Energy simulation adaptive analysis method, analysis system, medium and product

Abstract

The invention discloses an energy simulation adaptive analysis method, an analysis system, a medium and a product, and relates to the field of energy analysis. By implementing the application, the preset energy model general library comprises comprehensive modules from energy production, conversion, consumption to balance, and the analysis system can automatically screen the related analog variable set and analog variable calculation rule set from the preset energy model general library according to the specific energy analysis target; and a targeted energy target analysis model is constructed. The adaptive method can flexibly meet the analysis requirements of different regions, especially regions with complex energy systems, such as Ordos, and avoids the problem that a traditional standardized analysis framework is difficult to completely describe region features. Meanwhile, through quantitative calculation of the energy target analysis model, the analysis system can obtain a more accurate and reliable analysis result, and scientific decision support is provided for clean and efficient development of a regional energy system.

Description

Technical Field

[0001] This application relates to the field of energy analysis, and in particular to an adaptive energy simulation analysis method, analysis system, medium, and product. Background Technology

[0002] Ordos City, as an important energy base in my country, possesses a complete energy industry system and has established several industrial clusters worth hundreds of billions of yuan, including coal, oil, and natural gas. In recent years, under the dual requirements of optimizing the energy structure and protecting the ecological environment, Ordos City's energy development has faced the significant task of transformation and upgrading. How to achieve clean and efficient development of the energy system has become a crucial issue that urgently needs to be addressed.

[0003] Currently, energy analysis methods primarily employ the construction of energy balance sheets, which involves statistically analyzing data on the production, conversion, consumption, and transmission and distribution of various energy sources to establish an energy flow relationship matrix. Based on this, and combined with an energy statistical indicator system, trend extrapolation methods are used to predict future energy supply and demand. Furthermore, different scenario assumptions can be set to conduct qualitative analysis of the development path of the energy system.

[0004] However, the relevant technologies are difficult to adapt to the characteristics of energy systems in different regions, especially in regions like Ordos City, which have abundant energy types and complete industrial chains. The complex and diverse characteristics of its energy system are difficult to fully describe by a standardized analytical framework, which limits the accuracy and reliability of the analysis results. Summary of the Invention

[0005] This application provides an adaptive analysis method, analysis system, medium, and product for energy simulation, which can improve the accuracy and reliability of energy analysis results.

[0006] Firstly, this application provides an adaptive energy simulation analysis method applied to an analysis system. The method includes: receiving an energy analysis target; selecting a set of simulation variables from all energy variables in a pre-set energy model library, the pre-set energy model library including a terminal energy consumption module, a fossil energy production module, a power generation module, an energy conversion and refining module, an energy transmission and distribution module, a CO2 emission module, an energy system cost module, an energy security module, and an energy environment module; the set of simulation variables includes multiple variables necessary to achieve the energy analysis target; using the set of simulation variables as an index, determining a set of simulation variable calculation rules from all calculation rules in the pre-set energy model library, the set of simulation variable calculation rules including multiple rules necessary to achieve the energy analysis target; constructing an energy target analysis model based on the set of simulation variables and the set of simulation variable calculation rules, the energy target analysis model being used to quantitatively calculate each variable in the set of simulation variables; and calculating each variable in the set of simulation variables through the energy target analysis model to obtain simulation analysis results that satisfy the energy analysis target.

[0007] By adopting the above technical solution, the pre-set energy model library includes comprehensive modules covering energy production, conversion, consumption, and balance. The analysis system can automatically select relevant sets of simulated variables and calculation rules from the pre-set energy model library based on specific energy analysis objectives to construct targeted energy target analysis models. This adaptive approach can flexibly address the analysis needs of different regions, especially areas with complex energy systems like Ordos, avoiding the problem that traditional standardized analysis frameworks struggle to fully characterize regional features. Simultaneously, through quantitative calculations of the energy target analysis model, the analysis system can obtain more accurate and reliable analysis results, providing scientific decision support for the clean and efficient development of regional energy systems.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, the terminal energy consumption module includes an industrial energy consumption submodule, a transportation energy consumption submodule, a building energy consumption submodule, and an agricultural energy consumption submodule; the fossil energy production module includes a coal production submodule, a conventional oil production submodule, an unconventional oil production submodule, a natural gas production submodule, and an unconventional natural gas production submodule; the power generation module includes an onshore wind power module, a photovoltaic solar energy submodule, a solar thermal solar energy submodule, a geothermal power generation submodule, an ocean energy power generation submodule, a coal power generation submodule, a nuclear power generation submodule, and a natural gas power generation submodule; the energy conversion and refining module includes an oil refining submodule, a coal-to-oil submodule, a coal-to-gas submodule, a heating submodule, and a hydrogen production submodule; the energy environment module includes an energy and water submodule, an energy and air pollution submodule, and an energy and land use submodule.

[0009] By adopting the above technical solution, the sub-module structure of each module in the pre-set energy model library is refined. For example, the end-energy consumption module is divided into sub-modules such as industry, transportation, construction, and agriculture, and the fossil energy production module is divided into conventional and unconventional coal, oil, and natural gas production sub-modules. This allows for the accurate characterization of each link in the energy system. This modular design ensures the integrity of the pre-set energy model library while providing the possibility of flexible combination. Relevant sub-modules can be selected for modeling according to different energy analysis objectives, improving the practicality and adaptability of the model.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, the transportation energy consumption submodule includes a residential transportation unit and a freight transportation unit. The residential transportation unit includes an intra-city transportation submodule, an inter-city transportation submodule, and an international travel submodule. The freight transportation unit includes a domestic freight transportation submodule and an international freight transportation submodule. The building energy consumption submodule includes a residential building energy consumption unit and a public building energy consumption unit.

[0011] By adopting the above technical solution, the structure of the transportation energy consumption submodule and the building energy consumption submodule is further subdivided. The transportation energy consumption submodule is divided into residential transportation (including urban transportation, intercity transportation, and international travel) and freight transportation (including domestic transportation and international transportation). The latter is further divided into residential building energy consumption and public building energy consumption, enabling the model to more accurately depict the energy consumption characteristics of different energy-consuming entities. This refined division helps to identify key energy-consuming links, discover energy-saving potential, and provide a basis for the formulation of differentiated energy conservation and emission reduction policies.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, a set of simulated variables is selected from all energy variables in a preset energy model library. Specifically, this includes: performing natural language understanding on the energy analysis target to extract key information, including analysis dimensions, analysis indicators, constraints, and time nodes; matching the key information with a preset energy analysis template library; if the match is successful, calling the variables in the corresponding template as the set of simulated variables; if the match fails, calculating the semantic relevance between the key information and all energy variables; and selecting the set of simulated variables from energy supply variables, energy technology variables, energy demand variables, and energy balance variables based on a preset relevance threshold and semantic relevance.

[0013] By employing the above technical solution, the analysis system automatically extracts key information from the energy analysis objective using natural language understanding technology, and filters the set of simulated variables by matching them with an energy analysis template library or calculating semantic relevance. This intelligent variable filtering mechanism avoids omissions or subjective biases that may occur during manual filtering, improving the efficiency and accuracy of modeling. Simultaneously, setting a pre-defined relevance threshold as a filtering criterion ensures that the selected variables are sufficiently relevant to the energy analysis objective, preventing the introduction of irrelevant variables that could lead to a bloated model.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, the set of simulated variable calculation rules is determined from all calculation rules in the preset energy model library using the set of simulated variables as an index. Specifically, this includes: extracting target calculation rules with target variables as parameters from energy supply calculation rules, energy technology calculation rules, energy demand calculation rules, and energy balance calculation rules, where the target variables are any variables in the set of simulated variables; performing redundancy analysis and dependency determination on the target calculation rules to generate a structured set of simulated variable calculation rules.

[0015] By adopting the above technical solution, the analysis system extracts target calculation rules using the set of simulated variables as an index, and performs redundancy analysis and dependency determination, thus establishing a concise and structured set of simulated variable calculation rules. This method avoids the rule duplication or conflict problems that may exist in traditional energy models, improving the model's computational efficiency.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, after the step of constructing an energy target analysis model based on a set of simulated variables and a set of simulated variable calculation rules, the method further includes: displaying the set of simulated variables, the set of simulated variable calculation rules, and the energy target analysis model; receiving modification instructions from the user, and adjusting the set of simulated variables, the set of simulated variable calculation rules, and / or the energy target analysis model based on the modification instructions.

[0017] By adopting the above technical solution, the analysis system displays the set of simulated variables, the set of simulated variable calculation rules, and the energy target analysis model, and allows users to make adjustments through modification commands, thus realizing visualization and interactivity in the model construction process. This design enables users to intuitively understand the model structure, promptly identify and correct potential problems, and improve the accuracy and reliability of the model.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, after the step of calculating each variable in the set of simulated variables through the energy target analysis model to obtain the simulation analysis results that meet the energy analysis objectives, the method further includes: calculating the technical feasibility score, economic rationality score, and environmental impact score of the simulation analysis results based on a preset evaluation index database, wherein the evaluation index database includes technical feasibility assessment parameters, economic rationality assessment parameters, and environmental impact assessment parameters; when the technical feasibility score, economic rationality score, and / or environmental impact score are lower than the corresponding warning threshold, identifying the factors affecting the scores; and generating parameter adjustment suggestions for the factors affecting the scores.

[0019] By adopting the above technical solution, the analysis system introduces an evaluation index database to assess the simulation results from three dimensions: technical feasibility, economic rationality, and environmental impact. It also sets early warning thresholds for monitoring, enabling timely detection of potential problems in the simulation results. When the score for a certain dimension falls below the corresponding early warning threshold, the analysis system automatically identifies the factors influencing the score and generates parameter adjustment suggestions. This intelligent evaluation and feedback mechanism helps improve the reliability and practicality of the simulation results, ensuring a balance between technical, economic, and environmental aspects, and providing comprehensive reference for decision-making.

[0020] In a second aspect, embodiments of this application provide an analysis system comprising: one or more processors and a memory; the memory is coupled to the one or more processors and is used to store computer program code, the computer program code including computer instructions, wherein the one or more processors invoke the computer instructions to cause the analysis system to perform the method described in the first aspect and any possible implementation thereof.

[0021] Thirdly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on an analysis system, cause the analysis system to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on an analysis system, cause the analysis system to perform the method described in the first aspect and any possible implementation thereof.

[0023] Understandably, the analysis system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the methods provided in the embodiments of this application. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. By adopting the above technical solution, the pre-set energy model library includes comprehensive modules covering energy production, conversion, consumption, and balance. The analysis system can automatically select relevant sets of simulated variables and calculation rules from the pre-set energy model library based on specific energy analysis objectives to construct targeted energy target analysis models. This adaptive method can flexibly address the analysis needs of different regions, especially areas with complex energy systems like Ordos, avoiding the problem that traditional standardized analysis frameworks cannot fully characterize regional features. Simultaneously, through quantitative calculations of the energy target analysis model, the analysis system can obtain more accurate and reliable analysis results, providing scientific decision support for the clean and efficient development of regional energy systems.

[0025] 2. By adopting the above technical solution, the analysis system displays the set of simulated variables, the set of simulated variable calculation rules, and the energy target analysis model, and allows users to make adjustments through modification commands, thus realizing visualization and interactivity in the model construction process. This design enables users to intuitively understand the model structure, promptly identify and correct potential problems, and improve the accuracy and reliability of the model.

[0026] 3. By adopting the above technical solution, the sub-module structure of each module in the pre-set energy model library is refined. For example, the end-energy consumption module is divided into sub-modules such as industry, transportation, construction, and agriculture; the fossil energy production module is divided into conventional and unconventional coal, oil, and natural gas production sub-modules, etc., thereby enabling accurate characterization of each link in the energy system. This modular design not only ensures the integrity of the pre-set energy model library but also provides the possibility of flexible combination. Relevant sub-modules can be selected for modeling according to different energy analysis objectives, improving the practicality and adaptability of the model. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating an adaptive energy simulation analysis method in an embodiment of this application. Figure 2 This is another flowchart illustrating the energy simulation adaptive analysis method in the embodiments of this application; Figure 3 This is a schematic diagram of the physical device structure of the analysis system in the embodiments of this application. Detailed Implementation

[0028] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification of this application, the singular expressions “a,” “an,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0029] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0030] The following describes the process of the method provided in this implementation. Please refer to [link / reference]. Figure 1 This is a flowchart illustrating an adaptive analysis method for energy simulation in an embodiment of this application.

[0031] S101. Receive the energy analysis target and select the set of simulation variables from all energy variables in the preset energy model library. The preset energy model library includes the terminal energy consumption module, fossil energy production module, power generation module, energy conversion and refining module, energy transmission and import / export module, CO2 emission module, energy system cost module, energy security module, and energy environment module. The set of simulation variables includes multiple variables necessary to achieve the energy analysis target. The analysis system refers to the computer processing system used to perform adaptive energy simulation analysis. The energy analysis objective refers to the energy-related problem or task proposed by the user that needs to be analyzed, such as "analyzing the feasibility of achieving a 30% renewable energy share in Ordos City by 2030." The pre-built energy model library refers to a pre-established database collection containing various energy system models. Energy variables refer to quantifiable indicators in the energy system, such as "photovoltaic power generation capacity" and "annual natural gas production." The simulation variable set refers to the set of variables relevant to the energy analysis objective selected from all energy variables. The final energy consumption module represents the model components for the final use of energy. The fossil energy production module represents the model components for the production of traditional energy sources such as coal, oil, and natural gas. The power generation module represents the model components for various power generation methods. The energy conversion and refining module represents the model components for the energy form conversion process. The energy transmission, distribution, import, and export module represents the model components for the spatial flow of energy. The CO2 emission module represents the model components for carbon emission calculation. The energy system cost module represents the model components for economic analysis. The energy security module represents the model components for supply security analysis. The energy and environment module refers to the model components for environmental impact assessment.

[0032] The terminal energy consumption module includes an industrial energy consumption sub-module, a transportation energy consumption sub-module, a building energy consumption sub-module, and an agricultural energy consumption sub-module.

[0033] The industrial energy consumption submodule calculates industrial energy consumption based on the industrial energy consumption calculation formula; The formula for calculating industrial energy consumption is: ;

[0034] Where i represents the industrial sub-sector, j represents different energy types, including coal, oil, natural gas, electricity, and heat, and t represents different periods.

[0035] Industrial energy consumption accounts for more than 90% of total energy consumption in Ordos City. Based on the characteristics of Ordos City's industrial sector, the industrial sectors of Ordos City can be divided into coke, steel, cement, caustic soda, calcium carbide, fertilizer, methanol, coal-to-olefins, coal-to-ethylene glycol, coal-to-oil, and other industries.

[0036] There are three main factors influencing future industrial energy consumption: industry development level, industry technology level, and energy consumption structure. Within the energy system, industry output indicators can represent industry development level, comprehensive energy consumption per unit of output can represent industry technology level, and the proportion of different energy types can represent energy consumption structure. These factors can then be used to analyze and predict industrial energy consumption patterns.

[0037] The transportation energy consumption submodule includes residential transportation units and freight transportation units. Residential transportation units include urban transportation subunits, intercity transportation subunits, and international travel subunits. Freight transportation units include domestic freight transportation subunits and international freight transportation subunits. The main calculation formula is: 1. Total travel demand = average travel distance per person × city population; 2. Demand for each mode of travel = Total travel demand × Mode of travel; 3. Transportation energy consumption = operation of different modes of transportation × engine efficiency; Modes of transportation include walking, cycling, private cars, buses, and subways; Classification of Transportation Energy Consumption: 1. Petroleum consumption: mainly from traditional fuel vehicles; 2. Diesel and gas consumption: mainly from diesel vehicles; 3. Electricity consumption: from electric vehicles; 4. Hydrogen consumption: from clean energy vehicles such as natural gas.

[0038] The building energy consumption submodule includes residential building energy consumption units and public building energy consumption units. Residential building energy consumption mainly refers to energy consumption caused by residents' daily life, while public building energy consumption refers to energy consumption caused by institutions or venues that provide public services.

[0039] The building energy consumption submodule calculates building energy consumption based on the formulas for calculating cooling energy consumption, heating energy consumption, hot water energy consumption, lighting energy consumption, home appliance energy consumption, and cooking energy consumption. The formula for calculating cooling energy consumption is: ; The formula for calculating heating energy consumption is: ; The formula for calculating hot water energy consumption is: ; The formula for calculating lighting energy consumption is: ; The formula for calculating the energy consumption of home appliances is: Where 's' represents different household appliances; The formula for calculating cooking energy consumption is: .

[0040] The proportion of agricultural energy consumption in the final energy consumption in Ordos City is relatively low. Agricultural energy consumption is mainly manifested in agricultural machinery and agricultural irrigation. Therefore, the agricultural energy consumption sub-module calculates agricultural energy consumption based on the agricultural energy consumption calculation formula. The formula for calculating agricultural energy consumption is: .

[0041] The fossil energy production module includes a coal production submodule, a conventional oil production submodule, an unconventional oil production submodule, a natural gas production submodule, and an unconventional natural gas production submodule.

[0042] The power generation modules include onshore wind power modules, photovoltaic solar sub-modules, solar thermal solar sub-modules, geothermal power modules, ocean energy power modules, coal power modules, nuclear power modules, and natural gas power modules.

[0043] The energy conversion and refining module includes an oil refining submodule, a coal-to-oil submodule, a coal-to-gas submodule, a heating submodule, and a hydrogen production submodule.

[0044] The Energy and Environment module includes the Energy and Water submodule, the Energy and Air Pollution submodule, and the Energy and Land Use submodule.

[0045] Specifically, first, the analysis system receives the energy analysis objective input by the user, understanding its specific content and requirements through natural language processing technology. Then, the system accesses a pre-defined energy model library, which manages thousands of energy variables in functional modules. Based on the semantic information of the energy analysis objective, the system calculates the relevance of each energy variable to the objective and sets a relevance threshold (e.g., 0.6), selecting energy variables with relevance higher than the threshold to form a set of simulated variables. For example, for an energy analysis objective focusing on the proportion of renewable energy, the system will select variables related to renewable energy generation such as photovoltaic and wind power, as well as related simulated variables such as total power generation and electricity consumption. The selection process also considers the logical relationships between energy variables to ensure the completeness and consistency of the simulated variable set.

[0046] Optionally, in general, the set of simulated variables can be selected from all energy variables in the preset energy model library in the following ways, which are not limited here: Perform natural language understanding on the energy analysis objective to extract key information, including analysis dimensions, analysis indicators, constraints, and time points; match the key information with the preset energy analysis template library; if the match is successful, use the variables in the corresponding template as the set of simulated variables; if the match fails, calculate the semantic relevance between the key information and all energy variables; based on the preset relevance threshold and semantic relevance, select the set of simulated variables from energy supply variables, energy technology variables, energy demand variables, and energy balance variables.

[0047] Natural language understanding refers to the process of converting human language into structured information that computers can process, such as converting "analyzing carbon emissions in the transportation sector in 2030" into a computer-readable format. Key information refers to the core elements of energy analysis, including: analytical dimensions (e.g., sectoral or regional dimensions), analytical indicators (e.g., specific indicators to be examined, such as carbon emissions and energy consumption), constraints (e.g., limitations to be followed during the analysis, such as technological conditions and cost limits), and timeframes (e.g., specific dates or time periods for the energy analysis, such as 2025 or 2020-2030).

[0048] Specifically, first, the analysis system performs word segmentation and syntactic analysis on the input energy analysis target to identify keywords and parts of speech. Then, the analysis system extracts four types of key information from it using preset rules or machine learning models. For example, for "analyze the carbon emissions of the transportation sector in 2030", the analysis system will extract information such as the time point (2030), the analysis dimension (sector dimension - transportation), and the analysis indicator (carbon emissions).

[0049] The preset energy analysis template library represents a predefined set of standard energy analysis scenarios. Each template in the library contains a set of variables required for that specific scenario; these variables refer to the energy-related parameters necessary for that type of analysis scenario. Template matching is the process of comparing the extracted key information with each template in the preset energy analysis template library based on their similarity. A successful match indicates that a preset template highly similar to the current energy analysis target has been found.

[0050] Specifically, the analysis system compares the extracted key information with the features of each template in a pre-defined energy analysis template library, calculating the similarity. If the similarity of a template exceeds a pre-defined similarity threshold, it is considered a successful match, and the set of variables defined in that template is directly adopted. This method can quickly determine the variables needed for analysis, improving modeling efficiency. If no matching template is found, the system proceeds to the next step: a selection process based on semantic relevance.

[0051] Semantic relevance refers to the degree of similarity between two words or phrases in meaning, usually expressed numerically. The preset relevance threshold is the standard value for determining whether two words are related. Energy supply variables refer to parameters describing energy production and supply (such as crude oil production). Energy technology variables refer to parameters describing technologies related to energy conversion and utilization (such as power generation efficiency). Energy demand variables refer to parameters describing end-user energy demand (such as industrial electricity consumption). Energy balance variables refer to parameters describing the balance state of the energy system (such as the supply-demand gap).

[0052] Specifically, firstly, the analysis system calculates the semantic relevance between the extracted key information and each variable, using methods such as word vector models or knowledge graphs. For variables with semantic relevance exceeding a preset threshold, they are then categorized and filtered according to their class (supply, technology, demand, balance). For example, when analyzing carbon emissions from the transportation sector, highly relevant variables such as "vehicle fuel consumption" (demand variable) and "engine efficiency" (technology variable) might be selected. This approach allows for flexible selection of variables based on specific analytical needs.

[0053] S102. Using the set of simulated variables as an index, determine the set of simulated variable calculation rules from all the calculation rules in the preset energy model library. The set of simulated variable calculation rules includes multiple rules necessary to achieve the energy analysis objectives. Here, the simulated variable set refers to the combination of relevant variables selected in step S101. The index refers to keywords or identifiers used for retrieval and location. The calculation rule refers to the formula or algorithm describing the mathematical relationship between energy variables, such as "photovoltaic power generation = installed capacity × utilization hours". The simulated variable calculation rule set represents the combination of calculation rules that accompany the simulated variable set.

[0054] Specifically, firstly, the analysis system uses each variable in the set of simulated variables as an index key to search through all calculation rules in the pre-defined energy model library. For each variable, the analysis system extracts all calculation rules that include that variable. Then, the analysis system filters and organizes these calculation rules: firstly, it removes duplicate calculation rules; secondly, it analyzes the dependencies between calculation rules to ensure the rationality of the calculation order; thirdly, it verifies the completeness of the calculation rules to ensure that each variable has a corresponding calculation rule to support it; finally, it classifies and organizes these calculation rules according to supply rules, technology rules, demand rules, and balance rules to form a structured set of simulated variable calculation rules. For example, for the photovoltaic power generation variable, the relevant calculation rules include installed capacity calculation rules, utilization hours calculation rules, and power generation efficiency calculation rules, etc.

[0055] Optionally, under normal circumstances, the set of simulated variable calculation rules can be determined from all calculation rules in the preset energy model library using the set of simulated variables as an index. This can be achieved in the following ways, without limitation: extract target calculation rules with target variables as parameters from the energy supply calculation rules, energy technology calculation rules, energy demand calculation rules, and energy balance calculation rules. The target variables can be any variables in the set of simulated variables. Perform redundancy analysis and dependency determination on the target calculation rules to generate a structured set of simulated variable calculation rules.

[0056] Among these, energy supply calculation rules refer to the mathematical formulas or logical relationships used to calculate indicators related to energy production and supply, such as the formula for calculating crude oil production. Energy technology calculation rules refer to the calculation methods describing the characteristics of energy conversion and utilization technologies, such as the formula for calculating power generation efficiency. Energy demand calculation rules refer to the methods for calculating end-user energy demand, such as industrial electricity consumption forecasting models. Energy balance calculation rules refer to the mathematical expressions of the balance relationship in an energy system, such as the supply and demand balance equation. Target variables refer to the variables in the set of simulated variables that need to be calculated. Target calculation rules represent the calculation rules directly related to the target variables.

[0057] Specifically, the analysis system iterates through all types of calculation rules, checking whether the parameters involved in the rules include variables from the simulated variable set. If a calculation rule contains the target variable, it is marked as the target calculation rule. For example, if the simulated variable set contains "carbon emissions from the transportation sector," the analysis system will extract all rules used to calculate that variable, such as formulas like "carbon emissions = activity level × emission factor." This approach ensures that all calculation methods related to the target variable are obtained.

[0058] Redundancy analysis refers to the process of checking and eliminating duplicate or simplifiable computational rules. Dependency determination refers to determining the order and interrelationships between computational rules. A structured set of simulated variable computational rules represents an organized and optimized rule system with a clear computational order.

[0059] Specifically, first, the analysis system performs redundancy analysis on the extracted target calculation rules, identifying and deleting duplicate rules, and merging rules that can be simplified. Then, the system analyzes the dependencies between rules, determining which variables' calculations depend on the results of other variables. Based on these dependencies, the system organizes the rules into a directed acyclic graph structure, clearly defining the execution order of the rules. For example, if calculating carbon emissions in the transportation sector requires first calculating energy consumption, then the rules for calculating energy consumption will be prioritized. This process ensures the correctness and efficiency of the calculation process. The final rule set contains clear calculation steps and logical relationships between rules.

[0060] S103. Based on the set of simulated variables and the set of simulated variable calculation rules, construct an energy target analysis model. The energy target analysis model is used to realize the quantitative calculation of each variable in the set of simulated variables. Quantitative calculation refers to the process of calculating specific values ​​for variables using mathematical methods. It involves constructing a framework that integrates a set of dispersed simulated variables and their calculation rules into a complete computational framework.

[0061] Specifically, first, the analysis system categorizes the variables in the simulation variable set into decision variables (such as installed capacity), intermediate variables (such as power generation), and target variables (such as the proportion of renewable energy). Then, the system constructs a calculation sequence based on the logical relationships between the variables, determining which variables require priority calculation and which depend on the calculation results of other variables. Next, the system sorts and organizes the rules in the simulation variable calculation rule set according to the calculation sequence, forming a complete calculation chain. Based on this, the system also needs to set constraints for the model, such as variable value ranges and resource limitations. Finally, the system integrates these elements into a structured mathematical model, namely, the energy target analysis model. This model can receive input parameters and obtain the required simulation analysis results through a series of calculation rules.

[0062] S104. Using the energy target analysis model, calculate each variable in the set of simulation variables to obtain simulation analysis results that meet the energy analysis target.

[0063] The energy target analysis model represents the mathematical computational framework used to achieve energy analysis objectives. Computation refers to the process of processing data using mathematical methods. "Meeting" indicates that specific requirements have been met or are satisfied. Simulation analysis results refer to the quantitative analytical data obtained through calculations using the energy target analysis model.

[0064] Specifically, first, the analysis system collects and organizes the input parameters required for the energy target analysis model, including historical data, forecast data, and parameter settings. Then, the system executes the calculation rules in the energy target analysis model sequentially according to a pre-set calculation sequence. For each variable, the system performs numerical verification to ensure the results are within a reasonable range. If outliers are found, the system automatically records them and indicates possible causes. After completing the calculations for all variables, the system summarizes and organizes the key results, generating an analysis report containing numerical results, trend charts, and other information. For example, for the analysis of the proportion of renewable energy, the final simulation analysis results will include specific data such as renewable energy installed capacity, power generation, and proportion at different time points, as well as corresponding supporting analytical indicators such as economic costs and environmental benefits.

[0065] By adopting the above technical solution, the pre-set energy model library includes comprehensive modules covering energy production, conversion, consumption, and balance. The analysis system can automatically select relevant sets of simulated variables and calculation rules from the pre-set energy model library based on specific energy analysis objectives to construct targeted energy target analysis models. This adaptive approach can flexibly address the analysis needs of different regions, especially areas with complex energy systems like Ordos, avoiding the problem that traditional standardized analysis frameworks struggle to fully characterize regional features. Simultaneously, through quantitative calculations of the energy target analysis model, the analysis system can obtain more accurate and reliable analysis results, providing scientific decision support for the clean and efficient development of regional energy systems.

[0066] The following provides a more detailed description of the process of the method provided in this implementation. Please refer to [link / reference]. Figure 2 This is another flowchart illustrating the energy simulation adaptive analysis method in this application embodiment.

[0067] S201. Receive the energy analysis target and select a set of simulation variables from all energy variables in the preset energy model library. The preset energy model library includes the terminal energy consumption module, fossil energy production module, power generation module, energy conversion and refining module, energy transmission and distribution and import / export module, CO2 emission module, energy system cost module, energy security module, and energy environment module. The set of simulation variables includes multiple variables necessary to achieve the energy analysis target.

[0068] For details, please refer to step S101, which will not be repeated here.

[0069] S202. Using the set of simulated variables as an index, determine the set of simulated variable calculation rules from all the calculation rules in the preset energy model library. The set of simulated variable calculation rules includes multiple rules necessary to achieve the energy analysis objectives.

[0070] For details, please refer to step S102, which will not be repeated here.

[0071] S203. Based on the set of simulated variables and the set of simulated variable calculation rules, construct an energy target analysis model. The energy target analysis model is used to realize the quantitative calculation of each variable in the set of simulated variables.

[0072] For details, please refer to step S103, which will not be repeated here.

[0073] S204 displays the set of simulated variables, the set of simulated variable calculation rules, and the energy target analysis model.

[0074] In this context, "display" refers to the process of presenting information visually through a graphical user interface (GUI). A GUI is a visual software interface used for human-computer interaction. "Visual presentation" means converting data or information into intuitive forms such as graphs and tables.

[0075] Specifically, firstly, the analysis system defines an interactive graphical user interface, including a variable display area, a rule display area, and a model structure display area. In the variable display area, the system presents the set of simulated variables in a tree structure, categorizing variables as decision variables, intermediate variables, and target variables, and labeling the attribute information of each variable, such as data type and value range. In the rule display area, the system categorizes the set of simulated variable calculation rules into supply rules, technology rules, demand rules, and balance rules, using different colors to indicate the dependencies between rules. In the model structure display area, the system presents the overall framework of the energy target analysis model in the form of a flowchart, including the logical relationships between input parameters, calculation processes, and output results.

[0076] S205. Receive the user's modification instructions and adjust the set of simulation variables, the set of simulation variable calculation rules, and / or the energy target analysis model based on the modification instructions.

[0077] In this context, "modification command" refers to the specific operational commands used by the user to adjust the set of simulated variables, the set of simulated variable calculation rules, and / or the energy target analysis model. "User" refers to the operator using the analysis system. "Adjustment" indicates the process of modifying or optimizing model components.

[0078] Specifically, the analysis system provides various interactive tools in its graphical user interface, including a variable editor, a rule editor, and a model structure editor. Users can add or delete variables and modify variable attributes through the variable editor; modify calculation formulas and adjust rule priorities through the rule editor; and adjust the calculation process and set constraints through the model structure editor. The analysis system verifies in real time whether user modifications will lead to model inconsistencies, and will promptly alert the user if potential problems are detected. For each modification, the analysis system saves the modification record, supporting undo and redo operations. Finally, the analysis system updates the various components of the model based on the user-confirmed modification instructions, ensuring that the modified model maintains structural integrity and logical consistency.

[0079] S206. Using the energy target analysis model, calculate each variable in the set of simulation variables to obtain simulation analysis results that meet the energy analysis target.

[0080] For details, please refer to step S104, which will not be repeated here.

[0081] S207. Based on the preset evaluation index database, calculate the technical feasibility score, economic rationality score, and environmental impact score of the simulation analysis results respectively. The evaluation index database includes technical feasibility assessment parameters, economic rationality assessment parameters, and environmental impact assessment parameters.

[0082] The evaluation index database refers to a structured dataset used to store evaluation standards and parameters. The technical feasibility score represents a quantitative evaluation of the simulation analysis results in terms of technical implementation difficulty and maturity. The economic rationality score represents a quantitative indicator of the economic benefits and return on investment of the simulation analysis results. The environmental impact score represents a quantitative evaluation of the degree of impact of the simulation analysis results on the ecological environment. Technical feasibility assessment parameters represent specific indicators used to assess the difficulty of technical implementation, such as technology maturity and engineering complexity. Economic rationality assessment parameters represent specific indicators used to assess economic benefits, such as investment costs, operating costs, and rate of return. Environmental impact assessment parameters represent specific indicators used to assess environmental impact, such as carbon emissions, pollutant emissions, and resource consumption.

[0083] Specifically, firstly, the analysis system accesses a pre-set database of evaluation indicators, which contains benchmark values ​​and standards for different types of evaluation projects. For technical feasibility assessment, the system examines indicators such as technology maturity (0-1 points), engineering implementation difficulty (0-1 points), and system reliability (0-1 points), and calculates a weighted score (0-100 points) to obtain a technical feasibility score. For economic rationality assessment, the system calculates indicators such as investment payback period (years), net present value (ten thousand yuan), and internal rate of return (%), and converts them into an economic rationality score (0-100 points). For environmental impact assessment, the system measures indicators such as carbon emissions per unit of energy consumption (tons / ton of standard coal) and air pollutant emissions (kilograms / ton of standard coal), and converts them into an environmental impact score (0-100 points).

[0084] S208. When the scores for technical feasibility, economic rationality, and / or environmental impact are lower than the corresponding warning thresholds, identify the factors affecting the scores.

[0085] The warning threshold refers to the critical score value that triggers a warning, such as when the technical feasibility score is below 60. The scoring influencing factors indicate the specific reasons or parameters that lead to a low evaluation score.

[0086] Specifically, the analysis system first checks whether the scores for technical feasibility, economic rationality, and / or environmental impact are below the corresponding warning thresholds. For example, the warning threshold for the technical dimension is 60 points, for the economic dimension it is 70 points, and for the environmental dimension it is 75 points. When a score in a certain dimension is found to be below the corresponding warning threshold, the analysis system tracks the calculation process to pinpoint the specific indicator causing the low score. For example, if the technical feasibility score is low, the analysis system will identify whether there are problems such as immature technology or excessive engineering difficulty; if the economic rationality score is low, the analysis system will identify whether there are problems such as excessive investment or insufficient returns; if the environmental impact score is low, the analysis system will identify whether there are problems such as excessive emissions or excessive resource consumption.

[0087] S209. Suggestions for adjusting the parameters that affect the score.

[0088] Among them, parameter adjustment suggestions represent optimization solutions proposed for the problematic parameters. Generation refers to the process of forming specific content through calculation and analysis.

[0089] Specifically, the analysis system establishes a problem-solution knowledge base, which contains typical solutions to common problems. Based on the identified specific problem, the analysis system retrieves the corresponding solution from the knowledge base. For technical problems, it may suggest adopting a more mature technological approach or implementing solutions in phases; for economic problems, it may suggest optimizing engineering plans to reduce costs or seeking policy support to increase returns; for environmental problems, it may suggest adopting cleaner technologies or increasing environmental protection facilities. The analysis system will generate structured reports from these recommendations, including problem descriptions, improvement suggestions, and expected results, and will present these reports to the user through a graphical interface.

[0090] The analysis system in the embodiments of this invention is described below from the perspective of hardware processing. Please refer to [link / reference needed]. Figure 3 This is a schematic diagram of the physical device structure of the analysis system in an embodiment of this application.

[0091] It should be noted that, Figure 3 The structure of the analysis system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0092] like Figure 3As shown, the analysis system includes a CPU 301, which can perform various appropriate actions and processes based on a program stored in the read-only memory ROM 302 or a program loaded from the storage section 308 into the random access memory RAM 303, such as performing the methods described in the above embodiments. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An I / O interface 305 is also connected to the bus 304.

[0093] The following components are connected to I / O interface 305: input section 306 including audio input devices, push-button switches, etc.; output section 307 including a liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 308 including a hard disk, etc.; and communication section 309 including a network interface card such as a LAN (Local Area Network) card, modem, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0094] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by CPU 301, it performs the various functions defined in the present invention.

[0095] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0096] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0097] Specifically, the analysis system in this embodiment includes a processor and a memory. The memory stores a computer program, and when the computer program is executed by the processor, it implements the energy simulation adaptive analysis method provided in the above embodiment.

[0098] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the analysis system described in the above embodiments; or it may exist independently and not assembled into the analysis system. The storage medium carries one or more computer programs that, when executed by a processor of the analysis system, cause the analysis system to implement the energy simulation adaptive analysis method provided in the above embodiments.

[0099] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0100] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0101] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. An adaptive analysis method for energy simulation, characterized in that, Applied to an analysis system, the method includes: The system receives the energy analysis target and selects a set of simulation variables from all energy variables in the preset energy model library. The preset energy model library includes a terminal energy consumption module, a fossil energy production module, a power generation module, an energy conversion and refining module, an energy transmission and distribution and import / export module, a CO2 emission module, an energy system cost module, an energy security module, and an energy environment module. The set of simulation variables includes multiple variables necessary to achieve the energy analysis target. Using the set of simulated variables as an index, a set of simulated variable calculation rules is determined from all the calculation rules in the preset energy model library. The set of simulated variable calculation rules includes multiple rules necessary to achieve the energy analysis objective. Based on the set of simulated variables and the set of simulated variable calculation rules, an energy target analysis model is constructed. The energy target analysis model is used to realize the quantitative calculation of each variable in the set of simulated variables. By using the energy target analysis model, each variable in the set of simulated variables is calculated to obtain simulation analysis results that satisfy the energy analysis target.

2. The method according to claim 1, characterized in that, The terminal energy consumption module includes an industrial energy consumption submodule, a transportation energy consumption submodule, a building energy consumption submodule, and an agricultural energy consumption submodule. The fossil energy production module includes a coal production submodule, a conventional oil production submodule, an unconventional oil production submodule, a natural gas production submodule, and an unconventional natural gas production submodule. The power generation module includes an onshore wind power module, a photovoltaic solar energy sub-module, a solar thermal solar energy sub-module, a geothermal power generation module, an ocean energy power generation module, a coal power generation module, a nuclear energy power generation module, and a natural gas power generation module; The energy conversion and refining module includes an oil refining submodule, a coal-to-oil submodule, a coal-to-gas submodule, a heating submodule, and a hydrogen production submodule. The energy and environment module includes an energy and water submodule, an energy and air pollution submodule, and an energy and land use submodule.

3. The method according to claim 2, characterized in that, The transportation energy consumption submodule includes a residential transportation unit and a freight transportation unit. The residential transportation unit includes an intra-city transportation subunit, an inter-city transportation subunit, and an international travel subunit. The freight transportation unit includes a domestic freight transportation subunit and an international freight transportation subunit. The building energy consumption submodule includes residential building energy consumption units and public building energy consumption units.

4. The method according to claim 1, characterized in that, The step of selecting a set of simulation variables from all energy variables in the preset energy model library specifically includes: Natural language understanding is used to extract key information from the energy analysis objectives. The key information includes analysis dimensions, analysis indicators, constraints, and time nodes. The key information is matched with a preset energy analysis template library; If a match is found, the variables in the corresponding template are used as the set of simulated variables. If the matching fails, the semantic relevance between the key information and all energy variables is calculated. Based on a preset relevance threshold and the semantic relevance, the set of simulated variables is obtained by filtering from energy supply variables, energy technology variables, energy demand variables, and energy balance variables.

5. The method according to claim 4, characterized in that, The step of determining the set of simulation variable calculation rules from all calculation rules in the preset energy model library, using the set of simulation variables as an index, specifically includes: From the energy supply calculation rules, energy technology calculation rules, energy demand calculation rules, and energy balance calculation rules, target calculation rules with target variables as parameters are extracted, where the target variables are any variables in the set of simulation variables. Redundancy analysis and dependency determination are performed on the target calculation rules to generate a set of structured simulated variable calculation rules.

6. The method according to claim 1, characterized in that, After the step of constructing the energy target analysis model based on the set of simulated variables and the set of simulated variable calculation rules, the method further includes: Display the set of simulated variables, the set of simulated variable calculation rules, and the energy target analysis model; Receive modification instructions from users, and adjust the set of simulated variables, the set of simulated variable calculation rules, and / or the energy target analysis model based on the modification instructions.

7. The method according to claim 1, characterized in that, After the step of calculating each variable in the set of simulated variables using the energy target analysis model to obtain simulation analysis results that satisfy the energy analysis target, the method further includes: Based on a pre-set evaluation index database, the technical feasibility score, economic rationality score, and environmental impact score of the simulation analysis results are calculated respectively. The evaluation index database includes technical feasibility evaluation parameters, economic rationality evaluation parameters, and environmental impact evaluation parameters. When the technical feasibility score, the economic rationality score, and / or the environmental impact score are lower than the corresponding warning threshold, the factors affecting the scores are identified. Suggestions for adjusting the parameters generated by the factors influencing the scores.

8. An analysis system, characterized in that, The analysis system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the analysis system to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instructions are executed on the analysis system, the analysis system performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on the analysis system, the analysis system performs the method as described in any one of claims 1-7.

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