Energy-saving and carbon-reducing digital analysis method and system, storage medium and electronic equipment

Abstract

The invention provides an energy-saving and carbon-reducing digital analysis method and system, a storage medium and electronic equipment, and the method comprises the steps: collecting the energy-saving and carbon-reducing work big data of a studied city, and calculating the low-carbon economic comprehensive evaluation of the studied city based on the energy-saving and carbon-reducing work big data; comprehensively planning low-carbon economic comprehensive evaluation of each researched city, introducing fund control variables, and applying a fund regulation model to measure fund supervision strength of each researched city; establishing a correlation analysis model by using a geographic feature analysis method, analyzing spatial correlation between the researched cities, and optimizing fund distribution of the researched cities based on the spatial correlation and fund supervision strength; according to the researched city after fund distribution, a corresponding decision is provided, effect evaluation is carried out on the decision through a decision evaluation model, fund distribution is calibrated on the big data level and is aligned with cities with similar geographic features, and finally fund allocation is carried out based on the working gravity center of the cities, so that energy conservation and carbon reduction work is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of energy conservation and carbon reduction, and in particular to a digital analysis method, system, storage medium and electronic equipment for energy conservation and carbon reduction. Background Art

[0002] As environmental awareness continues to increase, ecological and environmental management departments at all levels need to master carbon-related energy data, environmental data and economic data within their jurisdiction, especially for industries and enterprises in demonstration areas.

[0003] Among the existing technologies, the main focus of energy conservation and carbon reduction is reflected in actual work, while the effects, costs, trends, etc. of energy conservation and carbon reduction reflected in the background big data are only reflected in a single way. No reasonable analysis method is used to analyze the indicators of energy conservation and carbon reduction, resulting in data gaps, which is not conducive to the leadership's overall planning of subsequent energy conservation and carbon reduction work. Summary of the invention

[0004] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a digital analysis method for energy conservation and carbon reduction, aiming to solve the technical problem that in the prior art, the main focus of energy conservation and carbon reduction is reflected in actual work, while the effects, costs, trends, etc. of energy conservation and carbon reduction reflected in the background big data are only reflected in a single way, and no reasonable analysis method is used to analyze the indicators of energy conservation and carbon reduction, resulting in data gaps, which is not conducive to the leadership level's overall planning of subsequent energy conservation and carbon reduction work.

[0005] In order to achieve the above object, the present invention is implemented by the following technical solutions:

[0006] A digital analysis method for energy saving and carbon reduction includes the following steps:

[0007] Collecting big data on energy conservation and carbon reduction work in the studied city, and calculating a comprehensive evaluation of the low-carbon economy of the studied city based on the big data on energy conservation and carbon reduction work;

[0008] Coordinate the comprehensive evaluation of the low-carbon economy of each of the studied cities, introduce capital control variables, and use the capital adjustment model to measure the capital supervision strength of each of the studied cities;

[0009] Using geographic feature analysis methods to establish a correlation analysis model, analyzing the spatial correlation between the studied cities, and optimizing the funding allocation of the studied cities based on the spatial correlation and the funding supervision intensity;

[0010] According to the studied cities after the allocation of funds, corresponding decisions are proposed, and the effectiveness of the decisions is evaluated through a decision evaluation model.

[0011] According to one aspect of the above technical solution, the steps of collecting big data on energy conservation and carbon reduction work of the studied city and calculating the comprehensive evaluation of the low-carbon economy of the studied city based on the big data on energy conservation and carbon reduction work include:

[0012] Select the cities under study that are persuasive and collect historical ecological evaluation indicators X of the cities under study from relevant departments. ij ;X ij represents the historical ecological evaluation index of the jth project in the i-th year of the studied city;

[0013] The historical ecological evaluation index X ij Perform standardization and calculate the specific gravity to obtain the index specific gravity P ij ;

[0014] Determine the indicator weight W according to the development strategy j , determine the low-carbon economy comprehensive evaluation dep of the studied city through a comprehensive evaluation formula;

[0015]

[0016] Here, m represents the year of the study.

[0017] According to one aspect of the above technical solution, the comprehensive evaluation of the low-carbon economy of each of the studied cities is coordinated, a capital control variable is introduced, and a capital adjustment model is used to measure the capital supervision strength of each of the studied cities. The specific steps include:

[0018] Statistics on the economic development level of the studied citiesfin it , and determine the financial control variables that have an impact on the low-carbon economy it (O1,O2,……,O k ), O k To invest funds in projects;

[0019] Based on the level of economic development fin it , the fund control variable sup it , and the comprehensive economic evaluation dep it Establish a funding adjustment model;

[0020] dep it =α1fin it +α2sup it +α3(fin it *sup it )+α4T it +u i +δ t +ε it ;

[0021] Among them, a1, a2, a3, and a4 are adjustment parameters, T it is the level of development of the project where the capital is invested, u i is the fixed attribute effect of the studied city, δ t is the time attribute effect of the studied city, ε it is a random interference term;

[0022] According to the planned inclination of the studied city, the adjustment parameters a1, a2, a3, and a4 are determined to obtain the capital control variable sup it .

[0023] According to one aspect of the above technical solution, the use of the geographic feature analysis method to establish a correlation analysis model to analyze the spatial correlation between the studied cities includes the following specific steps:

[0024] Based on geographical factors, the geographical attribute observation values ​​of each city under study and the geographical attribute weight matrix H are counted. ii’ ;

[0025] Establishing a correlation analysis model based on the observed values ​​of geographical attributes of each of the studied cities;

[0026]

[0027] Among them, I is the spatial correlation, θ i and θ i’ is the observed value of the geographical attribute between different studied cities, and S 2 are the mean and variance of the geographical attribute observations respectively, and n is the number of cities studied.

[0028] According to one aspect of the above technical solution, the optimization of the fund allocation of the studied city based on the spatial correlation and the fund supervision strength comprises the following specific steps:

[0029] Determine a plurality of benchmark cities based on the economic development level and geographical attribute observation values ​​of each of the studied cities;

[0030] Counting the spatial correlation of the benchmark cities, and matching each non-benchmark city with each benchmark city according to the similarity of the spatial correlation, to obtain a city adjustment sequence;

[0031] Based on the funding adjustment model, the adjustment parameters of all non-benchmark cities in the same city adjustment sequence are adjusted to be consistent with the benchmark cities in the column to optimize the funding allocation of the studied cities.

[0032] According to one aspect of the above technical solution, according to the studied city after the allocation of funds, a corresponding decision is made, and the effect of the decision is evaluated through a decision evaluation model. The specific steps include:

[0033] After the funds are allocated, the studied city will re-establish the fund adjustment model according to the economic development level. it and the fund control variable sup it Recalculate the comprehensive economic evaluation dep it ;

[0034] Based on actual needs, a variety of decision-making schemes are proposed to establish a decision-making evaluation model;

[0035]

[0036] In the formula, ρ * is the effect evaluation index of the decision-making scheme, n is the number of cities under study, r is the error variable, O represents the capital investment project, x ik is the factor of the k-th capital investment project in city i, is the expected capacity of the k-th capital investment project in city i, is the undesired capacity of the k-th capital investment project in city i, C1 is the total expected capacity of all capital investment projects, C2 is the total undesired capacity of all capital investment projects, is the parameter x ik The error variable, For parameters The error variable, For parameters The error variable

[0037] Set a threshold for the effect evaluation index, and study the decision-making scheme whose effect evaluation index is greater than the threshold as a reference scheme;

[0038] Using x in the reference scheme ik As the fund control variable sup it , calculate the comprehensive economic evaluation depi again t , select the comprehensive economic evaluation depi t The reference scheme that achieves the expected result is used as the application scheme to conduct financial regulation on the studied city.

[0039] The present invention also provides a digital analysis system for energy saving and carbon reduction, comprising:

[0040] Calculation module: used to collect big data of energy conservation and carbon reduction work in the studied city, and calculate the comprehensive evaluation of low-carbon economy of the studied city based on the big data of energy conservation and carbon reduction work;

[0041] The calculation module is specifically used to: select a city to be studied that has persuasive significance, and collect historical ecological evaluation indicators X of the city to be studied from relevant departments. ij ;X ij represents the historical ecological evaluation index of the jth project in the i-th year of the studied city;

[0042] The historical ecological evaluation index X ij Perform standardization and calculate the specific gravity to obtain the index specific gravity P ij ;

[0043] Determine the indicator weight W according to the development strategy j , determine the low-carbon economy comprehensive evaluation dep of the studied city through a comprehensive evaluation formula;

[0044]

[0045] Among them, m represents the year of study;

[0046] Measurement module: used to coordinate the comprehensive evaluation of the low-carbon economy of each of the studied cities, introduce capital control variables, and use the capital adjustment model to measure the capital supervision strength of each of the studied cities;

[0047] The measurement module is specifically used to: calculate the economic development level of the studied city it , and determine the financial control variables that have an impact on the low-carbon economy it (O1,O2,……,O k ), O k To invest funds in projects;

[0048] Based on the level of economic development fin it , the fund control variable sup it , and the comprehensive economic evaluation dep it Establish a funding adjustment model;

[0049] dep it =α1fin it +α2sup it +α3(fin it *sup it )+α4T it +u i +δ t +ε it ;

[0050] Among them, a1, a2, a3, and a4 are adjustment parameters, Ti t is the level of development of the project where the capital is invested, u iis the fixed attribute effect of the studied city, δ t is the time attribute effect of the studied city, ε it is a random interference term;

[0051] According to the planned inclination of the studied city, the adjustment parameters a1, a2, a3, and a4 are determined to obtain the capital control variable sup it ;

[0052] Optimization module: used to establish a correlation analysis model using a geographic feature analysis method, analyze the spatial correlation between the studied cities, and optimize the fund allocation of the studied cities based on the spatial correlation and the fund supervision strength;

[0053] The optimization module is specifically used to: count the geographical attribute observation values ​​and geographical attribute weight matrix H of each of the studied cities based on geographical factors ii’ ;

[0054] Establishing a correlation analysis model based on the observed values ​​of geographical attributes of each of the studied cities;

[0055]

[0056] Among them, I is the spatial correlation, θ i and θ i’ is the observed value of the geographical attribute between different studied cities, and S 2 are the mean and variance of the geographical attribute observations, respectively, and n is the number of cities studied;

[0057] Determine a plurality of benchmark cities based on the economic development level and geographical attribute observation values ​​of each of the studied cities;

[0058] Counting the spatial correlation of the benchmark cities, and matching each non-benchmark city with each benchmark city according to the similarity of the spatial correlation, to obtain a city adjustment sequence;

[0059] Based on the fund adjustment model, the adjustment parameters of all non-benchmark cities in the same city adjustment sequence are adjusted to be consistent with the benchmark city in the sequence, so as to optimize the fund allocation of the studied city;

[0060] Evaluation module: used to propose corresponding decisions based on the studied cities after the funds are allocated, and evaluate the effectiveness of the decisions through a decision evaluation model;

[0061] The evaluation module is specifically used to: re-establish the capital adjustment model for the studied city after the capital allocation, and fin itand the fund control variable sup it Recalculate the comprehensive economic evaluation dep it ;

[0062] Based on actual needs, a variety of decision-making schemes are proposed to establish a decision-making evaluation model;

[0063]

[0064] In the formula, ρ * is the effect evaluation index of the decision-making scheme, n is the number of cities under study, r is the error variable, O represents the capital investment project, x ik is the factor of the k-th capital investment project in city i, is the expected capacity of the k-th capital investment project in city i, is the undesired capacity of the k-th capital investment project in city i, C1 is the total expected capacity of all capital investment projects, C2 is the total undesired capacity of all capital investment projects, is the parameter x ik The error variable, For parameters The error variable, For parameters The error variable

[0065] Set a threshold for the effect evaluation index, and study the decision-making scheme whose effect evaluation index is greater than the threshold as a reference scheme;

[0066] Using x in the reference scheme ik As the fund control variable sup it , calculate the comprehensive economic evaluation dep again it , select the comprehensive economic evaluation dep it The reference scheme that achieves the expected result is used as the application scheme to conduct financial regulation on the studied city.

[0067] The present invention also provides a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the digital analysis method for energy saving and carbon reduction as described above.

[0068] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the digital analysis method for energy saving and carbon reduction as described above when executing the computer program.

[0069] Compared with the prior art, the present invention has the following beneficial effects:

[0070] By collecting big data on energy conservation and carbon reduction work in the studied cities (including carbon emissions, vegetation planting, etc.), we can calculate the comprehensive evaluation of the low-carbon economy of each studied city, and then clearly know the strength of each city's low-carbon economy. Then, by introducing capital control variables (including low-carbon education costs, population density, low-carbon industrial structure, etc.), we can get the impact of capital participation on the city's low-carbon economy; then, through the analysis of geographical characteristics, we can airdrop cities with high correlation, and use the city with the highest comprehensive evaluation of low-carbon economy as a benchmark to adjust the capital allocation of other cities with high spatial correlation. Finally, according to the future work focus of each city, we can determine the corresponding decision, evaluate the quality of the decision, and apply the capital allocation corresponding to the excellent decision to actual work;

[0071] The present invention calibrates the allocation of funds at the big data level, aligns with the best cities with similar geographical features, and finally allocates funds based on the work focus of each city to achieve energy conservation and carbon reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 This is a flow chart of the digital analysis method for energy saving and carbon reduction in the first embodiment of the present invention;

[0073] Figure 2 It is a structural block diagram of the energy-saving and carbon-reduction digital analysis system in the second embodiment of the present invention;

[0074] Figure 3 is a structural block diagram of an electronic device in a third embodiment of the present invention;

[0075] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0076] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0077] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0078] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0079] See also Figure 1 , which is a digital analysis method for energy saving and carbon reduction in the first embodiment of the present invention, comprising the following steps:

[0080] S10, collecting big data on energy conservation and carbon reduction work in the studied city, and calculating a comprehensive evaluation of the low-carbon economy of the studied city based on the big data on energy conservation and carbon reduction work;

[0081] S20, coordinate the comprehensive evaluation of the low-carbon economy of each of the studied cities, introduce capital control variables, and use the capital adjustment model to measure the capital supervision strength of each of the studied cities;

[0082] S30, using a geographic feature analysis method to establish a correlation analysis model, analyzing the spatial correlation between the studied cities, and optimizing the fund allocation of the studied cities based on the spatial correlation and the fund supervision strength;

[0083] S40, proposing corresponding decisions based on the studied cities after the fund allocation, and evaluating the effectiveness of the decisions through a decision evaluation model.

[0084] It can be understood that the present invention can calculate the comprehensive evaluation of the low-carbon economy of each studied city by collecting big data on energy conservation and carbon reduction work in the studied cities (including carbon emissions, vegetation planting, etc.), and then clearly know the strength of the low-carbon economy of each city, and then introduce capital control variables (including low-carbon education costs, population density, low-carbon industrial structure, etc.), and obtain the impact of capital participation on the city's low-carbon economy; then, through the analysis of geographical characteristics, the cities with higher airborne correlation are selected, and the cities with the highest comprehensive evaluation of low-carbon economy are used as the benchmark to adjust the capital allocation of other cities with higher spatial correlation; finally, according to the future work focus of each city, the corresponding decision is determined, and the quality of the decision is evaluated, and the capital allocation corresponding to the excellent decision is applied to actual work;

[0085] The present invention calibrates the allocation of funds at the big data level, aligns with the best cities with similar geographical features, and finally allocates funds based on the work focus of each city to achieve energy conservation and carbon reduction.

[0086] Furthermore, the step of collecting big data on energy conservation and carbon reduction work of the studied city and calculating a comprehensive evaluation of the low-carbon economy of the studied city based on the big data on energy conservation and carbon reduction work includes:

[0087] Select the cities under study that are persuasive and collect historical ecological evaluation indicators X of the cities under study from relevant departments. ij ;X ij represents the historical ecological evaluation index of the jth project in the i-th year of the studied city;

[0088] The historical ecological evaluation index X ij Perform standardization and calculate the specific gravity to obtain the index specific gravity P ij ;

[0089] Determine the indicator weight W according to the development strategy j , determine the low-carbon economy comprehensive evaluation dep of the studied city through a comprehensive evaluation formula;

[0090]

[0091] Here, m represents the year of the study.

[0092] Understandably, this step first selects representative cities to be studied, such as economically strong cities, industrially strong cities, and tourism strong cities, and then collects historical ecological evaluation indicators X of the cities to be studied in recent years. ij , and then calculate the historical ecological evaluation index curve of the studied city; then calculate the historical ecological evaluation index X of each index ij The index weight P ij , the indicators include carbon emissions, vegetation area, new vegetation, etc., and then calculate the weight of each indicator W j For example, the weight level of carbon emissions is higher because it directly determines the low-carbon results, and the weight level of new vegetation construction is second, which determines the development strategy, etc.; finally, the comprehensive evaluation is used to obtain the comprehensive evaluation of the low-carbon economy of each city under study, dep.

[0093] Furthermore, the comprehensive evaluation of the low-carbon economy of each of the studied cities is coordinated, a capital control variable is introduced, and a capital adjustment model is used to measure the capital supervision strength of each of the studied cities. The specific steps include:

[0094] Statistics on the economic development level of the studied citiesfin it , and determine the financial control variables that have an impact on the low-carbon economy it (O1,O2,……,O k ), O k To invest funds in projects;

[0095] Based on the level of economic development fin it , the fund control variable sup it , and the comprehensive economic evaluation depi t Establish a funding adjustment model;

[0096] dep it =α1fin it +α2sup it +α3(fin it *sup it )+α4T it +u i +δ t +ε it ;

[0097] Among them, a1, a2, a3, and a4 are adjustment parameters, T it is the level of development of the project where the capital is invested, u i is the fixed attribute effect of the studied city, δ t is the time attribute effect of the studied city, ε it is a random interference term;

[0098] According to the planned inclination of the studied city, the adjustment parameters a1, a2, a3, and a4 are determined to obtain the capital control variable sup it .

[0099] It is understandable that after obtaining the comprehensive evaluation of low-carbon economy dep of each studied city, a capital adjustment model is established. it is certain, the level of economic developmentfin it is certain, the capital control variable sup it It is also certain that only the capital control variable sup it The adjustment parameters are uncertain, but the adjustment parameters a1, a2, a3, and a4 can be preliminarily determined through the planned inclination of the studied city. The planned inclination represents the characteristics of the studied city and the future strategic direction based on the characteristics. For example, in an industrially strong city, it is difficult to meet the demand for new vegetation construction, so more funds need to be spent on environmental education and other aspects. Therefore, the corresponding supi t (O k ) provides a higher value of the adjustment parameter a2, and the economic development level fini t Provide a higher value of the adjustment parameter a1, fin it *sup it Providing a higher value of the adjustment parameter a3, the development level of vegetation new construction volume T itBy providing a slightly lower value of the adjustment parameter a4, and then determining the basic adjustment parameters a1, a2, a3, and a4, the updated capital control variable sup can be obtained. it The value of .

[0100] Furthermore, the use of the geographic feature analysis method to establish a correlation analysis model to analyze the spatial correlation between the studied cities includes the following specific steps:

[0101] Based on geographical factors, the geographical attribute observation values ​​of each city under study and the geographical attribute weight matrix H are counted. ii’ ;

[0102] Establishing a correlation analysis model based on the observed values ​​of geographical attributes of each of the studied cities;

[0103]

[0104] Among them, I is the spatial correlation, θ i and θ i′ is the observed value of the geographical attribute between different studied cities, and S 2 are the mean and variance of the geographical attribute observations respectively, and n is the number of cities studied.

[0105] It can be understood that in order to find the geographical correlation between the studied cities, a correlation analysis model was established. Because the development direction of each studied city is generally determined based on geographical attributes, it is hoped that cities with high geographical attribute correlation can move towards cities with higher economic development levels in the sequence. With similar capital control variables, firstly, the geographical attribute observation values ​​of each studied city and the geographical attribute weight matrix H are statistically analyzed based on geographical factors. ii’ , and then the spatial correlation I between cities is obtained based on the correlation analysis model. The higher the spatial correlation, the closer the geographical attributes between the two cities are.

[0106] Furthermore, the optimization of the fund allocation of the studied city based on the spatial correlation and the fund supervision strength comprises the following specific steps:

[0107] Determine a plurality of benchmark cities based on the economic development level and geographical attribute observation values ​​of each of the studied cities;

[0108] Counting the spatial correlation of the benchmark cities, and matching each non-benchmark city with each benchmark city according to the similarity of the spatial correlation, to obtain a city adjustment sequence;

[0109] Based on the funding adjustment model, the adjustment parameters of all non-benchmark cities in the same city adjustment sequence are adjusted to be consistent with the benchmark cities in the column to optimize the funding allocation of the studied cities.

[0110] It can be understood that, generally speaking, the city with the highest economic development level is determined as the benchmark city, and then the spatial correlation of each city is determined based on the geographical attribute observations, and then multiple city adjustment sequences are determined based on the spatial correlation of each city. For example, the economic development levels of City A and City B are the first and second respectively, but the spatial correlations are quite different. Therefore, City A and City B are the benchmark cities of different city adjustment sequences, and then other cities are treated as non-benchmark cities and divided into different city adjustment sequences according to the spatial correlation. Then the adjustment parameters a1, a2, a3, and a4 of the non-benchmark cities are updated to be consistent with the benchmark cities in the city adjustment sequence, so as to optimize the funding allocation of the studied cities, and other parameters remain unchanged. This spatial correlation can set an evaluation threshold, and the cities within this evaluation threshold are correlated with each other.

[0111] Furthermore, according to the studied cities after the allocation of funds, corresponding decisions are proposed, and the effectiveness of the decisions is evaluated through a decision evaluation model. The specific steps include:

[0112] After the funds are allocated, the studied city will re-establish the fund adjustment model according to the economic development level. it and the fund control variable sup it Recalculate the comprehensive economic evaluation dep it ;

[0113] Based on actual needs, a variety of decision-making schemes are proposed to establish a decision-making evaluation model;

[0114]

[0115] In the formula, ρ * is the effect evaluation index of the decision-making scheme, n is the number of cities under study, r is the error variable, O represents the capital investment project, x ik is the factor of the k-th capital investment project in city i, is the expected capacity of the k-th capital investment project in city i, is the undesired capacity of the k-th capital investment project in city i, C1 is the total expected capacity of all capital investment projects, C2 is the total undesired capacity of all capital investment projects, is the parameter x ik The error variable, For parameters The error variable, For parameters The error variable

[0116] Set a threshold for the effect evaluation index, and study the decision-making scheme whose effect evaluation index is greater than the threshold as a reference scheme;

[0117] Using x in the reference scheme ik As the fund control variable sup it , calculate the comprehensive economic evaluation dep again it , select the comprehensive economic evaluation dep it The reference scheme that achieves the expected result is used as the application scheme to conduct financial regulation on the studied city.

[0118] It is understandable that after re-adjusting the adjustment parameters of non-benchmark cities, the comprehensive economic evaluation dep it It has also been updated. By establishing a decision evaluation model and inputting various indicators (expected and unexpected) into the decision evaluation model, the effect evaluation index of the decision plan can be obtained. The main variable of the decision plan is the investment amount of various capital investment projects. By changing a variety of different investment ratios, the effect evaluation index of each decision plan can be obtained. The decision plan with an effect evaluation index higher than the effect evaluation index threshold is selected, and then the parameter x in the decision plan is used. ik , the factors of the k-th capital investment project in city i are used to recalculate the comprehensive economic evaluation dep it The expected results will be used as important application plans in the capital regulation of the studied cities.

[0119] In summary, the digital analysis method for energy conservation and carbon reduction in the above-mentioned embodiments of the present invention calibrates the allocation of funds at the big data level, aligns with the best cities with similar geographical characteristics, and finally allocates funds based on the work focus of each city to achieve energy conservation and carbon reduction.

[0120] Please refer to Figure 2 , which is a digital analysis system for energy saving and carbon reduction in a second embodiment of the present invention, comprising:

[0121] Calculation module 11: used to collect big data of energy conservation and carbon reduction work in the studied city, and calculate the comprehensive evaluation of low-carbon economy of the studied city based on the big data of energy conservation and carbon reduction work;

[0122] The calculation module 11 is specifically used to: select a city to be studied that has persuasive significance, and collect historical ecological evaluation indicators X of the city to be studied from relevant departments. ij ;X ij represents the historical ecological evaluation index of the jth project in the i-th year of the studied city;

[0123] The historical ecological evaluation index X ij Perform standardization and calculate the specific gravity to obtain the index specific gravity P ij ;

[0124] Determine the indicator weight W according to the development strategy j , determine the low-carbon economy comprehensive evaluation dep of the studied city through a comprehensive evaluation formula;

[0125]

[0126] Among them, m represents the year of study;

[0127] Metrics module 12: used to coordinate the comprehensive evaluation of the low-carbon economy of each of the studied cities, introduce capital control variables, and use the capital adjustment model to measure the capital supervision strength of each of the studied cities;

[0128] The measurement module 12 is specifically used to: calculate the economic development level of the studied city fin it , and determine the financial control variables that have an impact on the low-carbon economy it (O1,O2,……,O k ), O k To invest funds in projects;

[0129] Based on the level of economic development fin it , the fund control variable sup it , and the comprehensive economic evaluation dep it Establish a funding adjustment model;

[0130] dep it =α1fin it +α2sup it +α3(fin it *sup it )+α4T it +u i +δ t +ε it ;

[0131] Among them, a1, a2, a3, and a4 are adjustment parameters, T it is the level of development of the project where the capital is invested, u i is the fixed attribute effect of the studied city, δ t is the time attribute effect of the studied city, ε it is a random interference term;

[0132] According to the planned inclination of the studied city, the adjustment parameters a1, a2, a3, and a4 are determined to obtain the capital control variable sup it;

[0133] Optimization module 13: used to establish a correlation analysis model using a geographic feature analysis method, analyze the spatial correlation between the studied cities, and optimize the fund allocation of the studied cities based on the spatial correlation and the fund supervision strength;

[0134] The optimization module 13 is specifically used to: count the geographical attribute observation values ​​and geographical attribute weight matrix H of each of the studied cities based on geographical factors ii’ ;

[0135] Establishing a correlation analysis model based on the geographical attribute observation values ​​of each of the studied cities;

[0136]

[0137] Among them, I is the spatial correlation, θ i and θ i’ is the observed value of the geographical attribute between different studied cities, and S 2 are the mean and variance of the geographical attribute observations, respectively, and n is the number of cities studied;

[0138] Determine a plurality of benchmark cities according to the economic development level and geographical attribute observation values ​​of each of the studied cities;

[0139] Counting the spatial correlation of the benchmark cities, and matching each non-benchmark city with each benchmark city according to the similarity of the spatial correlation, to obtain a city adjustment sequence;

[0140] Based on the fund adjustment model, the adjustment parameters of all non-benchmark cities in the same city adjustment sequence are adjusted to be consistent with the benchmark city in the sequence, so as to optimize the fund allocation of the studied city;

[0141] Evaluation module 14: used to propose corresponding decisions according to the studied cities after the funds are allocated, and evaluate the effect of the decisions through a decision evaluation model;

[0142] The evaluation module 14 is specifically used to: re-establish the capital adjustment model for the studied city after the capital allocation, and to fin it and the fund control variable sup it Recalculate the comprehensive economic evaluation dep it ;

[0143] Based on actual needs, a variety of decision-making schemes are proposed to establish a decision-making evaluation model;

[0144]

[0145] In the formula, ρ * is the effect evaluation index of the decision-making scheme, n is the number of cities under study, r is the error variable, O represents the capital investment project, x ik is the factor of the k-th capital investment project in city i, is the expected capacity of the k-th capital investment project in city i, is the undesired capacity of the k-th capital investment project in city i, C1 is the total expected capacity of all capital investment projects, C2 is the total undesired capacity of all capital investment projects, is the parameter x ik The error variable, For parameters The error variable, For parameters The error variable

[0146] Set a threshold for the effect evaluation index, and study the decision-making scheme whose effect evaluation index is greater than the threshold as a reference scheme;

[0147] Using x in the reference scheme ik As the fund control variable sup it , calculate the comprehensive economic evaluation dep again it , select the comprehensive economic evaluation dep it The reference scheme that achieves the expected result is used as the application scheme to conduct financial regulation on the studied city.

[0148] The present invention also provides an electronic device, see Figure 3 , shown is an electronic device in the third embodiment of the present invention, including a memory 10, a processor 20, and a computer program 30 stored in the memory 10 and executable on the processor 20, and when the processor 20 executes the computer program 30, the above-mentioned digital analysis method for energy saving and carbon reduction is implemented.

[0149] Among them, the memory 10 includes at least one type of storage medium, and the storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 10 can be an internal storage unit of an electronic device, such as a hard disk of the electronic device. In other embodiments, the memory 10 can also be an external storage device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Further, the memory 10 can also include both an internal storage unit of the electronic device and an external storage device. The memory 10 can not only be used to store application software and various types of data installed in the electronic device, but also can be used to temporarily store data that has been output or is to be output.

[0150] Among them, in some embodiments, the processor 20 can be an electronic control unit (Electronic Control Unit, abbreviated as ECU, also known as a vehicle computer), a central processing unit (Central Processing Unit, CPU), a controller, a microcontroller, a microprocessor or other data processing chip, used to run the program code stored in the memory 10 or process data, such as executing access restriction programs, etc.

[0151] It should be pointed out that Figure 3 The structure shown does not constitute a limitation on the electronic device. In other embodiments, the electronic device may include fewer or more components than those shown in the figure, or combine certain components, or arrange the components differently.

[0152] The embodiment of the present invention further provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the digital analysis method for energy saving and carbon reduction as described above is implemented.

[0153] Those skilled in the art will appreciate that the logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For purposes of this specification, a "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0154] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), 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 disk read-only memory (CDROM). In addition, the computer-readable medium may even be a 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, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0155] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0156] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0157] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0158] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0159] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A digital analysis method for energy saving and carbon reduction, characterized in that: The steps include: Collecting big data on energy conservation and carbon reduction work in the studied city, and calculating a comprehensive evaluation of the low-carbon economy of the studied city based on the big data on energy conservation and carbon reduction work; Coordinate the comprehensive evaluation of the low-carbon economy of each of the studied cities, introduce capital control variables, and use the capital adjustment model to measure the capital supervision strength of each of the studied cities; Using geographic feature analysis methods to establish a correlation analysis model, analyzing the spatial correlation between the studied cities, and optimizing the funding allocation of the studied cities based on the spatial correlation and the funding supervision intensity; According to the studied cities after the allocation of funds, corresponding decisions are proposed, and the effectiveness of the decisions is evaluated through a decision evaluation model.

2. The energy saving and carbon reduction digital analysis method according to claim 1 is characterized in that: The steps of collecting the energy conservation and carbon reduction work big data of the studied city and calculating the low-carbon economy comprehensive evaluation of the studied city based on the energy conservation and carbon reduction work big data include: Select the cities under study that are persuasive and collect historical ecological evaluation indicators X of the cities under study from relevant departments. ij ;X ij represents the historical ecological evaluation index of the jth project in the i-th year of the studied city; The historical ecological evaluation index X ij Perform standardization and calculate the specific gravity to obtain the index specific gravity P ij ; Determine the indicator weight W according to the development strategy j , determine the low-carbon economy comprehensive evaluation dep of the studied city through a comprehensive evaluation formula; Here, m represents the year of the study.

3. The energy saving and carbon reduction digital analysis method according to claim 2 is characterized in that: The comprehensive evaluation of the low-carbon economy of the cities under study is coordinated, a capital control variable is introduced, and a capital adjustment model is used to measure the capital supervision strength of the cities under study. The specific steps include: Statistics on the economic development level of the studied citiesfin it , and determine the financial control variables that have an impact on the low-carbon economy it (O1, O2, ..., O k ), O k To invest funds in projects; Based on the level of economic development fin it , the fund control variable sup it , and the comprehensive economic evaluation dep it Establish a fund adjustment model; deposit it =α1fin it +α2sup it +α3(fin it *sup it )+α4T it +u i +d t +e it ; Among them, a1, a2, a3, and a4 are adjustment parameters, T it is the level of development of the project where the capital is invested, u i is the fixed attribute effect of the studied city, δ t is the time attribute effect of the studied city, ε it is a random interference term; According to the planned inclination of the studied city, the adjustment parameters a1, a2, a3, and a4 are determined to obtain the capital control variable sup it .

4. The energy saving and carbon reduction digital analysis method according to claim 3 is characterized in that: The method of using geographic feature analysis to establish a correlation analysis model and analyze the spatial correlation between the studied cities specifically includes the following steps: Based on geographical factors, the geographical attribute observation values ​​of each city under study and the geographical attribute weight matrix H are counted ii’ ; Establishing a correlation analysis model based on the geographical attribute observation values ​​of each of the studied cities; Among them, I is the spatial correlation, θ i and θ i’ is the observed value of the geographical attribute between different studied cities, and S 2 are the mean and variance of the geographical attribute observations respectively, and n is the number of cities studied.

5. The energy saving and carbon reduction digital analysis method according to claim 4 is characterized in that: The optimizing the fund allocation of the studied city based on the spatial correlation and the fund supervision strength comprises: Determine a plurality of benchmark cities according to the economic development level and geographical attribute observation values ​​of each of the studied cities; Counting the spatial correlation of the benchmark cities, and matching each non-benchmark city with each benchmark city according to the similarity of the spatial correlation, to obtain a city adjustment sequence; Based on the funding adjustment model, the adjustment parameters of all non-benchmark cities in the same city adjustment sequence are adjusted to be consistent with the benchmark cities in the column to optimize the funding allocation of the studied cities.

6. The energy saving and carbon reduction digital analysis method according to claim 5 is characterized in that: The steps of making corresponding decisions based on the cities studied after the allocation of funds and evaluating the effectiveness of the decisions through a decision evaluation model include: After the funds are allocated, the studied city will re-establish the fund adjustment model according to the economic development level. it and the fund control variable sup it Recalculate the comprehensive economic evaluation dep it ; Based on actual needs, a variety of decision-making schemes are proposed to establish a decision-making evaluation model; In the formula, ρ * is the effect evaluation index of the decision-making scheme, n is the number of cities under study, r is the error variable, O represents the capital investment project, x ik is the factor of the k-th capital investment project in city i, is the expected capacity of the k-th capital investment project in city i, is the undesired capacity of the k-th capital investment project in city i, C1 is the total expected capacity of all capital investment projects, C2 is the total undesired capacity of all capital investment projects, is the parameter x ik The error variable, For parameters The error variable, For parameters The error variable Set a threshold for the effect evaluation index, and study the decision-making scheme whose effect evaluation index is greater than the threshold as a reference scheme; Using x in the reference scheme ik As the fund control variable sup it , calculate the comprehensive economic evaluation dep again it , select the comprehensive economic evaluation dep it The reference scheme that achieves the expected result is used as the application scheme to conduct financial regulation on the studied city.

7. A digital analysis system for energy saving and carbon reduction, characterized in that: include: Calculation module: used to collect big data of energy conservation and carbon reduction work in the studied city, and calculate the comprehensive evaluation of low-carbon economy of the studied city based on the big data of energy conservation and carbon reduction work; Measurement module: used to coordinate the comprehensive evaluation of the low-carbon economy of each of the studied cities, introduce capital control variables, and use the capital adjustment model to measure the capital supervision strength of each of the studied cities; Optimization module: used to establish a correlation analysis model using a geographic feature analysis method, analyze the spatial correlation between the studied cities, and optimize the fund allocation of the studied cities based on the spatial correlation and the fund supervision strength; Evaluation module: used to propose corresponding decisions based on the studied cities after the funds are allocated, and to evaluate the effectiveness of the decisions through a decision evaluation model.

8. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the energy-saving and carbon-reduction digital analysis method as described in any one of claims 1 to 6 is implemented.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the digital analysis method for energy saving and carbon reduction according to any one of claims 1 to 6 is implemented.

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