Intelligent community service management method and system based on cloud computing

Abstract

The invention relates to the technical field of community management, in particular to an intelligent community service management method and system based on cloud computing, and the method comprises the following steps: S1, collecting community multi-dimensional data in real time through a heterogeneous data collection terminal; s2, constructing an edge-cloud cooperative processing framework; s3, implementing intelligent video analysis; s4, realizing data-driven resource scheduling; and S5, establishing an intelligent storage system. According to the invention, omnibearing perception of a community environment is realized through a multispectral camera and a distributed sensor network, data processing efficiency is improved by adopting an optimized lightweight algorithm and an edge-cloud collaborative architecture, and a visual decision platform is constructed in combination with adaptive resource scheduling and an intelligent analysis technology. A closed-loop management system integrating real-time monitoring, intelligent early warning and accurate operation and maintenance is formed, the community management efficiency is improved, the operation cost is reduced, stable operation of the system is guaranteed through multiple safety mechanisms, and an intelligent solution is provided for modern community services.

Description

Technical Field

[0001] This invention relates to the field of community management technology, specifically to an intelligent community service management method and system based on cloud computing. Background Technology

[0002] Traditional community management models suffer from inefficient manual inspections, making it difficult to detect safety hazards in a timely manner. Dispersed security systems are difficult to manage in a unified manner, equipment maintenance relies on manual experience, resulting in delayed responses. The lack of effective integration of various sensor data leads to insufficient decision-making basis. In existing technologies, video surveillance systems mostly use single-spectrum cameras, which significantly reduce recognition rates at night or in inclement weather. Environmental monitoring equipment usually operates independently, resulting in serious data silos. Equipment status monitoring relies on periodic manual inspections, making predictive maintenance impossible. At the same time, traditional cloud computing architectures suffer from problems such as large data transmission latency and high bandwidth consumption, making it difficult to meet the real-time requirements of community services. Summary of the Invention

[0003] To address these issues, the present invention provides an intelligent community service management method and system based on cloud computing.

[0004] This invention provides the following technical solution: an intelligent community service management method based on cloud computing, comprising the following steps: S1 collects multi-dimensional community data in real time through heterogeneous data acquisition terminals; S2, building an edge-cloud collaborative processing framework; S3 enables intelligent video analytics; S4 enables data-driven resource scheduling; S5 establishes an intelligent storage system; S6, building a visual decision-making platform; S7 features a continuous optimization mechanism.

[0005] As a preferred embodiment of the present invention, the community multidimensional data includes video surveillance data, environmental perception data, and equipment operation data. The video surveillance data is acquired by using a multispectral camera to collect visible light and infrared video streams. The environmental perception data is acquired by using a distributed sensor network to obtain temperature, humidity, PM2.5, and noise parameters. The equipment operation data is acquired by real-time monitoring of the operating status and energy consumption data of elevators, access control systems, and fire-fighting equipment.

[0006] As a preferred embodiment of the present invention, in step S2, more specifically: a lightweight data cleaning module is deployed at the edge node, an anomaly detection algorithm based on time series is used to remove invalid data, a multi-level data caching mechanism is established, differentiated retention durations are set according to data types, a dynamic QoS strategy is designed, and the highest transmission priority is allocated to security videos; in step S3, more specifically: an improved YOLOv7-tiny model is used for real-time multi-target detection, an attention mechanism is introduced to improve the accuracy of small target recognition, an adaptive frame sampling algorithm is developed, and the analysis frequency is increased in dense target areas; in step S4, more specifically: a multi-dimensional resource monitoring index system is constructed, including computing resources, network resources, and storage resources, and a dynamic scheduling model based on deep reinforcement learning is designed. The system achieves intelligent migration of computing tasks, dynamic allocation of network bandwidth, and elastic scaling of storage resources. Specifically, in S5, a video semantic indexing system is developed to support content-based rapid retrieval, automatic hierarchical storage of hot and cold data, differentiated compression strategies, and a blockchain-based evidence storage module to ensure the immutability of critical data. More specifically, in S6, a 3D digital twin interface is developed to provide a panoramic view of the community's operational status, integrate a multi-source early warning system, establish a tiered alarm mechanism, and provide data mining tools to support operational decision analysis. More specifically, in S7, a model performance monitoring system is established to periodically evaluate target detection accuracy, enable online parameter tuning of resource scheduling strategies, and develop an incremental learning framework to support the continuous evolution of algorithm models.

[0007] As a preferred embodiment of the present invention, the improved YOLOv7-tiny model in S3 includes introducing MobileNetV3 as the feature extraction backbone network, adding a CBAM attention module, using the CIoU loss function to optimize bounding box regression, and deploying a model quantization toolchain to achieve FP16 precision inference. In S4, the deep reinforcement learning model adopts state space definition, action space design, reward function construction, and training algorithms. The state space definition includes 20-dimensional system state features, the action space design supports 8 types of resource adjustment operations, the reward function construction comprehensively considers service quality and resource cost, and the training algorithm is based on the PPO distributed training framework.

[0008] As a preferred embodiment of the present invention, the three-dimensional digital twin interface in S6 enables lightweight rendering of building BIM models, virtual-real mapping of IoT devices, spatiotemporal visualization of abnormal events, and AR navigation of operation and maintenance work orders.

[0009] A cloud-based intelligent community service management system, employing any of the cloud-based intelligent community service management methods described above, includes an intelligent sensing layer, the output of which is electrically connected to a platform service layer, the output of which is electrically connected to a data governance layer, the output of which is electrically connected to an intelligent application layer, and the intelligent application layer containing a security system.

[0010] As a preferred embodiment of the present invention, the intelligent perception layer consists of a multimodal data acquisition terminal array, an edge computing gateway device cluster, and a 5G / WiFi 6 hybrid communication network; the platform service layer consists of a distributed message middleware, a microservice architecture business middleware, and a containerized algorithm repository; the data governance layer consists of a stream-batch integrated data processing engine, a spatiotemporal data management platform, and a federated learning training framework; the intelligent application layer consists of a community security monitoring system, an equipment operation and maintenance management system, and a resident service platform; and the security system consists of a multi-factor authentication module, a data encryption transmission channel, and a privacy computing sandbox environment.

[0011] As a preferred embodiment of the present invention, the platform service layer is further equipped with an elastic resource scheduler, which supports hybrid deployment of virtual machines and containers and provides fine-grained resource quota management. The device operation and maintenance management system is equipped with a full-link performance monitoring dashboard, an anomaly root cause analysis engine, and an automated fault recovery toolset. The multi-factor authentication module is equipped with an intelligent risk control system, which realizes user behavior profile analysis, real-time blocking of abnormal access, and early warning of non-security situations.

[0012] Compared with the prior art, the beneficial effects of the present invention are: This invention achieves comprehensive perception of the community environment through multispectral cameras and distributed sensor networks, improves data processing efficiency by employing optimized lightweight algorithms and an edge-cloud collaborative architecture, and constructs a visual decision-making platform by combining adaptive resource scheduling and intelligent analysis technologies. This forms a closed-loop management system that integrates real-time monitoring, intelligent early warning, and precise operation and maintenance, thereby improving community management efficiency while reducing operating costs. Furthermore, multiple security mechanisms ensure the stable operation of the system, providing an intelligent solution for modern community services. Attached Figure Description

[0013] Figure 1 This is a flowchart of the intelligent community service management method based on cloud computing of the present invention; Figure 2 This is a flowchart of the cloud-based intelligent community service management system of the present invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] Please see Figures 1-2 The technical solution provided by the present invention specifically includes the following embodiments: Example: A cloud-based intelligent community service management method, comprising the following steps: S1 collects multi-dimensional community data in real time through heterogeneous data acquisition terminals. The multi-dimensional community data includes video surveillance data, environmental perception data, and equipment operation data. Video surveillance data is acquired by using multispectral cameras to collect visible light and infrared video streams. Environmental perception data is acquired by using a distributed sensor network to obtain temperature, humidity, PM2.5, and noise parameters. Equipment operation data is acquired by real-time monitoring of the operating status and energy consumption data of elevators, access control, and fire protection equipment. The system employs a multispectral camera array to cover public areas of the community, enabling simultaneous acquisition of visible light and infrared video to ensure all-weather monitoring capabilities. The distributed sensor network deployment utilizes a cellular topology, with each sensor node integrating temperature, humidity, PM2.5, and noise detection modules. An adaptive sampling frequency adjustment strategy balances data accuracy and energy consumption. The equipment operation monitoring subsystem connects to elevator controllers, access control systems, and fire alarm systems via an industrial protocol conversion gateway, extracting equipment status register data in real time. The system uses a timestamp alignment algorithm to solve multi-source data synchronization issues and normalizes heterogeneous protocols such as Modbus and BACnet through the protocol adaptation layer of the edge computing gateway. The data acquisition terminal incorporates a Trusted Execution Environment (TEE) to ensure raw data security and employs differential privacy technology to process sensitive information. The system achieves highly reliable transmission through 5G / WiFi 6 dual-mode communication, dynamically switching transmission paths based on network quality to ensure the continuity and integrity of data acquisition.

[0016] S2, construct an edge-cloud collaborative processing framework; deploy a lightweight data cleaning module at the edge node, use a time-series-based anomaly detection algorithm to remove invalid data, establish a multi-level data caching mechanism, set differentiated retention durations according to data types, design dynamic QoS policies, and allocate the highest transmission priority to security videos; By deploying a lightweight data cleaning module at the edge, a sliding window mechanism is used to detect time-series anomalies. A dynamic threshold algorithm is used to identify sensor data anomalies. A multi-level data caching mechanism uses the LRU-K algorithm to manage storage resources. A dynamic QoS policy is implemented based on the DiffServ architecture, assigning EF service level to video streams, AF level to sensor data, and BE level to device status data. Data sharing channels are established between edge nodes to form a decentralized computing grid. When a single node is overloaded, neighboring nodes are automatically triggered to assist in computation. The cloud data processing center adopts a Lambda architecture. The real-time stream processing layer uses the Flink engine to perform complex event processing, and the batch processing layer uses Spark for deep analysis. The system uses a consistent hashing algorithm to achieve data sharding, ensuring load balancing between the edge and the cloud. Blockchain technology is used to maintain the auditability of the data processing process.

[0017] S3 implements intelligent video analysis; it adopts an improved YOLOv7-tiny model for real-time multi-target detection, introduces an attention mechanism to improve the accuracy of small target recognition, and develops an adaptive frame sampling algorithm to increase the analysis frequency in dense target areas; the improved YOLOv7-tiny model includes introducing MobileNetV3 as the feature extraction backbone network, adding a CBAM attention module, using the CIoU loss function to optimize bounding box regression, and deploying a model quantization toolchain to achieve FP16 precision inference. In S4, the deep reinforcement learning model adopts state space definition, action space design, reward function construction, and training algorithms. The state space definition includes 20-dimensional system state features, the action space design supports 8 types of resource adjustment operations, the reward function construction comprehensively considers service quality and resource cost, and the training algorithm is based on the PPO distributed training framework. The system employs MobileNetV3 as the backbone network and optimizes the convolutional layer configuration through neural architecture search technology to reduce computation while maintaining accuracy. Secondly, a CBAM attention module is introduced to enhance feature representation capabilities in both spatial and channel dimensions, significantly improving small target detection performance. Finally, the CIoU loss function is used to optimize bounding box regression, improving localization accuracy by considering overlapping areas, center point distance, and aspect ratio. An adaptive frame sampling algorithm is developed, dynamically adjusting the processing frequency based on scene complexity analysis. Full frame rate analysis is used in densely populated target areas, while frame skipping is used in sparse areas. The TensorRT toolchain is used for optimization during model deployment. An online learning mechanism is established to continuously optimize model performance through hard example mining. When detection accuracy decreases, a model fine-tuning process is automatically triggered. Video analysis results are spatiotemporally aligned with sensor data and then fed into the event inference engine to achieve cross-modal abnormal behavior recognition. The state space definition includes 20 dimensions of features such as CPU utilization, memory usage, and network throughput. The action space supports operations such as container instance scaling, virtual machine migration, and bandwidth adjustment. The reward function is designed as a weighted combination of service quality and resource cost. Service quality considers task completion latency and SLA default rate, while resource cost includes energy consumption and rental fees. The system adopts a distributed training framework to pre-train the policy network in a simulation environment and then adapt it to real-world scenarios through online learning. The resource monitoring module constructs a multi-dimensional indicator system to calculate and monitor container quota utilization, network link congestion, and storage IOPS and throughput. The scheduling decision engine analyzes the system status in real time, automatically triggering vertical scaling when hot services are detected and initiating dynamic virtual machine migration when node load imbalance is detected. The system introduces digital twin technology to pre-simulate the scheduling strategy effect in a virtual environment and verify its security before applying it to the production environment, ensuring the reliability of scheduling decisions.

[0018] S4 enables data-driven resource scheduling; it constructs a multi-dimensional resource monitoring indicator system, including computing resources, network resources, and storage resources, and designs a dynamic scheduling model based on deep reinforcement learning to achieve intelligent migration of computing tasks, dynamic allocation of network bandwidth, and elastic scaling of storage resources. S5 establishes an intelligent storage system; develops a video semantic indexing system to support content-based fast retrieval, realizes automatic hierarchical storage of hot and cold data, sets differentiated compression strategies, and deploys a blockchain evidence storage module to ensure the immutability of key data; Audit traceability is achieved through an intelligent storage system. The system deploys erasure coding mechanism to achieve a balance between storage efficiency and reliability, improves data reconstruction speed, and the storage resource scheduler adopts a predictive expansion algorithm to predict storage demand based on time series analysis, which can complete resource allocation in advance. The data lifecycle management system automatically executes retention policies and performs secure erasure of expired data. S6 builds a visual decision-making platform; develops a 3D digital twin interface to realize a panoramic display of the community's operational status, integrates a multi-source early warning system, establishes a hierarchical alarm mechanism, provides data mining tools, and supports operational decision analysis; the 3D digital twin interface in S6 realizes lightweight rendering of building BIM models, virtual-real mapping of IoT devices, spatiotemporal visualization of abnormal events, and AR navigation of operation and maintenance work orders; The system employs lightweight BIM technology to compress building models while retaining key details, enabling smooth rendering on the web. Virtual-physical mapping of IoT devices is achieved through a digital tagging system, with each physical device corresponding to a digital twin in the virtual scene. Status data is synchronized bidirectionally in real time. A dedicated shader is developed for the spatiotemporal visualization system of abnormal events, using particle effects to represent abnormal environmental parameters and light pillars to mark equipment fault locations. Maintenance work order AR navigation integrates SLAM technology, using a mobile phone camera to identify the on-site environment and overlay equipment parameters and maintenance instructions. The platform integrates a multi-source early warning system, establishing a three-level alarm mechanism of prompt, warning, and critical alerts, with different levels triggering different color flashes and sound prompts. The data mining toolbox provides algorithms such as cluster analysis and association rule mining to assist managers in discovering patterns in equipment faults. The system supports multi-terminal collaborative operation, with the command center's large screen displaying the overall situation and mobile terminals handling on-site issues, forming a closed-loop management process. S7, design a continuous optimization mechanism; establish a model performance monitoring system, regularly evaluate the target detection accuracy, realize online parameter tuning of resource scheduling strategy, develop an incremental learning framework, and support the continuous evolution of algorithm models.

[0019] An intelligent community service management system based on cloud computing employs a cloud-based intelligent community service management method. It includes an intelligent sensing layer, whose output is electrically connected to a platform service layer. The output of the platform service layer is electrically connected to a data governance layer, and the output of the data governance layer is electrically connected to an intelligent application layer. The intelligent application layer contains a security system. The intelligent sensing layer consists of a multimodal data acquisition terminal array, an edge computing gateway device cluster, and a 5G / WiFi 6 hybrid communication network. The platform service layer consists of a distributed message middleware, a microservice architecture business platform, and a containerized algorithm repository. The data governance layer consists of a stream-batch integrated data processing engine and a spatiotemporal data management system. The system comprises a management platform and a federated learning training framework. The intelligent application layer consists of a community security monitoring system, an equipment operation and maintenance management system, and a resident service platform. The security system consists of a multi-factor authentication module, a data encryption transmission channel, and a privacy computing sandbox environment. The platform service layer also includes an elastic resource scheduler, which supports hybrid deployment of virtual machines and containers and provides fine-grained resource quota management. The equipment operation and maintenance management system includes a full-link performance monitoring dashboard, an anomaly root cause analysis engine, and an automated fault recovery toolset. The multi-factor authentication module includes an intelligent risk control system, which enables user behavior profiling, real-time blocking of abnormal access, and early warning of non-security situations. The multi-factor authentication module integrates facial recognition, voiceprint verification, and dynamic tokens, balancing security and convenience through a risk-adaptive authentication strategy. The intelligent risk control system establishes a baseline of user behavior, identifies abnormal operations based on sequence pattern analysis, and blocks brute-force attacks in real time. Data encryption transmission uses the national cryptographic algorithm SM4, implementing double encryption at the transport and application layers. The privacy computing sandbox provides a secure execution environment, supporting joint analysis of multi-party data. Device fingerprint technology is used for terminal identification, generating unique identifiers through hardware features to prevent device impersonation. The security situation awareness platform aggregates data such as network traffic and login logs. The system implements a zero-trust architecture, requiring all access requests to undergo dynamic authorization checks and rejecting all unauthorized operations by default. The audit trail module records all sensitive operations, supporting forensic analysis and accountability.

[0020] In this invention, multi-dimensional community data is collected in real time through heterogeneous data acquisition terminals. An edge-cloud collaborative processing framework is constructed to achieve data cleaning and hierarchical transmission. An improved YOLOv7-tiny model is used for intelligent video analysis. Deep reinforcement learning is combined to achieve dynamic resource scheduling. An intelligent storage system supporting semantic indexing and blockchain notarization is established. A three-dimensional digital twin decision-making platform is developed to achieve panoramic visualization. The system adopts a microservice architecture and integrates security technologies such as multi-factor authentication and privacy computing. A continuous optimization mechanism ensures the iterative evolution of the algorithm model. Finally, a complete solution is formed, including an intelligent perception layer, a platform service layer, a data governance layer, and an intelligent application layer, to achieve intelligent management of community security monitoring, equipment operation and maintenance, and resident services.

[0021] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention.

Claims

1. A cloud computing-based intelligent community service management method, characterized in that: The method comprises the following steps: S1, collecting community multi-dimensional data in real time through an isomerism data collection terminal; S2, constructing an edge-cloud collaborative processing framework; S3, implementing intelligent video analysis; S4, realizing data-driven resource scheduling; S5, establishing an intelligent storage system; S6, constructing a visual decision-making platform; S7, designing a continuous optimization mechanism. 2.The cloud-computing-based intelligent community service management method according to claim 1, characterized in that: The community multi-dimensional data comprises video monitoring data, environmental perception data and equipment operation data, the video monitoring data is acquired by using a multi-spectrum camera to collect visible light and infrared video streams, the environmental perception data is acquired by a distributed sensor network to acquire temperature and humidity, PM2.5 and noise parameters, and the equipment operation data is acquired by real-time monitoring of the running state and energy consumption data of elevator, access control and fire-fighting equipment. 3.The cloud-computing-based intelligent community service management method according to claim 1, characterized in that: In S2, more specifically, a lightweight data cleaning module is deployed on the edge node, a time series-based anomaly detection algorithm is used to eliminate invalid data, a multi-level data caching mechanism is established, a differentiated retention period is set according to the data type, and a dynamic QoS strategy is designed to assign the highest transmission priority to security video; In S3, more specifically, an improved YOLOv7-tiny model is used for multi-target real-time detection, an attention mechanism is introduced to improve small target recognition accuracy, and an adaptive frame sampling algorithm is developed to increase the analysis frequency in target dense areas; In S4, more specifically, a multi-dimensional resource monitoring index system is constructed, including computing resources, network resources and storage resources, a dynamic scheduling model based on deep reinforcement learning is designed to realize intelligent migration of computing tasks, dynamic allocation of network bandwidth and elastic scaling of storage resources; In S5, more specifically, a video semantic indexing system is developed to support fast content-based retrieval, realize automatic hierarchical storage of hot and cold data, set up a differentiated compression strategy, deploy a blockchain storage module to ensure the non-tamperability of key data; In S6, more specifically, a three-dimensional digital twin interface is developed to realize panoramic display of community operation status, integrate a multi-source early warning system, establish a hierarchical alarm mechanism, provide data mining tools and support operation decision analysis; In S7, more specifically, a model performance monitoring system is established to regularly evaluate target detection accuracy, realize online parameter adjustment of resource scheduling strategy, develop an incremental learning framework to support continuous evolution of algorithm models.

4. The cloud-computing-based intelligent community service management method according to claim 3, characterized in that: The improved YOLOv7-tiny model in S3 includes introducing MobileNetV3 as a feature extraction backbone network, adding a CBAM attention module, using a CIoU loss function to optimize boundary box regression, and deploying a model quantization tool chain and realizing FP16 precision inference, the deep reinforcement learning model in S4 uses state space definition, action space design, reward function construction and training algorithm, the state space definition contains 20-dimensional system state features, the action space design supports 8 types of resource adjustment operations, the reward function construction considers service quality and resource cost comprehensively, and the training algorithm is based on a distributed training framework of PPO.

5. The cloud-computing-based intelligent community service management method according to claim 3, characterized in that: The three-dimensional digital twin interface in the S6 realizes lightweight rendering of a building BIM model, virtual-real mapping of Internet of Things equipment, abnormal event space-time visualization and AR navigation of operation and maintenance work orders.

6. A cloud computing-based intelligent community service management system, which adopts the cloud computing-based intelligent community service management method according to any one of claims 1-5. The output end of the intelligent perception layer is electrically connected with a platform service layer, the output end of the platform service layer is electrically connected with a data governance layer, the output end of the data governance layer is electrically connected with an intelligent application layer, and the intelligent application layer is loaded with a security guarantee system.

7. The cloud-computing-based intelligent community service management system according to claim 6, characterized in that: The intelligent perception layer is composed of a multi-modal data acquisition terminal array, an edge computing gateway device cluster and a 5G / WiFi6 hybrid communication network, the platform service layer is composed of a distributed message middleware, a micro-service architecture business middle platform and a containerized algorithm warehouse, the data governance layer is composed of a stream-batch integrated data processing engine, a space-time data management platform and a federated learning training framework, the intelligent application layer is composed of a community security monitoring system, a device operation and maintenance management system and a resident service platform, and the security guarantee system is composed of a multi-factor identity authentication module, a data encryption transmission channel and a privacy computing sandbox environment. 8.The cloud-computing-based intelligent community service management system according to claim 7, characterized in that: The platform service layer is also loaded with an elastic resource scheduler, the elastic resource scheduler supports hybrid deployment of virtual machines and containers and provides fine-grained resource quota management, the device operation and maintenance management system is loaded with a full-link performance monitoring board, an abnormal root cause analysis engine and an automatic fault recovery tool set, the multi-factor identity authentication module is loaded with an intelligent risk control system, and the intelligent risk control system realizes user behavior portrait analysis, abnormal access real-time blocking and non-security situation early warning. The three-dimensional digital twin interface in the S6 realizes lightweight rendering of a building BIM model, virtual-real mapping of Internet of Things equipment, abnormal event space-time visualization and AR navigation of operation and maintenance work orders.

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