An energy supply and demand balance analysis method based on fusion analysis for identifying influencing factors

Abstract

The application discloses an energy supply and demand balance analysis method based on fusion analysis and identification of influencing factors. By acquiring energy supply and demand data, using a grey correlation degree analysis algorithm, according to the energy supply and demand data, the correlation degree between each kind of energy supply and demand data and the energy supply and demand balance result is calculated to obtain a correlation degree sorting result. The target energy supply and demand data corresponding to the target correlation degree in the correlation degree sorting result is extracted, and the target energy supply and demand data is taken as a condition variable affecting the energy supply and demand balance. Using a fuzzy set qualitative comparison analysis algorithm, the importance of each condition variable is calculated to obtain an importance sorting result. The correlation degree sorting result and the importance sorting result are compared, and the target energy supply and demand balance influence analysis is carried out by using a target condition variable combination meeting a preset consistency condition. The use of the grey correlation degree analysis algorithm effectively makes up for the deficiency of the fuzzy set qualitative comparison analysis algorithm, and the analysis result is more persuasive and scientific.

Description

Technical Field

[0001] This application relates to the field of energy supply technology, and in particular to an energy supply and demand balance analysis method based on fusion analysis to identify influencing factors. Background Technology

[0002] A continuous and reliable energy supply is an important support for the rapid development of the national economy. However, due to factors such as climate change, geopolitics, significant changes in the external economic environment, drastic fluctuations in energy prices, and complex and volatile supply and demand situations, energy supply and demand imbalances can easily occur, which will have a significant impact on the social economy and people's livelihoods. Therefore, it is of great significance to analyze the impact of energy supply and demand balance.

[0003] Currently, the impact of energy supply and demand balance is usually analyzed simply by using linear relationships from panel data or past experience. However, the impact of energy supply and demand balance is often determined by multiple factors, so current impact analysis methods cannot guarantee the accuracy of the analysis results. Summary of the Invention

[0004] This application provides an energy supply and demand balance analysis method based on fusion analysis to identify influencing factors, in order to solve the technical problem of low accuracy in the current energy supply and demand balance impact analysis results.

[0005] To address the aforementioned technical problems, firstly, this application provides an energy supply and demand balance analysis method based on fusion analysis to identify influencing factors, including:

[0006] Obtain energy supply and demand data, which includes various types of supply data and various types of demand data;

[0007] Using the grey relational analysis algorithm, the correlation degree between each type of energy supply and demand data and the energy supply and demand balance result is calculated based on energy supply and demand data, and the correlation degree ranking result is obtained.

[0008] Extract the target energy supply and demand data corresponding to the target correlation in the correlation ranking results, and use the target energy supply and demand data as a conditional variable affecting the energy supply and demand balance;

[0009] Using a fuzzy set qualitative comparison analysis algorithm, the importance of each condition variable is calculated, and the importance ranking results are obtained.

[0010] By comparing the correlation ranking results and the importance ranking results, and using the combination of target condition variables that meet the preset consistency conditions, the supply and demand balance impact analysis of the target energy is conducted.

[0011] As a preferred method, the grey relational analysis algorithm is used to calculate the correlation degree between each type of energy supply and demand data and the energy supply and demand balance result based on energy supply and demand data, and to obtain the correlation degree ranking result, including:

[0012] Normalize energy supply and demand data;

[0013] Using a preset correlation coefficient calculation formula, the correlation coefficient between each type of energy supply and demand data and the energy supply and demand balance results is calculated based on the normalized energy supply and demand data.

[0014] Using a preset correlation calculation formula, the correlation degree of each type of energy supply and demand data is calculated based on the correlation coefficient corresponding to different time periods, and the correlation degree ranking results are obtained.

[0015] As a preferred option, the formula for calculating the correlation coefficient is:

[0016]

[0017] Where, ξ i y(k) is the correlation coefficient of the i-th energy supply and demand data in time period k, y(k) is the energy supply and demand balance result, and x i (k) represents the i-th energy supply and demand data in time period k, and ρ is the resolution coefficient.

[0018] As a preferred option, the formula for calculating the correlation degree is:

[0019]

[0020] Where, r i Let ξ be the correlation degree corresponding to the i-th energy supply and demand data. i (k) is the correlation coefficient of the i-th energy supply and demand data in time period k.

[0021] As a preferred method, a fuzzy set qualitative comparison analysis algorithm is used to calculate the importance of each condition variable, and the importance ranking results are obtained, including:

[0022] Based on a preset fuzzy set, each condition variable is encoded to obtain an encoding table, which includes the encoded values ​​of multiple condition variables.

[0023] Using pre-set analysis software, the importance of various combinations of condition variables is calculated based on the coding table;

[0024] Based on the importance of various combinations of condition variables, the importance of each condition variable is analyzed, and the importance ranking results are obtained.

[0025] As a preferred method, using pre-set analysis software, the importance of various combinations of condition variables is calculated based on a coding table, including:

[0026] Using pre-defined analysis software, the continuous coded values ​​in the coding table are converted into set dependent values, and a truth table is constructed based on the set dependent values;

[0027] Calculate the importance of multiple combinations of condition variables based on the truth table.

[0028] As a preferred approach, the correlation ranking results and importance ranking results are compared, and the supply and demand balance impact analysis of the target energy is conducted using the combination of target condition variables that meet the preset consistency conditions, including:

[0029] Compare the consistency between the relevance ranking results and the importance ranking results;

[0030] From the importance ranking results that meet the preset consistency conditions, identify multiple target condition variables that affect the balance of energy supply and demand;

[0031] Multiple target condition variables are combined as target condition variables to conduct a supply and demand balance impact analysis on the target energy.

[0032] Secondly, this application also provides an energy supply and demand balance analysis device, comprising:

[0033] The acquisition module is used to acquire energy supply and demand data, which includes various supply data and various demand data.

[0034] The first calculation module is used to calculate the correlation degree between each type of energy supply and demand data and the energy supply and demand balance result based on the gray relational analysis algorithm, and obtain the correlation degree ranking result.

[0035] The extraction module is used to extract the target energy supply and demand data corresponding to the target correlation in the correlation ranking results, and to use the target energy supply and demand data as a conditional variable affecting the energy supply and demand balance.

[0036] The second calculation module is used to calculate the importance of each condition variable using a fuzzy set qualitative comparison analysis algorithm, and obtain the importance ranking results.

[0037] The analysis module is used to compare the correlation ranking results and the importance ranking results, and to conduct a supply and demand balance impact analysis on the target energy using the combination of target condition variables that meet the preset consistency conditions.

[0038] Thirdly, this application also provides a computer device, including a processor and a memory, the memory being used to store a computer program, which, when executed by the processor, implements the energy supply and demand balance analysis method based on fusion analysis to identify influencing factors as described in the first aspect.

[0039] Fourthly, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the energy supply and demand balance analysis method based on fusion analysis to identify influencing factors as described in the first aspect.

[0040] Compared with the prior art, this application has at least the following beneficial effects:

[0041] By acquiring energy supply and demand data, including various supply and demand data, and using a grey relational analysis algorithm, the correlation degree between each type of energy supply and demand data and the energy supply and demand balance result is calculated, resulting in a correlation degree ranking. Target energy supply and demand data corresponding to the target correlation degree in the correlation degree ranking result are extracted and used as conditional variables affecting the energy supply and demand balance. A fuzzy set qualitative comparison analysis algorithm is used to calculate the importance of each conditional variable, resulting in an importance ranking. The correlation degree ranking result and the importance ranking result are compared, and the impact of target conditional variable combinations that meet preset consistency conditions on the supply and demand balance is analyzed. This approach achieves grey relational analysis of relevant data affecting the energy supply and demand balance, measures the degree of correlation between energy supply-side and demand-side factors, obtains highly correlated feature data, and then verifies the robustness of the grey relational analysis results using a fuzzy set qualitative comparison analysis algorithm. The grey relational analysis algorithm can effectively compensate for the shortcomings of the fuzzy set qualitative comparison analysis algorithm. It can analyze the degree to which the antecedent variable is a necessary condition for the result from a quantitative perspective. Therefore, the integration of the grey relational analysis algorithm and the fuzzy set qualitative comparison analysis algorithm makes the analysis results more convincing and scientific. It can identify and verify the influence mechanism of multiple factors in energy supply and demand balance, and make the relationship between various factors affecting energy supply and demand balance clearer. This allows for targeted adjustments to energy supply and demand plans to ensure a secure energy supply. Attached Figure Description

[0042] Figure 1 This is a flowchart illustrating an energy supply and demand balance analysis method based on fusion analysis to identify influencing factors, as shown in an embodiment of this application.

[0043] Figure 2 As shown in the embodiments of this application;

[0044] Figure 3 This is a schematic diagram of the structure of the energy supply and demand balance analysis device shown in the embodiments of this application;

[0045] Figure 4 This is a schematic diagram of the structure of a computer device shown in an embodiment of this application. Detailed Implementation

[0046] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0047] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating an energy supply and demand balance analysis method based on fusion analysis to identify influencing factors, provided as an embodiment of this application. The energy supply and demand balance analysis method based on fusion analysis to identify influencing factors in this application embodiment can be applied to computer devices, including but not limited to smartphones, laptops, tablets, desktop computers, physical servers, and cloud servers. Figure 1 As shown, the energy supply and demand balance analysis method based on fusion analysis to identify influencing factors in this embodiment includes steps S101 to S105, which are detailed below:

[0048] Step S101: Obtain energy supply and demand data, which includes various supply data and various demand data.

[0049] In this step, the supply data refers to data from the energy supply side, including but not limited to energy resource quantity, energy extraction capacity, development potential of new and alternative energy sources, and the security of imported energy; the demand data refers to data from the energy demand side, including but not limited to population growth data, economic development data, technological progress, and comprehensive data on consumption pattern transformation, including energy price data and energy-related policy data.

[0050] Step S102: Using the grey relational analysis algorithm, calculate the correlation degree between each type of energy supply and demand data and the energy supply and demand balance result based on the energy supply and demand data, and obtain the correlation degree ranking result.

[0051] In this step, the grey relational analysis algorithm is used to analyze the correlation between energy supply and demand data and energy supply and demand balance results, so as to analyze the interaction between the factors affecting energy supply and demand balance, thereby improving the accuracy of the analysis results and making the analysis results more convincing.

[0052] In some embodiments, step S102 includes:

[0053] The energy supply and demand data are normalized.

[0054] Using a preset correlation coefficient calculation formula, the correlation coefficient between each type of energy supply and demand data and the energy supply and demand balance result is calculated based on the normalized energy supply and demand data at different time periods.

[0055] Using a preset correlation calculation formula, the correlation degree corresponding to each type of energy supply and demand data is calculated based on the correlation coefficient corresponding to different time periods, and the correlation degree ranking result is obtained.

[0056] In this embodiment, energy supply and demand data are integrated into a dataset Y = [Y1, Y2, ..., Y]. n ] T , where Y n This represents the characteristic data for each type of energy supply and demand data, specifically including a characteristic data sequence x. i =[x i (1),x i (2),…,x i (n)] T , where i = 1, 2, ..., m, represents a row in the sequence, i.e. a feature.

[0057] Normalize energy supply and demand data:

[0058]

[0059] Where i = 1, 2, ..., m; k = 1, 2, ..., n.

[0060] Optionally, the correlation coefficient is calculated using the following formula:

[0061]

[0062] Where, ξ i (k) is the correlation coefficient of the i-th energy supply and demand data in time period k, y(k) is the energy supply and demand balance result, which is also known data in the energy supply and demand data, x i (k) represents the i-th energy supply and demand data in time period k, and ρ is the resolution coefficient.

[0063] Optionally, the correlation degree calculation formula is:

[0064]

[0065] Where, r i Let ξ be the correlation degree corresponding to the i-th energy supply and demand data. i (k) is the correlation coefficient of the i-th energy supply and demand data in time period k.

[0066] Step S103: Extract the target energy supply and demand data corresponding to the target correlation degree from the correlation degree ranking results, and use the target energy supply and demand data as a conditional variable affecting the energy supply and demand balance.

[0067] In this step, the correlation degree of each energy supply and demand data is obtained through step S102. All correlation degrees are sorted to obtain the correlation degree ranking result. Specific influencing factors that reflect the impact on energy supply and demand are extracted from the correlation degree ranking result. These influencing factors are used as condition variables, and the energy supply and demand balance result is used as the result variable. Optionally, a correlation degree greater than a preset value is used as the target correlation degree, and the target energy supply and demand data corresponding to the target correlation degree are extracted.

[0068] For example, assuming that the factors affecting energy supply obtained through steps S101 to S103 include energy resource quantity, energy extraction capacity, development potential of new and alternative energy sources, and security issues of imported energy, and that the factors affecting energy demand include population growth, economic development, technological progress, and changes in consumption patterns, then the relationship between these factors is as follows: Figure 2 As shown.

[0069] Step S104: Using the fuzzy set qualitative comparison analysis algorithm, calculate the importance of each condition variable to obtain the importance ranking result.

[0070] In this step, the robustness of the grey relational analysis results is tested using the fuzzy set qualitative comparison analysis algorithm. By combining the grey relational analysis algorithm with the fuzzy set qualitative comparison analysis algorithm, the influence mechanism of multiple factors affecting energy supply and demand balance can be identified and verified, making the relationship between various influencing factors on energy supply and demand balance clearer. This allows for targeted adjustments to energy supply and demand plans, ensuring a secure energy supply.

[0071] In some embodiments, step S104 includes:

[0072] Based on a preset fuzzy set, each of the condition variables is encoded to obtain an encoding table, which includes the encoded values ​​of various condition variables.

[0073] Using pre-set analysis software, the importance of various combinations of condition variables is calculated based on the coding table;

[0074] Based on the importance of various combinations of the condition variables, the importance of each condition variable is analyzed to obtain the importance ranking result.

[0075] In this embodiment, the preset fuzzy set can be a four-valued fuzzy set, a six-valued fuzzy set, or a continuous fuzzy set. Optionally, based on the preset fuzzy set, each element, i.e., the condition variable, is assigned a value encoding, the continuous values ​​in the encoding table are converted into set dependent values, and a truth table is constructed based on the set dependent values. The importance of various combinations of condition variables is then calculated based on the truth table.

[0076] Optionally, as shown in the table below, a three-valued fuzzy set adds a third value of 0.5 between [0] and [1]. 0.5 indicates that the corresponding condition variable is neither completely subordinate to nor completely unsubordinate to the factors affecting energy supply and demand balance. A four-valued fuzzy set divides each variable into 0, 0.33, 0.67, and 1 according to their degree of importance. 0 represents complete unsubordinate, 0.33 represents partial unsubordinate, 0.67 represents partial subordinate, and 1 represents complete subordinate. A six-valued fuzzy set adds 0.1, 0.4, 0.6, and 0.9 between [0] and [1]. 0 represents complete unsubordinate, 0.1 represents very unsubordinate, 0.4 represents somewhat unsubordinate, 0.6 represents somewhat subordinate, 0.9 represents very subordinate, and 1 represents complete subordinate. A continuous fuzzy set can take any value between 0 and 1, where 0 represents complete unsubordinate, 0 < 0 < 0.5. i <0.5 indicates partial non-subordinate; 0.5 is the intersection point, indicating neither "subordinate" nor "non-subordinate". <X i <1 indicates partial membership, and 1 indicates complete membership. For continuous fuzzy sets, three qualitative anchor points need to be determined: a membership score of 1 for points that completely belong to the outcome variable, a membership score of 0 for points that do not belong to the outcome variable, and a score of 0.5 for the point with the most ambiguous membership to the outcome variable.

[0077]

[0078] For example, among the influencing factors, the energy supply and demand balance outcome is treated as the result variable. Simultaneously, it's determined from which aspects the level of energy supply and demand balance is measured, such as short-term energy policies, energy price fluctuations, energy production, energy consumption, and energy storage. Each influencing factor is evaluated using an independent scoring method, such as a 0-5 scale, where 0 indicates the worst performance and 5 indicates the best performance. The sum of the scores for each influencing factor is the degree value of the energy supply and demand balance (0-25). Following the QCA (Quality, Cost, and Benefit) approach, this degree value is converted into the set membership value corresponding to the above elements. The table below sets the lower quartile (6.25), the mean (12.5), and the upper quartile (18.75) as the critical values ​​for not belonging to the set at all, the cross-critical values, and the critical values ​​for belonging to the set completely, respectively.

[0079]

[0080] Based on the above critical values, the influencing factors are coded. In a separate raw data folder, all textual descriptions related to the condition variables are extracted and organized into the same table. Combining the above assignment criteria for each condition variable, each condition variable is assigned a value, and finally, a coding table for all condition variables is obtained.

[0081] In some embodiments, calculating the importance of multiple combinations of condition variables using preset analysis software based on the coding table includes:

[0082] Using the preset analysis software, the continuous encoded values ​​in the encoding table are converted into set dependent values, and a truth table is constructed based on the set dependent values;

[0083] Based on the truth table, calculate the importance of various combinations of the condition variables.

[0084] In this embodiment, the above encoding table is imported into QCA software for data processing: First, the continuous values ​​in the encoding table are converted into set dependent values ​​to facilitate the subsequent construction of the truth table. The technical path conversion degree and technical path dependency degree in the data encoding table are both continuous values, which need to be converted into set dependent values ​​using the calibrate function in QCA software.

[0085] The conversion function formula operation is as follows:

[0086] yf Z =Calibrate(x,n1,n2,n3);

[0087] Among them, f Z The values ​​are continuous, and n1, n2, and n3 are the critical values ​​set by the assignment.

[0088] Based on the set membership values, a truth table is constructed. Using the fuzzy set qualitative comparison analysis algorithm, to ensure that each element matches the energy supply and demand balance target characteristics, a necessary condition analysis method is needed to determine that the independent variables are subsets of the outcome variables, i.e., the consistency value is less than 1. The necessary condition analysis method can be calculated using the following formula:

[0089]

[0090] X i "Y" refers to the membership score in the combination of condition variables. i "Y" refers to the membership score in the outcome variable, where all "Y" are members. i "All are less than or equal to the corresponding "X" i When the value is greater than the preset number of "Y", the consistency score is 1; when the value is greater than the preset number of "Y", the consistency score is 1. i The value exceeds the corresponding "X" i The value, if consistent, will be far less than 1;

[0091] Importing the truth table above into QCA software yields three solutions: a complex solution (without using "logical remainders"), an intermediate solution (only "logical remainders" that are meaningful based on the researcher's theoretical and practical knowledge are included), and a concise solution (using all "logical remainders" without evaluating their rationality). The intermediate solution is preferred, as it does not allow the elimination of necessary conditions, i.e., any supersets constituting the outcome and meaningful conditions that are necessary. Furthermore, by using QCA software, the combination of conditional variables that forms the energy supply and demand balance can be derived, and the table will present the overall consistency and overall coverage of the case samples, i.e., the explanatory power of the factor combinations on the total sample.

[0092] Furthermore, the different combinations of the obtained condition variables are analyzed to determine the importance ranking of the factors affecting the energy supply and demand balance. The ranking is then compared with the correlation ranking results obtained using the grey relational analysis algorithm to determine whether the results are consistent.

[0093] Step S105: Compare the correlation ranking results and the importance ranking results, and use the target condition variable combination that meets the preset consistency conditions to conduct a supply and demand balance impact analysis on the target energy.

[0094] In this embodiment, the combination of target condition variables determined by the grey relational analysis algorithm and the fuzzy set qualitative comparison analysis algorithm is used as the evaluation index for assessing the supply and demand balance of the target energy, thereby improving the accuracy of the analysis results of the impact of the supply and demand balance of the target energy.

[0095] In some embodiments, step 105 includes:

[0096] Compare the consistency between the correlation ranking results and the importance ranking results;

[0097] From the importance ranking results that satisfy the preset consistency conditions, determine multiple target condition variables that affect the energy supply and demand balance;

[0098] By combining multiple target condition variables as target condition variables, an impact analysis on the supply and demand balance of the target energy is conducted.

[0099] In this embodiment, the condition variables are verified by the algorithm results of the fuzzy set qualitative comparison analysis algorithm and the grey relational analysis algorithm, which further improves the accuracy and reliability of the condition variables as factors affecting the energy supply and demand balance.

[0100] To implement the energy supply and demand balance analysis method based on fusion analysis to identify influencing factors corresponding to the above method embodiments, and to achieve the corresponding functions and technical effects. See also Figure 3 , Figure 3 This diagram illustrates a structural block diagram of an energy supply and demand balance analysis device according to an embodiment of this application. For ease of explanation, only the parts relevant to this embodiment are shown. The energy supply and demand balance analysis device provided in this embodiment includes:

[0101] The acquisition module 301 is used to acquire energy supply and demand data, which includes various supply data and various demand data.

[0102] The first calculation module 302 is used to calculate the correlation degree between each type of energy supply and demand data and the energy supply and demand balance result based on the energy supply and demand data using a grey relational analysis algorithm, and obtain the correlation degree ranking result.

[0103] The extraction module 303 is used to extract the target energy supply and demand data corresponding to the target correlation degree in the correlation degree ranking result, and to use the target energy supply and demand data as a conditional variable affecting the energy supply and demand balance;

[0104] The second calculation module 304 is used to calculate the importance of each condition variable using a fuzzy set qualitative comparison analysis algorithm, and obtain the importance ranking result.

[0105] The analysis module 305 is used to compare the correlation ranking results and the importance ranking results, and to perform a supply and demand balance impact analysis on the target energy using the combination of target condition variables that meet the preset consistency conditions.

[0106] In some embodiments, the first computing module 302 is specifically used for:

[0107] The energy supply and demand data are normalized.

[0108] Using a preset correlation coefficient calculation formula, the correlation coefficient between each type of energy supply and demand data and the energy supply and demand balance result is calculated based on the normalized energy supply and demand data at different time periods.

[0109] Using a preset correlation calculation formula, the correlation degree corresponding to each type of energy supply and demand data is calculated based on the correlation coefficient corresponding to different time periods, and the correlation degree ranking result is obtained.

[0110] In some embodiments, the correlation coefficient is calculated using the following formula:

[0111]

[0112] Where, ξ i (k) is the correlation coefficient of the i-th energy supply and demand data in time period k, y(k) is the energy supply and demand balance result, and x i(k) represents the i-th energy supply and demand data in time period k, and ρ is the resolution coefficient.

[0113] In some embodiments, the correlation calculation formula is:

[0114]

[0115] Where, r i Let ξ be the correlation degree corresponding to the i-th energy supply and demand data. i (k) is the correlation coefficient of the i-th energy supply and demand data in time period k.

[0116] In some embodiments, the second computing module 304 includes:

[0117] An encoding unit is used to encode each of the condition variables based on a preset fuzzy set to obtain an encoding table, wherein the encoding table includes the encoded values ​​of the various condition variables;

[0118] The calculation unit is used to calculate the importance of multiple combinations of condition variables according to the coding table using preset analysis software;

[0119] The analysis unit is used to analyze the importance of each of the condition variables based on the importance of multiple combinations of the condition variables, and obtain the importance ranking result.

[0120] In some embodiments, the computing unit is specifically used for:

[0121] Using the preset analysis software, the continuous encoded values ​​in the encoding table are converted into set dependent values, and a truth table is constructed based on the set dependent values;

[0122] Based on the truth table, calculate the importance of various combinations of the condition variables.

[0123] In some embodiments, the analysis module 305 is specifically used for:

[0124] Compare the consistency between the correlation ranking results and the importance ranking results;

[0125] From the importance ranking results that satisfy the preset consistency conditions, determine multiple target condition variables that affect the energy supply and demand balance;

[0126] By combining multiple target condition variables as target condition variables, an impact analysis on the supply and demand balance of the target energy is conducted.

[0127] The energy supply and demand balance analysis device described above can implement the energy supply and demand balance analysis method based on fusion analysis to identify influencing factors in the above method embodiments. The options in the above method embodiments are also applicable to this embodiment, and will not be detailed here. The remaining content of this application embodiment can be referred to the content of the above method embodiments, and will not be repeated in this embodiment.

[0128] Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Figure 4 As shown, the computer device 4 of this embodiment includes: at least one processor 40 ( Figure 4 (Only one is shown in the diagram) a processor, a memory 41, and a computer program 42 stored in the memory 41 and executable on the at least one processor 40, which, when executing the computer program 42, implements the steps in any of the above method embodiments.

[0129] The computer device 4 can be a smartphone, tablet, desktop computer, cloud server, or other computing device. This computer device may include, but is not limited to, a processor 40 and a memory 41. Those skilled in the art will understand that... Figure 4 The computer device 4 is merely an example and does not constitute a limitation on the computer device 4. It may include more or fewer components than shown, or combine certain components, or different components, such as input / output devices, network access devices, etc.

[0130] The processor 40 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0131] In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 may be an external storage device of the computer device 4, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 4. Furthermore, the memory 41 may include both internal and external storage units of the computer device 4. The memory 41 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 41 can also be used to temporarily store data that has been output or will be output.

[0132] In addition, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps in any of the above method embodiments.

[0133] This application provides a computer program product that, when run on a computer device, enables the computer device to execute the steps described in the various method embodiments above.

[0134] In the several embodiments provided in this application, it will be understood that each block in the flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the figures. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved.

[0135] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0136] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the scope of protection of this application. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application for those skilled in the art.

Claims

1. A method for energy supply and demand balance analysis based on fusion analysis to identify influencing factors, characterized in that, include: Acquire energy supply and demand data, which includes various supply data and various demand data; wherein, the supply data is energy supply-side data, including energy resource quantity, energy extraction capacity, development potential of new energy and alternative energy, and security of imported energy; the demand data is energy demand-side data, including population growth data, economic development data, technological progress, and consumption pattern transformation data; Using the grey relational analysis algorithm, the correlation degree between each type of energy supply and demand data and the energy supply and demand balance result is calculated based on the energy supply and demand data, and the correlation degree ranking result is obtained. Extract the target energy supply and demand data corresponding to the target correlation in the correlation ranking results, and use the target energy supply and demand data as a conditional variable affecting the energy supply and demand balance; Using a fuzzy set qualitative comparison analysis algorithm, the importance of each condition variable is calculated to obtain the importance ranking results; By comparing the correlation ranking results and the importance ranking results, and using the combination of target condition variables that meet the preset consistency conditions, an analysis of the supply and demand balance impact of the target energy is conducted.

2. The energy supply and demand balance analysis method based on fusion analysis to identify influencing factors as described in claim 1, characterized in that, The grey relational analysis algorithm is used to calculate the correlation degree between each type of energy supply and demand data and the energy supply and demand balance result based on the energy supply and demand data, and to obtain the correlation degree ranking result, including: The energy supply and demand data are normalized. Using a preset correlation coefficient calculation formula, the correlation coefficient between each type of energy supply and demand data and the energy supply and demand balance result is calculated based on the normalized energy supply and demand data at different time periods. Using a preset correlation calculation formula, the correlation degree corresponding to each type of energy supply and demand data is calculated based on the correlation coefficient corresponding to different time periods, and the correlation degree ranking result is obtained.

3. The energy supply and demand balance analysis method based on fusion analysis to identify influencing factors as described in claim 2, characterized in that, The formula for calculating the correlation coefficient is: ； in, For the first Energy supply and demand data in a time period The correlation coefficient, The energy supply and demand balance result is as follows. For the time period The Energy supply and demand data, The resolution coefficient.

4. The energy supply and demand balance analysis method based on fusion analysis to identify influencing factors as described in claim 2, characterized in that, The formula for calculating the correlation degree is: ； in, For the first The correlation between individual energy supply and demand data For the first Energy supply and demand data in a time period The correlation coefficient.

5. The energy supply and demand balance analysis method based on fusion analysis to identify influencing factors as described in claim 1, characterized in that, The step of using a fuzzy set qualitative comparison analysis algorithm to calculate the importance of each condition variable and obtain the importance ranking results includes: Based on a preset fuzzy set, each of the condition variables is encoded to obtain an encoding table, which includes the encoded values ​​of various condition variables. Using pre-set analysis software, the importance of various combinations of condition variables is calculated based on the coding table; Based on the importance of various combinations of the condition variables, the importance of each condition variable is analyzed to obtain the importance ranking result.

6. The energy supply and demand balance analysis method based on fusion analysis to identify influencing factors as described in claim 5, characterized in that, The process of using pre-set analysis software to calculate the importance of various combinations of condition variables based on the coding table includes: Using the preset analysis software, the continuous encoded values ​​in the encoding table are converted into set dependent values, and a truth table is constructed based on the set dependent values; Based on the truth table, calculate the importance of various combinations of the condition variables.

7. The energy supply and demand balance analysis method based on fusion analysis to identify influencing factors as described in claim 1, characterized in that, The comparison of the correlation ranking results and the importance ranking results, and the analysis of the supply and demand balance impact on the target energy using a combination of target condition variables that meet preset consistency conditions, includes: Compare the consistency between the correlation ranking results and the importance ranking results; From the importance ranking results that satisfy the preset consistency conditions, determine multiple target condition variables that affect the energy supply and demand balance; By combining multiple target condition variables as target condition variables, an impact analysis on the supply and demand balance of the target energy is conducted.

8. An energy supply and demand balance analysis device, characterized in that, include: The acquisition module is used to acquire energy supply and demand data, which includes various supply data and various demand data. The supply data is energy supply-side data, including energy resource quantity, energy extraction capacity, development potential of new energy and alternative energy, and security of imported energy. The demand data is energy demand-side data, including population growth data, economic development data, technological progress, and consumption pattern transformation data. The first calculation module is used to use a grey relational analysis algorithm to calculate the correlation degree between each type of energy supply and demand data and the energy supply and demand balance result based on the energy supply and demand data, and obtain the correlation degree ranking result. The extraction module is used to extract the target energy supply and demand data corresponding to the target correlation in the correlation ranking results, and to use the target energy supply and demand data as a conditional variable affecting the energy supply and demand balance. The second calculation module is used to calculate the importance of each condition variable using a fuzzy set qualitative comparison analysis algorithm, and obtain the importance ranking result. The analysis module is used to compare the correlation ranking results and the importance ranking results, and to perform a supply and demand balance impact analysis on the target energy using a combination of target condition variables that meet preset consistency conditions.

9. A computer device, characterized in that, It includes a processor and a memory, the memory being used to store a computer program, which, when executed by the processor, implements the energy supply and demand balance analysis method based on fusion analysis to identify influencing factors as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the energy supply and demand balance analysis method based on fusion analysis to identify influencing factors as described in any one of claims 1 to 7.

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