Smart community service system based on real estate and community data collaboration

Abstract

The invention relates to the technical field of smart community service and real estate data collaboration, in particular to a smart community service system based on real estate and community data collaboration. According to the technical scheme, the system comprises a real estate and community data integration platform, a user demand portrait module, a full-scene service matching and scheduling module, a cross-scene service closed-loop management module, a community multi-party cooperation module, an intelligent operation decision-making module and a safety and authority management module, according to the method, deep fusion of real estate data and community data and full-process optimization of intelligent community service are realized, the barrier of the real estate data and the community data is broken through the collaborative operation of seven functional modules, accurate portraits required by users and intelligent matching and scheduling of full-scene service are realized, and the user experience is improved. A cross-scene service closed-loop and community multi-party cooperation system is constructed, the intelligent, personalized and efficient level of intelligent community service is remarkably improved, and an innovative technical solution is provided for development of the intelligent community service industry.

Description

Technical Field

[0001] This invention relates to the field of smart community services and real estate data collaboration technology, and in particular to a smart community service system based on real estate and community data collaboration. Background Technology

[0002] With the acceleration of urbanization, communities have become the core setting of residents' lives, and real estate, as a core component of communities, has a natural connection with community services. Currently, real estate services and community services are largely separate, forming a "lone wolf" situation.

[0003] In the real estate services sector, existing systems mostly focus on the transaction process itself, providing only basic functions such as property listings and transaction matching. They lack data integration with community life services, cannot provide users with accurate property recommendations based on information such as community amenities and resident atmosphere, and are also unable to convert real estate transaction users into long-term community service users.

[0004] In the community services sector, existing platforms are mostly limited to single convenient services (such as housekeeping and group buying), failing to fully utilize real estate data (such as apartment layout, building age, and homeowner family structure) to optimize service supply. For example, they cannot recommend suitable renovation plans based on apartment layout, nor can they predict maintenance needs based on building age, resulting in low service matching accuracy and a poor user experience.

[0005] The core problems and causes of existing technologies are as follows: Significant data barriers exist, with real estate and community data scattered across different systems, lacking a unified data integration and sharing mechanism. This prevents the full realization of data value and hinders cross-scenario service innovation. Service matching is inefficient; there is a lack of precise matching channels between community service supply and user demand. Demand profiling is not based on real estate characteristics and user behavior data, resulting in wasted service resources and difficulty in quickly meeting core user needs. Service breaks occur throughout the user lifecycle; key user demand scenarios such as real estate transactions, renovations, and community life lack effective connections, failing to achieve a closed loop of transactions, services, and retention, leading to low user stickiness and value conversion efficiency. Community governance and service collaboration is insufficient; multiple stakeholders, including property management companies, community committees, and service providers, lack a unified collaborative platform, resulting in delayed information transmission, low levels of refinement in community governance, and inadequate emergency response capabilities.

[0006] These problems make it difficult to improve the intelligence and personalization of smart community services, failing to meet residents' needs for a high-quality community life, and also limiting the integrated development of the real estate and community service industries.

[0007] In view of this, we propose a smart community service system based on the collaboration of real estate and community data to solve the existing problems. Summary of the Invention

[0008] The purpose of this invention is to provide a smart community service system based on the collaboration of real estate and community data, so as to solve the problems mentioned in the background art.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a smart community service system based on the collaboration of real estate and community data, including a real estate and community data integration platform, a user demand profiling module, a full-scenario service matching and scheduling module, a cross-scenario service closed-loop management module, a community multi-party collaboration module, an intelligent operation decision-making module, and a security and access control module. The modules work together to achieve deep integration of real estate data and community data and optimization of the entire process of smart community services.

[0010] Furthermore, the real estate and community data integration platform is responsible for integrating multi-dimensional data from real estate, communities, and users to build a unified data resource pool, providing data services for various functional modules, and possessing data collection, data cleaning and standardization, data storage and management, and data interface service functions.

[0011] Furthermore, the user demand profile module constructs multi-dimensional user demand profiles based on integrated real estate and community data, providing a basis for service matching. It has profile dimension construction functions, intelligent tag generation functions, and dynamic profile update functions.

[0012] Furthermore, the full-scenario service matching and scheduling module, based on user demand profiles and resource data from the data integration platform, enables the matching, scheduling, and execution of community services. It features service resource integration, matching algorithm functionality, intelligent service scheduling, and service quality monitoring capabilities.

[0013] Furthermore, the cross-scenario service closed-loop management module connects multiple service scenarios such as real estate transactions, renovation and remodeling, and community life, realizing closed-loop management of services throughout the user's entire life cycle. It has functions related to real estate transactions, extended services for renovation and remodeling, value-added services for community life, and service tracking and repurchase reminders.

[0014] Furthermore, the community multi-party collaboration module establishes a collaborative platform for multiple stakeholders, including property management companies, community neighborhood committees, service providers, residents, and government departments, optimizing community governance and service collaboration processes. It features collaboration functions for property management companies, community neighborhood committees, service providers, and government and enterprises.

[0015] Furthermore, the intelligent operation decision-making module, based on the massive data of the data integration platform, uses AI algorithms to analyze and mine data, providing intelligent decision support for community service operations. It has functions such as demand forecasting, resource optimization and allocation, event planning support, and risk warning.

[0016] Furthermore, the security and access control module ensures system data security, user privacy security, and service process compliance, standardizes the operation permissions of each user role, and has data security protection functions, user privacy protection functions, hierarchical access control functions, operation log and audit functions.

[0017] Compared with the prior art, the beneficial effects of the present invention are: This invention, through the coordinated operation of seven functional modules, breaks down the barriers between real estate data and community data, achieving precise user demand profiling, intelligent matching and scheduling of services across all scenarios, and constructing a cross-scenario service loop and a multi-party community collaboration system. This significantly improves the intelligence, personalization, and efficiency of smart community services, addressing many pain points of existing technologies. The real estate and community data integration platform integrates and standardizes multi-dimensional data, breaking down data silos and enabling real estate and community data to mutually empower each other, providing solid data support for precise services and intelligent operations, maximizing data value, and fully releasing data value. User demand profiles built based on real estate and community data, combined with intelligent matching algorithms, achieve precise matching between services and user needs, reducing invalid service recommendations, improving user service satisfaction and service resource utilization, and achieving precise and efficient service matching. It connects service links across multiple scenarios such as real estate transactions, renovations, and community life, achieving service coverage throughout the user's entire lifecycle, effectively improving user stickiness and value conversion efficiency, forming a virtuous cycle of transactions, services, repeat purchases, and recommendations, and realizing a cross-scenario service loop. The community multi-party collaboration module... A unified collaboration platform has been provided for property management companies, neighborhood committees, and service providers, optimizing information transmission and business processes, reducing communication costs, and improving the responsiveness and precision of community governance and services, resulting in a significant improvement in community collaboration efficiency. The intelligent operation decision-making module, through data mining and AI prediction, provides operators with intelligent suggestions on demand forecasting, resource allocation, and event planning, reducing the subjectivity and blindness of operational decisions, improving operational efficiency and the scientific nature of decision-making, and achieving an intelligent upgrade in operational decision-making. The security and access management module has constructed a comprehensive security protection system from multiple aspects, including data security, privacy protection, and access control, ensuring the security of system data and user information, standardizing the operational behavior of various users, ensuring the compliance of service processes, and achieving comprehensive and reliable security. For the first time, deep integration and collaborative application of real estate data and community data have been achieved, constructing a full-process smart community service system encompassing data, profiling, matching, services, collaboration, and decision-making. This solves core problems in traditional community services such as data fragmentation, inefficient matching, and insufficient collaboration, providing innovative technical solutions for the development of the smart community service industry, with significant practicality and industry innovation. Attached Figure Description

[0018] Figure 1This is a functional architecture diagram of the smart community service system based on real estate and community data collaboration according to the present invention. Detailed Implementation

[0019] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0020] Example 1 like Figure 1 As shown, a smart community service system based on the collaboration of real estate and community data includes a real estate and community data integration platform, a user demand precision profiling module, a full-scenario service matching and scheduling module, a cross-scenario service closed-loop management module, a community multi-party collaboration module, an intelligent operation decision-making module, and a security and access management module. The modules work together to achieve deep integration of real estate data and community data and full-process optimization of smart community services.

[0021] The real estate and community data integration platform serves as the core data support of the system. It is responsible for integrating multi-dimensional data from real estate, communities, and users, building a unified data resource pool, and providing data services for various functional modules. It has data collection, data cleaning and standardization, data storage and management, and data interface service functions.

[0022] For the data collection function, data is collected through multiple channels, including basic real estate data, real estate transaction data, basic community data, community service data, and user behavior data. Among them, basic real estate data includes address, apartment type, area, year of construction, and property rights information; real estate transaction data includes transaction price, sales cycle, and transaction status; basic community data includes community name, building distribution, supporting facilities, and property information; community service data includes housekeeping needs, group purchase orders, repair records, and activity participation; and user behavior data includes property browsing history, service appointment records, community interaction behavior, and consumption preferences.

[0023] For data cleaning and standardization functions, the collected heterogeneous data is cleaned, including removing duplicate data, correcting erroneous data, and supplementing missing data. The data is then formatted according to a unified standard to establish a standardized data classification system and data dictionary, ensuring the accuracy and consistency of the data.

[0024] For data storage and management, a distributed storage architecture is adopted to classify and store structured, semi-structured, and unstructured data. A data update mechanism is also established to ensure real-time updates of property data, community data, and service data. Structured data includes basic property information and transaction records; semi-structured data includes user reviews and service feedback; unstructured data includes property photos and community activity videos; property data includes changes in property ownership; and community data includes additions to supporting facilities.

[0025] For data interface service functions, standardized data interfaces are provided to support data calls and interactions between various modules within the system and external third-party platforms, enabling data sharing and reuse; among them, external third-party platforms include government systems, property management systems, and service provider systems.

[0026] The user demand profiling module constructs multi-dimensional user demand profiles based on integrated real estate and community data, providing a basis for precise service matching. It features profile dimension construction, intelligent tag generation, and dynamic profile update functions.

[0027] For the profile dimension construction function, a comprehensive profile system is established, covering property characteristics, family structure, behavioral preferences, and demand tags. Among them, property characteristics include apartment type, house area, year of construction, and decoration status; family structure includes number of family members, age distribution, occupation characteristics, and income level; behavioral preferences include service consumption frequency, consumption amount, activity participation type, and browsing focus; and demand tags include essential service tags, improvement service tags, and potential demand tags.

[0028] The intelligent tag generation function uses AI algorithms to analyze user data and automatically generate user tags. For example, it generates a tag for "aging-friendly renovation needs" based on "three-bedroom, two-living room apartment, elderly family members, and browsing aging-friendly renovation services"; and a tag for "new home furnishing needs" based on "newlywed families, frequent housekeeping and cleaning appointments, and participation in home furnishing group buying".

[0029] The dynamic profile update function captures changes in user data in real time and automatically updates user profile tags to ensure the timeliness and accuracy of the profile, providing precise support for subsequent service matching; among which, changes in user data include changes in real estate transaction status, changes in service consumption behavior, and adjustments in family structure.

[0030] The full-scenario service matching and scheduling module is based on user demand profiles and resource data from the data integration platform to achieve accurate matching, intelligent scheduling, and efficient execution of community services. It has service resource integration functions, accurate matching algorithm functions, intelligent service scheduling functions, and service quality monitoring functions.

[0031] Regarding the service resource integration function, it integrates various community service resources, including housekeeping services, repair services, daily consumption services, renovation and remodeling services, community activity services, and government service agency services. It establishes a standardized service resource database, recording service provider information, service scope, service prices, and service quality ratings. Among them, housekeeping services include cleaning, nannies, and childcare workers; repair services include appliance repair, house repair, and pipe dredging; daily consumption services include premium group buying, direct sourcing from the place of origin, and fresh food delivery; renovation and remodeling services include partial renovation, whole house renovation, and age-friendly renovation; community activity services include parent-child activities, cultural lectures, and neighborhood interactions; and government service agency services include social security consultation and document processing appointments.

[0032] For the precise matching algorithm function, based on user demand profiles and service resource library information, the intelligent matching algorithm recommends suitable services to users. For example, it recommends a combination service of "deep cleaning + formaldehyde testing in the children's room" for "a two-bedroom old house with a young child"; and recommends a service of "elderly-friendly handrail installation + annual appliance repair card" for "a three-bedroom house built 20 years ago with an elderly couple".

[0033] The intelligent service scheduling function intelligently allocates service resources based on the urgency of service needs, service location distribution, and the service capacity and availability of service providers. For example, for urgent repair needs (such as burst water pipes), it prioritizes matching the nearest and currently available repair personnel; for bulk group purchase orders, it optimizes delivery routes and improves delivery efficiency.

[0034] For the service quality monitoring function, a service quality evaluation system is established. Service quality is monitored and rated through multiple dimensions such as user ratings, service process records, and after-sales feedback, providing a reference for subsequent service resource optimization and matching. Among them, the service process records include on-site arrival time, service duration, and service results.

[0035] The cross-scenario service closed-loop management module connects multiple service scenarios such as real estate transactions, renovation and reconstruction, and community life, realizing closed-loop management of services throughout the user's entire life cycle. It has functions such as real estate transaction-related services, renovation and reconstruction extended services, community life value-added services, and service tracking and repurchase reminders.

[0036] For real estate transaction-related service functions, relevant services are automatically associated with real estate transaction users. For example, services such as "deep cleaning, formaldehyde removal, and interior design consultation" are pushed to homeowners who have just completed a second-hand home transaction; and services such as "moving services, appliance rental, and community move-in guide" are pushed to renters.

[0037] For extended services related to home renovation, we provide matching services for users who are renovating. For example, after the renovation is completed, we can recommend services such as "group purchase of soft furnishings, housekeeping and cleaning, and appliance cleaning"; during the renovation process, we can recommend services such as "renovation waste removal and temporary storage space rental".

[0038] For value-added services in community life, recommendations are provided based on users' long-term needs in the community. For example, personalized group-buying products are pushed based on users' consumption habits; new activities of interest are pushed based on their participation records in community activities; and services such as "nanny and baby product group-buying" are pushed based on changes in family structure (such as the birth of a newborn).

[0039] The service tracking and repurchase reminder function records the user's service history and automatically sends repurchase reminders for periodic services; it also conducts satisfaction surveys for users after the service is completed and recommends subsequent suitable services based on the feedback, forming a closed loop of service, feedback, and re-service; among them, periodic services include monthly house cleaning and quarterly appliance cleaning.

[0040] The community multi-party collaboration module builds a collaborative platform for multiple stakeholders, including property management companies, neighborhood committees, service providers, residents, and government departments, optimizing community governance and service collaboration processes. It has functions for property management collaboration, neighborhood committee collaboration, service provider collaboration, and government-enterprise collaboration.

[0041] For property management collaboration functions, online office tools are provided for property management companies, including functions such as owner information management, repair request processing, property fee collection, announcement posting, and community facility maintenance records. It also enables efficient communication between property management and residents; residents can submit repair requests and report problems through the system, which property management will receive and process in real time, with the processing progress updated to residents in real time.

[0042] Regarding the collaborative functions of community neighborhood committees, support is provided for them to release information such as community announcements, policy interpretations, and activity notices; organize community voting (such as community public affairs decision-making), volunteer recruitment, and neighborhood mutual assistance activities; and collect residents' opinions and suggestions to provide reference for community governance.

[0043] The system provides service providers with functions such as order management, service personnel scheduling, service progress tracking, and payment settlement. Suppliers can receive service orders, provide service progress feedback, and handle after-sales issues through the system, achieving efficient collaboration with the platform and users.

[0044] Regarding the government-enterprise collaboration function, it connects with the government's e-government service system to provide residents with services such as e-government service appointments, policy consultations, and information inquiries; it also pushes relevant data on community governance (such as the distribution of community service needs, facility usage, and hot issues raised by residents) to government departments to provide data support for government decision-making.

[0045] The intelligent operation decision-making module is based on the massive data of the data integration platform. It uses AI algorithms to analyze and mine the data, providing intelligent decision support for community service operations. It has functions such as demand forecasting, resource optimization and allocation, event planning support, and risk warning.

[0046] For demand forecasting, the system analyzes historical service data, changes in user profiles, seasonal factors, and community dynamics to predict the demand intensity and trends for different services. For example, it predicts the demand growth for housekeeping services and community activities during holidays; and it predicts peak demand for housing repairs in older residential areas.

[0047] For the resource optimization and allocation function, based on the demand forecast results, it provides the operator with service resource optimization suggestions, including the recruitment and allocation of service providers, the training and scheduling of service personnel, and the formulation of material procurement plans, so as to achieve reasonable allocation of resources and reduce operating costs.

[0048] For event planning support, based on user profiles and community interaction data, suitable community event themes, formats, and times are recommended. For example, for communities with a high concentration of young families, parent-child interactive activities are recommended; for communities with a large number of elderly residents, health and wellness lectures are recommended.

[0049] For the risk warning function, potential risks are identified through data analysis, including service quality risks (such as an abnormally high complaint rate for a certain supplier), security risks (such as frequent entry of strangers into the community or abnormal backgrounds of service personnel), and operational risks (such as a sudden drop in the order volume of a certain type of service), and warnings are issued in a timely manner to assist operators in taking countermeasures.

[0050] The security and access control module ensures system data security, user privacy security, and service process compliance, standardizes the operation permissions of each user role, and has data security protection functions, user privacy protection functions, hierarchical access control functions, operation log and audit functions.

[0051] For data security protection, sensitive data is stored and transmitted using encrypted technologies to prevent data leakage. A data backup mechanism is established to regularly back up system data, ensuring data security and recoverability. Sensitive data includes user ID numbers, bank card information, and property ownership information; encrypted storage and transmission technologies include AES encryption and HTTPS protocol.

[0052] Regarding user privacy protection features, we adhere to relevant privacy regulations, clearly define the scope of data collection and use, and allow users to independently set privacy permissions (such as whether to allow service providers to access their personal contact information). User behavior data is anonymized before being used for data analysis to protect user privacy.

[0053] For the hierarchical access control function, multiple levels of user roles are set up, and corresponding operation permissions are assigned to different roles. For example, resident users can only view service information and orders related to themselves; property management staff can only manage relevant affairs in their own community; and the super administrator has full system operation permissions. Among them, the multi-level user roles include super administrator, operations administrator, property management staff, service providers, resident users, and government staff.

[0054] The operation log and auditing functions record all users' system operation behaviors, forming detailed operation logs that support log querying and auditing, ensuring the traceability of operation behaviors and preventing unauthorized operations. System operation behaviors include login, data query, order submission, permission changes, etc.

[0055] The working principle of a smart community service system based on real estate and community data collaboration, as described in Embodiment 1, is as follows: A smart community service system based on the collaboration of real estate and community data includes a real estate and community data integration platform, a user demand precision profiling module, a full-scenario service matching and scheduling module, a cross-scenario service closed-loop management module, a community multi-party collaboration module, an intelligent operation decision-making module, and a security and access control module. It supports various users / partners such as residents, property management companies, community neighborhood committees, service providers, and government departments to realize full-process smart community services through multiple terminals (mini-program, APP, and PC).

[0056] During system deployment and initialization: For system deployment, a cloud-native architecture is adopted, supporting elastic scaling to ensure stable operation under high-concurrency scenarios. Simultaneously, third-party services are integrated, enabling data exchange through API interfaces. These high-concurrency scenarios include community group-buying activities and large-scale community event registration; third-party services include payment interfaces, map services, real-name authentication services, and government data interfaces.

[0057] For data initialization, in the initial stage of system launch, basic data initialization was completed through a combination of batch import and manual entry. Importing basic property data (information such as addresses, apartment types, areas, and construction years of 100 buildings and 5,000 households in a community in Changshu, obtained from the local housing management department), and basic community data (community name, building distribution, supporting facilities such as kindergartens, supermarkets, fitness equipment locations, and property management contact information); and entering information on the first batch of cooperative service providers (service scope, prices, and qualification certification information of 50 housekeeping companies, 30 repair service providers, and 20 fresh food suppliers, etc.).

[0058] In the process of user / partner onboarding: For residential users, they can complete mobile phone number registration and real-name authentication (uploading ID card photos or facial recognition) through the mini-program / app, fill in their home address (selecting the community, building, unit, and room number), and the system will automatically link the corresponding basic property data to complete the onboarding process.

[0059] For property management companies / neighborhood committees: Submit an application to join via PC, upload organizational qualification certificates, and after the system operator approves the application, assign a dedicated management account and permissions to complete the onboarding process; the organizational qualification certificates include business license and filing certificate.

[0060] For service providers: Submit an application for onboarding via PC, and upload materials such as business license, service qualification certificate, and health certificate of service personnel (such as domestic workers). After the operator reviews and approves the application (including qualification review and on-site inspection), the service provider will be entered into the service resource library and the onboarding will be completed.

[0061] For government departments, access to government data interfaces is used to obtain exclusive access rights, enabling data sharing and business collaboration.

[0062] In the operation and implementation of the real estate and community data integration platform: For data collection, the system collects data in real time through multiple channels. For example, real estate transaction data is obtained by connecting to the real estate transaction platform interface to obtain a user's second-hand house transaction record (transaction price of 1.2 million yuan, transaction date of 202X, month X, day X); community service data is collected through user order submissions (such as a user booking a housekeeping service on 202X, month X, day X) and service provider feedback (such as appliance repair records uploaded by repair service providers); user behavior data is automatically recorded through system logs (such as a user browsing the age-friendly renovation service page 3 times and registering for community parent-child activities).

[0063] For data cleaning and standardization, the system automatically cleans the collected data, such as removing duplicate service orders and correcting incorrectly entered house area data by users (changing "120 square meters" to "120m"). 2 (Add missing contact information for service providers.) Classify and archive data according to unified standards. For example, real estate data is classified into categories such as one-bedroom, one-living room, two-bedroom, one-living room, and three-bedroom, two-living room by "apartment type," and service data is classified into categories such as housekeeping, repair, and group buying by "service type."

[0064] For data storage and updates, a distributed database is used to store various types of data. Structured data is stored in a relational database, and unstructured data is stored in an object storage service. A real-time update mechanism is established. For example, when property ownership changes, the property information in the system is automatically synchronized and updated through the government data interface; when new community facilities are added (such as a new community library), the property management submits an update application through the system, and the community's basic data is automatically updated after approval. The structured data includes basic property information and order records, while the unstructured data includes property images and service process photos.

[0065] For data interface services, the system provides standardized data interfaces. For example, the property management system can use the interface to obtain the owner repair records of the community it manages; service providers can use the interface to receive user service orders; and the government system can use the interface to obtain community service demand distribution data to provide a reference for policy making.

[0066] In the implementation of the precise user needs profiling module: Regarding the construction of user profile dimensions, the system builds a comprehensive user profile with the following property features: "three bedrooms and two living rooms, 120 square meters". 2 The data shows that the family structure consists of 3 members, the couple is 35 years old, and the child is 5 years old. The behavioral preferences data shows that the family has booked housekeeping services 2 times, purchased fresh produce 10 times, and browsed children's room decoration cases 5 times in the past 3 months.

[0067] For intelligent tag generation, AI algorithms automatically generate tags based on user data, such as "families with young children", "high-frequency demand for housekeeping services", "potential demand for children's room decoration", and "preference for fresh food group buying".

[0068] For dynamic updates to user profiles, if a user's family structure changes (such as the birth of a newborn), the system will automatically add "Newborn Family" and "Childcare Service Needs" tags after the user updates their family information through the system. If a user frequently browses home appliance repair services recently, the system will add a "Potential Home Appliance Repair Needs" tag to ensure that the profile matches the user's needs in real time.

[0069] In the operation and implementation of the full-scenario service matching and scheduling module: Regarding service resource integration, the system integrates a service resource database covering various community services. For example, housekeeping services include daily cleaning (30 yuan / hour), deep cleaning (300 yuan / set), and childcare (6000 yuan / month); repair services include appliance repair (starting from 50 yuan / time) and pipe unclogging (80 yuan / time); and fresh produce group buying includes local vegetable packages (39 yuan / portion) and imported fruit packages (69 yuan / portion). The system also records the service range of each supplier (e.g., a housekeeping company covers XX community in Changshu and the surrounding 3 kilometers) and service quality rating (e.g., 4.8 points / 5 points).

[0070] For precise matching, User A's profile is tagged as "three bedrooms and two living rooms, family with young children, and high frequency of housekeeping service needs". The system recommends a combination service of "daily cleaning + special disinfection of children's room" (suitable for family structure and needs) through matching algorithm, and prioritizes housekeeping companies with a service quality rating of 4.8 or above that cover the user's community.

[0071] For service dispatch, when user B submits an "emergency repair for burst water pipe" request, the system automatically locates the user's location (Unit 3, Building 5, XX Community, Changshu), queries the service resource database for the three nearest (within 1 kilometer) and currently available repair service providers, and automatically pushes the order to them. Once one of the service providers accepts the order, the system immediately informs the user that "the repair personnel have accepted the order and are expected to arrive within 20 minutes," and updates the repair personnel's location in real time.

[0072] Regarding service quality monitoring, after user C completed the house cleaning service, the system automatically pushed a satisfaction survey (three dimensions: service attitude, cleaning effect, and on-time arrival). The user gave a score of 4.5 and commented, "The cleaning was very thorough, but the tools were a bit old." The system synchronized this evaluation to the service provider's backend, updated the provider's service quality rating (from 4.7 to 4.6), and reminded the provider to replace the tools.

[0073] In the operation and implementation of the cross-scenario service closed-loop management module: For real estate transaction-related services, when user D has just completed a second-hand house transaction in Changshu XX Community, the system automatically links his real estate transaction data and pushes a service package of "deep house cleaning (20% off for new owners), formaldehyde removal, and free appointment for decoration design consultation". When the user selects to book deep cleaning and decoration design consultation, the system generates the corresponding service order and synchronizes it to the relevant service provider.

[0074] For renovation and renovation extension services, after user E completes the renovation of their new house, the system will push the following services based on their renovation records: "Group purchase of soft furnishings (exclusive discounts on curtains and lighting fixtures), construction waste removal (discounted price of 50 yuan / truck), and home appliance cleaning package (199 yuan / 3 units)". After the user makes an appointment for the home appliance cleaning service, the system will automatically send a repurchase reminder one month after the service is completed: "Your home appliance cleaning service has been over for one month. It is recommended to clean regularly. Make an appointment now to enjoy a 10% discount."

[0075] For value-added services in community life, the system pushes a "Quarterly Organic Vegetable Subscription Service (999 yuan / 3 months, 12 deliveries per month)" based on user F's consumption record (8 purchases of fresh produce in group buying per month, preference for organic vegetables); and pushes a new "Calligraphy Exhibition" registration notice based on user F's participation record in community activities (participation in calligraphy interest class).

[0076] Regarding service tracking and repeat purchase reminders, for user G's twice-monthly house cleaning service, the system automatically sends a reminder on the 1st and 15th of each month: "Your house cleaning service is about to expire, would you like to make a reservation?"; user G also requested that "the cleaning time be adjusted to the weekend", the system recorded this request and synchronized it with the service provider, and subsequently automatically recommended available time slots based on weekends.

[0077] In the implementation of the community multi-party collaboration module: Regarding property management collaboration, resident user H submits a repair request for a "damaged hallway light" through the system. The system automatically pushes the request to the community's property management backend. After receiving the order, the property management staff updates the processing status to "under repair" and arranges for a repairman to come and fix it. After the repair is completed, photos of the repair are uploaded, and the system automatically synchronizes the processing result to user H. Once the user confirms, the repair request is closed. At the same time, the property management issues a "Community Elevator Maintenance Notice (Date: [Date] - [Date])" through the system, which is received by all residents.

[0078] For collaboration with community neighborhood committees, the system facilitates various initiatives. These include: publishing "Community Civilization Creation Voting Activities" where residents can vote online; issuing notices for "Senior Health Lectures" where residents can register; monitoring registration numbers and lists in real-time; and publishing event review photos after the event, which residents can like and comment on. The system also collects residents' suggestions on community facilities, compiling a needs report for submission to relevant departments.

[0079] For service provider collaboration, service provider I receives user housekeeping service orders through the system, views order details (service address, time, and requirements), arranges service personnel to provide services, uploads service photos during the service process, and submits a settlement application after the service is completed. The system automatically verifies the order information and user evaluation, completes the payment settlement, and transfers the funds to the supplier's account.

[0080] For government-enterprise collaboration, the system is connected to the Changshu government service system, allowing residents to book government services such as social security consultation and real estate registration inquiries through the system. The system regularly pushes community service data reports to government departments (such as a 10% monthly increase in demand for housekeeping services and a 30% increase in demand for age-friendly renovations in a certain community), providing data support for the government to formulate relevant community service policies.

[0081] In the implementation of the intelligent operation decision-making module: For demand forecasting, the system analyzed community service data from the past 6 months and found that the demand for housekeeping and air conditioning repair increased significantly from June to August each year (25% month-on-month). It predicted that the number of housekeeping orders in the community would reach 500 and the demand for air conditioning repair would reach 300 in June 202X, and issued a demand warning to the operator.

[0082] Regarding resource optimization, based on demand forecasting results, the system recommends that operators add 10 housekeeping service providers and 5 air conditioning repair service providers to the platform, while reminding existing service providers to increase their service personnel reserves. For community group buying demand, it is recommended that fresh food suppliers prepare goods in advance (such as increasing the stock of organic vegetables by 30%), optimize delivery routes, and ensure timely delivery.

[0083] For event planning support, the system analyzed the user profile of a certain community and found that 60% of users were families aged 30-45, of which 40% had young children. It was recommended to plan a "Parent-Child Fun Sports Meet" community event, suggesting the event be held on a weekend morning in the community's central square, and including parent-child games and children's gift redemption activities. After the event went live, 200 families registered, far exceeding expectations.

[0084] Regarding risk warnings, the system detected that the complaint rate of a certain domestic service provider rose from 5% to 20% within one month (mainly complaints about service personnel being late and poor service quality), and immediately issued a risk warning to the operator. After the operator intervened and investigated, it was found that the provider had insufficient training for its service personnel, and the provider was ordered to rectify the situation. During the rectification period, the provider's order-taking privileges were suspended to avoid affecting the user experience.

[0085] In the operation and implementation of the security and access control module: For data security protection, sensitive data such as users' ID card numbers and bank card information are stored using the AES-256 encryption algorithm, and data transmission is encrypted using the HTTPS protocol to prevent data theft. The system automatically backs up all data every morning at midnight. Backup data is stored on an off-site server and retains six months of backup records, allowing for rapid recovery in the event of data loss.

[0086] Regarding user privacy protection, users can choose privacy permissions in the system settings. For example, if they turn off "Allow service providers to obtain mobile phone numbers", the system will ensure communication through virtual number forwarding. When user behavior data is used for data analysis, user names, ID numbers and other identifying information will be automatically removed to ensure anonymization.

[0087] For hierarchical access control, the system sets different role permissions. For example, resident users can only view their own orders, book services, and participate in community activities; property management staff can only manage the handling of repair requests, the issuance of notices, and the query of resident information (with sensitive information hidden) within their community; service providers can only view their own orders and submit settlement applications; operations administrators have permissions such as user management, supplier review, and data viewing; and super administrators have full system operation permissions.

[0088] For operation logs and auditing, the system records all user actions, such as user login time, appointment service records, property management staff's procedures for handling repair requests, and records of suppliers modifying service prices. Operators can audit these operation logs, for example, to verify whether the processing of a particular order is compliant, or whether a supplier has illegally modified prices, ensuring the traceability of system operations.

[0089] Through the specific implementation methods described above, this system achieves deep integration of real estate data and community data, and optimizes the entire process of smart community services. It effectively solves the pain points of existing technologies such as data fragmentation, inefficient matching, and insufficient collaboration. It can be widely applied to various urban communities, providing residents with personalized and efficient smart community services, providing efficient collaboration tools for property management companies, community neighborhood committees, service providers, and other entities, and providing intelligent decision support for operators. It has strong practicality and promotional value.

[0090] Specifically, this involves addressing the integration and collaboration of real estate and community data, breaking down data barriers, and achieving deep integration and value mining of the two types of data; resolving the issue of accurately matching community services with user needs by combining real estate characteristics and community data to build user profiles and improve the targeting and effectiveness of service supply; addressing the problem of broken service loops throughout the user's lifecycle by achieving seamless integration from real estate transactions and renovations to community life services, thereby improving user stickiness and value conversion efficiency; addressing the problem of insufficient collaboration among multiple stakeholders in the community by building a unified collaboration platform, optimizing information transmission and business collaboration processes, and improving the efficiency of community governance and services; and resolving the issue of intelligent operation of community services by using data-driven approaches to achieve dynamic allocation of service resources, demand forecasting, and risk warning, thereby reducing operating costs.

[0091] The above specific embodiments are merely several preferred embodiments of the present invention. Based on the technical solutions of the present invention and the relevant teachings of the above embodiments, those skilled in the art can make various alternative improvements and combinations to the above specific embodiments.

Claims

1. A smart community service system based on the collaboration of real estate and community data, characterized in that: It includes a real estate and community data integration platform, a user demand profiling module, a full-scenario service matching and scheduling module, a cross-scenario service closed-loop management module, a community multi-party collaboration module, an intelligent operation decision-making module, and a security and access management module. These modules work together to achieve deep integration of real estate data and community data and full-process optimization of smart community services.

2. The smart community service system based on real estate and community data collaboration according to claim 1, characterized in that: The real estate and community data integration platform is responsible for integrating multi-dimensional data from real estate, communities, and users to build a unified data resource pool, providing data services for various functional modules, and possessing data collection, data cleaning and standardization, data storage and management, and data interface service functions.

3. The smart community service system based on real estate and community data collaboration according to claim 1, characterized in that: The user demand profiling module constructs multi-dimensional user demand profiles based on integrated real estate and community data, providing a basis for service matching. It has the functions of profile dimension construction, intelligent tag generation, and dynamic profile update.

4. A smart community service system based on real estate and community data collaboration according to claim 1, characterized in that: The full-scenario service matching and scheduling module is based on user demand profiles and resource data from the data integration platform to achieve matching, scheduling and execution of community services. It has service resource integration functions, matching algorithm functions, intelligent service scheduling functions, and service quality monitoring functions.

5. A smart community service system based on real estate and community data collaboration according to claim 1, characterized in that: The cross-scenario service closed-loop management module connects multiple service scenarios such as real estate transactions, renovation and remodeling, and community life, realizing closed-loop management of services throughout the user's entire life cycle. It has functions such as real estate transaction-related services, renovation and remodeling extended services, community life value-added services, and service tracking and repurchase reminders.

6. A smart community service system based on real estate and community data collaboration according to claim 1, characterized in that: The community multi-party collaboration module builds a collaborative platform for multiple stakeholders, including property management companies, neighborhood committees, service providers, residents, and government departments, optimizing community governance and service collaboration processes. It has functions for property management collaboration, neighborhood committee collaboration, service provider collaboration, and government-enterprise collaboration.

7. A smart community service system based on real estate and community data collaboration according to claim 1, characterized in that: The intelligent operation decision-making module is based on the massive data of the data integration platform. It uses AI algorithms to analyze and mine the data, providing intelligent decision support for community service operations. It has functions such as demand forecasting, resource optimization and allocation, event planning support, and risk warning.

8. A smart community service system based on real estate and community data collaboration according to claim 1, characterized in that: The security and access control module ensures system data security, user privacy security, and service process compliance, standardizes the operation permissions of each user role, and has data security protection functions, user privacy protection functions, hierarchical access control functions, operation log and audit functions.

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