Power grid equipment standard index difference analysis method

Abstract

The invention provides a power grid equipment standard index difference analysis method, which is based on an index system sorted by experts, extracts numeric and clause type indexes from a standard knowledge base through expert labeling and machine learning in combination with application of a semantic extraction technology, and sets the indexes through comparison rules. Different specified values of the same equipment and the same index in different standards are compared by utilizing technologies such as semantic analysis, intelligent differential analysis of the standard index is realized for the first time in the standardization field, and the method is not only suitable for evaluation of different equipment index applications, but also can be applied to other decision-making scenes needing multi-index evaluation, and has a wide application prospect. And the method has wide applicability.

Description

Technical Field

[0001] The present invention relates to the field of decision analysis and evaluation, and in particular to extracting equipment standard indicators through a machine learning method, and comparing different specified values ​​of the same equipment and the same indicators in different standards. Background Art

[0002] In today's decision analysis and evaluation field, especially in power equipment management, with the advancement of technology and the continuous updating of industry standards, accurate evaluation of equipment performance has become increasingly important. Traditional manual methods are often time-consuming and error-prone, and cannot capture the dynamic changes of standards in real time. For this reason, the industry urgently needs an automated and efficient method that can quickly extract and compare standard indicators of equipment to facilitate timely discovery and handling of differences.

[0003] In the past, technology usually relied on manual collection and comparison of equipment data under different standards, but this method is limited by manpower and time costs, and may be subjective and omissions. With the development of machine learning technology, especially the application of big data and artificial intelligence, it is possible to automatically process and intelligently analyze large amounts of data, providing decision makers with more accurate and comprehensive information support. Summary of the invention

[0004] The core content of the present invention is to propose a novel method, which uses machine learning technology to automatically extract and integrate the standard indicators of equipment, and then compares the specified values ​​of the same indicator of the same equipment in different standards in real time. Specifically, the method includes the following steps: Data collection: Combined with application semantic extraction technology, numerical, clause and tabular indicators are extracted through expert annotation and machine learning.

[0005] Data preprocessing: Clean and convert the extracted indicators into standardized data to ensure that all indicators are on a consistent measurement scale before comparison.

[0006] Feature extraction: Use machine learning algorithms, such as cluster analysis or feature selection, to extract key performance characteristics from massive amounts of data.

[0007] Comparative analysis: Based on the extracted features, calculate the differences between different standards of the same indicator of the device and perform statistical analysis.

[0008] Result presentation: Generate visual reports that clearly show the performance differences of equipment under different standards and their possible causes, supporting decision makers to make targeted improvement measures.

[0009] Through this invention, decision makers can quickly respond to standard changes, optimize equipment management and maintenance strategies, and improve overall operational efficiency and equipment reliability. At the same time, this method reduces the risk of human error and improves the scientificity and accuracy of decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to show the specific implementation of the present invention in detail, this article will be accompanied by a series of diagrams to assist in the explanation. The following are some possible drawings, which are not exhaustive, but are sufficient to help readers understand the key components and technical points of the present invention.

[0011] Figure 1 The business process of the present invention is demonstrated, the relationship between each process is clearly displayed, and the business process is analyzed by indicator differentiation.

[0012] Figure 2 :process Figure 2 The indicator extraction process of the present invention is demonstrated, including determining the name of the extracted indicator, formulating an indicator extraction plan, extracting indicators using machine learning and semantic analysis, and data normalization, and the logical relationship between each step is clearly demonstrated.

[0013] Figure 3 :Schematic diagram of the equipment standard indicator database, showing how to store and manage various performance indicator data of equipment under different standards.

[0014] Figure 4 : Indicator comparison survival warning example, showing the logical judgment process of indicator comparison Figure 5 : User interaction diagram, illustrating how users operate and view analysis results through a graphical interface.

[0015] Figure 6 : Comparison analysis interface, which shows the comparison results of different standards of the device on the same indicator, including difference values, statistical analysis results and a list of possible influencing factors.

[0016] Figure 7 : An example of a differential analysis decision support report that intuitively presents the results of the comparative analysis and optimization suggestions based on these results.

[0017] These drawings are only examples of embodiments of the present invention, not all possible implementations. The actual invention may include more or different drawings to adapt to different application scenarios and technical details. All of these drawings are intended to help readers better understand the technical connotation and application effects of the present invention. DETAILED DESCRIPTION

[0018] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0019] Based on the indicator system compiled by experts, numerical and clause-type indicators are extracted from the standard knowledge base in combination with application of semantic extraction technology, through expert annotation and machine learning. By setting comparison rules and using semantic analysis and other technologies, different specified values ​​of the same equipment and the same indicators in different standards are compared. This is the first time that intelligent differentiated analysis of standard indicators has been realized in the field of standardization.

[0020] Warning generation The establishment of indicator warning is to generate warnings when abnormal indicators are found under the set rules of standard indicators. Therefore, this report uses warnings as an objective indicator to warn of problems where indicator values ​​are inconsistent under the set rules.

[0021] 1) Index extraction Semantic duplication checking refers to the semantic level comparison and analysis of project proposals, feasibility study reports or other related texts to identify and compare semantic similarities and relevance between texts. It aims to detect possible plagiarism, plagiarism or reuse of content in the text, ensure the originality and independence of the project, and safeguard the credibility of the evaluation system.

[0022] 2) Indicator warning By combining the application of semantic extraction technology, through expert annotation and machine learning, numerical, clause and tabular indicators are extracted respectively. The numerical indicators are directly compared to output analysis results, and other indicators are provided with search and aggregation for indicator comparison and early warning.

[0023] In addition to providing a scientific and technological project evaluation method, the present invention also designs a corresponding system to implement this method. The system mainly consists of the following modules: Indicator retrieval module: Users can quickly find indicators, and can use indicators to quickly find the location of the standard chapters and clauses, and support the standards where the indicators are located.

[0024] Indicator differentiation list: Through indicator differentiation analysis, combined with semantic analysis and other technologies, comparative analysis is performed on indicators other than those with differentiation analysis.

[0025] Indicator differentiation analysis and comparison module: Directly compare the output analysis results of numerical indicators, and provide search and aggregation for other indicators for comparative display.

[0026] Differentiation analysis decision support report: Export according to the China Southern Power Grid differentiation analysis template to form a differentiation analysis report.

Claims

1. A method for analyzing differences in standard indicators of power grid equipment, characterized in that: The following steps are involved: Extract standard index parameter data of power grid equipment; Determine key performance indicators (KPIs) and establish a standardized database; Compare the original terms, application scenarios, equipment, and indicator values ​​of indicators with the same name, and mark the differences; Use statistical analysis methods (such as regression analysis and cluster analysis) to identify the significance of differences; Analyze the causes of the differences, which may include design, manufacturing, installation or operating conditions; Output detailed difference reports and improvement suggestions.

2. The method according to claim 1, characterized in that The statistical analysis method used also included the following steps: Semantic analysis: Perform semantic analysis of the indicator context to obtain the indicator application environment and equipment, as well as the Chinese characters for positive and negative or above and below zero, such as "+" and "-", corresponding to temperature, etc. Indicator comparison analysis: Based on the results of semantic analysis, standardize the indicator value description to facilitate indicator difference analysis.

3. Further, the method according to claim 1 or 2, further comprising the following steps when comparing the difference values: Leverage machine learning algorithms to predict likely future performance so corrective action can be taken in advance.

4. A method for analyzing differences in standard indicators of power grid equipment also includes a user interface for visualizing analysis results to facilitate user understanding and decision-making.

5. The present invention is applicable to the performance monitoring and optimization of power grid equipment in the electric power industry, effectively improving the operating efficiency and safety of the equipment. A system according to any preceding claim, characterised in that The system also has data analysis and visualization capabilities, and can generate evaluation reports and trend analysis charts for scientific and technological projects to provide support for decision-making.

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