CN113762802A - Artificial intelligence PPP project financing evaluation system - Google Patents
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Abstract
The invention relates to the field of artificial intelligence, in particular to an artificial intelligence PPP project financing evaluation system based on big data and machine learning. The system comprises a big data acquisition subsystem for acquiring PPP related data, a big data analysis and processing subsystem for solving the problem, a PPP financing evaluation index system construction subsystem for analyzing PPP financing influence factors in six dimensions, a PPP financing evaluation model construction subsystem for constructing an index builder for mining the financing evaluation subject by the subsystem, and a financing evaluation big data simulation and emulation subsystem for model integration, emulation and display. The invention effectively improves the efficiency and accuracy of PPP financing evaluation, and provides decision support for social capital and financial institutions to participate in PPP projects and promote high-quality development of public and private cooperation.
Description
Technical Field
The invention relates to big data, machine learning and artificial intelligence, in particular to an artificial intelligence PPP project financing evaluation system based on big data and machine learning.
Background
Financing is an important link in four links of 'throwing, melting, managing and withdrawing' of the PPP project, and a reasonable financing scheme and a standard financing behavior are important guarantees for the success of the PPP project. At present, PPP (Point-to-Point protocol) financability evaluation not only relates to a project, but also is influenced by factors such as a macroscopic policy, finance, market and the like, and relates to a plurality of stakeholders, and the financability evaluation faces heterogeneous multi-source and multi-dimensional related information inside and outside the project, so that the data size is large, the content is wide, the relationship is complex, and the information is dispersed. The evaluation made only by an expert system and some auxiliary systems with single functions has the disadvantages of strong subjectivity, long evaluation period, fussy organization, low accuracy and weak universality.
In recent years, scientific and technological progress enables the computer to be low in price, the application of big data and artificial intelligence technology is mature, and the effect is obvious. The financial department and the development and modification commission issue a large amount of PPP project data, various macro databases enable acquisition of macro economic, industry/region and market data, and the method is based on abundant basic data and utilizes a big data technology and an artificial intelligence algorithm to evaluate the financability of the PPP project. Compared with the traditional expert system method, the method has obvious advantages in terms of scale, efficiency, objectivity, accuracy, dynamics and cost, and is a novel creative system.
Chinese patent application No. 201911095099.7 discloses a PPP project investment decision analysis system based on financial measurement for intelligent adaptation of project characteristics, prediction and simulation of risk assessment. The system mainly comprises a financial measuring and calculating system, a risk assessment system and a visualization system, and only relates to microscopic financial data of a single project; the system does not relate to the macroscopic environments such as the financial capacity of a PPP local government, the industry market and the like, and does not perform comprehensive and omnibearing evaluation on the PPP financability.
Chinese patent application No. 201610958849.9 discloses a PPP project financial service system for assisting various government departments and financing institution in vertical financial services of information distribution, project matching, financing service and risk assessment of PPP projects, serving PPP project planning, consultation and risk assessment in internet mode, improving the efficiency of docking PPP projects and reducing docking cost. The system mainly comprises a PPP project information publishing module, a PPP project matching module, a PPP project financing module and a PPP project risk assessment module; the system focuses on information release and financing service of the PPP project, and system evaluation on the financing performance of the PPP project cannot be carried out.
Disclosure of Invention
In view of the defects of the prior art, the invention aims to provide an artificial intelligence PPP project financing evaluation system based on big data and machine learning, which can overcome the defects of the prior art, eliminate the correlation and collinearity among evaluation indexes, effectively solve the problems of multidimensional, multimode, multiattribute and isomerism of the PPP financing evaluation big data, and provide an artificial intelligence PPP project financing system for local governments, social capital and financial institutions while overcoming the problems of variable distribution assumption of the traditional regression model and unexplainable performance of the machine learning model.
In order to achieve the above purpose and other related purposes, the invention provides an artificial intelligence PPP project financing evaluation system based on big data and machine learning, which comprises a big data acquisition subsystem, a big data analysis and processing subsystem, a PPP financing evaluation index system construction subsystem, a PPP financing evaluation model construction subsystem and a financing evaluation big data simulation and simulation subsystem. The big data acquisition subsystem is a text mining and semantic analysis data normalizer and is used for acquiring PPP related data from the Internet, research reports, WeChat public accounts and the like; the big data analysis and processing subsystem is a PPP financing big data feature selection, classification, clustering and learning trainer and is used for solving the problems of multi-source, multi-dimensional and heterogeneous evaluation of PPP financing; the PPP financing influence factor analysis subsystem analyzes the PPP financing influence factors from six dimensions of time dimension, logic dimension, knowledge dimension, financing structure, financing function and operation mechanism based on a QCA method; the PPP financing evaluation index system construction subsystem is an index builder for mining financing evaluation themes from the inside and the outside of a project based on a big data technology; the PPP financing evaluation model building subsystem is a PPP financing evaluation model library; the financing evaluation big data simulation and emulation subsystem integrates, emulates and displays a model.
Optionally, in the above technical solution, the big data collection subsystem includes an internet (including a WeChat public number) collection project information database, a financial government and social capital cooperation center PPP database, and a macro (local government, social capital, industry, region, market) information database, and is located at the lowest layer of the system;
optionally, in the above technical solution, the basic data of the big data analysis and processing subsystem is from the big data acquisition subsystem and is located at a layer above the big data acquisition subsystem;
optionally, the PPP financing evaluation index system construction subsystem in the above technical solution is an index builder, which crawls a PPP project financing related text from the internet and a PPP related wechat platform by using a big data technology, identifies alternative indexes by concept similarity analysis and association rule mining with financing as a theme, and implements index screening by using semantic analysis and a reverse retrieval algorithm; analyzing the index network coacervate subgroup and the index core-edge position based on an index co-occurrence matrix constructed by the index statistical result to mine the correlation between indexes to determine the core index; finally, finishing index classification and weight calculation according to a social network analysis principle to form a final index system; providing an index system for the artificial intelligence PPP financing evaluation model construction subsystem, which is a key auxiliary module and is parallel to the PPP financing mode analysis subsystem;
optionally, in the above technical solution, the PPP financability evaluation model construction subsystem is composed of an artificial intelligence PPP financability evaluation model, and is located at a layer above the index builder of the PPP financability evaluation index system construction subsystem;
optionally, in the above technical solution, the financing evaluation big data simulation and emulation subsystem outputs a final score through model emulation, case verification, evaluation model comparison analysis and uncertainty analysis, and is located at the top level of the system.
As described above, the present invention has the following advantageous effects: integrating two large paradigms of model driving and data driving, adopting a large data technology, crawling external and internal information of a project, and combining network large data and project small data to construct a comprehensive, objective and dynamic financability evaluation index system; the method not only effectively overcomes the defects of overlarge subjective interference, weak universality and the like in the traditional index system construction, but also has the characteristics of objectivity, integrity and dynamic adjustability, and eliminates the correlation and collinearity among variables in the traditional evaluation index system construction process; designing a big data feature selection, classification and clustering algorithm to solve the problems of multi-dimension, multi-mode, multi-attribute and huge amount of the big data which can be evaluated by financing; constructing a PPP (Point-to-Point protocol) financing evaluation system based on big data and artificial intelligence and oriented to different perspectives of local governments, social capital and financial institutions; the problem of multidimensional disaster of big data evaluation is further solved, the problems of variable distribution hypothesis and unexplainable property of a machine learning model of a traditional regression model are solved, and the evaluation model has deep learning capacity, good stability and different decision-making subjects; shortening the evaluation period of the PPP project financing, effectively reducing the risk of the PPP project financing evaluation and improving the evaluation accuracy and universality.
The invention is further illustrated by the following figures and examples.
Drawings
FIG. 1 is a frame diagram of PPP financing evaluation dynamic index system construction according to the present invention.
FIG. 2 is a schematic diagram of the evaluation flow of the artificial intelligence PPP financability of the present invention.
FIG. 3 is a schematic diagram of an architecture of an artificial intelligence PPP financing evaluation system of the present invention.
FIG. 4 is a schematic diagram of a data collection processing flow of an artificial intelligence PPP financing evaluation system of the present invention.
Detailed Description
Referring to fig. 1 to 4, the present invention provides an artificial intelligence PPP project financing evaluation system, which includes:
the system comprises a big data acquisition subsystem, a big data analysis and processing subsystem, a PPP financing evaluation index system construction subsystem, a PPP financing evaluation model construction subsystem and a financing evaluation big data simulation and emulation subsystem.
The big data acquisition subsystem is a data normalizer and provides a basic database for big data analysis, and the system database comprises an internet collected project information database and a macroscopic (industry, region and market) information database.
The big data analysis and processing subsystem adopts a decision tree selection algorithm, a decision tree classification algorithm, K-Means and a random forest kernel model trainer; the characteristic selection algorithm of the decision tree selects the characteristic of the financing evaluation big data, the classification algorithm of the decision tree classifies the financing evaluation big data, the K-Means clusters the financing evaluation big data, and the random forest kernel model trainer trains the financing evaluation sample.
The PPP financing mode analysis subsystem is a PPP financing mode analyzer, provides discrimination conditions according to PPP financing connotation and PPP financing characteristics, and is an auxiliary module of the PPP financing evaluation model construction subsystem.
The PPP mode analysis subsystem analyzes PPP mode items of the information infrastructure from six dimensions of time dimension, logic dimension, knowledge dimension, financing structure, financing function and operation mechanism, and is an auxiliary module of the PPP financing evaluation model construction subsystem.
The PPP financing influence factor analysis subsystem is based on a QCA method, analyzes different areas from aspects of case selection, variable design, data calibration, qualitative comparison analysis, factor induction combination analysis, result specificity and universality analysis and the like, and is an auxiliary module of the PPP financing evaluation model construction subsystem in different industries. In parallel with the big data analysis and processing subsystem.
The PPP financing evaluation index system construction subsystem is an index builder, and indexes are constructed by applying eight methods, namely an evaluation theme, evaluation dimension definition, evaluation index correlation analysis, evaluation index causal relationship inspection, index clustering analysis, index correlation analysis, index similarity analysis and index node importance analysis; crawling PPP related texts through the Internet and 193 PPP WeChat public number specific data sources to form an analysis sample XML file, identifying sample keywords by using a Chinese word segmentation and keyword extraction algorithm, extracting keyword features through modular clustering, classifying word formation after dimension reduction to identify influence factors, further analyzing the influence factors by methods of semantic segmentation, reverse retrieval algorithm and the like, and identifying and verifying a complete set index; analyzing the index network through condensation subgroup analysis and core-edge structure analysis, and mining the correlation relationship between indexes by combining the correlation coefficient calculation result to obtain a final index and a core index; finally, determining the index category and weight through module clustering and feature vector centrality calculation to establish a PPP financing evaluation index system; the artificial intelligence PPP financing evaluation model construction subsystem provides an index system, is a key auxiliary module of the artificial intelligence PPP financing evaluation model construction subsystem, and is parallel to the PPP financing mode analysis subsystem.
The PPP financing evaluation model construction subsystem constructs an information infrastructure PPP financing evaluation model from five aspects of financing evaluation attribute and characteristic analysis, financing evaluation big data label analysis, financing evaluation variable analysis, financing service evaluation modeling and 360 project sketch construction; and dividing the original data set according to the industry attribute, and performing oversampling processing on samples with fewer types in the new sample set to ensure the balance of positive and negative sample sets of a single industry sub-training set, thereby realizing that each sub-training set can effectively describe the industry characteristics to which the sub-training set belongs and ensure the balance of the positive and negative samples in the industry. And then, the LightGBM algorithm is used as a base classifier model, the type field is directly used as an input variable, a histogram segmentation strategy is used for reference, and the LightGBM algorithm is used for optimizing the segmentation mode of the type field. And finally, dividing a PPP sample set according to the project industry attributes by using the LightGBM as a basic algorithm of the basic classifier, inputting the divided training sets into each basic classifier for training, fusing the training results through a Blending algorithm, and giving weights to each basic learner. It is located at the upper layer of the PPP financing evaluation index system construction subsystem, namely the index builder.
The financing evaluation big data simulation and simulation subsystem outputs a final score through model simulation, case verification, evaluation model comparative analysis and uncertain analysis, provides decision consultation and is positioned at the top layer of the system.
In conclusion, the method overcomes the defects of over-strong evaluation subjectivity, long evaluation period, complicated organization, low evaluation efficiency, low evaluation accuracy and the like in the conventional expert system method. Meanwhile, the invention also provides an artificial intelligence PPP project financing evaluation system based on big data and machine learning, which can cover different areas and evaluate the financing of different types of PPP projects, overcomes the defects of the prior art, shortens the PPP project financing evaluation period, effectively reduces the PPP project financing evaluation risk, improves the evaluation accuracy and universality and provides reference for standardization of the industrial PPP project financing evaluation.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (7)
1. An artificial intelligence PPP project financing evaluation system based on big data and machine learning is characterized by comprising: the system comprises a big data acquisition subsystem, a big data analysis and processing subsystem, a PPP financing evaluation index system construction subsystem, a PPP financing evaluation model construction subsystem and a PPP financing evaluation big data simulation and emulation subsystem;
the big data acquisition subsystem is a text mining and semantic analysis data normalizer; adopting a web crawler technology to crawl macro information such as policy, economy, finance, market and the like from a macro level, a middle level and a micro level, and micro information such as a PPP financing scheme, an implementation scheme, a research report and the like;
the big data analysis and processing subsystem integrates a random forest, a neural network and a support vector machine, faces to a decision-making main body and a financing theme, and adopts correlation analysis, cluster analysis and feature analysis to perform big data feature selection, data classification and dimension reduction;
the PPP financing influence factor analysis subsystem analyzes the PPP financing influence factors from six dimensions of time dimension, logic dimension, knowledge dimension, financing structure, financing function and operation mechanism based on the QCA method;
the PPP financing evaluation index system construction subsystem is a PPP financing dynamic evaluation index constructor, and constructs a PPP financing dynamic evaluation index system by mining events and topics associated with the financing evaluation from the external big data information in the project, selecting indexes by using big data technologies such as concept similarity analysis and association rule mining, and eliminating the correlation and collinearity among the indexes by using big data technologies such as association analysis and cluster analysis;
the PPP financing evaluation model building subsystem is a PPP financing evaluation model library, starts with PPP connotation and characteristic analysis, carries out PPP financing evaluation service modeling and builds a PPP financing evaluation basic model;
the large data simulation and emulation subsystem for the financing evaluation is a model emulation and evaluator, applies a machine learning algorithm on the basis of a PPP financing evaluation basic model, integrates two large paradigms of model driving and data driving, establishes an artificial intelligent evaluation model with deep learning capability, good stability and different decision subjects, and provides PPP financing evaluation large data display for government departments, social capital and financial institutions.
2. The big data and machine learning based artificial intelligence PPP project financing evaluation system as claimed in claim 1, wherein said big data and machine learning based artificial intelligence PPP project financing evaluation system can be an executable file or a program script file compiled after being written on UNIX operating system, LINUX operating system, apple IOS operating system and WINDOWS operating system in C language, C + + language, PYTHON language or JAVA language.
3. The artificial intelligence PPP project financing evaluation system based on big data and machine learning of claim 1, characterized in that the big data collection subsystem comprises an Internet collection project information database, a financial government and social capital cooperation center PPP database, and a macroscopic (local government, social capital, industry, region, market) information database, which are positioned at the lowest layer of the system.
4. The artificial intelligence PPP project financing evaluation system based on big data and machine learning of claim 1, wherein the big data analysis and processing subsystem base data comes from the big data collection subsystem and is located at the upper layer of the big data collection subsystem.
5. The system for evaluating artificial intelligence PPP project financing property based on big data and machine learning as claimed in claim 1, wherein said PPP financing property evaluation index system construction subsystem is a dynamic index builder, which provides an index system for said artificial intelligence PPP financing property evaluation model construction subsystem, which is a key auxiliary module thereof, in parallel with said big data analysis and processing subsystem.
6. The artificial intelligence PPP project financing evaluation system based on big data and machine learning as claimed in claim 1, wherein said PPP financing evaluation model construction subsystem is composed of artificial intelligence PPP financing evaluation model, which is located at the upper layer of said PPP financing evaluation index system construction subsystem and big data analysis and processing subsystem.
7. The artificial intelligence PPP project financing evaluation system based on big data and machine learning of claim 1, wherein said financing evaluation big data simulation and emulation subsystem provides visual financing evaluation result for government department, social capital and financial institution through model emulation, comprehensive integration and evaluation model comparison analysis, and is located at the top layer of the system.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106503913A (en) * | 2016-10-27 | 2017-03-15 | 东方融信（北京）科技发展有限责任公司 | A kind of PPP projects financial services system |
| CN110097459A (en) * | 2019-05-08 | 2019-08-06 | 重庆斐耐科技有限公司 | A kind of financial risks appraisal procedure and system based on big data technology |
| CN112132438A (en) * | 2020-09-17 | 2020-12-25 | 齐鲁股权交易中心有限公司 | Investment and financing docking and equity value evaluation system for regional equity market |
| CN112884590A (en) * | 2021-01-26 | 2021-06-01 | 浙江工业大学 | Power grid enterprise financing decision method based on machine learning algorithm |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106503913A (en) * | 2016-10-27 | 2017-03-15 | 东方融信（北京）科技发展有限责任公司 | A kind of PPP projects financial services system |
| CN110097459A (en) * | 2019-05-08 | 2019-08-06 | 重庆斐耐科技有限公司 | A kind of financial risks appraisal procedure and system based on big data technology |
| CN112132438A (en) * | 2020-09-17 | 2020-12-25 | 齐鲁股权交易中心有限公司 | Investment and financing docking and equity value evaluation system for regional equity market |
| CN112884590A (en) * | 2021-01-26 | 2021-06-01 | 浙江工业大学 | Power grid enterprise financing decision method based on machine learning algorithm |
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