Human and house data comprehensive treatment method, system and equipment based on multi-level address model and medium

Abstract

The invention discloses a human and house data comprehensive treatment method, system and equipment based on a multi-level address model, and a medium, belongs to the technical field of data treatment, and aims to solve the technical problem of how to effectively integrate human and house data sources of multiple parties and improve the accuracy, consistency and availability of human and house data. The defects that in the prior art, people and house data are scattered, address description is not standard, the incidence relation is inaccurate, data quality is uneven and a unified identification system is lacked are overcome. According to the technical scheme, the method comprises the steps that a five-level core data table comprising a community table, a building table, a house table, a people and house relation table and a personnel information table is constructed; the method comprises the following steps: accessing original human and house data from a plurality of data sources, performing intelligent analysis and standardization processing on address information, and obtaining data after standardization processing; establishing a house unique identification system and a human-house multi-dimensional relation model based on the standardized data; evaluating and repairing the data quality; and standardized human and house data service is provided for the outside through the data service interface.

Description

Technical Field

[0001] This invention relates to the field of data governance technology, specifically to a method, system, device, and medium for comprehensive governance of human and housing data based on a multi-level address model. Background Technology

[0002] With accelerated urbanization and increased population mobility, various levels of government have accumulated massive amounts of personnel and housing data. This data is of great significance for community governance and public safety. However, current management of personnel and housing data faces numerous challenges:

[0003] ① Data is scattered and heterogeneous: People and housing data are scattered across multiple systems such as community cloud, with different data standards and formats, forming data silos.

[0004] ② Non-standard address descriptions: The original address data contains a large number of non-standard expressions, such as "XX Road XX Lane XX No. XX Room" which may be abbreviated to "XX Road XX No." or use aliases, making address matching difficult.

[0005] ③ Inaccurate correlation: The relationship between people and housing lacks a unified standard and dynamic update mechanism, making it difficult to accurately reflect the actual situation, such as complex scenarios like co-renting, borrowing, and frequent changes.

[0006] ④ Inconsistent data quality: Data from different sources varies greatly in quality, with issues such as duplication, missing data, errors, and expiration, affecting the value of the data.

[0007] ⑤ Lack of a unified identification system: Different identifiers are used for houses and people in different systems, making it difficult to achieve cross-system data association and integration.

[0008] While some data integration solutions exist in current technologies, most focus on processing data from a single dimension or source, lacking systematic governance solutions for multi-source, heterogeneous human and housing data. Particularly in address standardization and relationship modeling, existing methods often rely on simple rules or manual intervention, failing to adapt to complex and ever-changing realities.

[0009] Therefore, how to effectively integrate multiple data sources of people and housing, improve the accuracy, consistency and availability of people and housing data, and overcome the shortcomings of existing technologies such as scattered people and housing data, non-standard address descriptions, inaccurate correlations, uneven data quality and lack of a unified identification system are technical problems that urgently need to be solved. Summary of the Invention

[0010] The technical objective of this invention is to provide a method, system, device, and medium for comprehensive governance of human and housing data based on a multi-level address model, in order to effectively integrate multiple human and housing data sources, improve the accuracy, consistency, and availability of human and housing data, and overcome the shortcomings of existing technologies such as scattered human and housing data, non-standard address descriptions, inaccurate correlations, inconsistent data quality, and lack of a unified identification system.

[0011] The technical objective of this invention is achieved as follows: a comprehensive management method for human and housing data based on a multi-level address model, the specific method of which is as follows:

[0012] A five-level core data table system is constructed, comprising a community table, a building table, a housing table, a person-housing relationship table, and a personnel information table. The community table stores basic community-level information, serving as the top-level unit for address management. The building table records building-level information through community ID association. The housing table stores detailed housing address information and housing attributes through building ID association. The personnel information table stores basic personnel information. The person-housing relationship table records multi-dimensional residential relationships between personnel and housing through housing ID and personnel ID association.

[0013] The system accesses raw housing data from multiple data sources, performs intelligent parsing and standardization on address information, and obtains standardized data.

[0014] A unique housing identification system and a multi-dimensional relationship model between people and housing are established based on the standardized data.

[0015] Assess and repair data quality to ensure its accuracy, integrity, and consistency;

[0016] Standardized human and housing data services are provided to external parties through data service interfaces.

[0017] As a preferred option, the community table is used to comprehensively record the community's basic information, geographical location, property details, and data source, and supports quick queries by region and name;

[0018] The building table records detailed information about each building, including its architectural features, geographical location, and relationship to its affiliated community. It supports efficient queries by community, geographical location, and building number, and ensures the uniqueness of building numbers within the same community.

[0019] The property table records detailed information for each property, including its specific location, property attributes, ownership information, and complete address. It also supports efficient queries by building, address, and property status, and ensures the uniqueness of unit numbers within the same building.

[0020] The personnel information form is used to comprehensively record personnel's basic information, occupation and work unit, contact information and emergency contact information, and supports quick query and retrieval by key information such as identity information, name and mobile phone number;

[0021] The Person-House Relationship Table is used to record various relationships between people and houses (such as owners, tenants, family members, etc.). It includes the time validity of the relationship, the primary and secondary relationships, and the data credibility assessment. It supports efficient querying by person, house, relationship type, and time range, and ensures that the relationship type of the same person under the same house is not repeated.

[0022] As a preferred approach, a hybrid algorithm combining rule-based and machine learning methods is used for intelligent address resolution, as detailed below:

[0023] Address segmentation: breaking down unstructured address text into semantic units;

[0024] Element identification: Identify address elements such as road, lane, number, and room;

[0025] Constructing hierarchical relationships: Building a hierarchical relationship between community, building, and house based on address elements;

[0026] Standardized output: Generates structured standard address information.

[0027] As a preferred option, the establishment of a unique property identification system is as follows:

[0028] A unique house code is generated based on the administrative region code, community code, building number, and house number;

[0029] Establish a spatial location index for buildings to support geospatial queries;

[0030] Set up a house state machine to manage the state changes throughout the house's life cycle.

[0031] As a preferred approach, the multi-dimensional relationship model between people and housing includes the relationship between housing types, the time dimension, the weight dimension, and the spatial dimension.

[0032] Among them, the types of residential relationships include property owners, tenants, permanent residents and temporary residents;

[0033] The time dimension includes the relationship's start time, end time, and effective status;

[0034] The weighting dimensions include relationship confidence and data source authority score;

[0035] The spatial dimension includes the relationship between primary residence, secondary residence, and workplace.

[0036] More preferably, the method also includes data lineage tracing, as follows:

[0037] Record the source, collection time, and processing pipeline for each piece of data;

[0038] Establish a data change history to support data traceability and version management;

[0039] Generate data quality reports and visualize the entire data processing process.

[0040] A comprehensive human and housing data governance system based on a multi-level address model, the system comprising:

[0041] The core data table construction module is used to build a five-level core data table system, including a community table, a building table, a housing table, a person-housing relationship table, and a personnel information table. The community table stores basic community-level information and serves as the top-level unit for address management. The building table records building-level information through community ID association. The housing table stores detailed housing address information and housing attributes through building ID association. The personnel information table stores basic personnel information. The person-housing relationship table records the multi-dimensional residential relationship between personnel and housing through housing ID and personnel ID association.

[0042] The data acquisition and preprocessing module is used to access raw human and room data from multiple data sources and perform preprocessing operations such as data deduplication, format conversion, and encoding standardization.

[0043] The address intelligent parsing and standardization module is used to intelligently parse and standardize address information to obtain standardized data.

[0044] The entity recognition and relationship building module is used to establish a unique identification system for houses and a multi-dimensional relationship model between people and houses based on standardized data.

[0045] The data quality assessment and repair module is used to assess and repair data quality to ensure the accuracy, integrity and consistency of the data;

[0046] The data service module is used to provide standardized human and housing data services to external parties through the data service interface.

[0047] As a preferred option, the address intelligent resolution and standardization module includes:

[0048] The address resolution submodule is used to resolve unstructured addresses;

[0049] The element identification submodule is used to identify key address elements such as road, lane, number, and room.

[0050] The hierarchical matching submodule is used to match address features with the community database and building database;

[0051] The standardized output submodule is used to generate structured standard addresses and populate them into the corresponding table;

[0052] The entity recognition and relationship building module includes:

[0053] The personnel deduplication and merging submodule is used to merge personnel entities based on multi-dimensional information such as identity information, name, and mobile phone number.

[0054] The unique property identifier submodule is used to generate a unique property code based on geographic location and address elements;

[0055] The relational reasoning submodule is used to infer the relationship between people and housing based on residence permits and household registration data;

[0056] The confidence calculation submodule is used to comprehensively consider factors such as the authority of the data source, update time, and field matching degree.

[0057] The data quality assessment and repair module includes:

[0058] The integrity assessment submodule is used to check for missing required fields;

[0059] The accuracy assessment submodule is used to verify the validity of the field formats of identity information and address;

[0060] The consistency assessment submodule is used to check data consistency across systems;

[0061] The timeliness assessment submodule is used to assess the frequency and timeliness of data updates;

[0062] The data service module provides data services to the outside world through a unified API interface, supporting services such as querying associated houses by person, querying associated people by house, and statistically analyzing population information and addresses by community.

[0063] An electronic device includes: a memory and at least one processor;

[0064] The memory stores computer-executed instructions;

[0065] The at least one processor executes the computer execution instructions stored in the memory, causing the at least one processor to execute the comprehensive human and housing data management method based on the multi-level address model described above.

[0066] A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method for comprehensive management of human and room data based on a multi-level address model.

[0067] The method, system, equipment, and medium for comprehensive management of human and room data based on a multi-level address model of the present invention have the following advantages:

[0068] (i) This invention adopts a five-level hierarchical data model, which conforms to actual address management habits, supports refined management from macro to micro, and makes the model design more reasonable;

[0069] (ii) This invention can effectively handle various non-standard address representations through address resolution, significantly improve the address matching accuracy, and make the address standardization effect better;

[0070] (III) This invention supports multi-dimensional and multi-type descriptions of human-room relationships, which can accurately reflect complex actual living conditions and make relationship descriptions more precise;

[0071] (iv) This invention, through a unified entity identifier and relational model, can effectively integrate multiple data sources, eliminate data silos, and enhance data fusion capabilities.

[0072] (v) The automated data processing flow and quality monitoring of the present invention greatly reduce the need for manual intervention, resulting in lower operation and maintenance costs;

[0073] (vi) This invention establishes a complete address standardization and data governance system by constructing a five-level association data model consisting of a community table, a building table, a house table, a person-house relationship table, and a personnel information table, which can systematically solve the problem of multi-source person-house data governance;

[0074] (VII) This invention innovatively designs a multi-level address structure model and adopts a hybrid address resolution algorithm based on rules and machine learning to realize the automatic conversion from the original non-standard address to the structured standard address; by establishing a unique identification system for houses and a multi-dimensional relationship model between people and houses, it supports the accurate description of complex residential relationships, can effectively integrate multiple data sources such as public security, civil affairs, and community cloud, significantly improve the accuracy, consistency and availability of people and houses data, and provide high-quality data support for applications such as smart communities, population management, and emergency command;

[0075] (viii) This invention establishes a multi-level address model and a standardized data architecture to achieve high-quality integration and intelligent management of people and housing data, providing accurate, complete and timely data support for upper-level applications. It is applicable to scenarios such as smart cities, community management, real estate management, and population statistics that require refined management of "people" and "housing" entities and their complex relationships. Attached Figure Description

[0076] The invention will be further described below with reference to the accompanying drawings.

[0077] Appendix Figure 1 This is a flowchart of a comprehensive governance method for human and housing data based on a multi-level address model. Detailed Implementation

[0078] The following detailed description of the integrated management method, system, equipment, and medium for human and housing data based on a multi-level address model of the present invention is provided with reference to the accompanying drawings and specific embodiments.

[0079] Example 1:

[0080] As attached Figure 1 As shown in the figure, this embodiment provides a comprehensive governance method for human and housing data based on a multi-level address model. The method is as follows:

[0081] S1. Construct a five-level core data table system, including a community table, a building table, a housing table, a person-housing relationship table, and a personnel information table. The community table stores basic community-level information and serves as the top-level unit for address management. The building table records building-level information through community ID association. The housing table stores detailed housing address information and housing attributes through building ID association. The personnel information table stores basic personnel information. The person-housing relationship table records the multi-dimensional residential relationship between personnel and housing through housing ID and personnel ID association.

[0082] S2. Access raw housing data from multiple data sources, perform intelligent parsing and standardization of address information, and obtain standardized data.

[0083] S3. Establish a unique housing identification system and a multi-dimensional relationship model between people and housing based on the standardized data;

[0084] S4. Assess and repair data quality to ensure data accuracy, integrity, and consistency;

[0085] S5 provides standardized human and housing data services to external parties through data service interfaces.

[0086] In step S1 of this embodiment, the community table comprehensively records the basic information, geographical location, property details, and data source of the community, and supports quick queries by region and name; the key code is as follows:

[0087]

[0088]

[0089] In step S1 of this embodiment, the building table records detailed information for each building, including its architectural features, geographical location, and relationship to its affiliated residential community. It supports efficient queries by community, geographical location, and building number, and ensures the uniqueness of building numbers within the same community. Key code is as follows:

[0090]

[0091]

[0092] In step S1 of this embodiment, the house table records detailed information for each house, including its specific location, property attributes, ownership information, and complete address. It supports efficient queries by building, address, and property status, and ensures the uniqueness of unit numbers within the same building. Key code is as follows:

[0093]

[0094]

[0095] In step S1 of this embodiment, the personnel information table (person) comprehensively records basic information, occupation and work unit, contact information, and emergency contact information of personnel, and supports quick query and retrieval by key information such as identity information, name, and mobile phone number; the key code is as follows:

[0096]

[0097]

[0098] In step S1 of this embodiment, the person-house-relation table (person_house_relation) records various relationships between people and houses (such as: owner, tenant, family member, etc.). It includes the time validity of the relationship, the primary and secondary relationships, and data reliability assessment. It supports efficient querying by person, house, relationship type, and time range, and ensures that the relationship type of the same person under the same house is not repeated. The key code is as follows:

[0099]

[0100]

[0101] In this embodiment, step S2, the intelligent resolution of the address uses a hybrid algorithm that combines rule-based and machine learning approaches, as detailed below:

[0102] S201. Address segmentation: breaking down unstructured address text into semantic units;

[0103] S202, Element Identification: Identify address elements such as road, lane, number, and room;

[0104] S203. Constructing hierarchical relationships: Constructing hierarchical relationships between communities, buildings, and houses based on address elements;

[0105] S204, Standardized Output: Generates structured standard address information.

[0106] The specific steps in step S3 of this embodiment for establishing a unique housing identification system are as follows:

[0107] ① Generate a unique house code based on the administrative region code, community code, building number, and house number;

[0108] ② Establish a spatial location index for houses to support geospatial queries;

[0109] ③ Set up a housing state machine to manage the changes in housing status throughout its life cycle.

[0110] The multi-dimensional relationship model of people and rooms in step S3 of this embodiment includes the relationship of residential type, time dimension, weight dimension and spatial dimension;

[0111] Among them, the types of residential relationships include property owners, tenants, permanent residents and temporary residents;

[0112] The time dimension includes the relationship's start time, end time, and effective status;

[0113] The weighting dimensions include relationship confidence and data source authority score;

[0114] The spatial dimension includes the relationship between primary residence, secondary residence, and workplace.

[0115] This embodiment includes data lineage tracing, as detailed below:

[0116] ① Record the source, collection time, and processing pipeline for each piece of data;

[0117] ②Establish a data change history to support data traceability and version management;

[0118] ③ Generate data quality reports and visualize the entire data processing process.

[0119] Example 2:

[0120] This embodiment provides a comprehensive human and housing data management system based on a multi-level address model. The system includes:

[0121] The core data table construction module is used to build a five-level core data table system, including a community table, a building table, a housing table, a person-housing relationship table, and a personnel information table. The community table stores basic community-level information and serves as the top-level unit for address management. The building table records building-level information through community ID association. The housing table stores detailed housing address information and housing attributes through building ID association. The personnel information table stores basic personnel information. The person-housing relationship table records the multi-dimensional residential relationship between personnel and housing through housing ID and personnel ID association.

[0122] The data acquisition and preprocessing module is used to access raw human and room data from multiple data sources and perform preprocessing operations such as data deduplication, format conversion, and encoding standardization.

[0123] The address intelligent parsing and standardization module is used to intelligently parse and standardize address information to obtain standardized data.

[0124] The entity recognition and relationship building module is used to establish a unique identification system for houses and a multi-dimensional relationship model between people and houses based on standardized data.

[0125] The data quality assessment and repair module is used to assess and repair data quality to ensure the accuracy, integrity and consistency of the data;

[0126] The data service module is used to provide standardized human and housing data services to external parties through the data service interface.

[0127] The address intelligent resolution and standardization module in this embodiment includes:

[0128] The address resolution submodule is used to resolve unstructured addresses;

[0129] The element identification submodule is used to identify key address elements such as road, lane, number, and room.

[0130] The hierarchical matching submodule is used to match address features with the community database and building database;

[0131] The standardized output submodule is used to generate structured standard addresses and populate them into the corresponding table.

[0132] The entity recognition and relationship construction module in this embodiment includes:

[0133] The personnel deduplication and merging submodule is used to merge personnel entities based on multi-dimensional information such as identity information, name, and mobile phone number.

[0134] The unique property identifier submodule is used to generate a unique property code based on geographic location and address elements;

[0135] The relational reasoning submodule is used to infer the relationship between people and housing based on residence permits and household registration data;

[0136] The confidence calculation submodule is used to comprehensively consider factors such as the authority of the data source, update time, and field matching degree.

[0137] The data quality assessment and repair module in this embodiment includes:

[0138] The integrity assessment submodule is used to check for missing required fields;

[0139] The accuracy assessment submodule is used to verify the validity of the field formats of identity information and address;

[0140] The consistency assessment submodule is used to check data consistency across systems;

[0141] The timeliness assessment submodule is used to evaluate the frequency and timeliness of data updates.

[0142] In this embodiment, the data service module provides data services to the outside world through a unified API interface, supporting services such as querying associated houses by person, querying associated people by house, and statistically analyzing population information and standardized addresses by community.

[0143] Example 3:

[0144] This embodiment also provides an electronic device, including: a memory and at least one processor;

[0145] The memory stores computer-executed instructions;

[0146] The at least one processor executes the computer execution instructions stored in the memory, causing the at least one processor to execute the comprehensive human and housing data management method based on a multi-level address model as described in any one of the present invention.

[0147] The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor can be a microprocessor or any conventional processor.

[0148] Memory is used to store computer programs and / or modules. The processor implements various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory. Memory can mainly include a program storage area and a data storage area. The program storage area can store the operating system, at least one application program required for a function, etc.; the data storage area can store data created based on the use of the terminal, etc. In addition, memory can also include high-speed random access memory, and can also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart memory cards (SMC), secure digital cards (SD cards), flash memory cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0149] Example 4:

[0150] This embodiment also provides a computer-readable storage medium storing multiple instructions, which are loaded by a processor to cause the processor to execute the multi-level address model-based comprehensive human and room data management method according to any embodiment of the present invention. Specifically, a system or apparatus equipped with a storage medium may be provided, on which software program code implementing the functions of any of the above embodiments is stored, and the computer (or CPU or MPU) of the system or apparatus can read and execute the program code stored in the storage medium.

[0151] In this case, the program code read from the storage medium can itself implement the function of any of the above embodiments, and therefore the program code and the storage medium storing the program code constitute part of the present invention.

[0152] Examples of storage media used to provide program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, program code can be downloaded from a server computer via a communication network.

[0153] Furthermore, it should be clear that not only can the program code read by the computer be executed, but also the operating system or other components operating on the computer can be instructed based on the program code to perform some or all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0154] Furthermore, it is understood that the program code read from the storage medium is written to the memory set in the expansion board inserted into the computer or to the memory set in the expansion unit connected to the computer. Then, based on the instructions of the program code, the CPU or other components installed on the expansion board or expansion unit execute some and all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0155] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for comprehensive management of residential data based on a multi-level address model, characterized in that, The method is specifically as follows: A five-level core data table including a community table, a building table, a house table, a person-house relationship table and a personnel information table is constructed; the community table stores community-level basic information and serves as a top-level unit of address management; the building table is associated with the community ID and records building-level information; the house table is associated with the building ID and stores house detailed address information and house attributes; the personnel information table stores personnel basic information; the person-house relationship table is associated with the house ID and the personnel ID and records the multi-dimensional residence relationship between personnel and houses; Raw person-house data is accessed from multiple data sources, address information is intelligently parsed and standardized, and standardized data is obtained; A house unique identification system and a person-house multi-dimensional relationship model are established based on the standardized data; Data quality is evaluated and repaired to ensure the accuracy, integrity and consistency of the data; Standardized person-house data services are provided to the outside through data service interfaces.

2. The multi-level address model-based room data synthesis management method according to claim 1, characterized in that, The community table is used to comprehensively record the basic information, geographic location, property details and data sources of the community, and supports quick queries by region and name; The building table is used to record the detailed information of each building, including building characteristics, geographic location and belonging community relationship, and supports efficient queries by community, geographic location and building number, and ensures the uniqueness of building numbers in the same community; The house table is used to record the detailed information of each house, including specific location, house attributes, property information and complete address, and supports efficient queries by building, address and house status, and ensures the uniqueness of house numbers in the same building; The personnel information table is used to comprehensively record the basic information, occupation and work unit, contact information and emergency contact information of personnel, and supports quick queries and searches by key information such as identity information, name and mobile phone number; The person-house relationship table is used to record various relationships between personnel and houses, including the time validity of the relationship, the primary and secondary relationship and the data credibility evaluation, and supports efficient queries by personnel, houses, relationship type and time range, and ensures that the relationship type of the same personnel in the same house is not repeated. 3.The method of claim 1, wherein, Intelligent address parsing adopts a hybrid algorithm combining rules and machine learning, specifically as follows: Address segmentation: unstructured address text is segmented into semantic units; Element recognition: identify address elements such as road, alley, number and room; Building hierarchical relationship: build a hierarchical relationship of community-building-house based on address elements; Standardized output: generate structured standard address information.

4. The multi-level address model based room data synthesis management method according to claim 1, characterized in that, The establishment of the house unique identification system is specifically as follows: Generate a house unique code based on administrative region code, community code, building number and house number; Establish a house spatial location index to support geographic spatial queries; Set up a house state machine to manage the state transition of the house life cycle.

5. The multi-level address model based room data synthesis management method according to claim 1, characterized in that, The person-house multi-dimensional relationship model includes residence type relationship, time dimension, weight dimension and spatial dimension; Among them, the residence relationship type includes property owner, tenant, permanent resident and temporary resident; The time dimension includes relationship start time, end time and effective state; The weight dimension includes relationship confidence and data source authority score; The spatial dimension includes a primary residence, a secondary residence, and a work location.

6. The multi-level address model-based room data synthesis management method according to any one of claims 1 to 5, characterized in that, The method further includes data bloodline tracking, specifically as follows: Record the source, collection time and processing pipeline of each piece of data; Establish a data change history to support data tracing and version management; Generate a data quality report to visually display the entire data processing process.

7. A room data comprehensive management system based on a multi-level address model, characterized in that, The system includes: A core data table construction module for constructing five-level core data tables including a community table, a building table, a house table, a person-house relationship table, and a personnel information table; the community table stores community-level basic information as the top-level unit of address management; the building table is associated by community ID to record building-level information; the house table is associated by building ID to store detailed address information and house attributes; the personnel information table stores personnel basic information; and the person-house relationship table is associated by house ID and personnel ID to record multi-dimensional residence relationships between personnel and houses; A data collection, access and preprocessing module for accessing raw person-house data from multiple data sources and performing preprocessing operations such as data deduplication, format conversion and encoding unification; An address intelligent analysis and standardization module for intelligently analyzing and standardizing address information to obtain standardized data; An entity recognition and relationship construction module for establishing a house unique identification system and a multi-dimensional person-house relationship model based on the standardized data; A data quality evaluation and repair module for evaluating and repairing data quality to ensure accuracy, completeness and consistency of the data; A data service module for providing standardized person-house data services to the outside through a data service interface.

8. The multi-level address model based room data synthesis management system according to claim 7, characterized in that, The address intelligent analysis and standardization module includes: An address analysis submodule for analyzing unstructured addresses; An element recognition submodule for identifying key address elements such as roads, alleys, numbers and rooms; A hierarchical matching submodule for matching address elements with community and building libraries; A standardized output submodule for generating structured standard addresses and filling them into corresponding tables; The entity recognition and relationship construction module includes: A personnel deduplication and merging submodule for merging personnel entities based on multi-dimensional information such as identity information, name and mobile phone number; A house unique identification submodule for generating a house unique code based on geographic location and address elements; A relationship reasoning submodule for reasoning person-house relationships based on residence cards and household registration data; A confidence calculation submodule for comprehensively considering factors such as data source authority, update time and field matching degree; The data quality evaluation and repair module includes: An integrity evaluation submodule for checking missing mandatory fields; An accuracy evaluation submodule for verifying the legality of field formats such as identity information and address; A consistency evaluation submodule for checking cross-system data consistency; A timeliness evaluation submodule for evaluating data update frequency and timeliness; The data service module provides data services to the outside through a unified API interface, supporting queries such as querying associated houses by personnel, querying associated personnel by houses, querying population information by community, and address standardization services.

9. An electronic device, comprising: It includes: A memory and at least one processor; The memory stores computer execution instructions. The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor executes the multi-level address model-based room data comprehensive management method as claimed in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, and when the processor executes the computer-executable instructions, the multi-level address model-based room data comprehensive management method as claimed in any one of claims 1 to 6 is implemented.

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