Smart city operation data management method based on cloud network system

Abstract

The invention relates to the technical field of data processing, discloses a smart city operation data management method based on a cloud network system, and aims to solve the comprehensive bottlenecks of existing smart city data management in the aspects of real-time performance, safety and self-adaptability. According to the method, a cloud side-end collaborative data acquisition and access architecture is constructed, a dynamic network awareness and resource scheduling mechanism is established, fine-grained access control based on attribute encryption and a block chain is implemented, and an intelligent data management engine is deployed to realize self-adaptive adjustment of storage and calculation strategies. And cross-domain data fusion and collaborative analysis are driven through a unified semantic model, and a city operation panoramic view is generated to support automatic response and aid decision making. According to the technical scheme, low-delay transmission of urban operation data, full-life-cycle privacy security protection, efficient utilization of resources and deep fusion of multi-source heterogeneous data in a high-concurrency environment are realized, and the intelligent level of urban governance and public service efficiency are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a smart city operation data management method based on a cloud network system. BACKGROUND

[0002] With the continuous development of cloud network systems that deeply integrate cloud computing and communication networks, their supporting role in smart city operations is increasingly prominent. Smart cities rely on massive, multi-source, and heterogeneous real-time data for city governance, public services, and resource scheduling. However, traditional data management architectures mostly use centralized storage and static processing modes, which are difficult to meet the business demands of high concurrency, low latency, and strong collaboration. Especially in cross-department and cross-regional city operation scenarios, data collection, transmission, fusion, and response need to be efficiently coordinated under a unified architecture, which poses severe challenges to the system's elastic expansion capability, data consistency guarantee, and security compliance.

[0003] Among them, the smart city operation data management based on the cloud network system aims to realize intelligent management and control of the whole life cycle of data through cloud edge coordination and network resource dynamic scheduling. This technical direction emphasizes sinking computing power to the network edge and combining global scheduling on the cloud to improve the real-time and reliability of data processing. However, existing technologies still have significant defects: on the one hand, traditional data management platforms lack adaptive ability to heterogeneous network environments and are difficult to maintain stable transmission of data flow under dynamic topology changes; on the other hand, city operation data involves a large amount of sensitive information, and existing solutions often sacrifice fine-grained access control and privacy protection mechanisms while achieving efficient sharing. In addition, data governance strategies mostly rely on manual configuration and cannot automatically adjust storage strategies and computing resource allocation according to business load and security posture, resulting in low overall energy efficiency and high operation and maintenance complexity.

[0004] Therefore, there is an urgent need for a smart city operation data management method that can deeply integrate cloud network capabilities, support dynamic collaboration and intelligent governance, to solve the comprehensive bottlenecks of existing technologies in real-time, security, and adaptability. SUMMARY

[0005] The purpose of the present application is to provide a smart city operation data management method based on a cloud network system, which can effectively solve the problems in the background technology.

[0006] To achieve the above purpose, the technical solution adopted by the present application is as follows:

[0007] A smart city operation data management method based on a cloud network system, comprising the following specific steps:

[0008] Step 1: Construct a cloud-edge-end collaborative data collection and access architecture. Real-time collection of multi-source heterogeneous operation data such as traffic, environment, energy, and security is achieved through the deployment of edge computing nodes in various regions of the city. Based on a unified data access protocol, data is uploaded to the cloud network fusion platform in a streaming manner.

[0009] Step 2: Establish a dynamic network perception and resource scheduling mechanism. Real-time monitoring of the topology, bandwidth utilization, and node load status of the cloud network system is achieved. Based on business priority and data timeliness requirements, dynamic allocation of network transmission paths and edge computing resources is achieved to ensure low-latency transmission of high-priority data streams.

[0010] Step 3: Implement fine-grained access control based on attribute encryption and blockchain. Hierarchical classification of city operation data uploaded to the platform is achieved. Combined with user identity, access context, and data sensitivity level, dynamic access policies are generated to ensure privacy and compliance during data sharing.

[0011] Step 4: Build an intelligent data governance engine. Based on real-time business load, security risk situation, and data access frequency, automatically adjust data storage levels, replica quantities, and computing scheduling strategies to achieve adaptive management throughout the data lifecycle.

[0012] Step 5: Perform cross-domain data fusion and collaborative analysis. Align and correlate data from different departments and regions under a unified semantic model to generate a panoramic view of city operation status. Based on this view, trigger automated response mechanisms or assist decision-making processes.

[0013] Preferably, the edge computing nodes in step 1 are deployed at key infrastructure nodes in the city, including but not limited to traffic signal control boxes, environmental monitoring stations, power substations, and community security gateways. Each node is equipped with at least 8-core processors and 32GB of memory, supporting local data preprocessing and anomaly detection, with a data upload delay of no more than 50 milliseconds.

[0014] Preferably, the dynamic network perception module in step 2 uses a software-defined network controller. It collects link state information every 100 milliseconds and predicts network congestion trends within the next 500 milliseconds using reinforcement learning algorithms. High-priority data streams are rerouted in advance to ensure that end-to-end transmission latency fluctuations are less than 10 milliseconds. The network congestion prediction model can be represented as:

[0015]

[0016] where represents the congestion probability at future time , , , and Q(t)Q(t) are the current link load, bandwidth utilization, and queue length, respectively. a prediction function trained by reinforcement learning.

[0017] Preferably, the attribute encryption in step 3 adopts a lattice-based post-quantum secure algorithm with a key length of 256 bits, the access policy is defined in the form of a Boolean expression, supports combined authorization of no less than 10 attribute dimensions, the policy update delay is no more than 200 milliseconds, and all access logs are stored in real time on-chain, with a block generation interval of 1 second.

[0018] Preferably, the intelligent data governance engine in step 4 has a multi-objective optimization model, which comprehensively considers storage cost, access delay, and energy consumption indicators. When the access frequency of a certain type of data is less than the threshold of 0.1 times per second for 7 consecutive days, it is automatically migrated from hot storage to cold storage, and the number of replicas is reduced to 2; when the security risk score exceeds 85 points, the data access permission is immediately frozen and the audit process is started. The data migration decision function is defined as follows:

[0019]

[0020] wherein, represents the storage level decision of data , is the access frequency of data on the th day.

[0021] Preferably, the unified semantic model in step 5 adopts an ontology-driven multi-source data alignment method, defines no less than 500 core city entity types and 2000 relationship predicates, and the data fusion accuracy is no less than 98%, and the collaborative analysis task can complete data correlation and result generation across more than 5 departments within 3 seconds.

[0022] Preferably, the method further comprises: establishing a two-way authentication channel between the edge computing node and the cloud, using the national SM2 algorithm for identity verification, and dynamically rotating the session key every 30 minutes to ensure end-to-end security of the data transmission process.

[0023] Preferably, the method further comprises: constructing a city operation data quality evaluation module to score the completeness, consistency, and timeliness of the collected data in real time, and when any dimension score is less than 90 points, automatically triggering data source verification or redundant collection mechanism to ensure the reliability of platform input data.

[0024] Preferably, the method further comprises: deploying a resource elasticity scaling controller to predict the computing and storage requirements for the next 24 hours based on the daily business load curve, and reserving resources on the cloud platform 1 hour in advance, with resource utilization fluctuation controlled within ±5%, and the overall energy efficiency ratio of the system improved by more than 30%.

[0025] Preferably, the method is applied to a municipal smart city operation center, supports simultaneous access to no less than 100,000 edge nodes, processes more than 100 TB of data per day, the key business response time is less than 1 second, and the system availability reaches 99.99%.

[0026] Compared with the prior art, the beneficial effects achieved by the present application are:

[0027] 1. By constructing a cloud-edge-end collaborative data acquisition architecture and a dynamic network perception scheduling mechanism, the present application realizes stable and low-latency transmission of city operation data in a high-concurrency and dynamic network environment, effectively overcoming the deficiencies of traditional centralized architecture in real-time performance; by fusing attribute encryption and blockchain technology, a fine-grained access control system covering the entire life cycle of data is established, which ensures efficient sharing while meeting strict privacy protection and compliance requirements;

[0028] 2. By introducing an intelligent data governance engine, adaptive adjustment of storage strategies and computing resources is realized, significantly reducing system operation and maintenance complexity and energy consumption; by unified semantic model-driven cross-domain data fusion, data silos between departments are broken down, forming a global view of city operation status, providing a solid data foundation for automated response and scientific decision-making. The above-mentioned technical features work together to solve the comprehensive bottlenecks of existing smart city data management methods in real-time performance, security and adaptability, and improve the intelligent level of city governance and public service efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 is the overall technical scheme architecture diagram of the data management method of the present application;

[0030] Figure 2 is the core principle framework diagram of the dynamic network perception and resource scheduling mechanism in the present application;

[0031] Figure 3 is the fine-grained access control logic flow framework diagram based on attribute encryption and blockchain in the present application;

[0032] Figure 4 is the adaptive management strategy and decision-making process framework diagram of the intelligent data governance engine in the present application;

[0033] Figure 5 is the multi-source data alignment and panoramic view generation interaction relationship diagram of cross-domain data fusion and collaborative analysis in the present application. DETAILED DESCRIPTION

[0034] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without creative efforts belong to the scope of the present application.

[0035] Embodiment 1

[0036] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to specific embodiments.

[0037] At present, with the development of cloud network systems in which cloud computing and communication networks are deeply integrated, the supporting role of the cloud network systems in smart city operation is increasingly prominent. Smart cities rely on massive, multi-source and heterogeneous real-time data for city governance, public services and resource scheduling. However, the traditional data management architecture adopts a centralized storage and static processing mode, which is difficult to meet the business requirements of high concurrency, low latency and strong collaboration. Especially in the cross-department and cross-regional city operation scenarios, the collection, transmission, fusion and response of data need to be efficiently coordinated under a unified architecture, which poses a severe challenge to the elastic expansion capability of the system, the data consistency guarantee and the security compliance. In view of the above technical problems, the present application proposes to build a cloud-edge-end collaborative data collection architecture, a dynamic network perception and resource scheduling mechanism, a fine-grained access control based on attribute encryption and blockchain, an intelligent data governance engine and cross-domain data fusion and collaborative analysis, to realize intelligent management and control of the whole life cycle of data, and to apply the same to a smart city operation data management method based on a cloud network system.

[0038] Reference Figure 1 The overall technical solution architecture of the present application includes an edge computing node layer, a cloud network integration platform layer and an upper layer application service layer. The edge computing node layer is deployed at key infrastructure nodes in each region of the city, including traffic signal control boxes, environmental monitoring stations, power substations and community security gateways, for real-time collection of multi-source and heterogeneous operation data such as traffic, environment, energy and security; the cloud network integration platform layer integrates a dynamic network perception module, an access control module, a data governance engine and a data fusion analysis module, and is responsible for data transmission scheduling, security control, storage optimization and semantic alignment; the upper layer application service layer triggers an automated response mechanism or an auxiliary decision-making process based on the generated panoramic view of the city operation status.

[0039] In the smart city operation data management method based on the cloud network system, the step (1) is to construct a cloud-edge-end collaborative data collection and access architecture. Real-time collection of multi-source heterogeneous operation data such as traffic, environment, energy, and security is performed through the deployment of edge computing nodes in various regions of the city, and the data is uploaded to the cloud network fusion platform in a streaming manner based on a unified data access protocol. Specifically, the edge computing nodes are deployed at key infrastructure nodes in the city, each node is configured with no less than 8-core processors and 32 GB of memory, and supports local data preprocessing and anomaly detection. The edge computing node has a built-in data collection agent module, which connects various types of sensor devices through standardized interfaces, including but not limited to traffic flow detectors, air quality sensors, smart meters, and video surveillance cameras. The collection frequency is dynamically adjusted according to the device type, ranging from 1 Hz to 100 Hz. The raw data collected is first standardized in format, timestamped, and preliminarily noise filtered locally, then packaged into message units conforming to the unified data access protocol, and uploaded to the cloud network fusion platform in a streaming manner through MQTT or HTTP / 2 protocol. To ensure real-time uploading, the system sets an upper limit of 50 milliseconds for data upload delay, which includes local processing time and network transmission time. If network interruption or node failure is detected, the edge computing node will enable a local caching mechanism with a maximum cache capacity of 16 GB, and after network recovery, the data will be transmitted in a priority queue manner to ensure data integrity. In addition, a two-way authentication channel is established between the edge computing node and the cloud, and the national SM2 algorithm is used for identity verification. The session key is dynamically rotated every 30 minutes to ensure end-to-end security during data transmission.

[0040] In the smart city operation data management method based on the cloud network system, the step (2) is to establish a dynamic network perception and resource scheduling mechanism. The topology structure, bandwidth utilization, and node load state of the cloud network system are monitored in real time, and the network transmission path and edge computing resources are dynamically allocated according to the business priority and data timeliness requirements to ensure low-latency transmission of high-priority data streams. Specifically, the dynamic network perception module uses a software-defined network controller, as shown in Figure 2 The controller collects link state information every 100 milliseconds, including real-time bandwidth utilization, node CPU and memory load, data packet queue length, and packet loss rate of each physical and virtual link. The collected state information is input into a network congestion prediction model trained by a reinforcement learning algorithm, which is used to predict the network congestion trend in the next 500 milliseconds and reroute high-priority data streams in advance. The network congestion prediction model can be represented as:

[0041]

[0042] where, represents the future time the congestion probability of the link, , Q(t)Q(t) are the link load, bandwidth utilization and queue length at the current time, respectively, is the prediction function trained by reinforcement learning. The neural network structure of the prediction function is a three-layer fully connected network, the input layer dimension is 3, the hidden layer dimension is 128, the output layer is a single probability value, the activation function uses ReLU, and the loss function is binary cross entropy. According to the prediction result, the controller dynamically calculates the optimal transmission path combined with the preset business priority label (for example, emergency command data is the highest priority, environmental monitoring data is medium priority). The path calculation adopts an improved Dijkstra algorithm, which considers the predicted congestion probability, physical distance and current link residual bandwidth in the weight function. The calculated new path instruction is issued to the corresponding network switching device to complete the seamless switching of data flow, ensuring that the end-to-end transmission delay fluctuation is less than 10 milliseconds. At the same time, the mechanism is also responsible for dynamically allocating edge computing resources. When it is detected that the load of a certain regional edge node exceeds the 80% threshold, the controller will offload part of the computing task to the adjacent node with lower load or the cloud, realizing the global optimization of computing resources.

[0043] In the above smart city operation data management method based on the cloud network system, the step (3) implements fine-grained access control based on attribute encryption and block chain, classifies the city operation data uploaded to the platform, and generates a dynamic access strategy combined with user identity, access context and data sensitivity level, to ensure the privacy security and compliance of the data in the sharing process. Specifically, see Figure 3The system first automatically classifies all accessed city operation data. The classification is based on data content sensitivity, divided into four levels of public, internal, confidential and top secret; the classification is based on data source and business field, divided into categories such as transportation, environment, energy and security. The classification result is stored as metadata together with the original data. The access control policy is defined in the form of a Boolean expression, for example "(department=traffic bureau AND job level >= section chief) OR (project=city brain AND security permission=authorized)". Attribute encryption uses a lattice-based post-quantum secure algorithm with a key length of 256 bits to ensure data security even in a future quantum computing environment. When a user initiates a data access request, the system verifies its digital certificate and extracts its identity attributes (such as department, job level, project ownership, etc.) and access context (such as access time, geographic location, device fingerprint). The system matches these attributes with the pre-set access policy, and if the match is successful, a temporary decryption key is dynamically generated to authorize this access. The policy update delay is no more than 200 milliseconds to respond to emergency security incidents. All access logs, including requesters, request times, access data, policy matching results and operation types, are stored in real time on the chain for evidence. The blockchain uses a consortium chain architecture, maintained by the city-level smart city operation center and various major government agencies, with a block generation interval of 1 second to ensure the non-tamperability and traceability of the logs.

[0044] In the above smart city operation data management method based on the cloud network system, step (4) constructs an intelligent data governance engine, which automatically adjusts the storage level, number of copies and computing scheduling strategy of data based on real-time business load, security risk situation and data access frequency, realizing adaptive management of the entire life cycle of data. Specifically, referring to Figure 4 , the intelligent data governance engine has a multi-objective optimization model built in, which considers storage cost, access delay and energy consumption indicators. The engine continuously monitors the access frequency of various data , where represents a specific data set, represents the number of days. When the average value of the access frequency of a certain type of data for 7 consecutive days is less than the threshold value of 0.1 times per second, the engine automatically triggers the data migration process, migrating it from high-performance hot storage (such as SSD arrays) to low-cost cold storage (such as object storage), and simultaneously reducing the number of copies from the default of 3 to 2 to save storage resources. The data migration decision function is defined as follows:

[0045]

[0046] where represents the storage level decision of data , is the data of the th day frequency of access.

[0047] In addition, the engine also integrates a security risk assessment module that analyzes the output of the network intrusion detection system, user behavior analysis system, and external threat intelligence in real time to generate a security risk score of 0 to 100. When the score exceeds 85, the engine immediately freezes the access rights of the relevant data and sends an alarm to the security audit center to start a complete audit process. At the same time, the engine is also responsible for the scheduling of computing resources, dynamically adjusting the CPU and memory resources allocated to different data processing tasks according to the task queue length and estimated execution time to ensure the timely completion of high-priority tasks.

[0048] In the smart city operation data management method based on the cloud network system described above, the step (5) performs cross-domain data fusion and collaborative analysis, aligns and associates data from different departments and regions under a unified semantic model, generates a city operation status panoramic view, and triggers an automatic response mechanism or an auxiliary decision-making process based on the view. Specifically, referring to Figure 5 , the system first constructs an ontology-driven unified semantic model. The model defines not less than 500 core city entity types (such as "road", "vehicle", "pollution source", "transformer station") and 2000 relationship predicates (such as "located in", "belongs to", "affects", "powers"). It provides a common understanding framework for multi-source heterogeneous data. The data fusion module receives data streams from different departments, uses entity linking and relationship extraction techniques to map records in the original data to corresponding entities and relationships in the unified semantic model. For example, associate "license plate number" from the traffic department with "vehicle owner information" from the public security department, or spatio-temporally align "PM2.5 concentration" from the environmental department with "wind speed and direction" from the meteorological department. The process uses a deep learning-based alignment algorithm, and the data fusion accuracy is not less than 98%. The fused data is organized into a dynamically updated knowledge graph, which is the city operation status panoramic view. Upper-layer application services perform collaborative analysis based on this view, for example, when detecting that the traffic congestion index of a certain area exceeds the threshold and there is high concentration of air pollution at the same time, the system can automatically generate a joint work order for traffic diversion and pollution source investigation and push it to the relevant departments. Such collaborative analysis tasks can complete data association and result generation across more than 5 departments within 3 seconds.

[0049] In addition, the method further comprises constructing a city operation data quality evaluation module. The module scores the completeness, consistency and timeliness of the collected data in real time. The completeness score is calculated based on the data field missing rate, the consistency score is verified based on data logic rules (such as vehicle speed cannot be negative) and historical data patterns, and the timeliness score is based on the time difference from data generation to platform reception. When the score of any dimension is lower than 90, the module automatically triggers data source verification or redundant collection mechanism, for example, sending collection instructions to backup sensors in the same area, or requiring the data source to resend missing data packets, to ensure the reliability of platform input data.

[0050] In addition, the method further comprises deploying a resource elasticity scaling controller. Based on historical business load data, the controller uses a time series prediction model (such as LSTM) to predict the computing and storage requirements for the next 24 hours. The prediction results are used to reserve or release resources on the cloud platform 1 hour in advance, realizing on-demand supply of resources. Through this mechanism, the system can control the resource utilization rate fluctuation within ±5%, avoiding resource idling and overload, and the overall energy efficiency ratio of the system is improved by more than 30%.

[0051] In this embodiment, the method is applied to a municipal smart city operation center, supporting simultaneous access to no less than 100,000 edge nodes, with a daily data processing capacity of more than 100 TB, a key business response time of less than 1 second, and a system availability of 99.99%.

[0052] Although embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and alterations can be made thereto without departing from the principles and spirit of the application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A smart city operation data management method based on a cloud network system, characterized in that: The specific steps include the following: Step 1: Construct a cloud-edge-device collaborative data collection and access architecture. Collect multi-source heterogeneous operational data such as traffic, environment, energy, and security in real time by deploying edge computing nodes in various urban areas, and upload the data to the cloud-network convergence platform in a streaming manner based on a unified data access protocol. Step 2: Establish a dynamic network awareness and resource scheduling mechanism to monitor the topology, bandwidth utilization and node load status of the cloud network system in real time. Based on business priority and data timeliness requirements, dynamically allocate network transmission paths and edge computing resources to ensure low-latency transmission of high-priority data streams. Step 3: Implement fine-grained access control based on attribute encryption and blockchain, classify and categorize the city operation data uploaded to the platform, and generate dynamic access policies based on user identity, access context and data sensitivity level to ensure privacy, security and compliance of data during the sharing process; Step 4: Build an intelligent data governance engine to automatically adjust the data storage level, number of replicas, and computing scheduling strategy based on real-time business load, security risk status, and data access frequency, so as to achieve adaptive management of the entire data lifecycle; Step 5: Perform cross-domain data fusion and collaborative analysis to align and associate data from different departments and regions under a unified semantic model, generate a panoramic view of the city's operation status, and trigger automated response mechanisms or auxiliary decision-making processes based on this view.

2. The smart city operation data management method based on a cloud network system according to claim 1, characterized in that: The edge computing nodes are deployed at key urban infrastructure nodes, including traffic signal control boxes, environmental monitoring stations, power substations, and community security gateways. Each node is equipped with no less than 8 core processors and 32GB of memory, supports local data preprocessing and anomaly detection, and the data upload latency does not exceed 50 milliseconds.

3. The smart city operation data management method based on a cloud network system according to claim 1, characterized in that: The dynamic network awareness and resource scheduling mechanism adopts a software-defined network controller, which collects the network link status information every 100 milliseconds, combines reinforcement learning algorithms to predict the network congestion trend within the next 500 milliseconds, and reroutes high-priority data streams in advance to ensure that the end-to-end transmission latency fluctuation is less than 10 milliseconds.

4. The smart city operation data management method based on a cloud network system according to claim 1, characterized in that: The attribute encryption uses a lattice-based post-quantum secure algorithm with a key length of 256 bits. The access policy is defined in the form of a Boolean expression, supports authorization of at least 10 attribute dimensions, has a policy update delay of no more than 200 milliseconds, and all access logs are uploaded to the blockchain for evidence storage in real time. The block generation interval is 1 second.

5. The smart city operation data management method based on a cloud network system according to claim 1, characterized in that: The intelligent data governance engine has a built-in multi-objective optimization model that comprehensively considers storage costs, access latency, and energy consumption indicators. When the access frequency of a certain type of data is lower than the threshold of 0.1 times per second for 7 consecutive days, it automatically migrates the data from hot storage to cold storage and reduces the number of copies to 2. When the security risk score is detected to exceed 85 points, the data access permissions are immediately frozen and the audit process is initiated.

6. The smart city operation data management method based on a cloud network system according to claim 1, characterized in that: The unified semantic model adopts an ontology-driven multi-source data alignment method, defines no less than 500 core urban entity types and 2,000 relational predicates, achieves a data fusion accuracy of no less than 98%, and can complete data association and result generation across more than 5 departments within 3 seconds for collaborative analysis tasks.

7. The smart city operation data management method based on a cloud network system according to claim 1, characterized in that: A two-way authentication channel is established between the edge computing node and the cloud, and the national cryptographic SM2 algorithm is used for identity verification. The session key is dynamically rotated every 30 minutes to ensure end-to-end security of data transmission.

8. The smart city operation data management method based on a cloud network system according to claim 1, characterized in that: A city operation data quality assessment module is built to score the completeness, consistency and timeliness of the collected data in real time. When the score of any dimension is lower than 90 points, the data source verification or redundant collection mechanism is automatically triggered to ensure the reliability of the data input to the platform.

9. A smart city operation data management method based on a cloud network system according to claim 1, characterized in that: Deploy resource elastic scaling controllers to predict computing and storage needs for the next 24 hours based on daily business load curves, reserve resources on the cloud platform one hour in advance, control resource utilization fluctuations within ±5%, and improve the overall system energy efficiency ratio by more than 30%.

10. A smart city operation data management method based on a cloud network system according to claim 1, characterized in that: The method is applied to a municipal smart city operation center, supporting the simultaneous access of no less than 100,000 edge nodes, processing more than 100TB of data per day, with a critical business response time of less than 1 second and a system availability of 99.99%.

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