Multi-source data fused power line tree barrier risk assessment and decision support system

Abstract

The invention provides a multi-source data fused power line tree barrier risk assessment and decision support system. The system mainly comprises the following steps of: 1, realizing real-time acquisition and storage of data by integrating various sensors and database interfaces; 2, preprocessing, cleaning and fusing the collected multi-source data, and extracting information valuable for tree barrier risk assessment; 3, based on the processed multi-source data, evaluating the tree obstacle risk of the power line by adopting an advanced algorithm model; and 4, making a scientific decision by a decision maker according to a risk assessment result. According to the multi-source data fused power line tree barrier risk assessment and decision support system provided by the invention, an efficient, accurate and reliable tree barrier risk assessment and decision support scheme is provided by combining advanced technologies such as big data analysis, multi-source data fusion and a decision support system according to the actual demand of power line tree barrier risk assessment.

Description

Technical Field

[0001] The present invention relates to the power industry, particularly to the technical field of power line tree fault risk assessment and decision-making support, and specifically relates to a power line tree fault risk assessment and decision-making support system integrating multi-source data. Background Art

[0002] With the continuous expansion of the power grid and the natural law of tree growth, the problem of the safety distance between power lines and trees has become increasingly prominent. Line faults caused by tree faults occur frequently, seriously threatening the stable operation of the power system and power supply safety. Therefore, it is of great significance to develop a system that can integrate multi-source data, accurately assess tree fault risks, and provide scientific decision-making support. Currently, the risk assessment of power line tree faults mainly relies on manual inspections and empirical judgments, which have problems such as long assessment cycles, strong subjectivity, and low accuracy. With the development of big data technology and artificial intelligence technology, the power industry has begun to explore using these new technologies to improve the efficiency and accuracy of tree fault risk assessment. However, existing technologies are often limited to the analysis of a single data source, ignoring the relevance and complementarity between multi-source data, resulting in incomplete and inaccurate assessment results. Therefore, a system that can integrate multi-source data and adopt advanced algorithm models is needed to improve the accuracy of tree fault risk assessment and decision-making efficiency. Summary of the Invention

[0003] To overcome the deficiencies of the above-mentioned prior art, the present invention provides a power line tree fault risk assessment and decision-making support system integrating multi-source data. This system can comprehensively utilize various data sources (such as meteorological data, geographic information data, tree growth data, power line operation data, etc.), and through big data analysis and multi-source data fusion technology, achieve a comprehensive and accurate assessment of the power line tree fault risk, and provide scientific decision-making support for decision-makers.

[0004] The present invention is realized through the following technical solutions:

[0005] A power line tree fault risk assessment and decision-making support system integrating multi-source data, comprising the following steps:

[0006] Step S1: Use a data acquisition module to collect various data sources related to the power line tree fault risk, and realize the real-time acquisition and storage of data;

[0007] Step S2: The data processing module is responsible for preprocessing, cleaning, and fusing the collected multi-source data.

[0008] Step S3: Based on the processed multi-source data, adopt an advanced algorithm model to assess the power line tree fault risk;

[0009] Step S4: According to the risk assessment results, provide scientific decision-making support for decision-makers.

[0010] Step 1 is specifically implemented as the following steps:

[0011] Step S1.1: Collect meteorological data such as wind speed, rainfall, and temperature through software;

[0012] Step S1.2: Collect real-time geographical information data such as terrain, soil type, and vegetation distribution through software;

[0013] Step S1.3: Collect real-time tree growth data such as tree species, tree age, tree height, and crown width through software;

[0014] Step S1.4: Collect real-time operation data of power lines such as line voltage level, load condition, and historical fault records through software.

[0015] Step 2 is specifically implemented as the following steps:

[0016] Step S2.1: Preprocess all the collected data, that is, denoise, fill in missing values, and detect outliers;

[0017] Step S2.2: Clean the preprocessed data, that is, improve the data quality through methods such as data normalization and standardization;

[0018] Step S2.3: Use multi-source data fusion technology to fuse the cleaned data, that is, integrate and analyze data from different sources, and extract valuable information for tree fault risk assessment.

[0019] Step 3 is specifically implemented as the following steps:

[0020] Step S3.1: Train multiple factors such as meteorological conditions, geographical environment, tree growth conditions, and power line operation conditions through methods such as machine learning or deep learning;

[0021] Step S3.2: Use an advanced algorithm model to assess the tree fault risk of power lines;

[0022] Step S3.3: The assessment results are presented in the form of risk levels, providing intuitive risk references for decision-makers.

[0023] Step 4 is specifically implemented as the following steps:

[0024] Step S4.1: According to the risk level, the decision support module displays risk warnings, gives real-time line optimization plans, and then gives emergency treatment suggestions. Through the visualization interface and intelligent recommendation system, it helps decision-makers make reasonable decisions quickly and reduce the risk of line faults caused by tree faults.

[0025] A power line tree fault risk assessment and decision support system for integrating multi-source data, including a data acquisition module, a data processing module, a risk assessment module, and a decision support module, where:

[0026] The data acquisition module is responsible for collecting various data sources related to the risk of power line tree faults;

[0027] The data processing module is responsible for preprocessing, cleaning, and fusing the collected multi-source data;

[0028] Based on the processed multi-source data, the risk assessment module uses advanced algorithm models to assess the risk of power line tree faults;

[0029] According to the risk assessment results, the decision support module provides scientific decision support for decision-makers.

[0030] Compared with the prior art, the present invention has the following beneficial technical effects:

[0031] The present invention provides a power line tree fault risk assessment and decision support system for integrating multi-source data, aiming to solve the problems such as long assessment cycle, strong subjectivity, and low accuracy rate existing in the current power line tree fault risk assessment. By adopting advanced technologies such as big data analysis, multi-source data fusion, and decision support system, the present invention can achieve a comprehensive and accurate assessment of the risk of power line tree faults and provide scientific decision support for decision-makers. It is believed that the implementation of the present invention will help improve the safe operation level of power lines and promote the sustainable and healthy development of the power industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is an architecture diagram of a power line tree fault risk assessment and decision support system for integrating multi-source data.

[0033] Figure 2 It is a flowchart of a power line tree fault risk assessment and decision support system for integrating multi-source data. DETAILED DESCRIPTION OF THE INVENTION

[0034] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can obtain variations based on this method. The basic principles defined in the following description can be used for equivalent solutions, similar solutions, etc. of the present invention.

[0035] Among them, the technical solution adopted by the present invention is: a power line tree fault risk assessment and decision support system for integrating multi-source data, mainly including four parts: a data acquisition module, a data processing module, a risk assessment module, and a decision support module. Each module communicates and collaborates through a data interface to jointly implement the functions of power line tree fault risk assessment and decision support.

[0036] Specifically, refer to the Figure 1 , Figure 1 which is the architecture diagram of the present invention. The power line tree fault risk assessment and decision support system that integrates multi-source data of the present invention includes four parts: a data acquisition module, a data processing module, a risk assessment module, and a decision support module. The data acquisition module realizes the real-time acquisition and storage of meteorological, geographical information, tree growth, and power line operation data by integrating a variety of sensors and database interfaces. The data processing module preprocesses the acquired multi-source data, including steps such as data cleaning, data fusion, and data standardization. The risk assessment module constructs a power line tree fault risk assessment model based on the preprocessed data. The decision support module generates risk warning information according to the risk assessment results to remind the operation and maintenance personnel to pay attention to high-risk areas; provides risk disposal suggestions, such as pruning trees, strengthening lines, adjusting line operation parameters, etc. Combining the operation status and maintenance plan of the power line, formulates a personalized tree fault risk prevention and control strategy to provide scientific and reasonable decision support for the power line operation and maintenance personnel.

[0037] Figure 2 is the operation flow chart of the present invention. First, the system starts and enters the operation process; subsequently, the data acquisition module starts to collect a variety of data sources related to the power line tree fault risk; then, preprocesses, cleans, and fuses the multi-source data; at the same time, uses an algorithm model to conduct a risk assessment on the power line tree fault; in addition, provides decision support according to the risk results; finally, the system completes the operation process and ends the operation.

[0038] In the preferred embodiment of the present invention, those skilled in the art should note that the operation wave impulse test system equipment, analysis software, etc. involved in the present invention can be regarded as the prior art.

[0039] Preferred embodiment.

[0040] The present invention discloses a power line tree fault risk assessment and decision support system that integrates multi-source data, including the following steps:

[0041] Step S1: Use the data acquisition module to collect a variety of data sources related to the power line tree fault risk to realize the real-time acquisition and storage of data;

[0042] Step S2: The data processing module is responsible for preprocessing, cleaning, and fusing the acquired multi-source data.

[0043] Step S3: Based on the processed multi-source data, use an advanced algorithm model to assess the power line tree fault risk;

[0044] Step S4: Provide scientific decision support for decision-makers according to the risk assessment results.

[0045] Among them, step S1 is specifically implemented as the following steps:

[0046] Step S1.1: Collect meteorological data such as wind speed, rainfall, and temperature through software;

[0047] Step S1.2: Collect real-time geographical information data such as terrain, soil type, and vegetation distribution through software;

[0048] Step S1.3: Collect real-time tree growth data such as tree species, tree age, tree height, and crown width through software;

[0049] Step S1.4: Collect real-time operation data of power lines such as line voltage level, load condition, and historical fault records through software.

[0050] Specifically, step S2 is specifically implemented as the following steps:

[0051] Step S2.1: Preprocess all the collected data, that is, denoise, fill in missing values, and detect outliers;

[0052] Step S2.2: Clean the preprocessed data, that is, improve the data quality through methods such as data normalization and standardization;

[0053] Step S2.3: Knowledge fusion Utilize domain knowledge and expert experience to integrate multi-source data at the semantic level, and adopt multi-source data fusion technology to fuse the cleaned data, that is, integrate and analyze data from different sources to extract valuable information for tree fault risk assessment.

[0054] Particularly, step S3 is specifically implemented as the following steps:

[0055] Step S3.1: Train multiple factors such as meteorological conditions, geographical environment, tree growth conditions, and power line operation conditions through methods such as machine learning or deep learning;

[0056] Step S3.2: Adopt an advanced algorithm model to evaluate the tree fault risk of power lines;

[0057] Step S3.3: The evaluation results are presented in the form of risk levels, providing intuitive risk references for decision-makers.

[0058] In addition, step S4 is specifically implemented as the following steps:

[0059] Step S4.1: Set an early warning threshold for risk early warning according to the risk assessment results;

[0060] Step S4.2: Trigger the early warning mechanism when the evaluation results exceed the threshold;

[0061] Step S4.3: The warning information can be promptly notified to relevant personnel through methods such as text messages, emails, and APP push;

[0062] Step S4.4: Emergency disposal suggestions provide targeted emergency disposal suggestions for different types of tree obstacle risk events. For example, for a line fault event caused by a fallen tree, it can be suggested to immediately organize a repair team for repair and strengthen the inspection efforts in the surrounding areas.

[0063] Step S4.5: Based on the risk assessment results and geographical information data, etc., a line optimization plan is proposed. For example, for high-risk areas, measures such as adjusting the line route or increasing the line height can be considered to reduce the tree obstacle risk.

[0064] Used to implement a power line tree obstacle risk assessment and decision support system integrating multi-source data, including a data acquisition module, a data processing module, a risk assessment module, and a decision support module, where:

[0065] The data acquisition module is responsible for collecting various data sources related to the power line tree obstacle risk;

[0066] The data processing module is responsible for preprocessing, cleaning, and fusing the collected multi-source data;

[0067] The risk assessment module is based on the processed multi-source data and uses advanced algorithm models to assess the power line tree obstacle risk;

[0068] The decision support module provides scientific decision support for decision-makers according to the risk assessment results.

[0069] Specifically, knowledge fusion utilizes domain knowledge and expert experience to integrate multi-source data at the semantic level. For example, by combining geographical information data and tree growth data, the tree growth trend and potential risk areas in a specific area can be inferred.

[0070] More specifically, feature fusion: Extract valuable feature variables for tree obstacle risk assessment from multi-source data and perform fusion analysis on these feature variables. By methods such as feature selection and feature dimensionality reduction, the accuracy and generalization ability of the assessment model are improved. Model fusion: Use methods such as ensemble learning to fuse multiple single assessment models to obtain more robust and accurate assessment results. Model fusion can include methods such as voting method, weighted average method, and Stacking.

[0071] Specifically, this system uses advanced algorithm models to assess the power line tree obstacle risk. The specific algorithm model can be selected and optimized according to actual requirements and data characteristics.

[0072] More specifically, machine learning-based evaluation models such as Support Vector Machine (SVM), Random Forest, and Gradient Boosting Decision Tree (GBDT). These models can handle high-dimensional data and non-linear relationships and are suitable for complex scenarios of power line tree fault risk assessment.

[0073] Preferably, deep learning-based evaluation models such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory Network (LSTM). These models can automatically extract data features and perform efficient calculations, and are suitable for processing large-scale datasets and real-time evaluation requirements.

[0074] Preferably, a hybrid model: combining the advantages of machine learning and deep learning to construct a hybrid evaluation model. For example, a deep learning model can be first used for feature extraction and dimensionality reduction, and then a machine learning model can be used for classification and regression prediction.

[0075] Specifically, the decision support module is one of the core functions of this system. By implementing functions such as risk warning, emergency disposal suggestions, and line optimization plans, it provides comprehensive decision support for decision-makers.

[0076] The beneficial effects of the present invention are that it designs a power line tree fault risk assessment and decision support system that integrates multi-source data. By integrating multi-source data and using advanced algorithm models for evaluation, it can more comprehensively reflect the actual situation of power line tree fault risks, improve the accuracy of evaluation results; enhance decision-making efficiency, provide real-time and intuitive risk assessment results and scientific decision support suggestions for decision-makers, help decision-makers quickly make reasonable decisions, and reduce decision-making costs and time costs; enhance system reliability, adopt modular design and multi-source data fusion technology to improve the stability and reliability of the system. Even when some data sources fail or abnormal data interfere, the system can still maintain a high evaluation accuracy and decision support ability; promote the development of the power industry. By improving the efficiency and accuracy of power line tree fault risk assessment, it helps to reduce line failure rates, improve power supply reliability and safety, and thus promote the sustainable and healthy development of the power industry.

[0077] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A power line tree fault risk assessment and decision support system integrating multi-source data, characterized in that It includes the following steps: Step S1: Use the data acquisition module to collect multiple data sources related to the tree obstacle risk of power lines, and realize the real-time collection and storage of data; Step S2: The data processing module is responsible for preprocessing, cleaning and fusing the collected multi-source data; Step S3: Based on the processed multi-source data, adopt an advanced algorithm model to evaluate the tree obstacle risk of power lines; Step S4: According to the risk assessment results, provide scientific decision-making support for decision-makers.

2. The power line tree fault risk assessment and decision support system integrating multi-source data according to claim 1, characterized in that, Step 1 is specifically implemented as the following steps: Step S1.1: Collect meteorological data such as wind speed, rainfall, and temperature through software; Step S1.2: Collect real-time geographical information data such as terrain, soil type, and vegetation distribution through software; Step S1.3: Collect real-time tree growth data such as tree species, tree age, tree height, and crown width through software; Step S1.4: Collect real-time operation data of power lines such as line voltage level, load condition, and historical fault records through software.

3. The power line tree fault risk assessment and decision support system integrating multi-source data according to claim 2, characterized in that Step 2 is specifically implemented as the following steps: Step S2.1: Preprocess all the collected data, that is, denoise, fill in missing values, and detect outliers; Step S2.2: Clean the preprocessed data, that is, improve the data quality through methods such as data normalization and standardization; Step S2.3: Use multi-source data fusion technology to fuse the cleaned data, that is, integrate and analyze data from different sources, and extract valuable information for tree obstacle risk assessment.

4. The power line tree obstacle risk assessment and decision support system integrating multi-source data according to claim 3, characterized in that, Step 3 is specifically implemented as the following steps: Step S3.1: Train multiple factors such as meteorological conditions, geographical environment, tree growth conditions, and power line operation conditions through machine learning or deep learning methods; Step S3.2: Adopt an advanced algorithm model to evaluate the tree obstacle risk of power lines; The evaluation results are presented in the form of risk levels, providing an intuitive risk reference for decision-makers.

5. The power line tree fault risk assessment and decision support system integrating multi-source data according to claim 4, characterized in that, Step 4 is specifically implemented as the following steps: Step S4.1: According to the risk level, the decision support module displays a risk warning, gives a line optimization plan in real time, and then gives emergency treatment suggestions. Through the visualization interface and intelligent recommendation system, it helps decision-makers make reasonable decisions quickly and reduce the risk of line failures caused by tree obstacles.

6. A power line tree fault risk assessment and decision support system for implementing the multi-source data fusion according to any one of claims 1-5, characterized in that It includes a data acquisition module, a data processing module, a risk assessment module, and a decision support module, where: The data acquisition module is responsible for collecting multiple data sources related to the tree obstacle risk of power lines; The data processing module is responsible for preprocessing, cleaning and fusing the collected multi-source data; The risk assessment module is based on the processed multi-source data and adopts an advanced algorithm model to evaluate the tree obstacle risk of power lines; The decision support module provides scientific decision-making support for decision-makers according to the risk assessment results.

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