Real estate information service system based on user data analysis

Abstract

The invention relates to the technical field of real estate information service, and provides a real estate information service system based on user data analysis, which comprises a user behavior depth perception module; a multi-dimensional user data fusion and mining module; a dynamic real estate information base; an accurate matching and intelligent recommendation engine; an emotion interaction feedback module; the system comprises an immersive visual display platform, a risk early warning and coping module and a cross-platform interaction module. According to the real estate information service system based on user data analysis, more accurate, personalized and immersive real estate information service is provided for the user through advanced technical means and innovative functional modules, meanwhile, the transaction risk is reduced, the timeliness and accuracy of real estate information are improved, cross-platform convenient use is achieved, and the user experience is improved. The method has remarkable beneficial effects, various requirements of the user in the real estate transaction process can be met, and the efficiency and quality of real estate transaction are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of real estate information services, and particularly relates to a real estate information service system based on user data analysis. Background Art

[0002] With the continuous development of the real estate market, people's demand for real estate information has become increasingly diversified and personalized. In traditional real estate information services, there are problems such as inaccurate, untimely information, and lack of personalized recommendations, resulting in users often spending a lot of time and effort in searching for real estate and it is difficult to find housing sources that meet their own needs. At the same time, there are also certain risks in the real estate transaction process, such as price fluctuations, transaction disputes, etc., bringing potential losses to users.

[0003] To solve these problems, in recent years, some real estate information service platforms have begun to try to use data analysis technology to improve service quality. However, the existing real estate information service systems still have deficiencies in the collection and analysis of user data, and are unable to deeply understand the real needs and preferences of users, resulting in a low matching degree between the recommended real estate and user needs. In addition, the existing real estate information display methods are also relatively single, making it difficult to bring an immersive experience to users and affecting the decision-making efficiency and accuracy of users.

[0004] In addition, in terms of preventing real estate transaction risks, the existing real estate information service systems often lack effective early warning mechanisms and coping strategies, and are unable to timely remind users to pay attention to potential risks, thus increasing the transaction risks and losses of users.

[0005] In summary, the existing real estate information service systems have many problems in aspects such as user demand matching, information display, and risk prevention. There is a need for a more advanced, intelligent, and personalized real estate information service system to meet the needs of users, improve the efficiency and quality of real estate transactions, and reduce transaction risks. The present invention is precisely proposed based on such a background, aiming to provide more accurate, personalized, and immersive real estate information services for users through user data analysis and advanced technical means, while effectively reducing the risks in real estate transactions.

[0006] Therefore, the present solution particularly proposes a real estate information service system based on user data analysis to solve the above problems. Summary of the Invention

[0007] To overcome the defects of the prior art, the purpose of the present invention is to provide a real estate information service system based on user data analysis.

[0008] To achieve the above object, the technical solution of the present invention is realized as follows: A real estate information service system based on user data analysis includes the following main parts:

[0009] The user behavior in-depth perception module uses behavior capture technology based on computer vision to collect real-time image data of users in different scenarios through high-definition cameras. It uses image recognition algorithms and deep learning models to accurately identify subtle behavior manifestations such as the eye movement trajectories and limb movement patterns of users, and converts them into analyzable behavior data. At the same time, it combines environment perception technology based on sensor networks, such as pressure sensors and temperature sensors, to comprehensively obtain the behavior information of users' interactions with the environment, and these information are classified and stored and updated in real time.

[0010] The multi-dimensional user data fusion and mining module uses data fusion algorithms to fuse the behavior data obtained by the user behavior in-depth perception module with the basic information of users (including age, gender, occupation, etc.), historical transaction data (such as housing purchase records, rental records, etc.), network social data (such as interaction information on real estate-related platforms), etc. Among them, algorithms based on association rule mining are used to deeply mine the potential associations and patterns between different data to construct a comprehensive demand portrait of users.

[0011] The dynamic real estate information database uses real-time data collection technology and distributed storage architecture to update the detailed information of housing sources in real time, including the real-time status of housing sources (such as whether they are for sale, sold, etc.), the dynamic changes in prices (through price monitoring algorithms and time series analysis), and the real-time evolution information of the surrounding environment (such as the progress of infrastructure construction), and uses semantic association technology to intelligently classify and associate housing source information.

[0012] The precise matching and intelligent recommendation engine, based on the user demand portrait obtained by the multi-dimensional user data fusion and mining module and the dynamic real estate information database, uses a hybrid recommendation strategy that combines collaborative filtering algorithms and content-based recommendation algorithms. By calculating the similarity between users and the matching degree between housing source features and user needs, highly suitable real estate recommendations are screened for users. At the same time, a reinforcement learning mechanism is used to adjust the recommendation results in real time according to users' feedback and behaviors.

[0013] The emotional interaction feedback module uses natural language processing technology and emotional analysis models to perform sentiment tendency analysis on text information such as users' evaluations and consultations. For example, word vector models and sentiment lexicons are used for sentiment classification and intensity evaluation, and the emotional feedback of users is accurately transmitted to the precise matching and intelligent recommendation engine to optimize subsequent recommendations.

[0014] The immersive visualization display platform uses virtual reality technology and three-dimensional modeling technology to convert the information of the recommended real estate into a realistic three-dimensional scene, and allows users to immerse themselves in experiencing the layout, space, etc. of the real estate through a stereoscopic display device. At the same time, it combines panoramic image technology to provide a full-range display of the exterior and surrounding environment of the real estate.

[0015] The risk warning and response module, based on a risk assessment model, comprehensively considers user credit data, real estate market fluctuation data, policy and regulation change data, etc., conducts risk prediction and assessment through algorithms such as logistic regression or decision tree algorithms, issues early warnings for possible transaction risks, and provides specific response suggestions according to the preset response strategy library.

[0016] Preferably, the image recognition algorithm in the user behavior in-depth perception module includes key steps such as feature extraction and target detection, and the deep learning model is trained and optimized using a convolutional neural network architecture.

[0017] Preferably, the association rule mining algorithm in the multi-dimensional user data fusion and mining module can set different support and confidence thresholds to screen valuable association rules.

[0018] Preferably, the price monitoring algorithm in the dynamic real estate information database can capture the trends and abnormal changes in price fluctuations in real time.

[0019] Preferably, the collaborative filtering algorithm in the precise matching and intelligent recommendation engine realizes recommendations through the calculation of user similarity, and the content-based recommendation algorithm is based on the matching degree between housing source features and user needs.

[0020] Preferably, the word vector model in the emotional interaction feedback module can be trained using technologies such as Word2Vec.

[0021] Preferably, the virtual reality technology in the immersive visualization display platform includes functional components such as position tracking and interaction control.

[0022] Preferably, the logistic regression algorithm or decision tree algorithm in the risk warning and response module determines the weights of risk factors and decision rules through training.

[0023] Preferably, it further includes a cross-platform interaction module, which uses a unified data interface and communication protocol to realize data interaction and synchronization between the system and various different terminal devices (such as mobile phones, computers, tablets, etc.), ensuring convenient use by users in different scenarios. This module uses data compression and encryption technologies to ensure the efficiency and security of data transmission.

[0024] The beneficial effects of the present invention are reflected in:

[0025] Provide accurate real estate recommendations:

[0026] Through multi-dimensional user data fusion and mining, deeply understand user needs and preferences, and combine the collaborative filtering algorithm and the content-based recommendation algorithm to provide real estate recommendations that highly match the user's needs, improving the probability for users to find their ideal properties.

[0027] The precise matching and intelligent recommendation engine can dynamically adjust the recommendation strategy according to the user's real-time feedback and behavioral data, further improving the accuracy and personalization of recommendations.

[0028] Enhance the user experience:

[0029] The user behavior in-depth perception module can capture the user's subtle behavioral manifestations, providing more comprehensive user information for the system, thus better meeting the user's needs.

[0030] The immersive visualization display platform uses virtual reality and panoramic image technologies, enabling users to feel the actual situation of the property as if they were on the spot, providing a more intuitive and real property viewing experience, and reducing the time and cost of on-site property viewing for users.

[0031] The emotional interaction feedback module can timely understand the user's emotional tendency and feedback on the recommended properties, enabling the system to better interact with the user and enhancing the user's sense of participation and satisfaction.

[0032] Reduce the risks of property transactions:

[0033] The risk warning and response module comprehensively considers various factors, gives early warnings of possible transaction risks, and provides targeted response suggestions to help users reduce transaction risks and ensure transaction security.

[0034] Improve the timeliness and accuracy of property information:

[0035] The dynamic property information database updates the detailed information of housing sources in real time, including price fluctuations, changes in the surrounding environment, etc., ensuring that the property information obtained by users is the latest and most accurate, providing a reliable basis for users' decision-making.

[0036] Enable convenient use across platforms:

[0037] The cross-platform interaction module enables the system to seamlessly interact and synchronize data with various terminal devices, facilitating users to access and use the property information service anytime and anywhere in different scenarios, improving the usability and convenience of the system.

[0038] In summary, this property information service system based on user data analysis provides users with more precise, personalized, and immersive property information services through advanced technical means and innovative functional modules. At the same time, it reduces transaction risks, improves the timeliness and accuracy of property information, and realizes convenient use across platforms, with significant beneficial effects, capable of meeting various needs of users in the property transaction process and enhancing the efficiency and quality of property transactions. Brief Description of the Drawings

[0039] In the drawings:

[0040] Figure 1 This is a schematic diagram of the system structure of the present invention. Specific implementation manners

[0041] The present invention will be further described in detail below with reference to the drawings and embodiments. Obviously, the described embodiments are only a part of the embodiments of the invention, rather than all of the embodiments. Without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the invention without making creative efforts belong to the scope of protection of the invention.

[0042] In addition, "a plurality of" means two or more. In addition, the technical solutions between various embodiments can be combined with each other, but it must be based on the ability of those of ordinary skill in the art to implement. When the combination of technical solutions conflicts with each other or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the invention.

[0043] Please refer to the attached specification Figure 1 , the present invention provides a real estate information service system based on user data analysis:

[0044] Implementation of the user behavior in-depth perception module:

[0045] Set high-definition cameras in places where users may contact real estate information, such as real estate display venues and online platforms, to ensure full coverage. The cameras are connected to the image processing server at the back end, and image recognition algorithms and deep learning models are run.

[0046] For the environmental perception technology based on the sensor network, devices such as pressure sensors and temperature sensors are arranged in relevant places. The data collected in real time by these sensors is transmitted to the data processing center for analysis and processing to obtain the behavior information of the user's interaction with the environment.

[0047] In the feature extraction step of the image recognition algorithm, methods such as SIFT (Scale-Invariant Feature Transform) or HOG (Histogram of Oriented Gradients) can be used to extract features from the user's image. For object detection, deep learning models such as YOLO (You Only Look Once) or Faster R-CNN (Faster Region-based Convolutional Neural Network) can be used to accurately identify the user's eye movement trajectory and limb movement pattern.

[0048] The deep learning model adopts a convolutional neural network architecture, such as VGGNet (Visual Geometry Group Network) or ResNet (Residual Network), and is trained with a large amount of labeled data to improve the recognition accuracy of user behavior.

[0049] Implementation of the multi-dimensional user data fusion and mining module:

[0050] Collect data from multiple data sources, such as the user behavior deep perception module, the user registration information system, the real estate transaction record database, and the user social platform. This data includes user behavior data, basic information, historical transaction data, and online social data, etc.

[0051] Use data cleaning and preprocessing techniques to clean and standardize the collected data, remove noise and outliers, and convert the data into a unified format.

[0052] Apply algorithms based on association rule mining, such as the Apriori algorithm or the FP-Growth (Frequent Pattern Growth) algorithm, set appropriate support and confidence thresholds, and mine the potential associations and patterns between different data. For example, it is found that users aged between 30 and 40 and with a white-collar occupation have a higher attention to small-sized properties near the commercial area.

[0053] Based on the mined associations and patterns, construct a comprehensive demand profile of users, including information such as the basic characteristics of users, preferred property types, geographical locations, price ranges, etc.

[0054] Implementation of the dynamic real estate information database:

[0055] Establish data interfaces with real estate agencies, developers, government real estate management departments, etc., to obtain detailed information about housing sources in real time, including the status of housing sources (for sale, sold, reserved, etc.), price changes, basic housing information (area, house type, decoration situation, etc.), and surrounding environment information (school, hospital, shopping mall and other supporting facilities).

[0056] Adopt price monitoring algorithms, such as ARIMA (AutoRegressive Integrated Moving Average) or GARCH (Generalized Autoregressive Conditional Heteroskedasticity), to monitor and analyze the dynamic changes of housing prices, and timely capture the price fluctuation trends and abnormal changes.

[0057] Utilize a distributed storage architecture, such as the Hadoop Distributed File System or Cassandra database, to store a vast amount of real estate information, ensuring high data reliability and scalability.

[0058] Through semantic association techniques, such as WordNet (a lexical semantic database) or knowledge graphs, perform intelligent classification and association of housing source information to facilitate user query and screening.

[0059] Implementation of the precise matching and intelligent recommendation engine:

[0060] Input the user demand portrait constructed by the multi-dimensional user data fusion and mining module and the housing source information in the dynamic real estate information database into the precise matching and intelligent recommendation engine.

[0061] Apply collaborative filtering algorithms to calculate the similarity between users, find other users similar to the target user, and make recommendations for the target user based on the preferences and behaviors of these similar users. For example, if user A is similar to user B in terms of age, occupation, housing purchase budget, etc., and user B shows interest in certain properties, then these properties can be recommended to user A.

[0062] Meanwhile, adopt content-based recommendation algorithms to analyze the matching degree between the characteristics of housing sources (such as geographical location, house area, price, etc.) and the user demand portrait, and recommend properties that meet the user's needs to the user.

[0063] Utilize a reinforcement learning mechanism to continuously adjust the recommendation strategy based on user feedback (such as click, browsing time, collection, consultation, etc.) and real-time behavior data to improve the accuracy and personalization of recommendations. For example, if the user shows a high level of interest in the recommended property, the system will further recommend similar properties; if the user is not interested in the recommended property, the system will adjust the recommendation strategy and try to recommend other types of properties.

[0064] Implementation of the emotional interaction feedback module:

[0065] On the user interface of the real estate information service system, set up function entrances such as user evaluation and consultation to encourage users to express their views and opinions on the recommended properties.

[0066] Apply natural language processing techniques to perform preprocessing operations on the user's text information, such as word segmentation, part-of-speech tagging, named entity recognition, etc.

[0067] Use sentiment analysis models, such as dictionary-based methods or machine learning-based methods (such as support vector machines, naive Bayes, etc.), to perform sentiment tendency analysis on the preprocessed text. For example, convert the text into vector form through a word vector model (such as Word2Vec), and then input it into the sentiment classifier for sentiment classification and intensity evaluation.

[0068] Timely transmit the user's emotional feedback information to the precise matching and intelligent recommendation engine so as to adjust the recommendation results according to the user's emotional needs.

[0069] Implementation of the immersive visualization display platform:

[0070] Use virtual reality technology and 3D modeling technology to model the internal structure, layout and appearance of the property, creating a realistic 3D scene. During the modeling process, 3D modeling software such as 3ds Max, Maya, etc., as well as virtual reality development engines such as Unity or Unreal Engine can be used.

[0071] Combine panoramic image technology, use a professional panoramic camera to shoot the surrounding environment and appearance of the property, generate panoramic images, and integrate them into the virtual reality scene to provide users with a full-range display of the property's appearance and surrounding environment.

[0072] Users enter the immersive visualization display platform by wearing virtual reality devices (such as HTC Vive, Oculus Rift, etc.) or using mobile devices that support virtual reality functions (such as mobile phones, tablets, etc.), and experience the actual situation of the property personally. During the display process, provide interactive functions, such as users can walk freely in the virtual scene, view house details, switch different decoration styles, etc.

[0073] Implementation of the risk warning and response module:

[0074] Collect relevant information such as user credit data (such as credit reports, repayment records, etc.), real estate market fluctuation data (such as housing price indices, trading volumes, etc.), policy and regulatory change data (such as purchase restriction policies, tax policies, etc.).

[0075] Use risk assessment models, such as logistic regression algorithms or decision tree algorithms, to analyze and process these data. During the model training process, determine the weights and decision rules of each risk factor. For example, factors such as a user's low credit score, a downward trend in the real estate market, and strict policy and regulatory restrictions on house purchases may increase transaction risks.

[0076] According to the risk assessment results, give early warnings of possible transaction risks. The warning information can be notified to users and relevant staff in a timely manner through system messages, emails, text messages, etc.

[0077] At the same time, extract corresponding response suggestions from the preset response strategy library, such as suggesting that users increase the down payment ratio, choose a more stable loan method, pay attention to policy and regulatory changes, etc., to reduce transaction risks.

[0078] Implementation of the cross-platform interaction module:

[0079] Define a unified data interface and communication protocol to ensure that the real estate information service system can perform data interaction and synchronization with a variety of different terminal devices (such as mobile phones, computers, tablets, etc.).

[0080] Develop corresponding interface services at the system backend to handle requests from different terminal devices and transmit data in a unified format.

[0081] Adopt data compression technologies such as Gzip or Deflate to compress the transmitted data, reduce the amount of data transmission, and improve transmission efficiency.

[0082] Apply data encryption technologies such as SSL (Secure Sockets Layer) or TLS (Transport Layer Security) to encrypt the transmitted data and ensure the security and privacy of the data.

[0083] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

[0084] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention. Any reference signs in the claims should not be regarded as limiting the claims involved.

Claims

1. A real estate information service system based on user data analysis, characterized in that, It includes the following main parts: The user behavior in-depth perception module adopts behavior capture technology based on computer vision. It uses high-definition cameras to collect image data of users in different scenarios in real time. By using image recognition algorithms and deep learning models, it accurately identifies subtle behavior manifestations such as the eye movement trajectories and limb movement patterns of users, and converts them into analyzable behavior data. At the same time, it combines environmental perception technology based on sensor networks, such as pressure sensors and temperature sensors, to comprehensively obtain the behavior information of users' interaction with the environment. These information are classified and stored and updated in real time. The multi-dimensional user data fusion and mining module uses data fusion algorithms to fuse the behavior data obtained by the user behavior in-depth perception module with the basic information of users (including age, gender, occupation, etc.), historical transaction data (such as housing purchase records, rental records, etc.), online social data (such as interaction information on real estate-related platforms), etc. Among them, algorithms based on association rule mining are used to deeply mine the potential associations and patterns between different data to construct a comprehensive demand portrait of users. The dynamic real estate information database uses real-time data collection technology and distributed storage architecture to update the detailed information of housing sources in real time, including the real-time status of housing sources (such as whether they are for sale, sold, etc.), the dynamic changes in prices (through price monitoring algorithms and time series analysis), the real-time evolution information of the surrounding environment (such as the progress of infrastructure construction, etc.), and uses semantic association technology to intelligently classify and associate housing source information. The precise matching and intelligent recommendation engine, based on the user demand portrait obtained by the multi-dimensional user data fusion and mining module and the dynamic real estate information database, uses a hybrid recommendation strategy that combines collaborative filtering algorithms and content-based recommendation algorithms. By calculating the similarity between users and the matching degree between housing source features and user needs, it screens out highly suitable real estate recommendations for users. At the same time, it uses a reinforcement learning mechanism to adjust the recommendation results in real time according to users' feedback and behaviors. The emotional interaction feedback module, with the help of natural language processing technology and emotional analysis models, conducts emotional tendency analysis on text information such as users' evaluations and consultations. For example, it uses word vector models and emotional lexicons for emotional classification and intensity evaluation, and accurately transmits users' emotional feedback to the precise matching and intelligent recommendation engine to optimize subsequent recommendations. The immersive visualization display platform uses virtual reality technology and three-dimensional modeling technology to transform the information of the recommended real estate into a realistic three-dimensional scene, allowing users to immerse themselves in experiencing the layout, space, etc. of the real estate through a stereoscopic display device. At the same time, it combines panoramic image technology to provide a full-range display of the appearance and surrounding environment of the real estate. The risk warning and response module, based on a risk assessment model, comprehensively considers user credit data, real estate market fluctuation data, policy and regulation change data, etc., and conducts risk prediction and assessment through algorithms such as logistic regression algorithms or decision tree algorithms. It issues early warnings for possible transaction risks and provides specific response suggestions according to the preset response strategy library.

2. The real estate information service system based on user data analysis according to claim 1, characterized in that, The image recognition algorithm in the user behavior in-depth perception module includes key steps such as feature extraction and object detection. The deep learning model is trained and optimized using a convolutional neural network architecture.

3. The real estate information service system based on user data analysis according to claim 1, characterized in that, The association rule mining algorithm in the multi-dimensional user data fusion and mining module can set different support and confidence thresholds to screen valuable association rules.

4. A real estate information service system based on user data analysis according to claim 1, characterized in that, The price monitoring algorithm in the dynamic real estate information database can capture the trends and abnormal changes in price fluctuations in real time.

5. The real estate information service system based on user data analysis according to claim 1, characterized in that, The collaborative filtering algorithm in the precise matching and intelligent recommendation engine realizes recommendations through similarity calculations among users, while the content-based recommendation algorithm is based on the matching degree between housing source features and user needs.

6. A real estate information service system based on user data analysis according to claim 1, characterized in that, The word vector model in the emotional interaction feedback module can be trained using technologies such as Word2Vec.

7. An information service system for real estate based on user data analysis according to claim 1, characterized in that The virtual reality technology in the immersive visualization display platform includes functional components such as position tracking and interaction control.

8. An information service system for real estate based on user data analysis according to claim 1, characterized in that, The logistic regression algorithm or decision tree algorithm in the risk warning and response module determines the weights of risk factors and decision rules through training.

9. The real estate information service system based on user data analysis according to claim 1, characterized in that It also includes a cross-platform interaction module that uses a unified data interface and communication protocol to achieve data interaction and synchronization between the system and various different terminal devices (such as mobile phones, computers, tablets, etc.), ensuring convenient use by users in different scenarios. This module uses data compression and encryption technologies to ensure the efficiency and security of data transmission.

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