Intelligent community management system based on artificial intelligence

Abstract

The invention discloses an intelligent community management system based on artificial intelligence, and relates to the field of intelligent community management, and the intelligent community management system comprises a scene perception pre-judgment module, a resource dynamic scheduling module, a man-machine cooperation interaction module and a full-period data closed-loop module, the scene perception pre-judgment module is a core data input end, and the full-period data closed-loop module is a core data output end. The information acquisition module is responsible for acquiring community multi-dimensional information and pre-judging a potential scene and providing a decision basis for subsequent modules; and the resource dynamic scheduling module is used for undertaking a result of the scene perception pre-judgment module and realizing adaptive allocation of human and material resources of the community. According to the intelligent community management system based on artificial intelligence, active risk avoidance and demand response can be realized, through the multi-modal fusion acquisition and behavior intention pre-judgment sub-module of the scene perception pre-judgment module, the existing passive alarm limitation is broken through, the problem can be recognized in advance, sufficient time is reserved for disposal, and the risk avoidance and demand response can be realized. The community safety risk and the resident life inconvenience are reduced.

Description

Technical Field

[0001] This invention relates to the field of smart community management, and in particular to a smart community management system based on artificial intelligence. Background Technology

[0002] Current smart community management systems mostly adopt a passive response and isolated module operation mode, relying on manual triggering commands or single device alarms, which has obvious technical limitations.

[0003] At the scene perception level, existing systems mostly rely on traditional surveillance cameras to collect video data, focusing on post-event tracing rather than pre-event prediction. Moreover, the data collection dimensions are single, and it is impossible to integrate multi-dimensional non-privacy data such as gait, equipment vibration, and water and electricity fluctuations, making it difficult to accurately capture residents' potential needs and facility hazards.

[0004] In terms of resource allocation, existing technologies use a fixed allocation mechanism, which means that resources such as elevators and maintenance personnel cannot be dynamically adjusted according to the real-time situation in the community, easily leading to the problem of both idle resources and backlogged demand.

[0005] In terms of human-computer interaction, most systems require residents to download a dedicated app to initiate services, which is cumbersome and unfriendly to the elderly, children, and other groups. Furthermore, they lack the ability to provide personalized push notifications based on user behavior.

[0006] At the data application level, the data in each module is fragmented and lacks a complete closed-loop optimization mechanism. The system's operational accuracy is difficult to improve over time and cannot meet the community's upgrade needs from responding to problems to providing proactive services.

[0007] Therefore, it is necessary to propose an artificial intelligence-based smart community management system to solve the above problems. Summary of the Invention

[0008] The main objective of this invention is to provide an artificial intelligence-based smart community management system that can effectively solve the problems in the background technology.

[0009] To achieve the above objectives, the technical solution adopted by the present invention is as follows: An AI-based smart community management system includes a scene perception and prediction module, a resource dynamic scheduling module, a human-machine collaborative interaction module, and a full-cycle data closed-loop module. The scene perception and prediction module is the core data input terminal, responsible for collecting multi-dimensional information of the community and predicting potential scenarios, providing decision-making basis for subsequent modules. The resource dynamic scheduling module is used to receive the results from the scene perception and prediction module to realize the adaptive allocation of community human and material resources. The human-machine collaborative interaction module is used to build a bridge between residents, staff and the system to achieve seamless interaction and precise response; The full-cycle data closed-loop module is used to realize the data flow closed loop and continuously optimize the system's prediction and scheduling accuracy.

[0010] Preferably, the scene perception and prediction module specifically includes: Multimodal fusion perception submodule: Collects non-privacy data through IoT terminals deployed in community public areas and at residents' doorsteps, processes it in real time using AI edge computing terminals, and distinguishes community residents from outsiders through gait recognition; Behavioral Intent Prediction Submodule: Based on a deep learning model, it analyzes the collected behavioral data to predict residents' needs and potential risks.

[0011] Preferably, the scene perception and prediction module further includes a scene classification and calibration submodule: classifying the prediction results according to the degree of urgency, including ordinary services, attention reminders, and emergency handling, and simultaneously marking the scene-related areas and required resources.

[0012] Preferably, the resource dynamic scheduling module specifically includes: Emergency resource intelligent allocation submodule: Based on the scenario calibration results, it automatically matches the corresponding resources and sends scheduling instructions; Public facilities adaptive control submodule: Links community public facilities and equipment to adjust their operating status according to predicted scenarios.

[0013] Preferably, the resource dynamic scheduling module also includes a human resource collaborative allocation submodule: updating the on-duty status of property and community staff in real time and intelligently allocating tasks based on scenario requirements.

[0014] Preferably, the human-computer collaborative interaction module specifically includes: Dual-modal interactive terminal sub-module: Deploy voice and motion-sensing interactive terminals in apartment buildings and community service centers. Residents do not need to download an APP; they can initiate service requests through voice commands or simple gestures, and the system responds based on the predicted results. Personalized service push submodule: Based on resident behavior data and prediction results, proactively push personalized services.

[0015] Preferably, the full-cycle data closed-loop module includes: Data cleaning and calibration submodule: Filters the data streams generated by each module, removes abnormal data, retains valid behavior and scheduling result data, and updates resident behavior profiles and equipment operation baseline data.

[0016] Preferably, the full-cycle data closed-loop module further includes: Collaborative optimization and iteration submodule: compares the scheduling results with the scene handling effect, and optimizes the prediction model and resource scheduling logic in reverse.

[0017] Compared with existing technologies, the present invention provides an artificial intelligence-based smart community management system, which has the following beneficial effects: This AI-based smart community management system enables proactive risk avoidance and demand response. Through multimodal fusion data collection in the scene perception and prediction module and behavioral intent prediction sub-module, it breaks through the limitations of existing passive alarms and can identify potential health risks for the elderly, elevator mechanical failures, and other issues in advance, allowing sufficient time for handling and reducing community safety risks and inconvenience to residents' lives.

[0018] This AI-based smart community management system can improve the efficiency of community resource utilization. The three sub-modules of the resource dynamic scheduling module work together to break the fixed resource allocation mode and adaptively adjust the facility operation status and manpower allocation according to the predicted scenario. For example, it can optimize elevator scheduling during peak hours and shut down the power supply of idle facilities at night, which reduces energy consumption and manpower waste while ensuring service response efficiency.

[0019] This AI-based smart community management system optimizes residents' service experience and adaptability: the dual-modal terminal of the human-computer collaborative interaction module does not rely on an APP and achieves seamless operation through voice and gestures, adapting to residents of all ages; the personalized service push sub-module proactively provides service reminders based on prediction results.

[0020] This AI-based smart community management system forms a continuously iterative, virtuous cycle. Through a full-cycle data loop module, it cleans and calibrates data in real time and optimizes predictive models and scheduling logic, gradually improving system accuracy over time without frequent manual adjustments, thus reducing community management and maintenance costs. It also ensures data security and privacy protection by processing data in real time through AI edge computing terminals. It collects only non-private information such as gait patterns for identity verification, without storing sensitive data, resolving the conflict between data collection and privacy protection in existing systems and enhancing residents' trust in the system. Attached Figure Description

[0021] Figure 1 This is a system block diagram of the present invention. Detailed Implementation

[0022] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0023] Example 1: like Figure 1As shown, an AI-based smart community management system includes a scene perception and prediction module, a resource dynamic scheduling module, a human-machine collaborative interaction module, and a full-cycle data closed-loop module. The scene perception and prediction module is the core data input terminal, responsible for collecting multi-dimensional information about the community and predicting potential scenarios, providing a basis for decision-making in subsequent modules. The scene perception and prediction module specifically includes: Multimodal fusion perception submodule: Through IoT terminals deployed in community public areas and at residents' doorsteps, non-privacy data such as people's gait, environmental temperature and humidity, equipment operating vibration frequency, and fluctuations in residents' water and electricity consumption are collected. The data is processed in real time using AI edge computing terminals to avoid data transmission delays. At the same time, gait recognition distinguishes community residents from outsiders, eliminating the risk of privacy leaks. The behavioral intent prediction submodule analyzes collected behavioral data based on a deep learning model to predict residents' needs and potential risks. For example, if it identifies that an elderly person spends more than one hour on the balcony at a fixed time for three consecutive days and uses less water and electricity, it predicts that there may be mobility issues or health problems; if it identifies abnormal vibration frequency in the elevator, it predicts the risk of mechanical failure. Scene classification and labeling submodule: Classifies the prediction results according to the degree of urgency, including ordinary service, attention reminder, and emergency response, and simultaneously labels the scene-related areas and required resources. For example, health risks of the elderly are labeled as "attention reminder" and associated with the community health service station; elevator malfunctions are labeled as "emergency response" and associated with the property maintenance department.

[0024] The resource dynamic scheduling module receives the results from the scene perception and prediction module, enabling adaptive allocation of community human and material resources. Specifically, the resource dynamic scheduling module includes: Emergency resource intelligent allocation submodule: Based on the scenario calibration results, it automatically matches the corresponding resources and sends dispatch instructions. For example, after receiving an elevator malfunction warning, it immediately locks the malfunctioning elevator, stops its operation, and pushes the malfunction location and historical operation data to the maintenance personnel. At the same time, it informs residents of the transfer plan through the unit building voice terminal and dispatches temporary guidance personnel to the site for evacuation. The public facilities adaptive control submodule: It links with community public facilities and equipment to adjust their operating status according to predicted scenarios. For example, if the evening peak travel period is predicted, the elevator floor intervals are adjusted in advance, and elevators are centrally dispatched to the 1st floor and high-frequency floors; if no people are expected to be active at night, the brightness of street lights in public areas is automatically reduced and the power to idle fitness equipment is turned off. The human resource allocation submodule updates the on-duty status of property management and community staff in real time and intelligently allocates tasks based on scenario needs. For example, if a reminder to pay attention to the elderly and a garbage collection request are received at the same time, staff members who are closer to the elderly's residence will be prioritized to visit them, and the garbage collection route will be adjusted simultaneously to ensure that both tasks are carried out efficiently.

[0025] The human-computer collaborative interaction module is used to build a bridge between residents, staff, and the system, enabling seamless interaction and precise response. The human-computer collaborative interaction module specifically includes: The dual-modal interactive terminal submodule deploys voice and motion-sensing interactive terminals in apartment buildings and community service centers. Residents do not need to download an app; they can initiate service requests through voice commands or simple gestures, and the system responds based on the predicted results. For example, if an elderly person says "I feel unwell," the terminal automatically links to previous health prediction data and pushes it simultaneously to the community health service station and their family. Personalized service push submodule: Based on resident behavior data and prediction results, proactively push personalized services. For example, if it is predicted that a resident has a child and the start of school is approaching, push information about community childcare registration; if it is identified that a resident frequently picks up packages at night, push reminders about the extended nighttime service of the parcel locker.

[0026] The full-cycle data closed-loop module is used to realize the closed-loop data flow and continuously optimize the system's prediction and scheduling accuracy. The full-cycle data closed-loop module includes: Data cleaning and calibration submodule: Filters the data streams generated by each module, removes abnormal data, retains valid behavior and scheduling result data, updates resident behavior profiles and equipment operation baseline data, and ensures the accuracy of the prediction model; The collaborative optimization and iteration submodule compares the scheduling results with the scenario handling effects, and then optimizes the prediction model and resource scheduling logic in reverse. For example, in handling elevator malfunctions, if the maintenance response time is too long, the scheduling priority is automatically adjusted, increasing the weight of the distance to maintenance personnel, and continuously optimizing the system's operating efficiency.

[0027] It should be noted that this invention is a smart community management system based on artificial intelligence, which includes the following features in its use: Sensing Terminal Deployment and Calibration: IoT terminals (not traditional surveillance cameras) are installed in designated locations such as community public areas, building entrances, and residents' doors, and connected to the multimodal fusion sensing submodule of the scene perception and prediction module. The equipment is debugged and calibrated using an AI edge computing terminal, adjusting gait recognition parameters, vibration frequency acquisition accuracy, and water and electricity fluctuation monitoring thresholds. Basic gait information of community residents is simultaneously recorded; this information is used only for identity verification and does not store private data. This completes the initial setting of the sensing range and data collection standards.

[0028] Baseline data entry and model training: The data cleaning and calibration submodule of the full-cycle data closed-loop module is used to enter the operating baseline parameters of community public facilities, such as elevators, streetlights, and fitness equipment, staff on-duty hours and service areas, and resource location information such as community health service stations. Based on historical community management data, the deep learning model of the behavior intention prediction submodule is initially trained, and behavioral analysis baselines for different groups such as the elderly and children are set. The urgency determination rules of the scenario classification and calibration submodule are entered, including ordinary services, attention reminders, and emergency handling.

[0029] Cross-module linkage debugging: Start the linkage test between the resource dynamic scheduling module and other modules to verify whether the signal of the scene perception and prediction module can be accurately transmitted to the scheduling module, and whether the terminal device of the human-machine collaborative interaction module can receive system instructions normally, ensuring that the data flow is smooth and the module response is delayed.

[0030] Daily automatic operation phase: No manual intervention is required at this stage. The system autonomously coordinates four modules to achieve proactive prediction and precise scheduling, specifically including: Scene Awareness and Prediction: The scene awareness and prediction module operates continuously. The multimodal fusion perception submodule collects data in real time, such as people's gait, environmental temperature and humidity, equipment vibration frequency, and fluctuations in residents' water and electricity usage, through IoT terminals. After being processed in real time by the AI ​​edge computing terminal, the data is transmitted to the behavior intent prediction submodule. The submodule analyzes the data based on a trained model to predict potential needs and risks. Then, the scene classification and labeling submodule classifies the data according to rules, labels the associated areas and required resources, generates prediction results, and pushes them to the resource dynamic scheduling module.

[0031] Resource Adaptive Scheduling: After receiving the prediction results, the three sub-modules respond synchronously. The Emergency Resource Intelligent Allocation sub-module, for emergency response scenarios such as abnormal elevator vibration, automatically locks the faulty equipment, suspends operation, pushes fault information to maintenance personnel, and links with the building's voice terminal to inform residents. The Public Facilities Adaptive Control sub-module, for predicted scenarios such as peak travel times and nighttime idleness, adjusts elevator operating intervals, street light brightness, and fitness equipment power supply. The Human Resources Coordination and Allocation sub-module, based on the real-time on-duty location of staff, prioritizes high-priority tasks, such as visiting elderly people with health risks, and simultaneously optimizes other work routes.

[0032] Data closed-loop synchronization: The full-cycle data closed-loop module collects operational data from each module in real time, the data cleaning and calibration sub-module removes abnormal data, the update resident behavior profile and equipment operation benchmark collaborative optimization iteration sub-module compares scheduling results and handling effects, and adjusts prediction model parameters and scheduling priorities in reverse, such as increasing the weight of personnel distance if maintenance response time is extended.

[0033] The human-machine collaborative interaction phase relies on the human-machine collaborative interaction module to enable residents to initiate requests seamlessly and for staff to respond precisely. Specifically, this includes: Resident-side operation: Residents do not need to download an app. They can initiate requests via voice commands or gestures through the dual-modal interactive terminals in their apartment buildings or community service centers. The terminal automatically links to the previous health data from the scene perception and prediction module, and simultaneously pushes the request and related information to the community health service station and the resident's family for accurate response. At the same time, the personalized service push sub-module proactively pushes reminders to residents based on the prediction results, such as custodial enrollment and extended express locker service. Residents can confirm receipt or cancel via terminal gestures.

[0034] Staff terminal operation: Staff receive task instructions (including location, requirements, and related data) pushed by the resource dynamic scheduling module, complete the handling according to the instructions (such as elevator maintenance, elderly visitation), and report the results through the terminal after the handling is completed. The system synchronizes the results to the full-cycle data closed-loop module as the basis for optimization.

[0035] The regular maintenance and iteration phase specifically includes: Equipment maintenance: Regularly check the operating status of IoT terminals, dual-modal interactive terminals, and AI edge computing terminals, clean the dust from the equipment, calibrate the data acquisition accuracy, and ensure that the sensing and interaction functions are normal.

[0036] Data and Model Maintenance: Through the full-cycle data closed-loop module, valid data is exported regularly and added to the training library of the behavior intent prediction sub-module to optimize the model analysis accuracy. Based on changes in community management needs, the judgment rules and resource scheduling priorities of the scenario classification calibration sub-module are fine-tuned.

[0037] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. An artificial intelligence-based smart community management system, comprising a scene perception and prediction module, a resource dynamic scheduling module, a human-machine collaborative interaction module, and a full-cycle data closed-loop module, characterized in that: The scene perception and prediction module is the core data input terminal, responsible for collecting multi-dimensional information from the community and predicting potential scenes, providing a basis for decision-making for subsequent modules; The resource dynamic scheduling module is used to receive the results from the scene perception and prediction module to realize the adaptive allocation of community human and material resources. The human-machine collaborative interaction module is used to build a bridge between residents, staff and the system to achieve seamless interaction and precise response; The full-cycle data closed-loop module is used to realize the data flow closed loop and continuously optimize the system's prediction and scheduling accuracy.

2. The smart community management system based on artificial intelligence according to claim 1, characterized in that: The scene perception and prediction module specifically includes: Multimodal fusion perception submodule: Collects non-privacy data through IoT terminals deployed in community public areas and at residents' doorsteps, processes it in real time using AI edge computing terminals, and distinguishes community residents from outsiders through gait recognition; Behavioral Intent Prediction Submodule: Based on a deep learning model, it analyzes the collected behavioral data to predict residents' needs and potential risks.

3. The smart community management system based on artificial intelligence according to claim 2, characterized in that: The scene perception and prediction module also includes a scene classification and calibration sub-module: classifying the prediction results according to the degree of urgency, including ordinary services, attention reminders, and emergency handling, and simultaneously marking the scene-related areas and required resources.

4. The smart community management system based on artificial intelligence according to claim 1, characterized in that: The resource dynamic scheduling module specifically includes: Emergency resource intelligent allocation submodule: Based on the scenario calibration results, it automatically matches the corresponding resources and sends scheduling instructions; Public facilities adaptive control submodule: Links community public facilities and equipment to adjust their operating status according to predicted scenarios.

5. The smart community management system based on artificial intelligence according to claim 4, characterized in that: The resource dynamic scheduling module also includes a human resource collaborative allocation sub-module: it updates the on-duty status of property and community staff in real time and intelligently allocates tasks based on scenario requirements.

6. The smart community management system based on artificial intelligence according to claim 1, characterized in that: The human-computer collaborative interaction module specifically includes: Dual-modal interactive terminal sub-module: Deploy voice and motion-sensing interactive terminals in apartment buildings and community service centers. Residents do not need to download an APP; they can initiate service requests through voice commands or simple gestures, and the system responds based on the predicted results. Personalized service push submodule: Based on resident behavior data and prediction results, proactively push personalized services.

7. The smart community management system based on artificial intelligence according to claim 1, characterized in that: The full-cycle data closed-loop module includes: Data cleaning and calibration submodule: Filters the data streams generated by each module, removes abnormal data, retains valid behavior and scheduling result data, and updates resident behavior profiles and equipment operation baseline data.

8. The smart community management system based on artificial intelligence according to claim 7, characterized in that: The full-cycle data closed-loop module also includes: Collaborative optimization and iteration submodule: compares the scheduling results with the scene handling effect, and optimizes the prediction model and resource scheduling logic in reverse.

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