Intelligent community management service system

Abstract

The application discloses a smart community management service system, and particularly relates to the field of service management, comprising a perception layer module, a communication layer module, a data processing layer module and an application layer module; the application realizes global intelligent management through the collaborative architecture of the perception layer module, the communication layer module, the data processing layer module and the application layer module; the perception layer is deployed with multi-source heterogeneous sensors, and data preprocessing is conducted in combination with edge computing; the communication layer adopts multi-protocol fusion transmission, supports 5G+NB-IoT dual-channel redundancy and end-to-end encryption; the data processing layer is based on a cloud-edge collaborative architecture, uses Kalman filtering denoising, D-S evidence theory fusion conflict information, LSTM-Attention prediction device failure, and constructs a digital twin simulation optimization energy consumption; the application layer provides a three-dimensional visual management and control platform, integrates security linkage, air conditioner group control strategy driven by reinforcement learning and convenient service scheduling, and supports cross-platform access and RBAC permission management.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of service management, in particular to a smart community management service system. BACKGROUND

[0002] With the accelerated advancement of urbanization in China, the number of urban communities continues to grow, the scale of communities continues to expand, and the demand of residents for community management services is increasingly diversified and refined.

[0003] According to statistics, about 65% of community safety accidents in China are caused by the failure to discover hidden dangers in time, the average repair response time of public facilities is more than 48 hours, and the energy waste rate is as high as 30%. At the same time, the demand of residents for community services is increasingly diversified, especially in the context of an aging society, the demand of the elderly for emergency assistance, health monitoring and other services has surged.

[0004] The traditional community management mode is facing unprecedented challenges; the current community management generally has problems such as data island, response lag, and service fragmentation, and property management personnel often need to deal with a large amount of and scattered security monitoring, equipment maintenance, and resident services, resulting in low management efficiency. SUMMARY

[0005] Therefore, the present application provides a smart community management service system to solve the problem of low management efficiency in the prior art.

[0006] In order to achieve the above purpose, the present application provides the following technical solutions:

[0007] The perception layer module: a plurality of Internet of Things sensors deployed in the community, used for real-time collection of environmental data, equipment state data and personnel activity data;

[0008] The communication layer module: including an edge gateway and a network transmission unit, used for encrypted transmission of the perception layer data to the cloud;

[0009] The data processing layer module: including a cloud server and a central processor, configured to fuse and analyze multi-source data to generate a community operation status report; and identify abnormal events through a pre-set algorithm model;

[0010] The application layer module: providing a user terminal interface and a management platform, executing security warning, energy consumption optimization, and convenient service scheduling instructions.

[0011] In the first aspect, the perception layer module, a three-dimensional monitoring system is constructed by Internet of Things sensor networks distributed at key nodes in the community. The module uses multi-source heterogeneous sensing technology to dynamically perceive the physical space in all directions. In terms of environmental monitoring, high-precision temperature and humidity sensors are deployed to track real-time indoor and outdoor climate changes. Combined with a laser scattering PM2.5 detector and a wideband noise monitoring terminal, an air quality and sound environment quality evaluation matrix is formed.

[0012] For community infrastructure, elevator operation state sensors with vibration analysis are installed, intelligent power distribution cabinet monitoring devices with current harmonic detection are integrated, and water and electricity dual meters with ultrasonic flow meters are equipped to millisecond-level sample equipment health and energy consumption. The personnel activity perception system is realized through multi-modal sensor fusion, including binocular passenger flow statistical cameras supporting infrared thermal imaging, millimeter wave radar fall detection devices deployed at unit entrances, and UWB positioning beacons embedded in public areas, cooperating with live detection face recognition terminals of the access control system, to build a non-contact personnel trajectory tracking network.

[0013] In the second aspect, the communication layer module uses a heterogeneous network architecture with multiple protocols to safely and efficiently transmit data. The communication layer module is composed of distributed edge gateway devices and a multi-level network transmission system. Each edge gateway is equipped with a quad-core ARM processor and a hardware encryption engine, and can simultaneously access multiple short-range communication protocols such as ZigBee, LoRa, and BLE, supporting adaptive pairing with various sensors in the perception layer.

[0014] The gateway has a built-in data preprocessing unit that compresses and extracts features from raw sensor data using lightweight algorithms, converts video streams into structured facial feature data, and aggregates continuous environmental monitoring data into 5-minute average payloads, reducing transmission data volume by more than 60%. The network transmission unit uses a dual-channel redundant design, with the main channel based on 5G SA networking for wide-area connectivity, and the backup channel ensuring key data transmission through an NB-IoT network. Mesh self-organizing network relay nodes are deployed in signal blind areas such as community underground garages to ensure full network coverage.

[0015] In a third aspect, the data processing layer module of the smart community management service system adopts a hybrid architecture of cloud computing and edge computing in cooperation, and an intelligent decision-making hub with multi-dimensional analysis capability is constructed. The cloud server cluster is based on Kubernetes containerized deployment, and adopts a micro-service architecture to realize elastic expansion. The real-time data processing engine is based on Apache Flink, and supports the stream processing of millions of events per second. The batch processing task is completed through the Spark distributed computing framework. The central processor is equipped with a special AI acceleration chip, and adopts a heterogeneous computing architecture to realize the cooperative operation of CPU+GPU+FPGA. The multi-source heterogeneous data uploaded by the perception layer is subjected to space-time alignment and feature fusion. First, the sensor data is denoised by Kalman filtering algorithm;

[0016]

[0017] wherein represents the current prediction state vector based on all observation data at the k-1 moment at the k moment; F k represents the state transition matrix, represents the state optimal estimation value obtained based on all observation data at the k-1 moment and before the k-1 moment; B k represents the control input matrix, u k represents the external control input at the k moment;

[0018] The multi-modal data fusion adopts D-S evidence theory to establish a confidence evaluation model, which is used to solve the multi-source information conflict problem of video analysis, infrared induction and the like in the security system. The calculation method is as follows:

[0019]

[0020] wherein m(A) represents the basic probability assignment function of event A after fusion; m1(B) and m2(C) represent the basic probability assignment functions of events B and C of two evidence sources respectively; K represents the conflict coefficient, and 1-K represents the normalization factor;

[0021] The anomaly detection subsystem deploys a multi-algorithm integrated detection pipeline: for device fault prediction, a LSTM-Attention hybrid network is used to model the time series data, and the loss function calculation method is as follows:

[0022]

[0023] wherein L represents the total loss value, N represents the sample quantity, y q represents the true label of the qth sample, p q represents the prediction probability of the model for the qth sample, λ represents the regularization coefficient, and θ represents the model parameter set;

[0024] For personnel abnormal behavior recognition, a three-dimensional spatio-temporal feature extraction network based on improved YOLOv5 is constructed, and the data processing layer module also includes a digital twin simulation engine. The finite element method is used to model the community energy consumption to realize the dynamic simulation of the building thermal field. The calculation method is as follows:

[0025]

[0026] Where T represents temperature, x and y represent spatial coordinates, t represents time, k x represents the thermal conductivity in the x direction, k y represents the thermal conductivity in the y direction, Q represents the internal heat source intensity, p represents the material density, c represents the specific heat capacity, represents the first-order partial derivative of temperature with respect to time, that is, the rate of change of temperature with respect to time.

[0027] In the fourth aspect, the application layer module of the intelligent community management service system constructs an intelligent interaction system for multi-role users, and realizes flexible combination of function modules through micro-frontend architecture. The management platform adopts a three-dimensional visualization console developed by React+WebGL technology stack, integrates a panoramic view of the digital twin community, supports zooming and floor penetration through gesture operation, and realizes real-time rendering of analysis results from the data processing layer. The security warning subsystem realizes a multi-level linkage response mechanism. When a fire warning is triggered, an emergency plan (path planning is optimized by A* algorithm) containing the optimal escape path is automatically generated to control the elevator forced landing, access control release and emergency lighting system, and customized evacuation guidance is pushed to the residents in the affected area. The energy consumption optimization module deploys an air conditioner group control strategy based on reinforcement learning, establishes a target function considering outdoor temperature and humidity, personnel density and price fluctuations, and the calculation method is as follows:

[0028] F1=min∑(α·E t +β·C t +γ·D t )

[0029] Where F1 represents the target function, a, b and g represent the weight factors, E t represents the energy consumption cost at time t, C t represents the comfort deviation at time t, and D t represents the device loss at time t.

[0030] Compared with the prior art, the present application has at least the following beneficial effects:

[0031] 1. The smart community management service system realizes intelligent management through a four-layer architecture: the perception layer deploys a multi-source heterogeneous sensor network, including environmental monitoring (temperature and humidity, PM2.5), equipment monitoring (elevator vibration analysis, smart meter), and personnel activity perception (UWB positioning, face recognition), and uses modular design and redundant networking to ensure data continuity; the communication layer is based on 5G / NB-IoT dual-channel transmission, and realizes safe and efficient data transmission through SM4 / SM2 encryption and edge computing, supporting 72-hour offline caching; the data processing layer uses a hybrid computing framework of Flink+Spark, and uses algorithms such as Kalman filtering, D-S evidence theory, and LSTM-Attention to realize multi-source data fusion analysis and anomaly detection, and builds a community state evaluation system containing six indicators; the application layer provides a three-dimensional visualization platform and a mobile terminal, integrating A* algorithm emergency path planning, reinforcement learning energy optimization, and mixed integer programming work order scheduling, supporting voice interaction and intelligent early warning.

[0032] 2. The present application can significantly improve the community security and prevention and control capability, optimize the resource allocation efficiency, and provide personalized services for residents through multi-source data fusion and intelligent algorithm application, and finally realize the transformation and upgrading of community management from "people's defense and people's rule" to "technical defense and intelligent rule". BRIEF DESCRIPTION OF DRAWINGS

[0033] In order to more directly illustrate the prior art and the present application, the following exemplary drawings are given. It should be understood that the specific shapes, structures shown in the drawings should not be regarded as limiting conditions in the implementation of the present application; for example, based on the technical concepts disclosed in the present application and the exemplary drawings, those skilled in the art can easily make routine adjustments or further optimizations to some units (components) in terms of increase / decrease / ownership division, specific shape, positional relationship, connection mode, size ratio relationship, etc.

[0034] Figure 1 The present application is a module connection diagram.

[0035] Figure 2 The present application is a communication layer data transmission process diagram. DETAILED DESCRIPTION

[0036] The present application will be further described in detail below in conjunction with the drawings.

[0037] Please refer to Figure 1 The present application relates to a smart community management service system, which includes a perception layer module, a communication layer module, a data processing layer module, and an application layer module.

[0038] Perception layer module: a plurality of Internet of Things sensors deployed in the community for real-time collection of environmental data, equipment status data, and personnel activity data;

[0039] In the perception layer module, a three-dimensional monitoring system is constructed by Internet of Things sensor networks distributed at key nodes in the community. The module uses multi-source heterogeneous sensing technology for all-around dynamic perception of physical space. In terms of environmental monitoring, high-precision temperature and humidity sensors are deployed to track real-time indoor and outdoor climate changes. Combined with laser scattering PM2.5 detectors and wideband noise monitoring terminals, an air quality and sound environment quality evaluation matrix is formed.

[0040] For community infrastructure, elevator operation state sensors with vibration analysis, intelligent power distribution cabinet monitoring devices with current harmonic detection, and water and electricity dual meters with ultrasonic flow meters are installed to perform millisecond-level sampling of equipment health and energy consumption. The personnel activity perception system is realized through multi-modal sensor fusion, including binocular passenger flow statistical cameras supporting infrared thermal imaging, millimeter wave radar fall detection devices deployed at unit entrances, and UWB positioning beacons embedded in public areas, in combination with live detection face recognition terminals of the access control system, to build a non-contact personnel trajectory tracking network.

[0041] All sensing nodes are designed modularly. The environmental monitoring unit is equipped with a dust cover with self-cleaning function and a solar auxiliary power supply unit. The facility monitoring sensor has an anti-electromagnetic interference shielding layer built-in. The security perception device integrates an IP66 protection level shell and an anti-tamper alarm device. The data acquisition terminal has an edge computing chip built-in, which can perform preliminary data filtering and outlier removal. The time stamp synchronization protocol is used to ensure the spatio-temporal alignment of multi-source data. The sampling frequency is dynamically adjusted according to the characteristics of the monitored object. For example, elevator vibration data is collected at a high frequency of 1 kHz, while the garbage can overflow monitoring uses an event-triggered reporting mechanism. The perception layer network adopts a hybrid topology of star and mesh. Redundant sensor nodes are set in key areas. When the main sensor fails, it can automatically switch to the backup node to ensure the continuity of data acquisition. Each sensing unit is implanted with a unique digital identity, which is bound to the spatial coordinates in the community BIM model, forming a virtual-real mapping Internet of Things perception system.

[0042] The communication layer module includes edge gateways and network transmission units, which are used to encrypt and transmit the data of the perception layer to the cloud.

[0043] In the communication layer module, a heterogeneous network architecture with multiple protocols is used for safe and efficient transmission of data. The communication layer module is composed of distributed edge gateway devices and a multi-level network transmission system. Each edge gateway is equipped with a quad-core ARM processor and a hardware encryption engine, which can simultaneously access multiple short-range communication protocols such as ZigBee, LoRa, and BLE, and support adaptive pairing with various sensors in the perception layer.

[0044] The gateway is provided with a data preprocessing unit, which compresses and extracts features of original sensing data through a lightweight algorithm, converts video stream into structured face feature data, and aggregates continuous environmental monitoring data into 5-minute average payload, so that the amount of transmission data is reduced by more than 60%; the network transmission unit adopts a dual-channel redundant design, the main channel is connected based on 5G SA networking to realize wide-area connection, the standby channel transmits key data through an NB-IoT network, and a mesh self-organizing network relay node is arranged in a signal blind area such as a community underground garage to ensure full network coverage.

[0045] An end-to-end encryption mechanism is adopted in the transmission process, the data is grouped and encrypted on the gateway side through the national SM4 algorithm, the cloud platform uses an SM2 public key system for key distribution management, each data packet is attached with a digital signature and a time watermark to prevent man-in-the-middle attacks and data tampering; the communication protocol stack optimizes the transmission priority of security data (such as smoke alarm) with high real-time requirements, establishes a dedicated QoS channel for end-to-end delay within 200 ms, and adopts a batch aggregation transmission mode for regular device state data to save bandwidth.

[0046] The system is provided with a network state intelligent monitoring subunit, which analyzes historical transmission quality data through a deep learning algorithm, dynamically predicts network congestion risks and switches transmission paths in advance, and automatically performs bandwidth resource allocation and protocol parameter optimization during night low-load periods. All gateway devices support remote OTA upgrade, firmware update and configuration adjustment can be performed through a cloud unified management platform, and a hardware trusted execution environment (TEE) built in the gateway ensures digital signature verification and integrity check of the upgrade package.

[0047] For temporary network interruption scenarios, the edge gateway is provided with a local storage buffer pool, which supports offline data caching for up to 72 hours, and after network recovery, data synchronization is completed through a breakpoint resume mechanism, and a data freshness verification process is triggered to automatically discard invalid data exceeding the time threshold.

[0048] Data processing layer module: including a cloud server and a central processor, configured to fuse and analyze multi-source data to generate a community operation status report; abnormal events are identified through a preset algorithm model;

[0049] The data processing layer module of the smart community management service system adopts a hybrid architecture of cloud computing and edge computing cooperation, and builds an intelligent decision-making hub with multi-dimensional analysis capability. The cloud server cluster is based on Kubernetes containerized deployment, and adopts a micro-service architecture to realize elastic expansion. The real-time data processing engine is based on Apache Flink, which supports million-level event stream processing per second. Batch processing tasks are completed through the Spark distributed computing framework. The central processor is equipped with a special AI acceleration chip, and adopts a heterogeneous computing architecture to realize the cooperative operation of CPU+GPU+FPGA. The multi-source heterogeneous data uploaded by the perception layer is subjected to space-time alignment and feature fusion. First, the Kalman filter algorithm is used to denoise the sensor data;

[0050]

[0051] wherein represents the current prediction state vector based on all observation data at the k-1 moment at the k moment; F k represents the state transition matrix, represents the state optimal estimation value obtained based on all observation data at the k-1 moment and before the k-1 moment; B k represents the control input matrix, u k represents the external control input at the k moment;

[0052] The multi-modal data fusion adopts the D-S evidence theory to establish a confidence evaluation model, which is used to solve the multi-source information conflict problem in the security system such as video analysis and infrared induction. The calculation method is as follows:

[0053]

[0054] wherein m(A) represents the basic probability assignment function of event A after fusion; m1(B) and m2(C) represent the basic probability assignment functions of events B and C of two evidence sources respectively; K represents the conflict coefficient, and 1-K represents the normalization factor;

[0055] The community operation state report generation module adopts a dynamic weight analysis method, constructs an evaluation system including 6 first-level indexes (safety, energy consumption, environment, equipment, service, and personnel) and 32 second-level indexes, determines the weight of each index through the analytic hierarchy process (AHP), and the calculation method is as follows:

[0056]

[0057] wherein W i represents the weight value of the i-th index, n represents the number of indexes, the element in the judgment matrix is a ij represents the relative importance ratio of the i-th index and the j-th index. denotes the product of all elements in the ith row of the judgment matrix; denotes the nth root of the above product, obtaining the geometric mean of the ith indicator;

[0058] The anomaly detection subsystem deploys a multi-algorithm integrated detection pipeline: for device failure prediction, a LSTM-Attention hybrid network is used to model time series data, and the loss function calculation method is as follows:

[0059]

[0060] Where L represents the total loss value, N represents the number of samples, y q denotes the true label of the qth sample, p q denotes the prediction probability of the model for the qth sample, λ denotes the regularization coefficient, and θ denotes the model parameter set;

[0061] For personnel abnormal behavior recognition, a three-dimensional spatio-temporal feature extraction network based on improved YOLOv5 is constructed, and the data processing layer module also includes a digital twin simulation engine. The finite element method is used to model the community energy consumption to realize the dynamic simulation of the building thermal field. The calculation method is as follows:

[0062]

[0063] Where T represents the temperature, x and y represent the spatial coordinates, t represents the time, k x denotes the thermal conductivity in the x direction, k y denotes the thermal conductivity in the y direction, Q denotes the internal heat source intensity, ρ denotes the material density, and c denotes the specific heat capacity, denotes the first-order partial derivative of temperature with respect to time, i.e. the rate of change of temperature with respect to time;

[0064] All analysis models are continuously optimized through online learning mechanism, and model incremental training is automatically executed every morning. When data distribution drift is detected to exceed the threshold, model reconstruction process is triggered. The system establishes a hierarchical data warehouse, real-time data is stored in TimescaleDB time series database, and analysis results are written into distributed graph database Neo4j. To ensure processing timeliness, GPU acceleration is used for key path calculation tasks. For example, TensorRT optimization inference engine is used for video analysis tasks, reducing processing delay to less than 150ms. The data processing layer provides standard Restful API and WebSocket interface to the outside world, supporting real-time data subscription and historical data backtracking requests in the application layer.

[0065] Application layer module: provides user terminal interface and management platform, executes security warning, energy optimization, and convenient service scheduling instructions.

[0066] Among the application layer modules, the application layer modules of the smart community management service system build an intelligent interaction system for multi-role users, and realize flexible combination of function modules through micro-frontend architecture. The management platform adopts a three-dimensional visual console developed by React+WebGL technology stack, integrates a digital twin community panoramic view, supports zooming and floor penetration through gesture operation, and realizes real-time rendering of analysis results from the data processing layer. The security warning subsystem realizes a multi-level linkage response mechanism. When a fire warning is triggered, an emergency plan (path planning is optimized by A* algorithm) containing the optimal escape path is automatically generated to control the elevator forced landing, access control release and emergency lighting system, and customized evacuation guidance is pushed to the residents in the affected area. The energy consumption optimization module deploys an air conditioning group control strategy based on reinforcement learning, establishes a target function considering outdoor temperature and humidity, personnel density and price fluctuations, and the calculation method is as follows:

[0067] F1 = min∑(α·E t +β·C t +γ·D t )

[0068] Where F1 represents the target function, α, β, γ represent the weight factor, E t represents the energy consumption cost at time t, C t represents the comfort deviation at time t, and D t represents the device wear at time t.

[0069] The convenience service scheduling introduces an operational research optimization model, and constructs a mixed integer programming for the repair work order allocation problem to obtain the optimal dispatching scheme by branch and bound method. The resident-end WeChat applet adopts lightweight design, integrates intelligent access control, property payment and neighborhood social functions, and the key interactive page first screen loading time is optimized to within 800ms; the property management background develops a work order whole life cycle tracking board, uses Gantt chart to visualize the maintenance progress, automatically identifies service overtime work orders and triggers the upgrade process;

[0070] A voice interaction interface is specially developed for the elderly user group, integrating ASR speech recognition and TTS broadcast engine, supporting dialect adaptive conversion; the platform establishes a service satisfaction evaluation model, analyzes user behavior sequences based on hidden Markov chain to identify potential dissatisfaction and give early warning.

[0071] All user operations are controlled by the RBAC permission model, with fine-grained permission granularity reaching the button level, and the audit log records the complete operation track; the mobile terminal application uses the React Native cross-platform framework, and through the hot update mechanism, it ensures that the user is not affected when the function is iterated, and the key business interface configuration is fused to degrade the strategy, and when the concurrent request exceeds the threshold, it is automatically switched to the local cache mode; the management platform deploys an intelligent voice assistant, supports natural language queries such as "display 3 buildings of the nearest one week water and electricity abnormal residents", and generates an instant response after analyzing the intent through the NLP engine.

[0072] The technical features of the above embodiments can be combined in any manner (as long as the combination of the technical features does not contradict), in order to make the description simple, not all possible combinations of the technical features in the above embodiments are described; these embodiments which are not explicitly written should also be considered as the scope of the present specification.

Claims

1. A smart community management and service system, characterized in that, include: Perception layer module: Multiple types of IoT sensors deployed within the community are used to collect environmental data, device status data, and personnel activity data in real time; Communication layer module: includes edge gateway and network transmission unit, used to encrypt and transmit perception layer data to the cloud; Data processing layer module: including cloud server and central processing unit, configured to perform fusion analysis of multi-source data, generate community operation status report; identify abnormal events through pre-built algorithm model; Application layer module: Provides user terminal interface and management platform, and executes security early warning, energy consumption optimization and public service scheduling instructions.

2. The smart community management service system according to claim 1, characterized in that: In the perception layer module, a three-dimensional monitoring system is built through an Internet of Things sensor network distributed at key nodes in the community. This module adopts multi-source heterogeneous sensing technology to achieve all-round dynamic perception of the physical space. In terms of environmental monitoring, high-precision temperature and humidity sensors are deployed to track indoor and outdoor climate changes in real time. Combined with laser scattering PM2.5 detectors and broadband noise monitoring terminals, an air quality and sound environment quality assessment matrix is ​​formed.

3. The smart community management service system according to claim 2, characterized in that: For community infrastructure, elevator operation status sensors with vibration analysis are installed, intelligent power distribution cabinet monitoring devices with integrated current harmonic detection are installed, and water and electricity dual metering devices equipped with ultrasonic flow meters are used to sample equipment health and energy consumption at the millisecond level. The personnel activity perception system is achieved through multimodal sensor fusion, including binocular passenger flow statistics cameras that support infrared thermal imaging, millimeter-wave radar fall detection devices deployed at the unit entrance, and UWB positioning beacons embedded in public areas. Together with the liveness detection facial recognition terminal of the access control system, a non-contact personnel trajectory tracking network is built.

4. The smart community management service system according to claim 1, characterized in that: The communication layer module employs a heterogeneous network architecture that integrates multiple protocols for secure and efficient data transmission. The communication layer module consists of distributed edge gateway devices and a multi-layered network transmission system, with each edge gateway equipped with a quad-core ARM processor and a hardware encryption engine. The gateway has a built-in data preprocessing unit that uses lightweight algorithms to compress and extract features from raw sensor data, converting video streams into structured facial feature data and aggregating continuous environmental monitoring data into a 5-minute average payload. The network transmission unit adopts a dual-channel redundancy design, with the main channel based on 5G SA networking to achieve wide-area connectivity and the backup channel using NB-IoT network to ensure critical data transmission. Mesh self-organizing network relay nodes are deployed in signal blind spots in the community's underground parking garage.

5. The smart community management service system according to claim 4, characterized in that: The transmission process employs an end-to-end encryption mechanism. At the gateway side, data is encrypted in blocks using the national cryptographic SM4 algorithm. The cloud platform uses the SM2 public key system for key distribution and management. Each data packet is appended with a digital signature and a time watermark. The communication protocol stack is optimized to prioritize security data transmission with high real-time requirements, and a dedicated QoS channel is established to achieve an end-to-end delay of less than 200ms.

6. The smart community management service system according to claim 1, characterized in that: The data processing layer module of the smart community management and service system adopts a hybrid architecture that combines cloud computing and edge computing to build an intelligent decision-making center. The cloud server cluster is deployed based on Kubernetes containers and uses a microservice architecture for elastic scaling. The real-time data processing engine is built on Apache Flink, while batch processing tasks are completed using the Spark distributed computing framework. The central processing unit performs spatiotemporal alignment and feature fusion on the multi-source heterogeneous data uploaded from the sensing layer. First, the sensor data is denoised using a Kalman filter algorithm; the specific calculation method is as follows: Where represents the current predicted state vector based on all observation data from time k-1 at time k; F k Represented as a state transition matrix, B represents the optimal state estimate obtained at time k-1, based on all observation data up to and including that time. k Represented as a control input matrix, u k This is represented as the external control input at time k.

7. The smart community management service system according to claim 1, characterized in that: Multimodal data fusion employs the DS evidence theory to establish a confidence assessment model, which is used to resolve the conflict problem of multi-source information such as video analysis and infrared sensing in security systems. The specific calculation method is as follows: Where m(A) represents the basic probability allocation function of event A after fusion; m1(B) and m2(C) represent the basic probability allocation functions of the two evidence sources for time B and C, respectively; K represents the conflict coefficient, and 1-K represents the normalization factor; The community operation status report generation module uses dynamic weighted analysis to construct an evaluation system containing 6 primary indicators and 32 secondary indicators. The weight of each indicator is determined through the analytic hierarchy process (AHP). The specific calculation method is as follows: Among them, W i Let a represent the weight value of the i-th indicator, n represent the number of indicators, and a be an element in the judgment matrix. ij It is expressed as the ratio of the relative importance of the i-th indicator to the j-th indicator; This can be expressed as the product of all elements in the i-th row of the judgment matrix; This can be expressed as the geometric mean of the i-th index obtained by taking the nth root of the above product.

8. The smart community management service system according to claim 1, characterized in that: In the application layer module, the smart community management service system constructs an intelligent interaction system for multi-role users, achieving flexible combination of functional modules through a micro-frontend architecture; the management platform adopts a 3D visualization console developed using the React+WebGL technology stack, integrating a panoramic view of the digital twin community and rendering analysis results from the data processing layer; the security early warning subsystem implements a multi-level linkage response mechanism, automatically generating an emergency plan containing the optimal escape route when a fire alarm is triggered, simultaneously controlling elevator emergency landing, access control release, and emergency lighting systems, and pushing customized evacuation guidance to residents in the affected area; the energy consumption optimization unit deploys an air conditioning group control strategy based on reinforcement learning, establishing objective functions for outdoor temperature and humidity, personnel density, and electricity price fluctuations, with the specific calculation method as follows: F1=min∑(α·E t +β·C t +γ·D t ) Where F1 represents the objective function, α, β, and γ represent weighting factors, and E t Let C represent the energy cost at time t. t Let D be the comfort deviation at time t. t The equipment loss at time t is expressed as follows: The convenient service dispatching system introduces an operations research optimization model. For the problem of allocating repair work orders, a mixed integer programming approach is constructed and the optimal dispatching scheme is obtained by using the branch and bound method. The WeChat mini-program for residents adopts a lightweight design and integrates smart access control, property fee payment, and neighborhood social functions. The property management backend has developed a work order full lifecycle tracking dashboard, which uses Gantt charts to visualize the repair progress, automatically identifies overdue work orders, and triggers the escalation process.

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