Intelligent resource optimization configuration method and system in cloud computing environment

Abstract

The invention discloses an intelligent resource optimization configuration method and system in a cloud computing environment. The method comprises the steps of collecting water conservancy station data in real time, uploading the data to a cloud end, performing dynamic labeling, and generating a resource label. Physical resources are virtualized into logic resources, and a resource partition mapping model is constructed at the cloud. And packaging the models, and establishing a collaborative optimization mechanism among the multi-region models. Based on cloud resource labels and virtualized partition information, in combination with real-time environment data, supply and demand matching is realized by adopting an optimization algorithm, and a resource allocation strategy is dynamically adjusted. According to the method, the refinement degree of resource management is effectively improved, and the flexibility and the real-time performance of resource allocation are improved. The flexibility and the coverage range of resource scheduling are improved, the cross-regional resource allocation requirement can be met, and therefore resource waste is remarkably reduced. Resource allocation is more intelligent, the scheduling cost caused by global resource imbalance is effectively reduced, and the resource utilization rate and allocation efficiency are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud computing resource optimization, and in particular to an intelligent resource optimization configuration method and system in a cloud computing environment. Background Art

[0002] With the rapid development of cloud computing technology, its application in the field of resource optimization and allocation is becoming more and more extensive, especially in multi-scenario dynamic resource scheduling and cross-regional collaborative optimization. Cloud computing can use virtualization technology to abstract physical resources into logical resources, and combine big data analysis with intelligent optimization algorithms to achieve efficient resource allocation. However, in practical applications, due to the dynamic nature of resource demand and the uncertainty of environmental factors, the existing system still faces many technical challenges in supply and demand balance, cross-regional allocation and global optimization. For example, the scheduling methods commonly used in current water resources management are usually based on static planning, which cannot quickly respond to sudden changes in supply and demand, resulting in resource waste or insufficient allocation. In addition, traditional resource scheduling is mostly based on a single region, lacks effective support for cross-regional collaborative optimization, and is prone to cause local resource overload or overall inefficiency.

[0003] Existing technologies have obvious deficiencies in the accuracy and real-time nature of resource scheduling. First, traditional methods have poor adaptability to dynamic environmental data, making it difficult to achieve multi-dimensional real-time analysis and rapid response. For example, in the face of a sudden increase in water demand, existing systems usually adopt fixed allocation rules, which are unable to adjust resource allocation strategies in real time according to environmental factors (such as rainfall, evaporation) and equipment status, thereby affecting allocation efficiency. Secondly, cross-regional resource collaborative optimization capabilities are limited, and most traditional models can only solve the supply and demand balance problem within a single region. They lack consideration of the optimization of resource flow paths and cross-regional allocation costs, resulting in low global resource utilization. In response to the above problems, there is an urgent need for an intelligent resource optimization and configuration method based on cloud computing, which can solve the imbalance between supply and demand in a dynamic environment and improve the accuracy and global efficiency of resource scheduling through technical means such as real-time data collection, virtualization modeling, and multi-region collaborative optimization. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: the problem of insufficient adaptability to dynamic environments: the present invention collects water conservancy station data in real time and generates resource tags, and makes dynamic adjustments based on environmental data (such as rainfall, evaporation, etc.), thereby significantly improving the adaptability of the resource allocation system to environmental changes.

[0006] Insufficient cross-regional resource coordination: By establishing a multi-regional collaborative optimization mechanism, the present invention achieves efficient allocation of cross-regional resource flows and avoids the problem of overload or insufficient allocation of resources in a single region.

[0007] Problems of insufficient optimization accuracy and efficiency: Through the improved linear programming algorithm and dynamic weight adjustment method, the present invention significantly improves the accuracy and efficiency of resource supply and demand matching, while reducing scheduling costs, and solves the technical problem that traditional methods have limited optimization capabilities for dynamic resource allocation.

[0008] To solve the above technical problems, the present invention provides the following technical solutions: an intelligent resource optimization configuration method in a cloud computing environment, comprising: real-time collection of water conservancy station data, uploading to the cloud, dynamic labeling, and generating resource tags.

[0009] Virtualize physical resources into logical resources and build a resource partition mapping model in the cloud.

[0010] Encapsulate the model and establish a collaborative optimization mechanism between multi-region models.

[0011] Based on cloud resource tags and virtualization partition information, combined with real-time environmental data, an optimization algorithm is used to achieve supply and demand matching and dynamically adjust resource allocation strategies.

[0012] As a preferred solution of the intelligent resource optimization configuration method under the cloud computing environment described in the present invention, the real-time collection of water conservancy station data includes installing sensor equipment in the water conservancy station, equipping it with a data gateway that supports the Internet of Things protocol, and realizing local sensor data collection.

[0013] Uploading to the cloud includes deploying a base data receiving interface on the cloud platform, using a distributed message queue to process sensor data streams, using cloud storage services to store raw data, and storing time series data through a database. Establish a data device type partitioning strategy to improve data storage and retrieval efficiency.

[0014] As a preferred solution of the intelligent resource optimization configuration method under the cloud computing environment described in the present invention, the dynamic labeling and resource label generation includes extracting the data features of the water conservancy station and dividing the data into different usage scenarios when deploying data in the cloud. According to the data features and event labeling results, resource label generation rules are designed, and the dynamically generated resource labels are stored in the label database.

[0015] As a preferred solution of the intelligent resource optimization configuration method in the cloud computing environment described in the present invention, wherein: the virtualization of physical resources into logical resources includes abstracting the physical resources into logical resource units according to their functional characteristics, and each logical resource unit contains virtual attributes, including capacity limit, service scope, and response time.

[0016] Building a resource partition mapping model in the cloud includes defining partition strategies and dividing resource partitions based on geographical locations and water supply requirements.

[0017] A mapping table is established in the cloud to bind logical resources to partitions, and logical resources are matched by the geographical location of the resources and the geographical boundaries of the partitions to ensure that each logical resource can be accessed.

[0018] As a preferred solution of the intelligent resource optimization configuration method in the cloud computing environment described in the present invention, wherein: the model is encapsulated and a collaborative optimization mechanism is established between multi-region models, including encapsulating the resource partition mapping model, determining the encapsulation target and interface definition, and constructing a model encapsulation container, which includes partition data processing logic, resource status update module and optimization algorithm engine.

[0019] Based on the global resource network, establish an interconnection mechanism between regions, define status synchronization rules, and define cross-regional collaboration trigger conditions.

[0020] As a preferred solution of the intelligent resource optimization configuration method under the cloud computing environment described in the present invention, the real-time environmental data includes real-time environmental data stored in the cloud, including rainfall P(t), evaporation E(t), water resource demand growth rate R(t), and the virtualized partition information is partition status data, including available water resource capacity C i (t) and the partition requirement D i (t).

[0021] Set the partition supply and demand balance objective function, expressed as:

[0022]

[0023] To perform dynamic resource scheduling between partitions, first calculate the partition scheduling weight γ based on the urgency of the partition demand i (t), expressed as:

[0024]

[0025] Partition scheduling weight γ i The larger (t) is, the more resources the partition requires.

[0026] Using the improved shortest path method, based on the edge weight w ij :

[0027]

[0028] Among them, δ ij represents the resource allocation cost from partition i to j, θ ij Indicates the path smoothness. ij The smaller the value, the better the path.

[0029] As a preferred solution of the intelligent resource optimization configuration method under the cloud computing environment described in the present invention, the dynamic adjustment of resource allocation strategy includes linear programming based on partition resource constraints and requirements, which is expressed as:

[0030]

[0031] Considering the impact of dynamic environment and coordinated optimization between partitions, an improved linear programming formula is constructed, using the exponential function e of the dynamic demand growth rate R(t) -R(t) Adjust the balance factor and introduce cross-partition scheduling items Optimize the deployment path through weights.

[0032] The precise degree of resource allocation optimization is expressed as:

[0033]

[0034] Among them, α represents the scheduling impact factor, balancing the optimization within and between partitions. t represents time, and t0 represents the initial time.

[0035] Ψ>0 indicates that there is an imbalance between resource supply and demand. The larger the value, the greater the imbalance.

[0036] Ψ=0 means that resource supply and demand are perfectly matched and no further adjustment is required.

[0037] An intelligent resource optimization configuration system in a cloud computing environment, characterized by: comprising:

[0038] The collection and labeling module collects water conservancy station data in real time, uploads it to the cloud, performs dynamic labeling, and generates resource tags.

[0039] The partition mapping module virtualizes physical resources into logical resources and builds a resource partition mapping model in the cloud.

[0040] The partitioning module encapsulates the model and establishes a collaborative optimization mechanism between multi-region models.

[0041] The optimization scheduling module uses optimization algorithms to achieve supply and demand matching and dynamically adjust resource allocation strategies based on cloud resource tags and virtualization partition information combined with real-time environmental data.

[0042] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0043] A computer-readable storage medium stores a computer program, which implements the steps of the method described above when executed by a processor.

[0044] The beneficial effects of the present invention are as follows: by collecting water conservancy station data in real time and generating resource tags, dynamic perception and scene classification of water resource status are realized, which effectively improves the refinement of resource management and provides a data basis for subsequent optimization. Through this step, changes in water resource supply and demand can be monitored in real time, providing an accurate basis for optimization decision-making, and ultimately improving the flexibility and real-time nature of resource allocation.

[0045] By virtualizing physical resources into logical resources and building a partition mapping model, flexible mapping of physical resources and virtualized resources is achieved, ensuring the accuracy of resource allocation. This step improves the flexibility and coverage of resource scheduling through partition strategies and logical binding, helps to meet cross-regional resource allocation needs, and thus significantly reduces resource waste.

[0046] By encapsulating the model and establishing a multi-region collaborative optimization mechanism, the collaborative optimization of cross-region resource allocation is achieved, overcoming the local optimization problem in traditional methods. This step makes resource allocation more intelligent by defining state synchronization rules and cross-region trigger conditions, effectively reducing the scheduling cost caused by global resource imbalance.

[0047] Through dynamic supply and demand matching optimization based on cloud resource tags and virtualization partition information, combined with real-time environmental data and improved linear programming algorithms, dynamic adjustment of supply and demand matching is achieved. This step effectively responds to sudden changes in resource demand, improves allocation accuracy through optimization algorithms, and ultimately achieves the global optimization goal of resource allocation, significantly improving resource utilization and allocation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0049] Figure 1 The first embodiment of the present invention provides an overall flow chart of an intelligent resource optimization configuration method and system in a cloud computing environment. DETAILED DESCRIPTION

[0050] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0051] Example 1, reference Figure 1 , is an embodiment of the present invention, and provides an intelligent resource optimization configuration method in a cloud computing environment, comprising:

[0052] S1: Collect water conservancy station data in real time, upload it to the cloud, perform dynamic annotation, and generate resource tags.

[0053] Install sensor equipment at the water conservancy station and equip it with a data gateway that supports the Internet of Things protocol to realize local sensor data collection.

[0054] It should be noted that the sensors include water level sensors, flow meters, thermometers, and hygrometers.

[0055] Furthermore, a cache module is deployed on the data gateway, and a circular cache queue is used to temporarily store short-term data to cope with network interruption scenarios. Set the reporting cycle and trigger mechanism: report basic data regularly (5 minutes in this invention). Instant reporting is triggered for abnormal values ​​(such as when the water level exceeds the threshold).

[0056] It should be noted that for the identification of outliers, a sliding window method based on time series is used to calculate the fluctuation range of the collected values, and the outlier threshold is generated in combination with historical statistical data. If the water level fluctuation exceeds 3 times the standard deviation of the historical interval, it will trigger an immediate report, and the cloud analysis module will determine the abnormal category, which is divided into equipment failure, environmental fluctuation or unknown cause.

[0057] Uploading to the cloud includes deploying a base data receiving interface on the cloud platform, using a distributed message queue to process sensor data streams, using cloud storage services to store raw data, and storing time series data through a database. Establish a data device type partitioning strategy to improve data storage and retrieval efficiency.

[0058] When deploying data in the cloud, extract the data features of the water conservancy station and divide the data into different usage scenarios. According to the data features and event annotation results, design resource tag generation rules and store the dynamically generated resource tags in the tag database.

[0059] It should be noted that the data processing module is deployed in the cloud, and the data features of the water conservancy station are extracted using machine learning algorithms (such as K-Means clustering or decision tree classification). According to the extracted features (such as water level change trends, flow peaks, etc.), the data is divided into different usage scenarios (such as "flood season", "dry season", "normal water supply"). Specific event data (such as "flood warning" and "drought warning") are dynamically annotated to generate meta-information such as time, location, and cause related to the event.

[0060] S2: Virtualize physical resources into logical resources and build a resource partition mapping model in the cloud.

[0061] According to the functional characteristics of physical resources, they are abstracted into logical resource units. Each logical resource unit contains virtual attributes, including capacity limit, service scope, and response time.

[0062] Building a resource partition mapping model in the cloud includes defining partition strategies and dividing resource partitions based on geographical locations and water supply requirements.

[0063] A mapping table is established in the cloud to bind logical resources to partitions, and logical resources are matched by the geographical location of the resources and the geographical boundaries of the partitions to ensure that each logical resource can be accessed.

[0064] It should be noted that for water resources (such as water pumps, regulating gates, reservoirs, etc.), their key attributes are collected, including equipment capacity parameters (such as flow, pressure, opening and closing status). Spatial information (geographic location, service scope). Time information (equipment operation time, maintenance cycle). Regular pull or real-time push through the equipment management system (SCADA) or the Internet of Things device interface. Use 3D modeling tools (such as Blender or AutoCAD) and descriptive languages ​​​​(such as XML or JSON) to establish a digital twin model of each device to describe its physical properties and interaction rules. Update the status information of the digital twin in combination with actual operation data to ensure that the model reflects the working status of the equipment in real time.

[0065] Furthermore, according to the functional characteristics of physical resources, they are abstracted into logical resource units: for example, a group of water pumps are abstracted into a "water supply capacity unit" and multiple water tanks are abstracted into "water storage units".

[0066] Each logical resource unit contains the following virtual attributes: capacity limit, service scope, response time, operating cost, etc.

[0067] Deploy a cloud resource virtualization platform and use virtualization technology (such as Kubernetes container management) to map physical resources to virtual logical resources. Assign a unique identifier (such as UUID) and its associated metadata to each logical resource. Dynamically update the status of logical resources through real-time data streams (such as MQTT). For example, if a physical device fails, the available capacity of the corresponding logical resource is automatically reduced. When physical resources are maintained or deactivated, the status of logical resources is synchronously adjusted to "unavailable".

[0068] Furthermore, resource zones can be divided based on geographical location, water supply demand or administrative divisions. For example, water supply zones can be divided based on the distribution of water resources in the region. Consider the actual coverage of the equipment to avoid overlapping areas.

[0069] Create a mapping table in the cloud to bind logical resources to partitions, and match logical resources by the geographical location of the resources and the geographical boundaries of the partitions. Ensure that each logical resource can be accessed uniquely or in multiple regions.

[0070] S3: Encapsulate the model and establish a collaborative optimization mechanism between multi-region models.

[0071] Encapsulate the resource partition mapping model, determine the encapsulation target and interface definition, and build a model encapsulation container, which includes partition data processing logic, resource status update module and optimization algorithm engine.

[0072] Based on the global resource network, establish an interconnection mechanism between regions, define status synchronization rules, and define cross-regional collaboration trigger conditions.

[0073] It should be noted that the encapsulation target and interface definition are determined, and the encapsulation target is to enable the resource partition mapping model to be modularly called while supporting cross-region collaborative optimization.

[0074] Define standardized interfaces:

[0075] Input: real-time resource status, demand forecast data, deployment requests, etc.

[0076] Output: optimized resource allocation strategy and scheduling path.

[0077] Interface protocol: Use REST API or gRPC to support efficient remote calls.

[0078] Use containerization technology (such as Docker) to encapsulate the resource partition mapping model into a portable service unit. The container includes: partition data processing logic and resource status update module.

[0079] Deploy multiple instances in the cloud platform using container orchestration tools (such as Kubernetes). Use load balancing services (such as NGINX or Kubernetes Ingress) to manage the call requests of model instances to ensure stable services under high concurrency.

[0080] Furthermore, each partition model regularly broadcasts its key status (such as remaining resource capacity, resource demand forecast) to the global status library. Define the status synchronization rules:

[0081] High-frequency synchronization (e.g., every minute) delivers core indicators (e.g., remaining capacity).

[0082] Low-frequency synchronization (such as every hour) transmits the status of all resources.

[0083] Define cross-region collaboration trigger conditions, for example:

[0084] Resource imbalance trigger: When the resource utilization rate of a certain area is higher than a threshold (such as 90%), a collaborative optimization request is triggered.

[0085] Sudden demand trigger: An external event causes a surge in resource demand in a single area (such as floods or droughts).

[0086] Furthermore, the triggering priority of cross-region collaborative optimization is sorted according to the urgency of resources. First, the supply and demand balance problem in resource-scarce areas is dealt with, and then the resource conflicts caused by emergencies are resolved. The collaborative triggering conditions optimize the triggering strategy through distributed collaborative algorithms (such as distributed reinforcement learning), so that each partition model can independently calculate its optimal adjustment plan after receiving the global state broadcast and submit it to the global coordination center.

[0087] Real-time collaborative triggering logic is implemented through event stream processing tools such as Apache Flink.

[0088] It should be noted that after completing resource virtualization and partition mapping model construction, the system broadcasts logical resource attributes and partition binding information through the cloud to provide basic data support for subsequent cross-region collaborative optimization. Through the resource status synchronization module, it ensures that all partition models have a consistent global resource view to achieve real-time collaborative optimization between partitions.

[0089] S4: Based on cloud resource tags and virtualization partition information, combined with real-time environmental data, an optimization algorithm is used to achieve supply and demand matching and dynamically adjust resource allocation strategies.

[0090] Real-time environmental data includes real-time environmental data stored in the cloud, including rainfall P(t), evaporation E(t), water resource demand growth rate R(t), and virtualized partition information is partition status data, including available water resource capacity C i (t) and the partition requirement D i (t).

[0091] Set the partition supply and demand balance objective function, expressed as:

[0092]

[0093] To perform dynamic resource scheduling between partitions, first calculate the partition scheduling weight γ based on the urgency of the partition demand i (t), expressed as:

[0094]

[0095] Partition scheduling weight γ i The larger (t) is, the more resources the partition requires.

[0096] Using the improved shortest path method, based on the edge weight w ij :

[0097]

[0098] Among them, δ ij represents the resource allocation cost from partition i to j, θ ij Indicates the path smoothness. ij The smaller the value, the better the path.

[0099] Linear programming based on partition resource constraints and requirements is expressed as:

[0100]

[0101] Considering the impact of dynamic environment and coordinated optimization between partitions, an improved linear programming formula is constructed, using the exponential function e of the dynamic demand growth rate R(t) -R(t) Adjust the balance factor and introduce cross-partition scheduling items Optimize the deployment path through weights.

[0102] The precise degree of resource allocation optimization is expressed as:

[0103]

[0104] Among them, α represents the scheduling impact factor, balancing the optimization within and between partitions. t represents time, and t0 represents the initial time.

[0105] Ψ>0 indicates that there is an imbalance between resource supply and demand. The larger the value, the greater the imbalance.

[0106] Ψ=0 means that resource supply and demand are perfectly matched and no further adjustment is required.

[0107] It should be noted that in the case of imbalance between resource supply and demand (Ψ>0), the system optimizes resource scheduling through a centralized cloud control center to ensure dynamic adjustment and matching of resources. The steps include resource status analysis, multi-region allocation model adjustment, and balance feedback mechanism.

[0108] Resource status analysis includes collecting the operating data of each resource node, uploading it to the cloud control platform, analyzing and evaluating the resource status, calculating the accuracy of current resource supply and demand, and obtaining the degree of imbalance, which is expressed as:

[0109]

[0110] Among them, C i represents the maximum capacity of each resource, α′ represents the environmental impact factor, r i is the resource requirement of node i, θ i is the environmental condition of node i, h i is the device health of node i, and n represents the number of nodes.

[0111] Adjustments to the multi-region allocation model include:

[0112] Partition mapping and dynamic adjustment: Based on the resource supply and demand accuracy Ψ in the previous stage, the system decides whether to adjust the resource allocation strategy. If Ψ>0, the system will enable the dynamic allocation model to transfer resources from the surplus area to the shortage area.

[0113] Resource flow and scheduling mechanism: The cloud platform uses a hierarchical mapping model to reallocate resources from areas with oversupply to areas with insufficient supply. The specific adjustment is carried out through the following optimization model:

[0114]

[0115] Among them, γ is the resource transfer ratio, β is the regional priority factor, represents the amount of resources between the i-th node and the j-th region after resource adjustment, represents the amount of resources between the i-th node and the j-th region before resource adjustment, R i Represents the set of resource requirements, environmental conditions, and device health of node i.

[0116] Automatic allocation and feedback: During the adjustment process, the cloud control system will monitor the resource usage of each area in real time and adjust the weight and transfer ratio based on the feedback data:

[0117]

[0118] Among them, adjusted is the adjusted resource supply and demand accuracy, κ is the correction coefficient, Indicates the impact of device health status on resource allocation.

[0119] When Ψ=0, that is, when resource supply and demand are fully matched, the system enters a stable state and no further resource allocation is required.

[0120] The above embodiments also include an intelligent resource optimization configuration system in a cloud computing environment, specifically:

[0121] The collection and labeling module collects water conservancy station data in real time, uploads it to the cloud, performs dynamic labeling, and generates resource tags.

[0122] The partition mapping module virtualizes physical resources into logical resources and builds a resource partition mapping model in the cloud.

[0123] The partitioning module encapsulates the model and establishes a collaborative optimization mechanism between multi-region models.

[0124] The optimization scheduling module uses optimization algorithms to achieve supply and demand matching and dynamically adjust resource allocation strategies based on cloud resource tags and virtualization partition information combined with real-time environmental data.

[0125] The computer device may be a server. The computer device includes a processor, a memory, an input / output interface (I / O for short) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data cluster data of a power monitoring system. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, an intelligent resource optimization configuration method in a cloud computing environment is implemented.

[0126] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0127] Embodiment 2 is an embodiment of the present invention, which provides an intelligent resource optimization configuration method and system in a cloud computing environment. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0128] Test preparation content and implementation details

[0129] Test objectives

[0130] In order to verify the technical advantages of the present invention, the existing traditional static allocation method is compared to evaluate the improvement effect of dynamic resource optimization on supply and demand balance, response efficiency and global resource utilization.

[0131] Three water resource zones (A, B, and C) were selected, each with different initial resource states and environmental variables. Two methods were used for comparison:

[0132] Existing technical methods: use static resource allocation strategies to configure resources according to fixed partition ratios without dynamic adjustment capabilities.

[0133] Our invented method: dynamic adjustment based on real-time environmental data and cloud optimization algorithm, combined with improved linear programming and partition weight optimization strategy.

[0134] The existing technology implementation process adopts fixed ratio allocation: the total resources are allocated according to the historical average demand of each partition, and the ratio is A:B:C=3:2:1.

[0135] The implementation process of our invention is as follows:

[0136] Sensor devices are installed in each partition to report real-time data every 5 minutes. The data is dynamically marked as resource tags and uploaded to cloud storage.

[0137] Based on the characteristics of physical devices, resources are abstracted into logical resource units, and their supply capabilities and coverage are recorded. A partition mapping model is built in the cloud to dynamically update the status of each partition.

[0138] Encapsulate the resource allocation model and adopt a cross-region collaborative optimization strategy. Define the partition supply and demand balance objective function, and dynamically calculate the partition weight based on real-time data to trigger resource reallocation.

[0139] Real-time analysis of the degree of supply and demand imbalance Ψ. When Ψ>0, the system optimizes the resource allocation path through an improved shortest path algorithm and dynamically adjusts the strategy in the feedback link until Ψ=0.

[0140] The experimental results are shown in Table 1.

[0141] Table 1 Experimental results

[0142]

[0143]

[0144] According to the table data, after resource allocation in the prior art method, the resource requirements of partitions A and C were not met, while partition B was over-allocated, resulting in a drop in overall resource utilization to 72%. In contrast, our invention makes the resource allocation of each partition closer to the actual demand through real-time dynamic adjustment, and the resource utilization rate is increased to 90%. Specifically, the supply-demand imbalance degree Ψ in our invention is reduced from 50 in the prior art to 10, significantly optimizing the accuracy of resource allocation.

[0145] Existing technical methods cannot be adjusted in time after supply and demand changes, and the allocation strategy lacks dynamism. However, our invention completes the adjustment within 5 minutes after the imbalance between supply and demand occurs, which fully demonstrates the real-time nature of dynamic optimization. This efficient response capability is particularly important for scenarios where resource demand changes drastically, such as when facing sudden floods or droughts, and can quickly optimize resource allocation to reduce losses.

[0146] Our invention achieves efficient cross-regional resource flow through partition mapping models and optimization algorithms. For example, by appropriately reducing allocation in partition B with lower demand and transferring resources to partitions A and C with higher demand, the overall imbalance is effectively reduced. At the same time, through shortest path optimization, the transmission cost of resource allocation is significantly reduced.

[0147] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent resource optimization configuration method in a cloud computing environment, characterized in that: include: Collect water conservancy station data in real time, upload it to the cloud, perform dynamic annotation, and generate resource tags; Virtualize physical resources into logical resources and build a resource partition mapping model in the cloud; Encapsulate the model and establish a collaborative optimization mechanism between multi-region models; Based on cloud resource tags and virtualization partition information, combined with real-time environmental data, an optimization algorithm is used to achieve supply and demand matching and dynamically adjust resource allocation strategies.

2. The intelligent resource optimization configuration method in a cloud computing environment according to claim 1, characterized in that: The real-time collection of water conservancy station data includes installing sensor equipment at the water conservancy station and equipping it with a data gateway supporting the Internet of Things protocol to realize local sensor data collection; Uploading to the cloud includes deploying a base data receiving interface on the cloud platform, using a distributed message queue to process sensor data streams, using cloud storage services to store raw data, and storing time series data through a database; establishing a data device type partitioning strategy to improve data storage and retrieval efficiency.

3. The intelligent resource optimization configuration method in a cloud computing environment according to claim 2, characterized in that: The dynamic labeling and resource label generation include extracting the data features of the water conservancy station and dividing the data into different usage scenarios when deploying data in the cloud; designing resource label generation rules based on data features and event labeling results, and storing the dynamically generated resource labels in a label database.

4. The intelligent resource optimization configuration method in a cloud computing environment according to claim 3, characterized in that: The virtualization of physical resources into logical resources includes abstracting the physical resources into logical resource units according to their functional characteristics, each of which contains virtual attributes, including a capacity limit, a service scope, and a response time; Building a resource partition mapping model in the cloud includes defining partition strategies and dividing resource partitions based on geographical location and water supply demand; A mapping table is established in the cloud to bind logical resources to partitions, and logical resources are matched by the geographical location of the resources and the geographical boundaries of the partitions to ensure that each logical resource can be accessed.

5. The intelligent resource optimization configuration method in a cloud computing environment according to claim 4, characterized in that: The step of encapsulating the model and establishing a collaborative optimization mechanism between the multi-region models includes encapsulating the resource partition mapping model, determining the encapsulation target and interface definition, and constructing a model encapsulation container, wherein the container includes partition data processing logic, a resource status update module, and an optimization algorithm engine; Based on the global resource network, establish an interconnection mechanism between regions, define status synchronization rules, and define cross-regional collaboration trigger conditions.

6. The intelligent resource optimization configuration method in a cloud computing environment according to claim 5, characterized in that: The real-time environmental data includes real-time environmental data stored in the cloud, including rainfall P(t), evaporation E(t), water resource demand growth rate R(t), and virtualized partition information is partition status data, including available water resource capacity C i (t) and the partition requirement D i (t); Set the partition supply and demand balance objective function, expressed as: To perform dynamic resource scheduling between partitions, first calculate the partition scheduling weight γ based on the urgency of the partition demand i (t), expressed as: Partition scheduling weight γ i The larger (t) is, the more resources the partition requires; Using the improved shortest path method, based on the edge weight w ij : Among them, δ ij represents the resource allocation cost from partition i to j, θ ij represents the path smoothness; w ij The smaller the value, the better the path.

7. The intelligent resource optimization configuration method in a cloud computing environment according to claim 6, characterized in that: The dynamic adjustment resource allocation strategy includes linear programming based on partition resource constraints and requirements, which is expressed as: Considering the impact of dynamic environment and coordinated optimization between partitions, an improved linear programming formula is constructed, using the exponential function e of the dynamic demand growth rate R(t) -R(t) Adjust the balance factor and introduce cross-partition scheduling items Optimize the deployment path through weights; The precise degree of resource allocation optimization is expressed as: Among them, α represents the scheduling impact factor, balancing the optimization within and between partitions; t represents time, and t0 represents the initial time; Ψ>0 indicates that there is an imbalance between resource supply and demand. The larger the value, the higher the imbalance; Ψ=0 means that resource supply and demand are perfectly matched and no further adjustment is required.

8. An intelligent resource optimization configuration system in a cloud computing environment using the method according to any one of claims 1 to 7, characterized in that: The collection and annotation module collects water conservancy station data in real time, uploads it to the cloud, performs dynamic annotation, and generates resource tags; The partition mapping module virtualizes physical resources into logical resources and builds a resource partition mapping model in the cloud; The partition module encapsulates the model and establishes a collaborative optimization mechanism between multi-region models; The optimization scheduling module uses optimization algorithms to achieve supply and demand matching and dynamically adjust resource allocation strategies based on cloud resource tags and virtualization partition information combined with real-time environmental data.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images