IT resource full-life-cycle intelligent management method and system based on strategy driving

Abstract

The invention belongs to the technical field of IT operation and maintenance management, and discloses a strategy-driven IT resource full-life-cycle intelligent management method and a strategy-driven IT resource full-life-cycle intelligent management system. The method comprises the following steps: collecting heterogeneous resource data through a multi-protocol resource probe, and carrying out standardization processing and storage based on a uniform resource model; the intelligent scheduling engine matches the resource data with the strategy rule in real time to generate a work order; the instruction generation module performs conflict verification and risk assessment in combination with the context to generate an atomic operation instruction; the instruction execution module calls a platform interface to execute an instruction through a plug-in adapter and collects a result; the feedback module updates a resource state and approaches an expected target according to a state convergence model; historical data are analyzed periodically, and strategy rules are optimized. According to the method, unified, automatic and intelligent closed-loop management of cross-platform and multi-type IT resources is realized, and the operation and maintenance efficiency, the safety and the resource utilization rate are improved.

Description

Technical Field

[0001] This invention relates to the field of IT operations and maintenance management technology, and in particular to a strategy-driven intelligent management method and system for the entire lifecycle of IT resources. Background Technology

[0002] Against the backdrop of enterprise digital transformation and the widespread adoption of cloud computing, IT infrastructure is becoming increasingly complex, encompassing various resource types such as physical servers, virtual machines, containers, and cloud host instances, and potentially distributed across heterogeneous environments like local data centers, private clouds, and public clouds. Traditional IT resource management models are typically handled by multiple independent tools or teams, creating management silos and leading to inconsistent resource views, cumbersome operational processes, low levels of automation, and poor resource utilization. While existing technologies such as CN119515000A propose a full lifecycle management model, they still have shortcomings in areas such as heterogeneous data fusion, precise policy matching, operational risk control, and system adaptive optimization. Therefore, a solution capable of achieving unified, intelligent, and automated closed-loop management is urgently needed.

[0003] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main objective of this invention is to provide a strategy-driven intelligent management method and system for the entire lifecycle of IT resources, aiming to solve the technical problems of fragmented management, low degree of automation, weak risk control, and system scalability in the existing technology.

[0005] To achieve the above objectives, this invention provides a policy-driven intelligent management method for the entire lifecycle of IT resources, comprising the following steps: By deploying resource probes in multiple environments, static attribute and dynamic performance data of heterogeneous IT resources are collected in a multi-protocol manner. Based on a unified resource model, standardized processing is performed to generate standardized resource data and store it in a resource database. The unified resource model includes at least 56 standard fields. The standardization process includes data format conversion, outlier cleaning, missing value completion, and scaling of each dimension of data to the [0,1] interval using a min-max normalization algorithm. The standardized resource data is obtained in real time through the intelligent scheduling engine and matched with the operation and maintenance rules predefined in the strategy center. When the match is successful, a work order is generated and pushed to the work order management module. The instruction generation module performs conflict verification and risk assessment based on the context information of the pending work order and the standardized resource data obtained from the resource database, generates an atomic operation instruction sequence, and stores it in the instruction queue. The instruction execution module obtains atomic operation instructions from the instruction queue, and executes the operation by calling the interface of the corresponding IT resource platform through the plug-in adapter based on the atomic operation instructions, and collects the execution results and resource status. The execution results and resource status are fed back to the resource database and intelligent scheduling engine to update the resource status, and the resource status is ensured to approach the desired target according to the state convergence model. Periodically analyze historical data and execution logs, generate strategy optimization suggestions through statistical analysis and predictive models, and update the rules of the strategy center to achieve continuous iterative optimization of the management system.

[0006] In one embodiment, matching the standardized resource data with predefined operation and maintenance rules in the policy center includes: Determine the resource type of the standardized resource data, and match the corresponding rule set according to the resource type; The resource key fields of the standardized resource data are compared with the triggering conditions of multiple rules in the rule set, wherein the composite conditions are calculated using short-circuit logic. When a match is found with multiple rules simultaneously, the rules are sorted by their overall score to complete the matching process. The overall score is determined by the formula... Calculate, where, The overall score for rule t is... For rule priority weights, To match the fit.

[0007] In one embodiment, the conflict verification involves obtaining the resource demand matrix of the currently existing operations on the target resource; calculating whether there is resource contention based on the resource demand vector of the pending work order and the resource demand matrix using a conflict determination formula; if resource contention exists, then processing is performed according to preset rules, including waiting, adjustment, or cancellation.

[0008] In one embodiment, the conflict determination formula is:

[0009] Among them, For the conflict outcome of resource type j, This represents the percentage of resource type j required by the current operation. The percentage of demand for the i-th existing operation. This represents the largest proportion of the supply of this resource.

[0010] In one embodiment, the risk assessment is to score based on operational complexity and resource importance, and calculate a risk value; if the risk value exceeds a preset threshold, a manual approval process is triggered.

[0011] In one embodiment, the state convergence model describes resource state changes using the following differential equation:

[0012] Let t represent the resource status at time t. For the desired state, This is the initial state. For state update coefficients.

[0013] In one embodiment, the statistical analysis includes calculating the average utilization rate and volatility of the resources, wherein the average utilization rate is calculated using the following formula:

[0014] The volatility calculation formula is as follows:

[0015] in, The average utilization rate within the period [T1, T2] Let be the instantaneous utilization rate at time t. Let T1 and T2 be the volatility, and T1 and T2 be the two time points, respectively.

[0016] Furthermore, to achieve the above objectives, this invention also proposes a policy-driven intelligent management system for the entire lifecycle of IT resources. This policy-driven intelligent management system is applied to the policy-driven intelligent management method for the entire lifecycle of IT resources described above. The system includes: The data acquisition and standardization module is used to collect static attribute and dynamic performance data of heterogeneous IT resources through resource probes deployed in multiple environments using multiple protocols, and to perform standardization processing based on a unified resource model to generate standardized resource data and store it in the resource database. The unified resource model includes at least 56 standard fields, and the standardization processing includes data format conversion, outlier cleaning, missing value completion, and scaling of each dimension of data to the [0,1] interval using a min-max normalization algorithm. The strategy matching and work order generation module is used to obtain the standardized resource data in real time through the intelligent scheduling engine, match the standardized resource data with the operation and maintenance rules predefined in the strategy center, generate a work order to be done when the match is successful, and push the work order to be done to the work order management module. The instruction generation and verification module is used to perform conflict verification and risk assessment based on the context information of the pending work order and the standardized resource data obtained from the resource database, generate an atomic operation instruction sequence and store it in the instruction queue. The instruction execution and status acquisition module is used to obtain atomic operation instructions from the instruction queue through the instruction execution module, and to execute the operation by calling the interface of the corresponding IT resource platform through the plug-in adapter based on the atomic operation instructions, and to collect the execution results and resource status. The feedback and status update module is used to feed back the execution results and the resource status to the resource database and the intelligent scheduling engine to update the resource status and ensure that the resource status approaches the desired target according to the status convergence model. The analysis and strategy iteration module is used to periodically analyze historical data and execution logs, generate strategy optimization suggestions through statistical analysis and predictive models, and update the rules of the strategy center to achieve continuous iterative optimization of the management system.

[0017] Furthermore, to achieve the above objectives, the present invention also proposes a policy-driven intelligent management device for the entire lifecycle of IT resources. The policy-driven intelligent management device for the entire lifecycle of IT resources includes: a memory, a processor, and a policy-driven intelligent management program for the entire lifecycle of IT resources stored in the memory and executable on the processor. The policy-driven intelligent management program for the entire lifecycle of IT resources is configured to implement the steps of the policy-driven intelligent management method for the entire lifecycle of IT resources as described above.

[0018] Furthermore, to achieve the above objectives, the present invention also proposes a storage medium storing a policy-driven intelligent management program for the entire lifecycle of IT resources. When the policy-driven intelligent management program for the entire lifecycle of IT resources is executed by a processor, it implements the steps of the policy-driven intelligent management method for the entire lifecycle of IT resources as described above.

[0019] This invention collects heterogeneous resource data through multi-protocol resource probes, standardizes and stores it based on a unified resource model, and then performs the necessary storage. An intelligent scheduling engine matches resource data with policy rules in real time to generate work orders. An instruction generation module performs conflict verification and risk assessment based on context, generating atomic operation instructions. An instruction execution module executes instructions and collects results by calling platform interfaces through plug-in adapters. A feedback module updates resource status and converges to the desired target based on a status convergence model. Historical data is periodically analyzed to optimize policy rules. This approach achieves unified, automated, and intelligent closed-loop management of cross-platform, multi-type IT resources, improving operational efficiency, security, and resource utilization. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the first embodiment of the strategy-driven intelligent management method for the entire lifecycle of IT resources according to the present invention. Figure 2 This is a structural block diagram of the first embodiment of the strategy-driven intelligent management system for the entire lifecycle of IT resources according to the present invention.

[0021] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0022] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0023] This invention provides a policy-driven intelligent management method for the entire lifecycle of IT resources, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of a strategy-driven intelligent management method for the entire lifecycle of IT resources according to the present invention.

[0024] In this embodiment, the policy-driven intelligent management method for the entire lifecycle of IT resources includes the following steps: Step S10: By deploying resource probes in multiple environments, static attribute and dynamic performance data of heterogeneous IT resources are collected in a multi-protocol manner, and standardized processing is performed based on a unified resource model to generate standardized resource data and store it in the resource database.

[0025] In this embodiment, the execution entity is a policy-driven intelligent management device for the entire lifecycle of IT resources. This policy-driven intelligent management device for the entire lifecycle of IT resources has functions such as data processing, data communication, and program execution. The policy-driven intelligent management device for the entire lifecycle of IT resources can be a computer terminal device or other network device, or other devices with similar functions. This embodiment does not limit the scope of such devices.

[0026] It's important to note that against the backdrop of enterprise digital transformation and the widespread adoption of cloud computing, IT infrastructure is becoming increasingly complex, encompassing various resource types such as physical servers, virtual machines, containers, and cloud host instances, and potentially distributed across heterogeneous environments like on-premises data centers, private clouds, and public clouds. Traditional IT resource management models are typically handled by multiple independent tools or teams, creating management silos and leading to inconsistent resource views, cumbersome operational processes, low levels of automation, and poor resource utilization. While existing technologies have proposed full lifecycle management models, they still have shortcomings in areas such as heterogeneous data integration, precise policy matching, operational risk control, and system adaptive optimization. Therefore, a solution capable of achieving unified, intelligent, and automated closed-loop management is urgently needed.

[0027] To address the aforementioned technical challenges, this embodiment employs a multi-protocol resource probe to collect heterogeneous resource data, performs standardized processing based on a unified resource model, and stores the data. An intelligent scheduling engine matches resource data with policy rules in real-time, generating work orders. An instruction generation module performs conflict verification and risk assessment based on context, generating atomic operation instructions. An instruction execution module executes instructions and collects results by calling platform interfaces through a plug-in adapter. A feedback module updates resource status and converges to the desired target based on a status convergence model. Historical data is periodically analyzed to optimize policy rules. This approach achieves unified, automated, and intelligent closed-loop management of cross-platform, multi-type IT resources, improving operational efficiency, security, and resource utilization. Specifically, it can be implemented as follows.

[0028] In the specific implementation, data from different dimensions are collected by resource probes and then converted into a unified format by a standardization module and stored in a resource database. The unified resource model includes at least 56 standard fields. The standardization process includes data format conversion, outlier cleaning, missing value completion, and scaling of each dimension of data to the [0,1] interval using a min-max normalization algorithm.

[0029] Specifically, this includes a resource probe module with a built-in environment adaptation submodule. It can proactively scan for resources in multiple environments via protocols such as SNMP, SSH, API, and Prometheus, with a default scanning cycle of 60 seconds. Upon discovering a new resource, it uses device fingerprinting technology to determine its type and automatically connects it, generating a unique identifier associated with the resource ledger. After connection, the probe collects data according to static and dynamic categories: static attributes are collected once plus incremental verification at midnight every day; dynamic performance indicators are collected in layers according to importance (core indicators every 10 seconds / time, non-core every 300 seconds / time); status information is event-driven plus 60-second polling verification. Collected data is transmitted in real-time to a Kafka buffer queue (one partition for every 1000 resources). The data processing module then performs format conversion, outlier cleaning, and missing value completion according to a unified resource model containing 56 standard fields. Finally, standardized data is generated and stored in an HBase distributed database, with the row key being the resource identifier and the column family divided according to data type.

[0030] To eliminate the magnitude differences between data from different dimensions, a min-max normalization method is used to uniformly scale the data of each dimension to the range of 0-1. The specific calculation formula is as follows:

[0031] in, For the standardized value of the j-th dimension of the i-th resource, These are the original collected values. and These represent the minimum and maximum values ​​of all resource collection values ​​for this dimension, respectively. Simultaneously, access effectiveness is evaluated from two dimensions: protocol matching and identity authentication. A complex function is defined to represent the access status. Access is considered effective when the access effectiveness coefficient (the product of protocol matching degree and identity authentication pass rate) is not less than 0.8 and there is no risk, thus avoiding misjudgment based on a single dimension.

[0032] Step S20: Obtain the standardized resource data in real time through the intelligent scheduling engine, match the standardized resource data with the operation and maintenance rules predefined in the strategy center, generate a pending work order when the match is successful, and push the pending work order to the work order management module.

[0033] In this embodiment, the policy center provides a visual interface where administrators can define rules based on trigger conditions, execution actions, and priorities. Multiple condition combinations are supported (e.g., "CPU utilization exceeds 80% for 5 minutes"). Execution actions are associated with preset templates (e.g., alarms, capacity expansion). Priorities are set in 1-10 levels and stored in the rule engine, which assigns a unique ID. The intelligent scheduling engine receives standardized data pushed from the resource database via RabbitMQ subscription and publish modes, stores it in a local Redis cache (cached for 10 minutes) and records a timestamp, and then groups and matches it according to the resource type and its corresponding exclusive rule set. Matching the standardized resource data with the predefined operation and maintenance rules in the policy center includes: The resource type of the standardized resource data is determined, and a corresponding rule set is matched according to the resource type. The key resource fields of the standardized resource data are compared with the trigger conditions of multiple rules in the rule set, wherein composite conditions are calculated using short-circuit logic. When multiple rules match simultaneously, they are sorted by comprehensive score to complete the matching process. The comprehensive score is calculated using the formula... Calculate, where, The overall score for rule t is... For rule priority weights, To match the fit.

[0034] Furthermore, after a work order is generated, the instruction generation stage ensures operational feasibility through multi-dimensional verification and transforms it into executable instructions. The instruction generation module retrieves work orders to be processed in batches every 10 seconds, first verifying the validity of the target resource ID and rule ID, and storing them in the processing queue after verification. Then, based on the target resource ID in the work order, it queries the resource database for context information, including the current running process, resource usage, associated resource information, and historical operation records for the past 24 hours, encapsulating this information into a unified data structure.

[0035] Step S30: The instruction generation module performs conflict verification and risk assessment based on the context information of the pending work order and the standardized resource data obtained from the resource database, generates an atomic operation instruction sequence, and stores it in the instruction queue.

[0036] It should be noted that conflict verification involves obtaining the resource demand matrix of currently existing operations on the target resource; based on the resource demand vector of the pending work order and the resource demand matrix, a conflict determination formula is used to calculate whether resource contention exists; if resource contention exists, the process is handled according to preset rules, including waiting, adjustment, or cancellation. The conflict determination formula is as follows:

[0037] Among them, For the conflict outcome of resource type j, This represents the percentage of resource type j required by the current operation. The percentage of demand for the i-th existing operation. This represents the largest proportion of the supply of this resource.

[0038] Furthermore, the risk assessment involves scoring based on operational complexity and resource importance to calculate a risk value; if the risk value exceeds a preset threshold, a manual approval process is triggered.

[0039] The state convergence model describes the resource state changes through the following differential equation:

[0040] Let t represent the resource status at time t. For the desired state, This is the initial state. For state update coefficients.

[0041] Step S40: Obtain atomic operation instructions from the instruction queue through the instruction execution module, and execute the operation by calling the interface of the corresponding IT resource platform through the plug-in adapter based on the atomic operation instructions, and collect the execution results and resource status.

[0042] In this embodiment, the instruction execution module receives instructions sequentially from the queue, verifies the integrity of fields such as ID and target resource, returns incomplete instructions for retry, and enters the execution buffer for complete instructions. Based on the target resource platform type (e.g., AWS cloud, VMware, K8s cluster), the corresponding pluggable adapter interface is called. The adapter uses a transformation matrix to map atomic instructions to the platform API, ensuring cross-platform compatibility. After execution, the adapter receives the platform's return result, while the resource probe collects the latest status of the target resource. The two are associated and encapsulated into an execution report. The report is pushed to the resource probe module and the intelligent scheduling engine via API, achieving closed-loop synchronization of execution, feedback, and updates. The state convergence model describes resource state changes using the following differential equation:

[0043] Let t represent the resource status at time t. For the desired state, This is the initial state. For state update coefficients.

[0044] Step S50: Feed the execution result and the resource status back to the resource database and intelligent scheduling engine to update the resource status, and ensure that the resource status approaches the desired target according to the state convergence model.

[0045] The intelligent scheduling engine executes the analysis process at 2:00 AM every day by default, extracting standardized data from the resource database for the past 7 days, historical work order data from the work order management system, and logs from the instruction execution module. After cleaning, the data is stored in the analysis database.

[0046] Step S60: Periodically analyze historical data and execution logs, generate strategy optimization suggestions through statistical analysis and predictive models, and update the rules of the strategy center to achieve continuous iterative optimization of the management system.

[0047] In this embodiment, statistical methods are used to calculate core indicators during the analysis process. The statistical analysis includes calculating the average utilization rate and volatility of resources. The formula for calculating the average utilization rate is as follows:

[0048] The volatility calculation formula is as follows:

[0049] in, The average utilization rate within the period [T1, T2] Let be the instantaneous utilization rate at time t. Let T1 and T2 be the volatility, and T1 and T2 be the two time points, respectively.

[0050] In this embodiment, heterogeneous resource data is collected through multi-protocol resource probes, standardized and stored based on a unified resource model; an intelligent scheduling engine matches resource data with policy rules in real time to generate work orders; an instruction generation module performs conflict verification and risk assessment based on context to generate atomic operation instructions; an instruction execution module executes instructions and collects results by calling platform interfaces through plug-in adapters; a feedback module updates resource status and approaches the desired target based on a status convergence model; and historical data is periodically analyzed to optimize policy rules. This approach achieves unified, automated, and intelligent closed-loop management of cross-platform, multi-type IT resources, improving operational efficiency, security, and resource utilization.

[0051] Furthermore, this embodiment of the invention also proposes a storage medium storing a policy-driven intelligent management program for the entire lifecycle of IT resources. When the policy-driven intelligent management program for the entire lifecycle of IT resources is executed by a processor, it implements the steps of the policy-driven intelligent management method for the entire lifecycle of IT resources described above.

[0052] Reference Figure 2 , Figure 2 This is a structural block diagram of the first embodiment of the strategy-driven intelligent management system for the entire lifecycle of IT resources according to the present invention.

[0053] like Figure 2As shown in the embodiments of the present invention, the policy-driven intelligent management system for the entire lifecycle of IT resources includes: The data acquisition and standardization module 10 is used to collect static attribute and dynamic performance data of heterogeneous IT resources through resource probes deployed in multiple environments in a multi-protocol manner, and to perform standardization processing based on a unified resource model to generate standardized resource data and store it in the resource database. The unified resource model includes at least 56 standard fields, and the standardization processing includes data format conversion, outlier cleaning, missing value completion, and scaling of each dimension of data to the [0,1] interval using a min-max normalization algorithm. The strategy matching and work order generation module 20 is used to obtain the standardized resource data in real time through the intelligent scheduling engine, match the standardized resource data with the operation and maintenance rules predefined in the strategy center, generate a work order to be done when the match is successful, and push the work order to be done to the work order management module. The instruction generation and verification module 30 is used to perform conflict verification and risk assessment based on the context information of the pending work order and the standardized resource data obtained from the resource database, generate an atomic operation instruction sequence and store it in the instruction queue. The instruction execution and status acquisition module 40 is used to obtain atomic operation instructions from the instruction queue through the instruction execution module, and to execute the operation by calling the interface of the corresponding IT resource platform through the plug-in adapter based on the atomic operation instructions, and to collect the execution results and resource status. The feedback and status update module 50 is used to feed back the execution result and the resource status to the resource database and the intelligent scheduling engine to update the resource status and ensure that the resource status approaches the desired target according to the status convergence model. The analysis and strategy iteration module 60 is used to periodically analyze historical data and execution logs, generate strategy optimization suggestions through statistical analysis and predictive models, and update the rules of the strategy center to achieve continuous iterative optimization of the management system.

[0054] In this embodiment, heterogeneous resource data is collected through multi-protocol resource probes, standardized and stored based on a unified resource model; an intelligent scheduling engine matches resource data with policy rules in real time to generate work orders; an instruction generation module performs conflict verification and risk assessment based on context to generate atomic operation instructions; an instruction execution module executes instructions and collects results by calling platform interfaces through plug-in adapters; a feedback module updates resource status and approaches the desired target based on a status convergence model; and historical data is periodically analyzed to optimize policy rules. This approach achieves unified, automated, and intelligent closed-loop management of cross-platform, multi-type IT resources, improving operational efficiency, security, and resource utilization.

[0055] This application embodiment also provides a policy-driven intelligent management device for the entire lifecycle of IT resources, including a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other through the communication bus. The memory is used to store the policy-driven intelligent management program for the entire lifecycle of IT resources. When the processor executes the program stored in the memory, it implements the above-mentioned policy-driven intelligent management method for the entire lifecycle of IT resources.

[0056] The communication bus mentioned in the above-mentioned policy-driven intelligent management device for the entire lifecycle of IT resources can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc.

[0057] The communication interface is used for communication between the aforementioned policy-driven intelligent management device for the entire lifecycle of IT resources and other devices.

[0058] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0059] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0060] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk (SSD)).

[0061] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0062] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0063] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

[0064] It should be understood that the above are merely illustrative examples and do not constitute any limitation on the technical solutions of the present invention. In specific applications, those skilled in the art can make settings as needed, and the present invention does not impose any restrictions on this.

[0065] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of this invention. In practical applications, those skilled in the art can select some or all of the workflow to achieve the purpose of this embodiment according to actual needs, and no restrictions are imposed here.

[0066] In addition, for technical details not described in detail in this embodiment, please refer to the policy-driven intelligent management method for the entire lifecycle of IT resources provided in any embodiment of the present invention, which will not be repeated here.

[0067] Furthermore, it should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0068] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0069] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory (ROM) / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0070] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

[0071] It is understood that the system provided in the embodiments of the present invention corresponds to the method provided in the embodiments of the present invention, and the explanation, examples and beneficial effects of the relevant content can be referred to the corresponding parts of the above methods.

Claims

1. A strategy-driven intelligent management method for the entire lifecycle of IT resources, characterized in that, The policy-driven intelligent management method for the entire lifecycle of IT resources includes: By deploying resource probes in multiple environments, static attribute and dynamic performance data of heterogeneous IT resources are collected in a multi-protocol manner. Based on a unified resource model, standardized processing is performed to generate standardized resource data and store it in a resource database. The unified resource model includes at least 56 standard fields. The standardization process includes data format conversion, outlier cleaning, missing value completion, and scaling of each dimension of data to the [0,1] interval using a min-max normalization algorithm. The standardized resource data is obtained in real time through the intelligent scheduling engine and matched with the operation and maintenance rules predefined in the strategy center. When the match is successful, a work order is generated and pushed to the work order management module. The instruction generation module performs conflict verification and risk assessment based on the context information of the pending work order and the standardized resource data obtained from the resource database, generates an atomic operation instruction sequence, and stores it in the instruction queue. The instruction execution module obtains atomic operation instructions from the instruction queue, and executes the operation by calling the interface of the corresponding IT resource platform through the plug-in adapter based on the atomic operation instructions, and collects the execution results and resource status. The execution results and resource status are fed back to the resource database and intelligent scheduling engine to update the resource status, and the resource status is ensured to approach the desired target according to the state convergence model. Periodically analyze historical data and execution logs, generate strategy optimization suggestions through statistical analysis and predictive models, and update the rules of the strategy center to achieve continuous iterative optimization of the management system.

2. The strategy-driven intelligent management method for the entire lifecycle of IT resources as described in claim 1, characterized in that, The step of matching the standardized resource data with the predefined operation and maintenance rules in the strategy center includes: Determine the resource type of the standardized resource data, and match the corresponding rule set according to the resource type; The resource key fields of the standardized resource data are compared with the triggering conditions of multiple rules in the rule set, wherein the composite conditions are calculated using short-circuit logic. When a match is found with multiple rules simultaneously, the rules are sorted by their overall score to complete the matching process. The overall score is determined by the formula... Calculate, where, The overall score for rule t is... For rule priority weights, To match the fit.

3. The strategy-driven intelligent management method for the entire lifecycle of IT resources as described in claim 1, characterized in that, The conflict verification involves obtaining the resource demand matrix of the current operations on the target resource; and calculating whether there is resource contention based on the resource demand vector of the pending work order and the resource demand matrix using a conflict determination formula. If resource contention exists, the process will be handled according to preset rules, including waiting, adjustment, or cancellation.

4. The strategy-driven intelligent management method for the entire lifecycle of IT resources as described in claim 3, characterized in that, The conflict determination formula is as follows: Among them, For the conflict outcome of resource type j, This represents the percentage of resource type j required by the current operation. The percentage of demand for the i-th existing operation. This represents the largest proportion of the supply of this resource.

5. The strategy-driven intelligent management method for the entire lifecycle of IT resources as described in claim 1, characterized in that, The risk assessment is based on scoring of operational complexity and resource importance, and then calculating a risk value. If the risk value exceeds the preset threshold, a manual approval process will be triggered.

6. The strategy-driven intelligent management method for the entire lifecycle of IT resources as described in claim 1, characterized in that, The state convergence model describes the resource state changes through the following differential equation: Let t represent the resource status at time t. For the desired state, This is the initial state. For state update coefficients.

7. The strategy-driven intelligent management method for the entire lifecycle of IT resources as described in claim 1, characterized in that, The statistical analysis includes calculating the average utilization rate and volatility of resources. The formula for calculating the average utilization rate is as follows: The volatility calculation formula is as follows: in, The average utilization rate within the period [T1, T2] Let be the instantaneous utilization rate at time t. Let T1 and T2 be the volatility, and T1 and T2 be the two time points, respectively.

8. A policy-driven intelligent management system for the entire lifecycle of IT resources, characterized in that, The policy-driven intelligent management system for the entire lifecycle of IT resources is applied to the policy-driven intelligent management method for the entire lifecycle of IT resources as described in any one of claims 1 to 7, wherein the system comprises: The data acquisition and standardization module is used to collect static attribute and dynamic performance data of heterogeneous IT resources through resource probes deployed in multiple environments using multiple protocols, and to perform standardization processing based on a unified resource model to generate standardized resource data and store it in the resource database. The unified resource model includes at least 56 standard fields, and the standardization processing includes data format conversion, outlier cleaning, missing value completion, and scaling of each dimension of data to the [0,1] interval using a min-max normalization algorithm. The strategy matching and work order generation module is used to obtain the standardized resource data in real time through the intelligent scheduling engine, match the standardized resource data with the operation and maintenance rules predefined in the strategy center, generate a work order to be done when the match is successful, and push the work order to be done to the work order management module. The instruction generation and verification module is used to perform conflict verification and risk assessment based on the context information of the pending work order and the standardized resource data obtained from the resource database, generate an atomic operation instruction sequence and store it in the instruction queue. The instruction execution and status acquisition module is used to obtain atomic operation instructions from the instruction queue through the instruction execution module, and to execute the operation by calling the interface of the corresponding IT resource platform through the plug-in adapter based on the atomic operation instructions, and to collect the execution results and resource status. The feedback and status update module is used to feed back the execution results and the resource status to the resource database and the intelligent scheduling engine to update the resource status and ensure that the resource status approaches the desired target according to the status convergence model. The analysis and strategy iteration module is used to periodically analyze historical data and execution logs, generate strategy optimization suggestions through statistical analysis and predictive models, and update the rules of the strategy center to achieve continuous iterative optimization of the management system.

9. A strategy-driven intelligent management device for the entire lifecycle of IT resources, characterized in that, The policy-driven intelligent management device for the entire lifecycle of IT resources includes: a memory, a processor, and a policy-driven intelligent management program for the entire lifecycle of IT resources stored in the memory and executable on the processor. The policy-driven intelligent management program for the entire lifecycle of IT resources is configured to implement the steps of the policy-driven intelligent management method for the entire lifecycle of IT resources as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium stores a policy-driven intelligent management program for the entire lifecycle of IT resources. When the policy-driven intelligent management program for the entire lifecycle of IT resources is executed by the processor, it implements the steps of the policy-driven intelligent management method for the entire lifecycle of IT resources as described in any one of claims 1 to 7.

Images