A Power Engineering Technological and Economic Analysis System Based on Big Data and Large Models

Abstract

This invention provides a power engineering techno-economic analysis system based on big data and large models. The system includes a data acquisition and integration terminal, a model analysis and prediction terminal, a decision support terminal, and a user interaction terminal. The data acquisition and integration terminal is used to collect various types of data related to power engineering. The model analysis and prediction terminal is used to model and predict the techno-economic characteristics of power engineering projects using various models. The decision support terminal is used to generate intelligent decision suggestions based on the analysis results of the model analysis and prediction terminal. The user interaction terminal is used to facilitate interaction between the user and the system. This invention, through the combination of big data and large models, achieves accurate modeling and intelligent decision support for the techno-economic characteristics of power engineering projects, improving the efficiency and accuracy of management and decision-making.

Description

Technical Field

[0001] This invention relates to the field of power engineering data processing system technology, and in particular to a power engineering technical and economic analysis system based on big data and large models. Background Technology

[0002] In modern power engineering projects, as project scale and complexity increase, the technical and economic characteristics of projects become more diverse and complex. Power engineering construction requires comprehensive consideration of technical and economic analysis, including factors such as project cost input, resource allocation, construction efficiency, and return on investment. Traditional decision-making and evaluation methods often fail to meet the multi-dimensional needs of complex projects and lack the ability to deeply mine and comprehensively analyze historical and real-time data. Furthermore, how to transform massive amounts of historical project data into effective knowledge that can guide current and future projects, thereby improving the efficiency and quality of project construction and management, is an urgent problem to be solved.

[0003] A review of publicly available technical solutions reveals that CN109858751A proposes an engineering project review system comprised of a user layer, application layer, platform layer, and data layer. This system includes a task module, a project review module, and a system management module. The task module primarily displays automatic push notifications to indicate the status of project review work within the system. The project review module enables online review and evaluation of scientific decision-making, investment control, and benchmarking management at each stage of power engineering project construction. The system management module handles system process design, monitoring, and historical data management. This system addresses issues such as unclear review workflows, ambiguous responsibilities, missing data, incomplete content, and insufficient benchmarking data in power engineering project construction. It achieves standardized, regulated, and information-based management of project review work, improving work quality and efficiency, and promoting the refinement of engineering project construction management. However, this solution primarily focuses on the standardization and optimization of the project review process, lacking sufficient support for technical and economic forecasting and optimization, and failing to provide in-depth support for multi-dimensional data analysis and dynamic optimization capabilities for power engineering projects. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of current systems by proposing a power engineering techno-economic analysis system based on big data and large models.

[0005] The present invention adopts the following technical solutions:

[0006] A power engineering techno-economic analysis system based on big data and large models is characterized in that the system includes a data acquisition and integration terminal, a model analysis and prediction terminal, a decision support terminal, and a user interaction terminal; the data acquisition and integration terminal is used to collect various types of data related to power engineering; the model analysis and prediction terminal is used to model and predict the techno-economic characteristics of power engineering projects using various models; the decision support terminal is used to generate intelligent decision suggestions based on the analysis results of the model analysis and prediction terminal; and the user interaction terminal is used to complete the interaction between the user and the system.

[0007] The data acquisition and integration terminal includes a data acquisition module, a historical data integration module, and a data processing and storage module. The data acquisition module is used to collect various types of data related to real-time power engineering projects. The historical data integration module is used to acquire and integrate historical power engineering project data. The data processing and storage module is used to preprocess various types of data related to real-time power engineering projects and historical power engineering project data, and store them according to the project type after preprocessing.

[0008] Furthermore, the model analysis and prediction end includes a feature processing module, a multi-model module, and a prediction input module. The feature processing module is used to process various types of real-time power engineering-related data and historical power engineering project data to generate standardized features that can be recognized and utilized by subsequent prediction models. The multi-model module includes multiple different types of prediction models related to power engineering, each prediction model being used for a specific analysis domain and providing accurate prediction results. The multiple different types of prediction models work together to complete a comprehensive analysis and prediction of the technical and economic aspects of an entire power engineering project. The prediction input module is used to input the various types of real-time power engineering-related data processed by the feature processing module into the various prediction models in the multi-model module, and to coordinate the data flow and input management between the prediction models, ensuring that each prediction model can receive the required feature data for efficient and accurate prediction analysis.

[0009] Furthermore, each prediction model in the multi-model module is built based on historical power engineering project data. After the feature processing module performs feature processing on the historical power engineering project data, the processed feature data is divided into a training set and a validation set to effectively train and evaluate the prediction model, thereby constructing a prediction model with good prediction capabilities. The training set is used to optimize the model parameters and learn the model structure in the prediction model, and the validation set is used to evaluate the performance of the prediction model on unseen data to ensure the model's generalization ability and prediction accuracy.

[0010] Furthermore, the training set contains a large amount of sample data extracted from historical power engineering project data and processed by the feature processing module; each sample data represents all the features contained in a historical power engineering project; during the training process of the prediction model, the training set generates multiple sets of variant samples of the original sample data by perturbing each sample data, thereby simulating different possible situations, and uses the original sample data and variant samples to train the prediction model together; and the variant samples share the same output label as the original sample data.

[0011] Furthermore, the specific execution process for perturbation processing of the sample data is as follows:

[0012] S11: For each feature in a given sample of data, calculate the perturbation factor by combining the historical data characteristics of that feature;

[0013]

[0014] Where, ∈ i γ is the perturbation factor for the i-th feature, and γ is the perturbation adjustment coefficient used to adjust the baseline strength of the data perturbation. This is the local volatility measure of the i-th feature; is the global volatility measure of the i-th feature; k is the sensitivity adjustment coefficient, used to adjust the sensitivity to changes in the disturbance factor.

[0015] Among them, for satisfy:

[0016]

[0017] in, This represents the set of feature values ​​for the i-th feature among all similar sample data within the time window of the current sample data in historical power engineering project data. The similar sample data refers to sample data with the same power engineering project type. T is the time window, which is set in advance according to the periodic characteristics of power engineering projects or the timeliness requirements of data. std() is the standard deviation calculation function.

[0018] Among them, for satisfy:

[0019]

[0020] in, This represents the set of feature values ​​for the i-th feature among all similar samples in historical power engineering project data.

[0021] S12: For each feature in the sample data, determine its perturbation range based on the perturbation factor; for the i-th feature of a certain sample data, its perturbation range is defined as (xi -∈ i ,x i +∈ i ); where x i Let be the feature value of the i-th feature in the sample data.

[0022] S13: For each feature in the sample data, a value is randomly generated within its perturbation range. After all features are generated, a new sample is formed, which is a variant of the original sample data.

[0023] S14: Repeat step S13 multiple times to obtain multiple variant samples of this sample data.

[0024] S15: Perform steps S11 to S14 for all sample data in the training set to generate variant samples for all sample data in the training set, thereby improving the generalization ability of the model training.

[0025] Furthermore, the decision support terminal includes a result receiving module, a difference analysis module, an optimization module, and a decision output module; the result receiving module is used to receive the analysis and prediction results from the model analysis and prediction terminal; the difference analysis module is used to receive the user-set ideal optimization target and identify the difference between the user's ideal optimization target and the analysis and prediction results provided by the analysis and prediction terminal; the optimization module is used to adjust and optimize various types of data related to real-time power engineering based on the difference analysis results; the decision output module is used to output the optimization results of the optimization module and display them to the user in a visual manner.

[0026] The beneficial effects achieved by this invention are as follows:

[0027] This invention integrates data acquisition, predictive modeling, and intelligent decision-making functions to achieve comprehensive analysis and accurate prediction of the technical and economic characteristics of power engineering projects. By perturbing the sample data during the training process of the predictive model, it improves the generalization ability and prediction accuracy of the predictive model and provides a flexible perturbation handling mechanism, enabling the model to adapt to complex engineering scenarios and changes. At the same time, through intelligent optimization and decision support functions, it provides more valuable and refined guidance for the management and decision-making of power engineering projects. Attached Figure Description

[0028] The invention will be further understood from the following description taken in conjunction with the accompanying drawings. The components in the drawings are not necessarily drawn to scale, but rather the emphasis is on illustrating the principles of the embodiments. In different views, the same reference numerals designate corresponding parts.

[0029] Figure 1 This is a schematic diagram of the internal framework of the system of the present invention.

[0030] Figure 2 This is a schematic diagram of the perturbation processing workflow for sample data according to the present invention.

[0031] Figure 3 This is a schematic diagram of the workflow of the optimization module of the present invention. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to its embodiments. It should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the present invention. Other systems, methods, and / or features of this embodiment will become apparent to those skilled in the art after reviewing the following detailed description. It is intended that all such additional systems, methods, features, and advantages are included within this specification, are included within the scope of the present invention, and are protected by the appended claims. Further features of the disclosed embodiments are described in the following detailed description, and these features will be apparent from the following detailed description.

[0033] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or component referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present patent. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0034] Example 1:

[0035] like Figure 1 As shown in the figure, this embodiment provides a power engineering technical and economic analysis system based on big data and large models. The system includes a data acquisition and integration terminal, a model analysis and prediction terminal, a decision support terminal, and a user interaction terminal. The data acquisition and integration terminal is used to collect various types of data related to power engineering. The model analysis and prediction terminal is used to model and predict the technical and economic characteristics of power engineering projects using various models. The decision support terminal is used to generate intelligent decision suggestions based on the analysis results of the model analysis and prediction terminal. The user interaction terminal is used to complete the interaction between the user and the system.

[0036] The data acquisition and integration terminal includes a data acquisition module, a historical data integration module, and a data processing and storage module. The data acquisition module is used to collect various types of data related to real-time power engineering projects. The historical data integration module is used to acquire and integrate historical power engineering project data. The data processing and storage module is used to preprocess various types of data related to real-time power engineering projects and historical power engineering project data, and store them according to the project type after preprocessing.

[0037] Furthermore, the model analysis and prediction end includes a feature processing module, a multi-model module, and a prediction input module. The feature processing module is used to process various types of real-time power engineering-related data and historical power engineering project data to generate standardized features that can be recognized and utilized by subsequent prediction models. The multi-model module includes multiple different types of prediction models related to power engineering, each prediction model being used for a specific analysis domain and providing accurate prediction results. The multiple different types of prediction models work together to complete a comprehensive analysis and prediction of the technical and economic aspects of an entire power engineering project. The prediction input module is used to input the various types of real-time power engineering-related data processed by the feature processing module into the various prediction models in the multi-model module, and to coordinate the data flow and input management between the prediction models, ensuring that each prediction model can receive the required feature data for efficient and accurate prediction analysis.

[0038] Furthermore, each prediction model in the multi-model module is built based on historical power engineering project data. After the feature processing module performs feature processing on the historical power engineering project data, the processed feature data is divided into a training set and a validation set to effectively train and evaluate the prediction model, thereby constructing a prediction model with good prediction capabilities. The training set is used to optimize the model parameters and learn the model structure in the prediction model, and the validation set is used to evaluate the performance of the prediction model on unseen data to ensure the model's generalization ability and prediction accuracy.

[0039] Furthermore, the training set contains a large amount of sample data extracted from historical power engineering project data and processed by the feature processing module; each sample data represents all the features contained in a historical power engineering project; during the training process of the prediction model, the training set generates multiple sets of variant samples of the original sample data by perturbing each sample data, thereby simulating different possible situations, and uses the original sample data and variant samples to train the prediction model together; and the variant samples share the same output label as the original sample data.

[0040] Further, such as Figure 2 As shown, the specific execution process for perturbation processing of sample data is as follows:

[0041] S11: For each feature in a given sample of data, calculate the perturbation factor by combining the historical data characteristics of that feature;

[0042]

[0043] Where, ∈ i γ is the perturbation factor for the i-th feature, and γ is the perturbation adjustment coefficient used to adjust the baseline strength of the data perturbation. This is the local volatility measure of the i-th feature; is the global volatility measure of the i-th feature; k is the sensitivity adjustment coefficient, used to adjust the sensitivity to changes in the disturbance factor.

[0044] Among them, for satisfy:

[0045]

[0046] in, This represents the set of feature values ​​for the i-th feature among all similar sample data within the time window of the current sample data in historical power engineering project data. The similar sample data refers to sample data with the same power engineering project type. T is the time window, which is set in advance according to the periodic characteristics of power engineering projects or the timeliness requirements of data. std() is the standard deviation calculation function.

[0047] Among them, for satisfy:

[0048]

[0049] in, This represents the set of feature values ​​for the i-th feature among all similar samples in historical power engineering project data.

[0050] S12: For each feature in the sample data, determine its perturbation range based on the perturbation factor; for the i-th feature of a certain sample data, its perturbation range is defined as (x i -∈ i ,x i +∈ i ); where x i Let be the feature value of the i-th feature in the sample data.

[0051] S13: For each feature in the sample data, a value is randomly generated within its perturbation range. After all features are generated, a new sample is formed, which is a variant of the original sample data.

[0052] S14: Repeat step S13 multiple times to obtain multiple variant samples of this sample data.

[0053] S15: Perform steps S11 to S14 for all sample data in the training set to generate variant samples for all sample data in the training set, thereby improving the generalization ability of the model training.

[0054] Furthermore, in this embodiment, the implementation code for the perturbation processing of the sample data is as follows:

[0055]

[0056]

[0057]

[0058]

[0059] Where: calculate_perturbation_factor is a function that calculates the perturbation factor.

[0060] The perturb_samples function perturbs sample data to generate variant samples.

[0061] The code example assumes that historical data and sample data have the `project_type` feature, which is used for classification and calculation of local volatility.

[0062] This solution constructs multiple prediction models by combining historical power engineering project data, thereby achieving comprehensive analysis and prediction of the technical and economic characteristics of power engineering. By generating variant samples through feature processing and perturbation of the sample data, the diversity of training data is effectively increased, thus improving the model's generalization ability and prediction accuracy. Simultaneously, during perturbation processing, the perturbation factor is dynamically set based on the historical statistical characteristics of features in the sample data and the statistical characteristics of data within the specified time period, ensuring the rationality and adaptability of the perturbation and avoiding the prediction model from becoming overly sensitive or exhibiting excessive bias. Ultimately, this enhances the prediction accuracy and stability of the prediction model in practical applications, providing more reliable information support for subsequent decision-making.

[0063] Example 2:

[0064] This embodiment should be understood to include at least all the features of any of the foregoing embodiments, and to further improve upon them;

[0065] This embodiment provides a power engineering techno-economic analysis system based on big data and large models. The system includes a data acquisition and integration terminal, a model analysis and prediction terminal, a decision support terminal, and a user interaction terminal. The data acquisition and integration terminal is used to collect various types of data related to power engineering. The model analysis and prediction terminal is used to model and predict the techno-economic characteristics of power engineering projects using various models. The decision support terminal is used to generate intelligent decision suggestions based on the analysis results of the model analysis and prediction terminal. The user interaction terminal is used to complete the interaction between the user and the system.

[0066] Furthermore, the decision support terminal includes a result receiving module, a difference analysis module, an optimization module, and a decision output module; the result receiving module is used to receive the analysis and prediction results from the model analysis and prediction terminal; the difference analysis module is used to receive the user-set ideal optimization target and identify the difference between the user's ideal optimization target and the analysis and prediction results provided by the analysis and prediction terminal; the optimization module is used to adjust and optimize various types of data related to real-time power engineering based on the difference analysis results; the decision output module is used to output the optimization results of the optimization module and display them to the user in a visual manner.

[0067] Furthermore, the specific workflow of the optimization module is as follows:

[0068] S21: Receive the difference analysis results from the difference analysis module and use them as the target optimization function for subsequent steps; the expression is:

[0069]

[0070] Where F represents the objective optimization function value, specifically indicating the quantitative difference between the user's ideal optimization objective and the model's analysis and prediction; d j Let c be the quantitative difference between the ideal optimization objective of the j-th user and the analysis and prediction results output by the model analysis and prediction end. j Let m be the weight of the j-th user ideal optimization objective, and m be the total number of user ideal optimization objectives.

[0071] S22: Based on the characteristics of real-time power engineering related data, set the effective range of each input feature, and generate an initial population within this range to ensure that each input feature value meets the set constraints; each individual in the initial population represents a possible solution, containing a specific set of input feature combinations, as the starting point of the optimization process; the input features are specifically the standardized features input into the prediction model in the multi-model module to complete the prediction.

[0072] S23: For the initial population, iteratively execute the selection, crossover, and mutation operations of the genetic algorithm to gradually optimize the individuals in the population; the specific implementation process is as follows:

[0073] Selection operation: Based on the fitness value of each individual in the current population, select the individuals with better performance from the current population; the calculation process of the fitness value is as follows:

[0074] S231: Input each individual in the current population into the multi-model module for prediction and obtain the analysis and prediction results;

[0075] S232: Calculate the objective optimization function value for each individual using the analysis and prediction results in step S231, and calculate its fitness value based on the objective optimization function value; in this scheme, the reciprocal of the objective optimization function value can be used as the fitness value.

[0076] Crossover operation: After the selection operation is completed, the input features of the selected individuals are cross-combined to generate new offspring individuals; the specific crossover operation process is as follows:

[0077] S233: Calculate the cross-selection probability for each input feature:

[0078]

[0079] Among them, P k,1 std(Y) represents the cross-selection probability of the k-th input feature at the current iteration number; α is a scaling factor used to control the baseline strength of cross-selection; k Y is the standard deviation of the k-th input feature in the current population. k Let be the set of all feature values ​​of the k-th input feature in the current population; ∈ is a very small positive number that is not zero.

[0080] S234: Based on the calculated cross-selection probability, the input features of the selected individuals are cross-combined to generate new offspring individuals.

[0081] Mutation operation: After the crossover operation, a mutation operation is performed on the generated offspring individuals, which randomly changes the input feature values ​​of certain input features; the specific implementation process is as follows:

[0082] S235: Calculate the variability for each individual:

[0083]

[0084] Where P2(t) is the variation rate of each input feature in the individual at the current iteration number t; P start β is the initial mutation rate of the population, set in the initial stage; β is a mutation rate adjustment parameter used to control the decay rate of the mutation rate; T max This is the preset maximum number of iterations.

[0085] S236: Based on the calculated mutation rate, randomly change the input features of the offspring individuals generated after the crossover operation, and ensure that the input feature values ​​after random change are within the preset effective range.

[0086] S24: Integrate the offspring individuals after the crossover and mutation operations with the current population, select the better performing individuals based on their fitness values ​​to form a new generation population, and repeat the selection, crossover, and mutation operation steps.

[0087] S25: When the number of iterations reaches the preset maximum number of iterations, the iteration process is stopped, and the individual with the highest fitness value in the current population is selected as the adjustment and optimization result and transmitted to the decision output module; the adjustment and optimization result is the optimal combination of input features.

[0088] Furthermore, the user interface can implement functions including but not limited to:

[0089] User input and requirements gathering: Users can input their optimization goals, constraints, and weight preferences through the interactive interface; the system will guide the model analysis and prediction end and the decision support end to perform corresponding analysis and optimization based on the user input.

[0090] Results Feedback and Display: Users can view the model prediction results and adjustment and optimization results in an intuitive and visual way through the interactive interface, which facilitates decision-making.

[0091] Adjustment and interactive feedback: Users can adjust and provide feedback on their past input based on the adjustment and optimization results, which will trigger the system's optimization process again.

[0092] This solution uses an objective optimization function to clearly define the difference between the user's ideal goal and the actual prediction result, serving as the direction and driving force for optimization. It employs a genetic algorithm to iteratively optimize the population, dynamically adjusting the combination of different features during crossover selection to improve population diversity and search capability. During mutation operations, the dynamic mutation rate is adjusted to give the population stronger exploration capabilities in the early stages and gradually converge in the later stages, avoiding premature entrapment in local optima. The streamlined process helps generate efficient, stable optimization results that meet the user's goals.

[0093] The content disclosed above is only a preferred and feasible embodiment of the present invention, and is not intended to limit the scope of protection of the present invention. Therefore, all equivalent technical changes made based on the content of the present invention specification and drawings are included within the scope of protection of the present invention. Furthermore, the elements therein can be updated as technology develops.

Claims

1. A power engineering techno-economic analysis system based on big data and large models, characterized in that, The system includes a data acquisition and integration terminal, a model analysis and prediction terminal, a decision support terminal, and a user interaction terminal. The data acquisition and integration terminal is used to collect various types of data related to power engineering. The model analysis and prediction terminal is used to model and predict the technical and economic characteristics of power engineering projects using various models. The decision support terminal is used to generate intelligent decision suggestions based on the analysis results of the model analysis and prediction terminal. The user interaction terminal is used to complete the interaction between the user and the system; The data acquisition and integration terminal includes a data acquisition module, a historical data integration module, and a data processing and storage module. The data acquisition module is used to collect various types of data related to real-time power engineering projects. The historical data integration module is used to acquire and integrate historical power engineering project data. The data processing and storage module is used to preprocess various types of data related to real-time power engineering projects and historical power engineering project data, and store them according to the project type after preprocessing. The model analysis and prediction end includes a feature processing module, a multi-model module, and a prediction input module; the multi-model module includes multiple different types of prediction models related to power engineering, and each prediction model in the multi-model module is constructed based on historical power engineering project data; After the feature processing module processes the historical power engineering project data, the processed feature data is divided into a training set and a validation set to effectively train and evaluate the prediction model, thereby constructing a prediction model with good prediction capabilities. The training set is used to optimize the model parameters and learn the model structure in the prediction model, while the validation set is used to evaluate the performance of the prediction model on unseen data to ensure the model's generalization ability and prediction accuracy. The training set contains a large amount of sample data extracted from historical power engineering project data and processed by the feature processing module. Each sample data point in the training set undergoes perturbation processing. The specific perturbation process for the sample data is as follows: S11: For each feature in a given sample of data, calculate the perturbation factor by combining the historical data characteristics of that feature; ； in, For the first A perturbation factor with certain characteristics. This is the disturbance adjustment coefficient, used to adjust the baseline strength of data disturbances; For the first A measure of the local volatility of a given characteristic; For the first A global volatility measure for this characteristic; This is the sensitivity adjustment coefficient, used to adjust the sensitivity to changes in the disturbance factor; Among them, for satisfy: ； in, This indicates the first (or second) sample of the same type within the time window of the current sample data in historical power engineering project data. The set of feature values ​​for a certain feature, wherein the sample data of the same type are sample data with the same type of power engineering project; The time window is set in advance based on the periodic characteristics of the power engineering project or the data timeliness requirements; This is the function for calculating standard deviation; Among them, for satisfy: ； in, This represents the first sample of its kind in all historical power engineering project data. The set of eigenvalues ​​of a feature; S12: For each feature in the sample data, determine its perturbation range based on the perturbation factor; for a given sample data point... This characteristic, whose perturbation range is defined as ;in For the first in this sample data The eigenvalues ​​of a feature; S13: For each feature in the sample data, a value is randomly generated within its perturbation range. After all features are generated, a new sample is formed, which is a variant sample of the original sample data. S14: Repeat step S13 multiple times to obtain multiple variant samples of this sample data; S15: Perform steps S11 to S14 for all sample data in the training set to generate variant samples for all sample data in the training set, thereby improving the generalization ability of the model training.

2. The power engineering technical and economic analysis system based on big data and large models according to claim 1, characterized in that, The feature processing module is used to process various types of real-time power engineering-related data and historical power engineering project data to generate standardized features that can be identified and utilized by subsequent prediction models. The multiple prediction models of different types work together to complete the comprehensive analysis and prediction of the technical and economic aspects of an entire power engineering project. The prediction input module is used to input the various types of real-time power engineering-related data processed by the feature processing module into the various prediction models in the multi-model module, and to coordinate the data flow and input management between the prediction models to ensure that each prediction model can receive the feature data it needs for efficient and accurate prediction analysis.

3. The power engineering technical and economic analysis system based on big data and large models according to claim 1, characterized in that, Each sample data represents all the features contained in a historical power engineering project; during the training process of the prediction model, the training set perturbs each sample data to generate multiple sets of variant samples of the original sample data, thereby simulating different possible situations, and uses the original sample data and variant samples to train the prediction model together; and the variant samples share the same output label as the original sample data.

4. The power engineering technical and economic analysis system based on big data and large models according to claim 1, characterized in that, The decision support terminal includes a result receiving module, a difference analysis module, an optimization module, and a decision output module. The result receiving module receives the analysis and prediction results from the model analysis and prediction terminal. The difference analysis module receives the user-defined ideal optimization target and identifies the difference between the user's ideal optimization target and the analysis and prediction results provided by the analysis and prediction terminal. The optimization module adjusts and optimizes various types of data related to real-time power engineering based on the difference analysis results. The decision output module outputs the optimization results from the optimization module and displays them to the user in a visual manner.

Images