Market supervision intelligent analysis system and method based on artificial intelligence

Abstract

The invention relates to the technical field of artificial intelligence, in particular to a market supervision intelligent analysis system and method based on artificial intelligence, and the system comprises a data collection module, a data preprocessing module, a feature extraction module, an intelligent analysis module, a risk early warning module, a decision support module and a user interaction interface. The system has the beneficial effects that automatic processing of market data is realized by integrating data acquisition, preprocessing, feature extraction, intelligent analysis and other modules. The complexity of manual operation is reduced, and the data processing efficiency is greatly improved. Through big data acquisition and a deep learning algorithm, the system can automatically identify and mark potential violation behaviors, and the accuracy and efficiency of market supervision are improved. In addition, the system can help supervision departments to early warn in advance through means of emotion analysis, trend prediction and the like, and market risks are effectively prevented.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and specifically to an artificial intelligence-based market supervision intelligent analysis system and method. Background Art

[0002] With the rapid development of the market economy, market supervision is facing increasingly complex challenges, including the proliferation of counterfeit and shoddy goods, price fraud, false advertising and other issues. These problems not only harm the rights and interests of consumers, but also affect fair competition in the market and the healthy development of the economy. Therefore, it is imperative to establish an efficient and intelligent market supervision and analysis system. Summary of the invention

[0003] The purpose of the present invention is to provide an artificial intelligence-based market supervision intelligent analysis system and method to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above-mentioned object, the present invention provides the following technical solutions: an artificial intelligence-based market supervision intelligent analysis system, comprising a data acquisition module, a data preprocessing module, a feature extraction module, an intelligent analysis module, a risk warning module, a decision support module and a user interaction interface;

[0005] The data collection module is used to collect data in real time or regularly from a variety of market data sources such as transaction records, social media, news reports, and consumer complaint platforms, and to perform preliminary data processing, including deduplication and format unification;

[0006] The data preprocessing module is used to further clean and process the collected raw data, identify and process outliers and missing values in the data, and perform data standardization and normalization operations.

[0007] Preferably, the feature extraction module uses natural language processing and image recognition AI technology to extract key features from the preprocessed data. The key features include abnormal price fluctuations, false advertising keywords, and consumer complaint hotspots. The output of the feature extraction module serves as the input basis for the subsequent intelligent analysis module.

[0008] Preferably, the intelligent analysis module is the core part of the system, which uses deep learning algorithms, including convolutional neural networks and recurrent neural networks, to learn and identify market behavior patterns; the intelligent analysis module can automatically classify market activities as compliant or non-compliant, issue early warnings for potential market violations, and predict market trends based on historical data.

[0009] Preferably, the risk warning module warns of potential market violations based on the output results of the intelligent analysis module, automatically generates a risk warning report, and lists in detail the type, degree, and possible impact of the violations; the output of the risk warning module serves as an important basis for market regulators to take further action.

[0010] Preferably, the decision support module combines the historical case library and expert system to provide personalized regulatory strategy recommendations to market regulators;

[0011] The decision support module can analyze the nature, characteristics and trends of illegal behaviors based on the output results of the risk warning module, provide targeted regulatory measures and plans to regulatory agencies, and continuously optimize and improve its own suggestions based on the feedback from regulatory agencies;

[0012] The user interface provides an intuitive and easy-to-use operation interface, allowing users to easily view analysis results, receive warning information and manage supervision tasks.

[0013] A method for an artificial intelligence-based market supervision intelligent analysis system comprises the following steps: using a data acquisition module to collect data in real time or regularly from a variety of market data sources such as transaction records, social media, news reports, and consumer complaint platforms, and performing preliminary processing on the collected data, including deduplication and format unification, to ensure the integrity and timeliness of the data, thereby laying a foundation for subsequent analysis.

[0014] Preferably, after data collection, the collected raw data is further cleaned and processed by a data preprocessing module, specifically including: identifying and processing outliers and missing values in the data to improve the accuracy and reliability of the data; standardizing and normalizing the data to make the data meet the processing and analysis requirements of subsequent algorithms.

[0015] Preferably, after data preprocessing, the feature extraction module uses natural language processing and image recognition I technology to extract key features from the processed data. The key features include abnormal price fluctuations, false advertising keywords, and consumer complaint hotspots; the extracted features are used as an important input basis for the subsequent intelligent analysis module.

[0016] Preferably, the intelligent analysis module utilizes deep learning algorithms, including convolutional neural networks and recurrent neural networks, to learn and identify market behavior patterns; automatically classify market activities as compliant or non-compliant, and issue early warnings for potential market violations; and predict market trends based on historical data to provide forward-looking information to market regulators.

[0017] Preferably, the method further comprises the following steps:

[0018] Through the risk warning module, according to the output results of the intelligent analysis module, potential market violations are warned, and a risk warning report is automatically generated, listing in detail the types, degrees, and possible impacts of the violations;

[0019] Through the decision support module, combined with the historical case library and the expert system, according to the output results of the risk warning module, the nature, characteristics, and trends of the violations are analyzed, and personalized regulatory strategy suggestions are provided for the market supervision agency, including targeted regulatory measures and plans;

[0020] Optimize and improve the suggestions of the decision support module according to the feedback from the supervision agency;

[0021] Through the user interface, an intuitive and easy-to-use operation interface is provided for users, enabling them to conveniently view the analysis results, receive warning information, and manage supervision tasks, ensuring the usability and practicality of the system.

[0022] Compared with the prior art, the beneficial effects of the present invention are:

[0023] The intelligent market supervision intelligent analysis system and method based on artificial intelligence proposed by the present invention realize the automated processing of market data by integrating modules such as data collection, preprocessing, feature extraction, and intelligent analysis. This not only reduces the tediousness of manual operations but also greatly improves the efficiency of data processing. Through big data collection and deep learning algorithms, the system can automatically identify and mark potential violations, improving the accuracy and efficiency of market supervision. In addition, this system can also help the supervision department issue early warnings and effectively prevent market risks through means such as sentiment analysis and trend prediction. Brief Description of the Drawings

[0024] Figure 1 It is a flow chart of the method of the present invention. Detailed Embodiments

[0025] In order to clearly and completely describe the objectives, technical solutions, and advantages of the present invention, the following further details the embodiments of the present invention with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are some embodiments of the present invention, rather than all embodiments, and are only used to explain the embodiments of the present invention and are not used to limit the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0026] Embodiment 1, the present invention provides a technical solution: a market supervision intelligent analysis system based on artificial intelligence technology, which aims to conduct a comprehensive and in-depth analysis of market data by integrating multiple AI technologies and algorithms to identify market violations, predict market trends, and provide decision support for market regulators. The system consists of multiple modules, each of which has a specific function and works together to achieve efficient and accurate market supervision.

[0027] 1) Data collection module: The data collection module is the entrance to the system and is responsible for collecting data from various market data sources. These data sources may include transaction records, social media, news reports, consumer complaint platforms, etc. The data collection module needs to monitor these data sources in real time or regularly to ensure the integrity and timeliness of the data. At the same time, the module also needs to perform preliminary processing on the data, such as deduplication and format unification, to lay the foundation for subsequent analysis.

[0028] 2) Data preprocessing module: The data preprocessing module is a stage for further cleaning and processing of the collected raw data. This module needs to identify and process problems such as outliers and missing values in the data to ensure the accuracy and reliability of the data. In addition, the data preprocessing module may also need to standardize and normalize the data to facilitate subsequent algorithm processing and analysis.

[0029] 3) Feature extraction module: The feature extraction module is a process of extracting key features from data using AI technologies such as natural language processing (NLP) and image recognition. These features may include abnormal price fluctuations, false advertising keywords, consumer complaint hotspots, etc. The output of the feature extraction module will be an important basis for the input of the subsequent intelligent analysis module.

[0030] 4) Intelligent Analysis Module: The intelligent analysis module is the core part of the system. It uses deep learning algorithms (such as convolutional neural networks (CNN), recurrent neural networks (RNN), etc.) to learn and identify market behavior patterns. The module can automatically classify market activities as compliant or illegal, and issue early warnings for potential market violations. At the same time, the intelligent analysis module can also predict market trends based on historical data and provide forward-looking information to market regulators.

[0031] 5) Risk warning module: The risk warning module warns of potential market violations based on the output of the intelligent analysis module. The module can automatically generate a risk warning report that lists in detail the type, degree, possible impact, and other information of the violations. The output of the risk warning module will serve as an important basis for market regulators to take further action.

[0032] 6) Decision support module: The decision support module combines the historical case library and expert system to provide personalized regulatory strategy recommendations for market regulators. Based on the output results of the risk warning module, this module can analyze the nature, characteristics and trends of violations and provide targeted regulatory measures and plans for regulators. At the same time, the decision support module can also continuously optimize and improve its own recommendations based on the feedback from the regulators.

[0033] 7) User interaction interface: The user interaction interface is a window for the system to interact with the user. This interface provides an intuitive and easy-to-use operation interface, allowing users to easily view analysis results, receive warning information, and manage supervision tasks. The design of the user interaction interface needs to focus on user experience and operability to ensure the ease of use and practicality of the system.

[0034] Embodiment 2, based on embodiment 1, proposes a method for a market supervision intelligent analysis system based on artificial intelligence, comprising the following steps: using a data acquisition module to collect data in real time or regularly from a variety of market data sources such as transaction records, social media, news reports, and consumer complaint platforms, and performing preliminary processing on the collected data, including deduplication and format unification, to ensure the integrity and timeliness of the data, laying the foundation for subsequent analysis.

[0035] After data collection, the collected raw data is further cleaned and processed through the data preprocessing module, including: identifying and processing outliers and missing values in the data to improve the accuracy and reliability of the data; standardizing and normalizing the data to make the data meet the processing and analysis requirements of subsequent algorithms.

[0036] After data preprocessing, the feature extraction module uses natural language processing and image recognition technology to extract key features from the processed data. The key features include abnormal price fluctuations, false advertising keywords, and consumer complaint hotspots; the extracted features are used as an important input basis for the subsequent intelligent analysis module.

[0037] Through the intelligent analysis module, deep learning algorithms, including convolutional neural networks and recurrent neural networks, are used to learn and identify market behavior patterns; market activities are automatically classified as compliant or non-compliant, and early warnings are issued for potential market violations; market trends are predicted based on historical data, providing forward-looking information to market regulators.

[0038] The following steps are also included:

[0039] The risk warning module warns of potential market violations based on the output of the intelligent analysis module and automatically generates a risk warning report that lists in detail the type, degree, and possible impact of the violations;

[0040] Through the decision support module, combining the historical case database and the expert system, analyze the nature, characteristics and trends of violations according to the output results of the risk warning module, and provide personalized regulatory strategy suggestions for market regulatory agencies, including targeted regulatory measures and plans;

[0041] Continuously optimize and improve the suggestions of the decision support module according to the feedback from regulatory agencies;

[0042] Provide an intuitive and easy-to-use operation interface for users through the user interaction interface, enabling users to conveniently view analysis results, receive warning information and manage regulatory tasks, ensuring the usability and practicality of the system.

[0043] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent analysis system for market supervision based on artificial intelligence, characterized in that: It includes a data collection module, a data preprocessing module, a feature extraction module, an intelligent analysis module, a risk warning module, a decision support module, and a user interaction interface; The data collection module is used to collect data from various market data sources such as transaction records, social media, news reports, and consumer complaint platforms in real time or regularly, and perform preliminary processing on the data, including duplicate removal and format unification; The data preprocessing module is used to further clean and process the collected raw data, identify and process outliers and missing values in the data, and perform data standardization and normalization operations.

2. The intelligent analysis system for market supervision based on artificial intelligence according to claim 1, characterized in that: The feature extraction module uses natural language processing and image recognition AI technologies to extract key features from the preprocessed data. The key features include abnormal price fluctuations, false publicity keywords, and consumer complaint hotspots; The output of the feature extraction module serves as the input basis for the subsequent intelligent analysis module.

3. The intelligent analysis system for market supervision based on artificial intelligence according to claim 2, characterized in that: The intelligent analysis module is the core part of the system. It uses deep learning algorithms, including convolutional neural networks and recurrent neural networks, to learn and identify market behavior patterns; the intelligent analysis module can automatically classify market activities as compliant or non-compliant, warn of potential market non-compliant behaviors, and predict market trends based on historical data.

4. The intelligent analysis system for market supervision based on artificial intelligence according to claim 3, wherein: The risk warning module warns of potential market non-compliant behaviors based on the output results of the intelligent analysis module, and automatically generates a risk warning report, detailing the types, degrees, and possible impacts of non-compliant behaviors; The output of the risk warning module serves as an important basis for the market supervision agency to take further actions.

5. The intelligent analysis system for market supervision based on artificial intelligence according to claim 4, characterized in that: The decision support module combines a historical case library and an expert system to provide personalized regulatory strategy suggestions for the market supervision agency; The decision support module can analyze the nature, characteristics, and trends of non-compliant behaviors based on the output results of the risk warning module, provide targeted regulatory measures and solutions for the supervision agency, and continuously optimize and improve its suggestions according to the feedback from the supervision agency; The user interaction interface provides an intuitive and easy-to-use operation interface, enabling users to conveniently view analysis results, receive warning information, and manage supervision tasks.

6. A method for an intelligent analysis system for market supervision based on artificial intelligence according to claim 5, characterized in that: It includes the following steps: Use the data collection module to collect data from various market data sources such as transaction records, social media, news reports, and consumer complaint platforms in real time or regularly, and perform preliminary processing on the collected data, including duplicate removal and format unification, to ensure the integrity and timeliness of the data and lay a foundation for subsequent analysis work.

7. A method according to claim 6, wherein: After data collection, further clean and process the collected raw data through the data preprocessing module. Specifically, it includes: identifying and processing outliers and missing values in the data to improve the accuracy and reliability of the data; performing data standardization and normalization operations to make the data meet the processing and analysis requirements of subsequent algorithms.

8. A method according to claim 7, wherein: After data preprocessing, use the feature extraction module to extract key features from the processed data using natural language processing and image recognition I technologies. The key features include abnormal price fluctuations, false publicity keywords, and consumer complaint hotspots; use the extracted features as an important input basis for the subsequent intelligent analysis module.

9. A method according to claim 8, characterized in that: The intelligent analysis module utilizes deep learning algorithms, including convolutional neural networks and recurrent neural networks, to learn and identify market behavior patterns; automatically classify market activities as compliant or non-compliant, and issue warnings for potential market violations; predict market trends based on historical data, and provide forward-looking information for market regulatory agencies.

10. A method according to claim 9, characterized in that: It also includes the following steps: Through the risk warning module, based on the output results of the intelligent analysis module, issue warnings for potential market violations, and automatically generate a risk warning report, which details the types, degrees, and possible impacts of the violations; Through the decision support module, in combination with the historical case database and the expert system, based on the output results of the risk warning module, analyze the nature, characteristics, and trends of the violations, and provide personalized regulatory strategy suggestions for market regulatory agencies, including targeted regulatory measures and plans; Continuously optimize and improve the suggestions of the decision support module according to the feedback from the regulatory agency; Provide an intuitive and user-friendly operation interface for users through the user interface, enabling users to conveniently view analysis results, receive warning information, and manage regulatory tasks, ensuring the usability and practicality of the system.

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