Big data intelligent analysis and data mining is an important means to extract more essential and useful regular information from massive data, and an important tool to mine intelligent and valuable information. The application of big data in venture capital project management can efficiently conduct mining, screening, identification and analysis of venture capital projects, and better provide intelligent support for investment companies to make venture capital decisions.
Designed by Zhang Jun, an outstanding venture capitalist in China A powerful big data mining system for project management and scenarios, e-sourcing (for VC) realizes the intensive sharing of network, computing, storage and security resources based on Python, OCR and RPA and other technologies and big data platforms. The integration and reuse of mining, OCR identification, AI analysis and other capabilities, e-sourcing (for VC) was first published by Zhang Jun in June 2020, and was awarded a computer software copyright registration certificate by the National Copyright Administration in 2022, with the registration number 2022SR0821422.
OCR technology is the abbreviation of Optical Character Recognition (Optical Character Recognition), which converts the text of various bills, newspapers, books, manuscripts and other printed materials into image information by scanning and other optical input methods, and then uses text recognition technology to convert the image information. Enter techniques for computers that can be used. At present, OCR technology is more closely integrated with other artificial intelligence products, making OCR technology widely used. In recent years, the RPA industry has become very popular, and it has been widely used in many fields such as e-commerce, finance, finance, government affairs, and manufacturing. The combination of OCR+RPA technology makes RPA technology even more powerful in the field of venture capital project mining. Zhang Jun applied the core principle of OCR technology combined with RPA technology to the research and development design of e-sourcing (for VC) to maximize the function of OCR technology.
In the OCR algorithm, the data set is the basis of algorithm training. The labeling of the data set is time-consuming and requires high labeling quality, and there is often a phenomenon of rework, which leads to less output of the OCR identification data set, while the OCR algorithm Training requires a large amount of data, which results in the asymmetry between the amount of data and the algorithm. Based on such problems, Zhang Jun used Python to generate OCR algorithm data sets, which can effectively increase the amount of data.
The application advantages of Python in enterprise financial data mining are very prominent. Zhang Jun uses Python as an extension tool and applies it in the development of e-sourcing (for VC), which greatly increases the flexibility and depth of data mining. Therefore, these advantages make Python applications the obvious choice for data mining. At present, the wind direction of the venture capital market has become extremely complex. If companies want to occupy a place in the venture capital market, they need to give full play to the application breadth of Python technology and expand their data analysis capabilities to other aspects. Zhang Jun uses Python data analysis to optimize the management methods and means of venture capital projects, and uses Python data analysis technology to effectively predict the consumption behavior of consumers, so as to fully grasp the market trend. In a word, e-sourcing (for VC) applies Python technology to forecasting, early warning and intelligent analysis of venture capital projects, so as to optimize the policy guidelines of venture capital from a macro perspective.
e-sourcing (for VC) is a built-in powerful data mining solution. Its core idea is to use a high-precision text recognition model to predict unlabeled data, obtain pseudo-labels, and select samples with high prediction confidence as training. data for training small models. Using e-sourcing (for VC), the accuracy of the recognition model was further improved to 79.4% (+1%); using e-sourcing (for VC), and the normalization height of the input image was increased from 32 to 48, and the prediction speed was comparable The recognition accuracy rate reached 73.98%; in multi-language scenarios, the venture capital model based on e-sourcing (for VC) improved the recognition accuracy rate by more than 5% on average in the four language families with evaluation sets.
From 2000 to 2002 and from 2006 to 2007, Zhang Jun successively worked as a business analyst and consultant in McKinsey for a number of consulting projects, mainly in advanced manufacturing, consumer services and telecommunications technology industries. He has rich business data mining and analysis technology and experience, and integrates intelligent data mining technology with Python, OCR and RPA, and creatively designs e-sourcing (for VC).
Specifically, based on deep network learning technology, Zhang Jun focuses on the extraction of industry OCR image text recognition industry information in complex environments, improving the industry OCR recognition engine and building a big data platform for venture capital projects, empowering industry enterprises through AI technology, Solve a series of key problems such as big data profiling, big data analysis and intelligent decision-making.
OCR applications in general scenarios on the market are relatively standardized and easy to implement maturely, while there are few customized applications for individual needs, which cannot solve specific information extraction in complex scenarios. The e-sourcing (for VC) designed by Zhang Jun, with the help of deep learning + image preprocessing, self-developed policy bill recognition module and glyph similarity phasor, specially developed a leading industry OCR recognition engine, e-sourcing (for VC) Provide enterprises with customized solutions for venture capital projects, meet the diverse market needs of a wide range of venture capital project application scenarios, and are widely used in the venture capital market. (Author/Zhang Xiaojuan)
Disclaimer: This article is reposted on this website to provide readers with more information, and the content does not constitute investment or consumption advice. If there is any doubt about the facts of the article, please verify with the relevant parties. The opinions of the article are not those of this website and are only for readers’ reference.