A large model-based intelligent real estate management system development method

Abstract

The application relates to a large model-based intelligent real estate management system development method. The method comprises the following steps: performing directional calling on a database based on the unique identifier of a system login of an operator to obtain a real estate management system operation table; extracting full user information of the operator by using login identifier primary key information; obtaining a preference feature set of the operator and a sorting result of a function demand, converting text information into a high-dimensional semantic vector through vectorization processing; obtaining a matching degree score and performing standardized quantitative assignment; generating an AI function list of the operator by using front-end dynamic rendering; correlating and mapping search business vocabulary, and simultaneously configuring semantic labels and search words; performing mode search on the search words by using a real estate large model to obtain real estate description data; and performing double-area function-description linkage presentation by using front-end and back-end data synchronization interfaces; and the large model-based intelligent real estate management system development is realized.

Description

Technical Field

[0001] This invention belongs to the field of large-scale model application technology and real estate information management technology, specifically involving a development method for an intelligent real estate management system based on a large-scale model. Background Technology

[0002] The current intelligent property management system still has the following areas for improvement: Existing information-based real estate management systems are characterized by their large functional system, with hundreds or even thousands of integrated functional modules. In the traditional design model, all functions are displayed in a fixed form of multi-level menus. Operators need to expand the menu level by level to locate and call the required functions, resulting in a cumbersome operation process and low interaction efficiency.

[0003] For system operators, especially new hires unfamiliar with the system's functional architecture, finding the corresponding functions quickly and accurately from complex multi-level menus is challenging, significantly increasing the operational threshold for business transactions. To address this issue, existing systems typically require detailed operation manuals of hundreds of pages. Even so, operators still need months or even years of practical experience to master all aspects of the system's usage. Summary of the Invention

[0004] To address the aforementioned problems in the existing technology, this invention provides a development method for an intelligent real estate management system based on a large model; The objective of this invention can be achieved through the following technical solutions: S1: Based on the unique identifier of the operator's system login, the database is retrieved to obtain the real estate management system operation table; the full user information of the operator is extracted using the login identifier primary key information; the corresponding scenarios of historical operation records are decomposed by frequency statistics to generate the operator's preference feature set, and the ranking results of potential functional requirements are obtained at the same time. S2: Vectorize the function and operation scenario tags in the ranking results of the potential functional requirements, and convert the text information into high-dimensional semantic vectors through the semantic model encoding module; calculate the cosine similarity between the high-dimensional semantic vectors to obtain the matching score, and simultaneously perform standardized quantization assignment. The specific process of performing standardized quantization assignment includes: Based on the matching score, global normalization is performed to obtain a standardized matching score. Utilize the priority configuration rules of functional operations to perform a dimensional allocation of priority coefficients; The fractal dimension matching result and the standardized matching degree score are calculated to obtain the quantitative assignment result; Based on the quantified assignment results, a secondary ranking of the functional scores is performed to generate a quantified functional sequence; the AI ​​function list of the operator is generated using dynamic rendering on the front end. The specific process of generating the AI ​​function list of the operator includes: An initial AI function list is constructed by arranging the function sequences in an initial list order. Based on the function configuration and prompt configuration in the full user information, the list structure is optimized and adapted. By leveraging the front-end and back-end interaction of the Dify large model development platform, a list visualization rendering is implemented to generate an AI function list for operators. S3: Based on the AI ​​function list, the search business terms are associated and mapped, and semantic tags and search terms are configured; the search terms are used to perform pattern search through the real estate big data model to obtain real estate description data; using the front-end and back-end data synchronization interface, dual-region function-description linkage presentation is performed. The specific process of dual-region function-description linkage presentation includes: Utilize the front-end and back-end data synchronization interface of the Dify platform to distribute the function identifiers to the AI ​​function list area; Based on the function identifier matching results in the AI ​​function list area, function indexing and positioning are performed. Based on the page rendering rules of the main interface display area, the function explanation content is arranged and displayed in a standardized format. The function clicks in the list area and the content in the display area are switched in real time to achieve a linked presentation of the function and description in both areas.

[0005] Specifically, the process of obtaining the property management system operation table includes: Database retrieval is anchored based on the unique identifier of the operator's system login, and the identifier field of the property management operator is anchored and matched to obtain the operation table of the property management system; Based on the field attributes of the operation table, data is layered, screened, and deduplicated to obtain a standardized real estate management system operation table.

[0006] Specifically, the process of extracting the full user information of the operators includes: Based on the primary key information of the login identifier in the standardized real estate management system operation table, a user information expansion and retrieval link is constructed. Tuowei retrieves multi-source data from the user information database and integrates four categories of information—identity, permissions, job position, and function—to obtain multi-dimensional user information. The user information is indexed by dimension to generate full user information for the operators.

[0007] Specifically, the process of dimensionally decomposing the corresponding scenarios of historical operation records includes: Based on the permission boundaries and job attributes of the full user information, a scenario decomposition and expansion mechanism is constructed. Based on four sub-dimensions—business category, operation time domain, functional module, and operation frequency—the historical operation records of the standardized real estate management system operation table are assigned dimension labels. Clustering and merging of operation records using dimension labels yields the clustering results for the corresponding dimension operation records.

[0008] Specifically, the process of obtaining the ranking results of potential functional requirements includes: The frequency of functional operations on the clustering results is quantified in all dimensions to obtain the quantified value of the operation frequency; Based on the job function requirements of operators using full user information, assign corresponding scenario-based weight coefficients to the relevant functions. The quantified value and the weighting coefficient are used to calculate the demand fit score; Based on the required fit score, the functional requirements are ranked, and the potential functional requirements of the operators are ranked.

[0009] Specifically, the vectorization process includes: Based on the sorting results of potential functional requirements and the operation scenario labels, functional-scenario text clustering data is obtained; Extract the feature information of the functional-scene text fusion data to generate a text feature vector set; By utilizing a locally deployed large model encoding module, discrete text feature vectors are transformed into high-dimensional semantic vectors.

[0010] Specifically, the process of obtaining the matching score includes: Based on the transformation results of high-dimensional semantic vectors, the cosine similarity between vectors is calculated to obtain the cosine similarity of semantic vectors, and at the same time, the initial similarity quantification score is obtained. Based on the demand fit score, the initial similarity quantification score is weighted and assigned to obtain a weighted similarity score. Functional filtering is performed using preset thresholds to obtain matching scores for the corresponding functions.

[0011] Specifically, the process of performing the association mapping includes: By building a function-keyword cluster library, the business keywords for the list function can be obtained; Semantic topology calculation is performed based on the business lexicon of the real estate management system. The business keywords are semantically expanded and associated with general search business terms; A retrieval index table is established based on the semantic topology association results, and an association mapping table of search business terms, semantic tags, and functional identifiers is obtained.

[0012] Specifically, the process of obtaining the property description data includes: Based on the semantic tags and search terms in the association mapping table, the search terms are input into the multi-modal search system of the local large model + Dify platform to obtain multi-modal search results; The multi-modal retrieval results are matched and synthesized based on the local knowledge base, and the dimensions of functional interpretation, business process and operation specification are matched and synthesized into structured text data. Data calibration is performed on the synthesized structured text data to generate calibrated clustered property description data.

[0013] The beneficial effects of this invention are as follows: By constructing a function-keyword cluster library and a retrieval index table, this invention semantically expands the association between search business terms and system functions, and combines a local large model with the multi-modal retrieval system of the Dify platform to achieve pattern search, supporting precise search, fuzzy search, and semantic search. Simultaneously, through a front-end and back-end data synchronization interface, it achieves a linked presentation of the function list area and the function-description dual-area display area in the main interface, solving the problems of single-dimensional function retrieval and low matching degree in traditional systems, and lowering the operational threshold for function retrieval.

[0014] The system generates and optimizes the AI ​​function list through standardized processes such as front-end dynamic rendering, global normalization mapping, and priority coefficient assignment, while also combining local knowledge base and large model capabilities. The system can solidify functions and provide intelligent prompts based on user-defined settings, eliminating the need for complex operation manuals. This significantly reduces personnel training and maintenance costs for the real estate management system, and enhances its versatility.

[0015] Replacing the traditional multi-level menu with an AI function list area and an AI dialogue area, and combining the natural language understanding and network search capabilities of a large model, this system not only enables intelligent retrieval of internal functions and business consultation but also meets the external information retrieval needs of operators, creating an integrated and intelligent system interface. This reduces unnecessary operations by operators, improves the smoothness of business processing, and, relying on personalized function matching and intelligent prompts, makes the system more tailored to the actual work needs of operators. Attached Figure Description

[0016] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0017] Figure 1 This is a flowchart illustrating a development method for an intelligent real estate management system based on a large model, according to the present invention. Figure 2 This is a flowchart illustrating the pattern retrieval and dual-region linkage presentation in this invention. Detailed Implementation

[0018] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0019] Please see Figure 1-2 A development method for an intelligent real estate management system based on a large model, comprising: S1: Based on the unique identifier of the operator's system login, the database is retrieved to obtain the real estate management system operation table; the full user information of the operator is extracted using the login identifier primary key information; the corresponding scenarios of historical operation records are decomposed by frequency statistics to generate the operator's preference feature set, and the ranking results of potential functional requirements are obtained at the same time. S2: Vectorize the function and operation scenario tags in the ranking results of the potential functional requirements, and convert the text information into high-dimensional semantic vectors through the semantic model encoding module; calculate the cosine similarity between the high-dimensional semantic vectors to obtain the matching score, and simultaneously perform standardized quantization assignment. The specific process of performing standardized quantization assignment includes: Based on the matching score, global normalization is performed to obtain a standardized matching score. Utilize the priority configuration rules of functional operations to perform a dimensional allocation of priority coefficients; The fractal dimension matching result and the standardized matching degree score are calculated to obtain the quantitative assignment result; Based on the quantified assignment results, a secondary ranking of the functional scores is performed to generate a quantified functional sequence; the AI ​​function list of the operator is generated using dynamic rendering on the front end. The specific process of generating the AI ​​function list of the operator includes: An initial AI function list is constructed by arranging the function sequences in an initial list order. Based on the function configuration and prompt configuration in the full user information, the list structure is optimized and adapted. By leveraging the front-end and back-end interaction of the Dify large model development platform, a list visualization rendering is implemented to generate an AI function list for operators. S3: Based on the AI ​​function list, the search business terms are associated and mapped, and semantic tags and search terms are configured; the search terms are used to perform pattern search through the real estate big data model to obtain real estate description data; using the front-end and back-end data synchronization interface, dual-region function-description linkage presentation is performed. The specific process of dual-region function-description linkage presentation includes: Utilize the front-end and back-end data synchronization interface of the Dify platform to distribute the function identifiers to the AI ​​function list area; Based on the function identifier matching results in the AI ​​function list area, function indexing and positioning are performed. Based on the page rendering rules of the main interface display area, the function explanation content is arranged and displayed in a standardized format. The function clicks in the list area and the content in the display area are switched in real time to achieve a linked presentation of the function and description in both areas.

[0020] As a preferred technical solution of the present invention, the specific process of obtaining the real estate management system operation table includes: Database retrieval is anchored based on the unique identifier of the operator's system login, and the identifier field of the property management operator is anchored and matched to obtain the operation table of the property management system; Based on the field attributes of the operation table, data is layered, screened, and deduplicated to obtain a standardized real estate management system operation table.

[0021] In this embodiment, an operator in a department of the real estate registration center uses their unique employee ID as the login identifier. After completing identity verification, they enter the intelligent real estate management system. The system backend immediately uses this employee ID as the search anchor point to accurately match the identifier field in the core relational database of the real estate management system, and retrieves all the original historical operation records of this operator to form an initial real estate management system operation table. This initial operation table contains eight core fields: operation function name, business module, operation execution time, operation result status, operation terminal type, operation business case number, operator position, and operation remarks, covering all categories of business operations such as initial real estate registration, transfer registration, mortgage registration, and application progress inquiry.

[0022] The system automatically activates a hierarchical deduplication model for government data, classifying raw records into three categories based on the field attributes of the operation table: core business operations, basic system operations, and invalid test operations. Core business operations refer to operations directly related to government processing, such as registration information entry, uploading of business materials, and business submission for review. Basic system operations refer to system-level operations without actual business significance, such as system login, logout, and password modification. Invalid test operations refer to operations with missing field information, duplicate submissions, or operations performed under test conditions, such as records that only display the operation time without a function name, or duplicate entries of the same business transaction number. The model uses a combination of rule-based filtering and manual verification to complete data deduplication, completion, and standardization, resulting in a standardized real estate management system operation table.

[0023] Specifically, the process of extracting the full user information of the operators includes: Based on the primary key information of the login identifier in the standardized real estate management system operation table, a user information expansion and retrieval link is constructed. Tuowei retrieves multi-source data from the user information database and integrates four categories of information—identity, permissions, job position, and function—to obtain multi-dimensional user information. The user information is indexed by dimension to generate full user information for the operators.

[0024] In this embodiment, the employee ID corresponding to the login identifier in the standardized real estate management system operation table is used as the primary key information. The system backend uses distributed data retrieval technology to build a three-level expansion retrieval link from the operation database to the user basic information database, the job permission information database, and the system function configuration database, realizing the linkage retrieval and synchronous integration of user data across databases and multiple sources: The user basic information database extracts the core identity information of the operator, including seven items: name, gender, department, job level, date of employment, office seat, and contact number; the job permission information database extracts the system operation permission and business processing permission information of the operator, including five items: system operation permission level, scope of accessible business modules, business approval permission, data viewing permission, and operation record query permission; the system function configuration database extracts the job and function association information of the operator, including six items: job name, job responsibilities, daily business scope, system operable functions, customizable configuration functions, and system restricted functions.

[0025] The retrieved 18 core information items across four categories—identity, permissions, position, and function—are input into the government data dimension integration and indexing model. The model integrates and categorizes this information according to four preset dimensions, adding unique dimension and business attribute tags to each item. For example: Identity - Department: a specific department; Permissions - Accessible Modules: all modules of real estate registration; Position - Core Responsibilities: first-time / transfer registration of real estate; Function - Restricted Functions: business approvals. Batch data export enables multi-dimensional indexing of information and generates complete user information for operators. Specifically, the process of dimensionally decomposing the corresponding scenarios of historical operation records includes: Based on the permission boundaries and job attributes of the full user information, a scenario decomposition and expansion mechanism is constructed. Based on four sub-dimensions—business category, operation time domain, functional module, and operation frequency—the historical operation records of the standardized real estate management system operation table are assigned dimension labels. Clustering and merging of operation records using dimension labels yields the clustering results for the corresponding dimension operation records.

[0026] In this embodiment, based on the secondary operation permission boundaries defined in the full user information of operators and the core business attributes of the real estate registration handling position, the system backend automatically constructs a job responsibility-adaptive scenario decomposition and expansion mechanism with four core sub-dimensions: business category, operation time domain, functional module, and operation frequency. For each sub-dimension, pre-set sub-labels that fit the real estate registration business are provided: the business category dimension includes five sub-labels: initial real estate registration, transfer registration, mortgage registration, cancellation registration, and seizure registration; the operation time domain dimension includes four sub-labels: weekday morning, weekday afternoon, end of month, and end of quarter; the functional module dimension includes five sub-labels: registration information entry module, business material upload module, application progress query module, business submission review module, and registration information modification module; and the operation frequency dimension includes three sub-labels: high frequency, medium frequency, and low frequency.

[0027] Subsequently, the real estate business dimension tag assignment model was launched. According to the four core sub-dimensions and preset sub-labels, dimension tags were assigned to each historical core business operation record in the standardized real estate management system operation table. Each operation record has four core dimension tags. For example, the record of an operator performing the real estate first registration information entry function is assigned four-dimensional tags: business category: first registration, operation time domain: weekday morning, function module: registration information entry module, and operation frequency: high frequency.

[0028] The K-means clustering algorithm optimized for government data is adopted. Using dimension labels as the core clustering feature, operation records with the same or similar dimension labels are clustered and merged. The algorithm dynamically sets the number of clusters to 6, integrating the operation records into 6 core operation scenario clusters, namely: high-frequency operation cluster for first-time real estate registration, high-frequency operation cluster for real estate transfer registration, medium-frequency operation cluster for real estate mortgage registration, medium-frequency operation cluster for application progress inquiry, low-frequency operation cluster for registration information modification, and low-frequency operation cluster for cancellation registration. The final clustering results of operation records for each dimension are obtained.

[0029] Specifically, the process of obtaining the ranking results of potential functional requirements includes: The frequency of functional operations on the clustering results is quantified in all dimensions to obtain the quantified value of the operation frequency; Based on the job function requirements of operators using full user information, assign corresponding scenario-based weight coefficients to the relevant functions. The quantified value and the weighting coefficient are used to calculate the demand fit score; Based on the demand fit score, the functional requirements are ranked, and the potential functional requirements of the operators are ranked.

[0030] In this embodiment, the clustering results of operation records of 6 core operation scenario clusters are input into the real estate function operation frequency quantification model. The model performs full-dimensional and refined quantitative calculation of the operation frequency of each function, and counts the actual number of times each function is executed under different business categories, different function modules, and different operation time domains to obtain the operation frequency quantification value corresponding to each function. Among them, the operation frequency quantification values ​​of the 6 core functions of real estate initial registration information entry, transfer registration information entry, mortgage registration material upload, application progress query, registration information modification, and cancellation registration information entry are among the highest.

[0031] Based on the functional requirements of real estate registration handling positions in the full user information of operators and the job responsibilities and core priority business responsibilities of government real estate management, a job responsibility matching scenario-based weight coefficient assignment model is used to assign differentiated scenario-based weight coefficients to different functions. The weight range is set between 0.5 and 0.9, following the principle of "core business functions have the highest weight, supporting business functions have the second highest weight, and auxiliary business functions have the lowest weight": registration information entry functions that are strongly related to the core business of the position (initial registration, transfer registration, cancellation registration) are uniformly set to 0.9; material uploading functions that support the core business (mortgage registration material uploading) are set to 0.8; and query and modification functions that are only auxiliary work (processing progress query, registration information modification) are set to 0.5.

[0032] The operation frequency quantification value of each function and the corresponding scenario-based weight coefficient are input into the government affairs demand adaptability calculation model one by one. The model completes the fusion calculation of the two through the core formula of operation frequency quantification value × scenario-based weight coefficient to obtain the demand adaptability score of each function. Then, all functions are ranked from high to low according to the demand adaptability score, generating a ranking result of potential functional requirements that are close to the actual work needs of operators and highly matched with job responsibilities. Among them, the demand adaptability scores of the three functions of real estate initial registration information entry, transfer registration information entry, and mortgage registration material upload are ranked in the top three, becoming the core potential functional requirements of operators.

[0033] Specifically, the vectorization process includes: Based on the sorting results of potential functional requirements and the labels of functional and operational scenarios, functional-scenario text clustering data is obtained; Extract the feature information of the functional-scene text fusion data to generate a text feature vector set; By utilizing a locally deployed large model encoding module, discrete text feature vectors are transformed into high-dimensional semantic vectors.

[0034] In this embodiment, all functions in the potential functional requirement ranking results are deeply integrated with the corresponding four-dimensional labels of the operation scenarios to form function-scenario text cluster data. This data is a UTF-8 encoded Chinese text string containing core semantic information such as function name, business category, operation time domain, and function module. The character length of a single cluster data is controlled between 10 and 50 characters. A typical input example is: Initial Registration Information Entry - Real Estate Initial Registration - Weekday Morning - Registration Information Entry Module - High-Frequency Operation, Mortgage Registration Material Upload - Real Estate Mortgage Registration - Weekday Afternoon - Material Upload Module - Medium-Frequency Operation. This cluster data serves as the core input data of the model. The model has a built-in dynamic truncation / padding mechanism that can adaptively adapt to text cluster data of different lengths, ensuring the standardization and consistency of the input data.

[0035] This semantic encoding model is an extension and optimization of the basic BERT-base architecture into a 6-layer core network. Targeted adjustments have been made to address the characteristics of short, strongly business-related real estate management texts. The functions of each layer are precisely adapted to the semantic encoding requirements of real estate texts. The specific design is as follows: Input embedding layer: Transforms the segmented text cluster data into a comprehensive embedding vector containing word semantic information, location information, and text segment information. It expands the basic vocabulary by 5,000+ real estate management professional vocabulary words and adds exclusive vocabulary embedding vectors for real estate registration, real estate transactions, maintenance fund management, etc., to enhance professional semantic representation capabilities. Convolutional Feature Extraction Layer: Multi-scale heterogeneous convolutional kernels are used to extract local semantic features of text, accurately capture short-distance business association features between functions and scenarios, and design differentiated convolutional kernel sizes for short real estate text features to adapt to semantic extraction of real estate business words of different lengths. Multi-head attention layer: 12 attention heads are set up. The attention weight of different words in the text is calculated through the self-attention mechanism. It automatically focuses on core business semantic words such as first registration, material upload, and mortgage registration, weakens auxiliary words without actual semantic meaning, and improves the accuracy of semantic encoding. Pooling layer: A hybrid pooling method of mean pooling and max pooling is adopted to aggregate and compress the high-dimensional feature vector after attention encoding, generating a single-sentence-level overall semantic feature vector, which retains both the core semantic features of the real estate text and the boundary semantic features of the text, thus avoiding the loss of feature information. Fully Connected Layer: A new dedicated real estate business feature mapping branch is added, which maps the pooled feature vectors to the model's preset unified semantic space to complete the unified processing of feature dimensions. At the same time, it strengthens the distinction between function and scenario business attributes and improves the semantic recognition of different business categories. Output layer: Outputs standardized high-dimensional semantic vectors, which are directly connected to the system's normalization module to ensure that the output vectors conform to the similarity calculation specifications of the real estate management system, providing standardized data for subsequent matching score calculation.

[0036] The convolutional kernels of the model's convolutional feature extraction layer employ a heterogeneous design with three scales, ensuring no redundancy and full coverage. This accurately adapts to the different semantic feature extraction needs of real estate management texts. The specific design and application scenarios are as follows: 1×2 small-size convolution kernel: used to extract the core semantic features of short texts such as real estate professional terms and function names, adapted to the semantic extraction of single business terms such as first registration, mortgage registration, and material upload, and capture the basic semantic information of the terms; 1×4 medium-sized convolution kernel: used to extract semantic features related to functions and scenarios, adapting to the semantic association extraction of combined texts such as initial registration information entry - initial real estate registration, mortgage registration material upload - material upload module, etc., and capturing the inherent business relationship between functions and scenarios; 1×6 large-size convolutional kernel: used to extract the overall semantic features of complex text fusion with multiple labels, adapted to the semantic extraction of text fusion of four-dimensional labels such as first registration information entry - first registration of real estate - weekday morning - registration information entry module, capturing the overall semantics and multi-dimensional correlation features of the text.

[0037] The three types of convolutional kernels work together and complement each other to achieve comprehensive and refined extraction of semantic features from real estate management texts, ensuring the integrity and accuracy of semantic encoding.

[0038] After performing full-process feature extraction and semantic encoding on the input function-scene text fusion data, the model outputs a 768-dimensional high-dimensional semantic vector. This vector is a floating-point numerical vector that accurately represents the overall semantic features and business relevance features of the function-scene in the input text. The output vector dimension of all function-scene texts within the same system remains consistent to ensure comparability in subsequent calculations. The output 768-dimensional high-dimensional semantic vector undergoes L2 normalization, unifying the magnitude of all vectors to 1 through a normalization algorithm. This eliminates dimensional differences and numerical deviations between different text vectors, ensuring that each function vector can be accurately compared for similarity within the same semantic space. The processed high-dimensional semantic vector is then directly input into the next round of matching score calculation.

[0039] Specifically, the process of obtaining the matching score includes: Based on the transformation results of high-dimensional semantic vectors, the cosine similarity between vectors is calculated to obtain the cosine similarity of semantic vectors, and at the same time, the initial similarity quantification score is obtained. Based on the demand fit score, the initial similarity quantification score is weighted and assigned to obtain a weighted similarity score. Functional filtering is performed using preset thresholds to obtain matching scores for the corresponding functions.

[0040] In this embodiment, all 768-dimensional high-dimensional semantic vectors after L2 normalization are input into the cosine similarity calculation model, and the formula is: , cosθ: Initial similarity metric score, ranging from [0,1]. A larger value indicates a stronger semantic association. : The 768-dimensional L2-normalized high-dimensional semantic vector (core function baseline vector) of the initial registration information. The remaining 768-dimensional L2-normalized high-dimensional semantic vectors for functions to be computed; :vector / The value of the i-th dimension; :vector / The modulus length (since L2 normalization has been performed) The actual calculation can be simplified to ).

[0041] Using the semantic vector of the core function of real estate registration handling post—initial registration information entry—as the benchmark, pairwise similarity calculations are performed on the semantic vectors of all other functions. By calculating the cosine value of the angle between the vectors, the semantic similarity between each function and the core function is obtained, and an initial similarity quantification score is generated for each function. The higher the score, the stronger the semantic association between the function and the core function. Among them, the initial similarity quantification scores of transfer registration information entry, cancellation registration information entry, and initial registration information entry are among the highest.

[0042] The requirement fit weighted cascade assignment model is activated, using the previously obtained requirement fit scores as weight coefficients to perform a weighted calculation on the initial similarity quantification scores. The weighted similarity score formula is as follows: , S w Weighted similarity score, the final semantic similarity quantification after weighting; S init The initial similarity metric score for this function is calculated using cosine similarity. F x : The requirement suitability score for this function (the score of matching function with job requirements calculated in the early stage); F core: Demand fit score for core function (initial registration information entry) (benchmark score, fixed value).

[0043] By using a weighted approach, core functions with higher demand fit scores receive a higher similarity weight, making the similarity calculation results more aligned with the actual job requirements of operators. After weighted calculation, the weighted similarity scores of each function are obtained, enabling precise and personalized adjustments to the similarity score.

[0044] A job-responsibility adaptive threshold screening mechanism is used. Based on the operator's real estate registration handling job attributes and business scope, combined with the results of previous operation scenario breakdown and functional requirement analysis, a similarity screening threshold is dynamically set to 0.2. All functions are then screened according to their weighted similarity scores: functions with a weighted similarity score higher than 0.2 are retained, while those with a weighted similarity score lower than 0.2 are eliminated. The eliminated functions are mostly auxiliary functions unrelated to the core business of the job and with extremely low operation frequency. The matching score corresponding to the retained functions is obtained. This score comprehensively integrates semantic similarity and requirement adaptability, reflecting the semantic relationship between functions and aligning with the actual needs of the job, thus achieving accurate screening of effective functions.

[0045] Specifically, the process of standardizing and quantifying the values ​​includes: Based on the matching score, global normalization is performed to obtain a standardized matching score. Utilize the priority configuration rules of functional operations to perform a dimensional allocation of priority coefficients; The fractal dimension matching result and the standardized matching degree score are calculated to obtain the quantitative assignment result; Based on the quantization assignment results, the functional scores are ranked in a secondary order to generate a quantized functional sequence.

[0046] In this embodiment, this step is achieved through a global Min-Max normalization model, a government business priority coefficient fractal dimension matching model, and a functional score secondary ranking model. The global normalization model maps the matching score to a fixed interval of 0-1 to eliminate the difference in dimensions. The priority coefficient fractal dimension matching model sets a matching interval of 1.0-1.2 in combination with the business priority rules of government real estate management. The secondary ranking model achieves accurate and orderly sorting of functions through quantitative assignment.

[0047] Implementation: After threshold screening, all matching scores for retained functions are input into a global Min-Max normalization model. The model uses the classic Min-Max normalization algorithm to map all matching scores to a fixed numerical range of 0-1. The core calculation formula is: Standardized Matching Score = (Original Matching Score - Minimum of All Scores) / (Maximum of All Scores - Minimum of All Scores). This normalization process completely eliminates dimensional differences and numerical deviations between matching scores of different functions, ensuring that the scores of each function are comparable on the same scale. The standardized matching score for each function is obtained, ranging from 0 to 1. A higher score indicates a higher degree of matching between the function and the operator's job requirements.

[0048] Based on the government affairs processing rules of the real estate management system and the core priority and urgency priority requirements of government work, the system pre-determines the "Real Estate Management Function Operation Priority Configuration Rules". According to these rules, the government affairs priority coefficient subdivision allocation model is activated. Based on the three core dimensions of business importance, urgency, and job relevance, priority coefficients are allocated to different functions. The priority coefficient range is set to 1.0-1.2: Registration information entry functions (initial registration, transfer registration) that are strongly related to the core business of real estate registration and have high urgency are uniformly set to a priority coefficient of 1.2; Material upload functions (mortgage registration material upload) that are complementary to the core business and have medium urgency are set to a priority coefficient of 1.0; Query functions that have no urgency requirements and are only auxiliary are set to a priority coefficient of 0.9 (if reserved, a value is assigned).

[0049] The priority coefficients of each function are quantified and their corresponding dimensions are combined with the standardized matching score for each function. The core calculation formula is: Quantitative Assignment Result = Standardized Matching Score × Priority Coefficient. This calculation enables the quantitative assignment and optimization of function scores, further enhancing the scores of core business functions. The quantitative assignment results of all functions are input into the secondary ranking model for function scores. The model ranks all functions from high to low according to the quantitative assignment results, generating a quantitative assignment function sequence from high to low scores. This sequence comprehensively considers function matching degree, business priority, and job requirements.

[0050] Specifically, the process of generating the AI ​​function list for operators includes: An initial AI function list is constructed by arranging the function sequences in an initial list order. Based on the function configuration and prompt configuration in the full user information, the list structure is optimized and adapted. By leveraging the front-end and back-end interaction of the Dify large model development platform, a list visualization rendering is implemented to generate an AI function list for operators.

[0051] In this embodiment, the system backend selects the top 8 core functions in descending order of their scores according to the quantitative assignment function sequence, and arranges them in an initial order to build an initial AI function list with a preliminary structure and accurate sorting. The top 8 core functions include real estate initial registration information entry, transfer registration information entry, mortgage registration material upload, application progress query, registration information modification, cancellation registration information entry, business submission review, and material preview and download, which fully covers the daily core business and supporting auxiliary work of operators.

[0052] The entire user information of operators is input into the job-responsibility-adaptive list structure optimization and adaptation model. The model combines the functional configuration information (operable functions, customizable functions) and prompt configuration information (operation prompts, function guidance configuration) to perform comprehensive and customized optimization and adaptation of the initial AI function list: For the two functions that operators frequently use, namely initial registration information entry and transfer registration information entry, quick operation entry points are added to support one-click access; for core business functions, floating prompt labels are added next to the function names, such as indicating next to initial registration information entry that a real estate ownership survey form and land use right certificate must be uploaded, and indicating next to mortgage registration material upload that PDF / image formats are supported, with a single file ≤50M; at the same time, the three types of custom operation functions in the list are retained, allowing operators to fix, delete, and manually sort functions to meet personalized operation needs.

[0053] Through the high-concurrency front-end and back-end interaction interface built on the Dify large model development platform, the optimized AI function list data is transmitted in real time to the front-end Vue3+Element Plus government affairs front-end visualization rendering model. The Dify platform's dedicated workflow for real estate management achieves real-time data synchronization, format conversion, and anomaly handling, ensuring the stability and accuracy of data transmission, with transmission latency controlled within 50ms. The front-end visualization rendering model strictly follows the operating habits and interface specifications of the government affairs real estate management system for list rendering: it adopts a fixed list layout on the left; each of the eight functions is paired with a dedicated real estate business icon to enhance visual recognition; the top three high-frequency core functions (initial registration information entry, transfer registration information entry, and mortgage registration material upload) are highlighted in red and bold for visual prominence, allowing operators to quickly locate core functions; at the same time, pagination, search, and filtering functions are added to the list to improve operational convenience. Finally, a personalized AI function list is generated for operators, which is tailored to the operator's job requirements, operating habits, and personalized configurations. The interface is user-friendly and easy to operate, allowing operators to directly click on functions in the list for one-click quick access without having to navigate through traditional multi-level menus.

[0054] Specifically, the process of performing the association mapping includes: By building a function-keyword cluster library, the business keywords for the list function can be obtained; Semantic topology calculation is performed based on the business lexicon of the real estate management system. The business keywords are semantically expanded and associated with general search business terms; A retrieval index table is established based on the semantic topology association results, and an association mapping table of search business terms, semantic tags, and functional identifiers is obtained.

[0055] In this embodiment, the system backend organized government real estate management experts to analyze the business attributes, core uses, operational characteristics, and business specifications of the eight functions in the AI ​​function list. For each function, 5-10 core business keywords were precisely matched. These core keywords cover dimensions such as function name, business category, operational actions, and core uses. For example, the five core keywords are: initial registration information entry, matching initial real estate registration, initial registration, property rights entry, registration information filling, and real estate ownership survey. The six core keywords are: transfer registration information entry, matching real estate transfer registration, transfer registration, property rights transfer, transfer information entry, and property transfer. All functions are bound one-to-one with their corresponding core business keywords, constructing a clearly structured and precisely mapped real estate function-keyword cluster library.

[0056] Based on the professional business terminology database of the real estate management system (including full-category professional terms such as real estate registration, real estate transactions, maintenance fund management, and surveying and mapping services), the locally deployed optimized Chinese large-scale model semantic expansion calculation model is launched to perform comprehensive semantic expansion calculations on all core business keywords in the cluster database. Through pre-trained natural language understanding capabilities, combined with real estate management professional corpus, the model accurately mines synonyms, related business terms, and other extended terms for each core keyword, realizing the semantic extension and expansion of professional terms. For example, for the core keyword "first registration," the model mines its synonym "initial registration," related terms "first property registration," and related business terms "real estate cadastral survey," "first registration acceptance," and "initial property rights review." For the core keyword "property transfer," the model mines its synonym "property transfer," and related business terms "transfer registration acceptance" and "property rights change registration."

[0057] The calculated business keyword expansion vocabulary is precisely semantically linked with general search business vocabulary. General search business vocabulary covers non-professional and colloquial search terms commonly used by government personnel in their daily operations, such as: first-time house certificate issuance, property transfer registration, and mortgage document upload. Through semantic matching technology, a precise semantic mapping relationship is established between general search terms and real estate professional keywords, enabling non-professional and colloquial general search terms to accurately match professional business keywords within the system, thus solving the retrieval problem of the disconnect between professional and general vocabulary.

[0058] The semantic expansion association results are input into the retrieval index table to generate the model. The model uniquely binds the search business terms (general terms + professional terms), semantic tags, and functional identifiers, adds a unique retrieval index code to each binding relationship, establishes a structured retrieval index table, and finally generates an association mapping table of search business terms-semantic tags-functional identifiers containing more than 50 related records.

[0059] Specifically, the process of obtaining the property description data includes: Based on the semantic tags and search terms in the association mapping table, the search terms are input into the multi-modal search system of the local large model + Dify platform to obtain multi-modal search results; The multi-modal retrieval results are matched and synthesized based on the local knowledge base, and the dimensions of functional interpretation, business process and operation specification are matched and synthesized into structured text data. Data calibration is performed on the synthesized structured text data to generate calibrated clustered property description data.

[0060] In this embodiment, when an operator enters search terms into the system, the system backend quickly extracts the corresponding semantic tags and core search terms from the association mapping table of search terms-semantic tags-functional identifiers. The core search terms are then input into the multi-modal retrieval system built on the local large-scale model (DeepSeek-R1-32B) and the Dify platform. This multi-modal retrieval system integrates the natural language understanding capabilities of the local large-scale model with the efficient retrieval capabilities of the Dify platform. It can automatically match the optimal retrieval method based on the characteristics of the search terms (professionalism, length, semantic features): precise retrieval is used for highly professional and standardized search terms; fuzzy retrieval is used for non-standardized or misspelled search terms; and semantic retrieval is used for colloquial and non-professional search terms, ensuring both accuracy and efficiency. The retrieval system quickly retrieves relevant data from the local knowledge base using the optimal retrieval method to obtain preliminary multi-modal retrieval results.

[0061] The local knowledge base is a dedicated knowledge base for the real estate management system, containing professional data on over 200 real estate management functions, covering five core dimensions: function definition, business process, operation specifications, material requirements, and precautions. The initial multi-modal search results are input into the real estate knowledge base matching and synthesis model. The model performs deep matching and synthesis between the initial search results and the precise professional data in the local knowledge base. It supplements, improves, and structurally integrates the search results from the four core dimensions of function definition, business process, operation specifications, and material requirements: supplementing missing dimensional information in the search results, accurately refining ambiguous information, and structuring scattered information, ultimately generating structured text data containing all four core dimensions, with complete information and a clear structure.

[0062] Structured text data is input into the government data calibration model for comprehensive and rigorous review and calibration: The accuracy of professional terminology is verified to ensure that there are no errors in terms such as those used in real estate registration survey forms and real estate registers; the completeness of business processes is verified to ensure that no core operational steps are omitted; the standardization of operational procedures is verified to ensure consistency with actual system operations; and the suitability of material requirements is verified to ensure that they match the latest material requirements of the local real estate registration center. Errors discovered during the review are corrected, missing information is supplemented, and non-standard expressions are standardized. This generates calibrated, complete, standardized, and practical real estate description data.

[0063] Specifically, the process of performing the linked presentation of dual-region function-description includes: Utilize the front-end and back-end data synchronization interface of the Dify platform to distribute the function identifiers to the AI ​​function list area; Based on the function identifier matching results in the AI ​​function list area, function indexing and positioning are performed. Based on the page rendering rules of the main interface display area, the function explanation content is arranged and displayed in a standardized format. The function clicks in the list area and the content in the display area are switched in real time to achieve a linked presentation of the function and description in both areas.

[0064] In this embodiment, the system backend utilizes the WebSocket high-concurrency front-end and back-end data synchronization interface built on the Dify large model development platform to send the unique function identifiers of the eight functions in the AI ​​function list to the AI ​​function list area on the system frontend via bidirectional real-time data transmission technology, thus completing the binding between the function identifiers and the display area. The AI ​​function list area displays the function names and corresponding business icons in the order of the quantified value assignment function sequence, allowing operators to view and click to call them intuitively.

[0065] When an operator clicks on a function in the AI ​​function list area, the system frontend immediately captures the function identifier and transmits it to the system backend in real time, activating the function indexing and positioning model. Based on this function identifier, the model performs a dual, precise matching and rapid retrieval in the association mapping table of search business terms, semantic tags, and function identifiers, and in the local knowledge base. It directly locates the property description data corresponding to the function through the function identifier, achieving rapid and accurate function indexing and positioning, and simultaneously retrieving the corresponding clustered property description data.

[0066] The retrieved property description data is transmitted in real time to the standardized rendering model of the front-end government affairs page. The model strictly follows the page display specifications of the government affairs property management system, and performs standardized formatting and visual optimization of the property description data to ensure the standardization, readability, and visual friendliness of the displayed content: the title uses 2-point boldface and marks the full name of the function, such as: Description of the function for entering information for the first registration of real estate; the four core dimensions (function explanation, business process, operation specifications, and material requirements) use 4-point bold Song typeface as second-level headings to distinguish the levels; the specific content of each dimension uses 12-point Song typeface, displayed in segments, with a line spacing of 1.5 times to improve readability; the business process is displayed as a step-by-step numbered list, the operation specifications are displayed as a bulleted list, and the material requirements are displayed in bold with bullet points; important information such as core operation requirements and key material lists are highlighted in red; at the same time, an immediate operation and quick access point is reserved at the bottom of the display area, allowing operators to directly click the entry to call the corresponding function, achieving a seamless connection between viewing the description and calling the function.

[0067] Through a dual-zone real-time interactive linkage model, a real-time, zero-latency, two-way interactive linkage mechanism is established between the AI ​​function list area and the main interface display area. This mechanism is based on WebSocket real-time data transmission technology, enabling synchronous updates of operations and content in both areas: when an operator switches between different functions in the AI ​​function list area, the content in the main interface display area will immediately update synchronously, seamlessly switching from the property description data of the original function to the property description data of the newly clicked function, without delay, lag, or loading wait; when the operator clicks on a previously viewed function again, the content in the display area can be quickly displayed again without repeated retrieval and rendering. This achieves a dual-zone function-description linkage presentation between the AI ​​function list area and the main interface display area. Operators can view complete and accurate description data of the corresponding function in real time by clicking on functions in the list area, greatly improving the convenience and efficiency of system operation, lowering the operating threshold, and allowing users to quickly master the function operation methods without consulting complex operation manuals.

[0068] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A development method for an intelligent real estate management system based on a large model, characterized in that, include: S1: Based on the unique identifier of the operator's system login, the database is retrieved to obtain the real estate management system operation table; The system extracts full user information of operators using login identifier primary key information; it also performs dimensional decomposition of corresponding scenarios in historical operation records through frequency statistics to generate operator preference feature sets and obtain the ranking results of potential functional requirements. S2: Vectorize the function and operation scenario tags in the ranking results of the potential functional requirements, and convert the text information into high-dimensional semantic vectors through the semantic model encoding module; calculate the cosine similarity between the high-dimensional semantic vectors to obtain the matching score, and simultaneously perform standardized quantization assignment. The specific process of performing standardized quantization assignment includes: Based on the matching score, global normalization is performed to obtain a standardized matching score. Utilize the priority configuration rules of functional operations to perform a dimensional allocation of priority coefficients; The fractal dimension matching result and the standardized matching degree score are calculated to obtain the quantitative assignment result; Based on the quantified assignment results, a secondary ranking of the functional scores is performed to generate a quantified functional sequence; the AI ​​function list of the operator is generated using dynamic rendering on the front end. The specific process of generating the AI ​​function list of the operator includes: An initial AI function list is constructed by arranging the function sequences in an initial list order. Based on the function configuration and prompt configuration in the full user information, the list structure is optimized and adapted. By leveraging the front-end and back-end interaction of the Dify large model development platform, a list visualization rendering is implemented to generate an AI function list for operators. S3: Based on the AI ​​function list, the search business terms are associated and mapped, and semantic tags and search terms are configured; the search terms are used to perform pattern search through the real estate big data model to obtain real estate description data; using the front-end and back-end data synchronization interface, dual-region function-description linkage presentation is performed. The specific process of dual-region function-description linkage presentation includes: Utilize the front-end and back-end data synchronization interface of the Dify platform to distribute the function identifiers to the AI ​​function list area; Based on the function identifier matching results in the AI ​​function list area, function indexing and positioning are performed. Based on the page rendering rules of the main interface display area, the function explanation content is arranged and displayed in a standardized format. The function clicks in the list area and the content in the display area are switched in real time to achieve a linked presentation of the function and description in both areas.

2. The method according to claim 1, characterized in that, The specific process of obtaining the real estate management system operation table includes: Database retrieval is anchored based on the unique identifier of the operator's system login, and the identifier field of the property management operator is anchored and matched to obtain the operation table of the property management system; Based on the field attributes of the operation table, data is layered, screened, and deduplicated to obtain a standardized real estate management system operation table.

3. The method according to claim 1, characterized in that, The specific process of extracting the operator's full user information includes: Based on the primary key information of the login identifier in the standardized real estate management system operation table, a user information expansion and retrieval link is constructed. Tuowei retrieves multi-source data from the user information database and integrates four categories of information—identity, permissions, job position, and function—to obtain multi-dimensional user information. The user information is indexed by dimension to generate full user information for the operators.

4. The method according to claim 1, characterized in that, The specific process of dimensionally decomposing the corresponding scenarios of historical operation records includes: Based on the permission boundaries and job attributes of the full user information, a scenario decomposition and expansion mechanism is constructed. Based on four sub-dimensions—business category, operation time domain, functional module, and operation frequency—the historical operation records of the standardized real estate management system operation table are assigned dimension labels. Clustering and merging of operation records using dimension labels yields the clustering results for the corresponding dimension operation records.

5. The method according to claim 1, characterized in that, The specific process for obtaining the ranking results of potential functional requirements includes: The frequency of functional operations on the clustering results is quantified in all dimensions to obtain the quantified value of the operation frequency; Based on the job function requirements of operators using full user information, assign corresponding scenario-based weight coefficients to the relevant functions. The quantified value and the weighting coefficient are used to calculate the demand fit score; Based on the required fit score, the functional requirements are ranked, and the potential functional requirements of the operators are ranked.

6. The method according to claim 1, characterized in that, The specific process of vectorization includes: Based on the sorting results of potential functional requirements and the operation scenario labels, functional-scenario text clustering data is obtained; Extract the feature information of the functional-scene text fusion data to generate a text feature vector set; By utilizing a locally deployed large model encoding module, discrete text feature vectors are transformed into high-dimensional semantic vectors.

7. The method according to claim 1, characterized in that, The specific process for obtaining the matching score includes: Based on the transformation results of high-dimensional semantic vectors, the cosine similarity between vectors is calculated to obtain the cosine similarity of semantic vectors, and at the same time, the initial similarity quantification score is obtained. Based on the demand fit score, the initial similarity quantification score is weighted and assigned to obtain a weighted similarity score. Functional filtering is performed using preset thresholds to obtain matching scores for the corresponding functions.

8. The method according to claim 1, characterized in that, The specific process of performing the association mapping includes: By building a function-keyword cluster library, the business keywords for the list function can be obtained; Semantic topology calculation is performed based on the business lexicon of the real estate management system. The business keywords are semantically expanded and associated with general search business terms; A retrieval index table is established based on the semantic topology association results, and an association mapping table of search business terms, semantic tags, and functional identifiers is obtained.

9. The method according to claim 1, characterized in that, The specific process for obtaining property description data includes: Based on the semantic tags and search terms in the association mapping table, the search terms are input into the multi-modal search system of the local large model + Dify platform to obtain multi-modal search results; The multi-modal retrieval results are matched and synthesized based on the local knowledge base, and the dimensions of functional interpretation, business process and operation specification are matched and synthesized into structured text data. Data calibration is performed on the synthesized structured text data to generate calibrated clustered property description data.

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