City planning effect evaluation method and system based on carbon emission

Abstract

The invention discloses an urban planning effect evaluation method and system based on carbon emission, and the method comprises the steps: dividing urban space units, and constructing a first space unit division matrix in combination with information data; obtaining a real-time data score, and adjusting the space unit to obtain space adjustment efficiency; updating the space units based on the space adjustment efficiency to obtain a second space unit division matrix; based on the upstream and downstream industry chains, constructing a directed weighted graph, and obtaining an importance sequence through a link analysis algorithm; in combination with a multi-criterion decision analysis method, determining comprehensive weights of carbon emission indexes, and judging key nodes of an industrial chain; a low-carbon technology is introduced, the industrial chain node dependency degree is adjusted, carbon emission is optimized, and evaluation data is obtained. According to the method, the economic and carbon emission characteristics of each block of the city in different development stages are considered, the space units are flexibly allocated by using the space adjustment rate, the industrial chain layout can be automatically adjusted along with the change of data, and the implementation efficiency of the carbon reduction policy is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of urban planning, and in particular to a city planning effect evaluation method and system based on carbon emissions. BACKGROUND

[0002] Currently, urban planning methods mainly focus on optimizing spatial layout and functional zoning, aiming to improve the livability and operational efficiency of cities through rational land use and infrastructure construction. Traditional urban planning focuses on the organization of physical space, such as the division of different functional areas such as residential areas, commercial areas, and industrial areas, to ensure that each part of the city can operate efficiently and meet the needs of residents.

[0003] However, the consideration of specific environmental impacts at the micro level, especially key environmental factors such as carbon emissions and energy consumption, is relatively insufficient. Existing technologies still have certain limitations in comprehensive analysis of environmental benefits and economic benefits in complex urban structures. For different entity industries, it is necessary to consider the type of industry and cannot adopt a unified carbon emission standard for planning and requirements. In addition, for industries with serious carbon emissions, the overall benefits of upstream and downstream may reduce the efficiency of carbon reduction policies. Secondly, existing evaluation tools and technologies often focus on static analysis and are difficult to accurately reflect the dynamic changes of carbon emissions in different parts of the city. Therefore, simple emission reduction measures are difficult to accurately reflect the changes and effectiveness of carbon emissions in different parts of the city. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a city planning effect evaluation method and system based on carbon emissions to solve the problem that existing technologies still have certain limitations in comprehensive analysis of environmental benefits and economic benefits in complex urban structures. For different entity industries, it is necessary to consider the type of industry and cannot adopt a unified carbon emission standard for planning and requirements. In addition, for industries with serious carbon emissions, the overall benefits of upstream and downstream may reduce the efficiency of carbon reduction measures.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides a city planning effect evaluation method based on carbon emissions, comprising: dividing a city area by a spatial unit, and constructing a first spatial unit division matrix based on spatial unit information data;

[0008] Based on the first spatial unit division matrix, real-time data is obtained for scoring, and the spatial unit is adjusted based on the scoring results to obtain a spatial adjustment efficiency;

[0009] updating the space unit based on the space adjustment efficiency, to obtain a second space unit division matrix;

[0010] Based on the upstream and downstream industrial chain of the second space unit division matrix, a directed weighted graph is constructed, and the importance ranking is obtained through a link analysis algorithm;

[0011] Based on the importance ranking, the comprehensive weight of the carbon emission index is determined by combining the multi-criteria decision analysis method, and the key nodes of the industrial chain are determined by combining the link analysis algorithm result;

[0012] Introduce low-carbon technology at the key nodes, and optimize carbon emissions by adjusting the dependence of the nodes of the industrial chain to obtain evaluation data.

[0013] As a preferred scheme of the city planning effect evaluation method based on carbon emission, wherein: the city area is divided into space units, and a first space unit division matrix is constructed by combining space unit information data, including:

[0014] The city area is divided into different basic space units based on the existing infrastructure and transportation network of the city;

[0015] The space unit information data is obtained based on the basic space unit, including the development time stage, economic benefit and carbon emission potential value;

[0016] The first space unit division matrix is obtained by reclassifying the matrix based on the functional attributes of the basic space unit and the space unit information data.

[0017] As a preferred scheme of the city planning effect evaluation method based on carbon emission, wherein: real-time data is obtained based on the first space unit division matrix for scoring, and the space unit is adjusted based on the scoring result, including:

[0018] The first score of each space unit in the first space unit division matrix is obtained by comprehensively scoring the energy consumption, carbon emission data and traffic flow in each space unit in the first space unit division matrix;

[0019] The basic space unit is divided into different functional space units based on the functional attributes, and the sum of the average comprehensive score and the standard deviation of different functional space units is calculated to obtain the score threshold;

[0020] Based on the functional attributes of the first space unit division matrix, the first score of each space unit is compared with the score threshold of the corresponding functional space unit, and if the first score is less than the score threshold, the corresponding space unit in the first space unit division matrix is adjusted.

[0021] As a preferred scheme of the city planning effect evaluation method based on carbon emission, wherein the space adjustment efficiency comprises:

[0022] The space unit after the area adjustment and the carbon reduction is scored comprehensively in a season as an adjustment period;

[0023] The difference between the current comprehensive score in each space unit adjustment period and the comprehensive score at the end of the last period is calculated, and the average difference is calculated as an optimization adjustment value;

[0024] The time required for the adjustment of different space units is counted,

[0025] The ratio of the optimization adjustment value to the time required for the adjustment is the space adjustment efficiency of the unit.

[0026] As a preferred scheme of the city planning effect evaluation method based on carbon emission, wherein the space adjustment efficiency is updated based on the space unit, and a second space unit division matrix is obtained, comprising:

[0027] The adjustment threshold and the efficiency threshold are set;

[0028] If the optimization adjustment value is less than the adjustment threshold or the space adjustment efficiency is less than the efficiency threshold, the space adjustment update is re-performed;

[0029] If the optimization adjustment value is greater than the adjustment threshold and the space adjustment efficiency is greater than the efficiency threshold, the update is stopped, and the second space unit division matrix is obtained.

[0030] As a preferred scheme of the city planning effect evaluation method based on carbon emission, wherein the directed weighted graph is constructed, and the importance ranking is obtained by a link analysis algorithm, comprising:

[0031] Based on the industrial unit in each industrial chain, the node is set, based on the supply relationship between two industries, the edge is set, and based on the dependence of the downstream industry on the upstream industry and the carbon emission intensity of the upstream industry, the weight of the edge is set;

[0032] The weighted graph is analyzed by the PageRank algorithm, and the industrial PageRank value is calculated;

[0033] Based on the PageRank value, the importance ranking of the industrial chain nodes in each space unit is performed.

[0034] As a preferred scheme of the city planning effect evaluation method based on carbon emission, wherein based on the importance ranking, the comprehensive weight of the carbon emission index is determined by combining the multi-criteria decision analysis method, and the key nodes of the industrial chain are determined by combining the link analysis algorithm result, comprising:

[0035] Based on the importance ranking, the spatial units with the same industrial chain node and within the ranking range are obtained;

[0036] The subjective weight of each spatial unit of the same industrial chain is determined by the fuzzy weighting method;

[0037] The spatial units with the same industrial chain are taken as upstream and downstream nodes, and the correlation degree of each upstream and downstream node is defined according to the upstream and downstream relationship;

[0038] Based on the correlation degree and carbon emission of each upstream and downstream node, the carbon emission index is normalized and the correlation matrix is constructed;

[0039] The entropy value of each spatial unit in the correlation matrix is calculated by the entropy weight method to obtain the objective weight of each spatial unit;

[0040] The comprehensive weight value of the carbon emission index of each spatial unit is obtained by combining the subjective and objective weights;

[0041] The PR threshold and the weight threshold are set, and if the industrial PageRank value and the comprehensive weight value of the spatial unit are both greater than the set two thresholds, the spatial unit is a key node.

[0042] In the second aspect, the present application provides a city planning effect evaluation system based on carbon emission, comprising:

[0043] A first spatial division module is used for dividing the city area by spatial units, and a first spatial unit division matrix is constructed by combining spatial unit information data;

[0044] A score adjustment module is used for obtaining real-time data for scoring based on the first spatial unit division matrix, and adjusting the spatial unit based on the scoring result to obtain a spatial adjustment efficiency;

[0045] A second spatial division module is used for updating the spatial unit based on the spatial adjustment efficiency to obtain a second spatial unit division matrix;

[0046] An ordering module is used for constructing a directed weighted graph based on the upstream and downstream industrial chains of the second spatial unit division matrix, and obtaining an importance ranking by a link analysis algorithm;

[0047] A key node identification module is used for determining the comprehensive weight of the carbon emission index based on the importance ranking and the multi-criteria decision analysis method, and judging the key node of the industrial chain in combination with the link analysis algorithm result;

[0048] A low-carbon adjustment module is used for introducing low-carbon technology at the key node and optimizing carbon emission by adjusting the dependence of the industrial chain node to obtain evaluation data.

[0049] In a third aspect, the present application provides an electronic device comprising:

[0050] a memory and a processor;

[0051] The memory is configured to store computer-executable instructions, and the processor is configured to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the method for evaluating the effect of urban planning based on carbon emissions.

[0052] In a fourth aspect, the present application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for evaluating the effect of urban planning based on carbon emissions.

[0053] Compared with the prior art, the present application has the following beneficial effects: by analyzing the functional attributes of the city and different development stages, considering the economic performance and carbon emission characteristics of each block of the city at different development stages, using spatial adjustment rate to flexibly allocate the city space unit, and combining the fuzzy weighting method and the entropy weighting method to calculate the weight, and combining the dynamic adjustment mechanism of PageRank analysis, the present application can automatically adjust and optimize the industrial chain layout according to the changes of data, provide basis for analyzing the key degree of carbon emissions from the perspective of industrial chain, provide basis for the adjustment of the industry, and improve the implementation efficiency of the carbon reduction policy. BRIEF DESCRIPTION OF DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0055] Figure 1 The overall flowchart of the method for evaluating the effect of urban planning based on carbon emissions according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0056] In order to make the above-mentioned objects, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0057] Embodiment 1

[0058] Reference Figure 1For an embodiment of the present application, a carbon emission-based urban planning effect evaluation method is provided, comprising:

[0059] S100: dividing the urban area by spatial units, combining spatial unit information data, and constructing a first spatial unit division matrix;

[0060] S200: scoring based on real-time data obtained from the first spatial unit division matrix, adjusting the spatial units based on the scoring results to obtain spatial adjustment efficiency;

[0061] S300: updating the spatial units based on the spatial adjustment efficiency to obtain a second spatial unit division matrix;

[0062] S400: constructing a directed weighted graph based on the upstream and downstream industrial chains of the second spatial unit division matrix, and obtaining importance ranking through link analysis algorithm;

[0063] S500: determining the comprehensive weight of the carbon emission index based on the importance ranking and combining the multi-criteria decision analysis method, and combining the link analysis algorithm results to determine the key nodes of the industrial chain;

[0064] S600: introducing low-carbon technology at the key nodes and optimizing carbon emissions by adjusting the dependency of the industrial chain nodes to obtain evaluation data.

[0065] It should be noted that traditional urban planning still focuses on macro-level spatial structure design to a large extent, and the consideration of specific environmental impact, especially key environmental factors such as carbon emissions and energy consumption, is relatively insufficient. Such planning mode can promote economic development to a certain extent, and urban planning needs to change to a more comprehensive direction, not only optimizing the spatial layout, but also going deep into each link of industrial activities and comprehensively considering its impact on the environment.

[0066] For different entities, due to the diversity of industry types and distinct characteristics of each, it is impossible to adopt a unified carbon emission standard for planning and requirements; for example, the carbon emission sources and intensity of different types of industries such as manufacturing, service and agriculture are very different in the production process; and carbon emission industries are often closely connected with upstream and downstream industrial chains, and any single link change may trigger a chain reaction, affecting the stability and competitiveness of the entire industrial chain. Existing evaluation techniques often focus on static analysis, lacking effective capture of dynamic evolution of urban systems such as population flow, economic activity, and environmental conditions. The interaction between these factors is dynamic and nonlinear, and the development and implementation of carbon reduction measures are often based on static assumptions, ignoring the uncertainty brought by the change of various elements within the city over time, thereby reducing the actual effect and efficiency of policy implementation.

[0067] Therefore, the application proposes a planning mechanism based on carbon emission indicators for urban regional space based on the steps S100-S600, obtains real-time effective data in a time period, performs secondary adjustment planning on the traditional space unit, performs comprehensive evaluation on the adjusted space unit based on the connection between the industrial chains to determine the core industrial chain nodes, and implements carbon emission measures on the key nodes to improve the carbon emission efficiency.

[0068] Embodiment 2

[0069] With reference to Figure 1 For an embodiment of the application, based on the above embodiment, a carbon emission-based urban planning effect evaluation method is provided.

[0070] In the embodiment of the application, the city area is divided into space units in step S100, and a first space unit division matrix is constructed in combination with the space unit information data, including the following steps A1-A3.

[0071] A1: dividing the city area into different basic space units based on the existing infrastructure and traffic network of the city;

[0072] In an implementable manner, in A1, the infrastructure and traffic network can be obtained by using geographic information system (GIS) technology, urban planning data, or remote sensing technology, etc., to preliminarily divide the city into space units;

[0073] A2: obtaining space unit information data based on the basic space units, including development time stage, economic benefit, and carbon emission potential value;

[0074] In the embodiment of the application, the development stage can be distinguished according to the construction period. For example, if the construction period is less than or equal to 2 years, it is set as an experimental node, if the construction period is greater than 2 years and less than or equal to 5 years, it is set as an engineering stage, and if the construction period is greater than 5 years, it is set as an industrial stage.

[0075] In an optional implementation manner, the development stage can also be distinguished according to the land development intensity, such as building density and height or infrastructure completeness.

[0076] In the embodiment of the application, the economic benefit can be calculated based on the return on investment (ROI). The return on investment is calculated by subtracting the total investment cost and operating cost from the annual tax revenue of the basic space unit and then dividing the total investment cost.

[0077] In an optional implementation manner, the economic benefit can also be calculated according to the employment creation and per capita output value, such as the number of employment positions or per capita GDP.

[0078] In the implementation manner of the present application, the carbon emission potential can be calculated according to the current carbon emission and the proportion of renewable energy use of the block, by subtracting the expected carbon emission after energy saving measures from the current carbon emission of the basic space unit, and then dividing the result by the current carbon emission, to obtain the carbon emission potential value.

[0079] In an alternative implementation manner, the carbon emission potential can also be calculated according to the energy structure optimization potential, such as the carbon emission reduction amount caused by the increase of the proportion of renewable energy or the improvement of energy efficiency.

[0080] A3: Based on the functional attributes of the basic space unit and the space unit information data, a first space unit division matrix is obtained through reclassification matrix division.

[0081] It should be noted that by analyzing the data of different development stages of the city, the economic performance and carbon emission characteristics of each block at different development stages are obtained, which facilitates the analysis of the carbon emission potential of blocks with certain functional attributes at a certain stage, so as to formulate differentiated management strategies; through the multi-dimensional reclassification matrix, the characteristics of each block can be more accurately identified and evaluated to predict future development trends.

[0082] In the implementation manner of the present application, the real-time data is scored based on the first space unit division matrix in step S200, and the space unit is adjusted based on the scoring result, including the following steps B1-B3:

[0083] B1: Based on the energy consumption, carbon emission data and traffic flow in each space unit in the first space unit division matrix, a comprehensive score of each space unit in the first space unit division matrix is obtained.

[0084] In the implementation manner of the present application, the first score is expressed as:

[0085] ;

[0086] Wherein, represents the comprehensive score of the i th unit at time t, represents the energy consumption of the i th unit at time t, represents the traffic flow of the i th unit at time t, represents the carbon emission of the i th unit at time t, represents the area of the i th unit, represents the area of the i th unit, represents the area of the i th unit, represents the area of the i th unit, represents the area of the i th unit, represents the area of the i th unit, represents the area of the i th unit, represents the area of the i th unit, represents the area of the i th unit, represents the area of the i th unit, represents the area of the i th unit, the population density of the i-th unit at time t; the GDP of the i-th unit at time t; the average comprehensive score of the i-th unit at time t; the average comprehensive score of the i-th unit at time t; the average comprehensive score of the i-th unit at time t;

[0087] In an alternative embodiment, the comprehensive score in B1 can also be calculated by combining the environmental quality with the above parameters.

[0088] In another alternative embodiment, the comprehensive score in B1 can also be calculated by combining the infrastructure completeness with the above parameters.

[0089] B2: Divide the basic spatial units into different functional spatial units based on the functional attributes, and calculate the sum of the average comprehensive score and the standard deviation of each functional spatial unit to obtain a score threshold;

[0090] B3: Based on the functional attributes of the first spatial unit division matrix, compare the first score of each spatial unit with the score threshold of the corresponding functional spatial unit. If the first score is less than the score threshold, adjust the corresponding spatial unit in the first spatial unit division matrix.

[0091] In the embodiments of the present application, the adjustment in B3 is to adjust the area of the corresponding spatial unit, which can be represented as:

[0092] ;

[0093] wherein represents the adjustment area of the i-th unit at time t;

[0094] In an alternative embodiment, the adjustment can also adjust the functional area definition. If the functional attributes of a certain spatial unit do not match its score result, it can be considered to redefine its main function, for example: to convert part of the industrial area into commercial or residential area.

[0095] ​​It should be noted that by combining the comprehensive score calculated by combining energy consumption, traffic flow, carbon emissions, and key parameters such as area, population density, and economic exposition, the calculation of each index considers its corresponding unit area or density, ensuring the fairness and rationality of the evaluation, and the comprehensive reflection of each dimension reflects the performance of the unit in resource use, economic vitality and environmental impact, which respectively considers the unit area of energy consumption, traffic flow and population density, and the relationship between economic output and carbon emissions, so as to comprehensively reflect the comprehensive evaluation of the basic spatial unit in the urban strategy; based on the comprehensive score adjustment of the basic spatial unit, the efficient use of resources is realized by a dynamic adjustment method, which avoids the irrationality and waste of resources caused by fixed area allocation, and further considers the adjustment efficiency of the spatial unit, which can quickly identify which adjustment measures are effective, and judges the adjustment enthusiasm of the spatial unit according to the adjustment efficiency, and realizes the effect of dynamic adjustment.

[0096] In the embodiment of the present application, the spatial adjustment efficiency obtained in step S200 includes the following steps C1-C4:

[0097] C1: The spatial unit after area adjustment and carbon reduction is comprehensively scored with season as the adjustment period;

[0098] C2: Calculate the difference between the current comprehensive score and the comprehensive score at the end of the last period in each spatial unit adjustment period, and calculate the average difference as the optimized adjustment value;

[0099] C3: Statistics the time required for adjustment of different spatial units,

[0100] C4: The ratio of the optimized adjustment value to the time required for adjustment is the spatial adjustment efficiency of the unit.

[0101] In the embodiment of the present application, the spatial unit is updated based on the spatial adjustment efficiency in step S300 to obtain the second spatial unit division matrix, which includes the following steps D1-D3:

[0102] D1: Set the adjustment threshold and the efficiency threshold;

[0103] In the embodiment of the present application, the adjustment threshold and the efficiency threshold in step D1 can be determined by statistical analysis of the data distribution of the current spatial unit division matrix, such as calculating the average value, standard deviation and other statistical quantities. For example, the efficiency threshold can be set to the average value of the adjustment efficiency of all current spatial units plus or minus several times the standard deviation, so as to ensure that most units can be reasonably adjusted;

[0104] In an optional embodiment, the adjustment threshold and the efficiency threshold can also be based on past experience or industry standards to set an empirical value as the threshold;

[0105] In another alternative embodiment, the adjustment threshold and the efficiency threshold can also be dynamically adaptive, starting with a relatively loose threshold to allow a larger adjustment range, and then gradually tightening the threshold as the iteration progresses until the desired steady state is reached.

[0106] D2: If the optimized adjustment value is less than the adjustment threshold or the spatial adjustment efficiency is less than the efficiency threshold, re-perform the spatial adjustment update;

[0107] D3: If the optimized adjustment value is greater than the adjustment threshold and the spatial adjustment efficiency is greater than the efficiency threshold, stop updating and obtain the second spatial unit division matrix.

[0108] In an alternative embodiment, the updating of the spatial unit in step S300 can also be adjusted by the simulated annealing algorithm or the genetic algorithm based on the spatial adjustment efficiency to find the optimal solution;

[0109] In another alternative embodiment, the updating of the spatial unit in step S300 can also be adjusted by multi-objective optimization to multiple conflicting objectives that may be involved, such as economic cost, environmental impact, etc.

[0110] In the embodiments of the present application, the directed weighted graph is constructed in step S400, and the importance ranking is obtained by the link analysis algorithm, including the following steps E1-E3:

[0111] E1: Based on the industrial units in each industrial chain, set the nodes, based on the supply relationship between two industries, set the edges, and based on the dependence of downstream industries on upstream industries and the carbon emission intensity of upstream industries, set the weight of the edges;

[0112] E2: Analyze the weighted graph by the PageRank algorithm to calculate the industrial PageRank value;

[0113] E3: Based on the PageRank value, rank the importance of the industrial chain nodes in each spatial unit.

[0114] It should be noted that constructing a directed weighted graph requires acquiring information data on spatial units within the industrial chain, which may include: economic benefit data, carbon emission data, and supply chain relationship data. Specifically, economic benefit data may include the unit product output value, profit margin, and return on investment of the industry; carbon emission data may include carbon emissions per unit product, energy consumption, and waste disposal status; and supply chain relationship data may include upstream raw material supply information, downstream product flow, and inter-industry interdependence. Using the NetworkX library as a complex network modeling tool, the collected industrial chain data is imported, and a directed weighted graph is created, with nodes, edges, and edge weights set based on the imported data. The directed weighted graph can accurately represent the upstream and downstream relationships in the industrial chain and reflect the inter-industry interdependence and carbon emission intensity in the model.

[0115] For example, calculating the industry PageRank value can be represented as:

[0116] ;

[0117] in, Indicates the first PageRank value of each node industry Indicates pointing to a node The set of all nodes Indicating the node industry The degree of exit, This represents the damping factor, which is set to 0.85 based on historical experience.

[0118] In this embodiment of the application, the PageRank calculation results can also be visualized using the Matplotlib tool to rank the importance of the industry chain nodes within each spatial unit, and spatial units of the same industry chain can be jointly labeled in the visualization.

[0119] In an optional implementation, step S400 involves constructing a directed weighted graph. The importance ranking is obtained through a link analysis algorithm. Furthermore, the HITS (Hyperlink-Induced Topic Search) algorithm can be used to identify industry units with high influence on other industries (i.e., as suppliers or customers) as authoritative nodes; while industry units connected to multiple authoritative nodes and acting as connectors are considered hub nodes. The importance ranking is then performed through iterative calculation.

[0120] In another alternative implementation, in step S400, a directed weighted graph is constructed, and the importance ranking is obtained through the link analysis algorithm. Additionally, a score is assigned to each node using the Katz centrality algorithm, which reflects the relative importance of the node in the entire network.

[0121] It should be pointed out that the present application preferably economic benefit data, carbon emission data and supply chain relationship data, constructs a weighted graph, which can accurately describe its carbon emission situation and position in the supply chain, uses PageRank algorithm to analyze the network of industry chain nodes, which can effectively identify the key nodes with high influence in the whole industry chain, quantifies the importance of each node in the industry chain by calculating the PageRank value of each node, accurately locates the nodes that need to be focused on and optimized, improves the efficiency of resource allocation; At the same time, the PageRank analysis results can also be visualized by Matplotlib tool, which can clearly distinguish different nodes in the same industry chain and their interdependence through color, symbol and other labeling methods, improve the convenience and accuracy of data interpretation, through this quantitative analysis, the nodes with high carbon emission and great influence on downstream industry can be effectively identified, and for these key nodes, emission reduction measures or technical optimization can be implemented first, so as to realize the optimized management of carbon emission without weakening the stability of the industry chain.

[0122] In the embodiment of the present application, the comprehensive weight of the carbon emission index is determined based on the importance ranking and combined with the multi-criteria decision analysis method in step S500, and the key nodes of the industry chain are determined combined with the results of the link analysis algorithm, including the following steps F1-F7:

[0123] F1: Based on the importance ranking, the spatial units with the same industry chain node and within the ranking range are obtained;

[0124] F2: Determine the subjective weight of each spatial unit of the same industry chain by fuzzy weighting method;

[0125] Exemplarily, the importance of each industry node, the influence of carbon emission on the whole industry chain and the economic benefit can be scored by experts in each industry, and the different indexes can be scored by using triangular fuzzy numbers, and the comprehensive fuzzy weight calculation is represented as:

[0126] ;

[0127] Wherein, represents the number of experts, represents the fuzzy weight of the first index, represents the lowest score of the first expert on the first index, represents the most likely score of the first expert on the first index, represents the highest score of the first expert on the first index.

[0128] The fuzzy weight is calculated as a deterministic weight by de-fuzzification formula, which is expressed as:

[0129] ;

[0130] wherein, is the deterministic weight of the i-th index, i.e., the final subjective weight.

[0131] F3: Taking the spatial units with the same industrial chain as upstream and downstream nodes, the correlation degree of each upstream and downstream node is defined according to the upstream and downstream relationship;

[0132] For example, based on the spatial units with the same industrial chain as nodes, the correlation degree of each upstream and downstream node is defined according to the upstream and downstream relationship and the upstream value. , which can be expressed as:

[0133] ;

[0134] wherein, is the product value obtained from the node i, is the total production value of the node i.

[0135] F4: Based on the correlation degree and carbon emission of each upstream and downstream node, the carbon emission index is normalized and the correlation matrix is constructed;

[0136] For example, the correlation matrix can be expressed as:

[0137]

[0138] wherein, is the carbon emission of the i-th index of the j-th spatial unit, is the total number of spatial unit nodes.

[0139] F5: The entropy value of each spatial unit in the correlation matrix is calculated by the entropy weight method to obtain the objective weight of each spatial unit;

[0140] F6: The comprehensive weight value of the carbon emission index of each spatial unit is obtained by combining the subjective and objective weights;

[0141] F7: The PR threshold and weight threshold are set, and if the industrial PageRank value and the comprehensive weight value of the spatial unit are both greater than the set two thresholds, the spatial unit is a key node.

[0142] ​​​​​​In the embodiments of the present application, the PR threshold and the weight threshold in step F7 can be determined by statistical analysis of the average value, standard deviation and other statistical quantities of the current spatial unit.

[0143] In an alternative embodiment, the adjustment threshold and the efficiency threshold can also be based on past experience or industry standards to set an empirical value as the threshold.

[0144] In an alternative embodiment, the determination of the comprehensive weight of the carbon emission index in step S500, combined with the link analysis algorithm result, to judge the key nodes of the industrial chain can also be based on the comparison and scoring of different influence aspects of carbon emissions by the analytic hierarchy process, and the TOPSIS method is used to sort the schemes according to the distance between the ideal solution and the negative ideal solution.

[0145] In another alternative embodiment, the determination of the comprehensive weight of the carbon emission index in step S500, combined with the link analysis algorithm result, to judge the key nodes of the industrial chain can also be measured by data envelopment analysis to measure the technical efficiency of the spatial unit, and combined with the Borda count method for importance ranking.

[0146] It should be noted that the fuzzy weighting method and entropy weight are preferred in the present application for weight calculation, in which the fuzzy numbers are converted into specific numerical values, so that the final weight calculation can reflect the opinions of experts and match the actual application; through information entropy calculation, it is ensured that the weight of each index is proportional to its information quantity; the fuzzy weighting method and entropy weight method are combined and applied to the weight distribution of the industrial chain in urban planning, and the multi-dimensional weight and the dynamic adjustment mechanism of PageRank analysis are combined, which can automatically adjust and optimize the industrial chain layout with the change of data, and continuously improve the system performance through closed-loop feedback; the combination of comprehensive weight and PageRank value can identify key nodes through double threshold, which not only considers the position of the node in the industrial chain, but also combines its actual influence on carbon emissions and economic benefits, so as to more accurately identify the nodes that need to be focused on, introduce the correlation degree of upstream and downstream nodes in the analysis of the industrial chain, and evaluate them in combination with carbon emissions and economic benefits, which can more accurately reflect the interaction between nodes, thereby providing more accurate decision support for urban planning.

[0147] In the embodiments of the present application, the introduction of low-carbon technology at the key node in step S600 and the optimization of carbon emissions by adjusting the dependence of the industrial chain nodes to obtain the evaluation data can include the following steps:

[0148] Based on the upstream and downstream nodes of the key node, the dependence is adjusted according to the carbon emissions, which is represented as:

[0149] ;

[0150] wherein represents the optimized node and the correlation degree of the node , represents the original correlation degree, represents the carbon emissions of the upstream and downstream nodes, represents the maximum carbon emissions in the industrial chain;

[0151] Based on the identified non-critical nodes, according to the industrial analysis and fixed assets within the spatial unit, the comprehensive score of the node after industrial transformation is calculated, and it is judged whether it exceeds the score threshold, and the area is adjusted;

[0152] Through the key nodes and non-critical nodes in the identified industrial chain, these nodes have a significant impact on the overall carbon emissions and economic benefits of the industrial chain, and through the adjustment dependence calculated based on carbon emission changes, it can provide guidance for the carbon reduction operation of the supply chain nodes. Reducing the dependence can make the upstream and downstream nodes find other supply chains, and at the same time, the nodes with reduced dependence have the motivation to reduce carbon and improve the enthusiasm of carbon reduction.

[0153] Further, tables can be automatically generated based on data for visualization, for example:

[0154] Statistics and determination of data, including industrial statistics data, carbon emission data, economic benefit data, upstream and downstream relationship data of industrial chain and expert score data;

[0155] Save data through CSV, generate automatic tables based on Pandas library and ExcelWriter tool, input data into automatically generated Excel tables, and visualize data through Seaborn library.

[0156] Through the automatic tool, the data related to the industrial chain is effectively sorted and managed, and an Excel table easy to analyze and display is generated. Through the process of data sorting to table generation, data verification is facilitated, achieving efficient and accurate data processing. The generated table not only has practicality, but also saves the tedious sorting work of numerous data. Through the visual display, each item of data is easily displayed, which is convenient for personnel to check and analyze.

[0157] Embodiment 3

[0158] The above is a schematic scheme of the carbon emission based urban planning effect evaluation method of the embodiment. It should be noted that the technical scheme of the carbon emission based urban planning effect evaluation system belongs to the same concept as the technical scheme of the carbon emission based urban planning effect evaluation method described above. The technical details of the carbon emission based urban planning effect evaluation system in the embodiment are not described in detail, and can be referred to the description of the technical scheme of the carbon emission based urban planning effect evaluation method described above.

[0159] The embodiment also provides a carbon emission based urban planning effect evaluation method system, comprising:

[0160] The acquisition module is configured to acquire a soybean sample data set.

[0161] The screening module is configured to perform feature selection and dimension screening on the preprocessed soybean sample data set to obtain a screened first data set.

[0162] The encoding module is configured to encode the first data set based on the base dimension to introduce weights to obtain encoded data.

[0163] The prediction module is configured to input the encoded data into a prediction model to predict the oil content and the protein content.

[0164] The analysis module is configured to perform interpretive analysis on the prediction model to obtain the influence data of the SNP site on the phenotype.

[0165] The embodiment also provides an electronic device suitable for carbon emission based urban planning effect evaluation, comprising a memory and a processor. The memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions to implement the carbon emission based urban planning effect evaluation method proposed in the above embodiment.

[0166] The embodiment also provides a storage medium having a computer program stored thereon. The program is executed by a processor to implement the carbon emission based urban planning effect evaluation method proposed in the above embodiment.

[0167] The storage medium proposed in the embodiment and the carbon emission based urban planning effect evaluation method proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in the embodiment can be referred to the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.

[0168] Those skilled in the art can clearly understand the present application by the above description of the embodiments, and the present application can be realized by software and necessary general hardware, and of course, can also be realized by hardware. Based on such understanding, the technical solutions of the present application or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a FLASH, a hard disk, or an optical disc, and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods of various embodiments of the present application.

[0169] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application, and although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and all should be covered in the scope of the claims of the present application.

Claims

1. A method for evaluating the effectiveness of urban planning based on carbon emissions, characterized in that, include: The urban area is divided into spatial units, and the first spatial unit division matrix is ​​constructed by combining spatial unit information data. Real-time data is obtained based on the first spatial unit partitioning matrix for scoring, and the spatial units are adjusted based on the scoring results to obtain spatial adjustment efficiency; The spatial units are updated based on the spatial adjustment efficiency to obtain the second spatial unit partitioning matrix; Based on the upstream and downstream industrial chains partitioned by the second spatial unit matrix, a directed weighted graph is constructed, and the importance ranking is obtained through the link analysis algorithm; Based on importance ranking and combined with multi-criteria decision analysis, the comprehensive weight of carbon emission indicators is determined, and the key nodes of the industrial chain are identified by combining the results of link analysis algorithm. Low-carbon technologies are introduced at the key nodes, and carbon emissions are optimized by adjusting the dependence of industrial chain nodes to obtain assessment data.

2. The method for evaluating the effectiveness of urban planning based on carbon emissions as described in claim 1, characterized in that, The urban area is divided into spatial units, and a first spatial unit division matrix is ​​constructed by combining spatial unit information data, including: The urban area is divided into different basic spatial units based on the city's existing infrastructure and transportation network; Based on the aforementioned basic spatial unit, spatial unit information data is obtained, including: development time stage, economic benefits, and carbon emission potential value; Based on the functional attributes of the basic spatial units and the spatial unit information data, a first spatial unit partitioning matrix is ​​obtained by reclassification matrix.

3. The method for evaluating the effectiveness of urban planning based on carbon emissions as described in claim 2, characterized in that, Based on the first spatial unit partitioning matrix, real-time data is obtained for scoring, and the spatial units are adjusted based on the scoring results, including: Based on the energy consumption, carbon emission data and traffic flow of each spatial unit in the first spatial unit division matrix, a comprehensive score is obtained to obtain the first score of each spatial unit in the first spatial unit division matrix; The basic spatial unit is divided into different functional spatial units based on functional attributes. The average comprehensive score and standard deviation of each functional spatial unit are calculated to obtain the score threshold. Based on the functional attributes of the first spatial unit partitioning matrix, the first score of each spatial unit is compared with the score threshold of the corresponding functional spatial unit. If the first score is less than the score threshold, the corresponding spatial unit in the first spatial unit partitioning matrix is ​​adjusted.

4. The method for evaluating the effectiveness of urban planning based on carbon emissions as described in claim 3, characterized in that, The obtained spatial adjustment efficiency includes: Using the seasons as an adjustment cycle, the aforementioned comprehensive score is applied to spatial units that have undergone area adjustments and carbon reduction measures. Calculate the difference between the current comprehensive score within the adjustment cycle of each spatial unit and the comprehensive score at the end of the previous cycle, and calculate the average difference as the optimization adjustment value; Statistics on the time required to adjust different spatial units. The ratio of the optimized adjustment value to the time required for adjustment is the spatial adjustment efficiency of the unit.

5. The method for evaluating the effectiveness of urban planning based on carbon emissions as described in claim 4, characterized in that, Based on the aforementioned spatial adjustment efficiency, the spatial units are updated to obtain the second spatial unit partitioning matrix, including: Set adjustment thresholds and efficiency thresholds; If the optimization adjustment value is less than the adjustment threshold or the space adjustment efficiency is less than the efficiency threshold, then the space adjustment update will be performed again. If the optimization adjustment value is greater than the adjustment threshold and the spatial adjustment efficiency is greater than the efficiency threshold, then the update is stopped, and the second spatial unit partitioning matrix is ​​obtained.

6. The method for evaluating the effectiveness of urban planning based on carbon emissions as described in claim 5, characterized in that, The construction of the directed weighted graph, and the obtaining of importance ranking through link analysis algorithm, includes: Nodes are set based on industrial units in each industrial chain, edges are set based on the supply relationship between two industries, and the weights of the edges are set based on the dependence of downstream industries on upstream industries and the carbon emission intensity of upstream industries. The PageRank algorithm is used to analyze the weighted graph and calculate the PageRank value for each industry. The importance of industry chain nodes within each spatial unit is ranked based on PageRank values.

7. The method for evaluating the effectiveness of urban planning based on carbon emissions as described in claim 6, characterized in that, Based on importance ranking and combined with multi-criteria decision analysis, the comprehensive weight of carbon emission indicators is determined. Furthermore, by incorporating the results of link analysis algorithms, key nodes in the industrial chain are identified, including: Based on importance ranking, spatial units with the same industry chain node and within the ranking range are obtained; Subjective weights are determined for each spatial unit in the same industrial chain using the fuzzy weighting method; Spatial units with the same industrial chain are used as upstream and downstream nodes, and the degree of correlation of each upstream and downstream node is defined according to the upstream and downstream relationship. The carbon emission indicators are normalized based on the correlation degree and carbon emission of each upstream and downstream node, and a correlation matrix is ​​constructed. The entropy value of each spatial unit in the correlation matrix is ​​calculated using the entropy weight method to obtain the objective weight of each spatial unit. The comprehensive weight value of carbon emission indicators for each spatial unit is obtained by combining subjective and objective weights; Set PR threshold and weight threshold. If both the industry PageRank value and the spatial unit's comprehensive weight value are greater than the two set thresholds, then the spatial unit is a critical node.

8. A system for evaluating the effectiveness of urban planning based on carbon emissions as described in any one of claims 1-7, characterized in that, include: The first spatial division module is used to divide urban areas by spatial units and construct the first spatial unit division matrix by combining spatial unit information data. The scoring adjustment module is used to score the spatial units based on the real-time data obtained from the first spatial unit partitioning matrix, and to adjust the spatial units based on the scoring results to obtain the spatial adjustment efficiency. The second spatial partitioning module is used to update the spatial units based on the spatial adjustment efficiency to obtain the second spatial unit partitioning matrix; The sorting module is used to divide the upstream and downstream industrial chains based on the second spatial unit matrix, construct a directed weighted graph, and obtain the importance ranking through the link analysis algorithm; The key node identification module is used to determine the comprehensive weight of carbon emission indicators based on importance ranking and multi-criteria decision analysis, and to identify key nodes in the industrial chain by combining the results of link analysis algorithm. The low-carbon adjustment module is used to introduce low-carbon technologies at the key nodes and optimize carbon emissions by adjusting the dependence of industrial chain nodes to obtain assessment data.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the carbon emission-based urban planning effect evaluation method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the carbon emission-based urban planning effectiveness evaluation method according to any one of claims 1 to 7.

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