Multi-tenant environment intelligent management method, device, equipment and storage medium

Abstract

The present invention relates to the technical field of multi-tenant environment, and discloses a multi-tenant environment intelligent management method, device, equipment and storage medium. The present invention obtains resource information and tenant information in the multi-tenant environment, constructs an environment simulation map, obtains real-time activity information of each tenant unit, and substitutes it into the environment simulation map to perform multi-dimensional data analysis, and generates a multi-dimensional analysis map including a real-time analysis layer, a historical reference layer and a future estimation layer. According to the multi-dimensional analysis map, resource allocation analysis is performed on each tenant unit, resource allocation countermeasures are formulated, and resource allocation processing is executed. The method realizes accurate resource allocation by dynamically monitoring and analyzing tenant activities, improves resource utilization efficiency and system stability; by combining historical and future data, the system's ability to cope with changes is improved, and user experience is optimized, thereby solving the problem in the prior art that resource allocation in a multi-tenant environment is difficult to achieve a balance between cost and experience.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-tenant environment, and in particular to a multi-tenant environment intelligent management method, device, equipment and storage medium. Background Art

[0002] A multi-tenant environment is a software architecture or system design pattern in which multiple independent tenants can share the same computing resource pool, such as servers, databases, storage, and networks.

[0003] In a multi-tenant environment, resource management is a key challenge. Different tenants may have different demands for system resources, and these demands change over time. Traditional resource management methods are usually based on static allocation or simple historical data prediction, which can easily lead to unbalanced resource allocation. On the one hand, over-allocation of resources will waste system resources and increase operating costs; on the other hand, insufficient resource allocation may lead to reduced system performance and affect user experience. Summary of the invention

[0004] The purpose of the present invention is to provide a multi-tenant environment intelligent management method, device, equipment and storage medium, aiming to solve the problem in the prior art that resource allocation in a multi-tenant environment is difficult to achieve a balance between cost and experience.

[0005] The present invention is implemented in this way. In a first aspect, the present invention provides a multi-tenant environment intelligent management method, comprising: Obtain resource information and tenant information of a multi-tenant environment, and construct a corresponding environment simulation map according to the resource information and tenant information of the multi-tenant environment; wherein the tenant information is used to describe the account information of the tenant unit that registers an account in the multi-tenant environment; Acquire the real-time activity information of each tenant unit, and substitute the real-time activity information of each tenant unit into the environmental simulation map, so that the environmental simulation map performs data analysis and processing in multiple dimensions according to the real-time activity information of each tenant unit to obtain a multi-dimensional analysis map; wherein the multi-dimensional analysis map includes a real-time analysis layer, a historical reference layer, and a future estimation layer; The resource allocation of each tenant unit is analyzed and processed according to the real-time analysis layer, historical reference layer and future estimation layer of the multi-dimensional analysis map to obtain a resource allocation strategy for the multi-tenant environment, and resource allocation processing is performed on the multi-tenant environment according to the resource allocation strategy.

[0006] In a second aspect, the present invention provides a multi-tenant environment intelligent management device, which is used to implement a multi-tenant environment intelligent management method as described in any one of the first aspects, including: An environment simulation module, used to obtain resource information and tenant information of a multi-tenant environment, and to construct a corresponding environment simulation map according to the resource information and tenant information of the multi-tenant environment; wherein the tenant information is used to describe the account information of the tenant unit that registers an account in the multi-tenant environment; A data analysis module, used to obtain the real-time activity information of each tenant unit, and substitute the real-time activity information of each tenant unit into the environmental simulation map, so that the environmental simulation map performs data analysis processing in multiple dimensions according to the real-time activity information of each tenant unit to obtain a multi-dimensional analysis map; wherein the multi-dimensional analysis map includes a real-time analysis layer, a historical reference layer, and a future estimation layer; The countermeasure analysis module is used to analyze and process the resource allocation of each tenant unit according to the real-time analysis layer, historical reference layer and future estimation layer of the multi-dimensional analysis map, so as to obtain the resource allocation countermeasure of the multi-tenant environment, and perform resource allocation processing on the multi-tenant environment according to the resource allocation countermeasure.

[0007] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, and when the processor executes the computer program, the method for intelligent management of a multi-tenant environment as described in any one of the first aspects is implemented.

[0008] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the processor executes a multi-tenant environment intelligent management method as described in any one of the first aspects.

[0009] The present invention provides a multi-tenant environment intelligent management method, which has the following beneficial effects: The present invention acquires resource information and tenant information in a multi-tenant environment, constructs an environmental simulation map, acquires real-time activity information of each tenant unit, and substitutes it into the environmental simulation map to perform multi-dimensional data analysis, thereby generating a multi-dimensional analysis map including a real-time analysis layer, a historical reference layer, and a future estimation layer. According to the multi-dimensional analysis map, resource allocation analysis is performed on each tenant unit, a resource allocation strategy is formulated, and resource allocation processing is executed. The method achieves accurate resource allocation and improves resource utilization efficiency and system stability by dynamically monitoring and analyzing tenant activities; by combining historical and future data, the system's ability to cope with changes is improved, user experience is optimized, and the problem that resource allocation in a multi-tenant environment in the prior art is difficult to achieve a balance between cost and experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1It is a schematic diagram of the steps of a multi-tenant environment intelligent management method provided by an embodiment of the present invention; Figure 2 It is a structural diagram of a multi-tenant environment intelligent management device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0011] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0012] The implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0013] Reference Figure 1 , Figure 2 As shown, a preferred embodiment of the present invention is provided.

[0014] In a first aspect, the present invention provides a multi-tenant environment intelligent management method, comprising: S1: Obtain resource information and tenant information of a multi-tenant environment, and construct a corresponding environment simulation map according to the resource information and tenant information of the multi-tenant environment; wherein the tenant information is used to describe the account information of the tenant unit that registers an account in the multi-tenant environment; S2: Acquire the real-time activity information of each tenant unit, and substitute the real-time activity information of each tenant unit into the environmental simulation map, so that the environmental simulation map performs data analysis and processing in multiple dimensions according to the real-time activity information of each tenant unit to obtain a multi-dimensional analysis map; wherein the multi-dimensional analysis map includes a real-time analysis layer, a historical reference layer, and a future estimation layer; S3: Analyze and process resource allocation for each tenant unit according to the real-time analysis layer, historical reference layer, and future estimation layer of the multi-dimensional analysis map to obtain a resource allocation strategy for the multi-tenant environment, and perform resource allocation processing on the multi-tenant environment according to the resource allocation strategy Specifically, in step S1 of the embodiment provided by the present invention, resource information of the current multi-tenant environment is obtained through API calls, system monitoring tools or directly from the underlying virtualization / container management platform. This information includes CPU, memory, storage, network bandwidth, etc., and the current status of resources is collected, such as resource utilization, number of available resources, historical records of resource allocation, etc. Resource information is the basic data of the environment simulation map. Accurately obtaining the resource status is crucial for subsequent resource allocation and optimization. Understanding the current resource availability and load conditions can provide a basis for subsequent resource allocation decisions, avoid resource bottlenecks or over-allocation, and provide real-time resource utilization, so that the system can have a comprehensive understanding of resource usage. Accurate resource data can improve the accuracy of the environment simulation map and provide a reliable basis for subsequent analysis and decision-making.

[0015] More specifically, tenant information is extracted from the user management system or tenant registration system, including the tenant's ID, account information, payment level, historical resource usage, etc., and the tenant's business nature, priority and resource demand type, such as compute-intensive, storage-intensive or network bandwidth-sensitive, are collected. Different tenants have different needs and priorities. Accurately collecting this information can help the system better allocate resources to meet the personalized needs of tenants. Tenant information is key data for building an environmental simulation map, which helps to simulate the resource usage patterns and load conditions of different tenants, and provide detailed information for each tenant so that the system can optimize resource allocation strategies in a targeted manner. By analyzing the tenant's information, potential resource demand conflicts can be discovered and measures can be taken in advance.

[0016] More specifically, based on resource information and tenant information, an environmental simulation graph is constructed using a graph database or other data structures to simulate the interaction between tenants and resources. In the graph, nodes can represent tenants and resources, and edges represent the usage relationship between tenants and resources, including usage frequency, resource occupancy rate, etc. The graph should also reflect resource usage and tenant activity patterns in different time periods to form a dynamic environmental simulation. The environmental simulation graph can intuitively display the interaction between resources and tenants in a multi-tenant environment, which is helpful for complex data analysis and resource allocation optimization. Through the simulation graph, potential problems in resource allocation can be foreseen, and resource strategies can be adjusted in time according to changes in tenant demand. Through visualization and simulation graphs, the system can better understand resource usage patterns in a multi-tenant environment and improve the accuracy and response speed of decision-making. Dynamic graphs facilitate real-time monitoring and analysis, allowing the system to make quick adjustments when problems occur to reduce the impact on users.

[0017] Specifically, in step S2 of the embodiment provided by the present invention, the real-time activity information of each tenant is obtained through a real-time monitoring system or a log collection tool. Including but not limited to the resource consumption of the tenant (such as CPU usage, memory usage, network traffic, etc.), request response time, task execution, etc., the tenant activity data is continuously collected using data collection tools (such as Prometheus, ELK Stack, etc.), and stored in a data warehouse or stream processing system for further analysis and processing. The real-time activity information is the key data reflecting the current tenant behavior, which can reveal the dynamic changes of system load and resource usage. Through continuous monitoring, abnormal situations in tenant behavior can be discovered in time, and measures can be taken quickly to avoid system performance degradation or resource exhaustion. Real-time monitoring and data collection can provide accurate tenant activity information, so that the system has a clearer understanding of the current resource allocation situation, improve the system's responsiveness, and can adjust the resource allocation strategy in time to adapt to the dynamic needs of tenants.

[0018] More specifically, the collected real-time activity information of tenants is updated to the previously constructed environmental simulation map, so that the tenant nodes and resource nodes in the map reflect the current system status in real time, ensuring that the map can be updated in real time and present the interaction between tenants and resources, such as resource utilization rate, tenant activity peak period, etc. The environmental simulation map needs to reflect the actual behavior and resource usage of tenants in a timely manner in order to accurately carry out subsequent data analysis and decision support. By substituting real-time data into the map, resource allocation can be dynamically adjusted to ensure maximum efficiency of resource utilization while avoiding resource conflicts. The real-time update of the map enables the system to dynamically track changes in tenant behavior, improving the flexibility and adaptability of the system. By substituting real-time data, the map can more accurately reflect the current system status and help make more accurate resource allocation decisions.

[0019] More specifically, based on real-time activity information, data analysis tools are used to perform multi-dimensional data processing and analysis on the environmental simulation map to generate a multi-dimensional analysis map. The analysis dimensions include but are not limited to tenants' real-time activities (real-time analysis layer), past behavior patterns (historical reference layer), and future resource demand forecasts (future estimation layer); real-time analysis layer: analyzes the resource consumption and activity patterns of current tenants, identifies potential resource hotspots or bottlenecks, historical reference layer: analyzes tenant usage patterns based on historical data, evaluates the effectiveness of historical resource allocation strategies, and looks for optimization space, future estimation layer: uses machine learning algorithms or prediction models to predict future resource demands based on historical and real-time data, and formulates response strategies in advance.

[0020] It is understandable that multi-dimensional analysis can provide a comprehensive perspective, help understand the complexity of tenant behavior, and formulate more precise resource allocation strategies. The real-time analysis layer ensures that the system can quickly respond to current tenant needs. The historical reference layer provides a basis for improving past strategies, and the future estimation layer helps the system to plan resource allocation in advance and avoid possible resource bottlenecks in the future. It provides a more comprehensive system status analysis, enabling the system to optimize resource allocation strategies in multiple dimensions to ensure maximum resource utilization efficiency. Through historical data analysis and future predictions, the system can make more forward-looking decisions while improving current responsiveness to ensure long-term system stability and performance optimization.

[0021] Specifically, in step S3 of the embodiment provided by the present invention, the current resource usage of each tenant is obtained from the real-time analysis layer, including key indicators such as resource utilization, number of requests, response time, etc., the current resource hotspot areas (such as CPU-intensive tasks or network traffic peaks) are identified, and the impact of these hotspots on the overall resource allocation is analyzed. In combination with real-time data, the resource bottlenecks that need to be prioritized are determined, and the urgency and importance of resource requirements are evaluated. The real-time analysis layer provides instant feedback on the current system status, enabling the system to quickly identify and respond to changes in resource requirements. By analyzing hotspots and bottlenecks, the system can adjust resource allocation in a timely manner to avoid resource conflicts and performance degradation, thereby improving the flexibility of resource allocation, being able to quickly respond to tenants' sudden demands, reducing system delays and resource waste, and optimizing the current resource utilization efficiency.

[0022] More specifically, analyze the past resource usage patterns of each tenant from the historical reference layer, identify long-term resource consumption trends and usage patterns, compare the effects of historical allocation strategies, find out which strategies have been successful or failed in the past, and adjust the current allocation strategy based on these lessons learned, determine the priority of tenants, and adjust the weight and priority of resource allocation according to their historical performance and business importance. Historical data can provide valuable references to help understand the long-term needs and behavior patterns of tenants, and provide a basis for formulating more robust resource allocation strategies. By drawing on past successful experiences, the system can avoid repeating past mistakes, improve the effectiveness of resource allocation strategies, and improve the robustness of resource allocation strategies, ensuring that resource allocation not only meets current needs, but also adapts to possible changes in the future, optimizes the fairness and efficiency of resource allocation, and ensures that key tenants and tasks are given priority.

[0023] More specifically, the forecast of tenants' future resource needs is obtained from the future estimation layer, including short-term and long-term resource usage trends. Based on the forecast results, resource allocation is planned in advance to ensure appropriate resource reservation and expansion before the peak of resource demand arrives. Different resource allocation scenarios are designed and simulated, the feasibility and risks of each scenario are analyzed, and the optimal resource allocation strategy is selected. By predicting future resource needs, the system can proactively plan resources to avoid resource shortages or over-allocation. Designing and testing different allocation schemes in advance can reduce uncertainty in resource allocation, improve the predictability of allocation strategies, and improve the foresight and strategic nature of resource allocation, ensuring that the system can respond calmly to future challenges, reduce resource waste, and avoid service interruptions or performance degradation caused by insufficient resources.

[0024] More specifically, data from the real-time analysis layer, historical reference layer, and future estimation layer are integrated to formulate a comprehensive resource allocation strategy, covering the immediate allocation, scheduling, and future planning of resources. The resource allocation strategy is matched with current tenant needs and system resource conditions to ensure the feasibility and effectiveness of the allocation strategy. The priority and weight of resource allocation are set according to the strategy, and plans are prepared to deal with sudden demands. Comprehensive analysis of data from each layer can provide a comprehensive and balanced resource allocation plan, ensure the rationality and efficiency of resource allocation, and ensure that the resource allocation strategy matches the actual situation, which can reduce friction and errors in execution and improve overall resource utilization. It provides a flexible, dynamic, and easy-to-adjust resource allocation framework that can adapt to changing tenant needs and system loads. Through the setting of plans, the system can respond to emergencies more robustly and reduce risks in resource allocation.

[0025] More specifically, according to the formulated resource allocation strategy, adjust the resource quota of each tenant, including CPU, memory, storage and network resources, etc., use automated tools or scripts to dynamically adjust resource allocation, and monitor resource usage in real time to ensure the accuracy of resource allocation, continuously evaluate the effect of resource allocation, and optimize and adjust the allocation strategy according to actual performance. Resource allocation strategies need to be implemented through actual operations. Only after execution can their effectiveness be verified and tenant needs can be met. Through real-time monitoring and adjustment, continuous optimization of resource allocation can be ensured, resource waste or shortage can be avoided, and the immediacy and accuracy of system resource allocation can be ensured, which improves the overall performance and stability of the system. Through continuous optimization and adjustment, the system can better adapt to dynamically changing tenant needs and environment.

[0026] The present invention provides a multi-tenant environment intelligent management method, which has the following beneficial effects: The present invention acquires resource information and tenant information in a multi-tenant environment, constructs an environmental simulation map, acquires real-time activity information of each tenant unit, and substitutes it into the environmental simulation map to perform multi-dimensional data analysis, thereby generating a multi-dimensional analysis map including a real-time analysis layer, a historical reference layer, and a future estimation layer. According to the multi-dimensional analysis map, resource allocation analysis is performed on each tenant unit, a resource allocation strategy is formulated, and resource allocation processing is executed. The method achieves accurate resource allocation and improves resource utilization efficiency and system stability by dynamically monitoring and analyzing tenant activities; by combining historical and future data, the system's ability to cope with changes is improved, user experience is optimized, and the problem that resource allocation in a multi-tenant environment in the prior art is difficult to achieve a balance between cost and experience.

[0027] Preferably, the steps of obtaining resource information and tenant information of a multi-tenant environment, and constructing a corresponding environment simulation map according to the resource information and tenant information of the multi-tenant environment include: S11: Acquire resource information of the multi-tenant environment, and construct a basic environment map according to the resource information of the multi-tenant environment; wherein the basic environment map is used to perform digital simulation feedback on the overall callable resources of the multi-tenant environment; S12: Obtain tenant information of the multi-tenant environment, and construct an analysis layer, a waiting layer, and a plurality of tenant nodes on the basic environment map according to the tenant information of the multi-tenant environment; wherein the analysis layer is arranged around the basic environment map, the waiting layer is arranged around the analysis layer, a plurality of tenant nodes respectively correspond to a plurality of tenant units, the tenant nodes are used to perform digital simulation feedback on the tenant units, when the tenant units are in a non-logged-in state, the tenant nodes corresponding to the tenant units are arranged in the waiting layer, and when the tenant units are in a logged-in state, the tenant nodes corresponding to the tenant units are arranged in the analysis layer; S13: dividing the basic environment map into resource allocation grids according to the number of the tenant nodes, so as to divide the basic environment map into a plurality of resource scheduling grids; S14: The basic environment map divided into a number of resource scheduling grids, the analysis layer, the waiting layer, and each tenant node are collectively used as the environment simulation map.

[0028] Specifically, available resource information in a multi-tenant environment is collected through a resource management system or monitoring tool, including but not limited to computing resources (CPU, memory), storage resources, network resources, etc. A basic environment map is constructed based on the collected resource information. The map uses digital simulation technology to reflect the distribution and callability of various resources in the current system, organizes resource nodes according to resource type and location, and visualizes the resource availability to facilitate subsequent resource scheduling and allocation.

[0029] It can be understood that the basic environment map provides an intuitive resource distribution model for the entire multi-tenant system, which can clearly reflect the status and distribution of various resources in the system, help system administrators to fully grasp the resource situation, provide data support for subsequent resource scheduling and optimization, improve the transparency of resource utilization, and reduce the possibility of resource waste and conflict.

[0030] More specifically, the information of each tenant is obtained from the management system of the multi-tenant environment, including the tenant's resource requirements, login status, usage behavior, etc., and a multi-layer structure is constructed on the basis of the basic environment map, including an analysis layer and a waiting layer, and tenant nodes are set on these layers according to the tenant information. The analysis layer is set around the basic environment map and is used to process and analyze resource requests of tenants in the logged-in state. The waiting layer is set around the analysis layer to accommodate tenants who are not currently logged in or whose resource requirements have not been met. According to the login status of the tenant, the tenant node is dynamically placed in the analysis layer or the waiting layer. The tenant node in the logged-in state is placed in the analysis layer, and the tenant node in the non-logged-in state is placed in the waiting layer.

[0031] It can be understood that through the design of a multi-layer structure, the system can clearly distinguish tenants in different states, and adjust the resource allocation strategy according to the tenant status. Dynamically adjusting the position of tenant nodes improves the system's response speed, ensuring that tenants in the logged-in state have priority in obtaining resources, and improving the user experience. The multi-layer structure makes system resource management more flexible and can effectively respond to complex resource scheduling requirements in a multi-tenant environment.

[0032] More specifically, the basic environment map is divided into resource allocation grids according to the number and distribution of tenant nodes. The basic environment map is divided into several resource scheduling grids to enable refined resource allocation for different tenants. Each resource scheduling grid corresponds to a certain number of tenant nodes to ensure that resource allocation can meet the needs of tenants while avoiding excessive concentration or waste of resources. Grids are divided using automated algorithms or rule engines, and grid boundaries are dynamically adjusted based on real-time tenant activities and resource usage.

[0033] It can be understood that grid processing improves the accuracy and efficiency of resource allocation, ensuring that resources can be allocated to each tenant fairly and efficiently. Dynamic adjustment of grid boundaries enables the system to respond to changes in resource demand in a timely manner and optimize the overall utilization of resources. Through the resource scheduling grid, the system can better achieve load balancing and avoid performance bottlenecks caused by uneven resource allocation.

[0034] More specifically, the basic environment map, analysis layer, waiting layer and various tenant nodes divided into several resource scheduling grids will be integrated to build a complete environment simulation map. The environment simulation map reflects the system's resource status and tenant activities in real time, and serves as the core basis for system resource scheduling and allocation. The environment simulation map is continuously updated and optimized to ensure that it can accurately reflect the dynamic changes of the system and provide timely support for resource scheduling.

[0035] It can be understood that the complete environmental simulation map provides a comprehensive and dynamic resource and tenant relationship model for a multi-tenant environment, supporting the system to perform more precise resource management and scheduling. The construction and update of the environmental simulation map enables the system to adapt to changes in tenant needs in real time, improving the flexibility and adaptability of the system. Through the environmental simulation map, the system can effectively predict and plan resources and reduce the risk of resource waste and performance degradation.

[0036] Preferably, the steps of obtaining the real-time activity information of each tenant unit, substituting the real-time activity information of each tenant unit into the environment simulation map, and making the environment simulation map perform multi-dimensional data analysis and processing according to the real-time activity information of each tenant unit to obtain a multi-dimensional analysis map include: S21: Acquire real-time activity information of each of the tenant units; wherein the real-time activity information includes account login status and account behavior characteristics, the account login status is used to describe whether the account of the tenant unit is in a logged-in state or a non-logged-in state, and the account behavior characteristics are used to describe the behavior form of data processing performed by the account of the tenant unit in the logged-in state; S22: according to the account login status in the real-time activity information of the tenant unit, driving the tenant node corresponding to the tenant unit to set the position; when the account login status of the tenant unit is a non-login state, driving the position of the tenant node corresponding to the tenant unit to be set in the waiting layer; when the account login status of the tenant unit is a login state, driving the position of the tenant node corresponding to the tenant unit to be set in the analysis layer; S23: According to the account behavior characteristics corresponding to the tenant node in the analysis layer, drive the corresponding tenant node to perform resource mapping processing on the basic environment map to obtain the ideal resource allocation characteristics of the tenant node, and integrate the ideal resource allocation characteristics of each tenant node in the analysis layer to obtain a real-time analysis layer; wherein the ideal resource allocation characteristics are used to describe the number of resource scheduling grids required to realize the account behavior characteristics; S24: Retrieving the account behavior features of the tenant node in the analysis layer in the historical records, comparing the account behavior features of the tenant node in the historical records with the account behavior features of the tenant node in the real-time activity information to obtain historical reference features of the account behavior features in the real-time activity information of the tenant node, and using the historical reference features of each of the tenant nodes together as a historical reference layer; S25: Schedule the account behavior characteristics of the tenant node in the waiting layer in the historical records, and perform estimated processing on the account behavior characteristics in the future based on the account behavior characteristics of the tenant node in the historical records to obtain the future estimated characteristics of the tenant node, and use the future estimated characteristics of each tenant node together as the future estimation layer.

[0037] Specifically, use a real-time monitoring system or log analysis tool to continuously obtain real-time activity information of each tenant unit. This information includes the account login status (describing whether the tenant unit's account is online) and account behavior characteristics (describing the operational behavior of the online account, such as data processing, resource requests, etc.). Real-time activity information is collected and transmitted through APIs or data streams to ensure that it can accurately reflect the current tenant unit's operational status.

[0038] It can be understood that real-time activity information provides the system with dynamic user operation feedback, which can promptly reflect the behavioral changes of tenant units. By capturing real-time activity information, the system can adjust resource allocation at any time and optimize system performance.

[0039] More specifically, the current status of the tenant unit is determined based on the account login status information obtained in real time. When the account is not logged in, the corresponding tenant node is moved to the waiting layer, indicating that the tenant does not need resource scheduling for the time being. When the account is logged in, the corresponding tenant node is moved to the analysis layer, indicating that the tenant currently needs system resources for data processing or other operations.

[0040] It can be understood that dynamically adjusting the positions of tenant nodes ensures that system resources are allocated preferentially to active tenants, thereby improving resource utilization efficiency. This position adjustment mechanism can reduce resource waste and give priority to meeting the needs of important users when resources are tight.

[0041] More specifically, the behavior characteristics of the tenant nodes in the analysis layer are analyzed in real time. Based on these behavior characteristics, the ideal resource allocation characteristics of the tenant nodes are calculated. Through resource mapping, these ideal resource allocation characteristics are converted into specific resource scheduling grids in the basic environment map to ensure that the behavior of the tenant units can be best supported. The ideal resource allocation characteristics of all tenant nodes in the analysis layer are integrated to form a real-time analysis layer, which reflects the current tenant demand for system resources in real time.

[0042] It can be understood that resource mapping processing ensures the accuracy and real-time nature of resource allocation, enabling the system to quickly respond to changes in tenants' resource requirements. The real-time analysis layer provides an overall view of resource requirements to help system administrators perform reasonable resource scheduling and optimization.

[0043] More specifically, the historical account behavior characteristics of the tenant nodes in the analysis layer are retrieved from the database and compared with the behavior characteristics in the real-time activity information. The differences between the real-time behavior and the historical behavior are analyzed, and the trends or anomalies of the tenant behavior are identified. These comparison results are summarized to form a historical reference layer, which serves as an important basis for tenant behavior prediction and resource allocation optimization in the analysis system.

[0044] It is understandable that the historical reference layer provides the system with historical data support for behavioral patterns, allowing the system to better understand the regularity and anomalies of tenant behavior. By comparing real-time and historical behaviors, the system can identify potential problems in a timely manner and make corresponding adjustments.

[0045] More specifically, for tenant nodes in the waiting layer, historical behavior characteristics and statistical models are used to predict their possible future behavior characteristics. Based on the prediction results, resource scheduling planning is carried out in advance to ensure a quick response when the tenant account logs in again or there is a resource demand. These future estimated characteristics are integrated to form a future estimation layer to provide data support for the system's future resource scheduling.

[0046] It is understandable that the future prediction layer makes the system forward-looking, and can plan ahead in resource scheduling and estimate future resource requirements in advance, which helps to improve the system's response speed and service quality and avoid resource competition and system congestion.

[0047] Preferably, the step of comparing the account behavior features of the tenant node in the historical records with the account behavior features of the tenant node in the real-time activity information to obtain the historical reference features of the account behavior features in the real-time activity information of the tenant node includes: S241: Compare the account behavior characteristics of the tenant node in the historical records with the account behavior characteristics of the tenant node in the real-time activity information to obtain the historical average duration and historical average change trend of the account behavior characteristics of the tenant node in the real-time activity information; wherein the historical average duration is used to describe the average duration of the account behavior characteristics in the historical records, and the historical average change trend is used to describe the average trend of the account behavior characteristics corresponding to the real-time activity information being transformed into other behavior forms in the historical records; S242: The historical average duration and the historical average change trend of the account behavior feature are used as historical reference features of the account behavior feature.

[0048] Specifically, the historical records of the tenant node are retrieved from the database to obtain the account behavior characteristics recorded therein. These characteristics may include data such as the user's operation type, frequency, and operation duration. These historical records are sorted in chronological order to ensure that the behavior evolution process of each tenant node can be clearly reflected.

[0049] It can be understood that by obtaining historical behavior characteristics, the system can provide a reference benchmark for the analysis of current behavior, which helps to identify the operation rules and behavior patterns of tenant nodes in different time periods.

[0050] More specifically, the account behavior characteristics in the current real-time activity information are compared with the account behavior characteristics in the historical records of the corresponding tenant nodes. The comparison focuses on the type, duration and change trend of the behavior. Using data analysis tools or algorithms, the average duration and average change trend of the behavior characteristics of each tenant node in the historical records are calculated, including the average time from the start to the end of the behavior characteristics, as well as the frequency and method of the transformation of the behavior characteristics into other behaviors.

[0051] It is understandable that the comparison process can reveal the similarities and differences between real-time behavior and historical behavior, and provide data support for further behavior prediction and resource scheduling. Through comparison, the system can identify abnormal behavior or potential system problems, such as the duration of a certain behavior feature being abnormally long or short.

[0052] More specifically, for each account behavior feature, calculate its average duration in the historical records. This value represents the average time required for the behavior feature to go from start to end in history. This calculation can be done using a simple mean calculation or a weighted average method, depending on the distribution and importance of the behavior feature.

[0053] It can be understood that the historical average duration provides a reference standard for the duration of real-time behavior characteristics, which helps the system identify abnormal behaviors or trends. This calculation also provides a time basis for the system's resource scheduling, and more accurate resource allocation can be made based on the duration of the behavior characteristics.

[0054] More specifically, we analyze the changes in the behavioral characteristics in the historical records and calculate the average frequency and mode of transformation of each behavioral characteristic into other behavioral characteristics. This step focuses on the transformation mode and frequency of the behavioral characteristics. This calculation can be based on time series analysis or other statistical methods, with the focus on identifying the transformation rules of the behavioral characteristics.

[0055] It is understandable that the historical average change trend provides a predictive basis for the change of real-time behavioral characteristics, helping the system predict the next behavioral characteristics. The system can prepare resources in advance based on this trend to ensure timely response when the behavioral characteristics change.

[0056] More specifically, the calculated historical average duration and historical average change trend are integrated into a set of historical reference features. These reference features provide a historical basis for the real-time behavior features of each tenant node. During the real-time analysis process, the system combines these historical reference features with the real-time behavior features for comprehensive analysis.

[0057] It is understandable that by using the historical average duration and change trend as historical reference features, the system can better understand and predict real-time behavior characteristics. These reference features provide a solid data foundation for the system in terms of resource scheduling, behavior monitoring, and anomaly detection, thereby improving the system's stability and response speed.

[0058] Preferably, the step of performing an estimation process on the account behavior characteristics in the future time according to the account behavior characteristics of the tenant node in the historical records to obtain the future estimated characteristics of the tenant node includes: S251: Analyze and process the time characteristics of the account behavior characteristics of various behavior forms of the tenant node in the historical records respectively, so as to obtain the behavior distribution characteristics of the tenant node; wherein the behavior distribution characteristics are used to describe the occurrence probability of the account behavior characteristics of various behavior forms of the tenant node under various time situations; S252: collecting time conditions of the multi-tenant environment to obtain time condition information of the multi-tenant environment; wherein the time condition information includes time period information and date information; S253: Analyze and process the occurrence probability of the behavior distribution characteristics of each of the tenant nodes according to the time situation information to obtain the future estimated characteristics of each of the tenant nodes; wherein the future estimated characteristics are used to describe the estimated probability of the tenant node performing various forms of account behavior characteristics in a future time period.

[0059] Specifically, extract various account behavior features of tenant nodes in historical records. Behavior forms may include login, data operation, resource request, etc. Analyze each behavior form and identify its time features. This includes the specific time period when the behavior occurs (such as morning, afternoon, night) and the date when the behavior occurs (weekdays, weekends, holidays, etc.). Using these time features, establish the association between behavior form and time, and obtain the behavior distribution features of tenant nodes. This distribution feature will describe the probability of occurrence of each behavior form under different time situations.

[0060] It is understandable that the behavior distribution characteristics provide basic data for future behavior prediction, helping the system understand the regularity of a certain form of behavior in different time situations. By analyzing the time characteristics of historical behavior, the system can establish a more accurate and targeted behavior prediction model.

[0061] More specifically, the time situation information in the current multi-tenant environment is collected, including time period information (such as morning, afternoon, night, etc.) and date information (such as Monday, weekends, holidays, etc.). The time situation information can be automatically obtained through the system clock or scheduler, and updated regularly according to the tenant's behavior pattern.

[0062] It can be understood that by collecting time situation information, the system can accurately analyze the characteristics of each time period. The time situation provides real-time data for further analysis and prediction of behavior distribution, ensuring that the prediction model can be updated in real time and consistent with the current time.

[0063] More specifically, based on the collected time situation information, the behavior distribution characteristics of each tenant node are further analyzed. The core of the analysis is to calculate the probability of occurrence of various behavior forms of the tenant node under the current time situation, and use probability analysis or machine learning algorithms (such as naive Bayes model or time series prediction) to calculate the future probability of occurrence of each behavior form based on time period and date information.

[0064] It is understandable that the occurrence probability analysis helps the system understand the possibility of different behavioral forms of tenant nodes in the current time context. This analysis not only improves the accuracy of the prediction, but also can identify potential high-frequency operation behaviors in advance and optimize the system's resource allocation.

[0065] More specifically, by combining the time situation information and the historical behavior distribution characteristics of the tenant nodes, the estimated probability of each tenant node performing various behaviors in the future time period is calculated, and these estimated probabilities are integrated into the future estimated features of the tenant nodes. The future estimated features can include the probability of a certain behavior occurring in a specific time period in the future and the possible behavior types.

[0066] It can be understood that the future estimation features provide forward-looking data support for the system, enabling the system to prepare the required resources in advance and optimize the response speed. By predicting the future behavior characteristics of tenant nodes, the system can avoid resource contention and delays, and improve service quality and user experience.

[0067] Preferably, the step of analyzing and processing resource allocation for each tenant unit according to the real-time analysis layer, the historical reference layer, and the future estimation layer of the multi-dimensional analysis map to obtain a resource allocation strategy for the multi-tenant environment includes: S31: performing ideal allocation processing according to the ideal resource allocation characteristics of each tenant node of the real-time analysis layer to obtain a first resource requirement quota corresponding to an ideal allocation processing mode, and substituting the first resource requirement quota into the basic environment map; S32: According to the historical average duration and historical average change trend of the account behavior characteristics of each tenant node in the historical reference layer, the basic environment map into which the first resource demand quota is substituted is subjected to a resource demand quota change analysis process to obtain a plurality of second resource demand quotas and quota weights corresponding to the various second resource demand quotas; wherein the quota weight is used to describe the possibility degree of the second resource demand quota; S33: According to the future prediction characteristics of each of the tenant nodes in the future prediction layer, analyzing and processing the theoretical reservation amount on the basic environment map to obtain a third resource requirement amount corresponding to the future prediction layer; wherein the third resource requirement amount is used to describe the resource amount that needs to be reserved in a future time period; S34: performing superposition calculation processing on the third resource demand quota according to various second resource demand quotas and quota weights corresponding to various second resource demand quotas, respectively, to obtain resource prediction distribution characteristics; wherein the resource prediction distribution characteristics include fourth resource demand quotas corresponding to various second resource demand quotas and quota weights corresponding to various fourth resource demand quotas; S35: Substituting the resource prediction distribution feature into the basic environment map, and making the basic environment map perform adaptability evaluation processing according to the resource prediction distribution feature, so as to obtain adaptability features of the resource prediction distribution feature corresponding to the basic environment map; wherein the adaptability features are used to describe the applicability of the resource prediction distribution feature in the basic environment map; S36: judging the adaptability feature according to a preset standard, and if the judgment result is in compliance, taking the first resource demand quota corresponding to the resource prediction distribution feature as a resource allocation strategy; S37: If the judgment result is non-compliance, the first resource demand amount corresponding to the resource prediction distribution characteristics will be reduced, and the corresponding adaptability characteristics of the first resource demand amount after the reduction will be calculated according to the above steps for evaluation, and this cycle will be repeated until the first resource demand amount whose adaptability characteristics meet the preset standards is obtained, and the first resource demand amount will be used as a resource allocation strategy.

[0068] Specifically, based on the data from the real-time analysis layer, the ideal resource allocation characteristics of each tenant node are calculated. The ideal resource allocation characteristics are calculated and allocated in the form of a resource scheduling grid in the basic environment map. The content may include the ideal configuration of resources such as CPU, memory, and network bandwidth. Based on these ideal configurations, the first resource demand amount of each tenant node is calculated, and the first resource demand amount is substituted into the basic environment map to view the total resource demand of the current system.

[0069] It can be understood that the first resource demand quota calculated in real time can provide a baseline for the current resource allocation that is closest to the actual demand. This method can timely reflect the resource demand of the current tenant node and effectively avoid over-allocation or under-allocation of resources.

[0070] More specifically, the data from the historical reference layer is used to analyze the account behavior characteristics of each tenant node (such as the historical average duration and the historical average change trend). In the basic environment map, the changes in the first resource demand quota are considered, and several second resource demand quotas are calculated. These second resource demand quotas are analyzed to determine the possibility of their occurrence (i.e., quota weight).

[0071] It is understandable that by analyzing historical data, the system can predict fluctuations in resource demand and provide multiple alternatives for possible resource changes. The quota weight can help the system identify which resource demand scenario is most likely to occur, thereby formulating a more flexible resource allocation strategy.

[0072] More specifically, based on the data of the future estimation layer, the possible future resource requirements of the tenant nodes are predicted. In the basic environment map, the third resource requirement amount is calculated according to the prediction results, which indicates the amount of resources that need to be reserved for the future time period.

[0073] It is understandable that future estimates can provide forward-looking suggestions for resource allocation, ensuring that the system can operate stably under high load or sudden demand in the future. The third resource demand quota adds fault tolerance and reserved space to the resource allocation of the system, improving the robustness of overall resource management.

[0074] More specifically, the second resource demand quota and its corresponding quota weight are superimposed on the third resource demand quota to generate a resource prediction distribution feature, which includes a fourth resource demand quota and its corresponding quota weight, which represents the comprehensive resource demand under multiple possibilities.

[0075] It can be understood that through superimposed calculations, the system can comprehensively consider historical, real-time and future resource demands to generate a more accurate resource prediction distribution. The resource prediction distribution characteristics can reflect the overall distribution of resource demands and provide a comprehensive reference basis for subsequent resource allocation strategies.

[0076] More specifically, the resource prediction distribution characteristics are substituted into the basic environment map to perform adaptability evaluation. The focus of adaptability evaluation is to determine the matching degree between the current resource allocation plan and the actual system environment. If the adaptability characteristics meet the preset standards, the current first resource demand amount is used as the final resource allocation strategy. If not, the first resource demand amount is gradually reduced and the adaptability evaluation is repeated until a resource allocation plan that meets the standards is found.

[0077] It can be understood that adaptive evaluation ensures that the resource allocation scheme is feasible and effective in the actual environment, avoiding performance problems caused by unreasonable resource allocation. Through cyclic evaluation and adjustment, the system can find the best balance between resource demand and system carrying capacity, and optimize resource utilization and service quality.

[0078] Reference Figure 2 As shown, in a second aspect, the present invention provides a multi-tenant environment intelligent management device, which is used to implement a multi-tenant environment intelligent management method as described in any one of the first aspects, including: An environment simulation module, used to obtain resource information and tenant information of a multi-tenant environment, and to construct a corresponding environment simulation map according to the resource information and tenant information of the multi-tenant environment; wherein the tenant information is used to describe the account information of the tenant unit that registers an account in the multi-tenant environment; A data analysis module, used to obtain the real-time activity information of each tenant unit, and substitute the real-time activity information of each tenant unit into the environmental simulation map, so that the environmental simulation map performs data analysis processing in multiple dimensions according to the real-time activity information of each tenant unit to obtain a multi-dimensional analysis map; wherein the multi-dimensional analysis map includes a real-time analysis layer, a historical reference layer, and a future estimation layer; The countermeasure analysis module is used to analyze and process the resource allocation of each tenant unit according to the real-time analysis layer, historical reference layer and future estimation layer of the multi-dimensional analysis map, so as to obtain the resource allocation countermeasure of the multi-tenant environment, and perform resource allocation processing on the multi-tenant environment according to the resource allocation countermeasure.

[0079] In this embodiment, for the specific implementation of each module in the above-mentioned device embodiment, please refer to the above-mentioned method embodiment, which will not be described in detail here.

[0080] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, and when the processor executes the computer program, the method for intelligent management of a multi-tenant environment as described in any one of the first aspects is implemented.

[0081] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the processor executes a multi-tenant environment intelligent management method as described in any one of the first aspects.

[0082] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-tenant environment intelligent management method, characterized in that: include: Obtain resource information and tenant information of a multi-tenant environment, and construct a corresponding environment simulation map according to the resource information and tenant information of the multi-tenant environment; wherein the tenant information is used to describe the account information of the tenant unit that registers an account in the multi-tenant environment; Acquire the real-time activity information of each tenant unit, and substitute the real-time activity information of each tenant unit into the environmental simulation map, so that the environmental simulation map performs data analysis and processing in multiple dimensions according to the real-time activity information of each tenant unit to obtain a multi-dimensional analysis map; wherein the multi-dimensional analysis map includes a real-time analysis layer, a historical reference layer, and a future estimation layer; The resource allocation of each tenant unit is analyzed and processed according to the real-time analysis layer, historical reference layer and future estimation layer of the multi-dimensional analysis map to obtain a resource allocation strategy for the multi-tenant environment, and resource allocation processing is performed on the multi-tenant environment according to the resource allocation strategy.

2. The multi-tenant environment intelligent management method according to claim 1, characterized in that: The steps of obtaining resource information and tenant information of a multi-tenant environment, and constructing a corresponding environment simulation map according to the resource information and tenant information of the multi-tenant environment include: Obtain resource information of the multi-tenant environment, and construct a basic environment map based on the resource information of the multi-tenant environment; wherein the basic environment map is used to perform digital simulation feedback on the overall callable resources of the multi-tenant environment; Obtain tenant information of the multi-tenant environment, and construct an analysis layer, a waiting layer, and a plurality of tenant nodes on the basic environment map according to the tenant information of the multi-tenant environment; wherein the analysis layer is arranged around the basic environment map, the waiting layer is arranged around the analysis layer, a plurality of tenant nodes respectively correspond to a plurality of tenant units, the tenant nodes are used to perform digital simulation feedback on the tenant units, when the tenant units are in a non-logged-in state, the tenant nodes corresponding to the tenant units are arranged in the waiting layer, and when the tenant units are in a logged-in state, the tenant nodes corresponding to the tenant units are arranged in the analysis layer; The basic environment map is divided into resource allocation grids according to the number of the tenant nodes, so as to divide the basic environment map into a plurality of resource scheduling grids; The basic environment map divided into a number of resource scheduling grids, the analysis layer, the waiting layer, and each tenant node are collectively used as the environment simulation map.

3. The multi-tenant environment intelligent management method according to claim 2, characterized in that: The steps of obtaining the real-time activity information of each tenant unit, substituting the real-time activity information of each tenant unit into the environment simulation map, and making the environment simulation map perform multi-dimensional data analysis and processing according to the real-time activity information of each tenant unit to obtain a multi-dimensional analysis map include: Acquire real-time activity information of each of the tenant units; wherein the real-time activity information includes account login status and account behavior characteristics, the account login status is used to describe whether the account of the tenant unit is in a logged-in state or a non-logged-in state, and the account behavior characteristics are used to describe the behavior form of data processing performed by the account of the tenant unit in the logged-in state; According to the account login status in the real-time activity information of the tenant unit, the tenant node corresponding to the tenant unit is driven to set the position; when the account login status of the tenant unit is a non-login state, the position of the tenant node corresponding to the tenant unit is driven to be set in the waiting layer; when the account login status of the tenant unit is a login state, the position of the tenant node corresponding to the tenant unit is driven to be set in the analysis layer; According to the account behavior characteristics corresponding to the tenant node in the analysis layer, drive the corresponding tenant node to perform resource mapping processing on the basic environment map to obtain the ideal resource allocation characteristics of the tenant node, and integrate the ideal resource allocation characteristics of each tenant node in the analysis layer to obtain a real-time analysis layer; wherein the ideal resource allocation characteristics are used to describe the number of resource scheduling grids required to realize the account behavior characteristics; Retrieving the account behavior features of the tenant node in the analysis layer in the historical records, comparing the account behavior features of the tenant node in the historical records with the account behavior features of the tenant node in the real-time activity information to obtain the historical reference features of the account behavior features in the real-time activity information of the tenant node, and using the historical reference features of each of the tenant nodes together as the historical reference layer; Schedule the account behavior characteristics of the tenant node in the waiting layer in the historical records, and perform estimated processing on the account behavior characteristics in the future based on the account behavior characteristics of the tenant node in the historical records to obtain the future estimated characteristics of the tenant node, and use the future estimated characteristics of each tenant node together as the future estimation layer.

4. The multi-tenant environment intelligent management method according to claim 3, characterized in that: The step of comparing the account behavior features of the tenant node in the historical records with the account behavior features of the tenant node in the real-time activity information to obtain the historical reference features of the account behavior features in the real-time activity information of the tenant node includes: Compare the account behavior features of the tenant node in the historical records with the account behavior features of the tenant node in the real-time activity information to obtain the historical average duration and historical average change trend of the account behavior features of the tenant node in the real-time activity information; wherein the historical average duration is used to describe the average duration of the account behavior features in the historical records, and the historical average change trend is used to describe the average trend of the account behavior features corresponding to the real-time activity information being transformed into other behavior forms in the historical records; The historical average duration and the historical average change trend of the account behavior feature are taken together as the historical reference features of the account behavior feature.

5. The multi-tenant environment intelligent management method according to claim 3, characterized in that: The step of performing an estimation process on the account behavior characteristics in the future time according to the account behavior characteristics of the tenant node in the historical records to obtain the future estimated characteristics of the tenant node includes: Analyze and process the time characteristics of the account behavior characteristics of various behavior forms of the tenant node in the historical records respectively, so as to obtain the behavior distribution characteristics of the tenant node; wherein the behavior distribution characteristics are used to describe the occurrence probability of the account behavior characteristics of various behavior forms of the tenant node under various time situations; Collecting time information of the multi-tenant environment to obtain time information of the multi-tenant environment; wherein the time information includes time period information and date information; The behavior distribution characteristics of each of the tenant nodes are analyzed and processed for occurrence probability based on the time situation information to obtain future estimated characteristics of each of the tenant nodes; wherein the future estimated characteristics are used to describe the estimated probability of the tenant node performing various forms of account behavior characteristics in a future time period.

6. The multi-tenant environment intelligent management method according to claim 3, characterized in that: The steps of analyzing and processing resource allocation for each tenant unit according to the real-time analysis layer, the historical reference layer, and the future estimation layer of the multi-dimensional analysis map to obtain a resource allocation strategy for the multi-tenant environment include: Performing ideal allocation processing according to the ideal resource allocation characteristics of each tenant node of the real-time analysis layer to obtain a first resource requirement quota corresponding to an ideal allocation processing mode, and substituting the first resource requirement quota into the basic environment map; According to the historical average duration and the historical average change trend of the account behavior characteristics of each tenant node in the historical reference layer, the basic environment map into which the first resource demand quota is substituted is subjected to a resource demand quota change analysis process to obtain a plurality of second resource demand quotas and quota weights corresponding to the various second resource demand quotas; wherein the quota weight is used to describe the possibility degree of the second resource demand quota; According to the future estimated characteristics of each of the tenant nodes in the future estimated layer, the theoretical reservation amount is analyzed and processed on the basic environment map to obtain a third resource requirement amount corresponding to the future estimated layer; wherein the third resource requirement amount is used to describe the resource amount that needs to be reserved in a future time period; According to various second resource demand quotas and quota weights corresponding to various second resource demand quotas, respectively, superimposed calculation processing is performed with the third resource demand quota to obtain resource prediction distribution characteristics; wherein the resource prediction distribution characteristics include fourth resource demand quotas corresponding to various second resource demand quotas and quota weights corresponding to various fourth resource demand quotas; Substituting the resource prediction distribution characteristics into the basic environment map, and making the basic environment map perform adaptability evaluation processing according to the resource prediction distribution characteristics, so as to obtain the adaptability characteristics of the resource prediction distribution characteristics corresponding to the basic environment map; wherein the adaptability characteristics are used to describe the applicability of the resource prediction distribution characteristics in the basic environment map; The adaptability feature is judged and processed according to a preset standard, and if the judgment result is compliance, the first resource demand amount corresponding to the resource prediction distribution feature is used as a resource allocation strategy; If the judgment result is non-compliance, the first resource demand amount corresponding to the resource prediction distribution characteristics will be reduced, and the corresponding adaptability characteristics of the first resource demand amount after the reduction will be calculated according to the above steps for evaluation. This cycle will be repeated until the first resource demand amount whose adaptability characteristics meet the preset standards is obtained, and the first resource demand amount will be used as a resource allocation strategy.

7. A multi-tenant environment intelligent management device, characterized in that: A method for intelligent management of a multi-tenant environment for implementing any one of claims 1 to 6, comprising: An environment simulation module, used to obtain resource information and tenant information of a multi-tenant environment, and to construct a corresponding environment simulation map according to the resource information and tenant information of the multi-tenant environment; wherein the tenant information is used to describe the account information of the tenant unit that registers an account in the multi-tenant environment; A data analysis module, used to obtain the real-time activity information of each tenant unit, and substitute the real-time activity information of each tenant unit into the environmental simulation map, so that the environmental simulation map performs data analysis processing in multiple dimensions according to the real-time activity information of each tenant unit to obtain a multi-dimensional analysis map; wherein the multi-dimensional analysis map includes a real-time analysis layer, a historical reference layer, and a future estimation layer; The countermeasure analysis module is used to analyze and process the resource allocation of each tenant unit according to the real-time analysis layer, historical reference layer and future estimation layer of the multi-dimensional analysis map, so as to obtain the resource allocation countermeasure of the multi-tenant environment, and perform resource allocation processing on the multi-tenant environment according to the resource allocation countermeasure.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, wherein: When the processor executes the computer program, the method for intelligent management of a multi-tenant environment as described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the processor is enabled to execute a multi-tenant environment intelligent management method as described in any one of claims 1-6.

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