction, and the creation of employment opportunities. Data preprocessing: preprocess the collected data to ensure data quality, including data cleaning, data normalization and data integration; Data analysis: Analyze pre-processed data to identify key trends, patterns, and insights. Analysis methods include trend analysis, correlation analysis, and predictive analysis; Data output: Output the analysis results in the form of reports, charts or indicators to provide real-time data support for the dynamic assessment and adaptation module, including providing comprehensive data analysis reports, highlighting the potential risks and opportunities of the project, and recommending priority directions for project evaluation and funding allocation.

3. The smart city carbon emission reduction project evaluation and capital flow allocation system according to claim 2 is characterized in that: The trend analysis uses time series analysis methods to reveal the trend of data changes over time. The calculation formula is: Among them, MA t is the moving average at time point t, n is the time window size, X t is the observed value at time point t; The association analysis uses the Pearson correlation coefficient to measure the linear relationship between two variables, and the calculation formula is: Where r is the correlation coefficient, x and y are the observed values ​​of the two variables, and n is the number of observation points; The forecast analysis uses linear regression analysis to predict future project benefits, and the calculation formula is: Y=β0+β1X1+β2X2+…+β n X n +, where Y is the project benefit index to be predicted, X1, X2,…, X n are factors that affect project benefits, β0,β1,…,β n are model parameters and is the error term.

4. The smart city carbon emission reduction project evaluation and capital flow allocation system according to claim 3 is characterized in that: The dynamic assessment and adaptation module includes: Real-time data reception and preprocessing: Real-time reception of analysis results on environmental, economic and social data provided by the data analysis module, and standardization of the received data to ensure data consistency and comparability; Apply the weighted comprehensive evaluation model: determine the indicators and weights for evaluating project benefits based on project goals and the results of real-time data analysis, and calculate the weighted comprehensive evaluation value of the project based on the selected indicators and weights to quantify the overall benefits of the project; Evaluate the impact of external changes: Select the improved LSTM model to evaluate the potential impact of external environmental changes on the project. Based on real-time data and trend analysis, run the improved LSTM model to obtain prediction results about the future performance of the project. Dynamically adjust project evaluation standards and methods: Analyze the output of the improved LSTM model, identify the impact of changes in the external environment on the project, and dynamically adjust the standards and methods of project evaluation based on the prediction results and weighted comprehensive evaluation values; Formulate adjustment suggestions: formulate project adjustment strategies based on the results of comprehensive evaluation and forecast analysis, including resource reallocation and strategy adjustment, and use genetic algorithms to optimize the adjustment strategies and find the best project adjustment plan; Implementation and feedback: Apply the optimized adjustment suggestions to project management and implementation, collect feedback on implementation results, and iterate and optimize the evaluation criteria, methods, and adjustment strategies based on actual results.

5. The smart city carbon emission reduction project evaluation and capital flow allocation system according to claim 4 is characterized in that: The weighted comprehensive evaluation model includes: Determine the evaluation index system and its weights: Suppose there are m evaluation indicators, which represent different benefit dimensions of the project. i Assigned with corresponding weight w i ,satisfy Calculate the weighted comprehensive evaluation value V: Among them, E i ′ is the standardized value of the i-th indicator.

6. The smart city carbon emission reduction project evaluation and capital flow allocation system according to claim 5 is characterized in that: The improved LSTM model includes: Multivariate input preparation: Set the time series data of n related factors as the input of the model, and the input X at each time point t t is a vector containing n factor values; LSTM unit calculation: For each time step t, the state and output of the LSTM unit are calculated, including the forget gate, input gate, output gate, new memory unit and final output. The calculation formula is: Forget gate:f t =σ(W f ·[h t-1 ,X t ]+b f ); Input gate:i t =σ(W i ·[h t-1 ,X t ]+b i ); Output gate: o t =σ(W o ·[h t-1 ,X t ]+b o ); New memory unit: c t =f t *c t-1 +i t *tanh(W c ·[h t-1 ,X t ]+b c ); Final output: h t =o t *tanh(c t ); Among them, σ is the sigmoid activation function, tanh is the hyperbolic tangent activation function, [h t-1 ,X t ] means to change the hidden state h of the previous time step t-1 and the input X at the current time step t For splicing, W f , W i , W o , W c and b f , b i , b o , b c They are the weight matrix and bias vector of the forget gate, input gate, output gate and new memory unit respectively; Application of attention mechanism: Using the output of all time steps {h1,h2,...,h T } Calculate the attention weight a for each time step t , and get the weighted hidden state The calculation formula is: Attention weight: a t =softmax(W a ·h t +b a ); Weighted hidden state: Among them, W a and b a are the weight matrix and bias vector of the attention layer, respectively. The softmax function is used to calculate the attention weights and weighted hidden states of each time step. is the hidden state h at all time steps t The weighted sum after adjustment of attention weights; Predicting output: Using weighted hidden states Through another linear layer to predict future benefits, we get the predicted value The calculation formula is: predict: Among them, W p and b p is the weight matrix and bias vector of the output layer, is the weighted hidden state at the final time step, used to predict the future benefit Y of the project.

7. The smart city carbon emission reduction project evaluation and capital flow allocation system according to claim 6 is characterized in that: The genetic algorithm comprises: Initialize the population: Randomly generate an initial population P, which consists of multiple individuals, each individual C k Represents a fund allocation plan, expressed as: P={C1,C2,...,C N }; Where N is the population size, and each individual C k is a vector containing the proportion of project funding allocation; Fitness function calculation: Among them, E i ′(C) represents the standardized value of the i-th benefit indicator under scheme C, w i is the weight of the indicator; choose: Among them, P sel (C k ) represents individual C k the probability of being selected to carry on to the next generation; Crossover: Randomly select a point as the crossover point, exchange the parts of the two parent individuals after this point, and generate a new individual, which is expressed as: C new1 =[C a1 ,C b2 ],C new2 =[C b1 ,C a2 ]; Among them, C a1 ,C b1 is the part of the parent individual before the crossover point, C a2 ,C b2 It is the part after the intersection; Variation: C k ′=C k ±Δ; Among them, C k ′ is the individual after mutation, Δ is the amount of change dynamically adjusted according to the mutation rate; Generation of a new generation of population: A new generation of population is selected based on the fitness of individuals, and individuals with high fitness are retained to form the next generation.

8. The smart city carbon emission reduction project evaluation and capital flow allocation system according to claim 7 is characterized in that: The comprehensive value assessment module includes: Multi-dimensional evaluation: A multi-dimensional evaluation framework is used, which contains predefined benefit indicators, including environmental benefit indicators, economic benefit indicators and social benefit indicators. The environmental benefit indicators include carbon emission reduction, energy efficiency, renewable energy use and environmental quality improvement. The economic benefit indicators include cost savings, return on investment and economic growth potential. The social benefit indicators include social participation, public satisfaction, job creation and health and safety improvement. Weight allocation: assign weights to each benefit indicator; Calculation of comprehensive benefit score: Calculate the comprehensive benefit score of the project based on the weighted score of each project benefit indicator. The calculation formula is: Among them, Z represents the comprehensive benefit score of the project, w r represents the weight of the rth benefit indicator, S r represents the corresponding standardized score, and q represents the total number of benefit indicators; Sustainable development contribution assessment: Use the comprehensive benefit score Z to assess the project’s contribution to the sustainable development goals of smart cities to guide the optimal allocation of funding flows.

9. The smart city carbon emission reduction project evaluation and capital flow allocation system according to claim 8, characterized in that: The fund flow allocation module includes: Comprehensive benefit score utilization: The capital flow allocation module receives the comprehensive benefit score Z of each project calculated by the comprehensive value assessment module. The score Z of each project g reflects its combined contribution to the environment, economy and society; Dynamic evaluation considerations: Consider the project adjustment suggestions made by the dynamic evaluation and adaptation module based on real-time data and market changes. g , ensuring that resource allocation can adapt to changes in the external environment and conditions; Resource allocation decision: Through the decision-making model, according to the comprehensive benefit score Z of the project g and adjustment suggestions A g Determine the funding allocation ratio for each project F g , the allocation model is expressed as: F g =f(Z g ,A g ,R), where F g is the proportion of funds allocated to project g, Z g is the comprehensive benefit score of the project, A g is the adjustment suggestion provided by the dynamic assessment and adaptation module, and R is the total available financial resources; Application of optimization algorithms: Apply integer programming models to solve optimization problems in fund allocation; Strategy execution: Based on the calculated fund allocation plan F g , allocate limited financial resources to different projects and implement funding flow strategies that are consistent with the smart city’s carbon reduction goals.

10. The smart city carbon emission reduction project evaluation and capital flow allocation system according to claim 9, characterized in that: The integer programming model includes: Define the decision variables: Let x g represents the number of funding units allocated to the g-th project, where g = 1, 2, …, e, and x g is a non-negative integer; Establish objective function: The goal is to maximize the total comprehensive benefit. The objective function is defined as: Among them, Z g is the comprehensive benefit score of the g-th project; Build constraints: Total funding limit: If there is a total funding B for allocation, the total funding allocation for all projects shall not exceed B, that is, Project funding allocation range: Set the minimum and maximum funding requirements for each project, i.e. L g ≤x g ≤U g , where L g and U g are the minimum and maximum funding requirements of the g-th project, respectively; Solve the optimization problem: find the x that maximizes Z g value that satisfies all constraints.

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