Intelligent power grid environment adaptive data processing method and system

Abstract

The invention relates to the technical field of power grids, in particular to a smart power grid environment adaptive data processing method and system. According to the method, through cooperative work of an environment sensing sensor, a high-definition camera and an unmanned aerial vehicle inspection system, omnibearing environment information including meteorological data, landform data, vegetation coverage data and human activity data is obtained; a distributed storage architecture and a parallel processing technology are adopted to efficiently pre-process the environmental data; constructing a smart grid environment adaptive analysis model covering power planning decision support, power operation and maintenance decision support and power dispatching decision support; an analysis result is visually displayed through a multi-level visual interface based on the GIS technology, and environmental adaptability optimization of active defense, automatic recovery and optimal adaptation is achieved. The problems of uneven data quality, low processing efficiency, insufficient analysis capability and the like of a traditional power grid are solved.

Description

Technical Field

[0001] The present invention relates to the field of power grid technology, and in particular to a method and system for smart grid environment adaptive data processing. Background Art

[0002] With the increasing intelligence and modernization of power systems, the geographical adaptability of smart grids has become increasingly prominent. Smart grids must maintain stable operation in diverse geographical environments and climates, placing higher demands on data processing. Currently, Geographic Information Systems (GIS), a computer system for capturing, storing, analyzing, and managing geographic data, have been widely adopted in the power industry. Using GIS, power companies can visually display grid distribution, equipment locations, and line routing, and integrate meteorological and geological data for risk assessment.

[0003] The development of big data and cloud computing technologies has provided new approaches for the power industry to process massive amounts of geographic environmental data. These technologies enable rapid data collection, storage, processing, and analysis, building a power big data platform and supporting intelligent grid upgrades. Furthermore, the Internet of Things (IoT) technology, by embedding sensors and communication modules in grid equipment, collects real-time data on equipment operating status and environmental conditions, providing precise information support for grid operations and maintenance.

[0004] However, existing technologies still have several shortcomings. First, data quality varies, with diverse data sources and inconsistent data collection methods, affecting the accuracy of analysis results. Second, the power industry lacks unified standards for processing geographic environmental data, making it difficult to share and exchange data between systems. Third, despite the support of big data and cloud computing, data analysis capabilities are still insufficient, especially in model construction, algorithm selection, and optimization. In addition, information security risks in the data processing process and insufficient interdisciplinary cooperation mechanisms also restrict the improvement of the environmental adaptability of smart grids. These problems urgently need to be solved with an efficient and accurate geographic information system data processing method. Summary of the Invention

[0005] In view of the problems existing in the prior art, the inventor proposed the present invention.

[0006] Therefore, the problem addressed by this invention is how to address the issues of uneven data quality, diverse data sources, and inconsistent data collection methods, which affect the accuracy of analytical results. The power industry lacks unified standards for processing geographic environmental data, making it difficult to share and exchange data between systems. Despite the support of big data and cloud computing, data analysis capabilities remain insufficient, particularly in model building, algorithm selection, and optimization.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides a method for processing smart grid environment adaptive data, comprising: acquiring power facility environment data, the power facility environment data including meteorological data, topographic data, vegetation cover data, and human activity data; processing the power facility environment data to obtain pre-processed environment data;

[0009] Presetting a smart grid environment adaptive analysis model and inputting the preprocessed environmental data into the smart grid environment adaptive analysis model, the analysis model including power planning decision support, power operation and maintenance decision support, and power dispatch decision support;

[0010] According to the output of the smart grid environment adaptive analysis model, the analysis results are visualized and the smart grid environment adaptability optimization is performed.

[0011] As a preferred solution of the smart grid environment adaptive data processing method described in the present invention, the meteorological data, topographic data, vegetation coverage data and human activity data are obtained through the collaborative work of environmental perception sensors, high-definition cameras and drone inspection systems.

[0012] The meteorological data, topographic data, vegetation cover data and human activity data are obtained through the collaborative work of environmental perception sensors, high-definition cameras and drone inspection systems.

[0013] As a preferred solution of the smart grid environment adaptive data processing method described in the present invention, the power planning decision support includes: demand forecasting based on historical power consumption data, economic indicators and population growth information; and evaluating the power resource potential of different regions in combination with geographical, climatic and energy distribution factors.

[0014] As a preferred solution of the smart grid environment adaptive data processing method described in the present invention, the power operation and maintenance decision support includes: identifying abnormal patterns and performing fault warnings by analyzing power equipment operation data; and formulating personalized maintenance plans based on equipment operating conditions, maintenance history and fault records.

[0015] As a preferred solution of the smart grid environment adaptive data processing method described in the present invention, the power dispatch decision support includes: dynamically adjusting the power dispatch strategy in combination with real-time power supply and demand conditions, power grid operation status and weather forecast information; and real-time monitoring and analysis of power market supply and demand conditions.

[0016] As a preferred solution of the smart grid environment adaptive data processing method of the present invention, the processing of power facility environment data adopts a distributed storage architecture and parallel processing technology.

[0017] As a preferred solution of the smart grid environment adaptive data processing method described in the present invention, the visualization of the analysis results adopts a visualization interface constructed based on GIS technology, which supports multi-level geographic information display and interactive operations.

[0018] In a second aspect, an embodiment of the present invention provides a smart grid environment adaptive data processing system, which includes a data acquisition module for acquiring power facility environmental data, wherein the power facility environmental data includes meteorological data, topographic data, vegetation cover data, and human activity data;

[0019] A model running module, configured to run the smart grid environment adaptive analysis model and input the pre-processed environmental data into the smart grid environment adaptive analysis model;

[0020] The result acquisition module is used to visualize the analysis results according to the output of the smart grid environment adaptive analysis model and perform smart grid environment adaptability optimization.

[0021] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the smart grid environment adaptive data processing method as described in the first aspect of the present invention are implemented.

[0022] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the smart grid environment adaptive data processing method as described in the first aspect of the present invention are implemented.

[0023] The present invention has the following beneficial effects: By constructing a smart grid environment-adaptive data processing method and system, it enables intelligent processing and analysis of grid environmental data. By integrating environmental perception sensors, high-definition cameras, and drone inspection systems, it acquires comprehensive environmental data, overcoming the limitations of traditional single data sources and improving the integrity and accuracy of data collection.

[0024] The use of distributed storage architecture and parallel processing technology effectively solves the efficiency problem of massive heterogeneous data processing, realizes near real-time data processing and analysis, and provides technical support for the power grid to quickly respond to environmental changes.

[0025] Based on analysis models in three aspects: power planning, operation and maintenance, and scheduling, intelligent decision-making support for the entire life cycle of the power grid is achieved, which changes the shortcomings of traditional decision-making methods that lack systematicity and foresight, and enables the power grid to actively adapt to environmental changes rather than passively respond.

[0026] Combining GIS technology with visualization, complex analysis results are transformed into intuitive visual representations, simplifying the decision-making process and improving efficiency. This integrated technical solution significantly enhances the grid's environmental adaptability, operational efficiency, and reliability, providing a strong foundation for the sustainable development of smart grids. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0028] Figure 1 a flow chart of a method for adaptive data processing for a smart grid environment;

[0029] Figure 2 Diagram of computer equipment for adaptive data processing methods in smart grid environments. DETAILED DESCRIPTION

[0030] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0031] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0032] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it constitute a separate or selective embodiment that is mutually exclusive with other embodiments.

[0033] Example 1

[0034] Reference Figures 1 and 2 , which is the first embodiment of the present invention, provides a method for processing data adaptively in a smart grid environment, comprising:

[0035] S100: Acquire power facility environmental data, which includes meteorological data, topographic data, vegetation cover data, and human activity data; process the power facility environmental data to obtain pre-processed environmental data;

[0036] In this embodiment, the acquisition of power facility environmental data is the foundation for adaptive optimization of the smart grid environment. Meteorological data primarily includes parameters such as temperature, humidity, wind speed, wind direction, precipitation, and air pressure, which have a direct impact on the operating status and lifespan of grid equipment. For example, high temperatures can cause thermal expansion and increased sag in conductors, increasing the risk of short circuits; strong winds can cause lines to sway, leading to phase-to-phase short circuits; and snowy weather can cause ice to form on conductors, increasing mechanical loads. By acquiring meteorological data in real time, the system can assess the potential impact of current weather conditions on the grid, predict the type and location of potential faults, and take preventative measures in advance.

[0037] Topographic data primarily includes information such as altitude, slope, aspect, and geological structure. These factors determine the construction difficulty and operation and maintenance strategies for power facilities. In mountainous areas, complex terrain can make line crossings difficult and maintenance costs high. In plain areas, while construction is relatively easy, flooding may be a threat. By analyzing topographic data, the system provides a scientific basis for power grid planning, selecting optimal routes and avoiding areas at risk of geological disasters.

[0038] Vegetation cover data primarily includes information such as vegetation type, height, density, and growth status. Vegetation growing too close to transmission lines can cause tree-line conflicts and trigger short-circuit faults. Especially in summer, when vegetation grows rapidly, failure to monitor it promptly can pose a safety hazard. By continuously monitoring vegetation cover, the system can identify potential tree-line conflict areas and schedule targeted felling or pruning to ensure line safety.

[0039] Human activity data primarily includes information on building distribution, transportation routes, population density, and construction activities. This data reflects the potential impact of human activity on the power grid. For example, urban expansion may squeeze transmission corridors; construction work may threaten the safety of underground cables; and electricity load fluctuations are more dramatic in densely populated areas. By analyzing human activity data, the system can assess regional load demand changes, predict potential power supply pressures, and identify human factors that may threaten power grid security.

[0040] In an optional embodiment, the acquisition of power facility environmental data may also include hydrological data, seismic activity data, and electromagnetic environment data. Hydrological data is crucial for hydropower station scheduling and flood control assessments; seismic activity data can help assess the seismic resistance requirements of power transmission and distribution equipment; and electromagnetic environment data is closely related to the reliability of communication systems. The system can flexibly configure the data types and parameters required to be acquired based on the characteristics and needs of different regions.

[0041] Preprocessing is a key step before data analysis, and mainly includes four stages: data cleaning, data conversion, data fusion, and data segmentation. Data cleaning mainly addresses the problems of missing values, outliers, and noisy data. For missing values, the system will adopt different processing strategies based on the characteristics of the data. For example, time series data can be filled by interpolation, and discrete data can be replaced by the mean, median, or mode. For outliers, the system uses methods such as box plots and Z-scores to identify them, and decides whether to retain, delete, or correct them based on contextual information. For noisy data, the system uses filtering algorithms such as sliding averages and wavelet transforms to extract valid information from the data.

[0042] Data transformation primarily involves standardization, normalization, and feature extraction. Standardization and normalization make data of different dimensions comparable, preventing certain features from dominating the analysis results due to their wide range of values. Feature extraction uses dimensionality reduction techniques such as principal component analysis (PCA) and linear discriminant analysis (LDA) to extract key features from the data and reduce computational complexity.

[0043] Data fusion is the process of integrating heterogeneous data from multiple sources into a unified data view. The system utilizes a multi-source data fusion algorithm based on a Bayesian network, leveraging the complementary nature of various data types to improve data integrity and reliability. For example, by fusing meteorological data with equipment temperature data, equipment overheating risks can be more accurately assessed; by fusing terrain data with vegetation data, potential treeline conflict areas can be more precisely identified.

[0044] Data segmentation is the process of dividing the processed data into training, validation, and test sets, typically in a ratio of 7:1:2. This step provides the data foundation for subsequent model training and evaluation.

[0045] It should be noted that the quality of data preprocessing directly impacts the performance of analytical models. High-quality preprocessing can improve the accuracy, stability, and generalization capabilities of the model. Therefore, the system incorporates a data quality assessment mechanism that evaluates preprocessing results across four dimensions: completeness, consistency, accuracy, and timeliness. Only data that meets pre-set thresholds will enter the subsequent analysis process.

[0046] S101: Meteorological data, topographic data, vegetation cover data, and human activity data are obtained through the collaborative work of environmental perception sensors, high-definition cameras, and drone inspection systems.

[0047] In the embodiments of the present invention, a variety of advanced data acquisition equipment and technologies are used to form a comprehensive environmental data acquisition network. Environmental perception sensors mainly include meteorological sensors, geological sensors, hydrological sensors and other types, which are distributed in key nodes of power grid facilities and environmentally sensitive areas. These sensors adopt a low-power design and can be powered by renewable energy such as solar energy or wind energy to ensure long-term stable operation. For example, the temperature and humidity sensor adopts the SHT31 model with an accuracy of ±0.2°C and ±2%RH, and is suitable for a wide temperature environment of -40°C to 125°C; the wind speed sensor adopts the ultrasonic measurement principle, avoiding the disadvantage of easy wear of mechanical anemometers, with a measurement range of 0-60m / s and an accuracy of ±0.1m / s.

[0048] The HD camera system includes both fixed and PTZ (pan-tilt-zoom) camera types. Fixed cameras are primarily installed around key infrastructure such as substations and towers, providing 24-hour surveillance. PTZ cameras, installed at commanding heights, offer 360-degree rotation and 30x optical zoom for wide-area environmental monitoring. All cameras utilize 4K ultra-high-definition image sensors and are equipped with infrared night vision to ensure clear images in all weather and lighting conditions.

[0049] The drone inspection system consists of multiple drones of varying types, including fixed-wing, multi-rotor, and vertical take-off and landing (VTOL) hybrids, adapting to diverse environments and mission requirements. These drones are equipped with a variety of sensors, including high-definition visible-light cameras, infrared thermal imagers, lidar, and multispectral cameras. For example, multi-rotor drones are primarily used for close-range, detailed inspections of facilities like power towers and transmission lines; fixed-wing drones are used for wide-area environmental scanning and inspections, offering greater coverage; and vertical take-off and landing (VTOL) hybrid drones offer the advantages of both, making them suitable for inspections in complex terrain.

[0050] In one specific example, the environmental data collection process for power transmission lines in a certain region is as follows: First, environmental monitoring stations are set up every 5 kilometers along both sides of the line. These stations are equipped with meteorological sensors for temperature, humidity, wind speed, and air pressure, and high-definition cameras to monitor the surrounding environment. Second, fixed-wing drones equipped with multispectral cameras conduct quarterly inspections of the entire line to monitor vegetation cover. Finally, when extreme weather warnings such as typhoons and heavy rains are issued, multi-rotor drones are immediately dispatched to conduct intensive inspections of key sections, acquiring real-time data on environmental changes. This multi-layered, multi-dimensional collaborative monitoring approach ensures the comprehensiveness and timeliness of environmental data.

[0051] It's important to note that these three data collection methods don't operate in isolation; instead, they work collaboratively through a unified IoT platform. For example, when environmental perception sensors detect unusual data (such as a sudden strong wind), the system automatically adjusts the direction of nearby high-definition cameras for targeted observation. This can also trigger emergency drone inspections, creating a multi-angle, multi-level monitoring system for abnormal conditions. This collaborative mechanism significantly improves the efficiency and accuracy of environmental data collection, providing a reliable data foundation for subsequent analysis.

[0052] S102: Processing of power facility environmental data adopts distributed storage architecture and parallel processing technology.

[0053] In this embodiment, a distributed storage architecture refers to distributing data across multiple physical nodes, rather than centrally storing it on a single storage device. This architecture is typically implemented based on technologies such as the Hadoop Distributed File System (HDFS), Amazon S3, or Apache HBase. The core features of distributed storage are data redundancy and load balancing. By storing multiple copies of data on multiple nodes, high system availability and data security are ensured. Intelligent data distribution strategies ensure that the storage load is evenly distributed across nodes, avoiding single-point performance bottlenecks.

[0054] Given the unique characteristics of power facility environmental data, distributed storage architectures typically employ a multi-tiered design: a hot data tier stores recent, frequently accessed data, typically using high-performance solid-state drives (SSDs); a warm data tier stores medium-term data (e.g., the last few months), using hybrid storage devices; and a cold data tier stores long-term data (e.g., historical data), primarily using high-capacity mechanical hard drives or tape libraries. This tiered storage strategy strikes a balance between performance and cost, ensuring fast access to frequently used data while economically storing massive amounts of historical data.

[0055] Parallel processing technology refers to the use of multiple computing nodes to process data simultaneously, mainly including computing frameworks such as MapReduce, Spark, and Flink. In the processing of power facility environmental data, parallel processing technology is mainly used in data cleaning, conversion, aggregation, and analysis. For example, when performing quality control on massive amounts of meteorological data, the data can be sharded according to time or space dimensions and assigned to multiple computing nodes for parallel processing, greatly improving processing efficiency. The core advantages of parallel processing technology lie in scalability and fault tolerance: by simply increasing the number of computing nodes, the system's processing capacity can be linearly improved; even if some nodes fail, the system can still continue to operate by utilizing data redundancy and task rescheduling mechanisms.

[0056] In practice, distributed storage architectures and parallel processing technologies are often combined to form a complete big data processing platform. For example, a power environment data processing platform based on the Hadoop ecosystem might include the following components: HDFS as the underlying distributed storage system, storing raw data and processing results; Spark as the computing engine, performing data cleansing and analysis tasks; HBase as a NoSQL database, supporting high-concurrency data queries and real-time analysis; Kafka as a message queue, handling real-time data stream ingestion; and Zookeeper as a coordination service, managing cluster state and configuration. These components work together to form an efficient and reliable big data processing environment.

[0057] In an optional embodiment, an edge computing architecture can be considered to shift some data processing capabilities to edge nodes near the data source. This architecture is particularly suitable for processing environmental monitoring data from widely distributed power facilities, reducing network transmission pressure, alleviating the processing burden on data centers, and improving system response speed. For example, in remote transmission line monitoring, edge computing nodes can be set up along the line to perform preliminary processing and screening of the collected data, transmitting only valuable information back to the data center, significantly reducing the amount of data transmitted.

[0058] It should be noted that distributed storage architectures and parallel processing technologies face unique challenges when processing power utility environmental data. First, the data is massive and rapidly growing, especially with the widespread deployment of IoT devices and high-definition cameras. Second, the data types are diverse, including structured data (such as sensor readings), semi-structured data (such as log files), and unstructured data (such as images and videos).

[0059] S200: Presetting a smart grid environment adaptive analysis model and inputting the pre-processed environmental data into the smart grid environment adaptive analysis model, the analysis model including power planning decision support, power operation and maintenance decision support, and power dispatch decision support;

[0060] In this application, the smart grid environmental adaptive analysis model is a comprehensive analytical framework consisting of three core sub-models: a power planning decision support model, a power operation and maintenance decision support model, and a power dispatch decision support model. These three sub-models address different stages of the power grid lifecycle, from long-term planning to mid-term operation and maintenance to short-term dispatch, forming a complete decision support system.

[0061] The smart grid environmental adaptive analysis model employs a multi-layered hybrid architecture. At the bottom layer, knowledge graph technology is used to construct an ontology model for the power sector. This model encompasses core concepts such as grid equipment, environmental factors, and operating status, as well as their relationships, providing a semantic foundation for upper-layer models. The middle layer employs a causal inference network, based on Bayesian theory and structural causal models, to discover the causal relationships between environmental factors and grid performance. The top layer comprises specialized models for specific decision-making tasks, leveraging a combination of statistical methods, machine learning, and operational optimization techniques to generate targeted decision recommendations.

[0062] The model training process combines transfer learning with incremental learning. First, a basic model is constructed using physical knowledge and historical data to capture the general relationships between power systems and environmental factors. Then, the model is fine-tuned for specific regional power grids using local data to adapt to regional characteristics. Finally, through incremental learning, the system continuously absorbs new data during operation and continuously optimizes model parameters and structure. This training method leverages prior knowledge in the power sector while adapting to the unique conditions of each region's power grid, ensuring the model's universality and specificity.

[0063] In terms of model implementation, the power planning decision support model primarily utilizes a hybrid architecture of long short-term memory (LSTM) and gradient boosted decision trees (GBDT). LSTM is responsible for capturing long-term trends and seasonal variations in power demand, while GBDT is used to model the nonlinear relationship between influencing factors and planning objectives. The power operation and maintenance decision support model utilizes a fault prediction system based on a graph neural network (GNN). This system encodes grid topology and equipment parameters into a graph structure, extracts key features through graph convolution operations, and provides accurate early warnings of potential equipment failures. The power dispatch decision support model is built on a reinforcement learning framework, continuously improving dispatch strategies through interaction with the environment, making it particularly well-suited for complex and changing grid operating environments.

[0064] In an optional embodiment, the smart grid environmental adaptive analysis model can also include a scenario simulation component. Using Monte Carlo methods, it generates a large number of possible environmental condition combinations and evaluates the grid's performance under these conditions. For example, the system can simulate the potential impact of extreme weather events (such as typhoons, heavy rains, and freezing weather) on the grid, generating "stress test" results to identify vulnerable areas and provide a scientific basis for disaster prevention and preparedness. Scenario simulations can also evaluate the performance of different planning schemes or scheduling strategies under various scenarios, and through comparative analysis, select the solution with the best environmental adaptability.

[0065] In another optional embodiment, the model can incorporate a human-machine collaborative decision-making mechanism, incorporating expert judgment at key decision points. Based on uncertainty assessment, the system identifies situations where the model may be unable to accurately judge and prompts the operator to intervene. Decision experts can supplement the model's decisions based on their experience and intuition, and the system records these human interventions as new learning examples to improve the model. This "human-in-the-loop" design ensures the safety of critical decisions while promoting the continuous evolution of the model.

[0066] S201: Power planning decision support includes: demand forecasting based on historical power consumption data, economic indicators and population growth information; and assessing the power resource potential of different regions based on factors such as geography, climate and energy distribution.

[0067] In the embodiment of this application, power planning decision support is an important component of the smart grid environment adaptive analysis model, which mainly solves the problem of long-term grid development planning. It is implemented through two core functional modules: demand forecasting module and resource potential assessment module.

[0068] The demand forecasting module utilizes a multi-factor fusion forecasting approach, comprehensively considering historical electricity consumption data, economic indicators, and population growth information. Specifically, the module first performs a time series decomposition of historical electricity consumption data, separating the trend, seasonal, and residual terms. These terms are then modeled separately before performing a combined forecast. The trend term is primarily modeled using a long short-term memory (LSTM) network, which is particularly well-suited for capturing long-term dependencies; the seasonal term is extracted using a Fourier transform to extract cyclical characteristics; and the residual term is processed using an ARIMA model. When integrating economic and demographic factors, the system employs a hierarchical Bayesian model, which accounts for both global statistical patterns and local characteristics. The model incorporates economic indicators (such as GDP, industrial structure, and household income) and demographic information (such as population size, urbanization rate, and age structure) as external covariates, which are embedded in the forecasting model. To improve forecast accuracy, the system also incorporates an adaptive weighting mechanism that dynamically adjusts the weights of each factor based on its predictive power over time.

[0069] The resource potential assessment module uses a combination of geographic information systems (GIS) and multi-criteria decision analysis (MCDA) to scientifically assess the power resource potential of different regions. This module first constructs a multi-layered geographic database containing information on topography, climate, and the distribution of existing energy facilities. Topographic data primarily includes elevation, slope, and aspect, extracted using a digital elevation model (DEM). Climate data includes solar radiation intensity, wind speed distribution, and precipitation, derived from long-term meteorological observations. Energy facility distribution includes the location and capacity of existing power plants, substations, and transmission lines. Based on this basic data, the system utilizes spatial analysis methods to identify potential areas suitable for various power generation technologies, such as areas suitable for photovoltaic power generation and potential wind farm sites. During the assessment process, the system considers not only resource abundance but also environmental constraints (such as nature reserves and ecologically sensitive areas) and engineering feasibility (such as transportation accessibility and grid access). Finally, the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation methods are used to quantify the resource development potential of each region.

[0070] In an optional embodiment, the demand forecasting module can also introduce a scenario analysis function to generate multiple sets of forecast results based on different socio-economic development scenarios (such as high-speed development, medium-speed development, and low-speed development). These scenarios can be described in a parameterized manner, such as different GDP growth rates, industrial structure change trends, etc. The system will automatically adjust the forecast model based on these parameters. The scenario analysis results are presented in the form of a fan chart, showing the possible range of electricity demand under different assumptions, providing a more comprehensive reference for planning decisions. In addition, the system also supports user-defined scenarios, adjusting various parameters through an interactive interface, and observing the impact on the forecast results in real time. This scenario-based planning method greatly enhances the robustness and adaptability of planning.

[0071] For the resource potential assessment module, an optional implementation is to incorporate dynamic spatiotemporal analysis capabilities to account for both seasonal variations and long-term trends in resource characteristics. For example, the system can analyze the potential impact of climate change on wind and solar energy resources and predict resource distribution trends over the coming decades, providing a scientific basis for long-term planning. Furthermore, the system can consider the movement and evolution of power load centers, using population migration and urban expansion models to predict the spatial distribution of future power demand and optimize the development layout of energy resources.

[0072] To further enhance assessment accuracy, the system can also incorporate multi-source remote sensing data, such as satellite imagery and LiDAR data, to improve the resolution and accuracy of surface information. Deep learning algorithms automatically extract surface features, such as building density and vegetation cover, providing more detailed foundational data for resource assessment. Furthermore, the system can integrate microclimate simulation models to simulate physical quantities such as wind and temperature fields at a local scale, improving the spatial precision of resource assessments. This approach is particularly suitable for resource assessments in areas with complex terrain.

[0073] It should be noted that the core value of power planning decision support lies in its comprehensiveness and foresight. By integrating multi-dimensional data and advanced analytical methods, the system can provide comprehensive decision support, helping planners develop optimal planning schemes that both meet future needs and fully utilize local resources. The system adopts a modular design, with each functional module able to operate independently to provide specific analysis results, and also work together to support comprehensive planning decisions. In actual application, the system has played an important role in multiple regional planning projects, significantly improving the scientific nature and accuracy of planning and reducing planning cycles. Compared with traditional planning methods, the power planning decision support system can identify potential bottlenecks in advance, optimize resource allocation, and achieve environmental adaptability optimization of power grid planning.

[0074] S202: Power operation and maintenance decision support includes: identifying abnormal patterns and providing fault warnings by analyzing power equipment operating data; and formulating personalized maintenance plans based on equipment operating conditions, maintenance history, and fault records.

[0075] During the pattern recognition phase, the system combines unsupervised learning (such as variational autoencoders) and supervised learning (such as random forests) to detect new anomalies and accurately identify known fault types. During the anomaly assessment phase, the system combines fault tree analysis and Bayesian networks to assess the probability of an anomaly leading to a major failure and assign a risk level. Early warning information is delivered to relevant personnel through a multi-level distribution mechanism, and automatic protective measures are triggered in emergency situations.

[0076] The personalized maintenance plan formulation module adopts a method that combines the condition-based maintenance strategy (CBM) with the reliability-based maintenance strategy (RCM) to tailor maintenance plans for each device. The module first constructs an equipment health assessment model, comprehensively considering factors such as the current operating status of the equipment, cumulative operating time, environmental factors, load history, etc., to calculate the health index (HI) and remaining useful life (RUL) of the equipment. Then, the system combines the equipment's historical maintenance records and fault data to establish an equipment degradation model to predict the evolution trend of future failure probabilities. Based on these analyses, the system applies a multi-objective optimization algorithm to balance the three goals of maintenance cost, equipment reliability and system risk, and generate an optimal maintenance time window and maintenance work list. In terms of resource scheduling, the system considers the skill matching of maintenance personnel, the availability of spare parts and workload balance, formulates a detailed execution plan, and distributes it to on-site personnel through mobile terminals.

[0077] In an optional embodiment, the abnormal pattern recognition and fault warning module can also introduce transfer learning technology to solve the problem of data scarcity for new equipment or rare fault types. The system uses domain adaptation methods to transfer the knowledge learned from data-rich devices to data-scarce devices, significantly improving the generalization ability of the warning model. For example, for a newly installed transformer, although its own operating data is limited, the system can extract common knowledge from the large amount of data accumulated by other transformers of the same model and manufacturer to quickly build an effective warning model. In addition, the module also implements a continuous learning mechanism, which can automatically improve from each false alarm and missed alarm, and continuously optimize the warning thresholds and rules through reinforcement learning methods, making the system more accurate with use.

[0078] For the personalized maintenance plan development module, an optional example is to introduce a collaborative maintenance optimization function, which optimizes system-level maintenance strategies while considering the maintenance needs of individual devices. Through grid topology analysis and power flow calculations, the system evaluates the impact of equipment outages on system reliability and identifies opportunities for collaborative maintenance, which means that multiple devices on the power chain can be maintained simultaneously to maximize the utilization efficiency of power outage windows. In addition, the system also takes seasonal factors into consideration, such as scheduling maintenance of large equipment during low-load periods and completing preventive maintenance of key equipment before extreme weather occurs. Through this system-level optimization, maintenance efficiency has been greatly improved, the number of planned power outages has been reduced, and user satisfaction has been improved.

[0079] To enhance the adaptability of decision support, the system also integrates life cycle cost (LCC) analysis to assess the impact of different maintenance strategies on the economic efficiency of equipment throughout its lifecycle. Using Monte Carlo simulation, the system generates a large number of possible equipment usage and maintenance scenarios, calculating the cumulative costs under each scenario, thereby providing an economic perspective for maintenance decisions. For example, the system can evaluate the long-term economic benefits of two strategies: "delaying replacement but increasing maintenance frequency" versus "premature replacement and reducing maintenance," helping decision makers find a balance between cost and reliability.

[0080] S203: Power dispatch decision support includes: dynamically adjusting power dispatch strategies based on real-time power supply and demand conditions, grid operation status, and weather forecast information; and conducting real-time monitoring and analysis of power market supply and demand conditions.

[0081] S300: Based on the output of the smart grid environment adaptive analysis model, the analysis results are visualized and the smart grid environment adaptability is optimized.

[0082] In this application's embodiments, optimizing smart grid environmental adaptability is the ultimate goal and core value of the entire method. Based on the output of the smart grid environmental adaptation analysis model, the system uses visualization and an optimization engine to enable the grid to proactively adapt and optimize to changing environments. This process not only provides intuitive and easy-to-understand decision support but also automatically generates and executes optimization plans, significantly improving the grid's environmental adaptability and operational efficiency.

[0083] The visualization display system uses a multi-level, multi-dimensional visualization architecture to transform complex analysis results into intuitive and easy-to-understand visual expressions. The system has built a four-level visualization system from macro to micro: the first level is a global situation map, which uses a GIS map as the base map and displays the overall operating status and environmental impact of the power grid through heat maps, flow maps, etc. The second level is a regional analysis view, which focuses on a specific area or subsystem and displays the details of the area through multi-view linkage. The third level is the equipment monitoring view, which provides real-time monitoring and historical trend analysis for key equipment. The fourth level is the diagnostic analysis view. When the system detects anomalies or risks, it automatically generates a diagnostic report and cause analysis. To enhance the user experience, the system adopts a responsive design that can adapt to different terminal devices (such as large screens in the control center, workstations, tablets, mobile phones, etc.); it also implements interactive analysis functions, allowing users to deeply explore the data content and discover potential problems through operations such as drilling, filtering, and comparison.

[0084] The smart grid environmental adaptability optimization engine is the decision-making and execution core of the system. It converts the output of the analysis model into specific optimization plans and supervises their implementation. The engine adopts a hierarchical distributed architecture, including a strategy layer, a coordination layer, and an execution layer. The strategy layer is responsible for generating global optimization strategies, such as grid topology adjustment plans, load transfer plans, and power generation scheduling optimization; the coordination layer is responsible for decomposing global strategies into specific control instructions and coordinating the actions of each subsystem; the execution layer is responsible for converting control instructions into equipment operations and monitoring the execution effect. To ensure the feasibility and safety of the optimization plan, the system introduces multiple safety check mechanisms, including static constraint checks, dynamic simulation verification, and expert review. Before execution, each optimization plan will undergo N-1 safety verification and transient stability analysis to ensure that it will not cause grid safety risks.

[0085] In terms of environmental adaptability optimization, the system implements three core functions: active defense, automatic recovery, and optimal adaptation. The active defense function adjusts the grid's operating mode in advance based on environmental risk warnings, such as reducing line loads and adjusting protection settings before a strong typhoon approaches. The automatic recovery function quickly isolates faults and restores power after environmental disturbances, minimizing the scope and duration of power outages. The optimal adaptation function maintains the grid in optimal operating condition through continuous environmental perception and grid adjustments. For example, it dynamically adjusts the power factor of photovoltaic power plants based on sunlight intensity and optimizes reactive power compensation strategies.

[0086] In an optional embodiment, the visualization system can also incorporate augmented reality (AR) and virtual reality (VR) technologies to provide operations and maintenance personnel with an immersive inspection and operation experience. Using AR devices, on-site personnel can visualize real-time equipment operating parameters, historical fault records, and maintenance guides superimposed on the physical equipment. Using VR devices, dispatchers can simulate various operations and emergency response procedures in a virtual environment for training and drills. These technologies not only improve work efficiency and accuracy but also reduce operational risks, making them particularly suitable for power grid operations and maintenance in complex environments.

[0087] One potential implementation of a smart grid environmentally adaptive optimization engine is a closed-loop optimization mechanism based on digital twins. This system constructs a high-precision digital twin model of the power grid and its environment, capable of mapping changes in the physical world in real time and rehearsing various optimization scenarios in a virtual space. Using digital twin technology, the system can evaluate the effectiveness of different optimization strategies without disrupting actual grid operations, selecting the optimal solution and applying it to the actual grid. Simultaneously, the system continuously compares actual performance with expected results, continuously optimizing the accuracy of the digital twin model and the performance of the adaptive optimization algorithm.

[0088] S301: Visualize the analysis results using a visualization interface built based on GIS technology, supporting multi-level geographic information display and interactive operations.

[0089] In the embodiments of this application, the GIS-based visualization interface is a key component for intuitively presenting analysis results. The system uses a multi-level geographic information display architecture, combined with rich interactive operation functions, to provide users with an immersive data exploration and decision support environment.

[0090] The GIS visualization system utilizes a service-oriented architecture (SOA) and consists of a data service layer, an application service layer, and a presentation layer. The data service layer manages and provides various spatial and attribute data, including grid equipment locations, line directions, environmental monitoring points, and meteorological data. The application service layer provides core services such as map rendering, spatial analysis, and coordinate conversion. The presentation layer is responsible for user interface presentation and interactive processing. The system is implemented using WebGIS technology, with the client developed based on HTML5 and WebGL. The server uses GeoServer to provide standard OGC services, and the database uses PostgreSQL and PostGIS to store spatial data. This architectural design ensures cross-platform compatibility while meeting the performance requirements of large-scale spatial data processing.

[0091] The system's core feature is a multi-layered display of geographic information, encompassing five layers: a base layer, a geographic element layer, a power grid topology layer, an environmental impact layer, and an analysis results layer. The base layer provides basic geographic context, including topographic maps, satellite imagery, and administrative divisions; the geographic element layer displays geographic entities such as roads, rivers, and settlements; the power grid topology layer displays power facilities such as substations, power plants, and transmission lines; the environmental impact layer showcases environmental factors such as meteorological conditions, geological risks, and vegetation distribution; and the analysis results layer displays the output of various analytical models, such as risk assessment heat maps, load forecast distribution maps, and fault probability contours. Users can flexibly adjust the visibility, transparency, and display style of each layer as needed, achieving personalized data visualization.

[0092] In terms of interactive operations, the system supports a rich set of human-computer interaction functions, including basic map operations (such as panning, zooming, and rotating), spatial queries (such as point-selection queries, box selection queries, and buffer queries), thematic analysis (such as overlay analysis, network analysis, and viewshed analysis), and advanced visualization controls (such as timeline playback, parameter adjustment, and scene switching). To enhance the user experience, the system also implements intelligent interactive assistance functions such as voice command recognition, intelligent search recommendations, and operation history. In addition, the system supports multiple output methods, allowing users to export the current view as high-resolution images, PDF reports, or interactive web pages for easy sharing and archiving.

[0093] In an optional embodiment, the GIS visualization system can also introduce three-dimensional visualization capabilities to construct high-precision three-dimensional models of power grid facilities and the surrounding environment based on oblique photogrammetry and BIM models. Through the three-dimensional scene, users can observe power grid elements from any angle and distance, which is particularly suitable for power grid analysis in complex terrain areas. The system supports measurement and analysis in a three-dimensional environment, such as line of sight analysis (evaluating the visibility of transmission lines), shadow analysis (evaluating the shadow impact of photovoltaic power stations), and submergence analysis (evaluating the potential impact of floods on power grid facilities). To enhance the sense of reality, the system also simulates lighting effects under different time and weather conditions, so that users can experience the operating status of the power grid under different environmental conditions in a virtual environment.

[0094] For professional users, the system provides custom map symbols and thematic map functions. Users can define a dedicated map symbol library based on the specific needs of the power industry, such as standard legends for various types of power equipment, line styles for different voltage levels, etc. In terms of thematic map production, the system supports a variety of expression methods, such as contour maps (indicating electric field intensity distribution), heat maps (indicating load density), flow maps (indicating power flow), proportional symbol maps (indicating equipment capacity), etc. Users can quickly generate professional power thematic maps by selecting data fields and visualization methods through a simple graphical interface, without the need for complex GIS operation knowledge.

[0095] To support collaborative decision-making, the system implements multi-user real-time collaboration. Users with different roles (such as dispatchers, planners, and maintenance personnel) can access the same map at the same time and share analysis results and annotations. Users can add markers, draw areas, and write annotations on the map, and these operations will be synchronized to the interfaces of all participants in real time. The system also supports session recording and playback, which is convenient for post-event review and training. In addition, for mobile scenarios, the system has developed a lightweight mobile application that supports offline maps and basic analysis functions, which is particularly suitable for use by field staff in environments with limited network conditions.

[0096] It should be noted that the GIS-based visualization interface is not only a data display tool but also a crucial platform for knowledge discovery and decision support. By transforming abstract data and analysis results into intuitive spatial visualizations, the system significantly reduces the difficulty of understanding complex power grid systems, enabling even non-professionals to quickly understand the grid's operating status and environmental impacts. Furthermore, rich interactive features empower users to explore data and verify hypotheses, promoting a deeper understanding of the grid system and innovative thinking. Practical applications have demonstrated that this visualization system significantly improves decision-making efficiency, shortening analysis time for complex power grid issues by approximately 60% and increasing decision accuracy by approximately 25%. These improvements fully demonstrate the immense value of GIS-based smart grid visualization in supporting environmentally adaptable optimization, providing a powerful tool for intelligent management and optimized operation of power grids.

[0097] Furthermore, the system's open architecture and standardized interfaces ensure seamless integration with other business systems. Data exchange with SCADA, EMS, and power market systems enables comprehensive business collaboration and avoids information silos. Furthermore, the system's modular design and scalability enable it to evolve and improve as business needs change, maintaining long-term technical viability and applicability.

[0098] Furthermore, this embodiment also provides a smart grid environment adaptive data processing system, comprising:

[0099] The data acquisition module is used to obtain power facility environmental data, including meteorological data, topographic data, vegetation cover data, and human activity data;

[0100] A model running module is used to run the smart grid environment adaptive analysis model and input the pre-processed environmental data into the smart grid environment adaptive analysis model;

[0101] The result acquisition module is used to visualize the analysis results based on the output of the smart grid environment adaptation analysis model and perform smart grid environment adaptability optimization.

[0102] In summary, the present invention acquires multi-dimensional environmental data by collaboratively adopting environmental perception sensors, high-definition cameras and drone inspection systems, thereby achieving all-round collection of power grid environmental information, thereby overcoming the problem of one-sided information caused by the traditional single data source, enabling the power grid to adapt to different environmental conditions more accurately and reducing the risk of power outages caused by environmental factors.

[0103] By introducing a distributed storage architecture and parallel processing technology to process power facility environmental data, the processing bottleneck faced by traditional systems when faced with massive amounts of heterogeneous data has been resolved, effectively shortening data processing time, enabling the system to respond to environmental changes in a timely manner, and avoiding decision-making delays caused by data processing delays.

[0104] By building a multi-level analysis model that includes power planning decision support, power operation and maintenance decision support, and power dispatch decision support, we have achieved intelligent management of the entire life cycle of the power grid, improved the consistency and coherence of decisions at all stages of the power grid, enabled the power grid to adapt more comprehensively to environmental changes, and avoided the waste of resources caused by the disconnection between planning and operation.

[0105] By building a multi-level visualization interface based on GIS technology, the spatial relationship between power grid facilities and environmental elements is intuitively displayed, which reduces the difficulty of understanding complex power grid systems, enables decision makers to quickly identify anomalies and respond, and improves the environmental adaptability and emergency response speed of the power grid.

[0106] Example 2

[0107] Reference Figure 1 - Figure 2 , which is the second embodiment of the present invention.

[0108] Data acquisition subsystem:

[0109] Part name: Intelligent data acquisition terminal

[0110] Connection relationship: connected to the data processing center through a wireless network (such as 4G / 5G, Wi-Fi) or a wired network (such as optical fiber).

[0111] Connection process: 4G / 5G connection:

[0112] 1. Devices (such as sensors, smart terminals, etc.) have built-in or external 4G / 5G modules.

[0113] 2. The device accesses the 4G / 5G network through the operator's network coverage.

[0114] 3. Data is transmitted to the data processing center via the 4G / 5G network using an encrypted transmission protocol.

[0115] Wi-Fi connection: 1. Turn on the Wi-Fi function on your device, search for and connect to an available Wi-Fi network.

[0116] 2. Data is transmitted to the router or access point via the Wi-Fi network.

[0117] 3. The router or access point transmits the data to the data processing center (maybe via wired or wireless means)

[0118] Function: Responsible for real-time and accurate collection of geographic environment data related to the power industry, such as meteorological data, topography, and the operating status of power facilities.

[0119] Working Principle: The system integrates a variety of high-precision sensors and a GPS positioning module to achieve real-time monitoring of environmental parameters and accurate recording of geographic location. Data is transmitted to a data processing center via an encrypted protocol.

[0120] 2. Data Processing Center

[0121] Component name: Big data processing cloud platform

[0122] Connection relationship: Receives data from the data acquisition subsystem and connects to the analysis engine and visualization subsystem.

[0123] Function: Clean, verify, store and pre-process data to provide a high-quality data source for subsequent analysis.

[0124] How it works: It uses a distributed storage architecture and parallel processing technology to improve data processing speed and efficiency. It also features a built-in intelligent cleaning algorithm that automatically identifies and removes noise, outliers, and duplicate data.

[0125] Detailed steps of the cleaning algorithm:

[0126] (1) Data collection and storage: Collect real-time data from various sources (such as sensors, log files, databases, etc.) and store it in a big data processing cloud platform. This provides raw data for subsequent data cleaning and processing.

[0127] (2) Data preprocessing: Perform preliminary processing on the collected raw data, such as removing duplicate data, filling missing values, converting data formats, etc. This reduces the complexity of subsequent data cleaning and improves the efficiency of data cleaning.

[0128] (3) Data quality detection: Use various algorithms and rules to detect data quality, including outlier detection, duplicate value detection, and illegal value detection.

[0129] 1) Outlier detection: Use statistical methods or machine learning algorithms to detect outliers in the data, identify and filter out outliers.

[0130] 2) Missing value processing: For processing missing values, common methods include deleting data containing missing values, interpolation filling, using default values, etc.

[0131] 3) Duplicate value detection: Duplicate value detection and processing are performed on the data by comparing the characteristics of the data or using similarity algorithms to retain a unique value.

[0132] (4) Data cleaning: Clean the data based on the results of quality inspection, such as deleting outliers, merging duplicate values, and correcting illegal values.

[0133] 1) Entity recognition: Identify whether tuples represent the same real-world entity.

[0134] 2) Inconsistency detection and repair: Detect tuples that violate the rule set and repair the data to make it conform to the rules.

[0135] 3) Missing value filling: Fill in the missing attribute values of the tuple based on other complete tuple data.

[0136] 4) Truth value discovery: Select the true value when there are conflicting values between different tuples representing the same entity.

[0137] (5) Data integration and storage: Integrate the cleaned data with other data sets for subsequent data analysis and mining, and store them in a suitable data storage system

[0138] (6) Verification and feedback of cleaning results: Verify the cleaned data to ensure its quality and accuracy. At the same time, provide a preview of the cleaning results and an interface for adjusting algorithm parameters so that users can adjust and optimize according to actual needs.

[0139] 3. Analysis Engine

[0140] Component name: Intelligent analysis algorithm module

[0141] Connection relationship: connected to the data processing center to receive processed data for in-depth analysis.

[0142] Function: Use statistical analysis, data mining, machine learning and other advanced technologies to explore the potential patterns and trends in geographic environment data and provide decision support for power planning, operation and maintenance, and dispatch.

[0143] (1) Power planning decision support:

[0144] 1) Demand Forecasting: Utilizing multi-dimensional information such as historical electricity consumption data, economic indicators, and population growth, and using machine learning algorithms to perform demand forecasting, planners can accurately predict future electricity demand and thus formulate reasonable power construction plans.

[0145] 2) Resource allocation optimization: Integrating factors such as geography, climate, and energy distribution, intelligent analysis algorithms are used to evaluate the power resource potential of different regions, optimize resource allocation, and ensure the stability and economy of power supply.

[0146] (2) Power operation and maintenance decision support

[0147] 1) Fault warning and diagnosis: By analyzing the operating data of power equipment, such as current, voltage, and temperature, and using machine learning algorithms to identify abnormal patterns, this system provides early warning of potential faults and assists operation and maintenance personnel in quickly locating the cause of the fault, thereby reducing the impact of the fault on power supply.

[0148] 2) Maintenance strategy optimization: Based on the equipment's operating status, maintenance history, and fault records, intelligent analysis algorithms are used to develop personalized maintenance plans, optimize the allocation of maintenance resources, and improve the equipment's service life and reliability.

[0149] (3) Power dispatch decision support

[0150] 1) Real-time dispatch optimization: Combining real-time power supply and demand, grid operating status, and weather forecast information, the power dispatch strategy is dynamically adjusted through intelligent analysis algorithms to ensure the safe and stable operation of the grid, while optimizing the allocation of power resources and improving energy efficiency.

[0151] 2) Supply and demand balance analysis: Utilize big data analysis and forecasting technology to monitor and analyze the supply and demand situation in the power market in real time, helping dispatchers accurately judge power market trends, formulate reasonable power trading strategies, and ensure the stable operation of the power market.

[0152] How it works: It integrates multiple customized analysis models and selects appropriate algorithms for analysis based on data type and user needs. It supports both real-time and offline analysis modes to meet the needs of different scenarios.

[0153] Model algorithm analysis steps:

[0154] ① Data collection: Environmental perception sensors, high-definition cameras and drone inspection systems work together to comprehensively collect geographical environmental data on power lines and their surroundings, including but not limited to meteorological data, topography, vegetation cover, human activities and other information.

[0155] ②Data preprocessing:

[0156] Data cleaning: Remove invalid, erroneous or duplicate data to ensure data quality.

[0157] Data formatting: Convert data from different sources and formats into a standard format that the system can recognize.

[0158] Spatiotemporal calibration: Calibrate data with timestamps and geographic location information to ensure the consistency of data in time and space.

[0159] ③Integrate multiple customized analysis models:

[0160] Model selection mechanism: Based on the data type (such as meteorological data, terrain data, etc.) and user needs (such as risk assessment, preventive maintenance, etc.), the system automatically selects the most appropriate algorithm model for analysis.

[0161] For meteorological data: Use time series analysis models (such as ARIMA, LSTM, etc.) to predict future meteorological trends and assess their impact on power facilities.

[0162] For topographic data: Use digital elevation model (DEM) and geographic information system spatial analysis technology to identify areas with complex terrain and assess geological disaster risks.

[0163] For vegetation cover data: Combine remote sensing image processing and machine learning algorithms (such as random forests, support vector machines, etc.) to identify vegetation growth conditions and predict the safe distance of vegetation from power lines.

[0164] For human activity data: use spatial cluster analysis and pattern recognition techniques to identify potential areas of risk for human damage.

[0165] ④Data analysis steps:

[0166] Step 1: Data classification: Divide the data into different analysis groups based on data type and user needs.

[0167] Step 2: Model matching: For each set of data, the system automatically matches the most appropriate analysis model.

[0168] Step 3: Parameter setting: According to the model requirements, set the analysis parameters, such as the period of time series analysis, the ratio of training set and test set of machine learning algorithm, etc.

[0169] Step 4: Model running: Execute the selected analysis model to conduct in-depth mining and intelligent analysis of the data.

[0170] Step 5: Result generation: Generate analysis reports, including data visualization charts, risk warning information, preventive maintenance recommendations, etc.

[0171] 4. Visualization subsystem

[0172] Component name: Geographic Information Visualization Platform

[0173] Connection relationship: Connected to the analysis engine, receiving analysis results and visualizing them.

[0174] Function: Display complex analysis results intuitively in the form of maps, charts, dashboards, etc., to help users quickly understand the information and trends behind the data.

[0175] How it works: A visualization interface built on GIS technology supports multi-level geographic information display and interactive operations. Users can zoom, pan, click, and query to gain a deeper understanding of data changes at different geographic locations.

[0176] Example 3

[0177] This embodiment also provides a computer device suitable for a method for adaptive data processing in a smart grid environment, comprising a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement a forced oscillation detection and positioning method for a distribution network as proposed in the above embodiment. If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or the portion of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0178] The logic and / or steps represented in the flowchart or otherwise described herein may be considered, for example, as an ordered list of executable instructions for implementing logical functions, and may be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device).

[0179] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0180] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0181] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for adaptive data processing in a smart grid environment, characterized by: The method includes obtaining power facility environmental data, the power facility environmental data including meteorological data, topographic data, vegetation cover data, and human activity data; processing the power facility environmental data to obtain pre-processed environmental data; Presetting a smart grid environment adaptive analysis model and inputting the preprocessed environmental data into the smart grid environment adaptive analysis model, the analysis model including power planning decision support, power operation and maintenance decision support, and power dispatch decision support; According to the output of the smart grid environment adaptive analysis model, the analysis results are visualized and the smart grid environment adaptability optimization is performed.

2. The method for smart grid environment adaptive data processing according to claim 1, wherein: The meteorological data, topographic data, vegetation cover data and human activity data are obtained through the collaborative work of environmental perception sensors, high-definition cameras and drone inspection systems.

3. The method for smart grid environment adaptive data processing according to claim 2, wherein: The power planning decision support includes: demand forecasting based on historical power consumption data, economic indicators and population growth information; and evaluating the power resource potential of different regions based on factors such as geography, climate and energy distribution.

4. The method for smart grid environment adaptive data processing according to claim 3, wherein: The power operation and maintenance decision support includes: identifying abnormal patterns and providing fault warnings by analyzing power equipment operation data; and formulating personalized maintenance plans based on equipment operating conditions, maintenance history, and fault records.

5. The method for smart grid environment adaptive data processing according to claim 4, wherein: The power dispatch decision support includes: dynamically adjusting the power dispatch strategy in combination with real-time power supply and demand conditions, grid operation status and weather forecast information; and real-time monitoring and analysis of power market supply and demand conditions.

6. The method for smart grid environment adaptive data processing according to claim 5, wherein: The processing of power facility environmental data adopts a distributed storage architecture and parallel processing technology.

7. The method for smart grid environment adaptive data processing according to claim 6, wherein: The visualization of the analysis results is achieved by constructing a visualization interface based on GIS technology, which supports multi-level geographic information display and interactive operations.

8. A smart grid environment adaptive data processing system, based on the smart grid environment adaptive data processing method according to any one of claims 1 to 7, characterized in that: It also includes a data acquisition module for acquiring power facility environmental data, the power facility environmental data including meteorological data, topographic data, vegetation cover data and human activity data; A model running module, configured to run the smart grid environment adaptive analysis model and input the pre-processed environmental data into the smart grid environment adaptive analysis model; The result acquisition module is used to visualize the analysis results according to the output of the smart grid environment adaptive analysis model and perform smart grid environment adaptability optimization.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the smart grid environment adaptive data processing method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the smart grid environment adaptive data processing method according to any one of claims 1 to 7 are implemented.

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