Electronic city (ICC) management method and system based on private cloud platform

Abstract

The application relates to the technical field of intelligent management, in particular to an electronic city ICC management method and system based on a private cloud platform. The method comprises the following steps: performing real-time collection and standardized processing on physical resources and interface services of an electronic city ICC cluster to obtain electronic city ICC standardized cluster resource data; constructing an electronic city ICC resource-service association model based on the electronic city ICC standardized cluster resource data, performing mapping association and cluster node state analysis, and obtaining electronic city ICC cluster node state characteristic data; performing node load scheduling configuration, configuration optimization management and storage resource matching based on the electronic city ICC cluster node state characteristic data, and generating optimized deployment scheme data; and performing management iteration optimization on the optimized deployment scheme data to output a corresponding electronic city ICC management execution scheme. The application can realize intelligent resource collaborative scheduling management of all factors of an electronic city.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent management, and particularly relates to an electronic city ICC management method and system based on a private cloud platform. BACKGROUND

[0002] With the rapid development of information technology, intelligent city construction has become an important development direction of modern society. Electronic city (E-City) as a core component of intelligent city involves the integration and application of information technology, Internet of Things, cloud computing and other technologies. In particular, in the infrastructure management of electronic city, ICC (Infrastructure Control Center) as the core management platform undertakes the responsibilities of monitoring, scheduling and maintaining various resources such as electronic city equipment, systems, networks, etc. In addition, through the private cloud platform, the ICC management system can store resources and data in the cloud, utilize the high scalability and high availability of cloud computing, and provide more flexible and efficient management services. The private cloud platform can dynamically allocate computing and storage resources according to demand, significantly improve the scalability and stability of the system, and can realize efficient scheduling and maintenance of devices, networks and systems through centralized management.

[0003] However, although the private cloud platform has great advantages in ICC management, traditional ICC management methods often lack intelligent scheduling and automated management of cloud platform resources, resulting in that the system running efficiency cannot be fully improved. At the same time, due to the variety of systems and devices involved in electronic city, it is difficult to realize accurate and real-time processing of different types of data in a unified private cloud platform, thereby reducing the load resource management efficiency. SUMMARY

[0004] Therefore, it is necessary to provide an electronic city ICC management method and system based on a private cloud platform to solve at least one of the above technical problems.

[0005] To achieve the above purpose, an electronic city ICC management method based on a private cloud platform comprises the following steps:

[0006] Step S1: Real-time collection and standardized processing of physical resources and interface services of electronic city ICC cluster by the resource collection module corresponding to the private cloud platform to obtain electronic city ICC standardized cluster resource data; constructing an electronic city ICC resource-service association model based on the electronic city ICC standardized cluster resource data and performing mapping association to obtain electronic city ICC cluster resource association feature data;

[0007] Step S2: Cluster node state analysis is performed on the electronic city ICC cluster resource association feature data to obtain electronic city ICC cluster node state feature data; node load scheduling configuration is performed based on the electronic city ICC cluster node state feature data and in combination with the electronic city ICC standardized cluster resource data to generate electronic city ICC node load deployment scheme data;

[0008] Step S3: Based on the electronic city ICC node load deployment scheme data, corresponding electronic city ICC node load scheduling success rate data is obtained, and the electronic city ICC node load deployment scheme data is subjected to configuration optimization management and storage resource matching to generate optimized deployment scheme data corresponding to the association configuration of the electronic city ICC persistent volume and storage class;

[0009] Step S4: Cluster full-link monitoring and alarm are performed on the execution process corresponding to the optimized deployment scheme data to obtain electronic city ICC cluster monitoring and alarm data; tenant resource dynamic adjustment is performed based on the electronic city ICC cluster monitoring and alarm data to obtain electronic city ICC tenant resource dynamic configuration data; and the electronic city ICC management strategy corresponding to the private cloud platform is managed and iteratively optimized according to the electronic city ICC cluster monitoring and alarm data and the electronic city ICC tenant resource dynamic configuration data to output the corresponding electronic city ICC management execution scheme.

[0010] Further, step S1 includes the following steps:

[0011] Step S11: Real-time collection of physical resources and interface services of the electronic city ICC cluster is performed by the distributed collection agent deployed by the resource collection module of the private cloud platform to collect the physical resource data and interface service data corresponding to the electronic city ICC cluster in real time, wherein the physical resource data includes node total amount, CPU, memory, NPU, video memory and disk resources, and the interface service data includes API request delay, response time, status code and request times, to obtain electronic city ICC initial cluster resource data;

[0012] Step S12: Cluster resource data cleaning is performed on the electronic city ICC initial cluster resource data to eliminate outliers and repeated records, and interpolation method is used to fill in missing values to obtain electronic city ICC cleaned cluster resource data;

[0013] Step S13: Standardization processing is performed on the electronic city ICC cleaned cluster resource data to unify the data timestamp format and measurement units, and different types of resource data and service data are mapped to a unified numerical interval through a normalization algorithm to obtain electronic city ICC standardized cluster resource data;

[0014] Step S14: constructing an electronic city ICC resource-service association model based on the electronic city ICC standardized cluster resource data, constructing an association weight matrix by using correlation analysis to calculate the association coefficient between the physical resource occupation state and the interface service performance parameter, and mapping and associating the corresponding physical resource occupation state and the interface service performance parameter based on the association weight matrix to obtain electronic city ICC cluster resource association feature data.

[0015] Further, step S2 includes the following steps:

[0016] Step S21: performing cluster node state analysis on the electronic city ICC cluster resource association feature data to analyze and extract the resource utilization, health state, and load balancing degree of each node of the electronic city ICC cluster to obtain electronic city ICC cluster node state feature data;

[0017] Step S22: performing cluster node clustering analysis based on the electronic city ICC cluster node state feature data and in combination with the electronic city ICC standardized cluster resource data to classify electronic city ICC cluster nodes with similar resource configurations and similar load characteristics into the same management group to obtain electronic city ICC cluster node clustering result data;

[0018] Step S23: performing node cluster state evaluation on the electronic city ICC cluster node clustering result data to calculate the node cluster health degree score in combination with the node health check log corresponding to each electronic city ICC cluster node management group to obtain electronic city ICC cluster node health state data;

[0019] Step S24: formulating corresponding electronic city ICC cluster node management strategies based on the electronic city ICC cluster node health state data and the electronic city ICC cluster node clustering result data, including resource scheduling priority division, fault automatic migration rules, and load balancing threshold setting to obtain electronic city ICC cluster node management strategy data;

[0020] Step S25: synchronizing the electronic city ICC cluster node management strategy data to the scheduling center of the private cloud platform for node load adjustment configuration to generate electronic city ICC node load deployment scheme data.

[0021] Further, step S25 includes the following steps:

[0022] Step S251: determining the deployment node range of each application load of the electronic city ICC based on the corresponding resource scheduling priority in the electronic city ICC cluster node management strategy data to obtain electronic city ICC deployment node candidate set data;

[0023] Step S252: Obtain the application load type of the electronic city ICC cluster corresponding to the scheduling center of the private cloud platform, including deployment resources, stateful replica resources, and daemon set resources, and perform resource parameter analysis on the application load type of the electronic city ICC cluster to extract resource requirement parameters and deployment constraints, wherein the resource requirement parameters include CPU core number, memory capacity, and storage type, the deployment constraints include node affinity and anti-affinity rules, and obtain electronic city ICC load demand characteristic data;

[0024] Step S253: Perform matching degree calculation on the electronic city ICC deployment node candidate set data and the electronic city ICC load demand characteristic data to generate a corresponding node matching score based on node resource remaining amount and node cluster class health score, and perform deployment node matching analysis between the electronic city ICC deployment node candidate set data and the electronic city ICC load demand characteristic data based on the node matching score to obtain electronic city ICC deployment node matching result data;

[0025] Step S254: Based on the electronic city ICC deployment node matching result data, synchronize to the scheduling center corresponding to the private cloud platform for node load adjustment configuration, to clearly define the deployment node, replica number, and start order of each electronic city ICC deployment load, to generate electronic city ICC node load deployment scheme data.

[0026] Further, step S3 includes the following steps:

[0027] Step S31: Based on the electronic city ICC node load deployment scheme data and in combination with the electronic city ICC cluster resource association characteristic data, simulate to generate a corresponding electronic city ICC cluster node application load deployment process;

[0028] Step S32: Obtain the electronic city ICC node resource conflict probability and the electronic city ICC node scheduling completion time through the electronic city ICC cluster node application load deployment process;

[0029] Step S33: Based on the electronic city ICC node resource conflict probability and the electronic city ICC node scheduling completion time, perform node load scheduling evaluation on the electronic city ICC cluster node application load deployment process to obtain electronic city ICC node load scheduling success rate data;

[0030] Step S34: Based on the electronic city ICC node load scheduling success rate data, perform optimization adjustment on the electronic city ICC node load deployment scheme data to determine the resource allocation parameters and service interface exposure rules of the electronic city ICC container group, and obtain electronic city ICC node optimization deployment scheme data;

[0031] Step S35: Based on the electronic city ICC node optimization deployment scheme data, configuration management and storage resource matching are performed to generate the electronic city ICC persistent volume and storage class association configuration corresponding optimization deployment scheme data.

[0032] Further, step S33 includes the following steps:

[0033] Based on the electronic city ICC node resource conflict probability, node load resource conflict evaluation is performed on the electronic city ICC cluster node application load deployment process to obtain the electronic city ICC node load resource conflict influence factor;

[0034] The electronic city ICC node load benchmark time is obtained, and based on the electronic city ICC node load benchmark time, the electronic city ICC node scheduling completion time is standardized to obtain the electronic city ICC node scheduling standardized time;

[0035] Based on the electronic city ICC node load resource conflict influence factor and combined with the electronic city ICC node scheduling standardized time, node load scheduling score quantization is performed to obtain the electronic city ICC node load scheduling comprehensive score;

[0036] According to the preset node load scheduling score threshold, the electronic city ICC node load scheduling comprehensive score is compared and judged. When the electronic city ICC node load scheduling comprehensive score is higher than the preset node load scheduling score threshold, it is judged that the scheduling is successful, and the ratio between the number of nodes corresponding to the scheduling success and the total number of nodes is calculated to obtain the electronic city ICC node load scheduling success rate data.

[0037] Further, step S35 includes the following steps:

[0038] Step S351: By extracting the storage demand parameters corresponding to each application load from the electronic city ICC node optimization deployment scheme data, including storage capacity, access mode and performance level, and by combining resource application type for storage demand classification, storage demand classification data is obtained;

[0039] Step S352: Based on the storage demand classification data, the storage resource pool information corresponding to the private cloud platform is queried to filter out the storage class corresponding to the demand, including storage medium type, dynamic supply capacity and recycling strategy, to obtain candidate storage class data; The performance matching degree of the candidate storage class data is evaluated to compare and analyze the application storage demand by combining the historical input / output operations per second, throughput and storage class to obtain storage class matching score data;

[0040] Step S353: Determine the target storage class according to the storage class matching score data, and create a persistent volume claim based on the storage capacity in the storage requirement classification data, while associating the target storage class with the mapping relationship of the persistent volume claim, to obtain persistent volume claim configuration data;

[0041] Step S354: Bind the persistent volume claim configuration data with the corresponding application load to record the allocation state, mounting path and access permission of the persistent volume claim, and generate the association configuration data of the persistent volume and the storage class; based on the association configuration data of the persistent volume and the storage class, perform association configuration optimization on the electronic city ICC node optimization deployment scheme data to generate the optimization deployment scheme data corresponding to the association configuration of the electronic city ICC persistent volume and the storage class.

[0042] Further, step S4 includes the following steps:

[0043] Step S41: Perform cluster full-link real-time monitoring on the execution process corresponding to the optimization deployment scheme data by deploying a distributed monitoring agent to collect the corresponding physical resource indicators and application resource indicators in the execution process of the optimization deployment scheme data, wherein the physical resource indicators include CPU usage, memory usage and network throughput, and the application resource indicators include container restart times and service response time, to obtain electronic city ICC cluster real-time monitoring indicator data;

[0044] Step S42: Perform time series trend analysis on the electronic city ICC cluster real-time monitoring indicator data to identify the usage trend of each resource indicator, including peak period and growth rate, and abnormal wave points, to obtain electronic city ICC cluster resource indicator trend analysis data;

[0045] Step S43: Set the electronic city ICC cluster resource alarm rule group based on the electronic city ICC cluster resource indicator trend analysis data, to define the corresponding alarm threshold, alarm level and notification method based on the electronic city ICC cluster resource alarm rule group, to obtain electronic city ICC cluster alarm rule configuration data; based on the electronic city ICC cluster alarm rule configuration data, perform cluster monitoring alarm triggering processing on the execution process corresponding to the optimization deployment scheme data, to obtain electronic city ICC cluster monitoring alarm data;

[0046] Step S44: The tenant management data corresponding to the electronic city ICC is collected, including tenant ID, tenant resource usage, resource quota, and role permission, and the tenant resource usage is compared and analyzed with the resource quota to calculate the resource usage rate and excess risk, and the electronic city ICC tenant resource usage data is obtained; a tenant permission-resource usage association model is constructed based on the electronic city ICC cluster monitoring alarm data and the electronic city ICC tenant resource usage data, and the corresponding resource quota is dynamically adjusted based on the electronic city ICC tenant resource usage data, including temporary expansion and quota reduction, and the electronic city ICC tenant resource dynamic configuration data is obtained;

[0047] Step S45: The electronic city ICC management strategy corresponding to the private cloud platform is iteratively optimized according to the electronic city ICC cluster monitoring alarm data and the electronic city ICC tenant resource dynamic configuration data, to output the corresponding electronic city ICC management execution scheme.

[0048] Further, step S45 includes the following steps:

[0049] Step S451: The corresponding physical resource usage details and application resource performance indicators are extracted from the electronic city ICC cluster monitoring alarm data, wherein the physical resource usage details include CPU and memory usage proportion of each node, and the application resource performance indicators include deployment replica survival rate and service interface response time, to obtain the electronic city ICC cluster monitoring resource details data;

[0050] Step S452: The electronic city ICC cluster monitoring resource details data is subjected to resource multi-dimensional statistical analysis according to time dimension, tenant dimension and application dimension, to obtain the electronic city ICC cluster resource analysis data;

[0051] Step S453: The electronic city ICC cluster alarm rule configuration data corresponding to the electronic city ICC cluster monitoring alarm data is integrated to count the corresponding alarm times, processing rate and average processing time, and the classification and summary are performed according to the alarm level and tenant dimension, to obtain the electronic city ICC cluster alarm statistical data;

[0052] Step S454: The electronic city ICC tenant permission audit report is generated based on the electronic city ICC tenant resource dynamic configuration data, including resource usage compliance check and optimization suggestion, to generate the electronic city ICC tenant audit data;

[0053] Step S455: generating a corresponding electronic city ICC cluster management evaluation report by integrating the electronic city ICC cluster resource analysis data, the electronic city ICC cluster alarm statistical data and the electronic city ICC tenant audit data, and iteratively optimizing the management strategy of the private cloud platform corresponding to the electronic city ICC based on the electronic city ICC cluster management evaluation report to output a corresponding electronic city ICC management execution scheme.

[0054] Further, the application also provides an electronic city ICC management system based on a private cloud platform, which is used for executing the electronic city ICC management method based on a private cloud platform as described above.

[0055] The cluster resource service association module is used for collecting and standardizing the physical resources and interface services of the electronic city ICC cluster by the resource collection module corresponding to the private cloud platform, so as to obtain the electronic city ICC standardized cluster resource data; the electronic city ICC resource-service association model is constructed based on the electronic city ICC standardized cluster resource data and is mapped and associated, so as to obtain the electronic city ICC cluster resource association characteristic data.

[0056] The cluster node load scheduling module is used for analyzing the cluster node state based on the electronic city ICC cluster resource association characteristic data, so as to obtain the electronic city ICC cluster node state characteristic data; the node load scheduling configuration is performed based on the electronic city ICC cluster node state characteristic data and in combination with the electronic city ICC standardized cluster resource data, so as to generate the electronic city ICC node load deployment scheme data.

[0057] The configuration optimization resource matching module is used for obtaining the electronic city ICC node load scheduling success rate data based on the electronic city ICC node load deployment scheme data, and performing configuration optimization management and storage resource matching on the electronic city ICC node load deployment scheme data, so as to generate the optimized deployment scheme data corresponding to the association configuration of the electronic city ICC persistent volume and the storage class.

[0058] The electronic city management iterative optimization module is used for performing cluster full-link monitoring and alarming on the execution process corresponding to the optimized deployment scheme data, so as to obtain the electronic city ICC cluster monitoring and alarming data; performing tenant resource dynamic adjustment based on the electronic city ICC cluster monitoring and alarming data, so as to obtain the electronic city ICC tenant resource dynamic configuration data; iteratively optimizing the management strategy of the private cloud platform corresponding to the electronic city ICC based on the electronic city ICC cluster monitoring and alarming data and the electronic city ICC tenant resource dynamic configuration data, so as to output a corresponding electronic city ICC management execution scheme.

[0059] The application has the following beneficial effects:

[0060] 1、The electronic city ICC management method based on the private cloud platform has the beneficial effects that, compared with the prior art, the physical resources and interface services of the electronic city ICC cluster are collected and standardized in real time by the resource collection module of the private cloud platform, which ensures that the collected data has a unified format and standard, facilitating subsequent data processing and analysis, the standardized processing helps to eliminate the differences between different resources, so that different types of hardware, software resources and interface services can be managed under the same standard, the standardized data can effectively improve the efficiency of resource scheduling and management, thereby optimizing the allocation and use of resources, and the establishment of the resource-service association model can also provide basic data support for the load prediction and optimization of the cluster, thereby laying a solid foundation for subsequent cluster optimization and service adjustment. Secondly, after obtaining the standardized data of the electronic city ICC cluster resources, the load situation, performance bottleneck and performance of each node at different time periods can be further identified by analyzing the cluster node state, and the node state feature data can help to deeply understand the health status and resource demand of each node in the cluster, thereby providing accurate basis for subsequent load scheduling, and by combining the standardized cluster resource data, the node load can be scheduled and configured to reasonably allocate the computing and storage resources in the cluster, ensure that each node can efficiently run within its performance bearing range, reasonable load scheduling can not only improve the overall performance of the cluster, but also avoid individual node failure due to excessive load, increase the reliability and stability of the cluster, provide fine management basis for subsequent cluster resource optimization and scheduling, and enhance the adaptability of the cluster under different workloads. Then, after completing the load scheduling configuration, the electronic city ICC node load scheduling success rate data is obtained, and by analyzing these data, it can be identified which nodes have a low load scheduling success rate, thereby providing specific improvement direction for subsequent optimization. Further, based on the load scheduling data, the storage resources are matched and optimized to generate an optimization deployment scheme associated with the storage class, the optimization of the storage resources not only focuses on the utilization of the storage capacity, but also includes the improvement of the storage access speed, delay and reliability, the generation of the optimization scheme ensures that the storage resources can efficiently support the operation demand of each node in the cluster, and still guarantee high performance when the data traffic surges, dynamically adjusts the allocation of storage resources to avoid excessive concentration or uneven distribution of storage resources, thereby fully improving the system operation efficiency. Finally, after the whole optimization deployment scheme is generated, the cluster full-link monitoring and alarm can detect any abnormal situation in the execution process in real time, the monitoring mechanism can penetrate into each link of the cluster, timely capture potential faults or performance bottlenecks, and rapidly notify the administrator through the alarm mechanism, and the health monitoring data of the cluster provides reliable data support for subsequent tenant resource dynamic adjustment.Based on this monitoring and alarm data, the resource configuration of tenants can be adjusted in real time to ensure that the resource needs of each tenant are met in a timely manner, avoid service interruptions or performance degradation caused by resource shortages or unbalanced allocation, and further optimize management strategies to achieve iterative optimization of the management of the Electronic City ICC cluster by the private cloud platform. As the cluster operation changes, the management strategy will be continuously adjusted and optimized, thereby improving the efficiency of cluster load resource management.

[0061] 2. The electronic city ICC management system based on a private cloud platform proposed in this invention consists of a cluster resource service association module, a cluster node load scheduling module, a configuration optimization resource matching module, and an electronic city management iterative optimization module. It can realize any electronic city ICC management method based on a private cloud platform as described in this invention. It is used to combine the operations between computer programs running on various modules to realize the electronic city ICC management method based on a private cloud platform. The internal structure of the system cooperates with each other, which can greatly reduce repetitive work and manpower input, and can quickly and effectively provide a more accurate and efficient electronic city ICC management process based on a private cloud platform, thereby simplifying the operation process of the electronic city ICC management system based on a private cloud platform. Attached Figure Description

[0062] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0063] Figure 1 This is a flowchart illustrating the steps of the electronic city ICC management method based on a private cloud platform according to the present invention.

[0064] Figure 2 for Figure 1 A detailed flowchart of step S1. Detailed Implementation

[0065] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0066] To achieve the above objectives, please refer to Figures 1 to 2 This invention provides a method for managing electronic city ICCs based on a private cloud platform, the method comprising the following steps:

[0067] Step S1: the physical resources and interface services of the electronic city ICC cluster are collected and standardized by the resource collection module corresponding to the private cloud platform, so as to obtain electronic city ICC standardized cluster resource data; an electronic city ICC resource-service association model is constructed based on the electronic city ICC standardized cluster resource data and is mapped and associated, so as to obtain electronic city ICC cluster resource association characteristic data;

[0068] Step S2: the electronic city ICC cluster resource association characteristic data is analyzed to obtain electronic city ICC cluster node state characteristic data; based on the electronic city ICC cluster node state characteristic data and in combination with the electronic city ICC standardized cluster resource data, node load scheduling configuration is performed, so as to generate electronic city ICC node load deployment scheme data;

[0069] Step S3: based on the electronic city ICC node load deployment scheme data, corresponding electronic city ICC node load scheduling success rate data is obtained, and the electronic city ICC node load deployment scheme data is configured, optimized, managed and matched with storage resources, so as to generate optimized deployment scheme data corresponding to the association configuration of the electronic city ICC persistent volume and the storage class;

[0070] Step S4: the execution process of the optimized deployment scheme data is monitored and alarmed in the whole link of the cluster, so as to obtain electronic city ICC cluster monitoring and alarming data; based on the electronic city ICC cluster monitoring and alarming data, tenant resource dynamic adjustment is performed, so as to obtain electronic city ICC tenant resource dynamic configuration data; based on the electronic city ICC cluster monitoring and alarming data and the electronic city ICC tenant resource dynamic configuration data, the electronic city ICC management strategy corresponding to the private cloud platform is iteratively optimized, so as to output the corresponding electronic city ICC management execution scheme.

[0071] In the embodiment of the application, please refer to Figure 1 The electronic city ICC management method based on the private cloud platform includes the following steps:

[0072] Step S1: the physical resources and interface services of the electronic city ICC cluster are collected and standardized by the resource collection module corresponding to the private cloud platform, so as to obtain electronic city ICC standardized cluster resource data; an electronic city ICC resource-service association model is constructed based on the electronic city ICC standardized cluster resource data and is mapped and associated, so as to obtain electronic city ICC cluster resource association characteristic data;

[0073] In the embodiment of the application, 10 distributed collection agents (collecting once every 10 seconds) are deployed by the resource collection module of the private cloud platform to collect the physical resources and interface services of the ICC cluster (20 nodes) of the electronic city in real time. The physical resource collection indexes include the total number of nodes (20), CPU usage rate (0-100%, accuracy 0.1%), memory capacity (0-512 GB, accuracy 0.1 GB), NPU computing power (0-200 TOPS), video memory (0-64 GB), and disk IO (0-1000 MB / s); the interface service collection indexes include API request delay (0-1000 ms), response time (0-500 ms), status code (200 / 404 / 500), and request number (integer). The data is collected continuously for 24 hours, and after the initial data is obtained, the standardized processing is performed: the timestamp is unified as “YYYY-MM-DDHH:MM:SS”, the physical resource unit is converted into GB / TOPS, etc., and the data is mapped to the [0, 1] interval through min-max (for example, CPU usage rate 50% is mapped to 0.5). Based on the standardized data, the resource-service association model is constructed, the covariance matrix (81x81) is calculated, the first 10 principal components (cumulative contribution rate 92%) are extracted, the physical resource and interface service parameters are mapped and associated (for example, the correlation coefficient of CPU usage rate and response time is 0.85), the resource association characteristic data of the ICC cluster of the electronic city is obtained, which contains a 10-dimensional feature vector and an association weight, all processing is based on a fixed algorithm without subjective intervention.

[0074] Step S2: performing cluster node state analysis on the resource association characteristic data of the ICC cluster of the electronic city to obtain ICC cluster node state characteristic data; performing node load scheduling configuration based on the ICC cluster node state characteristic data and in combination with the ICC standardized cluster resource data to generate ICC node load deployment scheme data;

[0075] In the embodiment of the application, by analyzing the cluster node state of the ICC cluster resource association feature data of the electronic city, the resource utilization rate (CPU 30%-70%, memory 20%-60%) of each node, the health state (determined to be normal by the disk failure rate <1%, network packet loss rate <0.1%), and the load balancing degree (calculated based on the CPU usage rate standard deviation, <10% for balanced) are extracted to form node state feature data. In combination with the standardized cluster resource data (node configuration: 5 32-core / 512 GB, 10 16-core / 256 GB, and 5 8-core / 128 GB), node load scheduling configuration is performed: 32-core nodes are deployed for core services (2 instances per node, CPU limit 4 cores per instance), 16-core nodes are deployed for general services (4 instances per node, CPU limit 2 cores per instance), and 8-core nodes are deployed for test services (8 instances per node, CPU limit 1 core per instance). The load balancing threshold (32-core node CPU > 80% triggers balancing) and the fault migration rule (load is migrated to the same configuration node when the node is abnormal) are configured to generate electronic city ICC node load deployment scheme data, including the business type, instance quantity, and resource limit parameters of each node. The scheme strictly matches the node configuration and business demand, and there is no resource over-allocation.

[0076] Step S3: Based on the electronic city ICC node load deployment scheme data, the corresponding electronic city ICC node load scheduling success rate data is obtained, and the electronic city ICC node load deployment scheme data is configured and optimized for management and storage resource matching to generate optimized deployment scheme data corresponding to the association configuration of the electronic city ICC persistent volume and the storage class;

[0077] In the embodiment of the application, based on the electronic city ICC node load deployment scheme data, the deployment process is simulated and the node load scheduling success rate is calculated: 4 out of 5 32-core nodes are successfully scheduled (score > 90 points), 8 out of 10 16-core nodes are successfully scheduled, and 2 out of 5 8-core nodes are successfully scheduled, with a total success rate of (4+8+2) / 20=70%. For the failed nodes, the CPU limit of the 32-core node is increased from 4 cores to 4.5 cores, and the memory limit of the 16-core node is increased from 8 GB to 9 GB. Simultaneously, storage resource matching is performed: core services require 500 GB NVMe storage (IOPS 10000), general services require 2000 GB SSD (IOPS 5000), and test services require 1000 GB SATA (IOPS 1000). Persistent volume claims are created and associated with target storage classes (recycle strategy: core services are deleted, and general services are retained). Optimized deployment scheme data is generated, including adjusted resource parameters and storage volume binding information. The optimization is based on the scheduling failure reason, and the storage matching is based on the performance demand to ensure that there is no conflict in the configuration.

[0078] Step S4: Perform full-link cluster monitoring and alarms on the execution process corresponding to the optimized deployment scheme data to obtain the Electronic City ICC cluster monitoring and alarm data; dynamically adjust tenant resources based on the Electronic City ICC cluster monitoring and alarm data to obtain the Electronic City ICC tenant resource dynamic configuration data; perform management iteration and optimization on the Electronic City ICC management strategy corresponding to the private cloud platform based on the Electronic City ICC cluster monitoring and alarm data and the Electronic City ICC tenant resource dynamic configuration data to output the corresponding Electronic City ICC management execution scheme.

[0079] In this embodiment of the invention, a distributed monitoring agent (collecting data every 30 seconds) is deployed to monitor physical resources (CPU / memory utilization, network throughput) and application metrics (container restart count, response time) during the execution of optimized deployment scheme data, generating 11,520 records within 48 hours. Alarm rules are set: CPU > 80% triggers a critical alarm (SMS notification), memory > 70% triggers a general alarm (email), and response time > 200ms triggers an emergency alarm (SMS + phone call), generating a total of 120 alarms (10 emergency, 30 critical, and 80 general). Based on the alarm data and tenant management information (resource usage and quotas for tenants A / B / C), dynamic adjustments are made: tenant B's memory quota increases from 30% to 35% (utilization rate 83%), and tenant C's CPU quota decreases from 20% to 18% (utilization rate 67%). Optimize management strategies by combining monitoring and adjustment results: increase the scheduling priority of tenant B, shorten the emergency alarm response time to 5 minutes, and output management execution plan, including resource allocation table, alarm process, and tenant permission rules. All optimizations are based on actual data and have no subjective settings.

[0080] Furthermore, as an embodiment of the present invention, reference is made to... Figure 2 As shown, Figure 1 A detailed flowchart of step S1 is shown below. In this embodiment, step S1 includes the following steps:

[0081] Step S11: The distributed acquisition agent deployed by the resource acquisition module of the private cloud platform is used to collect physical resources and interface services of the Electronic City ICC cluster in real time. The physical resource data includes the total number of nodes, CPU, memory, NPU, video memory and disk resources. The interface service data includes API request latency, response time, status code and number of requests. The initial cluster resource data of Electronic City ICC is obtained.

[0082] In the embodiment of the application, 10 distributed collection agents are deployed by the resource collection module of the private cloud platform (deployed on 10 nodes of the ICC cluster of the electronic city), each of which collects physical resources and interface services of the cluster in real time at an interval of 10 seconds. Physical resource data collection: the total number of nodes (fixed at 20 nodes), CPU usage rate (0-100%, accuracy 0.1%), memory occupancy (0-512 GB, accuracy 0.1 GB), NPU computing power (0-200 TOPS, accuracy 1 TOPS), video memory occupancy (0-64 GB, accuracy 0.1 GB), and disk IO rate (0-1000 MB / s, accuracy 1 MB / s) are obtained through the built-in sensors of the server. Interface service data collection: API request delay (0-1000 ms, accuracy 1 ms), response time (0-500 ms, accuracy 1 ms), status code (200 / 404 / 500, etc.), and request number (cumulative value every 10 seconds, integer) are recorded by a network packet capturing tool. Continuous collection is performed for 24 hours to generate the initial cluster resource data of the ICC of the electronic city, which includes 8640 physical resource records (24x3600÷10) of 20 nodes and 8640 interface service records. Each record is marked with a collection timestamp (accurate to milliseconds) and an agent ID to ensure that the data covers all nodes and service interfaces of the cluster without interruption.

[0083] Step S12: The initial cluster resource data of the ICC of the electronic city is cleaned to remove abnormal values and repeated records, and an interpolation method is used to fill in missing values to obtain cleaned resource data of the ICC cluster of the electronic city.

[0084] In the embodiment of the application, the initial cluster resource data of the ICC of the electronic city is cleaned, and the abnormal value removal adopts the 3σ criterion: the CPU usage rate mean is 50%, and the standard deviation is 15%. 120 records exceeding 50%±45% (i.e. <5% or >95%) are determined as abnormal and removed. The memory occupancy mean is 200 GB, and the standard deviation is 50 GB. 80 records less than 50 GB or greater than 350 GB are removed. Repeated record removal: compare records with the same timestamp and node ID. Keep 1 record with the highest CPU usage rate and delete the remaining 300 repeated records. Linear interpolation is used to fill in missing values: the memory occupancy of a node at 10:00:00 is 200 GB, and the missing value at 10:00:10 is 210 GB when the memory occupancy is 220 GB at 10:00:20. The request number of the interface service at 10:00:00 is 50 times, and the intermediate value is 60 times when the request number is 70 times at 10:00:20. The cleaned resource data of the ICC cluster of the electronic city includes 8140 physical resource records and 8340 interface service records of 20 nodes. The data integrity rate is improved to 99.5%, and all cleaning rules are based on fixed thresholds and mathematical methods without subjective judgment.

[0085] Step S13: Standardizing the resource data of the electronic city ICC cluster after cleaning to unify the data timestamp format and measurement units, and mapping different types of resource data and service data to a unified numerical interval through a normalization algorithm to obtain electronic city ICC standardized cluster resource data;

[0086] In the embodiment of the application, by standardizing the resource data of the electronic city ICC cluster after cleaning, the timestamp is uniformly converted to the format of "YYYY-MM-DDHH:MM:SS" (such as 2024-01-01 10:00:00), the measurement units are unified: memory / video memory is converted to GB, disk IO rate is kept as MB / s, and the number of requests is kept as an integer. The normalization algorithm uses min-max mapping to map the data to the interval [0, 1]: the CPU usage rate (0-100%) mapping formula is x'=x / 100; the memory occupation (50-350GB) mapping formula is x'=(x-50) / 300; the API request delay (0-1000ms) mapping formula is x'=x / 1000. The status code is converted to a numerical value: 200→1, 404→0.5, 500→0. The electronic city ICC standardized cluster resource data is obtained, all numerical values are kept to three decimal places, physical resources and service data are in the interval [0, 1], the timestamp format is unified, and different types of data can be directly used for correlation analysis without unit or format differences.

[0087] Step S14: Based on the electronic city ICC standardized cluster resource data, an electronic city ICC resource-service correlation model is constructed to calculate the correlation coefficient between the physical resource occupation state and the interface service performance parameter using correlation analysis to construct a correlation weight matrix, and based on the correlation weight matrix, the corresponding physical resource occupation state and the interface service performance parameter are mapped and correlated to obtain electronic city ICC cluster resource correlation feature data.

[0088] In the embodiment of the present application, the electronic city ICC resource-service association model is constructed based on the ICC standardized cluster resource data, the correlation analysis adopts Pearson coefficient calculation: the covariance of CPU usage (x) and API response time (y) is 0.05, the standard deviation is x=0.2, y=0.25, and the correlation coefficient is 0.05 ÷ (0.2×0.25)=1.0; the correlation coefficient of memory occupation and request number is 0.8. The association weight matrix (6×4 order) is constructed, the row represents the physical resource (CPU / memory / NPU / video memory / disk IO / node total amount), the column represents the service parameter (delay / response time / status code / request number), and the matrix element is the correlation coefficient (retaining two decimal places). Based on the matrix, the physical resource and the service parameter are mapped and associated: the CPU usage is associated with the response time (weight 1.0), the memory occupation is associated with the request number (weight 0.8), and the NPU computing power is associated with the delay (weight 0.7). The electronic city ICC cluster resource association characteristic data is obtained, including a 6×4 weight matrix and 10 groups of strong association pairs (coefficient>0.7), each association pair is marked with resource type, service parameter and weight value, and the association relationship is based on strict statistical calculation without subjective setting.

[0089] Further, step S2 includes the following steps:

[0090] Step S21: The electronic city ICC cluster resource association characteristic data is analyzed to extract the resource usage, health status and load balancing degree of each node of the electronic city ICC cluster, so as to obtain the electronic city ICC cluster node state characteristic data.

[0091] In the embodiment of the present application, by analyzing the cluster node state of the electronic city ICC cluster resource association feature data (including the resource and service association matrix of 20 nodes), the resource utilization is calculated by CPU utilization (node 1 is 60%, node 2 is 55%…), memory utilization (node 1 is 70%, node 2 is 65%…), and disk IO utilization (node 1 is 40%, node 2 is 35%…), and the weighted average value (weights are 0.4, 0.4, and 0.2) is taken. The score of node 1 is 60%×0.4+70%×0.4+40%×0.2=60%. The health state analysis is based on the state code (200 accounts for more than 99% healthy, less than 95% abnormal), the number of bad tracks (less than 1 healthy), and the network delay (less than 10 ms healthy). Node 1 meets all three requirements, and the health state is marked as “normal”. The load balancing degree is calculated by the standard deviation of the CPU utilization of each node (the cluster mean is 50%, the standard deviation of node 1-5 is 5%, the balancing degree is 90%; the standard deviation of node 6-10 is 15%, the balancing degree is 70%). The electronic city ICC cluster node state feature data is generated, including the resource utilization (0-100%) of 20 nodes, the health state (normal / abnormal), and the load balancing degree (0-100%). All indexes are calculated based on fixed formula, and the analysis result directly reflects the actual state of the node.

[0092] Step S22: Based on the electronic city ICC cluster node state feature data and combined with the electronic city ICC standardized cluster resource data, cluster node clustering analysis is performed to classify the electronic city ICC cluster nodes with similar resource configuration and similar load characteristics into the same management group, and the electronic city ICC cluster node clustering result data is obtained.

[0093] In the embodiment of the application, the K-means clustering algorithm is used for cluster node clustering analysis based on the ICC cluster node state feature data (resource utilization, health status, balance degree) and standardized cluster resource data (CPU core number, memory capacity, etc.). The algorithm input features are CPU core number (8 cores / 16 cores / 32 cores), memory capacity (128 GB / 256 GB / 512 GB), resource utilization (0-50% / 50%-80%), and load balance degree (>80% / 60%-80% / <60%), and the number of clusters is set to 3. Nodes 1-5 (32 cores, 512 GB, utilization rate 30-40%, balance degree >80%) are clustered into management group 1; nodes 6-15 (16 cores, 256 GB, utilization rate 50-60%, balance degree 60-80%) are clustered into management group 2; and nodes 16-20 (8 cores, 128 GB, utilization rate 60-70%, balance degree <60%) are clustered into management group 3. The resource configuration difference of nodes in the same group is <10%, and the load characteristic deviation is <5%. The ICC cluster node clustering result data is obtained, including the node ID list of the three management groups, the feature mean value (such as the mean value of management group 1: 32 cores, 512 GB, utilization rate 35%), the clustering process is based on the calculation of the Euclidean distance of the features, the difference between groups is significant, and the consistency within the group is high.

[0094] Step S23: Perform node cluster state evaluation on the ICC cluster node clustering result data to calculate the corresponding node cluster health score in combination with the node health check log corresponding to each ICC cluster node management group, and obtain ICC cluster node health status data;

[0095] In the embodiment of the present application, the node cluster state evaluation is performed on the electronic city ICC cluster node clustering result data, and the node health check log (recorded once every hour, including CPU temperature, disk IO error number, and network packet loss rate) of each management group is extracted. The CPU temperature of the five nodes in management group 1 is less than 60℃ (full score 10 points), the disk IO error number is 0 (full score 20 points), the network packet loss rate is less than 0.05% (full score 20 points), the health check pass rate is 100% (full score 50 points), and the total health score is 10+20+20+50=100 points. The CPU temperature of three nodes in management group 2 is 60-70℃ (deduct 2 points per node), and the health check pass rate is 98% (deduct 1 point), so the total health score is 10+20+20+49-3*2=93 points. The disk IO error number of two nodes in management group 3 is 1 (deduct 5 points per node), and the packet loss rate is 0.1-0.2% (deduct 5 points), so the total health score is 10+10+15+45-2*5=65 points. The electronic city ICC cluster node health state data is generated, including the health score (0-100 points) and the deduction item details of the three management groups. The score is directly linked to the log data, and the evaluation result quantitatively reflects the overall health level of the cluster.

[0096] Step S24: Based on the electronic city ICC cluster node health state data and the electronic city ICC cluster node clustering result data, the corresponding electronic city ICC cluster node management strategy is formulated, including resource scheduling priority division, fault automatic migration rule, and load balancing threshold setting, to obtain electronic city ICC cluster node management strategy data;

[0097] In the embodiment of the present application, the electronic city ICC cluster node management strategy is formulated based on the electronic city ICC cluster node health state data (management group 1: 100 points, group 2: 93 points, group 3: 65 points) and the clustering result data (node configuration, load characteristics). The resource scheduling priority is: management group 1 (health score 100 points) is set as priority 1 (core business), group 2 (93 points) is set as priority 2 (ordinary business), and group 3 (65 points) is set as priority 3 (test business). The fault automatic migration rule is: when the nodes in the group are abnormal, the load is migrated to the healthy nodes in the same group (the nodes in group 1 are migrated to other nodes in group 1), and when there are not enough nodes in the same group, the load is migrated to the group with lower priority (group 1 is migrated to group 2 when there are not enough nodes). The load balancing threshold is: the CPU usage rate of group 1 is greater than 80% to trigger balancing, the CPU usage rate of group 2 is greater than 85%, and the CPU usage rate of group 3 is greater than 90%. The electronic city ICC cluster node management strategy data is generated, including priority division, migration rule, and balancing threshold. The strategy parameters are positively correlated with the health score (the group with high health score has high priority), the rule ensures service continuity in case of failure, and the balancing threshold is set based on the node carrying capacity without subjective adjustment.

[0098] Step S25: Synchronize the electronic city ICC cluster node management strategy data to the private cloud platform corresponding dispatch center to configure node load adjustment, to generate electronic city ICC node load deployment scheme data.

[0099] In the embodiment of the application, by synchronizing the electronic city ICC cluster node management strategy data (priority 1-3, migration rules, etc.) to the private cloud platform dispatch center, the node load adjustment configuration is performed. The management group 1 (node 1-5) of priority 1 deploys core business (transaction system, CPU demand 4 cores / instance), each node runs 2 instances, and the total load is controlled within 60% of CPU usage; the management group 2 (node 6-15) of priority 2 deploys ordinary business (log system, CPU demand 2 cores / instance), each node runs 4 instances; the management group 3 (node 16-20) of priority 3 deploys test business (development tools, CPU demand 1 core / instance), each node runs 8 instances. Configure the fault migration trigger condition (node abnormality lasts for 10 seconds), automatically migrate to the same group node (such as node 1 is abnormal, its load is migrated to node 2), generate electronic city ICC node load deployment scheme data, including the deployment business type, instance number, migration target of each node, the scheme strictly follows the management strategy, the load allocation matches the node carrying capacity, and the configuration parameters are without manual intervention.

[0100] Further, step S25 includes the following steps:

[0101] Step S251: Determine the deployment node range of each application load of the electronic city ICC based on the corresponding resource scheduling priority in the electronic city ICC cluster node management strategy data, to obtain electronic city ICC deployment node candidate set data;

[0102] In the embodiment of the application, the deployment node range of each application load is determined based on the corresponding resource scheduling priority (core service load priority 1, general service priority 2, and test service priority 3) in the ICC cluster node management strategy data of the electronic city. The core service load (such as a transaction system) of priority 1 is limited to be deployed on nodes 1-5 (CPU remaining amount > 4 cores, memory remaining > 64 GB, and node health degree > 95%); the general service (such as log analysis) of priority 2 is limited to be deployed on nodes 6-15 (CPU remaining amount > 2 cores, memory remaining > 32 GB, and health degree > 90%); and the test service (such as development and debugging) of priority 3 is limited to be deployed on nodes 16-20 (CPU remaining amount > 1 core, memory remaining > 16 GB, and health degree > 85%). The health degree of each node is calculated by a disk failure rate (< 1%), a network packet loss rate (< 0.1%), and a CPU temperature (< 70°C), and the score range is 0-100. The ICC deployment node candidate set data of the electronic city is generated, which includes a node ID list corresponding to the three priorities, resource remaining threshold values, and health degree threshold values. The candidate set data strictly corresponds to the scheduling priority, the node range is divided based on fixed resource indicators, and there is no elastic adjustment.

[0103] Step S252: Obtain the application load types corresponding to the ICC cluster of the electronic city through the scheduling center corresponding to the private cloud platform, including deployment resources, stateful replica resources, and daemon set resources, and perform resource parameter analysis on the application load types corresponding to the ICC cluster of the electronic city to extract resource requirement parameters and deployment constraint conditions, wherein the resource requirement parameters include CPU core number, memory capacity, and storage type, the deployment constraint conditions include node affinity and anti-affinity rules, and ICC load demand feature data is obtained.

[0104] In the embodiment of the application, the application load types of the electronic city ICC cluster are obtained by the scheduling center corresponding to the private cloud platform: deployment resource (web server application), stateful copy resource (database application), daemon set resource (monitoring agent). Resource parameter analysis is performed on each type: the resource requirement parameters of the web server application are CPU core number 2 cores, memory capacity 8 GB, storage type SSD; the database application is CPU core number 4 cores, memory 32 GB, storage type NVMe; the monitoring agent is CPU core number 0.5 cores, memory 1 GB, storage type HDD. Deployment constraint condition analysis: the web server requires node affinity rules (node label contains “web = yes”), anti-affinity rules (not deployed on the same node as the database); the database requires node affinity (label “db = yes”), anti-affinity (at most one copy on the same node); the monitoring agent requires affinity (label “monitor = yes”), and there is no anti-affinity. The electronic city ICC load demand characteristic data is obtained, including the resource requirement parameters (accurate to 0.1 core, 1 GB) and constraint conditions (label matching rules) of the three types of loads. The parsing process is based on fixed field extraction of the load configuration file, and there is no subjective interpretation.

[0105] Step S253: Matching degree calculation is performed on the electronic city ICC deployment node candidate set data and the electronic city ICC load demand characteristic data to generate corresponding node matching scores based on node resource remaining amount and node cluster class health score, and deployment node matching analysis is performed between the electronic city ICC deployment node candidate set data and the electronic city ICC load demand characteristic data based on the node matching scores to obtain electronic city ICC deployment node matching result data;

[0106] In the embodiment of the application, by matching the electronic city ICC deployment node candidate set data (nodes 1-5, 6-15, 16-20) with the load demand feature data, the node matching score = resource remaining score x 0.6 + health score x 0.4. The web server application matches node 6-15: node 6 has 3 cores of CPU remaining (demand 2 cores, score 1.0), 40 GB of memory remaining (demand 8 GB, score 1.0), resource score 1.0; health score 92 points (score 0.92), matching score = 1.0 x 0.6 + 0.92 x 0.4 = 0.968. Node 7 has 1.5 cores of CPU remaining (score 0.75), matching score 0.81. The database application matches node 1-5: node 3 has 5 cores of CPU remaining (demand 4 cores, score 1.0), 80 GB of memory remaining (demand 32 GB, score 1.0), resource score 1.0; health score 96 points (score 0.96), matching score 0.984. The monitoring agent matches node 16-20: node 18 meets the demand for each resource, matching score 0.89. Based on the score, the top 3 nodes are selected as the matching result, and the electronic city ICC deployment node matching result data is obtained, which includes 3 candidate nodes and corresponding scores (retaining three decimal places) for each load, the calculation rule is fixed, and the score directly reflects the adaptation degree of the node and the load.

[0107] Step S254: Based on the electronic city ICC deployment node matching result data, the node load adjustment configuration is synchronized to the corresponding dispatch center of the private cloud platform to determine the deployment node, the number of replicas, and the start order of each electronic city ICC deployment load, so as to generate the electronic city ICC node load deployment scheme data.

[0108] In the embodiment of the application, by matching the results data of the electronic city ICC deployment nodes (Web server matching nodes 6, 8, 10, scores 0.968, 0.95, 0.94; database matching nodes 3, 1, 2, scores 0.984, 0.97, 0.96; monitoring agent matching nodes 18, 19, 20, scores 0.89, 0.88, 0.87), the node load adjustment configuration is synchronized to the scheduling center. It is clear that the Web server is deployed in node 6 (3 replicas), 8 (2 replicas), 10 (2 replicas), and the start order is 6→8→10; the database is deployed in node 3 (primary replica), 1 (secondary replica), 2 (secondary replica), and the start order is 3→1→2; the monitoring agent is deployed in node 18-20 with 1 replica each and is started in parallel. The CPU limit (Web server single replica 2 cores, database 4 cores), memory limit (Web server 8 GB, database 32 GB), storage mounting path (SSD mounted to / web, NVMe mounted to / db) are configured, and the electronic city ICC node load deployment scheme data is generated, including load name, deployment node, replica quantity, start order, resource limit parameter. The replica quantity in the scheme is positively correlated with the remaining amount of node resources (the node with high remaining amount is allocated more replicas), the start order follows the principle of priority of master node, all configuration parameters are determined based on matching score and load demand, and there is no manual intervention.

[0109] Further, step S3 includes the following steps:

[0110] Step S31: Simulate the corresponding electronic city ICC cluster node application load deployment process based on the electronic city ICC node load deployment scheme data and in combination with the electronic city ICC cluster resource association feature data;

[0111] In the embodiment of the application, the application load deployment process of the cluster nodes is simulated by using the ICC node load deployment scheme data (including load distribution of 20 nodes, number of copies of 3 types of applications and resource limit) and the cluster resource association characteristic data (CPU and response time correlation coefficient 1.0, memory and request number correlation coefficient 0.8) based on the electronic city. The simulation environment is built according to the actual configuration of the nodes (group 1: 32-core CPU, 512 GB memory; group 2: 16-core, 256 GB; group 3: 8-core, 128 GB), and the event-driven model is used to reproduce the deployment process: at the first second, the transaction system deployment instruction (single copy 2-core CPU, 8 GB memory) is issued to group 1 node, and at the third second, the CPU usage of group 1 node increases from 50% to 55%; at the fifth second, the log system deployment instruction (single copy 2-core, 8 GB) is issued to group 2 node, and at the eighth second, the memory occupancy of group 2 increases from 100 GB to 108 GB; at the tenth second, the test tool deployment instruction (single copy 1-core, 1 GB) is issued to group 3 node, and at the twelfth second, the disk IO of group 3 increases from 200 MB / s to 210 MB / s. The simulation lasts for 1 hour, the resource changes (accurate to 0.1-core CPU, 0.1 GB memory) and instruction response time (accurate to 0.1 second) of each deployment step are recorded, the process data containing 1800 deployment events are generated, and the simulation process strictly follows the resource association characteristics without overallocation.

[0112] Step S32: Obtain the corresponding electronic city ICC node resource conflict probability and electronic city ICC node scheduling completion time through the electronic city ICC cluster node application load deployment process;

[0113] In the embodiment of the application, 1800 event data of the electronic city ICC cluster node application load deployment process are obtained, and node resource conflict probability and scheduling completion time are obtained. Resource conflict probability statistics: when the management group 1 deploys the transaction system, 3 times of CPU resource contention (requesting 2 cores but actually remaining 1.8 cores) occur, and the conflict probability = 3 ÷ 150 × 100% = 2%; when the group 2 deploys the log system, 8 times of insufficient memory (requesting 8 GB but remaining 7 GB) occur, and the conflict probability = 8 ÷ 160 × 100% = 5%; when the group 3 deploys the test tool, 12 times of disk IO contention (requesting 10 MB / s but remaining 8 MB / s) occur, and the conflict probability = 12 ÷ 150 × 100% = 8%. Scheduling completion time measurement: the time length from the deployment instruction issuing to the application starting successfully, the group 1 is 10 seconds (transaction system) on average, the group 2 is 15 seconds (log system) on average, and the group 3 is 20 seconds (test tool) on average, the time is recorded to 0.1 second, the pure scheduling time after excluding the network delay (fixed 5 ms) is 9.95 seconds for the group 1, 14.95 seconds for the group 2, and 19.95 seconds for the group 3. A data set containing the conflict probability (2%, 5%, 8%) and the scheduling completion time (10 seconds, 15 seconds, 20 seconds) of the three management groups is generated, and the data directly comes from the event record and has no estimated component.

[0114] Step S33: based on the electronic city ICC node resource conflict probability and the electronic city ICC node scheduling completion time, the node load scheduling evaluation of the electronic city ICC cluster node application load deployment process is performed, and the electronic city ICC node load scheduling success rate data is obtained.

[0115] In the embodiment of the application, the node load scheduling evaluation is performed based on the node resource conflict probability (group 12%, group 25%, group 38%) and the scheduling completion time (group 110 seconds, group 215 seconds, group 320 seconds). The evaluation adopts a scoring system: 2 points are deducted for each 1% increase in conflict probability, and 1 point is deducted for each 1 second over the benchmark in scheduling time (benchmark group 110 seconds, group 215 seconds, group 320 seconds). Among the 5 nodes of group 1, 3 nodes have no conflict and the time is 10 seconds, with a score of 100-0-0=100 points; 2 nodes have 1 conflict and the time is 12 seconds, with a score of 100-2-2=96 points. The score threshold is set to 95 points, and group 1 has 3 successful nodes (100 points) and 2 failed nodes (96 points < 95). Among the 10 nodes of group 2, 2 nodes have no conflict and the time is 15 seconds, with a score of 100 points; 8 nodes have 1 conflict and the time is 18 seconds, with a score of 100-2-3=95 points (equal to the threshold), and the success is determined. The 5 nodes of group 3 all have 2 conflicts and the time is 25 seconds, with a score of 100-4-5=91 points < 95, and all fail. The success rate = (3+10+0) ÷ 20 x 100% = 65%, the electronic city ICC node load scheduling success rate data is generated, the evaluation rule is based on fixed deduction standard, and the success rate is calculated as the proportion of actual successful nodes without subjective adjustment.

[0116] Step S34: The electronic city ICC node load scheduling success rate data is used to optimize and adjust the electronic city ICC node load deployment scheme data, to determine the resource allocation parameters and service interface exposure rules corresponding to the electronic city ICC container group, and to obtain the electronic city ICC node optimized deployment scheme data;

[0117] In the embodiment of the application, the node load deployment scheme data is optimized and adjusted based on the electronic city ICC node load scheduling success rate data (65%). The resource allocation parameters are optimized: the CPU of the group 1 transaction system single copy is increased from 2 cores to 2.2 cores (to avoid conflict when 1.8 cores are left), and the memory is increased from 8 GB to 8.5 GB; the memory of the group 2 log system single copy is increased from 8 GB to 9 GB (to cover the 7 GB remaining scenario); the group 3 test tool disk IO quota is reduced from 10 MB / s to 8 MB / s (to match the actual remaining amount). The service interface exposure rule is adjusted: the group 1 transaction system uses NodePort to expose the interface (port 30001), the group 2 log system uses ClusterIP (port 8080), and the group 3 test tool disables the external interface. After optimization, the conflict probability is reduced to group 10%, group 21%, and group 32%, and the scheduling success rate is improved to 90%. The electronic city ICC node optimized deployment scheme data is generated, including the adjusted CPU / memory / IO parameters (accurate to 0.1 units), interface type and port number, and the parameter adjustment amount is calculated based on the resource gap in the conflict scenario, and the rule setting conforms to the node network configuration without conflict port allocation.

[0118] Step S35: Based on the electronic city ICC node optimization deployment scheme data, the configuration management and storage resource matching are performed to generate the optimized deployment scheme data corresponding to the association configuration of the electronic city ICC persistent volume and the storage class.

[0119] In the embodiment of the application, the configuration management and storage resource matching are performed based on the electronic city ICC node optimization deployment scheme data (including 500GB storage requirement of the transaction system, 2000GB of the log system, and 1000GB of the test tool). The configuration management includes storage requirement parameter extraction (capacity, access mode, performance level) and classification (high-performance shared, medium-performance independent, and low-performance read-only), and matching of NVMe (IOPS 12000), SSD (IOPS 6000), and SATA (IOPS 1500) storage classes in the private cloud storage resource pool. Persistent volume claims are created: the transaction system is bound to a 500GB NVMe volume (access mode ReadWriteMany), the log system is bound to a 2000GB SSD volume (ReadWriteOnce), and the test tool is bound to a 1000GB SATA volume (ReadOnlyMany). The mounting path ( / opt / trade, / opt / log, / opt / dev) and the permission (775, 755, 444) are set. The optimized deployment scheme data: the storage configuration command (adding volume mounting parameters) is updated, the IO scheduling strategy (NVMe uses the noop algorithm, and SSD uses the deadline) is adjusted, the final scheme containing the storage association configuration is generated, the scheme ensures that the storage class performance meets the requirement (IOPS compliance rate 100%), the permission and the access mode are consistent (the read-only volume permission is 444), and there is no configuration conflict.

[0120] Further, step S33 includes the following steps:

[0121] Based on the electronic city ICC node resource conflict probability, the node load resource conflict evaluation is performed on the electronic city ICC cluster node application load deployment process to obtain an electronic city ICC node load resource conflict influence factor;

[0122] In the embodiment of the present application, the node load resource conflict evaluation is performed on the cluster node application load deployment process based on the electronic city ICC node resource conflict probability (group 1 conflict probability 2%, group 2 5%, group 3 8%). The conflict probability is calculated by the number of CPU resource contention times in the past 24 hours (group 1 occurs 3 times, group 2 8 times, group 3 12 times) divided by the total deployment times (group 1 1150 times, group 2 2160 times, group 3 3150 times). The evaluation indexes include conflict duration (group 1 average 10 seconds, group 2 15 seconds, group 3 20 seconds), the number of affected instances (group 1 average 1, group 2 2, group 3 3), and resource utilization fluctuation amplitude (group 1 5%, group 2 8%, group 3 12%). The influence factor calculation formula is: conflict probability x 0.4 + (duration / 30) x 0.2 + (affected instance number / 5) x 0.2 + (fluctuation amplitude / 20) x 0.2. The calculation result is: group 1 influence factor = 2% x 0.4 + (10 / 30) x 0.2 + (1 / 5) x 0.2 + (5 / 20) x 0.2 = 0.008 + 0.0067 + 0.008 + 0.005 = 0.0277; group 2 = 5% x 0.4 + (15 / 30) x 0.2 + (2 / 5) x 0.2 + (8 / 20) x 0.2 = 0.02 + 0.01 + 0.016 + 0.008 = 0.054; group 3 = 8% x 0.4 + (20 / 30) x 0.2 + (3 / 5) x 0.2 + (12 / 20) x 0.2 = 0.032 + 0.0133 + 0.024 + 0.012 = 0.0813, which is the electronic city ICC node load resource conflict influence factor, the numerical range is 0-0.1, directly reflecting the influence degree of conflict on deployment, the calculation is based on historical data and fixed weight, without subjective adjustment.

[0123] Preferably, the electronic city ICC node load reference time is obtained, and the electronic city ICC node scheduling completion time is standardized based on the electronic city ICC node load reference time to obtain the electronic city ICC node scheduling standardized time.

[0124] In the embodiment of the present application, the electronic city ICC node load reference time (10 seconds for group 1, 15 seconds for group 2, and 20 seconds for group 3) is obtained, which is the average time for the node to complete load scheduling in a non-conflict state (through 100 times of successful scheduling statistics, the fastest 8 seconds, the slowest 12 seconds, and the average 10 seconds for group 1). The node scheduling completion time (12 seconds for a certain scheduling of group 1, 20 seconds for group 2, and 25 seconds for group 3) is subjected to scheduling reference standardization processing, and the standardized time = actual completion time / reference time. The calculation result is that the standardized time for group 1 = 12 / 10 = 1.2, the standardized time for group 2 = 20 / 15 = 1.333, and the standardized time for group 3 = 25 / 20 = 1.25. For the overtime scheduling (25 seconds for a certain scheduling of group 1), the standardized time = 25 / 10 = 2.5; for the early completion (8 seconds for a certain scheduling of group 1), the standardized time = 8 / 10 = 0.8. The electronic city ICC node scheduling standardized time is generated, and the value > 1 represents overtime, < 1 represents early completion, and = 1 represents compliance with the reference. All calculations are based on the ratio of the actual time to the reference time, and the standardized result eliminates the differences between different groups, facilitating unified evaluation of scheduling efficiency.

[0125] Preferably, the electronic city ICC node load resource conflict influence factor is used to combine the electronic city ICC node scheduling standardized time to perform node load scheduling scoring quantization, and obtain an electronic city ICC node load scheduling comprehensive score.

[0126] In the embodiment of the present application, the electronic city ICC node load resource conflict influence factor (0.0277 for group 1, 0.054 for group 2, and 0.0813 for group 3) and the node scheduling standardized time (1.2 for group 1, 1.333 for group 2, and 1.25 for group 3) are used to perform node load scheduling scoring quantization. The scoring formula is: 100-(influence factor*1000)-(standardized time*20). The score of group 1 = 100-(0.0277*1000)-(1.2*20) = 100-27.7-24 = 48.3; the score of group 2 = 100-(0.054*1000)-(1.333*20) = 100-54-26.66 = 19.34; and the score of group 3 = 100-(0.0813*1000)-(1.25*20) = 100-81.3-25 =-6.3. For the scheduling of early completion (the standardized time of group 1 is 0.8), the score = 100-27.7-(0.8*20) = 100-27.7-16 = 56.3; for the overtime scheduling (the standardized time of group 1 is 2.5), the score = 100-27.7-50 = 22.3, and an electronic city ICC node load scheduling comprehensive score is obtained, which ranges from-10 to 60 points. The score is negatively correlated with the influence factor and the standardized time (the smaller the conflict and the shorter the time, the higher the score), and the calculation process uses a fixed formula without flexible coefficient adjustment.

[0127] Preferably, the electronic city ICC node load scheduling comprehensive score is compared with a preset node load scheduling score threshold, and when the electronic city ICC node load scheduling comprehensive score is higher than the preset node load scheduling score threshold, it is judged that the scheduling is successful, and the ratio between the number of nodes corresponding to the scheduling success and the total number of nodes is calculated to obtain the electronic city ICC node load scheduling success rate data.

[0128] In the embodiment of the application, the electronic city ICC node load scheduling comprehensive score is compared with a preset node load scheduling score threshold (40 points, based on the lowest score of historical successful scheduling). Among the 5 nodes of management group 1, 3 nodes have scores of 48.3, 56.3 and 45.2, all of which are greater than 40 points, and it is determined that the scheduling is successful; 2 nodes have scores of 38.5 and 39.1, both of which are less than 40 points, and it is determined that the scheduling fails. Among the 10 nodes of group 2, 2 nodes have scores of 42.1 and 41.5, and the scheduling is successful; 8 nodes have scores less than 40 points, and the scheduling fails. The 5 nodes of group 3 have scores less than 40 points (-6.3, -5.8, etc.), and the scheduling fails. The number of nodes whose scheduling is successful is 3+2+0=5, the total number of nodes is 20, and the success rate is calculated as 5÷20×100%=25%, and the electronic city ICC node load scheduling success rate data is obtained, including the successful node ID, the failed node ID and the success rate value (25%). The comparison and judgment are strictly based on the 40-point threshold, the success rate is calculated as the actual ratio of the successful nodes to the total nodes, the result directly reflects the scheduling effect, and there is no subjective correction.

[0129] Further, the step S35 includes the following steps:

[0130] Step S351: The storage demand parameters corresponding to each application load are extracted from the electronic city ICC node optimization deployment scheme data, including storage capacity, access mode and performance level, and the storage demand classification data is obtained by classifying the storage demand according to the resource application type.

[0131] In the embodiment of the application, the storage requirement parameters of each application load are extracted from the electronic city ICC node optimization deployment scheme data: the transaction system (core business) requires a storage capacity of 500 GB, an access mode of read-write multi-node, a performance level of level one (IOPS≥10000); the log system (ordinary business) requires 2000 GB, an access mode of read-write single node, a performance level of level two (IOPS≥5000); the development tool (test business) requires 1000 GB, an access mode of read-only multi-node, a performance level of level three (IOPS≥1000). The storage requirement classification is combined with the resource application type: the transaction system is classified as a “high-performance shared storage class” due to the level-one performance and multi-node access; the log system is classified as a “medium-performance independent storage class” due to the level-two performance and single-node access; and the development tool is classified as a “low-performance read-only storage class” due to the level-three performance and read-only characteristics. The storage requirement classification data is generated, including the requirement parameters (capacity accurate to 1 GB, IOPS threshold fixed) and classification labels of the three storage classes, and the classification rule is based on the combination of performance level and access mode without cross categories.

[0132] Step S352: query the storage resource pool information corresponding to the private cloud platform based on the storage requirement classification data to filter out the storage class that meets the requirement, including the storage medium type, dynamic supply capacity and recovery strategy, to obtain candidate storage class data; and perform performance matching degree evaluation on the candidate storage class data to obtain storage class matching score data by comparing and analyzing the historical input / output operations per second, throughput of the storage class and the application storage requirement;

[0133] In the embodiment of the application, the private cloud platform storage resource pool information is queried based on the storage requirement classification data: the high-performance shared storage class matches the storage medium type of NVMe (delay < 1 ms), the dynamic supply capability (supporting the creation of a 500 GB volume within 5 minutes), and the recycling strategy of deletion (data is cleared after the volume is deleted); the medium-performance independent storage class matches the SSD (delay < 5 ms), the dynamic supply capability (the creation of a 2000 GB volume within 10 minutes), and the recycling strategy of reservation; and the low-performance read-only storage class matches the SATA (delay < 10 ms), the dynamic supply capability (the creation of a 1000 GB volume within 15 minutes), and the recycling strategy of archiving. The performance matching degree of the candidate storage class is evaluated: the NVMe class has a historical IOPS of 12000 (demand 10000) and a throughput of 500 MB / s (demand 300 MB / s), the matching score = (12000 / 10000) x 50 + (500 / 300) x 50 = 60 + 83.33 = 143.33; the SSD class has an IOPS of 6000 (demand 5000) and a throughput of 200 MB / s (demand 150 MB / s), the score = (6000 / 5000) x 50 + (200 / 150) x 50 = 60 + 66.67 = 126.67; and the SATA class has an IOPS of 1500 (demand 1000) and a throughput of 100 MB / s (demand 50 MB / s), the score = (1500 / 1000) x 50 + (100 / 50) x 50 = 75 + 100 = 175. The storage class matching score data is obtained, and the score > 100 is qualified (full score 200), and the value directly reflects the performance compliance degree.

[0134] Step S353: determining the target storage class according to the storage class matching score data, creating a persistent volume claim based on the storage capacity in the storage requirement classification data, associating the mapping relationship between the target storage class and the persistent volume claim, and obtaining persistent volume claim configuration data;

[0135] In the embodiment of the application, the corresponding storage class with the highest score is selected as the target storage class according to the storage class matching score data (NVMe class 143.33, SSD class 126.67, SATA class 175): the high-performance shared storage class selects NVMe, the medium-performance independent storage class selects SSD, and the low-performance read-only storage class selects SATA. Based on the storage requirement classification data, the persistent volume claim is created: a volume with a capacity of 500 GB, an access mode of ReadWriteMany, and an associated NVMe storage class is declared for the transaction system; a volume with a capacity of 2000 GB, an access mode of ReadWriteOnce, and an associated SSD storage class is declared for the log system; and a volume with a capacity of 1000 GB, an access mode of ReadOnlyMany, and an associated SATA storage class is declared for the development tool. The mapping relationship is realized by binding the storage class ID and the volume claim ID (such as NVMe class ID: sc-001 binding volume claim ID: pvc-001), and the persistent volume claim configuration data is generated, which includes the volume claim capacity, the access mode, the target storage class ID, and the mapping relationship. The configuration parameters strictly correspond to the requirement classification, and there is no capacity overage or mode mismatch.

[0136] Step S354: Bind the persistent volume claim configuration data with the corresponding application load to record the allocation state, mounting path, and access permission of the persistent volume claim, and generate the associated configuration data of the persistent volume and the storage class; based on the associated configuration data of the persistent volume and the storage class, the electronic city ICC node optimization deployment scheme data is associated and configured to generate the optimization deployment scheme data corresponding to the associated configuration of the electronic city ICC persistent volume and the storage class.

[0137] In the embodiment of the application, the persistent volume claim configuration data is bound with the corresponding application load: the transaction system is bound with pvc-001 (NVMe volume), the allocation state is “bound”, the mounting path is / opt / trade, and the access permission is 775 (read / write / execute); the log system is bound with pvc-002 (SSD volume), the allocation state is “bound”, the mounting path is / opt / log, and the permission is 755; and the development tool is bound with pvc-003 (SATA volume), the allocation state is “bound”, the mounting path is / opt / dev, and the permission is 444 (read-only). After recording the binding information, the electronic city ICC node optimization deployment scheme data is associated and configured: the storage configuration of the transaction system deployment node is updated (an NVMe volume mounting command is added), the IO scheduling strategy of the log system is adjusted (the deadline algorithm is set), and the volume write operation of the development tool is limited (through permission control). The optimization deployment scheme data is generated, which includes the volume binding information, mounting parameters, and optimized storage configuration commands of the three types of applications. The scheme ensures one-to-one correspondence between the volume and the application, the permission setting meets the access mode requirements, and the optimization operation is based on the storage class characteristics and has no conflict configuration.

[0138] Further, step S4 comprises the following steps:

[0139] Step S41: Collecting physical resource indicators and application resource indicators corresponding to the execution process of the optimization deployment scheme data by deploying distributed monitoring agents to perform cluster full-link real-time monitoring on the execution process of the optimization deployment scheme data, wherein the physical resource indicators include CPU usage, memory usage and network throughput, the application resource indicators include container restart times and service response time, and obtaining electronic city ICC cluster real-time monitoring indicator data;

[0140] In the embodiment of the application, the execution process of the optimization deployment scheme data is monitored in real time by deploying one distributed monitoring agent on each of the 20 nodes of the electronic city ICC cluster (collecting data once every 30 seconds, with a fixed sampling frequency). The physical resource indicators are collected as follows: the CPU usage is obtained through the node kernel counter (with an accuracy of 0.1%); the memory usage is calculated based on the difference between the total memory and the free memory (with an accuracy of 0.1%); and the network throughput is collected through the network card flow statistical module (in units of MB / s, with an accuracy of 0.1 MB / s). The application resource indicators are collected as follows: the container restart times are counted through the container runtime interface (integer); and the service response time is calculated by intercepting the time stamps of the service request and response (in units of ms, with an accuracy of 1 ms). The data is collected continuously for 48 hours, and the following data is recorded: for the nodes in the management group 1, the CPU usage is 30%-60%, the memory usage is 20%-50%, the network throughput is 100-300 MB / s, the container restart times are 0, and the service response time is 10-50 ms; and for the nodes in the management group 2, the CPU usage is 40%-70%, the memory usage is 30%-60%, the network throughput is 200-400 MB / s, the container restart times are 1-3, and the service response time is 30-80 ms. The electronic city ICC cluster real-time monitoring indicator data is obtained, which includes 6 indicators of each node within 48 hours, 1 record every 30 seconds, with no data loss or interruption in collection.

[0141] Step S42: Performing time series trend analysis on the electronic city ICC cluster real-time monitoring indicator data to identify the usage trend of each resource indicator, including peak periods and growth rates, and abnormal wave points, and obtaining electronic city ICC cluster resource indicator trend analysis data;

[0142] In the embodiment of the present application, by performing time trend analysis on the real-time monitoring index data (11520 records in 48 hours) of the electronic city ICC cluster, the sliding window algorithm (window size 1 hour) is used to calculate the hourly mean. CPU usage trend: management group 1 rises from 30% to 60% (growth rate 7.5% / hour) from 8:00-12:00, maintains 60% (peak period) from 12:00-14:00, and drops to 30% from 20:00-24:00; management group 2 remains 40%-70% (peak period) from 9:00-15:00, with a growth rate of 5% / hour. The memory usage trend is consistent with the CPU, and the network throughput peaks appear from 10:00-16:00 (group 1 reaches 300MB / s, group 2 reaches 400MB / s). Abnormal wave point identification: management group 1 has a sudden drop in CPU usage to 10% at 13:00 (lasting 5 minutes), which is determined to be abnormal; management group 2 has a sudden increase in service response time to 200ms at 16:00 (normal <80ms), which is determined to be abnormal. The electronic city ICC cluster resource index trend analysis data is obtained, including the 48-hour trend curve of each index, the peak period (accurate to the hour), the growth rate (% / hour), and the time and value of the abnormal wave point. The analysis algorithm parameters are fixed, the trend identification is based on the actual data change rate, and there is no subjective judgment.

[0143] Step S43: setting the electronic city ICC cluster resource alarm rule group based on the electronic city ICC cluster resource index trend analysis data, defining the corresponding alarm threshold, alarm level and notification method based on the electronic city ICC cluster resource alarm rule group, obtaining the electronic city ICC cluster alarm rule configuration data; performing cluster monitoring alarm trigger processing on the execution process corresponding to the optimized deployment scheme data based on the electronic city ICC cluster alarm rule configuration data, obtaining the electronic city ICC cluster monitoring alarm data;

[0144] In the embodiment of the application, the resource alarm rule group is set based on the ICC cluster resource index trend analysis data (CPU peak value 60%, memory peak value 60%, network throughput peak value 400 MB / s). The CPU usage alarm threshold: more than 70% triggers a general alarm (based on the peak value 60% up by 10%), more than 80% triggers an important alarm, and more than 90% triggers an emergency alarm; the memory usage threshold is the same as the CPU. Network throughput: more than 450 MB / s (peak value 400 MB / s up by 12.5%) general alarm, 500 MB / s important alarm, and 550 MB / s emergency alarm. Application index: container restart number > 5 times general alarm, > 10 times important alarm; service response time > 100 ms general alarm, > 200 ms important alarm, and > 300 ms emergency alarm. The alarm level corresponds to the notification method: emergency alarm through SMS + phone notification, important alarm through SMS notification, and general alarm through email notification. The ICC cluster alarm rule configuration data is obtained, including three-level thresholds (accurate to 0.1 units) of six indexes, level and notification method, the threshold is set based on the peak value plus a fixed proportion, to ensure that the alarm is triggered before the abnormality occurs, and there is no threshold overlap. Based on the configuration data, when the CPU usage reaches 91%, an emergency alarm is triggered immediately, the phone notification process is executed, and an alarm record is generated.

[0145] Step S44: The tenant management data corresponding to the ICC is collected, including tenant ID, tenant resource usage, resource quota, and role permission, and the tenant resource usage is compared and analyzed with the resource quota to calculate the resource usage rate and excess risk, and the ICC tenant resource usage data is obtained; the tenant permission-resource usage association model is constructed based on the ICC cluster monitoring alarm data and the ICC tenant resource usage data, and the corresponding resource quota is dynamically adjusted based on the ICC tenant resource usage data, including temporary expansion and quota reduction, and the ICC tenant resource dynamic configuration data is obtained;

[0146] In the embodiment of the application, the ICC tenant management data of the electronic city is collected: tenant A (ID: 001) resource usage CPU 20%, memory 15%, quota CPU 30%, memory 20%, and role permission administrator; tenant B (ID: 002) usage CPU 30%, memory 25%, quota CPU 40%, memory 30%, and permission common user; and tenant C (ID: 003) usage CPU 15%, memory 10%, quota CPU 20%, memory 15%, and permission visitor. The resource usage rate is compared and analyzed: tenant A CPU usage rate = 20% / 30% ≈ 67%, and memory = 15% / 20% = 75%; tenant B CPU = 30% / 40% = 75%, and memory = 25% / 30% ≈ 83%; and tenant C CPU = 15% / 20% = 75%, and memory = 10% / 15% ≈ 67%. The excess risk is calculated: tenant B memory usage rate 83% (> 80%), high risk level; and the risk levels of the remaining tenants are low. The tenant permission-resource usage correlation model is constructed, and the resource usage rates of the administrator permission tenants are 67%-75%, the common user 83%, and the visitor 67%. The resource quota is dynamically adjusted: the memory quota of tenant B is increased from 30% to 35% (temporary expansion), and the CPU quota of tenant C is reduced from 20% to 18% (quota reduction). The ICC tenant resource dynamic configuration data of the electronic city is obtained, including tenant ID, usage, quota, permission, risk level, and adjusted quota, the quota adjustment amount is calculated based on the difference between the usage rate and the threshold value, the correlation model is fitted based on the actual data, and there is no permission-resource mismatching situation.

[0147] Step S45: according to the electronic city ICC cluster monitoring alarm data and the electronic city ICC tenant resource dynamic configuration data, the management strategy of the electronic city ICC corresponding to the private cloud platform is iteratively optimized to output the corresponding electronic city ICC management execution scheme.

[0148] In the embodiment of the present application, the electronic city ICC management strategy of the private cloud platform is iteratively optimized according to the electronic city ICC cluster monitoring alarm data (5 emergency alarms, 20 important alarms, and 50 general alarms within 48 hours) and the tenant resource dynamic configuration data (tenant B expansion and tenant C reduction). The resource scheduling strategy is as follows: the resource scheduling priority of tenant B is upgraded from level two to level one (matching high usage rate), and the resource scheduling priority of tenant C is downgraded from level three to level four. The alarm processing flow is as follows: the emergency alarm response time is shortened from 10 minutes to 5 minutes, and the number of personnel handling important alarms is increased. The tenant permission management is as follows: the resource usage of the visitor permission tenant during non-working hours (20:00-8:00) is limited. The output electronic city ICC management execution scheme includes the optimized scheduling priority list, alarm processing time limit, permission restriction rule, and resource quota adjustment command. The priority adjustment in the scheme is based on the tenant usage rate, and the response time reduction is based on the historical processing time. After the strategy optimization, the resource utilization rate is improved by 10%, the alarm processing efficiency is improved by 20%, and there is no strategy conflict.

[0149] Further, step S45 includes the following steps:

[0150] Step S451: Obtain the electronic city ICC cluster monitoring resource detail data by extracting the corresponding physical resource usage details and application resource performance indicators from the electronic city ICC cluster monitoring alarm data, wherein the physical resource usage details include the CPU and memory usage proportion of each node, and the application resource performance indicators include the deployment replica survival rate and service interface response time.

[0151] In the embodiment of the present application, the physical resource usage details and application resource performance indicators are extracted from the electronic city ICC cluster monitoring alarm data (collected once every 5 minutes, covering 24 hours). The physical resource usage details: the CPU usage of the 5 nodes of management group 1 accounts for 40%-60% (average 50%), the memory usage accounts for 30%-50% (average 40%); the CPU of the 10 nodes of management group 2 accounts for 50%-70% (average 60%), the memory accounts for 40%-60% (average 50%); the CPU of the 5 nodes of management group 3 accounts for 60%-80% (average 70%), the memory accounts for 50%-70% (average 60%), all the percentages are accurate to 1%. The application resource performance indicators: the transaction system deployment replica survival rate is 100% (5 replicas are normal), the service interface response time is 10-30 ms (average 20 ms); the log system survival rate is 98% (1 of 20 replicas is abnormal), the response time is 50-100 ms (average 70 ms); the test tool survival rate is 95% (0.5 of 10 replicas is abnormal), the response time is 100-200 ms (average 150 ms), the survival rate is an integer, and the response time is accurate to 1 ms. The electronic city ICC cluster monitoring resource detail data is obtained, including the usage percentage range, average and application performance indicators of the 3 types of resources, and the data is directly from the monitoring record without estimation and correction.

[0152] Step S452: obtaining the electronic city ICC cluster resource analysis data by performing resource multi-dimensional statistical analysis on the electronic city ICC cluster monitoring resource detail data according to the time dimension, the tenant dimension and the application dimension;

[0153] In the embodiment of the present application, the resource multi-dimensional statistical analysis is performed on the electronic city ICC cluster monitoring resource detail data according to the time dimension, the tenant dimension and the application dimension. The time dimension: the CPU usage is counted every hour, the average of management group 1 is 55% from 8:00 to 12:00, the average is 60% from 12:00 to 18:00, and the average is 45% from 18:00 to 24:00, and a 24-hour trend curve is generated. The tenant dimension: the CPU peak of tenant A (using group 1 resource) is 50%, the peak of tenant B (using group 2) is 65%, and the peak of tenant C (using group 3) is 75%, and the resource percentage of each tenant (A accounts for 20%, B accounts for 50%, and C accounts for 30%) is calculated. The application dimension: the CPU consumption of the transaction system is 20%, the memory is 15%, the CPU of the log system is 30%, the memory is 25%, the CPU of the test tool is 20%, and the memory is 20%, and the resource consumption percentage of each application is counted. The electronic city ICC cluster resource analysis data is obtained, including the statistical chart, average, peak and percentage value of the 3 dimensions, the time dimension interval is fixed, the tenant and application dimension division is based on the resource allocation record, and the analysis result quantitatively reflects the resource usage rule.

[0154] Step S453: Based on the electronic city ICC cluster monitoring alarm data integration corresponding to the electronic city ICC cluster alarm rule configuration data statistics corresponding alarm number, processing rate, average processing time, and through the classification summary according to the alarm level and tenant dimension, to obtain the electronic city ICC cluster alarm statistics data;

[0155] In the embodiment of the application, based on the electronic city ICC cluster monitoring alarm data (120 alarms generated within 24 hours) and the alarm rule configuration data (emergency alarm threshold CPU>90%, memory>80%; important alarm CPU>80%, memory>70%; general alarm CPU>70%, memory>60%), the alarm number is counted: 10 emergency alarms (8 CPU overruns, 2 memory overruns), 30 important alarms, and 80 general alarms. The processing rate calculation: 10 emergency alarms are all processed, the processing rate is 100%; 27 important alarms are processed, the processing rate is 90%; 64 general alarms are processed, the processing rate is 80%. The average processing time: 5 minutes for emergency alarm, 15 minutes for important alarm, and 30 minutes for general alarm. According to the alarm level and tenant dimension classification summary: tenant A triggers 3 emergency alarms and 10 important alarms, tenant B triggers 5 emergency alarms and 15 important alarms, and tenant C triggers 2 emergency alarms and 5 important alarms. The electronic city ICC cluster alarm statistics data is obtained, including the number of alarms of each level, the processing rate, the average processing time and the tenant distribution, the statistics rule is based on fixed threshold, the processing time is accurate to 1 minute, and there is no subjective statistical deviation.

[0156] Step S454: Based on the electronic city ICC tenant resource dynamic configuration data, the corresponding electronic city ICC tenant permission audit report is generated, including resource use compliance check and optimization suggestion, to generate electronic city ICC tenant audit data;

[0157] In the embodiment of the application, a tenant permission audit report is generated based on the electronic city ICC tenant resource dynamic configuration data (tenant A configures CPU upper limit 50%, memory 40%; tenant B configures CPU 70%, memory 60%; tenant C configures CPU 80%, memory 70%). Resource use compliance check: tenant A CPU actual peak 50% (not exceeding the limit), memory 35% (compliant); tenant B CPU peak 65% (not exceeding 70%), memory 55% (compliant); tenant C CPU peak 75% (not exceeding 80%), memory 65% (compliant), all are determined to be compliant. Optimization suggestions: tenant A increases 10% CPU quota (current usage rate is close to the upper limit), tenant B adjusts memory allocation to 55% (actual use is lower than the configuration), tenant C limits resource use during non-working hours (CPU upper limit is reduced to 50% from 20:00 to 8:00). The electronic city ICC tenant audit data is generated, including the compliance results of each tenant, the difference between resource use and configuration, and specific optimization suggestions. The compliance determination is based on the comparison between actual use and configuration, the suggestions are based on resource usage rate trends, and there is no generalization content.

[0158] Step S455: The corresponding electronic city ICC cluster management evaluation report is generated by integrating the electronic city ICC cluster resource analysis data, the electronic city ICC cluster alarm statistical data and the electronic city ICC tenant audit data, and the management strategy of the private cloud platform corresponding to the electronic city ICC is iteratively optimized based on the electronic city ICC cluster management evaluation report to output the corresponding electronic city ICC management execution scheme.

[0159] In the embodiment of the application, the cluster management evaluation report is generated by integrating the electronic city ICC cluster resource analysis data (resource proportion in each dimension), the alarm statistical data (alarm indicators at each level) and the tenant audit data (compliance and suggestions). The report shows that: the CPU resource is tight during 8:00-18:00 (the mean value of group 1 exceeds 50%), the urgent alarm is handled in time but the number is relatively large (10), the tenant resource configuration is overall compliant but there is optimization space. Based on the report, the management strategy of the electronic city ICC of the private cloud platform is iteratively optimized: adjust the resource scheduling priority (increase the priority of tenant A during 8:00-18:00), modify the alarm threshold (CPU>95%, memory>85% for urgent alarm), and adopt the tenant resource adjustment suggestion (increase the CPU quota of A). The electronic city ICC management execution scheme is output, including the scheduling priority period table after optimization, the new alarm threshold and the tenant resource configuration command. The adjustment range in the scheme is calculated based on the peak value and the mean value difference of the resource analysis data, the threshold is modified by referring to the historical alarm reasons, and it is ensured that the execution scheme can be directly implemented and there is no configuration conflict.

[0160] Further, the application also provides an electronic city ICC management system based on a private cloud platform, used for executing the electronic city ICC management method based on the private cloud platform as described above, and the electronic city ICC management system based on the private cloud platform comprises:

[0161] A cluster resource service association module is configured to collect and standardize the physical resources and interface services of the electronic city ICC cluster through the resource collection module corresponding to the private cloud platform, so as to obtain the standardized cluster resource data of the electronic city ICC; the resource-service association model of the electronic city ICC is constructed based on the standardized cluster resource data of the electronic city ICC, and is mapped and associated, so as to obtain the cluster resource association characteristic data of the electronic city ICC;

[0162] A cluster node load scheduling module is configured to analyze the cluster node state based on the cluster resource association characteristic data of the electronic city ICC, so as to obtain the cluster node state characteristic data of the electronic city ICC; the node load scheduling configuration is performed based on the cluster node state characteristic data of the electronic city ICC and the standardized cluster resource data of the electronic city ICC, so as to generate the node load deployment scheme data of the electronic city ICC;

[0163] A configuration optimization resource matching module is configured to obtain the corresponding node load scheduling success rate data of the electronic city ICC based on the node load deployment scheme data of the electronic city ICC, and perform the configuration optimization management and storage resource matching on the node load deployment scheme data of the electronic city ICC, so as to generate the optimization deployment scheme data corresponding to the persistent volume and the storage class association configuration of the electronic city ICC;

[0164] An electronic city management iterative optimization module is configured to perform the cluster full-link monitoring and alarm on the execution process corresponding to the optimization deployment scheme data, so as to obtain the cluster monitoring and alarm data of the electronic city ICC; the tenant resource dynamic adjustment is performed based on the cluster monitoring and alarm data of the electronic city ICC, so as to obtain the tenant resource dynamic configuration data of the electronic city ICC; the management iterative optimization is performed on the electronic city ICC management strategy corresponding to the private cloud platform based on the cluster monitoring and alarm data of the electronic city ICC and the tenant resource dynamic configuration data of the electronic city ICC, so as to output the corresponding electronic city ICC management execution scheme.

[0165] The above is only a specific embodiment of the application, which enables those skilled in the art to understand or implement the application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the application. Therefore, the application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for managing an ICC (Internet City) based on a private cloud platform, characterized in that, Comprise the following steps: Step S1: through the private cloud platform corresponding to the resource acquisition module of the electronic city ICC cluster for physical resources and interface service real-time collection and standardization processing, to obtain the electronic city ICC standardized cluster resource data; based on the electronic city ICC standardized cluster resource data, the electronic city ICC resource-service association model is constructed and mapped, and the electronic city ICC cluster resource association characteristic data is obtained; Step S2: the electronic city ICC cluster resource association characteristic data is analyzed to obtain the electronic city ICC cluster node state characteristic data; based on the electronic city ICC cluster node state characteristic data and combined with the electronic city ICC standardized cluster resource data, the node load scheduling configuration is generated to obtain the electronic city ICC node load deployment scheme data; Step S3: based on the electronic city ICC node load deployment scheme data, the corresponding electronic city ICC node load scheduling success rate data is obtained, and the electronic city ICC node load deployment scheme data is configured and optimized, and the storage resource is matched to generate the optimized deployment scheme data corresponding to the electronic city ICC persistent volume and storage class association configuration; Step S4: the execution process of the optimized deployment scheme data is monitored and alarmed to obtain the electronic city ICC cluster monitoring and alarming data; Based on the electronic city ICC cluster monitoring and alarming data, the tenant resource dynamic adjustment is carried out, and the electronic city ICC tenant resource dynamic configuration data is obtained; according to the electronic city ICC cluster monitoring and alarming data and the electronic city ICC tenant resource dynamic configuration data, the electronic city ICC management strategy corresponding to the private cloud platform is managed and iteratively optimized to output the corresponding electronic city ICC management execution scheme. 2.The private cloud platform based ICC management method of claim 1, wherein, Step S1 includes the following steps: Step S11: the distributed collection agent deployed by the resource acquisition module corresponding to the private cloud platform collects the physical resources and interface services of the electronic city ICC cluster in real time to collect the physical resource data and interface service data corresponding to the electronic city ICC cluster in real time, wherein the physical resource data includes node total amount, CPU, memory, NPU, video memory and disk resources, and the interface service data includes API request delay, response time, status code and request times, to obtain the electronic city ICC initial cluster resource data; Step S12: the electronic city ICC initial cluster resource data is cleaned to eliminate abnormal values and repeated records, and the interpolation method is used to fill in the missing values to obtain the electronic city ICC cluster cleaned resource data; Step S13: the electronic city ICC cluster cleaned resource data is standardized to unify the data timestamp format and measurement unit, and different types of resource data and service data are mapped to a unified numerical interval through normalization algorithm to obtain the electronic city ICC standardized cluster resource data; Step S14: constructing an electronic city ICC resource-service association model based on the electronic city ICC standardized cluster resource data, constructing an association weight matrix by calculating the association coefficients between the physical resource occupation state and the interface service performance parameters using correlation analysis, and mapping and associating the corresponding physical resource occupation state and the interface service performance parameters based on the association weight matrix to obtain electronic city ICC cluster resource association feature data. 3.The electronic city (ICC) management method based on a private cloud platform according to claim 1, wherein, Step S2 includes the following steps: Step S21: analyzing the electronic city ICC cluster resource association feature data to extract the resource utilization, health status, and load balancing degree of each node of the electronic city ICC cluster to obtain electronic city ICC cluster node state feature data; Step S22: performing cluster node clustering analysis based on the electronic city ICC cluster node state feature data and in combination with the electronic city ICC standardized cluster resource data to classify electronic city ICC cluster nodes with similar resource configurations and similar load characteristics into the same management group to obtain electronic city ICC cluster node clustering result data; Step S23: evaluating the node cluster state of the electronic city ICC cluster node clustering result data to calculate the node cluster health score in combination with the node health check logs corresponding to each electronic city ICC cluster node management group to obtain electronic city ICC cluster node health status data; Step S24: formulating corresponding electronic city ICC cluster node management strategies based on the electronic city ICC cluster node health status data and the electronic city ICC cluster node clustering result data, including resource scheduling priority division, fault automatic migration rules, and load balancing threshold setting, to obtain electronic city ICC cluster node management strategy data; Step S25: synchronizing the electronic city ICC cluster node management strategy data to the scheduling center of the private cloud platform for node load adjustment configuration to generate electronic city ICC node load deployment scheme data. 4.The electronic city (ICC) management method based on a private cloud platform according to claim 3, wherein, Step S25 includes the following steps: Step S251: determining the deployment node range of each application load of the electronic city ICC based on the corresponding resource scheduling priority in the electronic city ICC cluster node management strategy data to obtain electronic city ICC deployment node candidate set data; Step S252: obtaining the application load type of the electronic city ICC cluster through the scheduling center of the private cloud platform, including deployment resources, stateful replica resources, and daemon set resources, and performing resource parameter analysis on the application load type of the electronic city ICC cluster to extract resource demand parameters and deployment constraints, wherein the resource demand parameters include CPU core number, memory capacity, and storage type, and the deployment constraints include node affinity and anti-affinity rules to obtain electronic city ICC load demand feature data; Step S253: performing electronic city ICC application load deployment based on the electronic city ICC load demand feature data and the electronic city ICC deployment node candidate set data to obtain electronic city ICC application load deployment result data; Step S253: Calculate the matching degree between the e-City ICC deployment node candidate set data and the e-City ICC load demand feature data, generate a corresponding node matching score based on the node resource remaining amount and the node cluster class health score, and perform deployment node matching analysis between the e-City ICC deployment node candidate set data and the e-City ICC load demand feature data based on the node matching score, to obtain e-City ICC deployment node matching result data; Step S254: Based on the e-City ICC deployment node matching result data, synchronize to the corresponding dispatch center of the private cloud platform for node load adjustment configuration, to clearly define the deployment node, replica number, and start order of each e-City ICC deployment load, to generate e-City ICC node load deployment scheme data. 5.The electronic city (ICC) management method based on a private cloud platform according to claim 1, wherein, Step S3 includes the following steps: Step S31: Based on the e-City ICC node load deployment scheme data and in combination with the e-City ICC cluster resource association feature data, simulate to generate a corresponding e-City ICC cluster node application load deployment process; Step S32: Obtain the e-City ICC node resource conflict probability and the e-City ICC node scheduling completion time through the e-City ICC cluster node application load deployment process; Step S33: Based on the e-City ICC node resource conflict probability and the e-City ICC node scheduling completion time, evaluate the e-City ICC cluster node application load deployment process for node load scheduling, to obtain e-City ICC node load scheduling success rate data; Step S34: Based on the e-City ICC node load scheduling success rate data, optimize and adjust the e-City ICC node load deployment scheme data, to determine the resource allocation parameters and service interface exposure rules of the e-City ICC container group, to obtain e-City ICC node optimization deployment scheme data; Step S35: Based on the e-City ICC node optimization deployment scheme data, perform configuration management and storage resource matching, to generate optimized deployment scheme data corresponding to the e-City ICC persistent volume and storage class association configuration. 6.The electronic city (ICC) management method based on a private cloud platform according to claim 5, wherein, Step S33 includes the following steps: Based on the e-City ICC node resource conflict probability, evaluate the e-City ICC cluster node application load deployment process for node load resource conflict, to obtain an e-City ICC node load resource conflict influence factor; Obtain the e-City ICC node load benchmark time, and based on the e-City ICC node load benchmark time, perform scheduling benchmark standardization processing on the e-City ICC node scheduling completion time, to obtain an e-City ICC node scheduling standardized time; Based on the e-City ICC node load resource conflict influence factor and in combination with the e-City ICC node scheduling standardized time, perform node load scheduling scoring quantization, to obtain an e-City ICC node load scheduling comprehensive score; The electronic city ICC node load scheduling comprehensive score is compared according to the preset node load scheduling score threshold value, and when the electronic city ICC node load scheduling comprehensive score is higher than the preset node load scheduling score threshold value, it is judged that the scheduling is successful, and the ratio between the number of nodes corresponding to the scheduling success and the total number of nodes is calculated to obtain the electronic city ICC node load scheduling success rate data. 7.The electronic city (ICC) management method based on a private cloud platform according to claim 5, wherein, Step S35 includes the following steps: Step S351: Extract the storage demand parameters corresponding to each application load from the electronic city ICC node optimization deployment scheme data, including storage capacity, access mode and performance level, and obtain storage demand classification data by classifying the storage demand according to the resource application type; Step S352: Query the storage resource pool information corresponding to the private cloud platform based on the storage demand classification data to filter out the storage classes that meet the demand, including storage medium type, dynamic supply capacity and recycling strategy, to obtain candidate storage class data; and perform performance matching degree evaluation on the candidate storage class data to compare and analyze the application storage demand by combining the historical input / output operations per second, throughput and storage class, to obtain storage class matching score data; Step S353: Determine the target storage class according to the storage class matching score data, and create a persistent volume claim based on the storage capacity in the storage demand classification data, and associate the mapping relationship between the target storage class and the persistent volume claim, to obtain persistent volume claim configuration data; Step S354: Bind the persistent volume claim configuration data with the corresponding application load to record the allocation state, mounting path and access permission of the persistent volume claim, and generate association configuration data of the persistent volume and the storage class; and perform association configuration optimization on the electronic city ICC node optimization deployment scheme data based on the association configuration data of the persistent volume and the storage class, to generate optimization deployment scheme data corresponding to the association configuration of the electronic city ICC persistent volume and the storage class. 8.The private cloud platform based ICC management method of electronic city according to claim 1, wherein, Step S4 includes the following steps: Step S41: Real-time monitor the execution process of the optimization deployment scheme data through the deployment of a distributed monitoring agent to collect physical resource indicators and application resource indicators corresponding to the execution process of the optimization deployment scheme data, wherein the physical resource indicators include CPU usage, memory usage and network throughput, and the application resource indicators include container restart times and service response time, to obtain electronic city ICC cluster real-time monitoring indicator data; Step S42: Perform time series trend analysis on the electronic city ICC cluster real-time monitoring indicator data to identify the usage trend of each resource indicator, including peak period and growth rate, and abnormal wave points, to obtain electronic city ICC cluster resource indicator trend analysis data; Step S43: Based on the electronic city ICC cluster resource index trend analysis data, set the electronic city ICC cluster resource alarm rule group, define the corresponding alarm threshold, alarm level and notification method based on the electronic city ICC cluster resource alarm rule group, and obtain the electronic city ICC cluster alarm rule configuration data; based on the electronic city ICC cluster alarm rule configuration data, perform cluster monitoring alarm trigger processing on the execution process corresponding to the optimization deployment scheme data, to obtain the electronic city ICC cluster monitoring alarm data; Step S44: By collecting the tenant management data corresponding to the electronic city ICC, including tenant ID, tenant resource usage, resource quota and role permission, and comparing and analyzing the tenant resource usage and resource quota, the resource usage rate and excess risk are calculated to obtain the electronic city ICC tenant resource usage data; based on the electronic city ICC cluster monitoring alarm data and the electronic city ICC tenant resource usage data, a tenant permission-resource usage association model is constructed, and the corresponding resource quota is dynamically adjusted based on the electronic city ICC tenant resource usage data, including temporary expansion and quota reduction, to obtain the electronic city ICC tenant resource dynamic configuration data; Step S45: According to the electronic city ICC cluster monitoring alarm data and the electronic city ICC tenant resource dynamic configuration data, the electronic city ICC management strategy corresponding to the private cloud platform is managed and iteratively optimized to output the corresponding electronic city ICC management execution scheme. 9.The electronic city (ICC) management method based on a private cloud platform according to claim 8, wherein, Step S45 includes the following steps: Step S451: Extract the corresponding physical resource usage details and application resource performance indicators from the electronic city ICC cluster monitoring alarm data, wherein the physical resource usage details include CPU and memory usage percentage of each node, and the application resource performance indicators include deployment replica survival rate and service interface response time, to obtain the electronic city ICC cluster monitoring resource details data; Step S452: Perform resource multi-dimensional statistical analysis on the electronic city ICC cluster monitoring resource details data according to time dimension, tenant dimension and application dimension to obtain the electronic city ICC cluster resource analysis data; Step S453: Based on the electronic city ICC cluster monitoring alarm data, integrate the corresponding electronic city ICC cluster alarm rule configuration data to count the corresponding alarm times, processing rate and average processing time, and classify and summarize according to alarm level and tenant dimension to obtain the electronic city ICC cluster alarm statistical data; Step S454: Based on the electronic city ICC tenant resource dynamic configuration data, generate the corresponding electronic city ICC tenant permission audit report, including resource usage compliance check and optimization suggestion, to generate the electronic city ICC tenant audit data; Step S455: Integrate the electronic city ICC cluster resource analysis data, the electronic city ICC cluster alarm statistical data and the electronic city ICC tenant audit data to generate the corresponding electronic city ICC cluster management evaluation report, and based on the electronic city ICC cluster management evaluation report, the electronic city ICC management strategy corresponding to the private cloud platform is managed and iteratively optimized to output the corresponding electronic city ICC management execution scheme.

10. An electronic city (ICC) management system based on a private cloud platform, characterized by, The application discloses a private cloud platform-based electronic city ICC management system for executing a private cloud platform-based electronic city ICC management method as claimed in claim 1. A cluster resource service association module is configured to collect and standardize physical resources and interface services of the electronic city ICC cluster through a resource collection module corresponding to the private cloud platform, so as to obtain electronic city ICC standardized cluster resource data; an electronic city ICC resource-service association model is constructed based on the electronic city ICC standardized cluster resource data and is mapped and associated, so as to obtain electronic city ICC cluster resource association characteristic data. A cluster node load scheduling module is configured to analyze the electronic city ICC cluster resource association characteristic data to obtain electronic city ICC cluster node state characteristic data; and node load scheduling configuration is performed based on the electronic city ICC cluster node state characteristic data and in combination with the electronic city ICC standardized cluster resource data, so as to generate electronic city ICC node load deployment scheme data. A configuration optimization resource matching module is configured to obtain corresponding electronic city ICC node load scheduling success rate data based on the electronic city ICC node load deployment scheme data, and to perform configuration optimization management and storage resource matching on the electronic city ICC node load deployment scheme data, so as to generate optimized deployment scheme data corresponding to the association configuration of the electronic city ICC persistent volume and the storage class. An electronic city management iterative optimization module is configured to perform cluster full-link monitoring and alarm on an execution process corresponding to the optimized deployment scheme data, so as to obtain electronic city ICC cluster monitoring and alarm data; to perform tenant resource dynamic adjustment based on the electronic city ICC cluster monitoring and alarm data, so as to obtain electronic city ICC tenant resource dynamic configuration data; and to perform management iteration optimization on the electronic city ICC management strategy corresponding to the private cloud platform based on the electronic city ICC cluster monitoring and alarm data and the electronic city ICC tenant resource dynamic configuration data, so as to output corresponding electronic city ICC management execution scheme.

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