A unified monitoring and intelligent operation and maintenance platform for intelligent enterprise information infrastructure

Abstract

The application discloses a kind of unified monitoring and intelligent operation and maintenance platform of wisdom enterprise information infrastructure, it is related to operation and maintenance intelligent technology field, comprising the following steps: S1, based on the construction of basic characteristic parameter set of information infrastructure operation characteristics;S2, using basic characteristic parameter set to carry out domain construction;S3, using domain set to carry out associated domain construction;S4, using associated domain set to generate state domain;S5, based on state domain set to generate global situation.This application can make the system form unified integrated association expression between steady state domain, dynamic domain and behavior domain by setting the associated strength construction method based on domain unified expression and generating three types of associated domain set according to domain type, so that the internal relationship between monitoring parameters can be accurately presented, thereby improving the overall accuracy and intelligent level of unified monitoring and intelligent operation and maintenance platform.

Description

Technical Field

[0001] This invention relates to the field of intelligent operation and maintenance technology, specifically to a unified monitoring and intelligent operation and maintenance platform for intelligent enterprise information infrastructure. Background Technology

[0002] Against the backdrop of the rapid evolution of IT infrastructure development in large enterprises, IT infrastructure has gradually formed a complex form characterized by "multi-platform, multi-architecture, and multi-regional" architectures, encompassing server clusters, virtualization resources, container platforms, storage arrays, network devices, and various security protection systems. Simultaneously, the cloudification, distribution, and microservice-based nature of business applications have resulted in infrastructure operations exhibiting multi-dimensional characteristics across devices, systems, and business chains. Traditional infrastructure operation and maintenance models often rely on segmented monitoring tools, with monitoring parameters scattered across different systems, lacking cross-dimensional structured representation. This leads to an understanding of the basic operational status remaining at a local perspective, making it difficult to support the needs of intelligent, predictive, and globally collaborative operations and maintenance.

[0003] In existing technologies, due to the lack of a unified structural domain expression framework and cross-domain association construction process, the correlation between monitoring parameters cannot be systematically identified, and the potential interactions across dimensions (steady-state—dynamic—behavior) are difficult to quantify and express. As a result, the system cannot obtain a real and derivable set of related domains, and thus cannot form a basic input for the overall situational awareness that supports large-scale enterprise-level infrastructure. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a unified monitoring and intelligent operation and maintenance platform for smart enterprise information infrastructure, thereby resolving the problems mentioned in the background section.

[0005] To achieve the above objectives, the present invention provides the following technical solution: In a first aspect, embodiments of the present invention provide a unified monitoring and intelligent operation and maintenance platform for intelligent enterprise information infrastructure, comprising the following steps: Includes the following steps: S1. Construct a set of basic characterization parameters based on the operational characteristics of information infrastructure; S2. Use the basic representation parameter set to construct the structural domains and obtain the set of structural domains; S3. Use the set of structural domains to construct the associated domains, and obtain the set of associated domains; S4. Use the associated domain set to generate the state domain set; S5. Generate the global situation based on the state domain set to obtain the global operational situation set.

[0006] To further optimize this technical solution, step S1 is used to extract a set of basic representation parameters directly used in the subsequent step S2 from the original operating data of the information infrastructure, according to three dimensions: steady-state characteristics, dynamic response characteristics, and internal behavior characteristics. Step S1, in the process of constructing the parameter set, includes the following steps: extracting steady-state characterization parameters to obtain a set of steady-state characterization parameters. ; Dynamic response parameter extraction, resulting in a dynamic response parameter set. ; Internal behavioral parameters are extracted to obtain a set of internal behavioral parameters. ; Parameter unification processing and output yield the basic representation parameter set. To further optimize this technical solution, step S2 is based on the set of basic representation parameters output in step S1. The operating parameters of information infrastructure are structurally divided to form a set of structural domains for subsequent state evaluation and model inference. ; Step S2, during the construction of the structural domain, includes the following process: The basis for domain partitioning is determined; Domain unit construction; The set of structural domains is formed.

[0007] To further optimize this technical solution, step S2 derives three structural domains based on the parameter type: Static structural domain :correspond ; Dynamic domain :correspond ; Behavioral structural domain :correspond ; The final set of structures is formed through the structure domain composition operation: .

[0008] To further optimize this technical solution, step S3 serves to optimize the set of structural domains formed in step S2. Building upon this foundation, the dispersed parameter structures within the structural domains are further transformed into a set of association domains capable of describing the intrinsic relationships between different structural domains. ; Step S3, in the process of constructing the associated domain, includes the following steps: Selection and correlation identification of cross-domain parameter pairs; Calculation of correlation strength; Generation of related fields.

[0009] To further optimize this technical solution, in step S3, when performing the selection and correlation identification of cross-domain parameter pairs, the set of structural domains is... Parameter pairs from different structural domains are combined to form a cross-domain candidate parameter pair set: ; in This is used to ensure that cross-domain analysis does indeed come from different representational dimensions; right For each pair of candidate elements, the correlation identification technique is used to determine their correlation, resulting in a Boolean judgment result: ; like If this parameter is not passed, then proceed to the next subprocess; if If so, discard it.

[0010] To further optimize this technical solution, in step S3, when performing the correlation strength calculation, for parameter pairs determined to be correlateable, the correlation strength is calculated. This strength is used as a quantitative value to express the degree of association; The correlation strength calculation follows the logic outlined below: ; , : Basic representation parameters derived from the structural domain; , The importance of the original parameters in the structural domain; The value range is [0,1], and the higher the value, the stronger the correlation between the two parameters.

[0011] To further optimize this technical solution, in step S3, when generating the association domain, the association identification result and the association strength calculation result are combined to form the final association domain set. ; Related fields The generation follows these principles: ; in The classification is based on the combination of structural domain types in step S2, including: Steady-state–dynamic domain correlation; Dynamic-behavioral structural domain association; Steady-state – behavioral domain association; Each type generates a corresponding association field. The set of related domains satisfies: ; In the formula, Corresponding steady-state–dynamic domain correlation; Corresponding to dynamic-behavioral structural domain association; Corresponding to steady-state – behavioral structural domain association.

[0012] To further optimize this technical solution, step S4 involves, based on the three types of association domain structures formed in step S3, using state representation construction and feature aggregation techniques to integrate each association domain... Transformed into independent and representative information infrastructure state domain items Furthermore, the state domains are structurally aggregated according to the type of the associated domains, ultimately resulting in a set of state domains. Its expression is: ; In the formula, Steady-state – Dynamic state domain; Dynamic – Behavioral State Domain; Steady-state – behavioral state domain.

[0013] To further optimize this technical solution, step S5 is based on the three types of state domain sets obtained in step S4. This data will be integrated to generate a comprehensive operational status set that describes the overall operational status of the current information infrastructure. .

[0014] In a second aspect, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the computer program instructions, when executed by the processor, implement the steps of a unified monitoring and intelligent operation and maintenance platform for a smart enterprise information infrastructure as described in the first aspect of the present invention.

[0015] Thirdly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, they implement the steps of a unified monitoring and intelligent operation and maintenance platform for a smart enterprise information infrastructure as described in the first aspect of the present invention.

[0016] Compared with existing technologies, this invention provides a unified monitoring and intelligent operation and maintenance platform for smart enterprise information infrastructure, which has the following beneficial effects: This unified monitoring and intelligent operation and maintenance platform for intelligent enterprise information infrastructure, by setting up a correlation strength construction method based on a unified expression of structural domains and generating three types of correlation domain sets according to the structural domain type, enables the system to form a unified integrated correlation expression across the steady-state domain, dynamic domain, and behavioral domain, thus accurately presenting the intrinsic relationships between monitoring parameters. This approach not only solves the problem of existing technologies being unable to construct cross-domain correlations, but also provides a structured and quantifiable input foundation for subsequent steps in forming the state domain and generating the overall situation, thereby improving the overall accuracy and intelligence level of the unified monitoring and intelligent operation and maintenance platform. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating a unified monitoring and intelligent operation and maintenance platform for intelligent enterprise information infrastructure proposed in this invention. Figure 2 This is a schematic diagram illustrating the structural domain construction process of a unified monitoring and intelligent operation and maintenance platform for intelligent enterprise information infrastructure proposed in this invention. Figure 3 This is a schematic diagram illustrating the association domain construction process of a unified monitoring and intelligent operation and maintenance platform for intelligent enterprise information infrastructure proposed in this invention. Figure 4 This is a schematic diagram illustrating the overall situation generation process of a unified monitoring and intelligent operation and maintenance platform for intelligent enterprise information infrastructure proposed in this invention. Detailed Implementation

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0020] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0022] Example 1: Refer to Figures 1-4 This is the first embodiment of the present invention, which provides a unified monitoring and intelligent operation and maintenance platform for intelligent enterprise information infrastructure, including the following steps: S1. Construct a set of basic characterization parameters based on the operational characteristics of information infrastructure; Step S1 is used to extract a set of basic representation parameters from the raw operational data of the information infrastructure, according to three dimensions: steady-state characteristics, dynamic response characteristics, and internal behavior characteristics, which can be directly used in the subsequent step S2. ; Step S1, in the process of constructing the parameter set, includes the following steps: Steady-state characterization parameter extraction: Steady-state parameters reflect the typical state of equipment under long-term stable operating conditions, and their extraction process includes: Stable range identification: Using mature "sliding window stability judgment technology", the stable range is identified based on whether the rate of change of the indicator is consistently less than a set threshold (commonly used in the industry, such as 1% to 3% rate of change). Interval determination criteria: The mean changes little and the variance remains at a low level within the window; The steady-state parameter extraction includes: The effective value of the current is calculated from the stable range using the industry-standard effective value extraction method. RMS voltage ; phase It is obtained through mature phase detection algorithms (such as those based on signal intersection detection); Health It is derived through mature hardware health assessment models (such as statistics based on the stability of resource usage changes and alarm frequency); steady state temperature The average value within the stable range of the temperature sensor is used; This leads to the set of steady-state characterization parameters: .

[0023] Dynamic response parameter extraction: Dynamic parameters reflect the response characteristics of information infrastructure when load changes, event triggers, or resource adjustments occur. The extraction process includes: Identification of dynamically changing segments: Using mature rate of change detection technology, the event triggering interval is automatically identified when the rate of change of the indicator crosses a preset threshold (such as CPU utilization changing by more than 5% within a sampling period). Extraction of rise and fall processes: Rising process: By using a phase segmentation technique based on the rate of change, the start and end points of the rapid rise of the indicator are identified, and then the rise time and magnitude are calculated; Attenuation process: A mature inflection point identification method (such as based on the first-order difference sign change) is used to identify the attenuation inflection point and extract the attenuation duration; Extraction of dynamic event-related metrics: Dynamic health is obtained using mature system volatility detection methods. .

[0024] Transient frequency change It is obtained through a frequency drift detection method based on discrete sequences.

[0025] This leads to the set of dynamic response parameters: .

[0026] Internal behavioral parameter extraction: Behavioral parameters reflect the logical characteristics of events, processes, and operation sequences within the system, and their extraction process includes: Event sequence construction: Event types are extracted from logs using sophisticated event template recognition technology, and event sequences are constructed in chronological order. ; Event Deviation Detection: Event deviations are identified using industry-standard fixed-threshold deviation detection methods (such as the number of deviations or deviation intervals based on normal sequences). ; Event correlation analysis: Use mature event template matching or association rule analysis (such as event association mining based on frequent itemsets) to identify logical relationships between events and generate... ; This leads to the set of internal behavioral parameters: .

[0027] Parameter standardization and output: To ensure that the subsequent step S2 can construct the structural domain in a unified parameter space, the above three types of parameters need to be standardized. This requires using industry-standard parameter normalization and type unification techniques, including: Dimensional unification: For parameters with different units (such as current, voltage, and temperature), a mature interval mapping technique is used to map them to the same interval (such as 0 to 1). Uniform type: For categorical parameters (such as event type and deviation category), use industry-standard sequence encoding methods (such as event ID encoding and rule-based label encoding). Unified structure: Organize steady-state, dynamic, and behavioral parameters into parameter sets according to a fixed structure. , To avoid structural differences between different devices causing subsequent steps to be unparseable; this parameter set serves as the direct input for step S2.

[0028] S2. Use the basic representation parameter set to construct the structural domains and obtain the set of structural domains; Step S2 is based on the set of basic representation parameters output in step S1. The operating parameters of information infrastructure are structurally divided to form a set of structural domains that can be used for subsequent state evaluation and model inference. .

[0029] Step S2, during the construction of the structural domain, includes the following process: Domain partitioning is determined based on: To ensure that the construction logic of the structural domain is interpretable and reproducible, this step first bases the construction logic on the parameter set. The domain division is based on the following settings: Steady-state parameters As the basic variables for constructing static domains, they mainly reflect the electrical and environmental steady state of the equipment during stable operation; Dynamic response parameters As a transitional feature variable in the construction of dynamic domain, it describes the response characteristics of the device under sudden changes, disturbances or load variations. Internal behavioral parameters As structural variables constructed from behavioral domains, they describe the device's behavior chain, action sequence, and deviation pattern; To ensure that the partitioning of structural domains has clear boundaries, a structural function is introduced: ; in: Indicates from The selected subset of parameters; For the constructed single structural domain; This represents a structured mapping rule whose function is to convert parameters of different types into structured domains based on type consistency, time consistency, and semantic consistency.

[0030] Domain unit construction: In actual construction, a unified structure mapping model is used to aggregate the parameter set into structural domain units according to attributes and runtime semantics, and a structure mapping formula is introduced: ; In the formula: : Belongs to parameter set A single parameter; : Parameter weights or importance indicators used for structural representation; The determination of parameters is achieved through mature parameter normalization and weighting techniques (such as min-max normalization + entropy-based weighting) to ensure that each parameter has consistent dimensions and clear importance in the structural domain representation. Based on the parameter type, three basic structural domains are constructed: Static structural domain :correspond ; Dynamic domain :correspond ; Behavioral structural domain :correspond ; Each structural field is a set expression of the above structural form. .

[0031] Domain set formation: After obtaining the various types of structural fields, the final output is formed through structural field combination operations: ; The entire set This is the final result of this step, which will be used for structural relationship modeling in the next step.

[0032] Traditional technologies typically classify or cluster operational data, but do not divide the system into domains according to the three-dimensional structure of steady state, dynamic state, and behavior. The domain structure construction logic in step S2 is based on the operational semantics of information infrastructure, dividing the parameter set into three structural dimensions of stable, dynamic, and internal behavior according to the operational mechanism, so that the structural domain expression is closer to the equipment's operational mechanism.

[0033] Traditional structured processing generally relies on a single parameter set or a single dimension of information; this step uses the complete three types of parameter sets from step S1 and constructs a multi-domain structure according to a unified structured mapping rule, so that the structural association modeling in subsequent steps has continuity, interpretability and high adaptability.

[0034] S3. Use the set of structural domains to construct the associated domains, and obtain the set of associated domains; The purpose of step S3 is to complete the set of structural domains formed in step S2. Building upon this foundation, the dispersed parameter structures within the structural domains are further transformed into a set of association domains capable of describing the intrinsic relationships between different structural domains. .

[0035] Step S3, in the process of constructing the associated domain, includes the following steps: Selection and correlation identification of cross-domain parameter pairs: Set of structural domains Parameter pairs from different structural domains are combined to form a cross-domain candidate parameter pair set: ; in This is used to ensure that cross-domain analysis does indeed come from different representational dimensions; right For each pair of candidate elements, a mature correlation identification technique is used to determine their relevance, including: For steady-state parameter pairs and dynamic domain parameter pairs, the consistency of their evolution patterns over time is analyzed using the mature time-window sliding correlation matching technique. For dynamic domains and internal behavior domains, mature event sequence pattern matching technology is used to determine the mapping relationship between behavior triggers and dynamic changes; For the steady-state domain and the internal behavior domain, the consistency of their representation value range and change trajectory is determined by using mature steady-state feature similarity calculation technology. Using the above identification method, a Boolean judgment result is obtained: ; like If this parameter is not passed, then proceed to the next subprocess; if If so, discard it; In this process and To provide a reference for the importance of parameters in the structural domain, guiding whether to perform correlation identification, but not directly involved in the calculation of the Boolean judgment result, this point is related to step S2. The definition remains consistent, that is , and All three originate from the same normalization and weighting process in step S2. They are importance calibration values ​​calculated separately for different parameters. The three are completely identical in their generation mechanism, and are only distinguished by the different indices of the corresponding parameters.

[0036] Calculation of correlation strength: For parameter pairs that are determined to be correlated, calculate their correlation strength. This strength is used as a quantitative value to express the degree of association; The correlation strength calculation follows the logic outlined below: ; , : Basic representation parameters derived from the structural domain; , The importance of the original parameters in the structural domain is used to provide a weight reference in the calculation; The calculation process based on this formula is as follows: We employ mature normalized statistical correlation analysis techniques and semantic matching scoring models, and incorporate them in the calculation. , The contribution of the weighting factor to moderating correlation is obtained through the following method. : Will , Transformed into a scale value within a unified interval; Similarity scoring is based on the synchronicity or order of parameters; Combination and Increase the contribution weight of key parameters; The value range is [0,1], and the higher the value, the stronger the correlation between the two parameters.

[0037] Generation of related fields: The association identification results are combined with the association strength calculation results to form the final association domain set. ; Related fields The generation follows these principles: ; in The classification is based on the combination of structural domain types in step S2, including: Steady-state–dynamic domain correlation; The association identification results are combined with the association strength calculation results to form the final association domain set. ; Related fields The generation follows these principles: ; in The classification is based on the combination of structural domain types in step S2, including: Steady-state–dynamic domain correlation; Dynamic-behavioral structural domain association; Steady-state – behavioral domain association; Each type generates a corresponding association field. The set of related domains satisfies: ; Each of them Each parameter pair contains all parameters that satisfy the correlation requirement and whose correlation strength has been calculated. These are the core inputs for constructing the global collaborative situation in step S4.

[0038] Existing technologies generally employ single-dimensional or single-scenario monitoring parameter correlation methods, such as static calculations based on the correlation between device indicators or single-domain behavioral analysis based on event streams. These technologies typically lack cross-domain structured parameter systems and cannot construct unified correlations between parameters of three completely different natures: steady-state, dynamic, and behavioral. In contrast, step S3 is based on the unified set of structural domains formed in step S2. Parameter pairs are selected based on three types of cross-domain logic: semantic consistency, temporal consistency, and internal behavioral consistency. This is then combined with a normalized association strength model to form a unified set of association domains. This enables the construction of associations to possess cross-domain uniformity, consistent structural expression, and adjustable parameter importance.

[0039] S4. Use the associated domain set to generate the state domain set; Step S4, based on the three types of association domain structures formed in step S3, uses mature state representation construction and feature aggregation techniques to integrate each association domain... Transformed into independent and representative information infrastructure state domain items Furthermore, the state domains are structurally aggregated according to the type of the associated domains, ultimately resulting in a set of state domains. .

[0040] Step S4 includes the following process: Extracting basic terms from the state domain based on the association domain structure: Traverse each associated field The triplet elements in: Extracting main parameters ; Extract related parameters ; Extracting correlation strength ; Preserving the importance of structural domains , (Originally from step S2, obtained through normalization and weighted classification); right Perform basic consistency processing: Using mature interval partitioning techniques (such as the three-segment interval partitioning method based on statistical distribution) to... The numerical range is mapped to three levels—weak, medium, and strong—to obtain a clearly defined correlation strength level item: This indicates a weak correlation. , indicating the strength of the association; This indicates a strong association. Generate state basic items : The structure is ; in, .

[0041] Three types of state components are generated based on the structural domain type: Based on the three types of structural domains formed by S2 , , Source mapping, Based on the three types of correspondences that appear in S3 during association construction (steady-state–dynamic, dynamic–behavior, steady-state–behavior), Mapped to three types of state representations.

[0042] The execution method is as follows: according to The type determines the target state domain category: like Source and The state domain type is denoted as = "steady state – dynamic"; like Source and Then the state domain type is recorded = "Dynamic-Behavior"; like Source and Then the state domain type is recorded = "steady state-behavior"; Construct state components based on target type

[0043] Is The state terms formed on this basis have the following structure: ; In the formula, : Main parameter; : Associated parameters; , Importance of structural domains; : Correlation strength; Association level; State domain category.

[0044] Generate the final state domain set by aggregating similar state items: according to For all Perform categorization and aggregation: all = "steady-state – dynamic" → aggregated into ; all = "Dynamic-Behavior" → Aggregated as ; all = "steady state – behavior" → aggregated as ; Form a set of state domains Its expression is: ; In the formula, Steady-state – Dynamic state domain; Dynamic – Behavioral State Domain; Steady-state – behavioral state domain.

[0045] S5. Generate the global situation based on the state domain set to obtain the global operational situation set; Step S5 is based on the three types of state domain sets obtained in step S4. This data will be integrated to generate a comprehensive operational status set that describes the overall operational status of the current information infrastructure. .

[0046] Step S5 in the global situation generation process includes the following steps: Perform unified preprocessing on all state domains: For each state domain output in step S4 Before entering the situation generation phase, preprocessing is performed using the following mature technologies: Parameter scaling: Mature feature normalization techniques (such as zero-mean normalization based on standard deviation and centering) are employed to... , Parameters with different dimensions are converted into unit scales so that they can be expressed by the same vector structure; Hierarchical structure transformation: Discrete levels ( The rank number is converted into a rank number that can be used for vector encoding. This conversion is accomplished using mature category encoding methods (such as one-hot encoding or ordinal encoding). Structure unification: All processed fields are arranged in a fixed order, as follows: Steady-state parameters, dynamic parameters, behavioral parameters, importance rating, correlation strength level, 】 This sorting rule is implemented using the mature "fixed field sequence feature concatenation technology" in engineering. Thus ensuring all Before entering the situation generation stage, all have a unified and combinable structural expression.

[0047] Based on the classification-based situation vector construction: For each type of state domain, a corresponding situation vector is generated, forming a three-segment structure: right , , All Separately, the standardized structural units are assembled into vector fragments according to the scene sequence using a mature "sequential feature assembly" technique. ; The final constructed global situation vector is: ; in" "" indicates a vector concatenation operation, which is achieved through mature structural feature concatenation technology.

[0048] Generate a global operational status set based on a multi-state fusion strategy: After obtaining the global situation vector Then, it needs to be incorporated into the final global operational status quo set. The process of its formation includes: Situation vector encapsulation: Encapsulating the obtained single situation vector As a description of the system situation in the current period or current analysis window; Aggregating and forming a situational awareness dataset: Employing mature "time-series situational awareness aggregation" technology, it aggregates situations generated at different times. Forming a set: ; The final result is strictly consistent with the step titles: Obtain the overall operational status set .

[0049] Global operational status quo

[0050] It can be directly used as the core status input for a unified monitoring and intelligent operation and maintenance platform for smart enterprise information infrastructure, enabling the platform to build a holistic operational view of various infrastructure resources. The platform receives...

[0051] Then, the content of the structural units can be matched with the platform's existing monitoring object model to generate a unified operation panel covering network, computing, storage, and business systems, ensuring that status information from different sources remains consistent at the structural level. The platform can also be based on...

[0052] The segmented situational awareness structure presents local, related, and global states, enabling operations and maintenance personnel to continuously observe the overall situation from fine-grained details within a single interface. Furthermore,

[0053] It can also be directly referenced by the platform's alarm module, resource scheduling module, or historical archive module to generate event-level change records, cross-domain resource load analysis results, or cross-system status correlation results, thereby ensuring that the infrastructure operational status can maintain a unified expression and consistent interpretation across different functional modules. In this way,

[0054] Enabling the platform to have globally consistent and clearly structured operational status inputs is a necessary foundation for building unified monitoring and intelligent operation and maintenance capabilities.

[0055] Existing technologies only monitor the situation for single-type indicators or single-domain relationships, lacking a unified situation generation method based on a "multi-state domain logical structure".

[0056] Step S5 uses the three types of structural relationships (steady-state-dynamic, dynamic-behavior, and steady-state-behavior) already constructed in S4 as the basis for splicing, which is structurally different from traditional methods.

[0057] Existing technologies mostly use direct feature stacking to construct situation vectors, which cannot guarantee semantic continuity between cross-domain indicators.

[0058] Step S5 is based on The structured classification and splicing makes the global situation structure segmented and interpretable.

[0059] Current situation generation technologies typically rely on complex models to achieve indicator fusion.

[0060] This step fully utilizes mature standardization, coding, and splicing technologies to construct a logically transparent and explainable system.

[0061] Example 2: This example provides a practical application scenario for a unified monitoring and intelligent operation and maintenance platform for a smart enterprise's information infrastructure: A large manufacturing enterprise has deployed an IT infrastructure covering network equipment, server clusters, production management systems, industrial control gateways, and various business systems within its production park. Based on this solution, the enterprise has built a unified monitoring and intelligent operation and maintenance platform to achieve centralized display and traceable management of the overall operational status.

[0062] The platform first integrates real-time operational information from network switches, application servers, database systems, industrial control equipment, and other sources. Through steady-state feature extraction, dynamic behavior description, and internal behavior analysis, it transforms the operational status of all infrastructure into a parameter set with the same structure. At this stage, the system can generate standardized parameter representations based on the device's own operating characteristics without needing to understand business attributes, thus enabling all heterogeneous devices to obtain a basic state description in a consistent format.

[0063] The platform is based on the parameter set The importance and correlation of parameters are automatically used to form a set of structural domains with fixed structures for each type of equipment. The server node's structure domain consists of CPU behavior domain, memory behavior domain, disk I / O domain, and network interaction domain; the database server, on the other hand, includes unique structures such as transaction pressure domain and connection behavior domain. This unification of structure domains enables the platform to perform horizontal comparisons of the operational characteristics of different systems, establishing a common structural foundation for subsequent cross-system analysis.

[0064] The platform generates a set of associated domains based on the actual operational interactions between structural domains. .

[0065] The request-response behavior between the business application server and the database server forms a stable association domain; The "control command-status feedback" behavior between the edge controller and the upper management system forms another type of association domain.

[0066] Through these related domains, the platform can automatically identify which systems have operational dependencies, thus enabling the complete expression of cross-system state linkages and laying a structural foundation for subsequent global situational analysis.

[0067] The platform combines the hierarchical characteristics of each structural domain and associated domain to generate a set of state domains for the operational status of devices and systems. In a specific business process, the system outputs a status domain that includes the application server's load status, database latency status, and the real-time status level of the network link. The generation of this status domain allows the platform to observe the current operational status of key components at the business level, thus supporting a unified monitoring view across devices and systems.

[0068] The platform integrates multiple state domains from different business processes and systems to generate a comprehensive operational status set. In the actual interface, the platform presents the current operational status of the enterprise's IT infrastructure in a comprehensive situational view, including: Are there operational bottlenecks in the business process? Does a certain type of device cluster experience overall load fluctuations? Which systems have experienced significant changes in their inter-system interactions? Do devices in certain areas exhibit similar abnormal patterns?

[0069] The structured representation enables the platform to present the operational status of the entire enterprise in a clear and unified manner, providing high-precision basis for decision-making and operation and maintenance.

[0070] Example 3: This example also provides a computer device applicable to a unified monitoring and intelligent operation and maintenance platform for a smart enterprise information infrastructure, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize a unified monitoring and intelligent operation and maintenance platform for a smart enterprise information infrastructure as proposed in the above examples.

[0071] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements a unified monitoring and intelligent operation and maintenance platform for intelligent enterprise information infrastructure as proposed in the above embodiments.

[0072] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0073] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0074] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0075] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0076] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0077] It should be noted that 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 preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A unified monitoring and intelligent operation and maintenance platform for intelligent enterprise information infrastructure, characterized in that, The platform performs unified monitoring and intelligent operation and maintenance, including the following steps: S1. Construct a set of basic characterization parameters based on the operational characteristics of information infrastructure; S2. Use the basic representation parameter set to construct the structural domains and obtain the set of structural domains; Based on the set of basic representation parameters output in step S1 The operating parameters of information infrastructure are structurally divided to form a set of structural domains for subsequent state evaluation and model inference. The process of constructing a structural domain includes the following steps: determining the basis for domain partitioning; constructing domain units; and forming a set of structural domains. S3. Use the set of structural domains to construct the associated domains, and obtain the set of associated domains; The set of structural domains formed in step S2 Building upon this foundation, the dispersed parameter structures within the structural domains are further transformed into a set of association domains capable of describing the intrinsic relationships between different structural domains. The process of constructing the association domain includes the following steps: selection of cross-domain parameter pairs and identification of their relevance; calculation of association strength; generation of the association domain; and finally, the generation of three types of association domain structures, including steady-state-dynamic structure domain association, dynamic-behavioral structure domain association, and steady-state-behavioral structure domain association. S4. Use the associated domain set to generate the state domain set; Based on the three types of association domain structures formed in step S3, each association domain... Transformed into independent and representative information infrastructure state domain items The state domains are then aggregated in a structured manner according to their types to obtain a set of state domains. ; S5. Generate the global situation based on the state domain set to obtain the global operational situation set; Based on the state domain set obtained in step S4 This data will be integrated to generate a comprehensive operational status set that describes the overall operational status of the current information infrastructure. .

2. The unified monitoring and intelligent operation and maintenance platform for intelligent enterprise information infrastructure according to claim 1, characterized in that, Step S1 is used to extract a set of basic representation parameters from the original operational data of the information infrastructure, according to three dimensions: steady-state characteristics, dynamic response characteristics, and internal behavior characteristics, which are directly used in the subsequent step S2. Step S1, in the process of constructing the parameter set, includes the following steps: Steady-state characterization parameters are extracted to obtain a set of steady-state characterization parameters. ; Dynamic response parameter extraction, resulting in a dynamic response parameter set. ; Internal behavioral parameters are extracted to obtain a set of internal behavioral parameters. ; Parameter unification processing and output yield the basic representation parameter set. .

3. The unified monitoring and intelligent operation and maintenance platform for intelligent enterprise information infrastructure according to claim 2, characterized in that, Step S2 derives three structural domains based on the parameter type: Static structural domain :correspond ; Dynamic domain :correspond ; Behavioral structural domain :correspond ; The final set of structures is formed through the structure domain composition operation: 。 4. The unified monitoring and intelligent operation and maintenance platform for intelligent enterprise information infrastructure according to claim 1, characterized in that, In step S3, when performing the selection of cross-domain parameter pairs and the identification of their relevance, the set of structural domains will be... Parameter pairs from different structural domains are combined to form a cross-domain candidate parameter pair set: ； in This is used to ensure that cross-domain analysis does indeed come from different representational dimensions; , : Basic representation parameters derived from the structural domain; , The importance of the original parameters in the structural domain; right For each pair of candidate elements, the correlation identification technique is used to determine their correlation, resulting in a Boolean judgment result: ； like If this parameter is not passed, then proceed to the next subprocess; if If so, discard it.

5. The unified monitoring and intelligent operation and maintenance platform for intelligent enterprise information infrastructure according to claim 4, characterized in that, In step S3, when performing the association strength calculation, the association strength of parameter pairs that are determined to be associatable is calculated. This strength is used as a quantitative value to express the degree of association; The correlation strength calculation follows the logic outlined below: ； The value range is [0,1], and the higher the value, the stronger the correlation between the two parameters.

6. The unified monitoring and intelligent operation and maintenance platform for intelligent enterprise information infrastructure according to claim 5, characterized in that, In step S3, when generating the association domains, the association identification results are combined with the association strength calculation results to form the final set of association domains. ; Related fields The generation follows these principles: ； in The classification is based on the combination of structural domain types in step S2, including: Steady-state–dynamic domain correlation; Dynamic-behavioral structural domain association; Steady-state – behavioral domain association; Each type generates a corresponding association field. The set of related domains satisfies: ； In the formula, Corresponding steady-state–dynamic domain correlation; Corresponding to dynamic-behavioral structural domain association; Corresponding to steady-state – behavioral structural domain association.

7. The unified monitoring and intelligent operation and maintenance platform for intelligent enterprise information infrastructure according to claim 1, characterized in that, The set of state domains obtained in step S4 Its expression is: ； In the formula, Steady-state – Dynamic state domain; Dynamic – Behavioral State Domain; Steady-state – behavioral state domain.

Images