An interval entropy weighted evaluation method for subjective and objective mixed planning of low-carbon cities

Abstract

The present invention discloses a method for evaluating low-carbon cities using mixed planning interval entropy weighting, which relates to the fields of urban management and low-carbon evaluation technology. The method includes real-time collection and preprocessing of indicator data, establishing a user behavior prediction model based on the indicator data to predict the user's indicator data; dynamically adjusting the weight distribution of subjective and objective evaluation indicators based on the predicted indicator data, constructing a comprehensive evaluation model to calculate the comprehensive score of each area in the city; and formulating optimization measures for each area based on the comprehensive score. A support vector machine is used to establish the prediction model, and dynamic weight adjustment is used to ensure that the evaluation results are highly consistent with real-time data, avoiding the lag problem of traditional static evaluation. By dynamically adjusting the weight distribution and constructing the comprehensive evaluation model, a more accurate and flexible low-carbon city evaluation is achieved. The comprehensive scores of urban areas are calculated using a nonlinear transformation function, which can better reflect the changes in the marginal effects of evaluation indicators under different load conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of urban management and low-carbon evaluation, in particular to an interval entropy weighted evaluation method for subjective and objective mixed planning of low-carbon cities. Background Art

[0002] With the intensification of global climate change and increasingly serious environmental problems, the construction of low-carbon cities has gradually become one of the important goals of sustainable development in countries around the world. In this context, the evaluation and planning of low-carbon development in cities is particularly critical. Existing low-carbon city evaluation methods mostly rely on static evaluation systems, that is, using fixed evaluation indicators and weights to measure the carbon emissions, energy consumption and environmental participation of residents in different areas of the city. However, factors such as urban carbon emission characteristics, energy consumption and resident behavior are highly dynamic, resulting in the inability of static evaluation systems to fully reflect the actual situation at various stages of urban development. The evaluation results are lagging and inaccurate. In addition, traditional low-carbon city evaluation methods have certain limitations in the integration of subjective and objective factors, making it difficult to fully balance and integrate residents' environmental awareness and actual low-carbon performance. This leads to a lack of accurate reference based on real-time data when city managers formulate low-carbon policies, which affects the effective implementation of low-carbon policies.

[0003] Existing technologies also have obvious deficiencies in the weight allocation of low-carbon city evaluations. Most methods use fixed weights set by default or expert experience, which makes it difficult to cope with the dynamic changes in various regions at different stages of development. There are large differences in carbon emission characteristics, energy use habits and resident behavior patterns in different areas of the city. These differences are constantly adjusted with changes in time and environment. If the weights cannot be dynamically adjusted according to real-time data, the evaluation results may not reflect the actual situation, resulting in the low-carbon performance of some areas being underestimated or overestimated. Traditional methods fail to make full use of big data technology and find it difficult to analyze and process complex multi-dimensional data in real time, resulting in large deviations in the evaluation results. In particular, in the prediction of user behavior, existing technologies generally lack the ability to dynamically predict user behavior and carbon emission trends by combining historical data and real-time data, which further limits the accuracy and reliability of the low-carbon city evaluation system. Summary of the Invention

[0004] In view of the problems existing in the above-mentioned existing low-carbon city subjective and objective mixed planning interval entropy weighting evaluation method, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is to solve the problems of static evaluation, inflexible weight distribution, delayed data processing, etc. in the prior art.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a low-carbon city subjective and objective mixed planning interval entropy weighted evaluation method, which includes:

[0007] Collect and pre-process indicator data in real time, build a user behavior prediction model based on the indicator data, and predict the user's indicator data;

[0008] Dynamically adjust the weight distribution of subjective and objective evaluation indicators based on the predicted indicator data, and build a comprehensive evaluation model to calculate the comprehensive score of each area in the city;

[0009] Formulate optimization measures for each region based on the comprehensive scores;

[0010] Store and back up data.

[0011] As a preferred solution of the low-carbon city subjective and objective mixed planning interval entropy weighted evaluation method of the present invention, wherein: the real-time collection of indicator data refers to establishing a comprehensive evaluation indicator system, including subjective evaluation indicators and objective evaluation indicators, and collecting indicator data from different target data sources in different areas of the city in real time;

[0012] The target data sources include online feedback platforms, community management departments, urban transportation management bureaus, energy companies, and environmental protection departments;

[0013] The subjective evaluation indicators include resident satisfaction, policy support and community participation;

[0014] The objective evaluation indicators include traffic flow, energy consumption and carbon emission intensity.

[0015] As a preferred solution of the interval entropy weighted evaluation method for the subjective and objective mixed planning of low-carbon cities described in the present invention, the preprocessing refers to preprocessing the collected indicator data, including using the Z-score method to detect outliers on the indicator data, deleting the detected outliers and using the interpolation method to fill the missing data, and normalizing all indicator data.

[0016] As a preferred solution of the interval entropy weighted evaluation method for the subjective and objective mixed planning of low-carbon cities of the present invention, wherein: the user behavior prediction model is established based on the indicator data, and the user's indicator data is predicted by integrating the pre-processed indicator data into a data set;

[0017] Use support vector machine method to build user behavior prediction model;

[0018] Split the dataset into training and testing sets;

[0019] Substitute the training set into the user behavior prediction model for training;

[0020] Select radial basis function as kernel function;

[0021] Use grid search method to optimize model parameters, evaluate the accuracy of each set of parameters and select the optimal parameter combination;

[0022] Use the test set to validate the model, calculate the evaluation indicators, and adjust the model based on the evaluation indicators;

[0023] Substitute the real-time indicator data into the trained user behavior prediction model to obtain the predicted indicator data.

[0024] As a preferred solution of the interval entropy weighted evaluation method for the subjective and objective mixed planning of low-carbon cities described in the present invention, wherein: the dynamic adjustment of the weight distribution of subjective and objective evaluation indicators based on the predicted indicator data refers to using the hierarchical analysis method to construct a subjective evaluation indicator judgment matrix and an objective evaluation indicator judgment matrix respectively;

[0025] The subjective evaluation index judgment matrix is ​​obtained by comparing the importance of subjective evaluation indicators by experts. The third-order matrix C is constructed to represent the relative importance of the three subjective evaluation indicators. The subjective evaluation index judgment matrix is:

[0026] , where C 12 Indicates the importance score of the first subjective evaluation index data relative to the second subjective evaluation index data;

[0027] Calculating the weight of the subjective evaluation index based on the importance score, including calculating the product of the importance score of each row in the subjective evaluation index judgment matrix, taking the xth root of the product of each row to obtain the weight value of each row, and normalizing the weight value of each row;

[0028] The objective evaluation index judgment matrix is ​​obtained by calculating the maximum and minimum values ​​of each objective evaluation index data and performing forward normalization processing to obtain a standardized objective evaluation index judgment matrix;

[0029] Calculate the entropy value of each objective evaluation indicator and normalize the entropy value to obtain the weight of the objective evaluation indicator;

[0030] Construct a time-varying weight function to dynamically adjust the weight. The formula is:

[0031] , where P n (t) represents the value of the nth evaluation index in the index data at time point t, P n (t-1) represents the value of the nth evaluation indicator in the indicator data at time point t-1, is the regulating factor, represents the acceleration adjustment factor, Represents the symbol of partial derivative, W n,0 Indicates the weight of the nth evaluation index before adjustment in the index data, Wn (t) represents the adjusted weight of the nth evaluation indicator in the indicator data at time point t.

[0032] As a preferred solution of the low-carbon city subjective and objective mixed planning interval entropy weighted evaluation method of the present invention, wherein: the construction of a comprehensive evaluation model to calculate the comprehensive score of each area in the low-carbon city includes:

[0033] A comprehensive evaluation model is constructed to calculate the comprehensive scores S(t) of different areas in the city. The formula is:

[0034] , where W i (t) represents the adjusted weight of the i-th subjective evaluation index at time point t, is the S-type nonlinear function in the nonlinear transformation function, D i (t) represents the value of the i-th subjective evaluation index at time point t, represents the attenuation factor, dt represents the small change of the integral variable t, h represents the total number of subjective evaluation indicators, W j (t) represents the weight of the jth objective evaluation index after adjustment at time point t, O j (t) represents the value of the jth objective evaluation index at time point t, m represents the total number of objective evaluation indicators, and T is the entire acquisition period.

[0035] As a preferred solution of the low-carbon city subjective and objective mixed planning interval entropy weighted evaluation method of the present invention, wherein: the optimization measures for each area are formulated according to the comprehensive score, which means setting the comprehensive score threshold according to the comprehensive score of different areas in the city. and And divide the interval into, including,

[0036] like , indicating that the current target area of ​​the city is in the high-scoring range, then the city should maintain its existing advantages, continue to implement existing low-carbon policies and measures, explore cutting-edge technologies, promote zero-carbon buildings, develop smart grids, and introduce more clean energy technologies;

[0037] like , indicating that the current city's target area is in the medium score range, then focus on the weak links, find the weak links that affect the comprehensive score by analyzing the indicator data, and perform targeted optimization.

[0038] Optimize citizen participation and recommend enhancing residents' environmental awareness through publicity and education activities to promote the improvement of community environmental protection participation;

[0039] like , indicating that the current target area of ​​the city is in the low-scoring range, then emergency measures should be implemented and taken to deal with environmental problems in the area, including reducing high-emission industries and promoting green infrastructure construction, and strengthening policy supervision. Low-scoring areas usually have poor policy implementation. It is recommended to increase the implementation of environmental protection policies and strengthen environmental protection monitoring and supervision mechanisms.

[0040] A computer device comprises: a memory and a processor; the memory stores a computer program, and is characterized in that: when the processor executes the computer program, it implements the steps of the low-carbon city subjective and objective mixed planning interval entropy weighted evaluation method.

[0041] A computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the computer program implements the steps of a method for entropy weighted evaluation of subjective and objective mixed planning intervals of low-carbon cities.

[0042] The beneficial effects of the present invention are: a prediction model is established using a support vector machine, and the evaluation results are highly matched with real-time data through dynamic weight adjustment, avoiding the lag problem of traditional static evaluation. By dynamically adjusting weight distribution and constructing a comprehensive evaluation model, a more accurate and flexible low-carbon city evaluation is achieved. The nonlinear transformation function is used to calculate the comprehensive score of the urban area, which can better reflect the changes in the marginal effect of the evaluation indicators under different load conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1 Flowchart of the interval entropy weighted evaluation method for subjective and objective mixed planning of low-carbon cities.

[0045] Figure 2 Schematic diagram of the process of making optimization recommendations for each area based on the comprehensive score. DETAILED DESCRIPTION

[0046] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0047] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0048] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it constitute a separate or selective embodiment that is mutually exclusive with other embodiments.

[0049] Reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a method for evaluating the interval entropy weighting of subjective and objective mixed planning of low-carbon cities. The method for evaluating the interval entropy weighting of subjective and objective mixed planning of low-carbon cities includes the following steps:

[0050] S1. Collect and pre-process indicator data in real time, build a user behavior prediction model based on the indicator data, and predict the user's indicator data;

[0051] Specifically, real-time collection of indicator data refers to establishing a comprehensive evaluation indicator system, including subjective and objective evaluation indicators, and collecting indicator data from different target data sources in different areas of the city in real time;

[0052] The target data sources include online feedback platforms, community management departments, urban transportation management bureaus, energy companies, and environmental protection departments;

[0053] The subjective evaluation indicators include resident satisfaction (measuring residents' acceptance and satisfaction with low-carbon policies), policy support (assessing the degree of support policymakers have for low-carbon city construction, reflecting the willingness and actions of decision-makers), and community participation (measuring residents' enthusiasm for participating in environmental protection activities, reflecting citizens' awareness of participating in low-carbon city construction).

[0054] The objective evaluation indicators include traffic flow (the number of vehicles per hour in the target area), energy consumption (the amount of energy consumed in the target area) and carbon emission intensity (the waste recycling rate in the target area).

[0055] By establishing a comprehensive evaluation index system and collecting subjective and objective indicator data from different areas of the city in real time, we can obtain key information on the development of low-carbon cities in a comprehensive and timely manner. Subjective evaluation indicators such as resident satisfaction, policy support and community participation reflect the attitudes and participation of citizens and policymakers in low-carbon policies; objective evaluation indicators such as traffic flow, energy consumption and carbon emission intensity provide actual data support for low-carbon development in the region. Overall, this process achieves comprehensive coverage and accurate collection of data, providing an efficient and reliable data basis for subsequent comprehensive evaluation of low-carbon cities, ensuring a more scientific and accurate evaluation.

[0056] Furthermore, performing preprocessing refers to preprocessing the collected indicator data, including detecting outliers on the indicator data using the Z-score method, deleting the detected outliers and filling the missing data using the interpolation method, and normalizing all indicator data.

[0057] Through the preprocessing steps, the Z-score method is used to detect outliers on the collected indicator data, effectively identify and delete abnormal data, and use the interpolation method to fill missing data to ensure the integrity and reliability of the data. All indicators are normalized so that indicators of different dimensions are unified within the same range to facilitate subsequent analysis and calculation. The beneficial effect of this preprocessing method is to improve data quality, avoid model errors caused by outliers and missing data, and ensure that the results of the subsequent comprehensive evaluation model are more accurate and stable.

[0058] In addition, a user behavior prediction model is established based on the indicator data. Predicting the user's indicator data means integrating the pre-processed indicator data into a data set;

[0059] Use support vector machine method to build user behavior prediction model;

[0060] Split the dataset into training and testing sets;

[0061] Substitute the training set into the user behavior prediction model for training;

[0062] Select radial basis function as kernel function;

[0063] Use grid search method to optimize model parameters, evaluate the accuracy of each set of parameters and select the optimal parameter combination;

[0064] Use the test set to validate the model, calculate the evaluation indicators, and adjust the model based on the evaluation indicators;

[0065] Substitute the real-time indicator data into the trained user behavior prediction model to obtain the predicted indicator data.

[0066] The present invention integrates the preprocessed indicator data into a data set, adopts the support vector machine method to establish a user behavior prediction model, combines the radial basis function kernel and the grid search method to optimize the model parameters, ensures that the accuracy of the model on different data sets is maximized, and by dividing the data set into a training set and a test set, the trained model can effectively predict the user's indicator data and be adjusted and optimized according to the verification results of the test set. This method not only improves the prediction accuracy of the model, but also can dynamically adapt to real-time data changes, ensuring more accurate predictions of user behavior and indicator data, and providing a reliable basis for subsequent evaluation and decision-making.

[0067] S2. Dynamically adjust the weight distribution of subjective and objective evaluation indicators based on the predicted indicator data, and build a comprehensive evaluation model to calculate the comprehensive score of each area in the city;

[0068] Specifically, dynamically adjusting the weight distribution of subjective and objective evaluation indicators based on predicted indicator data refers to using the hierarchical analysis method to construct a subjective evaluation indicator judgment matrix and an objective evaluation indicator judgment matrix respectively;

[0069] The subjective evaluation index judgment matrix is ​​obtained by comparing the importance of subjective evaluation indicators by experts. The third-order matrix C is constructed to represent the relative importance of the three subjective evaluation indicators. The subjective evaluation index judgment matrix is:

[0070] , where C 12 Indicates the importance score of the first subjective evaluation index data relative to the second subjective evaluation index data;

[0071] Importance rating follows the following 9-level scale:

[0072] 1: Both indicators are equally important;

[0073] 3: One indicator is slightly more important than another;

[0074] 5: One indicator is significantly more important than another indicator;

[0075] 7: One indicator is very important relative to another indicator;

[0076] 9: One indicator is extremely important relative to another indicator;

[0077] 2, 4, 6, 8: intermediate values ​​between the above scores, used for more detailed comparison;

[0078] Calculate the weight of the subjective evaluation index based on the importance score, including calculating the product of the importance score of each row in the subjective evaluation index judgment matrix, taking the xth root of the product of each row to obtain the weight value of each row (x is the order of the subjective evaluation index judgment matrix), and normalizing the weight value of each row so that the sum of the weight values ​​of each row is 1;

[0079] If the weight value of the row is large, the corresponding indicator is more important than other indicators, indicating that experts believe that the indicator has a greater impact on the evaluation of low-carbon cities and is given a higher priority in the decision-making process;

[0080] If the weight value of the row is small, the corresponding indicator is less important than other indicators, indicating that experts believe that the indicator has little impact on the evaluation of low-carbon cities and is given a lower priority in the decision-making process;

[0081] In subjective evaluation, the traditional hierarchical analysis method simply performs pairwise comparisons. However, this invention further subdivides subjective evaluation indicators, such as resident satisfaction, policy support, and public participation, and performs more detailed decomposition and pairwise comparisons on the indicators. This more detailed hierarchical division helps to obtain more accurate weight distribution.

[0082] The objective evaluation index judgment matrix is ​​obtained by calculating the maximum and minimum values ​​of each objective evaluation index data and performing forward normalization processing to obtain a standardized objective evaluation index judgment matrix;

[0083] Calculate the entropy value of each objective evaluation indicator, normalize the entropy value to obtain the weight of the objective evaluation indicator, and ensure that the total weight is 1;

[0084] In weight adjustment, the more common approach is to statically allocate or linearly adjust based on data, that is, to calculate weights based on direct changes in data. Common weight adjustment methods rarely consider the impact of accelerated changes in historical data on weights.

[0085] The introduction of the historical change rate term captures the rate at which the indicator changes over time, thereby dynamically adjusting the current weight. This is different from the traditional static weight calculation method and enables the weight to reflect the changes in the indicator in real time. The expression is: , where P n (t) represents the value of the nth evaluation index in the index data at time point t, P n (t-1) represents the value of the nth evaluation indicator in the indicator data at time point t-1;

[0086] The acceleration term is introduced to reflect the impact of the rate of change of indicator data on the weight. By capturing the acceleration trend of data changes, the weight can be adjusted more sensitively. If the data fluctuates greatly, the acceleration term can further smooth or intensify the change of weight, providing a more sensitive response mechanism. The expression is: ,in, Represents the partial derivative symbol, which is used to express the rate of change of a multivariable function relative to a specific variable, P n (t) represents the value of the nth evaluation indicator in the indicator data at time point t;

[0087] The time-varying weight function is used to adjust the weight according to the changes in time and data. The usual time-varying weight function formula is relatively simple. It only adjusts the weight according to a certain trend of time or data. The formula is:

[0088] ,in, It is an adjustment factor used to balance the weight changes caused by data changes and control the impact of current data changes on weights. It represents the change of the nth evaluation indicator in the indicator data at time point t, which is usually the difference between the current indicator data and the indicator data at the previous time point. n,0 Indicates the weight of the nth evaluation index before adjustment in the index data, W n (t) represents the adjusted weight of the nth evaluation indicator in the indicator data at time point t;

[0089] Based on the existing time-varying weight function, a time-varying weight function is constructed to dynamically adjust the weight. The formula is:

[0090] , where P n (t) represents the value of the nth evaluation index in the index data at time point t, P n (t-1) represents the value of the nth evaluation indicator in the indicator data at time point t-1, is the regulating factor, Represents the acceleration adjustment factor, which is used to smooth the impact of acceleration changes on weights. Represents the symbol of partial derivative, which is used to express the rate of change of a multivariable function relative to a specific variable, W n,0 Indicates the weight of the nth evaluation index before adjustment in the index data, W n (t) represents the adjusted weight of the nth evaluation indicator in the indicator data at time point t.

[0091] In the process of constructing weight distribution, the present invention fully combines the hierarchical analysis method and the entropy method to realize the dynamic adjustment of subjective evaluation indicators and objective evaluation indicators, and ensure the scientific nature and flexibility of the evaluation system. In the process of constructing subjective evaluation indicators, the hierarchical analysis method is used, and the experts compare each subjective evaluation indicator in pairs, judge the importance of each indicator based on the 9-level scaling rule, and construct a third-order matrix to represent the relative importance of the three subjective evaluation indicators. By calculating the product of each indicator and taking the nth root, the normalized weight value is generated. This method can ensure the accuracy of weight distribution on the basis of subdividing subjective indicators and further enhance the reliability of evaluation results. Compared with the traditional hierarchical analysis method, the present invention makes a more detailed division of subjective evaluation indicators, which helps to improve the accuracy of subjective indicator weights and avoid rough estimation. In the process of constructing objective evaluation indicators, the entropy method is used. First, the maximum and minimum values ​​of each objective evaluation indicator are normalized to standardize them. Data, calculate the entropy value of each objective indicator, and distribute weights based on the entropy value to ensure that the total weight is 1. This step can dynamically reflect the actual impact of each objective indicator on the evaluation results, and avoids the deviation that may be caused by static weight distribution. The present invention introduces a time-varying weight function and innovatively adds a historical change rate term and an acceleration term, so that the weight can be dynamically adjusted according to the changing trend and acceleration of the data. The historical change rate term is used to capture the rate of change of data over time, and the acceleration term reflects the second-order derivative of the data change, that is, the acceleration or deceleration of the changing trend. Through these dynamic adjustment mechanisms, the weight distribution of the present invention can not only reflect data changes in real time, but also has a more sensitive response capability. Especially when the data fluctuates greatly, the acceleration term can smooth or amplify the amplitude of the weight adjustment, thereby improving the accuracy and flexibility of the evaluation model. This makes the evaluation system not only suitable for stable data, but also can effectively cope with complex and changing environments, and ultimately improve the accuracy and scientificity of low-carbon city evaluation.

[0092] Furthermore, a comprehensive evaluation model is constructed to calculate the comprehensive scores of each region in a low-carbon city, including:

[0093] The use of nonlinear transformation functions can improve the sensitivity and accuracy of the comprehensive evaluation model by reasonably reflecting the characteristics of the data, especially under high load conditions, avoiding misjudgments caused by linear relationships and reflecting the diminishing characteristics of the editing effect of resource use;

[0094] Use the S-type nonlinear function in the nonlinear transformation function Calculate the marginal diminishing effect value of the indicator data at time point t using the formula:

[0095] Among them, D i (t) represents the value of the i-th subjective evaluation index at time point t, 、 is a function parameter used to control the shape of the function;

[0096] It is used to perform nonlinear transformation on input data. This transformation can make the data show different sensitivities in different ranges, thus better reflecting the actual situation.

[0097] w controls the initial gain of the indicator data, reflecting the influence of the data in the initial stage;

[0098] q controls the impact of indicator data growth on the score. A larger value can slow down the score growth under high load conditions.

[0099] Considering the decreasing effect of time on the indicators, to ensure that long-term evaluation is more reasonable, the attenuation function is introduced, and the expression is: ,in, represents the attenuation factor;

[0100] A comprehensive evaluation model is constructed to calculate the comprehensive scores S(t) of different areas in the city. The formula is:

[0101] , where W i (t) represents the adjusted weight of the i-th subjective evaluation index at time point t, is the S-type nonlinear function in the nonlinear transformation function, D i (t) represents the value of the i-th subjective evaluation index at time point t, represents the attenuation factor, dt represents the small change of the integral variable t, h represents the total number of subjective evaluation indicators, W j (t) represents the weight of the jth objective evaluation index after adjustment at time point t, O j (t) represents the value of the jth objective evaluation index at time point t, m represents the total number of objective evaluation indicators, and T is the entire acquisition period.

[0102] This comprehensive evaluation model introduces an S-type nonlinear transformation function to accurately adjust the data sensitivity in different intervals in order to better reflect the actual situation of various regions in low-carbon cities. Traditional linear models may lead to oversimplified misjudgments when processing data, especially under high-load conditions, and it is difficult to fully reflect the marginal diminishing effect of resource use. The application of the S-type nonlinear function solves this problem. It can respond quickly when the data is small, and when the data grows to a certain extent, the response becomes gradually flat, thereby avoiding the problem of rapid growth of scores under high-load conditions and ensuring the rationality and accuracy of the model. The marginal diminishing effect is reflected through this function, so that as the indicator grows, its impact on the score gradually decreases, reflecting the diminishing characteristics of resource use. The model also further considers the impact of time on the indicator by introducing an attenuation function. By setting the attenuation factor, the model can be used for long-term The influence of data is reasonably weakened to ensure that earlier data will not excessively affect the current evaluation results. This design makes the model more reasonable in long-term evaluation, avoids the interference of historical data on current decision-making, and thus enhances the adaptability of the model. When calculating the comprehensive scores of various regions in the city, the model dynamically adjusts the weights of subjective and objective evaluation indicators. The subjective indicators obtain weights through the expert scoring method, while the objective indicators are weighted through the entropy method. This method not only integrates the subjective judgment of experts, but also objectively measures the differences between different data in each region through the entropy method. The dynamic adjustment of weights enables the model to reflect the low-carbon development of each region in real time as the data changes. The model realizes the accurate comprehensive evaluation of various regions in low-carbon cities through the combination of nonlinear functions, marginal diminishing effects, attenuation functions and dynamic weight adjustments, providing a scientific basis for the formulation and optimization of low-carbon policies.

[0103] S3. Develop optimization measures for each region based on the comprehensive scores;

[0104] Specifically, formulating optimization measures for each area based on the comprehensive scores means setting comprehensive score thresholds based on the comprehensive scores of different areas in the city. and And divide the interval into, including,

[0105] like When , the target area of ​​the current city is judged to be in the high score range, indicating that the area has performed very well in low-carbon development and has reached the set high standards;

[0106] Optimization suggestions for high-scoring ranges include:

[0107] Maintain existing advantages and continue to implement existing low-carbon policies and measures;

[0108] Explore cutting-edge technologies, promote zero-carbon buildings, develop smart grids, and introduce more clean energy technologies;

[0109] like If the score is , the target area of ​​the current city is judged to be in the medium score range, indicating that the performance of the area in low-carbon development is average and does not meet the set high standards;

[0110] Optimization suggestions for the medium score range include:

[0111] Focus on weak links, find the weak links that affect the comprehensive score by analyzing indicator data, and conduct targeted optimization;

[0112] Optimize citizen participation and recommend enhancing residents' environmental awareness through publicity and education activities to promote the improvement of community environmental protection participation;

[0113] like If , the target area of ​​the current city is judged to be in the low score range, indicating that the area performs poorly in low-carbon development, and there may be significant environmental problems and inadequate implementation of low-carbon policies;

[0114] Optimization suggestions for the low-scoring range include:

[0115] Implement urgent measures to address environmental issues in the region, including reducing high-emission industries and promoting green infrastructure;

[0116] Strengthen policy supervision. Low-scoring areas usually have poor policy implementation. It is recommended to increase the implementation of environmental protection policies and strengthen environmental protection monitoring and supervision mechanisms.

[0117] This invention achieves precise low-carbon development management by dividing regional performance according to comprehensive scores and formulating optimization measures for each region in different score ranges. For high-scoring regions, it is recommended to maintain existing advantages and explore cutting-edge technologies, such as zero-carbon buildings and smart grids; medium-scoring regions are targeted for optimization by identifying weak links and increasing resident participation; low-scoring regions need to take urgent measures to improve environmental problems and strengthen policy supervision. Through this hierarchical optimization method, it is ensured that the performance of each region in low-carbon development can be improved and enhanced in a targeted manner, thereby improving the overall low-carbon management efficiency of the city.

[0118] S4, store data and back it up;

[0119] Specifically, storing data and backing it up means designing a structured database and storing data in time series. The stored data includes evaluation indicators, weights, time decay factors, comprehensive scores and optimization suggestions, and implementing data backup and security management measures, including cloud storage, redundant backup and data encryption.

[0120] This design ensures the systematicness and continuity of data by constructing a structured database and storing evaluation indicators, weights, time decay factors, comprehensive scores and optimization suggestions in time series, providing reliable data support for low-carbon city evaluation. At the same time, the implementation of security management measures such as cloud storage, redundant backup and data encryption ensures the integrity, security and recoverability of data, and prevents data loss and leakage. Overall, this solution improves the efficiency and security of data storage, and provides a solid technical foundation for future low-carbon city management and decision-making.

[0121] This is the second embodiment of the present invention, which is different from the previous embodiment in that:

[0122] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0123] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0124] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, and then editing, interpreting, or processing in another suitable manner as necessary, and then storing it in a computer memory.

[0125] The third embodiment of the present invention provides an interval entropy-weighted evaluation method for low-carbon city planning based on a mix of subjective and objective factors. To demonstrate its effectiveness, a comprehensive evaluation index system was first established, including resident satisfaction, policy support, and community engagement (subjective evaluation indicators), as well as traffic flow, energy consumption, and carbon emission intensity (objective evaluation indicators). Data was collected from online feedback platforms, community management departments, urban transportation management bureaus, energy companies, and environmental protection departments.

[0126] In the data preprocessing stage, the Z-score method is used to detect outliers in the indicator data, and the interpolation method is used to fill the missing data. Then all the indicator data are normalized; secondly, the support vector machine (SVM) method is used to establish a user behavior prediction model, and the data set is divided into a training set and a test set; finally, the radial basis function is used as the kernel function, and the grid search method is used to optimize the model parameters, the optimal parameter combination is selected, and the test set is used for model verification.

[0127] In the weight allocation stage, the hierarchical analysis method is used to construct the subjective evaluation index judgment matrix and the objective evaluation index judgment matrix. The importance scores are obtained by experts through pairwise comparison of the importance of subjective evaluation indicators, and the weights of subjective evaluation indicators are calculated. For objective evaluation indicators, the entropy value of each indicator is calculated and normalized to obtain the weight. A time-varying weight function is constructed to dynamically adjust the weight to reflect the changes in indicator data over time.

[0128] Finally, a comprehensive evaluation model was constructed to calculate the comprehensive scores of different areas in the city, and optimization measures were formulated for each area based on the scores. The calculation of the comprehensive score took into account the weights of subjective and objective evaluation indicators, as well as the S-shaped nonlinear function in the nonlinear transformation function.

[0129] Table 1 Comprehensive evaluation data of low-carbon city regions

[0130]

[0131] By comparing the data in the table, we can clearly see the performance of each area on different indicators. For example, Area A scored high in resident satisfaction and community participation, but low in energy consumption and carbon emission intensity. This may mean that the area performs well in terms of resident quality of life and community activities, but needs improvement in energy use and environmental impact.

[0132] Region C performed well across all indicators, achieving the highest overall score, indicating a balanced approach to low-carbon urban planning. Region D, while scoring highest in policy support, also scored high in energy consumption and carbon intensity. This may indicate that while the region has strong policy support, it still needs to improve energy efficiency and reduce carbon emissions in practice.

[0133] The calculation of the comprehensive score shows that Region F has the highest comprehensive score, indicating that it performs well across all indicators, especially in resident satisfaction and community participation. This demonstrates the advantages of the embodiments of the present invention compared to the prior art, namely, through real-time data collection and prediction models, dynamic adjustment of weight distribution, and construction of a comprehensive evaluation model, which can more accurately reflect the actual performance of each region and provide a scientific basis for policy formulation.

[0134] In summary, a hybrid interval entropy-weighted evaluation method for low-carbon city planning, combining subjective and objective factors, introduces a time-varying weight function and a nonlinear transformation function. This method can more flexibly respond to the dynamic changes in urban development and provide more targeted optimization measures for each region. This method not only improves the accuracy of the evaluation but also provides a new perspective and tool for sustainable urban development.

[0135] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

Claims

1. A low-carbon city subjective and objective mixed planning interval entropy weighted evaluation method, characterized by: include, Collect and pre-process indicator data in real time, build a user behavior prediction model based on the indicator data, and predict the user's indicator data; Dynamically adjust the weight distribution of subjective and objective evaluation indicators based on the predicted indicator data, and build a comprehensive evaluation model to calculate the comprehensive score of each area in the city; Formulate optimization measures for each region based on the comprehensive scores; Store data and back it up; The dynamically adjusting the weight distribution of subjective and objective evaluation indicators based on the predicted indicator data refers to using the hierarchical analysis method to construct a subjective evaluation indicator judgment matrix and an objective evaluation indicator judgment matrix respectively; The subjective evaluation index judgment matrix is ​​obtained by comparing the importance of subjective evaluation indicators by experts. The third-order matrix C is constructed to represent the relative importance of the three subjective evaluation indicators. The subjective evaluation index judgment matrix is: ， Among them, C 12 Indicates the importance score of the first subjective evaluation index data relative to the second subjective evaluation index data; Calculating the weight of the subjective evaluation index based on the importance score, including calculating the product of the importance score of each row in the subjective evaluation index judgment matrix, taking the xth root of the product of each row to obtain the weight value of each row, and normalizing the weight value of each row; The objective evaluation index judgment matrix is ​​obtained by calculating the maximum and minimum values ​​of each objective evaluation index data and performing forward normalization processing to obtain a standardized objective evaluation index judgment matrix; Calculate the entropy value of each objective evaluation indicator and normalize the entropy value to obtain the weight of the objective evaluation indicator; Construct a time-varying weight function to dynamically adjust the weight. The formula is: ， Among them, P n (t) represents the value of the nth evaluation index in the index data at time point t, P n (t-1) represents the value of the nth evaluation indicator in the indicator data at time point t-1, is the regulating factor, represents the acceleration adjustment factor, Represents the symbol of partial derivative, W n,0 Indicates the weight of the nth evaluation index before adjustment in the index data, W n (t) represents the adjusted weight of the nth evaluation indicator in the indicator data at time point t; The construction of a comprehensive evaluation model to calculate the comprehensive scores of each region in a low-carbon city includes: A comprehensive evaluation model is constructed to calculate the comprehensive scores S(t) of different areas in the city. The formula is: ， Among them, W i (t) represents the adjusted weight of the i-th subjective evaluation index at time point t, is the S-type nonlinear function in the nonlinear transformation function, D i (t) represents the value of the i-th subjective evaluation index at time point t, represents the attenuation factor, dt represents the small change of the integral variable t, h represents the total number of subjective evaluation indicators, W j (t) represents the weight of the jth objective evaluation index after adjustment at time point t, O j (t) represents the value of the jth objective evaluation index at time point t, m represents the total number of objective evaluation indicators, and T is the entire acquisition period.

2. The interval entropy weighted evaluation method for subjective and objective mixed planning of low-carbon cities according to claim 1 is characterized by: The real-time collection of indicator data refers to establishing a comprehensive evaluation indicator system, including subjective evaluation indicators and objective evaluation indicators, and collecting indicator data from different target data sources in different areas of the city in real time; The target data sources include online feedback platforms, community management departments, urban transportation management bureaus, energy companies, and environmental protection departments; The subjective evaluation indicators include resident satisfaction, policy support and community participation; The objective evaluation indicators include traffic flow, energy consumption and carbon emission intensity.

3. The interval entropy weighted evaluation method for subjective and objective mixed planning of low-carbon cities according to claim 2 is characterized by: The preprocessing refers to preprocessing the collected indicator data, including using the Z-score method to detect outliers on the indicator data, deleting the detected outliers and using the interpolation method to fill the missing data, and normalizing all indicator data.

4. The interval entropy weighted evaluation method for subjective and objective mixed planning of low-carbon cities according to claim 3 is characterized by: The user behavior prediction model is established based on the indicator data, and predicting the user's indicator data refers to integrating the pre-processed indicator data into a data set; Use support vector machine method to build user behavior prediction model; Split the dataset into training and testing sets; Substitute the training set into the user behavior prediction model for training; Select radial basis function as kernel function; Use grid search method to optimize model parameters, evaluate the accuracy of each set of parameters and select the optimal parameter combination; Use the test set to validate the model, calculate the evaluation indicators, and adjust the model based on the evaluation indicators; Substitute the real-time indicator data into the trained user behavior prediction model to obtain the predicted indicator data.

5. The interval entropy weighted evaluation method for subjective and objective mixed planning of low-carbon cities according to claim 4 is characterized by: The optimization measures for each area based on the comprehensive score are formulated to set the comprehensive score threshold according to the comprehensive scores of different areas in the city. and And divide the intervals, include, like , indicating that the current target area of ​​the city is in the high-scoring range, then the city should maintain its existing advantages, continue to implement existing low-carbon policies and measures, explore cutting-edge technologies, promote zero-carbon buildings, develop smart grids, and introduce more clean energy technologies; like , indicating that the current city's target area is in the medium score range, then focus on the weak links, find the weak links that affect the comprehensive score by analyzing the indicator data, and perform targeted optimization. Optimize citizen participation and recommend enhancing residents' environmental awareness through publicity and education activities to promote the improvement of community environmental protection participation; like , indicating that the current target area of ​​the city is in the low-scoring range, then emergency measures should be implemented and taken to deal with environmental problems in the area, including reducing high-emission industries and promoting green infrastructure construction, and strengthening policy supervision. Low-scoring areas usually have poor policy implementation. It is recommended to increase the implementation of environmental protection policies and strengthen environmental protection monitoring and supervision mechanisms.

6. The interval entropy weighted evaluation method for subjective and objective mixed planning of low-carbon cities according to claim 5 is characterized by: The said storing and backing up of data refers to designing a structured database and storing data in time series. The stored data includes evaluation indicators, weights, time decay factors, comprehensive scores and optimization suggestions, and implementing data backup and security management measures, including cloud storage, redundant backup and data encryption.

7. A computer device comprising: memory and processor; The memory stores a computer program, characterized in that when the processor executes the computer program, the steps of the low-carbon city subjective and objective mixed planning interval entropy weighted evaluation method according to any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the interval entropy weighted evaluation method for low-carbon city subjective and objective mixed planning are implemented according to any one of claims 1 to 7.

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