Multi-dimensional data analysis method for entrepreneurship incubation platform

Abstract

The invention relates to a multidimensional data analysis method for an entrepreneurship incubation platform, which improves the incubation efficiency and success rate of entrepreneurship enterprises through systematic data processing and intelligent analysis. Comprising a data acquisition module for acquiring multi-source data in multiple modes; the multi-dimensional data modeling module is used for constructing a data cube by utilizing an OLAP (On-Line Analytical Processing) technology to realize multi-angle observation; the data analysis module is used for carrying out deep portraying on the enterprise by using data mining algorithms such as clustering and causal analysis, and predicting a future trend by using a time sequence and a regression model; the machine learning module is used for constructing an intelligent recommendation system and providing customized incubation strategies for enterprises; the visual display module is used for displaying the analysis result in the forms of charts and instrument panels and supporting interactive operation; and the dynamic feedback and optimization module is used for updating an analysis result in real time and sending early warning to a manager through an anomaly detection mechanism. According to the invention, through comprehensive and dynamic data analysis, scientific decision is supported, and rapid growth and development of entrepreneurship enterprises are promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis and management, and in particular to a multidimensional data analysis method for an entrepreneurial incubation platform. Background Art

[0002] Incubation platforms are designed to provide start-ups with resources, funding and network support to help them grow rapidly. However, most current incubation platforms generally lack data analysis tools in the process of enterprise management and decision-making, making it difficult to comprehensively and accurately assess the development status and potential risks of start-ups, resulting in low efficiency in the use of incubation resources.

[0003] Existing incubation platforms usually rely on simple financial statements, industry analysis reports and empirical judgments when evaluating startups. These traditional methods can only analyze a single dimension or static indicators of an enterprise, and cannot dynamically capture the overall picture of the enterprise's development. Due to the lack of multi-dimensional data integration and in-depth mining, it is difficult for managers to identify the problems and opportunities facing the enterprise in a timely manner, resulting in delayed or inaccurate incubation strategies, which affects the growth efficiency of the enterprise.

[0004] In addition, most existing analysis systems lack intelligence and dynamic adjustment capabilities, and are unable to automatically update analysis results and adjust incubation strategies based on the latest data changes. This limitation makes it difficult for platform managers to make scientific decisions in the face of a complex and changing market environment, and misses the opportunity to optimize the enterprise incubation process.

[0005] In recent years, data analysis technology has been widely used in the business field, especially multidimensional data analysis methods. Through the integration of multi-source data and multidimensional modeling, the correlation behind the data can be deeply explored in multiple dimensions, providing decision makers with a comprehensive analytical perspective. At the same time, the development of data mining and machine learning technology has made automated evaluation and prediction possible, providing technical support for the intelligent management of the platform. However, applying these advanced data analysis technologies to entrepreneurial incubation platforms and combining them with the specific needs of the platform to form a systematic multidimensional data analysis method still faces many technical challenges and innovation opportunities.

[0006] In view of this, the inventor specially designed a multi-dimensional data analysis method for an entrepreneurial incubation platform, which resulted in this case. Summary of the invention

[0007] 1. Technical issues to be resolved The purpose of this application is to provide a multidimensional data analysis method for an entrepreneurial incubation platform, which at least solves the problem that existing entrepreneurial incubation platforms are unable to use multidimensional data analysis methods to comprehensively and dynamically evaluate and predict the performance of entrepreneurial enterprises, resulting in unscientific management decisions, low incubation efficiency and success rate.

[0008] (II) Technical solution In order to solve the above technical problems, the present invention provides the following technical solutions: The present application provides a multidimensional data analysis method for an entrepreneurial incubation platform, comprising the following steps: S10, Data Collection Module: Collect multi-source data of startups, including corporate financial data, market operation data, team member data, patent and technology data, social media data and industry report data, and standardize and clean the data; S20, multidimensional data modeling module: multidimensional modeling of the collected data, definition of data dimensions and measurements, and construction of data cubes through OLAP technology to achieve multi-angle observation and analysis of data; S30, Data Analysis Module: Use data mining algorithms to conduct multi-dimensional portrait analysis of entrepreneurial enterprises. Data mining algorithms include cluster analysis, correlation analysis, and causal analysis. And use prediction models to analyze and predict the future development trends of enterprises. Prediction models include time series analysis and regression analysis. S40, Machine Learning Module: Build an intelligent recommendation system based on machine learning, automatically evaluate the key indicators of startups through classification and regression algorithms, and recommend customized incubation strategies; S50, visualization display module: used to display the analysis results in a visualized form, including charts, dashboards and heat maps, and supports interactive operations of data; S60, Dynamic Feedback and Optimization Module: Dynamically updates analysis results and recommended content based on real-time data changes, and optimizes the accuracy and reliability of the analysis model through continuous learning of historical data.

[0009] In a further solution, the data collection module collects multi-source data of start-up enterprises in the following ways: through API interface, data crawler and manual input.

[0010] In a further embodiment, the data cube constructed by OLAP technology in the multidimensional data modeling module further includes: Data slicing: extracting a data subset of a specified dimension from a data cube; Data rotation: adjust the dimensional perspective of the data cube to change the way the data is presented; Data drilling: Analyze data along specific dimensions to reveal more detailed information.

[0011] In a further embodiment, the multidimensional data modeling module includes custom modeling, which customizes data dimensions and metrics according to requirements and adjusts the structure of the data cube.

[0012] In a further embodiment, the causal analysis includes a Granger causality test method to detect the causal relationship between different variables, and uses bivariate and multivariate regression models to verify and quantitatively evaluate the causal relationship to output the confidence and impact of the causal relationship.

[0013] In a further solution, the dynamic feedback and optimization module includes an automatic learning mechanism based on real-time data streams to continuously optimize the weights and parameter configurations of the data analysis model; it realizes real-time anomaly detection, and when abnormal fluctuations occur in the data, it sends an early warning to the manager and provides response suggestions.

[0014] In a further solution, the anomaly detection includes automatic detection and classification of anomalies in the data stream based on an autoencoder neural network model; combined with time series analysis, real-time monitoring of abnormal fluctuations in key indicators, and generation of corresponding risk warning reports.

[0015] In a further embodiment, the algorithms used in the machine learning module include random forest, support vector machine (SVM) and neural network, which are used to automatically evaluate key indicators of entrepreneurial enterprises and recommend incubation strategies.

[0016] In a further solution, the incubation strategy includes combining the enterprise's growth potential and risk level to generate a personalized incubation strategy through a multi-objective optimization algorithm; using a rule-based recommendation system to recommend corresponding resource allocation and market entry strategies for different types of entrepreneurial enterprises.

[0017] In a further solution: the data acquisition module also includes a data security and privacy protection method, which is used to protect the security and privacy of entrepreneurial enterprise data during the collection, transmission and storage process through data encryption, access control and data anonymization.

[0018] (III) Beneficial effects Compared with the prior art, the present invention has the following beneficial effects: Comprehensively collect multi-source data and perform standardized processing to ensure the comprehensiveness and accuracy of the data, helping managers to gain an in-depth understanding of the company's operating conditions. Secondly, the data cube constructed using OLAP technology can achieve flexible multi-dimensional analysis and enhance the scientific nature of decision-making. Data mining and machine learning algorithms can accurately identify the growth potential and risks of enterprises, provide personalized incubation strategies, improve resource allocation efficiency and reduce investment risks. Visual display makes complex data easy to understand and improves decision-making speed. Dynamic feedback and optimization mechanisms ensure rapid response and early warning to real-time data, enhancing the adaptability and accuracy of decision-making.

[0019] The specific implementation modes of the present invention are further described in detail below in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In the attached picture: Figure 1 A flowchart of the steps of the multidimensional data analysis method of the business incubation platform of the present invention; DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The following embodiments are used to illustrate the present invention.

[0022] like Figure 1 As shown, the present invention relates to a multidimensional data analysis method applied to an entrepreneurial incubation platform, which aims to conduct a comprehensive and dynamic evaluation and prediction of entrepreneurial enterprises by integrating multi-source data and applying multidimensional data modeling, data mining, machine learning, visualization and dynamic feedback optimization technologies, so as to support incubation platform managers to make scientific decisions, thereby improving incubation efficiency and success rate.

[0023] The first step of the present invention is a data collection and preprocessing module, which obtains multi-source data of entrepreneurial enterprises from multiple channels, including enterprise financial data, market operation data, team member data, patent and technology data, social media data and industry report data. The data collection module uses API interface, data crawler and manual input to obtain data. This diversified data collection method not only increases the breadth of data sources, but also improves the real-time and accuracy of data, providing a solid foundation for subsequent analysis. To ensure data quality, a RESTful API design is adopted, which supports multiple data formats such as JSON and XML, and integrates the OAuth 2.0 authentication mechanism to ensure data security and source reliability.

[0024] After data collection, the present invention further standardizes and cleans the data. In specific implementation, abnormal data points are automatically identified and corrected through machine learning algorithms, including outlier detection and error data filling. At the same time, rule-based filters and natural language processing techniques are used to perform semantic analysis on text data and remove noise data. This process ensures the consistency, completeness and accuracy of the data, laying the foundation for subsequent modeling and analysis.

[0025] The multidimensional data modeling module uses OLAP (online analytical processing) technology to build data cubes, supporting multi-angle observation and analysis of data. Data modeling defines the dimensions (such as time, geographic location, enterprise size, etc.) and metrics (such as revenue growth rate, user activity, etc.) of the collected data through multidimensional modeling, forming a data structure that can be flexibly analyzed.

[0026] Data cubes support operations such as data slicing, rotation, and drilling. Data slicing is achieved through a dynamic partitioning algorithm, which efficiently slices the data cube, thereby improving the speed and real-time performance of data extraction; data rotation uses principal component analysis (PCA) to reduce dimensions to simplify data structure and optimize data presentation; and the data drilling function allows in-depth analysis of data in specific dimensions to reveal more detailed information. These functions enable managers to quickly gain data insights from different angles, which helps to more fully understand the current status and potential of startups.

[0027] The data analysis module uses a variety of data mining algorithms to conduct multi-dimensional portrait analysis of startups. Specifically, algorithms such as cluster analysis, causal analysis, and time series analysis are used to conduct in-depth evaluation of enterprises. Cluster analysis uses the K-means algorithm combined with the Silhouette Score for optimization to identify different grouping characteristics of enterprises; causal analysis uses the Granger causality test method to detect the causal relationship between different variables, and further uses a multivariate regression model to quantitatively evaluate the causal relationship. Time series analysis is used to predict the revenue growth of enterprises and assist platform managers in making strategic decisions.

[0028] The machine learning module constructs a business development prediction model to intelligently evaluate and predict the key indicators of startups. The model training process includes steps such as feature selection, data preprocessing, model training and verification. The recursive feature elimination (RFE) algorithm is used to screen key features, the adaptive learning rate adjustment strategy is used to optimize the neural network model, and cross-validation is used to prevent model overfitting to ensure the accuracy of the prediction results and the generalization ability of the model. Through these technologies, the system can automatically identify the growth potential and risk level of the enterprise, and output personalized incubation strategy recommendations to provide managers with intelligent decision-making support.

[0029] The visualization module presents the results of data analysis in a variety of visualization forms, including charts, dashboards, and heat maps, and supports interactive operations on data. WebSocket technology is used to achieve real-time communication with the background data source to ensure the timeliness of data display, and a cache strategy is integrated to optimize the refresh speed and reduce the impact of network delays on user experience. Users can customize data views through interactive methods such as dragging, zooming, and filtering, or use voice recognition to adjust data display for a more convenient operation experience. These visualization and interactive functions can help managers more intuitively grasp the dynamic performance of the enterprise and support rapid response and adjustment.

[0030] The dynamic feedback and optimization module dynamically updates the analysis results and recommended content through real-time response to data changes, and continuously optimizes the accuracy and reliability of the analysis model. Based on the autoencoder neural network model, this module automatically detects and classifies abnormal points in the data stream, and combines time series analysis to monitor abnormal fluctuations of key indicators in real time and generate corresponding risk warning reports. The system uses reinforcement learning algorithms to automatically adjust model parameters and continuously learns users' operating habits through feedback loop mechanisms, thereby optimizing model performance and recommendation strategies and improving user satisfaction.

[0031] The real-time adjustment capability of the dynamic feedback and optimization module ensures that the system can respond quickly to the latest data changes, adjust the analysis model in a timely manner, maintain the accuracy of data analysis and prediction, and provide intelligent support for continuous improvement of the entrepreneurial incubation platform.

[0032] In summary: The multidimensional data analysis method of the present invention effectively solves the problems of traditional entrepreneurial incubation platforms in terms of single data analysis dimension, lagging decision-making, and lack of personalized strategies. Through the integration and in-depth mining of multi-source data, platform managers can fully and dynamically understand the performance and potential risks of entrepreneurial enterprises, thereby improving the scientific nature and accuracy of incubation strategies. The visualization and interactive functions of the data make complex data analysis results easier to understand and apply, while the real-time feedback and optimization functions ensure that the system's analytical capabilities continue to improve. Overall, the present invention has significant innovation and practical application value in data processing, analysis, and intelligent decision support, and provides advanced technical means for the efficient management of entrepreneurial incubation platforms.

[0033] The present invention is described above by way of example in conjunction with the accompanying drawings. It is obvious that the specific implementation of the present invention is not limited to the above-mentioned method. As long as various non-substantial improvements are made using the method concept and technical solution of the present invention, or the concept and technical solution of the present invention are directly applied to other occasions without improvement, they are all within the protection scope of the present invention.

Claims

1. A multidimensional data analysis method for an entrepreneurial incubation platform, characterized in that: The following steps are involved: S10, Data Collection Module: Collect multi-source data of startups, including corporate financial data, market operation data, team member data, patent and technology data, social media data and industry report data, and standardize and clean the data; S20, multidimensional data modeling module: multidimensional modeling of the collected data, definition of data dimensions and measurements, and construction of data cubes through OLAP technology to achieve multi-angle observation and analysis of data; S30, Data Analysis Module: Use data mining algorithms to conduct multi-dimensional portrait analysis of entrepreneurial enterprises. Data mining algorithms include cluster analysis, correlation analysis, and causal analysis. And use prediction models to analyze and predict the future development trends of enterprises. Prediction models include time series analysis and regression analysis. S40, Machine Learning Module: Build an intelligent recommendation system based on machine learning, automatically evaluate the key indicators of startups through classification and regression algorithms, and recommend customized incubation strategies; S50, visualization display module: used to display the analysis results in a visualized form, including charts, dashboards and heat maps, and supports interactive operations of data; S60, Dynamic Feedback and Optimization Module: Dynamically updates analysis results and recommended content based on real-time data changes, and optimizes the accuracy and reliability of the analysis model through continuous learning of historical data.

2. The multidimensional data analysis method of the entrepreneurship incubation platform according to claim 1 is characterized in that: The data collection module collects multi-source data of start-up enterprises in the following ways: through API interface, data crawler and manual input.

3. The multidimensional data analysis method of the entrepreneurship incubation platform according to claim 1 is characterized in that: The data cube constructed by OLAP technology in the multidimensional data modeling module also includes: Data slicing: extracting a data subset of a specified dimension from a data cube; Data rotation: adjust the dimensional perspective of the data cube to change the way the data is presented; Data drilling: Analyze data along specific dimensions to reveal more detailed information.

4. The multidimensional data analysis method of the entrepreneurship incubation platform according to claim 3 is characterized in that: The multidimensional data modeling module includes custom modeling, which customizes data dimensions and metrics according to needs and adjusts the structure of the data cube.

5. The multidimensional data analysis method of the entrepreneurship incubation platform according to claim 1, characterized in that: The causal analysis includes the Granger causality test method to detect the causal relationship between different variables, and uses bivariate and multivariate regression models to verify and quantitatively evaluate the causal relationship to output the confidence level and impact of the causal relationship.

6. The multidimensional data analysis method of the entrepreneurship incubation platform according to claim 1, characterized in that: The dynamic feedback and optimization module includes an automatic learning mechanism based on real-time data streams to continuously optimize the weights and parameter configurations of the data analysis model; it implements real-time anomaly detection, and when abnormal fluctuations occur in the data, it sends an early warning to the manager and provides response suggestions.

7. The multidimensional data analysis method of the entrepreneurship incubation platform according to claim 6, characterized in that: The anomaly detection includes automatic detection and classification of anomalies in data streams based on an autoencoder neural network model; combined with time series analysis, real-time monitoring of abnormal fluctuations in key indicators, and generation of corresponding risk warning reports.

8. The multidimensional data analysis method of the entrepreneurship incubation platform according to claim 1, characterized in that: The algorithms used in the machine learning module include random forest, support vector machine (SVM) and neural network, which are used to automatically evaluate the key indicators of startups and recommend incubation strategies.

9. The multidimensional data analysis method for the entrepreneurship incubation platform according to claim 8, characterized in that: The incubation strategy includes combining the growth potential and risk level of the enterprise to generate a personalized incubation strategy through a multi-objective optimization algorithm; using a rule-based recommendation system to recommend corresponding resource allocation and market entry strategies for different types of entrepreneurial enterprises.

10. The multidimensional data analysis method of the business incubation platform according to claim 1, characterized in that: The data collection module also includes a data security and privacy protection method, which is used to protect the security and privacy of entrepreneurial enterprise data during collection, transmission and storage through data encryption, access control and data anonymization.

Images