Urban resident low-carbon life diagnosis method and system based on process management perspective

Abstract

The invention provides an urban resident low-carbon life diagnosis method and system based on a process management perspective, and relates to the technical field of urban resident low-carbon life diagnos.The method comprises the steps that process links are divided according to preset process management types, five PICOF process links are obtained, and a diagnosis index system is constructed; multi-source data classification collection is carried out, data classification collection information is obtained, an urban resident low-carbon life construction level value calculation model is constructed, and a comprehensive evaluation result is obtained; value processing of the maximum value and the minimum value is carried out on score variables in the urban resident low-carbon life construction level value calculation model, and then extreme abnormal values are eliminated; after the extreme abnormal values are eliminated, the positive score variables and the negative score variables are subjected to standardization processing to obtain processing results, comprehensive evaluation of the low-carbon life construction level of urban residents is achieved, and the accuracy and reliability of the evaluation results are improved through abnormal value elimination and score variable standardization processing.

Description

Technical Field

[0001] The present invention proposes a method and system for diagnosing low-carbon life of urban residents based on a process management perspective, and relates to the technical field of diagnosing low-carbon life of urban residents. Background Art

[0002] Traditionally, the evaluation of low-carbon living standards of urban residents often relies on a single indicator or a few indicators. This method is difficult to fully reflect the multi-dimensional characteristics of low-carbon living construction. In addition, the single data source also limits the accuracy and comprehensiveness of the evaluation, making it difficult for the evaluation results to be used as a basis for effective decision-making. Summary of the invention

[0003] The present invention provides a method and system for diagnosing low-carbon life of urban residents based on the perspective of process management, which is used to solve the problems that the current research on the diagnostic method and system for low-carbon life of urban residents is insufficient, and it is difficult to comprehensively, objectively, intelligently and quantitatively evaluate the specific situation of low-carbon life of urban residents.

[0004] The present invention proposes a method and system for diagnosing low-carbon life of urban residents based on a process management perspective, the method comprising:

[0005] S1. Divide the process links according to the preset process management types to obtain the five process links of PICOF, and construct a diagnostic indicator system based on the information of the five process links of PICOF;

[0006] S2. Classify and collect data from multiple sources of the diagnostic indicator system data to obtain data classification and collection information, and construct a calculation model for the low-carbon life construction level value of urban residents based on the data collection information to obtain a comprehensive evaluation result;

[0007] S3. Perform maximum and minimum value processing on the score variables in the calculation model of the low-carbon life construction level of urban residents, and then exclude extreme outliers;

[0008] S4. After excluding extreme outliers, the positive score variables and negative score variables are standardized to obtain the processing results.

[0009] Furthermore, the S1 includes:

[0010] Obtaining preset process management types, wherein the preset process management types include planning management, implementation management, inspection management, result management, and feedback management;

[0011] Classify the process links of process management according to the preset management types to obtain the five process links of PICOF;

[0012] The five process links of PICOF include planning (P), implementation (I), inspection (C), results (O) and feedback (F);

[0013] Obtain the index information of the five links of PICOF, and construct a diagnostic index system based on the index information.

[0014] Further, the S2 includes:

[0015] Set preset collection categories according to the types of diagnostic indicator systems, classify and collect diagnostic indicator system data through multi-source channels according to the preset collection categories, and obtain multi-source classified collection data;

[0016] Preprocessing the multi-source classified collected data to obtain preprocessed multi-source classified collected data;

[0017] A calculation model for the construction level value of low-carbon life for urban residents is constructed, and the calculation formula of the calculation model for the construction level value of low-carbon life for urban residents is:

[0018]

[0019] Among them, V d is the low-carbon performance level of urban residents in the five process links of dimension d, including the performance value V of planning P link dP 、Implementation of Phase I Performance Value V dI , Check the performance value V of the C link dC , Result O stage performance value V dO And feedback F link performance value V dF ,s are five process links, W s is the weight of the five process links in the low-carbon construction process, including the planning P link weight W P 、Implement I link weight W I , Check the weight W of the C link C , Result O link weight W O and the weight W of the feedback link F F , V ds It is the low-carbon life construction level value of urban residents at the link scale;

[0020] The calculation formula of the low-carbon life construction level value calculation model for urban residents at the link scale is:

[0021]

[0022] Among them, V dsi is the score of indicator i in the five process links, which is also the value of the city's low-carbon construction level on the indicator scale, and n is the number of indicators i in the five process links;

[0023] The calculation formula of the indicator scale urban residents' low-carbon life construction level value calculation model is:

[0024]

[0025] Among them, V dsi-j is the value of the score variable j of indicator i in the five process links, and m is the number of score variables j;

[0026] The low-carbon performance level values ​​of the urban residents in the five process links of dimension d are the comprehensive evaluation results.

[0027] Further, the S3 includes:

[0028] Calculate the maximum and minimum values ​​of the score variable according to the score variable data;

[0029] The calculation formula for the maximum value is:

[0030]

[0031] Among them, MAX qz is the maximum value, x j is the value of the score variable j, is the average value of score variable j among a group of cities, Represents the standard deviation of this set of data;

[0032] The calculation formula for the minimum value is:

[0033]

[0034] Among them, MIN qz is the minimum value;

[0035] The maximum value and the minimum value are used to exclude extreme outliers of the score variable of the quantitative attribute in the natural data among cities.

[0036] Further, the S4 includes:

[0037] After excluding extreme outliers, normal score variables and negative score variables were standardized;

[0038] The standardized calculation formula for the positive score variable is:

[0039] =

[0040] Among them, V dsi-j is the value of the score variable j of indicator i in the five process links, x j is the value of the score variable j, xh is the optimal value of the score variable j in a group of cities; l is the worst value of the score variable j in a group of cities;

[0041] The standardized calculation formula for the negative score variable is:

[0042] =

[0043] Among them, x u is the optimal value of the score variable j in a group of cities; x e is the worst value of the score variable j in a group of cities.

[0044] Furthermore, the system comprises:

[0045] An indicator system construction module is used to divide the process links according to the preset process management types, obtain the five process links of PICOF, and construct a diagnostic indicator system based on the information of the five process links of PICOF;

[0046] The model comprehensive evaluation module is used to classify and collect data from multiple sources of the diagnostic indicator system data, obtain data classification collection information, and construct a calculation model for the low-carbon life construction level value of urban residents based on the data collection information to obtain a comprehensive evaluation result;

[0047] The outlier processing module is used to process the maximum and minimum values ​​of the score variables in the urban residents' low-carbon life construction level value calculation model, and then exclude extreme outliers;

[0048] The standardization module is used to standardize the positive score variables and the negative score variables after excluding the extreme outliers to obtain the processing results.

[0049] Furthermore, the indicator system construction module includes:

[0050] A management type classification module is used to obtain preset process management types, wherein the preset process management types include planning management, implementation management, inspection management, result management and feedback management;

[0051] A link classification module is used to classify the process links of process management according to the preset management types to obtain the five process links of PICOF;

[0052] The five process links of PICOF include planning (P), implementation (I), inspection (C), results (O) and feedback (F);

[0053] The construction module is used to obtain the index information of the five links of PICOF and construct a diagnostic index system according to the index information.

[0054] Furthermore, the model comprehensive evaluation module includes:

[0055] A data collection classification module is used to set preset collection classifications according to the types of diagnostic indicator systems, and classify and collect diagnostic indicator system data through multi-source channels according to the preset collection classifications to obtain multi-source classified collection data;

[0056] A preprocessing module, used for preprocessing the multi-source classified collected data to obtain preprocessed multi-source classified collected data;

[0057] The model building module is used to build a calculation model for the low-carbon life construction level value of urban residents. The calculation formula of the calculation model for the low-carbon life construction level value of urban residents is:

[0058]

[0059] Among them, V d is the low-carbon performance level of urban residents in the five process links of dimension d, including the performance value V of planning P link dP 、Implementation of Phase I Performance Value V dI , Check the performance value V of the C link dC , Result O stage performance value V dO And feedback F link performance value V dF ,s are five process links, W s is the weight of the five process links in the low-carbon construction process, including the planning P link weight W P 、Implement I link weight W I , Check the weight W of the C link C , Result O link weight W O and the weight W of the feedback link F F , V ds It is the low-carbon life construction level value of urban residents at the link scale;

[0060] The calculation formula of the low-carbon life construction level value calculation model for urban residents at the link scale is:

[0061]

[0062] Among them, V dsi is the score of indicator i in the five process links, which is also the value of the city's low-carbon construction level on the indicator scale, and n is the number of indicators i in the five process links;

[0063] The calculation formula of the indicator scale urban residents' low-carbon life construction level value calculation model is:

[0064]

[0065] Among them, V dsi-j is the value of the score variable j of indicator i in the five process links, and m is the number of score variables j;

[0066] The comprehensive evaluation module is used for the low-carbon performance level values ​​of the city residents in the five process links of the d dimension, which is the comprehensive evaluation result.

[0067] Furthermore, the outlier processing module includes:

[0068] A value limiting calculation module is used to calculate the maximum value and the minimum value of the score variable according to the score variable data;

[0069] The calculation formula for the maximum value is:

[0070]

[0071] Among them, MAX qz is the maximum value, x j is the value of the score variable j, is the average value of score variable j among a group of cities, Represents the standard deviation of this set of data;

[0072] The calculation formula for the minimum value is:

[0073]

[0074] Among them, MIN qz is the minimum value;

[0075] The anomaly elimination module is used to eliminate the extreme anomaly values ​​of the score variable of the quantitative attribute in the natural data among cities through the maximum value and the minimum value.

[0076] Furthermore, the standardization module includes:

[0077] A processing startup module is used to standardize normal score variables and negative score variables after excluding extreme outliers;

[0078] The positive standardization module is used to standardize the positive score variables;

[0079] The standardized calculation formula for the positive score variable is:

[0080] =

[0081] Among them, Vdsi-j is the value of the score variable j of indicator i in the five process links, x j is the value of the score variable j, x h is the optimal value of the score variable j in a group of cities; l is the worst value of the score variable j in a group of cities;

[0082] Negative standardization module, used to standardize negative score variables;

[0083] The standardized calculation formula for the negative score variable is:

[0084] =

[0085] Among them, x u is the optimal value of the score variable j in a group of cities; x e is the worst value of the score variable j in a group of cities.

[0086] Beneficial effects of the present invention: Through the division of the five links of PICOF and the construction of the corresponding diagnostic indicator system, all aspects of the low-carbon life construction of urban residents can be comprehensively evaluated. Based on the classification collection of data from multiple sources and the comprehensive evaluation model, the evaluation results are more objective and accurate. The comprehensiveness and accuracy of the data are the basis for the reliability of the evaluation results. By excluding extreme outliers and standardizing the score variables, the influence of the score variables brought by extreme data can be eliminated. The evaluation results can provide decision-making support for users in promoting the low-carbon life construction of urban residents, including optimizing resource allocation, promoting environmentally friendly behaviors, etc. Through the feedback link (F), the evaluation results can be fed back to relevant departments and residents to promote the attention and participation in the construction of low-carbon life, forming a virtuous circle of continuous improvement. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 A schematic diagram of a diagnostic method for low-carbon life of urban residents based on a process management perspective;

[0088] Figure 2 Provide a statistical distribution map of the construction level of each link of low-carbon life for urban residents;

[0089] Figure 3 This is a distribution map of scores of cities of different sizes in various aspects of low-carbon life of urban residents. DETAILED DESCRIPTION

[0090] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0091] In one embodiment of the present invention, a method and system for information mining based on big data is proposed by the present invention, and the method comprises:

[0092] S1. Divide the process links according to the preset process management types to obtain the five process links of PICOF, and construct a diagnostic indicator system based on the information of the five process links of PICOF;

[0093] S2. Classify and collect data from multiple sources of the diagnostic indicator system data to obtain data classification and collection information, and construct a calculation model for the low-carbon life construction level value of urban residents based on the data collection information to obtain a comprehensive evaluation result;

[0094] S3. Perform maximum and minimum value processing on the score variables in the calculation model of the low-carbon life construction level of urban residents, and then exclude extreme outliers;

[0095] S4. After excluding extreme outliers, the positive score variables and negative score variables are standardized to obtain the processing results.

[0096] The working principle of the above technical solution is as follows: Figure 1 As shown, the present invention proposes a diagnostic method and system for low-carbon life of urban residents based on the perspective of process management, with the construction of a diagnostic indicator system as the core content. The process of establishing a low-carbon lifestyle is a management process. Therefore, the promotion of low-carbon life of urban residents also depends on the continuous iteration of the five links of planning (P), implementation (I), inspection (C), results (O) and feedback (F). Therefore, the present invention will analyze the specific connotation of promoting low-carbon life of urban residents around the five process links of PICOF and construct a corresponding diagnostic indicator system.

[0097] Based on the preset process management types, the whole process is subdivided into five main links, namely P (planning), I (implementation), C (checking), O (optimization), and F (feedback). Each link has its specific management content and goals. A corresponding diagnostic indicator system is constructed for the five links of PICOF. These indicators should be able to fully reflect all aspects of the low-carbon life construction of urban residents, including but not limited to energy consumption, carbon emissions, resource utilization efficiency, environmental protection behavior, etc. Data related to diagnostic indicators are collected from multiple sources to ensure the comprehensiveness and accuracy of the data. Based on the collected data, a calculation model is constructed to evaluate the overall level of low-carbon life construction of urban residents. The model combines various indicators to form a comprehensive evaluation result.

[0098] Perform extreme value analysis on the score variables in the calculation model to determine the maximum and minimum values. These extreme values ​​may be caused by data errors or special circumstances and need to be processed. According to the maximum and minimum values, exclude those extreme outliers that deviate significantly from the normal range to avoid interference with the overall evaluation results. In the evaluation model, some indicators are better as they are higher (such as resource utilization efficiency), which are called positive score variables; some indicators are better as they are lower (such as carbon emissions), which are called negative score variables. Standardize the positive and negative score variables, that is, convert them into dimensionless values ​​for comparison and synthesis. Standardization can eliminate the differences between different indicators due to different units or dimensions.

[0099] like Figure 2 As shown, the results show that the data of the planning and result links belong to the left-skewed distribution, which means that most of them are distributed at high scores, but the high scores of the planning link are concentrated around 84.05 points, and the high scores of the result link are concentrated around 48.81 points; while the implementation and inspection belong to the right-skewed distribution or peak distribution, which means that most of them are distributed at low scores, or the data distribution is very concentrated in the middle.

[0100] like Figure 3 As shown, the following conclusions can be drawn: ① In the planning (P) stage, the average scores of different city sizes range from 75.76 points to 79.20 points. There is no significant difference between the scores of different city sizes, but it is much higher than the average scores of the other four stages, which shows that the sample cities have clear goals and requirements for residents' low-carbon lifestyles.

[0101] ② In the implementation (I) and inspection (C) stages, there are significant differences in the scores of cities of different sizes, and the performance results are: super-large and extra-large cities (the average scores of the two stages are 60.83 points and 68.04 points respectively) > large cities (36.89 points and 55.36 points) > medium-sized cities (32.42 points and 48.43 points) > small cities (26.43 points and 39.42 points). This shows that the implementation and inspection of low-carbon construction of urban residents in large, medium and small cities need to be further strengthened. In general, the average scores of sample cities in the implementation and inspection stages are 33.94 and 49.14 points respectively. It can be seen that the score of the inspection stage is higher than that of the implementation stage. It can be concluded that the overall mechanism for the low-carbonization of residents' living and consumption habits in the implementation stage is relatively weak, but the supervision and inspection system for low-carbon living, low-carbon travel and low-carbon consumption of residents in the inspection stage is relatively complete. Therefore, the score of the inspection stage is better than that of the implementation stage. It also shows that many cities lack the overall institutional framework to guide the low-carbonization of residents' living and consumption, and the measures to implement residents' low-carbon living and consumption are relatively scattered. ③ In the result (O) stage, the average score of cities of different sizes ranges from 45.52 points to 48.64 points. It can be seen that there is no significant difference between the scores of different city sizes. However, in the super-large and mega-city scales, the result stage scores the lowest among the five stages, but in the large, medium and small city scales, the implementation stage and feedback stage scores the worst, which shows that different city sizes have various shortcomings in each link of the low-carbon construction process.

[0102] ④ In the feedback (F) link, there are significant differences in the scores of cities of different sizes, and the performance results are: super-large and extra-large cities (52.98 points) > large cities (30.16 points) > medium-sized cities (25.79 points) > small cities (20.09 points), which shows that the feedback link is not only the worst among the five links, but also has great differences in performance among cities. In the feedback link, whether it is the reward mechanism for residents' low-carbon living consumption habits or the innovative optimization plan adopted by departments to guide residents' low-carbon living consumption habits, the scores are low. This phenomenon shows that there is a lack of awareness of following up and giving feedback on residents' low-carbon living consumption.

[0103] The technical effect of the above technical solution is: through the division of the five links of PICOF and the construction of the corresponding diagnostic indicator system, it is possible to comprehensively evaluate all aspects of the low-carbon life construction of urban residents and provide a scientific basis for decision-making. Based on the classification and comprehensive evaluation model of data from multiple sources, the evaluation results are more objective and accurate. The comprehensiveness and accuracy of the data are the basis for the reliability of the evaluation results. By excluding extreme outliers and standardizing the score variables, the influence of the score variables brought by extreme data can be eliminated. The evaluation results can provide decision-making support for relevant institutions in promoting the construction of low-carbon life for urban residents, including optimizing resource allocation and promoting environmentally friendly behaviors. Through the feedback link (F), the evaluation results can be fed back to relevant departments and residents to promote their attention and participation in the construction of low-carbon life, forming a virtuous circle of continuous improvement.

[0104] In one embodiment of the present invention, the S1 includes:

[0105] Obtaining preset process management types, wherein the preset process management types include planning management, implementation management, inspection management, result management, and feedback management;

[0106] Classify the process links of process management according to the preset management types to obtain the five process links of PICOF;

[0107] The five process links of PICOF include planning (P), implementation (I), inspection (C), results (O) and feedback (F);

[0108] Obtain the index information of the five links of PICOF, and construct a diagnostic index system based on the index information.

[0109] The working principle of the above technical solution is to construct an indicator system for the planning link of urban low-carbon construction in the dimension of urban residents from three aspects: low-carbon living, low-carbon travel, and low-carbon consumption.

[0110] The level of the inspection (C) link of a city's process of guiding residents to participate in the low-carbon construction management process can be diagnosed from two aspects: the mechanism guarantee of residents' low-carbon living and consumption habits, and the resource guarantee of residents' low-carbon living and consumption habits. The inspection (C) link is a further deepening of the implementation (I) link of the low-carbon construction level of urban residents, and a dynamic inspection of the implementation process and effect.

[0111] Accurately measuring the performance of low-carbon construction in the dimension of urban residents is an important part of understanding urban low-carbon construction. The indicators of the results (O) link mainly evaluate the results of guiding residents' low-carbon living and energy-saving habits, the results of guiding residents' low-carbon travel habits, and the results of guiding residents' low-carbon consumption habits.

[0112] The evaluation index of residents' participation in the feedback (F) link of the urban low-carbon construction management process is used to diagnose whether the city summarizes the low-carbon behaviors and methods of residents and feeds back to the promotion and improvement measures. The evaluation content is mainly encouragement and optimization content, in order to fully tap the emission reduction potential of residents' behavior. Guide the indicators, scoring variables, and scoring rules of the diagnostic indicator system for each link of urban residents' low-carbon life.

[0113] The technical effect of the above technical solution is: through the classification of the five process links of PICOF, systematic management of process management is achieved. Each link has its specific management goals and tasks to ensure that the project is effectively managed throughout the life cycle.

[0114] The constructed diagnostic indicator system can accurately evaluate the execution of the project in the five links of PICOF. By collecting and analyzing indicator data, problems and deficiencies in the project execution process can be discovered in a timely manner, providing a basis for project improvement. The setting of the feedback F link enables the project to be continuously improved. By reviewing, summarizing and giving feedback on project results, lessons learned can be summarized, potential problems and improvement opportunities can be discovered, and reference and improvement directions can be provided for future projects. The diagnostic indicator system provides decision-making support for managers. Managers can understand the overall situation, key links and potential risks of the project based on indicator data, so as to make more scientific and reasonable decisions. The management of the five process links of PICOF promotes collaboration among project teams. Each link needs to be connected, coordinated and supported to ensure the smooth progress of the project and the achievement of goals. This kind of collaboration helps to enhance the cohesion and execution of the project team.

[0115] In one embodiment of the present invention, S2 includes:

[0116] Set preset collection categories according to the types of diagnostic indicator systems, classify and collect diagnostic indicator system data through multi-source channels according to the preset collection categories, and obtain multi-source classified collection data;

[0117] Preprocessing the multi-source classified collected data to obtain preprocessed multi-source classified collected data;

[0118] A calculation model for the construction level value of low-carbon life for urban residents is constructed, and the calculation formula of the calculation model for the construction level value of low-carbon life for urban residents is:

[0119]

[0120] Among them, V d is the low-carbon performance level of urban residents in the five process links of dimension d, including the performance value V of planning P link dP 、Implementation of Phase I Performance Value V dI , Check the performance value V of the C linkdC , Result O stage performance value V dO And feedback F link performance value V dF ,s are five process links, W s is the weight of the five process links in the low-carbon construction process, including the planning P link weight W P 、Implement I link weight W I , Check the weight W of the C link C , Result O link weight W O and the weight W of the feedback link F F , V ds is the low-carbon life construction level of urban residents at the link scale; the weights are 0.2, 0.2, 0.15, 0.3, and 0.15 respectively, so W s ={W p ,W I ,W C ,W O ,W F} = {0.2,0.2,0.15,0.3,0.15}.

[0121] The calculation formula of the low-carbon life construction level value calculation model for urban residents at the link scale is:

[0122]

[0123] Among them, V dsi is the score of indicator i in the five process links, which is also the value of the city's low-carbon construction level on the indicator scale, and n is the number of indicators i in the five process links;

[0124] The calculation formula of the indicator scale urban residents' low-carbon life construction level value calculation model is:

[0125]

[0126] Among them, V dsi-j is the value of the score variable j of indicator i in the five process links, and m is the number of score variables j; the number of score variables m will vary for different indicators.

[0127] The low-carbon performance level values ​​of the urban residents in the five process links of dimension d are the comprehensive evaluation results.

[0128] Calculate the score change value of each indicator in each process link in real time according to the actual data of each indicator in each process link, calculate the indicator score change coefficient according to the score change value of each indicator, and make a qualification judgment on each indicator according to the score change coefficient to obtain a qualification judgment result;

[0129] According to the qualification determination result, the actual parameters of the unqualified indicators are adjusted in a targeted manner until the scores of the indicators are qualified scores, and then the comprehensive evaluation results are updated to obtain updated evaluation results;

[0130] The calculation formula of the index score variation coefficient is:

[0131]

[0132] Among them, B zxs is the index score variation coefficient, ΔV dsi-j is the upward change in the value of the score variable j of indicator i in the five process links, It is the downward change in the value of the score variable j of indicator i in the five process links.

[0133] When ΔV in the formula dsi-j Greater than , thus making B zxs When it is a positive number, the coefficient of change of the index score is determined to be a positive change;

[0134] When ΔV in the formula dsi-j Less than or equal to , thus making B zxs When it is a negative number, it means that the score change coefficient is changing in a negative direction;

[0135] Compare the index score change coefficient with the preset coefficient threshold range to obtain the coefficient comparison result;

[0136] When the index score change coefficient is within the preset coefficient threshold range, the corresponding index of the score change coefficient is judged as qualified, and the specific parameters of the index are not adjusted;

[0137] When the index score change coefficient is outside the preset coefficient threshold range, the corresponding index of the score change coefficient is judged as unqualified, and it is obtained whether the index score change coefficient is a positive change or a negative change. When it is a positive change, the specific parameter corresponding to the indicator is adjusted downward; when it is a negative change, the specific parameter corresponding to the indicator is adjusted upward until the index score change coefficient is within the preset coefficient threshold range, and then the adjustment is stopped.

[0138] The working principle of the above technical solution is: the calculation model of the construction level value of low-carbon life of urban residents. The level is determined by the performance values ​​of five construction management process links. The formula is as follows:

[0139]

[0140] Among them, V d is the low-carbon performance level of urban residents in the five process links of dimension d, including the performance value V of planning P link dP、Implementation of Phase I Performance Value V dI , Check the performance value V of the C link dC , Result O stage performance value V dO And feedback F link performance value V dF ,s are five process links, W s is the weight of the five process links in the low-carbon construction process, including the planning P link weight W P 、Implement I link weight W I , Check the weight W of the C link C , Result O link weight W O and the weight W of the feedback link F F , V ds is the low-carbon life construction level of urban residents at the link scale; the weights are 0.2, 0.2, 0.15, 0.3, and 0.15 respectively, so W s ={W p ,W I ,W C ,W O ,W F} = {0.2,0.2,0.15,0.3,0.15}.

[0141] The calculation formula of the low-carbon life construction level value calculation model for urban residents at the link scale is:

[0142]

[0143] Among them, V dsi is the score of indicator i in the five process links, which is also the value of the city's low-carbon construction level on the indicator scale, and n is the number of indicators i in the five process links;

[0144] The calculation formula of the indicator scale urban residents' low-carbon life construction level value calculation model is:

[0145]

[0146] Among them, V dsi-j is the value of the score variable j of indicator i in the five process links, and m is the number of score variables j; the number of score variables m will vary for different indicators.

[0147] The low-carbon performance level values ​​of the urban residents in the five process links of dimension d are the comprehensive evaluation results.

[0148] The technical effect of the above technical scheme is: by collecting data through multiple source channels, the comprehensiveness and accuracy of the data are ensured, thereby improving the accuracy of the calculation of the low-carbon life construction level value of urban residents. Combining five process links and multiple indicators, it can fully reflect the overall situation of low-carbon life construction of urban residents and the specific situation of each link, and realize comprehensive evaluation. According to the importance and influence of the five process links in the low-carbon construction process, the weights are reasonably allocated to ensure the scientificity and rationality of the evaluation results. The calculation model has a clear calculation formula and clear calculation steps, which is easy to operate and implement. The calculation results can provide scientific basis and decision-making support for planning management, project implementation, etc., and have strong practicality. Through continuous data collection and calculation, the progress of low-carbon life construction of urban residents can be monitored and evaluated in real time, providing data support for continuous improvement and optimization, and promoting the sustainable development of low-carbon construction. The score of each indicator of each process link is calculated in real time, and the score change coefficient is calculated based on the score change. This real-time nature ensures that problems can be quickly identified and handled, thereby improving the efficiency and response speed of the entire process. It ensures that when the score change of the indicator accumulates to a certain extent, timely and automatic qualification judgment and calculation update can be carried out to improve the efficiency of problem solving. Based on the positive and negative and size of the score change coefficient, it can intelligently decide whether the specific parameters of the indicator need to be adjusted and how to adjust them. When the score change coefficient is within the preset threshold range, no intervention is performed; when it exceeds the threshold, the parameters are adjusted up and down according to the positive and negative direction of the score change coefficient until the requirements are met. This intelligent adjustment mechanism helps to ensure the stability and optimization of the system. By adjusting the unqualified indicators, the technology can continuously update the comprehensive evaluation results to ensure the accuracy and timeliness of the evaluation results. This update mechanism helps to reflect the true state of the system or process and provide a basis for subsequent decision-making. Through real-time evaluation, precise problem location, intelligent adjustment and comprehensive evaluation update, the technology helps to optimize the overall performance of the system or process, improve production efficiency, reduce costs, reduce failure rates, etc. It can be applied to various systems or processes with multiple process links and multiple indicators, and has strong scalability and flexibility. At the same time, by adjusting parameters such as the preset coefficient threshold range, it can be customized and optimized according to actual needs.

[0149] In summary, this technology achieves effective management and optimization of systems or processes through real-time evaluation, precise problem location, intelligent adjustment and comprehensive evaluation update, thereby improving overall performance and reducing costs.

[0150] In one embodiment of the present invention, S3 includes:

[0151] Calculate the maximum and minimum values ​​of the score variable according to the score variable data;

[0152] The calculation formula for the maximum value is:

[0153]

[0154] Among them, MAX qz is the maximum value, x j is the value of the score variable j, is the average value of score variable j among a group of cities, Represents the standard deviation of this set of data;

[0155] The calculation formula for the minimum value is:

[0156]

[0157] Among them, MIN qz is the minimum value;

[0158] The maximum value and the minimum value are used to exclude extreme outliers of the score variable of the quantitative attribute in the natural data among cities.

[0159] The working principle of the above technical solution is: the calculation model of the low-carbon life construction level of urban residents based on the score variable scale, the value of the evaluation index is determined by many factors, and these influencing factors are called score variables (j). In the present invention, the value of the low-carbon life construction level of urban residents is based on the percentage system, and the score variable of the quantitative attribute needs to be standardized from 0 to 100 points. In order to exclude the extreme outliers of the score variable of the quantitative attribute in the natural data between cities, it is necessary to process the maximum and minimum values. The specific method is: the maximum value is based on Calculated, the minimum value is based on Calculated, where xj is the value of the score variable j, represents the average value of score variable j among a group of cities, Represents the standard deviation of this set of data.

[0160] The technical effect of the above technical solution is: by calculating the maximum and minimum values ​​of the score variable, extreme outliers that are far from the trend of the data set can be identified and excluded. The existence of these outliers may distort the analysis results. Therefore, excluding these outliers helps to improve the quality and reliability of the data. In data analysis, extreme outliers may lead to deviations in statistical results or misleading conclusions. By excluding these outliers, it can be ensured that the analysis is more accurate and closer to the actual situation. The method of using maximum and minimum values ​​to exclude outliers is a robust statistical method. No strict assumptions are required about the data distribution, so it can be applied to various types of data sets. In the process of data-based decision-making, accurate and reliable data is crucial. By accurately evaluating the level of low-carbon life construction of urban residents, resources can be allocated more reasonably, resource allocation can be optimized, and resource utilization efficiency can be improved. By calculating the maximum and minimum values ​​of the score variable and excluding extreme outliers based on these values, the quality and reliability of the data can be improved, and the accuracy and robustness of the analysis can be enhanced.

[0161] In one embodiment of the present invention, the S4 includes:

[0162] After excluding extreme outliers, normal score variables and negative score variables were standardized;

[0163] The standardized calculation formula for the positive score variable is:

[0164] =

[0165] Among them, V dsi-j is the value of the score variable j of indicator i in the five process links, x j is the value of the score variable j, x h is the optimal value of the score variable j in a set of cities; there are two ways to obtain x h Value: (a) If there is a relevant standard that specifies a benchmark or optimal value for the scoring variable j, x h The value is directly taken from the benchmark (optimal) value; (b) if there is no relevant standard, then x h = , x l is the worst value of the score variable j in a group of cities; there are two ways to obtain x l Value: (a) If there is a relevant standard that specifies a minimum value for the scoring variable j, x l The value is directly taken as the lowest value; (b) If there is no relevant standard, then x l = ;

[0166] The standardized calculation formula for the negative score variable is:

[0167] =

[0168] Among them, x u is the optimal value of the score variable j in a group of cities; there are two ways to obtain x u (a) If there is a relevant standard that specifies a benchmark or optimal value for the scoring variable j, x u (b) If there is no relevant standard, then .x e is the worst value of the score variable j in a group of cities. There are two ways to obtain x e (a) If there is a relevant standard that specifies the maximum value of the score variable j, x e The value is directly taken as the maximum value; (b) If there is no relevant standard, then x e = .

[0169] The working principle of the above technical solution is: after excluding extreme outliers, in order to make different score variables comparable, it is necessary to standardize the normal score variables and negative score variables. The main purpose of standardization is to convert the data into unitless relative values ​​so that they conform to the normal distribution and have the same scale. For positive score variables (i.e., variables with higher scores and better performance), the principle of standardization is to convert their values ​​into proportions relative to the optimal value. First, determine the optimal value x of the score variable j h and the worst value x l If there is a relevant standard that specifies a benchmark or optimal value, use it directly; otherwise, determine the optimal and worst values ​​through the data itself. Then, calculate the value V of the score variable j for each indicator i dsi-j With the optimal value x h For negative score variables (i.e., variables with lower scores and better performance), the principle of standardization is to convert their values ​​into a ratio relative to the worst value. First, determine the optimal value x of the score variable j u and the worst value x e Similarly, if there is a relevant standard that specifies a benchmark or optimal value, it is used directly; otherwise, it is determined by the data itself. Then, the value of each score variable j is calculated relative to the worst value x e The reciprocal of the ratio (or the worst value minus the actual value divided by the difference between the worst value and the best value) is used to obtain the standardized value.

[0170] The technical effect of the above technical solution is: standardization makes different score variables have the same scale, so that they can be directly compared. This is crucial for comprehensive evaluation, ranking or weight allocation analysis. Standardization eliminates the dimensional differences and order of magnitude differences between different score variables, avoiding the impact of these differences on the analysis results. Therefore, the analysis results based on standardized data are more accurate and reliable. Standardized data can provide strong support for strategic planning, etc. By comparing the standardized scores between different cities or different indicators, we can clearly understand the performance level of each city or indicator, so as to make more scientific and reasonable decisions. Standardized data has clear meaning and interpretability. For example, a variable with a standardized score of 0.8 means that its performance has reached 80% of the optimal value. The standardization method does not rely on specific data distribution or assumptions, so it has strong adaptability and flexibility. It can be applied to data sets of various types and sizes to meet different analysis needs.

[0171] In one embodiment of the present invention, the system comprises:

[0172] An indicator system construction module is used to divide the process links according to the preset process management types, obtain the five process links of PICOF, and construct a diagnostic indicator system based on the information of the five process links of PICOF;

[0173] The model comprehensive evaluation module is used to classify and collect data from multiple sources of the diagnostic indicator system data, obtain data classification collection information, and construct a calculation model for the low-carbon life construction level value of urban residents based on the data collection information to obtain a comprehensive evaluation result;

[0174] The outlier processing module is used to process the maximum and minimum values ​​of the score variables in the urban residents' low-carbon life construction level value calculation model, and then exclude extreme outliers;

[0175] The standardization module is used to standardize the positive score variables and the negative score variables after excluding the extreme outliers to obtain the processing results.

[0176] The working principle of the above technical solution is as follows: the present invention proposes a diagnostic method and system for low-carbon life of urban residents based on the perspective of process management. The process of establishing a low-carbon life form is a management process. Therefore, the promotion of low-carbon life of urban residents also depends on the continuous iteration of five links: planning (P), implementation (I), inspection (C), results (O) and feedback (F). Therefore, the present invention will analyze the specific connotation of promoting low-carbon life of urban residents around the five process links of PICOF and construct a corresponding diagnostic indicator system.

[0177] The technical effect of the above technical solution is: through the division of the five links of PICOF and the construction of the corresponding diagnostic indicator system, it is possible to comprehensively evaluate all aspects of the low-carbon life construction of urban residents and provide a scientific basis for decision-making. Based on the classification and comprehensive evaluation model of data from multiple sources, the evaluation results are more objective and accurate. The comprehensiveness and accuracy of the data are the basis for the reliability of the evaluation results. By excluding extreme outliers and standardizing the score variables, the influence of the score variables brought by extreme data can be eliminated. The evaluation results can provide decision-making support for relevant institutions in promoting the construction of low-carbon life for urban residents, including optimizing resource allocation and promoting environmentally friendly behaviors. Through the feedback link (F), the evaluation results can be fed back to relevant departments and residents to promote their attention and participation in the construction of low-carbon life, forming a virtuous circle of continuous improvement.

[0178] In one embodiment of the present invention, the indicator system construction module includes:

[0179] A management type classification module is used to obtain preset process management types, wherein the preset process management types include planning management, implementation management, inspection management, result management and feedback management;

[0180] A link classification module is used to classify the process links of process management according to the preset management types to obtain the five process links of PICOF;

[0181] The five process links of PICOF include planning (P), implementation (I), inspection (C), results (O) and feedback (F);

[0182] The construction module is used to obtain the index information of the five links of PICOF and construct a diagnostic index system according to the index information.

[0183] The working principle of the above technical solution is: clearly define the preset types of process management, including planning management, implementation management, inspection management, result management and feedback management. These types constitute the entire life cycle of process management, and each type manages a specific stage or task. According to the preset types of process management, the process links of process management are classified to obtain the five process links of PICOF: The planning P link corresponds to planning management, which mainly focuses on the goal setting, plan formulation and resource allocation of the project. The implementation I link corresponds to implementation management, which involves the specific execution of the project, progress control and task allocation. The inspection C link corresponds to inspection management, which mainly focuses on quality monitoring, risk identification and problem solving during the project execution process. The result O link corresponds to result management, which evaluates the completion of the project, the achievement of the effect and the output of the results. The feedback F link corresponds to feedback management, which reviews, summarizes and gives feedback on the project results in order to provide reference and improvement direction for future projects.

[0184] Obtain the indicator information of the five links of PICOF, which should be able to fully reflect the key elements and performance of each link. According to the indicator information, build a diagnostic indicator system. The system should be hierarchical, systematic and operational, and can accurately evaluate the implementation of the project in the five links of PICOF.

[0185] The technical effect of the above technical solution is: through the classification of the five process links of PICOF, systematic management of process management is achieved. Each link has its specific management goals and tasks to ensure that the project is effectively managed throughout the life cycle.

[0186] The constructed diagnostic indicator system can accurately evaluate the execution of the project in the five links of PICOF. By collecting and analyzing indicator data, problems and deficiencies in the project execution process can be discovered in a timely manner, providing a basis for project improvement. The setting of the feedback F link enables the project to be continuously improved. By reviewing, summarizing and giving feedback on project results, lessons learned can be summarized, potential problems and improvement opportunities can be discovered, and reference and improvement directions can be provided for future projects. The diagnostic indicator system provides decision-making support for managers. Managers can understand the overall situation, key links and potential risks of the project based on indicator data, so as to make more scientific and reasonable decisions. The management of the five process links of PICOF promotes collaboration among project teams. Each link needs to be connected, coordinated and supported to ensure the smooth progress of the project and the achievement of goals. This kind of collaboration helps to enhance the cohesion and execution of the project team.

[0187] In one embodiment of the present invention, the model comprehensive evaluation module includes:

[0188] A data collection classification module is used to set preset collection classifications according to the types of diagnostic indicator systems, and classify and collect diagnostic indicator system data through multi-source channels according to the preset collection classifications to obtain multi-source classified collection data;

[0189] A preprocessing module, used for preprocessing the multi-source classified collected data to obtain preprocessed multi-source classified collected data;

[0190] The model building module is used to build a calculation model for the low-carbon life construction level value of urban residents. The calculation formula of the calculation model for the low-carbon life construction level value of urban residents is:

[0191]

[0192] Among them, V d is the low-carbon performance level of urban residents in the five process links of dimension d, including the performance value V of planning P link dP 、Implementation of Phase I Performance Value V dI , Check the performance value V of the C linkdC , Result O stage performance value V dO And feedback F link performance value V dF ,s are five process links, W s is the weight of the five process links in the low-carbon construction process, including the planning P link weight W P 、Implement I link weight W I , Check the weight W of the C link C , Result O link weight W O and the weight W of the feedback link F F , V ds is the low-carbon life construction level of urban residents at the link scale; the weights are 0.2, 0.2, 0.15, 0.3, and 0.15 respectively, so W s ={W p ,W I ,W C ,W O ,W F} = {0.2,0.2,0.15,0.3,0.15}.

[0193] The calculation formula of the low-carbon life construction level value calculation model for urban residents at the link scale is:

[0194]

[0195] Among them, V dsi is the score of indicator i in the five process links, which is also the value of the city's low-carbon construction level on the indicator scale, and n is the number of indicators i in the five process links;

[0196] The calculation formula of the indicator scale urban residents' low-carbon life construction level value calculation model is:

[0197]

[0198] Among them, V dsi-j is the value of the score variable j of indicator i in the five process links, and m is the number of score variables j; the number of score variables m will vary for different indicators.

[0199] The comprehensive evaluation module is used for the low-carbon performance level values ​​of the city residents in the five process links of the d dimension, which is the comprehensive evaluation result.

[0200] The working principle of the above technical solution is: according to the type of diagnostic indicator system, set the preset collection category. For example, for the five process links of planning P, implementation I, inspection C, results O, and feedback F, set the corresponding data collection categories respectively. Through multiple source channels, the data of the diagnostic indicator system is classified and collected to obtain multi-source classified collection data. The collected multi-source classified collection data is preprocessed, including data cleaning (removing outliers, duplicate values, etc.), data conversion (standardization, normalization, etc.) and data integration steps to obtain preprocessed multi-source classified collection data. Based on the preprocessed data, the low-carbon performance level values ​​of urban residents in the five process links of the d dimension are calculated according to the formula. This value is composed of the performance values ​​(V dP 、V dI 、V dC 、V dO 、V dF ) and the weight of the corresponding link (W P , W I , W C , W O , W F ) are jointly determined. The urban residents' low-carbon life construction level value (V dsi ) calculation: In each link, according to the score value (V dsi ) and the number of indicators (n). The indicator scale of urban residents' low-carbon life construction level value (V dsi-j ) calculation: On the indicator scale, further according to the value of the score variable (j) of each indicator (i) (V dsi-j ) and the number of scoring variables (m).

[0201] The technical effects of the above technical scheme are: by collecting data through multiple source channels, the comprehensiveness and accuracy of the data are ensured, thereby improving the accuracy of the calculation of the low-carbon life construction level of urban residents. Combining five process links and multiple indicators, it can fully reflect the overall situation of low-carbon life construction of urban residents and the specific situation of each link, and realize comprehensive evaluation. According to the importance and influence of the five process links in the low-carbon construction process, the weights are reasonably allocated to ensure the scientificity and rationality of the evaluation results. The calculation model has a clear calculation formula and clear calculation steps, which is easy to operate and implement. The calculation results can provide scientific basis and decision-making support for planning management, project implementation, etc., and have strong practicality. Through continuous data collection and calculation, the progress of low-carbon life construction of urban residents can be monitored and evaluated in real time, providing data support for continuous improvement and optimization, and promoting the sustainable development of low-carbon construction.

[0202] In one embodiment of the present invention, the outlier processing module includes:

[0203] A value limiting calculation module is used to calculate the maximum value and the minimum value of the score variable according to the score variable data;

[0204] The calculation formula for the maximum value is:

[0205]

[0206] Among them, MAX qz is the maximum value, x j is the value of the score variable j, is the average value of score variable j among a group of cities, Represents the standard deviation of this set of data;

[0207] The calculation formula for the minimum value is:

[0208]

[0209] Among them, MIN qz is the minimum value;

[0210] The anomaly elimination module is used to eliminate the extreme anomaly values ​​of the score variable of the quantitative attribute in the natural data among cities through the maximum value and the minimum value.

[0211] The working principle of the above technical solution is: the calculation model of the low-carbon life construction level of urban residents based on the score variable scale, the value of the evaluation index is determined by many factors, and these influencing factors are called score variables (j). In the present invention, the value of the low-carbon life construction level of urban residents is based on the percentage system, and the score variable of the quantitative attribute needs to be standardized from 0 to 100 points. In order to exclude the extreme outliers of the score variable of the quantitative attribute in the natural data between cities, it is necessary to process the maximum and minimum values. The specific method is: the maximum value is based on Calculated, the minimum value is based on Calculated, where xj is the value of the score variable j, represents the average value of score variable j among a group of cities, Represents the standard deviation of this set of data.

[0212] The technical effect of the above technical solution is: by calculating the maximum and minimum values ​​of the score variables, extreme outliers that are far from the trend of the data set can be identified and excluded. The existence of these outliers may distort the analysis results. Therefore, excluding these outliers helps to improve the quality and reliability of the data. In data analysis, extreme outliers may lead to deviations in statistical results or misleading conclusions. By excluding these outliers, it can be ensured that the analysis is more accurate and closer to the actual situation. The method of using maximum and minimum values ​​to exclude outliers is a robust statistical method. There is no need to make strict assumptions about the data distribution, so it can be applied to various types of data sets. In the process of data-based decision-making, accurate and reliable data is crucial. By excluding outliers, it can be ensured that the policies, plans and strategies based on these data are more scientific, reasonable and effective. By accurately evaluating the level of low-carbon life construction of urban residents, resources can be allocated more reasonably, resource allocation can be optimized, and resource utilization efficiency can be improved. By excluding outliers, the progress and effectiveness of low-carbon life construction of urban residents can be more accurately evaluated, which helps to find problems, improve deficiencies, and promote the development of cities in a more low-carbon and sustainable direction. By calculating the maximum and minimum values ​​of the score variables and excluding extreme outliers based on these values, the quality and reliability of the data can be improved, the accuracy and robustness of the analysis can be enhanced, data-based decision making can be supported, resource allocation can be optimized, and the sustainable development of the city can be promoted.

[0213] In one embodiment of the present invention, the standardization module includes:

[0214] A processing startup module is used to standardize normal score variables and negative score variables after excluding extreme outliers;

[0215] The positive standardization module is used to standardize the positive score variables;

[0216] The standardized calculation formula for the positive score variable is:

[0217] =

[0218] Among them, V dsi-j is the value of the score variable j of indicator i in the five process links, x j is the value of the score variable j, x h is the optimal value of the score variable j in a set of cities; there are two ways to obtain x h Value: (a) If there is a relevant standard that specifies a benchmark or optimal value for the scoring variable j, x h The value is directly taken from the benchmark (optimal) value; (b) if there is no relevant standard, then x h = , x l is the worst value of the score variable j in a group of cities; there are two ways to obtain x l Value: (a) If there is a relevant standard that specifies a minimum value for the scoring variable j, x l The value is directly taken as the lowest value; (b) If there is no relevant standard, then x l = ;

[0219] Negative standardization module, used to standardize negative score variables;

[0220] The standardized calculation formula for the negative score variable is:

[0221] =

[0222] Among them, x u is the optimal value of the score variable j in a set of cities; there are two ways to obtain x u (a) If there is a relevant standard that specifies a benchmark or optimal value for the scoring variable j, x u (b) If there is no relevant standard, then .x e is the worst value of the score variable j in a group of cities. There are two ways to obtain x e (a) If there is a relevant standard that specifies the maximum value of the score variable j, x e The value is directly taken as the maximum value; (b) If there is no relevant standard, then x e = .

[0223] The working principle of the above technical solution is: after excluding extreme outliers, in order to make different score variables comparable, it is necessary to standardize the normal score variables and negative score variables. The main purpose of standardization is to convert the data into unitless relative values ​​so that they conform to the normal distribution and have the same scale. For positive score variables (i.e., variables with higher scores and better performance), the principle of standardization is to convert their values ​​into proportions relative to the optimal value. First, determine the optimal value x of the score variable j h and the worst value x l If there is a relevant standard that specifies a benchmark or optimal value, use it directly; otherwise, determine the optimal and worst values ​​through the data itself. Then, calculate the value V of the score variable j for each indicator i dsi-j With the optimal value x hFor negative score variables (i.e., variables with lower scores and better performance), the principle of standardization is to convert their values ​​into a ratio relative to the worst value. First, determine the optimal value x of the score variable j u and the worst value x e Similarly, if there is a relevant standard that specifies a benchmark or optimal value, it is used directly; otherwise, it is determined by the data itself. Then, the value of each score variable j is calculated relative to the worst value x e The reciprocal of the ratio (or the worst value minus the actual value divided by the difference between the worst value and the best value) is used to obtain the standardized value.

[0224] The technical effect of the above technical solution is: standardization makes different score variables have the same scale, so that they can be directly compared. This is crucial for comprehensive evaluation, ranking or weight allocation analysis. Standardization eliminates the dimensional differences and order of magnitude differences between different score variables, avoiding the impact of these differences on the analysis results. Therefore, the analysis results based on standardized data are more accurate and reliable. Standardized data can provide strong support for strategic planning, etc. By comparing the standardized scores between different cities or different indicators, we can clearly understand the performance level of each city or indicator, so as to make more scientific and reasonable decisions. Standardized data has clear meaning and interpretability. For example, a variable with a standardized score of 0.8 means that its performance has reached 80% of the optimal value. The standardization method does not rely on specific data distribution or assumptions, so it has strong adaptability and flexibility. It can be applied to data sets of various types and sizes to meet different analysis needs.

[0225] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A diagnostic method for low-carbon life of urban residents based on process management perspective, characterized in that: The method comprises: S1. Divide the process links according to the preset process management types to obtain the five process links of PICOF, and construct a diagnostic indicator system based on the information of the five process links of PICOF; S2. Classify and collect data from multiple sources of the diagnostic indicator system data to obtain data classification and collection information, and construct a calculation model for the low-carbon life construction level value of urban residents based on the data collection information to obtain a comprehensive evaluation result; S3. Perform maximum and minimum value processing on the score variables in the calculation model of the low-carbon life construction level of urban residents, and then exclude extreme outliers; S4. After excluding extreme outliers, the positive score variables and negative score variables are standardized to obtain the processing results.

2. According to claim 1, a method for diagnosing low-carbon life of urban residents based on process management perspective is characterized in that: The S1 includes: Obtaining preset process management types, wherein the preset process management types include planning management, implementation management, inspection management, result management, and feedback management; Classify the process links of process management according to the preset management types to obtain the five process links of PICOF; The five process links of PICOF include planning (P), implementation (I), inspection (C), results (O) and feedback (F); Obtain the index information of the five links of PICOF, and construct a diagnostic index system based on the index information.

3. According to claim 1, a method for diagnosing low-carbon life of urban residents based on process management perspective is characterized in that: The S2 includes: Set preset collection categories according to the types of diagnostic indicator systems, classify and collect diagnostic indicator system data through multi-source channels according to the preset collection categories, and obtain multi-source classified collection data; Preprocessing the multi-source classified collected data to obtain preprocessed multi-source classified collected data; A calculation model for the construction level value of low-carbon life for urban residents is constructed, and the calculation formula of the calculation model for the construction level value of low-carbon life for urban residents is: Among them, V d is the low-carbon performance level of urban residents in the five process links of dimension d, including the performance value V of planning P link dP 、Implementation of Phase I Performance Value V dI , Check the performance value V of the C link dC , Result O stage performance value V dO And feedback F link performance value V dF ,s are five process links, W s is the weight of the five process links in the low-carbon construction process, including the planning P link weight W P 、Implement I link weight W I , Check the weight W of the C link C , Result O link weight W O and the weight W of the feedback link F F , V ds The low-carbon life construction level value of urban residents at the link scale; The calculation formula of the low-carbon life construction level value calculation model for urban residents at the link scale is: Among them, V dsi is the score of indicator i in the five process links, which is also the value of the city's low-carbon construction level on the indicator scale, and n is the number of indicators i in the five process links; The calculation formula of the indicator scale urban residents' low-carbon life construction level value calculation model is: Among them, V dsi-j is the value of the score variable j of indicator i in the five process links, and m is the number of score variables j; The low-carbon performance level values ​​of the urban residents in the five process links of dimension d are the comprehensive evaluation results.

4. According to claim 1, a method for diagnosing low-carbon life of urban residents based on process management perspective is characterized in that: The S3 includes: Calculate the maximum and minimum values ​​of the score variable according to the score variable data; The calculation formula for the maximum value is: Among them, MAX qz is the maximum value, x j is the value of the score variable j, is the average value of score variable j among a group of cities, Represents the standard deviation of this set of data; The calculation formula for the minimum value is: Among them, MIN qz is the minimum value; The maximum value and the minimum value are used to exclude extreme outliers of the score variable of the quantitative attribute in the natural data among cities.

5. According to claim 1, a method for diagnosing low-carbon life of urban residents based on process management perspective is characterized in that: The S4 includes: After excluding extreme outliers, normal score variables and negative score variables were standardized; The standardized calculation formula for the positive score variable is: = Among them, V dsi-j is the value of the score variable j of indicator i in the five process links, x j is the value of the score variable j, x h is the optimal value of the score variable j in a group of cities; l is the worst value of the score variable j in a group of cities; The standardized calculation formula for the negative score variable is: = Among them, x u is the optimal value of the score variable j in a group of cities; x e is the worst value of the score variable j in a group of cities.

6. A low-carbon life diagnosis system for urban residents based on process management perspective, characterized in that: The system comprises: An indicator system construction module is used to divide the process links according to the preset process management types, obtain the five process links of PICOF, and construct a diagnostic indicator system based on the information of the five process links of PICOF; The model comprehensive evaluation module is used to classify and collect data from multiple sources of the diagnostic indicator system data, obtain data classification collection information, and construct a calculation model for the low-carbon life construction level value of urban residents based on the data collection information to obtain a comprehensive evaluation result; The outlier processing module is used to process the maximum and minimum values ​​of the score variables in the urban residents' low-carbon life construction level value calculation model, and then exclude extreme outliers; The standardization module is used to standardize the positive score variables and the negative score variables after excluding the extreme outliers to obtain the processing results.

7. According to claim 6, a low-carbon life diagnosis system for urban residents based on process management perspective is characterized in that: The indicator system building modules include: A management type classification module is used to obtain preset process management types, wherein the preset process management types include planning management, implementation management, inspection management, result management and feedback management; A link classification module is used to classify the process links of process management according to the preset management types to obtain the five process links of PICOF; The five process links of PICOF include planning (P), implementation (I), inspection (C), results (O) and feedback (F); The construction module is used to obtain the index information of the five links of PICOF and construct a diagnostic index system according to the index information.

8. According to claim 6, a low-carbon life diagnosis system for urban residents based on process management perspective is characterized in that: The model comprehensive evaluation module includes: A data collection classification module is used to set preset collection classifications according to the types of diagnostic indicator systems, and classify and collect diagnostic indicator system data through multi-source channels according to the preset collection classifications to obtain multi-source classified collection data; A preprocessing module, used for preprocessing the multi-source classified collected data to obtain preprocessed multi-source classified collected data; The model building module is used to build a calculation model for the low-carbon life construction level value of urban residents. The calculation formula of the calculation model for the low-carbon life construction level value of urban residents is: Among them, V d is the low-carbon performance level of urban residents in the five process links of dimension d, including the performance value V of planning P link dP 、Implementation of Phase I Performance Value V dI , Check the performance value V of the C link dC , Result O stage performance value V dO And feedback F link performance value V dF ,s are five process links, W s is the weight of the five process links in the low-carbon construction process, including the planning P link weight W P 、Implement I link weight W I , Check the weight W of the C link C , Result O link weight W O and the weight W of the feedback link F F , V ds The low-carbon life construction level value of urban residents at the link scale; The calculation formula of the low-carbon life construction level value calculation model for urban residents at the link scale is: Among them, V dsi is the score of indicator i in the five process links, which is also the value of the city's low-carbon construction level on the indicator scale, and n is the number of indicators i in the five process links; The calculation formula of the indicator scale urban residents' low-carbon life construction level value calculation model is: Among them, V dsi-j is the value of the score variable j of indicator i in the five process links, and m is the number of score variables j; The comprehensive evaluation module is used for the low-carbon performance level values ​​of the city residents in the five process links of the d dimension, which is the comprehensive evaluation result.

9. According to claim 6, a low-carbon life diagnosis system for urban residents based on process management perspective is characterized in that: The outlier processing module comprises: A value limiting calculation module is used to calculate the maximum value and the minimum value of the score variable according to the score variable data; The calculation formula for the maximum value is: Among them, MAX qz is the maximum value, x j is the value of the score variable j, is the average value of score variable j among a group of cities, Represents the standard deviation of this set of data; The calculation formula for the minimum value is: Among them, MIN qz is the minimum value; The anomaly elimination module is used to eliminate the extreme anomaly values ​​of the score variable of the quantitative attribute in the natural data among cities through the maximum value and the minimum value.

10. According to claim 6, a low-carbon life diagnosis system for urban residents based on process management perspective is characterized in that: The standardization module includes: A processing startup module is used to standardize normal score variables and negative score variables after excluding extreme outliers; The positive standardization module is used to standardize the positive score variables; The standardized calculation formula for the positive score variable is: = Among them, V dsi-j is the value of the score variable j of indicator i in the five process links, x j is the value of the score variable j, x h is the optimal value of the score variable j in a group of cities; x l is the worst value of the score variable j in a group of cities; Negative standardization module, used to standardize negative score variables; The standardized calculation formula for the negative score variable is: = Among them, x u is the optimal value of the score variable j in a group of cities; x e is the worst value of the score variable j in a group of cities.

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