An optimization analysis method and system based on multi-dimensional energy data

Abstract

This invention discloses an optimization analysis method and system based on multi-dimensional energy data. The invention acquires regional energy type data and transmission equipment data, and determines multi-dimensional energy assessment indicators. A multi-dimensional optimization set is constructed based on the energy optimization set and the transmission equipment optimization set. A matter-element extension model is constructed based on the multi-dimensional optimization set and the multi-dimensional energy assessment indicators to determine the hierarchical correlation degree between each multi-dimensional energy assessment indicator and each multi-dimensional optimization pair in the multi-dimensional optimization set, thereby constructing a multi-dimensional correlation sequence. The correlation coefficient of each multi-dimensional energy assessment indicator is calculated using the grey relational coefficient based on the multi-dimensional correlation sequence. The optimal multi-dimensional energy optimization scheme is selected according to actual needs based on the correlation coefficients of the carbon dioxide emission sequence, energy conversion rate sequence, and energy transmission efficiency sequence. This invention avoids the one-sidedness of single-indicator evaluation and ensures the comprehensiveness of the optimization scheme.

Description

Technical Field

[0001] This invention relates to the field of energy management and data analysis technology, specifically to an optimization analysis method and system based on multi-dimensional energy data. Background Technology

[0002] With the continuous development of society and the economy, the demand for energy is constantly increasing, and emerging energy technologies such as clean energy and renewable energy are also emerging. As these emerging energy technologies continue to develop, they place more complex and sophisticated demands on energy management systems. How to achieve efficient, precise management and optimization of energy use while ensuring a stable and secure energy supply has become a crucial issue.

[0003] Traditional energy optimization management methods often rely on single energy consumption or production data, using statistical analysis to assess energy efficiency or machine learning and neural network learning to predict and propose improvement measures. However, this approach ignores the complex relationships and interactions between various elements within the energy system, making it difficult to comprehensively and dynamically reflect the actual operating status of the energy system. Consequently, planning results may only meet one or two indicators. Summary of the Invention

[0004] The purpose of this invention is to provide an optimization analysis method and system based on multi-dimensional energy data, which considers the complex relationships and mutual influences between various elements within the energy system to optimize energy.

[0005] This invention provides the following technical solution: an optimization analysis method based on multi-dimensional energy data, comprising:

[0006] Acquire data on energy types and transmission equipment within the region, and calculate the total energy conversion rate and carbon dioxide emission rate of each energy source, as well as the transmission rate of each transmission device;

[0007] Determine multi-dimensional energy assessment indicators; the multi-dimensional energy assessment indicators include energy transmission efficiency assessment indicators, energy conversion rate assessment indicators, energy economic benefit assessment indicators, and carbon dioxide emission assessment indicators.

[0008] Determine the energy optimization set and the transmission equipment optimization set, construct multi-dimensional optimization pairs based on each element in the energy optimization set and the transmission equipment optimization set, and input all multi-dimensional optimization pairs into the multi-dimensional optimization set;

[0009] Based on the multidimensional optimization set and multidimensional energy assessment index, a matter-element extension model is constructed. The hierarchical correlation degree between each multidimensional energy assessment index and each multidimensional optimization pair in the multidimensional optimization set is determined, and the correlation degree of multidimensional energy assessment index for each multidimensional optimization pair is calculated to construct a multidimensional correlation sequence.

[0010] The correlation coefficient of each multi-dimensional energy assessment indicator is calculated using the grey relational coefficient.

[0011] All multi-dimensional optimization pairs are ranked according to their correlation number based on the multi-dimensional correlation sequence, and the best multi-dimensional energy optimization scheme is selected according to actual needs.

[0012] According to the above technical solution, the data of the transmission equipment includes the total energy input to the transmission equipment and the total energy output of the transmission equipment; the transmission equipment includes power transmission equipment, energy transmission equipment, heat pipe network and gas transmission pipeline;

[0013] The transmission rate of each transmission device is calculated based on the total energy input and output of each transmission device.

[0014] The energy type data includes the consumption of each energy type and the energy converted from each energy type. The total energy conversion rate of each energy type is calculated based on the consumption of each energy type and the energy converted from each energy type.

[0015] The carbon dioxide emission rate of each energy source is calculated based on its consumption and corresponding carbon dioxide emissions.

[0016] The energy conversion rate of each energy source is calculated based on its consumption and the energy converted from it.

[0017] The energy conversion rates of different types of energy are different under the same quantity. Therefore, the different allocation of each type of energy in the energy optimization scheme will also lead to changes in the total energy conversion rate. Similarly, the setting of different transmission equipment will also lead to changes in the total energy transmission efficiency. Changes in the total energy conversion rate and energy transmission efficiency will indirectly affect energy economic benefits and carbon dioxide emissions.

[0018] According to the above technical solution, energy optimization is carried out with the best energy economy as the optimization objective, or with the lowest carbon dioxide emissions as the optimization objective, or with both the best energy economy and the lowest carbon dioxide emissions as the optimization objective. The obtained energy optimization data is then filtered and put into the energy optimization set.

[0019] The transmission equipment is optimized with the goal of minimizing energy transmission loss. The resulting optimized transmission equipment data is then filtered and added to the transmission equipment optimization set.

[0020] The energy optimization and transmission equipment optimization methods can utilize game theory, particle optimization methods, genetic mutation methods, etc.

[0021] According to the above technical solution, the step of calculating the degree of correlation includes:

[0022] Determine the corresponding energy and economic benefits for each multi-dimensional optimization;

[0023] The carbon dioxide emissions corresponding to the multi-dimensional optimization are calculated based on the energy optimization data and the carbon dioxide emission rate of each energy source in each multi-dimensional optimization pair.

[0024] Based on the energy optimization data in each multi-dimensional optimization pair and the total energy conversion rate of each energy source, calculate the energy conversion rate corresponding to the multi-dimensional optimization. Specifically, use the total energy conversion rate of each energy source to calculate the energy after conversion of the corresponding energy in the multi-dimensional optimization pair, sum the total energy after conversion of each energy source to obtain the total conversion energy of the multi-dimensional optimization pair, and then calculate the ratio of the total conversion energy of the multi-dimensional optimization pair to the total amount before conversion of each multi-dimensional optimization to obtain the energy conversion rate corresponding to the multi-dimensional optimization.

[0025] The energy transmission efficiency corresponding to the multi-dimensional optimization is calculated based on the multi-dimensional optimization data, energy conversion rate, and transmission rate of each transmission device in the multi-dimensional optimization pair. Specifically, the energy after transmission by each transmission device is calculated based on the total conversion energy of the multi-dimensional optimization pair using the transmission rate of each transmission device. The sum of the energy after transmission by each transmission device is calculated, and then the ratio of the sum of the energy after transmission by each transmission device to the total conversion energy of the multi-dimensional optimization pair is calculated to obtain the energy transmission efficiency corresponding to the multi-dimensional optimization.

[0026] The correlation degree formula was used to calculate the correlation degree between the energy economic benefits of the multi-dimensional optimization pair and the energy economic benefits evaluation index level, the correlation degree between the energy conversion rate of the multi-dimensional optimization pair and the energy conversion rate evaluation index level, the correlation degree between the energy transmission efficiency of the multi-dimensional optimization pair and the energy transmission efficiency evaluation index level, and the correlation degree between the carbon dioxide emissions of the multi-dimensional optimization pair and the carbon dioxide emissions evaluation index level.

[0027] The correlation degree of multi-dimensional energy assessment indicators in the multi-dimensional optimization pair is calculated based on the weight ratio of each multi-dimensional energy assessment indicator level. The correlation degree of multi-dimensional energy assessment indicators includes the correlation degree of energy economic benefit assessment indicators, energy conversion rate assessment indicators, energy transmission efficiency assessment indicators, and carbon dioxide emission assessment indicators.

[0028] According to the above technical solution, the formula for the degree of hierarchical correlation is:

[0029]

[0030] In the formula, K q (V i V represents the correlation between multi-dimensional energy assessment index i and its corresponding multi-dimensional energy assessment index level q in multi-dimensional optimization. i V represents the data corresponding to the multi-dimensional energy assessment index i in the multi-dimensional optimization approach. iq ρ(.) represents the range of values ​​for the multi-dimensional energy assessment index i under level q, and ρ(.) represents the difference calculation function.

[0031] in,

[0032]

[0033] In the formula, a iq b represents the lower bound of the range of values ​​for the multi-dimensional energy assessment index i at level q. iq b represents the upper bound of the range of values ​​for the multi-dimensional energy assessment index i at level q. pi This represents the upper bound of the values ​​of the multi-dimensional energy assessment index i across all levels. This represents the lower bound of the range of values ​​for the multi-dimensional energy assessment index i after standardization of level q. a represents the upper bound of the range of values ​​for the multi-dimensional energy assessment index i after standardization, q. pi This represents the lower bound of the value of the multidimensional energy assessment index i across all levels.

[0034] The matter-element extension model describes the object to be evaluated and its indicators through ordered triples. The establishment of the matter-element extension model is existing technology and will not be described in detail here.

[0035] According to the above technical solution, the formula for calculating the correlation of the multi-dimensional energy assessment indicators is as follows:

[0036]

[0037] In the formula, S i This represents the correlation between multi-dimensional energy assessment indicators, where Q represents the number of levels of multi-dimensional energy assessment indicator i, and a ji This indicates the weight of multidimensional energy assessment index i in multidimensional energy assessment index level j.

[0038] According to the above technical solution, the multi-dimensional correlation sequence includes an energy economic benefit sequence, a carbon dioxide emission sequence, an energy conversion rate sequence, and an energy transmission efficiency sequence;

[0039] The energy economic benefit sequence includes the energy economic benefits of multi-dimensional optimization pairs, the correlation between the energy economic benefits of multi-dimensional optimization pairs and the level of each energy economic benefit evaluation indicator, and the correlation between the energy economic benefit evaluation indicators.

[0040] The carbon dioxide emission sequence includes the carbon dioxide emissions of the multi-dimensional optimization pair, the correlation between the carbon dioxide emissions of the multi-dimensional optimization pair and the level of each carbon dioxide emission assessment index, and the correlation between the carbon dioxide emission assessment index.

[0041] The energy transmission efficiency sequence includes the energy transmission efficiency of multi-dimensional optimization pairs, the correlation between the energy transmission efficiency of multi-dimensional optimization pairs and the level of each energy transmission efficiency evaluation index, and the correlation between the energy transmission efficiency evaluation index.

[0042] The energy conversion rate sequence includes the energy conversion rate of the multi-dimensional optimization pair, the correlation between the energy conversion rate of the multi-dimensional optimization pair and the level of each energy conversion rate evaluation index, and the correlation between the energy conversion rate evaluation index.

[0043] Using the energy economic benefit sequence as the reference sequence, and the carbon dioxide emission sequence, energy conversion rate sequence, and energy transmission efficiency sequence as comparison sequences, the correlation coefficient between the comparison sequences and the reference sequence is calculated using the grey relational coefficient calculation method.

[0044] Another embodiment is also included, a multi-dimensional energy data-based optimization analysis system, comprising:

[0045] The data acquisition module is used to acquire data on energy types and transmission equipment within the region;

[0046] The indicator determination module is used to determine multi-dimensional energy assessment indicators; the multi-dimensional energy assessment indicators include energy transmission efficiency assessment indicators, energy conversion rate assessment indicators, energy economic benefit assessment indicators, and carbon dioxide emission assessment indicators.

[0047] The multi-dimensional optimization pair determination module is used to determine the energy optimization set and the transmission equipment optimization set, construct multi-dimensional optimization pairs based on each element in the energy optimization set and the transmission equipment optimization set, and input all multi-dimensional optimization pairs into the multi-dimensional optimization set;

[0048] The hierarchical correlation module constructs a matter-element extension model based on a multi-dimensional optimization set and multi-dimensional energy assessment indicators, determines the hierarchical correlation between each multi-dimensional energy assessment indicator and each multi-dimensional optimization pair in the multi-dimensional optimization set, calculates the overall correlation level of each multi-dimensional optimization pair, and constructs a multi-dimensional correlation sequence.

[0049] The multi-dimensional optimization module uses the grey relational coefficient to calculate the correlation number of each multi-dimensional energy assessment index. Based on the correlation number of carbon dioxide emission sequence, energy conversion rate sequence and energy transmission efficiency sequence, it ranks all multi-dimensional optimization pairs according to their merits and selects the best multi-dimensional energy optimization scheme according to actual needs.

[0050] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0051] 1. This invention introduces energy transmission efficiency assessment indicators, energy conversion rate assessment indicators, energy economic benefit assessment indicators, and carbon dioxide emission assessment indicators to analyze the actual efficiency of different energy utilization and transmission schemes, consider cost-effectiveness, and also constrain and optimize carbon emissions, thereby improving overall energy utilization efficiency and reducing energy waste.

[0052] 2. This invention utilizes the matter-element extension model to calculate the correlation between energy optimization plans and the improvement of each indicator. Based on the correlation between each energy optimization plan and the improvement of each indicator, it uses the grey relational coefficient to consider the correlation between indicators, and selects different optimization schemes according to different needs. This invention can comprehensively consider multiple factors, improving the scientificity and accuracy of decision-making. It avoids the one-sidedness of single indicator evaluation and ensures the comprehensiveness and rationality of optimization schemes. Attached Figure Description

[0053] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0054] Figure 1 This is a flowchart of the steps of an optimization analysis method based on multi-dimensional energy data according to the present invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] Example 1

[0057] An optimization analysis method based on multi-dimensional energy data, the optimization method steps include:

[0058] S1. Obtain data on energy types and transmission equipment within the region; transmission equipment data includes the total energy input to the transmission equipment and the total energy output of the transmission equipment; transmission equipment includes power transmission equipment, energy transmission equipment, heat pipe networks, and gas pipelines;

[0059] The transmission rate of each transmission device is calculated based on the total energy input and output of each transmission device.

[0060] Energy type data includes the consumption of each energy source and the energy converted from each energy source. The total energy conversion rate of each energy source is calculated based on the consumption of each energy source and the energy converted from each energy source.

[0061] Market demand information includes energy demand and energy selling prices, and the economic benefits of energy are calculated based on energy demand and energy selling prices;

[0062] The carbon dioxide emission rate of each energy source is calculated based on its consumption and corresponding carbon dioxide emissions.

[0063] The energy conversion rate of each energy source is calculated based on its consumption and the energy converted from it.

[0064] S2. Determine multi-dimensional energy assessment indicators; the multi-dimensional energy assessment indicators include energy transmission efficiency assessment indicators, energy conversion rate assessment indicators, energy economic benefit assessment indicators, and carbon dioxide emission assessment indicators.

[0065] S3. Determine the energy optimization set and the transmission equipment optimization set. Based on each element in the energy optimization set and the transmission equipment optimization set, construct a multi-dimensional optimization pair and input all the multi-dimensional optimization pairs into the multi-dimensional optimization set.

[0066] Specifically, energy optimization is carried out with the goal of achieving optimal energy economy, minimizing carbon dioxide emissions, or achieving both optimal energy economy and minimum carbon dioxide emissions. The resulting energy optimization data is then filtered and placed into an energy optimization set. Similarly, transmission equipment optimization is carried out with the goal of minimizing energy transmission losses. The resulting transmission equipment optimization data is then filtered and placed into a transmission equipment optimization set.

[0067] For example, if the energy optimization set A = {a1, a2, a3, a4} and the transmission equipment optimization set B = {b1, b2, b3}, then the multi-dimensional optimization pairs that can be formed are: a1-b1, a1-b2, a1-b3, a2-b1, a2-b2, a2-b2, a2-b3, a3-b1, a3-b2, a3-b3, a4-b1, a4-b2, a4-b3.

[0068] S4. Construct a matter-element extension model based on a multi-dimensional optimization set and multi-dimensional energy assessment indicators. Determine the hierarchical correlation degree between each multi-dimensional energy assessment indicator and each multi-dimensional optimization pair in the multi-dimensional optimization set, and calculate the correlation degree of the multi-dimensional energy assessment indicators for each multi-dimensional optimization pair to construct a multi-dimensional correlation sequence. Specific steps include:

[0069] S401. Determine the energy economic benefits corresponding to each multi-dimensional optimization pair; calculate the carbon dioxide emissions corresponding to the multi-dimensional optimization based on the energy optimization data in each multi-dimensional optimization pair and the carbon dioxide emission rate of each energy source.

[0070] S402. Calculate the energy conversion rate based on the energy optimization data in each multi-dimensional optimization pair and the total energy conversion rate of each energy source;

[0071] S403. Calculate energy transmission efficiency based on multi-dimensional optimization data, energy conversion rate, and transmission rate of each transmission device under the same energy transmission.

[0072] S404. Using the graded correlation formula, calculate the correlation between the energy economic benefits of the multi-dimensional optimization pair and the grade of the energy economic benefits evaluation index, the correlation between the energy conversion rate of the multi-dimensional optimization pair and the grade of the energy conversion rate evaluation index, the correlation between the energy transmission efficiency of the multi-dimensional optimization pair and the grade of the energy transmission efficiency evaluation index, and the correlation between the carbon dioxide emissions of the multi-dimensional optimization pair and the grade of the carbon dioxide emissions evaluation index.

[0073] S405. Calculate the correlation degree of the multi-dimensional energy assessment indicators in the multi-dimensional optimization pair according to the weight ratio of each multi-dimensional energy assessment indicator level. The correlation degree of the multi-dimensional energy assessment indicators includes the correlation degree of energy economic benefit assessment indicators, the correlation degree of energy conversion rate assessment indicators, the correlation degree of energy transmission efficiency assessment indicators, and the correlation degree of carbon dioxide emission assessment indicators.

[0074] The formula for the degree of hierarchical correlation is as follows:

[0075]

[0076] In the formula, K q (V i V represents the correlation between multi-dimensional energy assessment index i and its corresponding multi-dimensional energy assessment index level q in multi-dimensional optimization. i V represents the data corresponding to the multi-dimensional energy assessment index i in the multi-dimensional optimization approach. iq ρ(.) represents the range of values ​​for the multi-dimensional energy assessment index i under level q, and ρ(.) represents the difference calculation function.

[0077] The formula for calculating the correlation of multi-dimensional energy assessment indicators is as follows:

[0078]

[0079] In the formula, S i This represents the correlation between multi-dimensional energy assessment indicators, where Q represents the number of levels of multi-dimensional energy assessment indicator i, and a jiThis indicates the weight of multidimensional energy assessment index i in multidimensional energy assessment index level j.

[0080] Among them, the multi-dimensional correlation sequences include energy economic benefits sequence, carbon dioxide emissions sequence, energy conversion rate sequence and energy transmission efficiency sequence;

[0081] The energy economic benefit sequence includes the energy economic benefits of multi-dimensional optimization pairs, the correlation between the energy economic benefits of multi-dimensional optimization pairs and the level of each energy economic benefit assessment indicator, and the correlation between the energy economic benefit assessment indicators.

[0082] The carbon dioxide emission sequence includes the carbon dioxide emissions of the multi-dimensional optimization pair, the correlation between the carbon dioxide emissions of the multi-dimensional optimization pair and the level of each carbon dioxide emission assessment index, and the correlation between the carbon dioxide emission assessment index.

[0083] The energy transmission efficiency sequence includes the energy transmission efficiency of multi-dimensional optimization pairs, the correlation between the energy transmission efficiency of multi-dimensional optimization pairs and the level of each energy transmission efficiency evaluation index, and the correlation between the energy transmission efficiency evaluation index.

[0084] The energy conversion rate sequence includes the energy conversion rate of the multi-dimensional optimization pair, the correlation between the energy conversion rate of the multi-dimensional optimization pair and the level of each energy conversion rate evaluation index, and the correlation between the energy conversion rate evaluation index.

[0085] S5. Calculate the correlation coefficient of each multi-dimensional energy assessment indicator using the grey relational coefficient. Specifically, use the energy economic benefit sequence as the reference sequence, and the carbon dioxide emission sequence, energy conversion rate sequence, and energy transmission efficiency sequence as the comparison sequence. Calculate the correlation coefficient between the comparison sequence and the reference sequence using the grey relational coefficient calculation method.

[0086] S6. Sort all multi-dimensional optimization pairs according to the correlation coefficients of carbon dioxide emission sequence, energy conversion rate sequence and energy transmission efficiency sequence respectively, and select the best multi-dimensional energy optimization scheme according to actual needs.

[0087] When the actual demand is to minimize carbon dioxide emissions and maximize energy economic benefits, the optimal sequence ranked by the correlation coefficient of carbon dioxide emission sequences can be selected as the best multidimensional energy optimization scheme.

[0088] When considering energy economic benefits, carbon dioxide emissions, energy conversion rate, and energy transmission efficiency in a comprehensive manner, the optimal multi-dimensional energy optimization scheme can be selected by statistically analyzing the correlation coefficients of the corresponding carbon dioxide emission series, energy conversion rate series, and energy transmission efficiency series, or by plotting curves and selecting focal points and poles.

[0089] Example 2: An optimization analysis system based on multi-dimensional energy data, comprising:

[0090] The data acquisition module is used to acquire data on energy types and transmission equipment within the region;

[0091] The indicator determination module is used to determine multi-dimensional energy assessment indicators; the multi-dimensional energy assessment indicators include energy transmission efficiency assessment indicators, energy conversion rate assessment indicators, energy economic benefit assessment indicators, and carbon dioxide emission assessment indicators.

[0092] The multi-dimensional optimization pair determination module is used to determine the energy optimization set and the transmission equipment optimization set, construct multi-dimensional optimization pairs based on each element in the energy optimization set and the transmission equipment optimization set, and input all multi-dimensional optimization pairs into the multi-dimensional optimization set;

[0093] The hierarchical correlation module constructs a matter-element extension model based on a multi-dimensional optimization set and multi-dimensional energy assessment indicators, determines the hierarchical correlation between each multi-dimensional energy assessment indicator and each multi-dimensional optimization pair in the multi-dimensional optimization set, calculates the overall correlation level of each multi-dimensional optimization pair, and constructs a multi-dimensional correlation sequence.

[0094] The multi-dimensional optimization module uses the grey relational coefficient to calculate the correlation number of each multi-dimensional energy assessment index. Based on the correlation number of each multi-dimensional energy assessment index, it ranks all multi-dimensional optimization pairs according to their merits and selects the best multi-dimensional energy optimization scheme according to actual needs.

[0095] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0096] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimization analysis based on multi-dimensional energy data, characterized in that, The method comprises the following steps: acquiring energy type data and transmission equipment data in a region, and calculating total energy conversion rate and carbon dioxide emission rate of each energy type and transmission rate of each transmission equipment; determining multi-dimensional energy evaluation indexes, wherein the multi-dimensional energy evaluation indexes comprise energy transmission efficiency evaluation indexes, energy conversion rate evaluation indexes, energy economic benefit evaluation indexes, and carbon dioxide emission amount evaluation indexes; determining an energy optimization set and a transmission equipment optimization set, constructing multi-dimensional optimization pairs based on each element in the energy optimization set and the transmission equipment optimization set, and inputting all the multi-dimensional optimization pairs into a multi-dimensional optimization set; constructing a matter-element extension model based on the multi-dimensional optimization set and the multi-dimensional energy evaluation indexes, determining the grade correlation degree of each multi-dimensional optimization pair in the multi-dimensional optimization set and the multi-dimensional energy evaluation indexes, calculating the multi-dimensional energy evaluation index correlation degree of each multi-dimensional optimization pair, and constructing a multi-dimensional correlation sequence; calculating the correlation numbers of each multi-dimensional energy evaluation index by using a grey correlation number calculation method; sorting all the multi-dimensional optimization pairs according to the correlation numbers of the multi-dimensional correlation sequence, and selecting the best multi-dimensional energy optimization scheme; performing energy optimization with the optimization target of optimal energy economy, the optimization target of minimum carbon dioxide emission amount, or the optimization target of optimal energy economy and minimum carbon dioxide emission amount, and screening the obtained energy optimization data and inputting the screened data into the energy optimization set; performing transmission equipment optimization with the optimization target of minimum energy transmission loss, and screening the obtained transmission equipment optimization data and inputting the screened data into the transmission equipment optimization set; the multi-dimensional correlation sequence comprises an energy economic benefit sequence, a carbon dioxide emission amount sequence, an energy conversion rate sequence, and an energy transmission efficiency sequence; the energy economic benefit sequence comprises energy economic benefits of the multi-dimensional optimization pairs, the correlation degrees of the energy economic benefits of the multi-dimensional optimization pairs and each energy economic benefit evaluation index grade, and energy economic benefit evaluation index correlation degrees; the carbon dioxide emission amount sequence comprises carbon dioxide emission amounts of the multi-dimensional optimization pairs, the correlation degrees of the carbon dioxide emission amounts of the multi-dimensional optimization pairs and each carbon dioxide emission amount evaluation index grade, and carbon dioxide emission amount evaluation index correlation degrees; the energy transmission efficiency sequence comprises energy transmission efficiencies of the multi-dimensional optimization pairs, the correlation degrees of the energy transmission efficiencies of the multi-dimensional optimization pairs and each energy transmission efficiency evaluation index grade, and energy transmission efficiency evaluation index correlation degrees; the energy conversion rate sequence comprises energy conversion rates of the multi-dimensional optimization pairs, the correlation degrees of the energy conversion rates of the multi-dimensional optimization pairs and each energy conversion rate evaluation index grade, and energy conversion rate evaluation index correlation degrees; taking the energy economic benefit sequence as a reference sequence, taking the carbon dioxide emission amount sequence, the energy conversion rate sequence, and the energy transmission efficiency sequence as comparison sequences, and calculating the correlation numbers of the comparison sequences and the reference sequence by using a grey correlation number calculation method. 2.The method of claim 1, wherein, the transmission equipment data comprises total energy input into the transmission equipment and total energy output from the transmission equipment, and the transmission equipment comprises power transmission equipment, energy transmission equipment, a heat pipe network, and a gas pipeline. The energy source category data includes the consumption of each energy source, the energy after conversion of each energy source, and the carbon dioxide emission of each energy source. 3.The method of claim 1, wherein, The grade correlation calculation step includes: determining the energy economic benefit of each multi-dimensional optimization pair; calculating the carbon dioxide emission of each multi-dimensional optimization pair based on the energy optimization data in each multi-dimensional optimization pair and the carbon dioxide emission rate of each energy source; calculating the energy conversion rate of each multi-dimensional optimization pair based on the energy optimization data in each multi-dimensional optimization pair and the total energy conversion rate of each energy source; calculating the energy transmission efficiency of each multi-dimensional optimization pair based on the multi-dimensional optimization data in the multi-dimensional optimization pair, the energy conversion rate, and the transmission rate of each transmission device; calculating the correlation of the energy economic benefit of each multi-dimensional optimization pair and the energy economic benefit evaluation index grade, the correlation of the energy conversion rate of each multi-dimensional optimization pair and the energy conversion rate evaluation index grade, the correlation of the energy transmission efficiency of each multi-dimensional optimization pair and the energy transmission efficiency evaluation index grade, and the correlation of the carbon dioxide emission of each multi-dimensional optimization pair and the carbon dioxide emission evaluation index grade using the grade correlation formula; calculating the multi-dimensional energy evaluation index correlation in each multi-dimensional optimization pair according to the weight proportion of each multi-dimensional energy evaluation index grade, which includes the energy economic benefit evaluation index correlation, the energy conversion rate evaluation index correlation, the energy transmission efficiency evaluation index correlation, and the carbon dioxide emission evaluation index correlation. 4.The method of claim 1, wherein, The grade correlation formula: In the formula, K q (V i ) represents the correlation degree of the multi-dimensional energy evaluation index i and its corresponding multi-dimensional energy evaluation index grade q in the multi-dimensional optimization, V i represents the data corresponding to the multi-dimensional energy evaluation index i in the multi-dimensional optimization, V iq represents the value range of the multi-dimensional energy evaluation index i under the q grade, and ρ(.) represents a difference calculation function.

5. The method of claim 1, wherein, The multi-dimensional energy evaluation index correlation calculation formula: In the formula, S i represents the correlation degree of the multi-dimensional energy evaluation index, Q represents the number of grades of the multi-dimensional energy evaluation index i, a ji represents the weight of the multi-dimensional energy evaluation index i corresponding to the multi-dimensional energy evaluation index grade j.

6. An optimization analysis system based on multi-dimensional energy data, characterized by, An optimization analysis method based on multi-dimensional energy data according to any one of claims 1-5, comprising: a data acquisition module for acquiring energy source category data and transmission device data in a region; an index determination module for determining multi-dimensional energy evaluation indexes, which include an energy transmission efficiency evaluation index, an energy conversion rate evaluation index, an energy economic benefit evaluation index, and a carbon dioxide emission evaluation index; a multi-dimensional optimization pair determination module for determining an energy optimization set and a transmission device optimization set, constructing a multi-dimensional optimization pair based on each element in the energy optimization set and the transmission device optimization set, and inputting all multi-dimensional optimization pairs into a multi-dimensional optimization set; a grade correlation module for constructing a matter-element extension model based on the multi-dimensional optimization set and the multi-dimensional energy evaluation indexes, determining the grade correlation of each multi-dimensional energy evaluation index and each multi-dimensional optimization pair in the multi-dimensional optimization set, calculating the overall correlation grade of each multi-dimensional optimization pair, and constructing a multi-dimensional correlation sequence; a multi-dimensional optimization module for calculating the correlation number of each multi-dimensional energy evaluation index using a grey correlation number, sorting all multi-dimensional optimization pairs according to the correlation number of the carbon dioxide emission sequence, the energy conversion rate sequence, and the energy transmission efficiency sequence, and selecting the best multi-dimensional energy optimization scheme according to actual needs.

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