Optimization analysis method and system based on multi-dimensional energy data

Abstract

The invention discloses an optimization analysis method and system based on multi-dimensional energy data, and the method comprises the steps: obtaining energy type data and transmission equipment data in a region, determining a multi-dimensional energy evaluation index, and constructing a multi-dimensional optimization set based on an energy optimization set and a transmission equipment 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 association degree between each multi-dimensional energy evaluation index and each multi-dimensional optimization pair in the multi-dimensional optimization set, and further constructing a multi-dimensional association sequence; calculating the correlation number of each multi-dimensional energy evaluation index by using a grey correlation coefficient based on the multi-dimensional correlation sequence; and selecting an optimal multi-dimensional energy optimization scheme according to the correlation numbers of the carbon dioxide emission sequence, the energy conversion rate sequence and the energy transmission efficiency sequence and actual requirements. According to the method, the one-sidedness of single index evaluation is avoided, and the comprehensiveness of an optimization scheme is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management and data analysis, and specifically to an optimization analysis method and system based on multi-dimensional energy data. Background Art

[0002] With the continuous development of social economy, the demand for energy is also increasing, and emerging energy technologies such as clean energy and renewable energy are also emerging. With the continuous development of these emerging energy technologies such as clean energy and renewable energy, more complex and refined requirements are put forward for the energy management system. How to achieve efficient, precise management and optimization of energy use under the premise of ensuring stable and safe energy supply has become a crucial issue.

[0003] Traditional energy optimization management methods often rely on single energy consumption data or production data, evaluate energy efficiency through statistical analysis, or make predictions through machine learning and neural network learning to propose improvement measures. However, this method ignores the complex relationship and mutual influence between the various elements within the energy system, and it is difficult to fully and dynamically reflect the actual operation status of the energy system, resulting in the planning results only meeting one or two indicators. Summary of the invention

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

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

[0006] Obtain data on energy types and transmission equipment in the region, and calculate the total energy conversion rate and carbon dioxide emission rate of each energy source and the transmission rate of each transmission equipment;

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

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

[0009] Based on the multidimensional optimization set and the multidimensional energy evaluation index, a matter-element extension model is constructed to determine the hierarchical correlation between each multidimensional energy evaluation index and each multidimensional optimization pair in the multidimensional optimization set, and the correlation between the multidimensional energy evaluation index of each multidimensional optimization pair is calculated to construct a multidimensional correlation sequence;

[0010] The grey correlation coefficient is used to calculate the correlation number of each multi-dimensional energy assessment index;

[0011] All multidimensional optimization pairs are ranked according to the correlation numbers of the multidimensional correlation sequences, and the best multidimensional energy optimization solution is selected according to actual needs.

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

[0013] Calculate the transmission rate at each transmission device based on the total amount of energy input to each transmission device and the total amount of energy output therefrom;

[0014] The energy type data includes the consumption corresponding to each energy source and the energy after each energy is converted, and the total energy conversion rate of each energy source is calculated based on the consumption corresponding to each energy source and the energy after each energy is converted;

[0015] Calculate the CO2 emission rate of each energy source based on the corresponding consumption of each energy source and the corresponding CO2 emissions;

[0016] The energy conversion rate of each energy source is calculated based on the corresponding consumption of each energy source and the energy after the corresponding energy conversion.

[0017] The energy conversion rates of different types of energy are different under the same amount. Therefore, the different allocation amounts of each energy in the energy optimization plan will also lead to changes in the total energy conversion rate. Similarly, the settings 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 also indirectly affect the energy economic benefits and carbon dioxide emissions.

[0018] According to the above technical solution, energy optimization is performed with the best energy economy as the optimization target or the lowest carbon dioxide emissions as the optimization target or the best energy economy and the lowest carbon dioxide emissions as the optimization target, and the obtained energy optimization data is screened and put into the energy optimization set;

[0019] The transmission equipment is optimized with the minimum energy transmission loss as the optimization goal, and the obtained transmission equipment optimization data is screened and put into the transmission equipment optimization set.

[0020] The energy optimization and transmission equipment optimization methods can utilize game theory, particle optimization methods, genetic variation methods, and the like.

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

[0022] Determine the energy economic benefits corresponding to each multi-dimensional optimization pair;

[0023] 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;

[0024] The energy conversion rate corresponding to the multi-dimensional optimization is calculated based on the energy optimization data in each multi-dimensional optimization pair and the total energy conversion rate of each energy; specifically, the total energy conversion rate of each energy is used to calculate the energy after the corresponding energy conversion in the multi-dimensional optimization pair, and the total energy after each energy conversion is summed to obtain the total conversion energy of the multi-dimensional optimization pair, and then the total conversion energy of the multi-dimensional optimization pair is calculated to the total amount of each multi-dimensional optimization before conversion. The ratio of the total conversion energy of the multi-dimensional optimization pair to the total amount of each multi-dimensional optimization before conversion is calculated 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 the corresponding transmission device is calculated based on the total conversion energy of the multi-dimensional optimization pair using the transmission rate of each transmission device, and the sum of the energy after transmission by each transmission device is calculated, and then the ratio of the sum of the energy transmitted 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 between the energy economic benefit of the multi-dimensional optimization pair and the energy economic benefit evaluation index level, the correlation between the energy conversion rate of the multi-dimensional optimization pair and the energy conversion rate evaluation index level, the correlation between the energy transmission efficiency of the multi-dimensional optimization pair and the energy transmission efficiency evaluation index level, and the correlation between the carbon dioxide emissions of the multi-dimensional optimization pair and the carbon dioxide emissions evaluation index level are calculated using the level correlation formula.

[0027] The correlation of the multidimensional energy evaluation indicators in the multidimensional optimization pair is calculated according to the weight ratio of each multidimensional energy evaluation indicator level. The multidimensional energy evaluation indicator correlation includes the correlation of energy economic benefit evaluation indicators, the correlation of energy conversion rate evaluation indicators, the correlation of energy transmission efficiency evaluation indicators, and the correlation of carbon dioxide emission evaluation indicators.

[0028] According to the above technical solution, the grade correlation formula is:

[0029]

[0030] In the formula, K q (V i ) represents the correlation between the multi-dimensional energy evaluation index i and its corresponding multi-dimensional energy evaluation index level 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 It represents the value range of the multi-dimensional energy assessment index i at level q, and ρ(.) is represented as the difference calculation function.

[0031] in,

[0032]

[0033] In the formula, a iq represents the lower bound of the value range of multi-dimensional energy assessment index i of level q, b iq represents the upper bound of the value range of multi-dimensional energy assessment index i of level q, b pi is the upper bound of the value of the multi-dimensional energy evaluation index i at all levels, represents the lower bound of the value range of the multi-dimensional energy assessment index i of level q after normalization, represents the upper bound of the value range of the multi-dimensional energy assessment index i of level q after normalization, a pi It represents the lower bound of the value of the multi-dimensional energy assessment index i at 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 belongs to the existing technology and will not be described in detail here.

[0035] According to the above technical solution, the multi-dimensional energy evaluation index correlation calculation formula is:

[0036]

[0037] In the formula, S i represents the correlation of multi-dimensional energy evaluation indicators, Q represents the number of levels of multi-dimensional energy evaluation indicator i, and a ji It represents the weight of multi-dimensional energy assessment index i corresponding to multi-dimensional energy assessment index level j.

[0038] According to the above technical solution, the multi-dimensional association 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 benefit of the multi-dimensional optimization pair, the correlation between the energy economic benefit of the multi-dimensional optimization pair and each energy economic benefit evaluation index level, and the energy economic benefit evaluation index correlation;

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

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

[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 each energy conversion rate evaluation index level, and the energy conversion rate evaluation index correlation;

[0043] The energy economic benefit sequence is taken as the reference sequence, and the carbon dioxide emission sequence, energy conversion rate sequence and energy transmission efficiency sequence are taken as the comparison sequence. The grey correlation coefficient calculation method is used to calculate the correlation number between the comparison sequence and the reference sequence.

[0044] Another embodiment includes an optimization analysis system based on multi-dimensional energy data, including:

[0045] Data acquisition module, used to obtain energy type data and transmission equipment data in the area;

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

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

[0048] The level correlation module constructs a matter-element extension model based on the multidimensional optimization set and the multidimensional energy evaluation index, determines the level correlation between each multidimensional energy evaluation index and each multidimensional optimization pair in the multidimensional optimization set, calculates the overall correlation level of each multidimensional optimization pair, and constructs a multidimensional correlation sequence;

[0049] The multidimensional optimization module uses the grey correlation coefficient to calculate the correlation numbers of various multidimensional energy evaluation indicators, and ranks all multidimensional optimization pairs according to the correlation numbers of the carbon dioxide emission sequence, energy conversion rate sequence, and energy transmission efficiency sequence, and selects the best multidimensional energy optimization solution according to actual needs.

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

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

[0052] 2. The present invention uses the matter-element extension model to calculate the correlation between each indicator optimization and improvement of the energy optimization sub-case, and uses the grey correlation coefficient to consider the correlation between indicators based on the correlation between each energy optimization sub-case and each indicator optimization and improvement, and selects different optimization schemes according to different needs. The present invention can comprehensively consider multiple factors to improve the scientificity and accuracy of decision-making. It avoids the one-sidedness of single indicator evaluation and ensures the comprehensiveness and rationality of the optimization scheme. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0054] Figure 1 It is a step flow chart of an optimization analysis method based on multi-dimensional energy data of the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work 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 energy type data and transmission equipment data in the region; the transmission equipment data includes the total amount of energy input by the transmission equipment and the total amount of energy output by the transmission equipment; the transmission equipment includes power transmission equipment, new energy transmission equipment, heat pipe network and gas pipeline;

[0059] Calculate the transmission rate at each transmission device based on the total amount of energy input to each transmission device and the total amount of energy output therefrom;

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

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

[0062] Calculate the CO2 emission rate of each energy source based on the corresponding consumption of each energy source and the corresponding CO2 emissions;

[0063] The energy conversion rate of each energy source is calculated based on the corresponding consumption of each energy source and the energy after the corresponding energy conversion.

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

[0065] S3, determining an energy optimization set and a transmission equipment optimization set, constructing a multi-dimensional optimization pair based on each element in the energy optimization set and the transmission equipment optimization set, and inputting all the multi-dimensional optimization pairs into the multi-dimensional optimization set;

[0066] Specifically, energy optimization is performed with optimal energy economy as the optimization target, or with minimum carbon dioxide emissions as the optimization target, or with optimal energy economy and minimum carbon dioxide emissions as the optimization targets, and the obtained energy optimization data are screened and put into the energy optimization set; transmission equipment optimization is performed with minimum energy transmission loss as the optimization target, and the obtained transmission equipment optimization data are screened and put into the transmission equipment optimization set.

[0067] For example, 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 constructed 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 the multidimensional optimization set and the multidimensional energy evaluation index, determine the hierarchical correlation between each multidimensional energy evaluation index and each multidimensional optimization pair in the multidimensional optimization set, calculate the correlation between the multidimensional energy evaluation index of each multidimensional optimization pair, and construct a multidimensional correlation sequence; the specific steps include:

[0069] S401, determining the energy economic benefits corresponding to each multi-dimensional optimization pair; calculating 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, calculating 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;

[0071] S403, calculating the energy transmission efficiency 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 when transmitting the same energy;

[0072] S404, using the grade correlation formula, respectively calculating the correlation between the energy economic benefit of the multi-dimensional optimization pair and the energy economic benefit evaluation index grade, the correlation between the energy conversion rate of the multi-dimensional optimization pair and the energy conversion rate evaluation index grade, the correlation between the energy transmission efficiency of the multi-dimensional optimization pair and the energy transmission efficiency evaluation index grade, and the correlation between the carbon dioxide emissions of the multi-dimensional optimization pair and the carbon dioxide emissions evaluation index grade;

[0073] S405. Calculate the correlation of the multidimensional energy evaluation indicators in the multidimensional optimization pair according to the weight ratio of each multidimensional energy evaluation indicator level, wherein the multidimensional energy evaluation indicator correlation includes the correlation of the energy economic benefit evaluation indicator, the correlation of the energy conversion rate evaluation indicator, the correlation of the energy transmission efficiency evaluation indicator, and the correlation of the carbon dioxide emission evaluation indicator.

[0074] Among them, the formula for grade association is:

[0075]

[0076] In the formula, K q (V i ) represents the correlation between the multi-dimensional energy evaluation index i and its corresponding multi-dimensional energy evaluation index level 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 It represents the value range of the multi-dimensional energy assessment index i at level q, and ρ(.) is represented as the difference calculation function.

[0077] Among them, the calculation formula for the correlation of multi-dimensional energy evaluation indicators is:

[0078]

[0079] In the formula, S i represents the correlation of multi-dimensional energy evaluation indicators, Q represents the number of levels of multi-dimensional energy evaluation indicator i, and a jiIt represents the weight of multi-dimensional energy assessment index i corresponding to multi-dimensional energy assessment index level j.

[0080] Among them, the multi-dimensional correlation sequence includes energy economic benefit sequence, carbon dioxide emission sequence, energy conversion rate sequence and energy transmission efficiency sequence;

[0081] The energy economic benefit sequence includes the energy economic benefit of the multi-dimensional optimization pair, the correlation between the energy economic benefit of the multi-dimensional optimization pair and each energy economic benefit evaluation index level, and the correlation between the energy economic benefit evaluation index;

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

[0083] The energy transmission efficiency sequence includes the energy transmission efficiency of the multi-dimensional optimization pair, the correlation between the energy transmission efficiency of the multi-dimensional optimization pair 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 indicator, and the correlation between the energy conversion rate evaluation indicators.

[0085] S5. Use the grey correlation coefficient to calculate the correlation number of each multi-dimensional energy evaluation index. The specific implementation is: take the energy economic benefit sequence as the reference sequence, the carbon dioxide emission sequence, the energy conversion rate sequence and the energy transmission efficiency sequence as the comparison sequence, and use the grey correlation coefficient calculation method to calculate the correlation number of the comparison sequence and the reference sequence.

[0086] S6. Rank all multi-dimensional optimization pairs according to the correlation numbers of the carbon dioxide emission sequence, the energy conversion rate sequence, and the energy transmission efficiency sequence, and select the best multi-dimensional energy optimization solution 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 numbers of the carbon dioxide emission sequences can be selected as the best multi-dimensional energy optimization solution.

[0088] When comprehensively considering energy economic benefits, carbon dioxide emissions, energy conversion rate and energy transmission efficiency, all multi-dimensional optimizations can be used to perform statistical analysis on the correlation numbers of the corresponding carbon dioxide emissions sequence, energy conversion rate sequence and energy transmission efficiency sequence to select the best multi-dimensional energy optimization solution, or draw a curve graph and select the best multi-dimensional energy optimization solution by selecting the focal point.

[0089] Embodiment 2 An optimization analysis system based on multi-dimensional energy data includes:

[0090] Data acquisition module, used to obtain energy type data and transmission equipment data in the area;

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

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

[0093] The level correlation module constructs a matter-element extension model based on the multidimensional optimization set and the multidimensional energy evaluation index, determines the level correlation between each multidimensional energy evaluation index and each multidimensional optimization pair in the multidimensional optimization set, calculates the overall correlation level of each multidimensional optimization pair, and constructs a multidimensional correlation sequence;

[0094] The multidimensional optimization module uses the grey correlation coefficient to calculate the correlation number of each multidimensional energy evaluation indicator, sorts all multidimensional optimization pairs according to the correlation number of each multidimensional energy evaluation indicator, and selects the best multidimensional energy optimization solution according to actual needs.

[0095] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0096] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An optimization analysis method based on multi-dimensional energy data, It is characterized in that include: Obtain data on energy types and transmission equipment in the region, and calculate the total energy conversion rate and carbon dioxide emission rate of each energy source and the transmission rate of each transmission equipment; Determine multi-dimensional energy evaluation indicators; the multi-dimensional energy evaluation indicators include energy transmission efficiency evaluation indicators, energy conversion rate evaluation indicators, energy economic benefit evaluation indicators, and carbon dioxide emission evaluation indicators; Determine an energy optimization set and a transmission equipment optimization set, construct a multi-dimensional optimization pair based on each element in the energy optimization set and the transmission equipment optimization set, and input all the multi-dimensional optimization pairs into the multi-dimensional optimization set; Based on the multidimensional optimization set and the multidimensional energy evaluation index, a matter-element extension model is constructed to determine the hierarchical correlation between each multidimensional energy evaluation index and each multidimensional optimization pair in the multidimensional optimization set, and the correlation between the multidimensional energy evaluation index of each multidimensional optimization pair is calculated to construct a multidimensional correlation sequence; The grey correlation coefficient is used to calculate the correlation number of each multi-dimensional energy assessment index; All multidimensional optimization pairs are ranked according to the correlation numbers of the multidimensional correlation sequences, and the best multidimensional energy optimization solution is selected.

2. According to claim 1, an optimization analysis method based on multi-dimensional energy data, It is characterized in that The transmission equipment data includes the total amount of energy input by the transmission equipment and the total amount of energy output by the transmission equipment; the transmission equipment includes power transmission equipment, new energy transmission equipment, thermal power pipelines and gas pipelines; The energy type data includes the consumption of each energy source, the energy after conversion of each energy source, and the carbon dioxide emissions generated by each energy source.

3. According to claim 1, an optimization analysis method based on multi-dimensional energy data, It is characterized in that Energy optimization is performed with the best energy economy as the optimization target or the lowest carbon dioxide emission as the optimization target or the best energy economy and the lowest carbon dioxide emission as the optimization target, and the obtained energy optimization data is screened and put into the energy optimization set; The transmission equipment is optimized with the minimum energy transmission loss as the optimization goal, and the obtained transmission equipment optimization data is screened and put into the transmission equipment optimization set.

4. According to claim 1, an optimization analysis method based on multi-dimensional energy data, It is characterized in that The step of calculating the rank correlation comprises: 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; Calculate the energy conversion rate corresponding to the multi-dimensional optimization 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 transmission efficiency corresponding to the multi-dimensional optimization based on the multi-dimensional optimization data, energy conversion rate and transmission rate of each transmission device in the multi-dimensional optimization pair; The correlation between the energy economic benefit of the multi-dimensional optimization pair and the energy economic benefit evaluation index level, the correlation between the energy conversion rate of the multi-dimensional optimization pair and the energy conversion rate evaluation index level, the correlation between the energy transmission efficiency of the multi-dimensional optimization pair and the energy transmission efficiency evaluation index level, and the correlation between the carbon dioxide emissions of the multi-dimensional optimization pair and the carbon dioxide emissions evaluation index level are calculated using the level correlation formula. The correlation of the multidimensional energy evaluation indicators in the multidimensional optimization pair is calculated according to the weight ratio of each multidimensional energy evaluation indicator level. The multidimensional energy evaluation indicator correlation includes the correlation of energy economic benefit evaluation indicators, the correlation of energy conversion rate evaluation indicators, the correlation of energy transmission efficiency evaluation indicators, and the correlation of carbon dioxide emission evaluation indicators.

5. According to claim 1, an optimization analysis method based on multi-dimensional energy data, It is characterized in that The grade association formula is: In the formula, K q (V i ) represents the correlation between the multi-dimensional energy evaluation index i and its corresponding multi-dimensional energy evaluation index level 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 It represents the value range of the multi-dimensional energy assessment index i at level q, and ρ(.) is represented as the difference calculation function.

6. The optimization analysis method based on multi-dimensional energy data according to claim 1, It is characterized in that The calculation formula for the correlation of the multi-dimensional energy evaluation indicators is: In the formula, S i represents the correlation of multi-dimensional energy evaluation indicators, Q represents the number of levels of multi-dimensional energy evaluation indicator i, and a ji It represents the weight of multi-dimensional energy assessment index i corresponding to multi-dimensional energy assessment index level j.

7. The optimization analysis method based on multi-dimensional energy data according to claim 1, It is characterized in that 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; The energy economic benefit sequence includes the energy economic benefit of the multi-dimensional optimization pair, the correlation between the energy economic benefit of the multi-dimensional optimization pair and each energy economic benefit evaluation index level, and the energy economic benefit evaluation index correlation; The carbon dioxide emission sequence includes the carbon dioxide emission of the multi-dimensional optimization pair, the correlation between the carbon dioxide emission of the multi-dimensional optimization pair and each carbon dioxide emission evaluation index level, and the carbon dioxide emission evaluation index correlation; The energy transmission efficiency sequence includes the energy transmission efficiency of the multi-dimensional optimization pair, the correlation between the energy transmission efficiency of the multi-dimensional optimization pair and each energy transmission efficiency evaluation index level, and the energy transmission efficiency evaluation index correlation; 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 each energy conversion rate evaluation index level, and the energy conversion rate evaluation index correlation; The energy economic benefit sequence is taken as the reference sequence, and the carbon dioxide emission sequence, energy conversion rate sequence and energy transmission efficiency sequence are taken as the comparison sequence. The grey correlation coefficient calculation method is used to calculate the correlation number between the comparison sequence and the reference sequence.

8. An optimization analysis system based on multi-dimensional energy data, It is characterized in that include: Data acquisition module, used to obtain energy type data and transmission equipment data in the area; An indicator determination module is used to determine multi-dimensional energy evaluation indicators; the multi-dimensional energy evaluation indicators include energy transmission efficiency evaluation indicators, energy conversion rate evaluation indicators, energy economic benefit evaluation indicators, and carbon dioxide emission evaluation indicators; A multi-dimensional optimization pair determination module is used to determine an energy optimization set and a transmission equipment optimization set, construct a multi-dimensional optimization pair based on each element in the energy optimization set and the transmission equipment optimization set, and input all the multi-dimensional optimization pairs into the multi-dimensional optimization set; The level correlation module constructs a matter-element extension model based on the multidimensional optimization set and the multidimensional energy evaluation index, determines the level correlation between each multidimensional energy evaluation index and each multidimensional optimization pair in the multidimensional optimization set, calculates the overall correlation level of each multidimensional optimization pair, and constructs a multidimensional correlation sequence; The multidimensional optimization module uses the grey correlation coefficient to calculate the correlation numbers of various multidimensional energy evaluation indicators, and ranks all multidimensional optimization pairs according to the correlation numbers of the carbon dioxide emission sequence, energy conversion rate sequence, and energy transmission efficiency sequence, and selects the best multidimensional energy optimization solution according to actual needs.

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