Method and system for optimizing real estate marketing strategy by using AIGC technology

Abstract

According to the method and system for optimizing the real estate marketing strategy through the AIGC technology, the real estate market can be analyzed more comprehensively and deeply on the basis of the AIGC technology, the personalized and accurate marketing strategy is provided, the sales benefit is improved, and compared with a traditional real estate marketing method, the method and system have the advantages that the cost is reduced, and the efficiency is improved. According to the method and the system provided by the invention, the sales conversion rate can be remarkably improved, the marketing cost is reduced, and the user satisfaction is improved.

Description

Technical Field

[0001] The present invention relates to the field of real estate marketing technology, and in particular to a method and system for optimizing real estate marketing strategies using AIGC technology. Background Art

[0002] At present, the real estate market is becoming increasingly competitive, and traditional real estate marketing strategies often fail to make full use of advanced technical means to promote real estate sales more accurately and efficiently. The application of artificial intelligence technology (AI) in real estate marketing has become an industry trend. However, the existing real estate marketing system has not yet fully utilized AI technologies such as natural language processing and image recognition. Therefore, it is necessary to provide a real estate marketing strategy optimization method and system based on AIGC (AI+language processing+image recognition+data mining) technology. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system for optimizing real estate marketing strategies using AIGC technology, so as to solve the problems raised in the background technology and facilitate promotion.

[0004] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is: The system that uses AIGC technology to optimize real estate marketing strategies mainly includes the following components and features: a. Natural language processing module, which uses natural language processing technology to analyze relevant information on the real estate market, including user comments and news reports, to obtain market trends and user feedback; b. Image recognition module, which uses image recognition technology to analyze real estate pictures and extract key features, including the appearance and internal structure of the house, to provide users with more intuitive information; c. Data mining module, which uses data mining technology to analyze historical transaction data and user housing purchase preference information to predict future market trends and optimize personalized marketing strategies; d. Artificial intelligence decision engine, based on the analysis results of the above modules, uses artificial intelligence algorithms to generate optimized real estate marketing strategies.

[0005] Furthermore, the natural language processing module has the following components and features: a. Text data collection: This component is responsible for collecting text data related to the real estate market from various data sources, including user reviews, news reports, and social media posts, for analyzing market trends and user feedback; b. Word segmentation and part-of-speech tagging, which splits the text into independent words, helps to understand the grammatical structure and semantic information of the text; c. Sentiment analysis: Through sentiment analysis technology, the sentiment polarity of user comments and other texts is judged to understand the sentiment tendency in the market; d. Entity recognition: The entity recognition component is used to identify key entities in the text, including place names, building names, and house features, for subsequent analysis and decision-making; e. Topic modeling: Through topic modeling technology, the text is analyzed to identify the themes and topics, understand the focus of users, and thus better identify market needs; f. Semantic similarity calculation: This component is used to calculate the semantic similarity between texts, helping to compare the relevance and similarity between different texts, thereby gaining a more comprehensive understanding of market information.

[0006] Furthermore, the data mining module includes the following components and features: a. Image acquisition: This component is responsible for collecting real estate-related image data from different sources, including photos of the house's exterior and internal structure images; b. Image preprocessing: Before image analysis, the image needs to be preprocessed, including image scaling, rotation, and denoising operations, to ensure the quality and consistency of the input image; c. Feature extraction: This function helps the system understand the visual information of the property by extracting key features in the image, including the building appearance, room layout and decoration style; d. Object detection: The object detection component is used to identify specific objects in the image, including houses, gardens, and garages, which helps to conduct more detailed analysis of the property and provide more accurate information; e. Image classification: Image classification technology classifies real estate images into different categories to better understand the diversity of the real estate market; f. Image segmentation: The image segmentation component is used to divide the image into different regions to extract more detailed information; g. Style recognition: The style recognition function can help the system distinguish the decoration style of the property to better meet the personalized needs of users; h. Visual Similarity Computation: This component is used to calculate the visual similarity between images to identify similar or identical properties, which helps in providing potential options that the user is interested in.

[0007] Furthermore, the data mining module includes the following components and features: a. Data collection: This component is responsible for collecting historical data of the real estate market from different data sources, including transaction records, user behavior data, and market trend data. These data will be used to mine potential associations and patterns; b. Data cleaning: Before data analysis, data needs to be cleaned, including processing missing values, outliers, and duplicate values ​​to ensure data quality and consistency; c. Feature Engineering: Feature engineering involves transforming and extracting data to create new features and enhance the expressiveness of data; d. Association rule mining: By using association rule mining technology, we can analyze the relationship between different attributes, including the relationship between house price and geographical location, house size, to reveal hidden market trends; e. Cluster analysis: Cluster analysis is used to group data into different clusters to help identify different segments in the market; f. Classification and prediction models: Using machine learning algorithms to build classification and prediction models, including predicting housing price trends and user home buying behavior, helps the system predict future market changes more accurately; g. Time series analysis: Time series analysis is used to process time-related data, helping to identify seasonal and cyclical changes in the real estate market and providing support for timing-sensitive marketing strategies; h. User model establishment: By mining user behavior data, a user model is constructed to understand the user's housing purchase preferences, behavioral habits and other information, providing a basis for personalized marketing.

[0008] Furthermore, the artificial intelligence decision engine includes the following components and features: a. Decision rules engine: used to define and execute decision rules. The rules can be based on previous experience, data model output and market analysis, which helps to automate the decision-making process; b. Real-time data processing: The engine needs to process real-time data streams and make corresponding decisions in real-time situations, which is particularly important for a rapidly changing market; c. Personalized recommendation system: The system involves user interaction. The engine includes a personalized recommendation system that provides customized real estate recommendations and marketing information by analyzing user historical data; d. Feedback and optimization: The engine can collect user feedback and decision results, and improve the accuracy and adaptability of decisions by learning and continuously optimizing the model; e. Risk management: Risk management is a key aspect for the real estate industry. The engine will integrate a risk assessment module to assess the risks of transactions and make corresponding decision-making recommendations; f. Explainability: The engine is able to provide explanations for decisions, enabling users and relevant stakeholders to understand why a decision was made.

[0009] The method of optimizing real estate marketing strategy using AIGC technology includes the following steps: Step 1: Obtain real estate market information, including natural language processing and image recognition; Step 2: Analyze user feedback and market trends; Step 3: Mining historical data and building a user model; Step 4: Combine data analysis and user model to generate optimized marketing strategies; Step 5: Implement marketing strategies through AI decision-making engines.

[0010] As an improvement, the beneficial effects of the present invention are: The present invention provides a method and system for optimizing real estate marketing strategies using AIGC technology. Based on AIGC technology, the present invention can analyze the real estate market more comprehensively and deeply, and provide personalized and accurate marketing strategies, thereby improving sales efficiency. Compared with traditional real estate marketing methods, the method and system provided by the present invention can significantly improve sales conversion rate, reduce marketing costs, and improve user satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a system schematic diagram of the present invention. DETAILED DESCRIPTION

[0012] In order to make the contents of the present invention more clearly understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0013] The system that uses AIGC technology to optimize real estate marketing strategies mainly includes the following components and features: a. Natural language processing module, which uses natural language processing technology to analyze relevant information on the real estate market, including user comments and news reports, to obtain market trends and user feedback; b. Image recognition module, which uses image recognition technology to analyze real estate pictures and extract key features, including the appearance and internal structure of the house, to provide users with more intuitive information; c. Data mining module, which uses data mining technology to analyze historical transaction data and user housing purchase preference information to predict future market trends and optimize personalized marketing strategies; d. Artificial intelligence decision engine, based on the analysis results of the above modules, uses artificial intelligence algorithms to generate optimized real estate marketing strategies.

[0014] The natural language processing module has the following components and features: a. Text data collection: This component is responsible for collecting text data related to the real estate market from various data sources, including user reviews, news reports, and social media posts, for analyzing market trends and user feedback; b. Word segmentation and part-of-speech tagging, which splits the text into independent words, helps to understand the grammatical structure and semantic information of the text; c. Sentiment analysis: Through sentiment analysis technology, the sentiment polarity of user comments and other texts is judged to understand the sentiment tendency in the market; d. Entity recognition: The entity recognition component is used to identify key entities in the text, including place names, building names, and house features, for subsequent analysis and decision-making; e. Topic modeling: Through topic modeling technology, the text is analyzed to identify the themes and topics, understand the focus of users, and thus better identify market needs; f. Semantic similarity calculation: This component is used to calculate the semantic similarity between texts, helping to compare the relevance and similarity between different texts, thereby gaining a more comprehensive understanding of market information.

[0015] The data mining module includes the following components and features: a. Image acquisition: This component is responsible for collecting real estate-related image data from different sources, including photos of the house's exterior and internal structure images; b. Image preprocessing: Before image analysis, the image needs to be preprocessed, including image scaling, rotation, and denoising operations, to ensure the quality and consistency of the input image; c. Feature extraction: This function helps the system understand the visual information of the property by extracting key features in the image, including the building appearance, room layout and decoration style; d. Object detection: The object detection component is used to identify specific objects in the image, including houses, gardens, and garages, which helps to conduct more detailed analysis of the property and provide more accurate information; e. Image classification: Image classification technology classifies real estate images into different categories to better understand the diversity of the real estate market; f. Image segmentation: The image segmentation component is used to divide the image into different regions to extract more detailed information; g. Style recognition: The style recognition function can help the system distinguish the decoration style of the property to better meet the personalized needs of users; h. Visual Similarity Computation: This component is used to calculate the visual similarity between images to identify similar or identical properties, which helps in providing potential options that the user is interested in.

[0016] The data mining module includes the following components and features: a. Data collection: This component is responsible for collecting historical data of the real estate market from different data sources, including transaction records, user behavior data, and market trend data. These data will be used to mine potential associations and patterns; b. Data cleaning: Before data analysis, data needs to be cleaned, including processing missing values, outliers, and duplicate values ​​to ensure data quality and consistency; c. Feature Engineering: Feature engineering involves transforming and extracting data to create new features and enhance the expressiveness of data; d. Association rule mining: By using association rule mining technology, we can analyze the relationship between different attributes, including the relationship between house price and geographical location, house size, to reveal hidden market trends; e. Cluster analysis: Cluster analysis is used to group data into different clusters to help identify different segments in the market; f. Classification and prediction models: Using machine learning algorithms to build classification and prediction models, including predicting housing price trends and user home buying behavior, helps the system predict future market changes more accurately; g. Time series analysis: Time series analysis is used to process time-related data, helping to identify seasonal and cyclical changes in the real estate market and providing support for timing-sensitive marketing strategies; h. User model establishment: By mining user behavior data, a user model is constructed to understand the user's housing purchase preferences, behavioral habits and other information, providing a basis for personalized marketing.

[0017] The AI ​​decision engine includes the following components and features: a. Decision rules engine: used to define and execute decision rules. The rules can be based on previous experience, data model output and market analysis, which helps to automate the decision-making process; b. Real-time data processing: The engine needs to process real-time data streams and make corresponding decisions in real-time situations, which is particularly important for a rapidly changing market; c. Personalized recommendation system: The system involves user interaction. The engine includes a personalized recommendation system that provides customized real estate recommendations and marketing information by analyzing user historical data; d. Feedback and optimization: The engine can collect user feedback and decision results, and improve the accuracy and adaptability of decisions by learning and continuously optimizing the model; e. Risk management: Risk management is a key aspect for the real estate industry. The engine will integrate a risk assessment module to assess the risks of transactions and make corresponding decision-making recommendations; f. Explainability: The engine is able to provide explanations for decisions, enabling users and relevant stakeholders to understand why a decision was made.

[0018] The method of optimizing real estate marketing strategy using AIGC technology includes the following steps: Step 1: Obtain real estate market information, including natural language processing and image recognition; Step 2: Analyze user feedback and market trends; Step 3: Mining historical data and building a user model; Step 4: Combine data analysis and user model to generate optimized marketing strategies; Step 5: Implement marketing strategies through AI decision-making engines.

[0019] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A system that uses AIGC technology to optimize real estate marketing strategies, which mainly includes the following components and features: a. Natural language processing module, which uses natural language processing technology to analyze relevant information on the real estate market, including user comments and news reports, to obtain market trends and user feedback; b. Image recognition module, which uses image recognition technology to analyze real estate pictures and extract key features, including the appearance and internal structure of the house, to provide users with more intuitive information; c. Data mining module, which uses data mining technology to analyze historical transaction data and user housing purchase preference information to predict future market trends and optimize personalized marketing strategies; d. Artificial intelligence decision engine, based on the analysis results of the above modules, uses artificial intelligence algorithms to generate optimized real estate marketing strategies.

2. The system for optimizing real estate marketing strategies using AIGC technology according to claim 1, characterized in that: The natural language processing module has the following components and features: a. Text data collection: This component is responsible for collecting text data related to the real estate market from various data sources, including user reviews, news reports, and social media posts, for analyzing market trends and user feedback; b. Word segmentation and part-of-speech tagging, which splits the text into independent words, helps to understand the grammatical structure and semantic information of the text; c. Sentiment analysis: Through sentiment analysis technology, the sentiment polarity of user comments and other texts is judged to understand the sentiment tendency in the market; d. Entity recognition: The entity recognition component is used to identify key entities in the text, including place names, building names, and house features, for subsequent analysis and decision-making; e. Topic modeling: Through topic modeling technology, the text is analyzed to identify the themes and topics, understand the focus of users, and thus better identify market needs; f. Semantic similarity calculation: This component is used to calculate the semantic similarity between texts, helping to compare the relevance and similarity between different texts, thereby gaining a more comprehensive understanding of market information.

3. The system for optimizing real estate marketing strategies using AIGC technology according to claim 1, characterized in that: The data mining module includes the following components and features: a. Image acquisition: This component is responsible for collecting real estate-related image data from different sources, including photos of the house's exterior and internal structure images; b. Image preprocessing: Before image analysis, the image needs to be preprocessed, including image scaling, rotation, and denoising operations, to ensure the quality and consistency of the input image; c. Feature extraction: This function helps the system understand the visual information of the property by extracting key features in the image, including the building appearance, room layout and decoration style; d. Object detection: The object detection component is used to identify specific objects in the image, including houses, gardens, and garages, which helps to conduct more detailed analysis of the property and provide more accurate information; e. Image classification: Image classification technology classifies real estate images into different categories to better understand the diversity of the real estate market; f. Image segmentation: The image segmentation component is used to divide the image into different regions to extract more detailed information; g. Style recognition: The style recognition function can help the system distinguish the decoration style of the property to better meet the personalized needs of users; h. Visual Similarity Computation: This component is used to calculate the visual similarity between images to identify similar or identical properties, which helps in providing potential options that the user is interested in.

4. The system for optimizing real estate marketing strategies using AIGC technology according to claim 1, characterized in that: The data mining module includes the following components and features: a. Data collection: This component is responsible for collecting historical data of the real estate market from different data sources, including transaction records, user behavior data, and market trend data. These data will be used to mine potential associations and patterns; b. Data cleaning: Before data analysis, data needs to be cleaned, including processing missing values, outliers, and duplicate values ​​to ensure data quality and consistency; c. Feature Engineering: Feature engineering involves transforming and extracting data to create new features and enhance the expressiveness of data; d. Association rule mining: By using association rule mining technology, we can analyze the relationship between different attributes, including the relationship between house price and geographical location, house size, to reveal hidden market trends; e. Cluster analysis: Cluster analysis is used to group data into different clusters to help identify different segments in the market; f. Classification and prediction models: Using machine learning algorithms to build classification and prediction models, including predicting housing price trends and user home buying behavior, helps the system predict future market changes more accurately; g. Time series analysis: Time series analysis is used to process time-related data, helping to identify seasonal and cyclical changes in the real estate market and providing support for timing-sensitive marketing strategies; h. User model establishment: By mining user behavior data, a user model is constructed to understand the user's housing purchase preferences, behavioral habits and other information, providing a basis for personalized marketing.

5. The system for optimizing real estate marketing strategies using AIGC technology according to claim 1, characterized in that: The artificial intelligence decision engine includes the following components and features: a. Decision rules engine: used to define and execute decision rules. The rules can be based on multiple factors such as previous experience, data model output and market analysis, which helps to automate the decision-making process; b. Real-time data processing: The engine needs to process real-time data streams and make corresponding decisions in real-time situations, which is particularly important for a rapidly changing market; c. Personalized recommendation system: The system involves user interaction. The engine includes a personalized recommendation system that provides customized real estate recommendations and marketing information by analyzing user historical data; d. Feedback and optimization: The engine can collect user feedback and decision results, and improve the accuracy and adaptability of decisions by learning and continuously optimizing the model; e. Risk management: Risk management is a key aspect for the real estate industry. The engine will integrate a risk assessment module to assess the risks of transactions and make corresponding decision-making recommendations; f. Explainability: The engine is able to provide explanations for decisions, enabling users and relevant stakeholders to understand why a decision was made.

6. The method of optimizing real estate marketing strategy using AIGC technology includes the following steps: Step 1: Obtain real estate market information, including natural language processing and image recognition; Step 2: Analyze user feedback and market trends; Step 3: Mining historical data and building a user model; Step 4: Combine data analysis and user model to generate optimized marketing strategies; Step 5: Implement marketing strategies through AI decision-making engines.

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