システム障害が発生した場合、障害の原因を正確に分析することが重要です。障害分析ツールは、エンジニアが問題を迅速に特定して解決するのに役立ち、システムの安定性と可用性を向上させます。 PHP エディター Baicao は、原理、種類、アプリケーション シナリオをカバーする障害分析ツールに関する知識を詳しく紹介します。この記事は、障害分析テクノロジーを習得し、システムの運用と保守の効率と信頼性を向上させるのに役立ちます。
つまり、障害分析ツールは企業の IT 環境に不可欠な部分です。適切なツールを選択することで、企業は効率を向上させ、ダウンタイムを削減し、ネットワーク環境を保護し、より賢明な意思決定を行うことができます。
ida は、最初に exe ファイルを追加することを選択することも、exe プログラムを開いた後にマウスで ida にドラッグすることもできます。 ida を使用すると、exe プログラムを逆アセンブルし、プロセスを確認し、ほとんどのシステム機能やその他の情報を自動的に識別できます。
リバースエンジニアリング、特に F5 プラグイン関数を使用して C コードを確認するのに非常に役立ちます。非常に便利。
can は分析ツールではありません。広く使用されているフィールドバスであり、産業用計測および制御および産業用オートメーションの分野で大きな応用が期待されています。
CANはController Area Network (CAN)の略称で、車載電子製品の開発・生産で有名なドイツのBOSCH社によって開発され、最終的には国際規格(ISO11898)となりました。これは、世界で最も広く使用されているフィールドバスの 1 つです。 北米と西ヨーロッパでは、CAN バス プロトコルが車載コンピュータ制御システムおよび組み込み産業用制御 LAN の標準バスとなっており、基盤となるプロトコルとして CAN を使用し、大型トラックや重工業用に特別に設計された J1939 プロトコルがあります。機械車両。
近年、その高い信頼性と良好なエラー検出能力が注目され、厳しい周囲温度、強い電磁放射、大きな振動が存在する車載コンピュータ制御システムや産業環境で広く使用されています。
全体として、SWOT は 2 つの部分に分けることができます。SW は主に内部条件の分析に使用され、OT は主に外部条件の分析に使用されます。 SWOTマトリックス分析を通じて、自社にとって有益で利用する価値のある要因と、自社にとって悪で避けるべき要因を見つけ出し、本質的な問題を発見し、解決策を模索し、開発の方向性を明確にすることができます。
この分析に基づいて、問題を時点と重要度によって分類し、ノードとキーポイントを把握できます...
Failure Mode and Effects Analysis (FMEA) and Fault Tree Analysis (FTA) are analytical techniques that have been widely used in reliability engineering. These techniques have been successfully applied abroad to solve various quality problems. In the ISO 9004:2000 version standard, FMEA and FTA analysis have been used as methods for risk assessment of design and development, as well as validation and changes of products and processes. Currently, our country basically only applies FMEA and FTA technology to reliability design analysis. According to foreign literature and the practice of some Chinese enterprise technicians, FMEA and FTA can be applied to process (process) analysis and quality problem analysis. Quality is a concept with a broad connotation, and reliability is one of its aspects. Through FMEA and FTA analysis, various potential quality problems and failure modes and their causes (including design defects, process problems, environmental factors, aging, wear and processing errors, etc.) that affect product quality and reliability were identified. After taking Corrective measures in design and process improve product quality and ability to withstand various interferences. According to literature reports, about 50% of the quality improvement of a world-class automobile company is achieved through FMEA and FTA/ETA.
1. EXCEL
As an entry-level tool, Excel is the most basic and important data analysis tool. Excel has a variety of powerful functions, such as creating forms, pivot tables, VBA, etc. The Excel system is so huge that no analysis tool can surpass it, ensuring that everyone can analyze data according to their own needs. It can meet the needs of most data analysis work, and also provides a very friendly operation interface. It is very easy to use for users with basic statistical theory, but the amount of data processed is small.
2. SPSS
SPSS is the first statistical software in the world to adopt a graphical menu-driven interface. Its most prominent feature is that the operation interface is extremely friendly and the output results are beautiful. As long as users master certain Windows operating skills and are proficient in statistical analysis principles, they can use this software to serve specific scientific research work. SPSS uses a method similar to EXCEL tables to input and manage data. The data interface is relatively universal and can easily read data from other databases. Its statistical processes include commonly used and relatively mature statistical processes, which can fully meet the work needs of non-statistical professionals.
3. SAS
SAS is one of the world's largest software companies and a global leader in business intelligence and analytics software and services. SAS is very popular among advanced users because of its powerful functions and programmability. It is precisely because of this that it is one of the most difficult software to master and is mostly used in corporate work. You need to write SAS programs to process the data and perform analysis. If an error occurs in a program, it is difficult to find and correct the error.
4. R
R is a language used for statistical calculations and graphics. It is not only a language, but also an environment for data calculation and analysis. Its main features are free, open source, and a complete range of various modules. In R's comprehensive archive network CRAN, a large number of third-party function packages are provided, covering everything from statistical computing to machine learning, from finance From analysis to biological information, from social network analysis to natural language processing, from various databases and various language interfaces to high-performance computing models, it can be said to be all-encompassing and all-encompassing. This is why R is gaining more and more attention in all walks of life. An important reason why practitioners in all industries love it.
5. Python
Python is an object-oriented, interpreted computer programming language. Python syntax is concise and clear. Reading a good Python program feels like reading English. Python is relatively active in data analysis and interaction, exploratory computing, and data visualization. Python also has powerful programming capabilities. This programming language is different from R or matlab. Python has some very powerful data analysis capabilities. Python can also be used to crawl, write games, and automate operation and maintenance. It has a wide range of applications in these fields. Application, these advantages enable one technology to solve all business service problems, which fully reflects that Python is conducive to the integration of various businesses. If you use Python, you can greatly improve the efficiency of data analysis.
6. SQL
It is no exaggeration to say that SQL is an essential skill for all positions in the data direction. It is relatively easy to get started. In summary, it is adding, deleting, modifying and checking. The knowledge points that need to be mastered in SQL mainly include data definition language, data manipulation language and data control language; in the data manipulation language, understand the execution order and grammatical order of SQL, master the important functions in SQL, and understand the Similarities and differences of various joins. All in all, SQL is a necessary skill if you want to get into data analysis.
7. BI tools
Business intelligence BI is born for data analysis, and it was born from a high starting point. The aim is to shorten the time from business data to business decisions and use data to influence decisions. BI tools are designed according to the data analysis process. It starts with data processing, data cleaning, then data modeling, and finally data visualization, using diagrams to identify problems and influence decisions.
Take Yixin ABI as an example, which integrates core functions such as ETL data processing, data modeling, data visualization, data analysis, data reporting, and mobile applications. Data collection and supplementary entry can be achieved through form filling and form filling. Data sources can be integrated and processed in advance, and various visual charts can be generated by simply dragging and dropping.
A Dump file is also called a memory dump file or a memory snapshot file. It is a memory image of a process. It is a snapshot of a process or system at a given time, such as when the process crashes or when the process has other problems. Even at any time, we can use tools to back up the memory of the system or a process for debugging and analysis.
The dump file contains module information, thread information, stack call information, exception information and other data of program running.
I recommend you to try FineBI. The multi-dimensional analysis of this tool is still fresh in my memory.
Tools for data analysis include "Analysis of Variance", "Correlation Coefficient", "Covariance", "Exponential Smoothing", "Fourier Analysis", "Histogram", "Random Number Generator", "Ranking and Percentage Ranking", "Regression" and "Sampling" ""t test" etc.
The basic tools of corporate finance include:
Cash held by enterprises, funds deposited in financial institutions, equity securities (stocks), bond securities (bonds), and stock price indexes.
And represent contractual rights or obligations to receive or pay financial assets in future periods, such as accounts receivable, accounts payable, other receivables, other payables, deposits, deposits, customer loans, customers Deposits, bond investments, bonds payable, etc.
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