General field matching method and system in operation and maintenance scene and electronic equipment

Abstract

The invention relates to the technical field of computer data processing and information system operation and maintenance, in particular to a universal field matching method and system in an operation and maintenance scene and electronic equipment, and the method comprises the following steps: acquiring multi-source basic data and historical field matching data; constructing an initial field regular standard and an initial priority model based on historical field matching data, and extracting multi-source basic data to obtain four types of core features and effective features; and optimizing the initial field regularization standard and the initial priority model based on the effective features to obtain a regular expression matching rule, a structured association key rule and a fuzzy matching threshold rule, and executing a matching task on the rules to obtain a real-time matching result and exception handling feedback. And the rule is optimized based on the real-time matching result and the exception processing feedback to obtain a field matching standard, and the field matching efficiency under different operation and maintenance scenes is improved.

Description

Technical Field

[0001] This application relates to the technical field of computer data processing and information system operation and maintenance, and in particular to a general field matching method, system and electronic equipment for operation and maintenance scenarios. Background Technology

[0002] In modern IT operations and maintenance systems, field matching is a core foundational step for achieving multi-system data collaboration, fault location, and business traceability. It is widely used in various operations and maintenance scenarios, such as server log analysis, work order workflow association, monitoring data integration, and asset information verification. Its core objective is to establish correspondences between fields in different data sources to achieve unified parsing and value mining of cross-system data, providing data support for operations and maintenance decisions.

[0003] As enterprises expand their business scale and deepen their digital transformation, operation and maintenance scenarios exhibit significant dynamic evolution characteristics: On the one hand, business scenarios are constantly expanding, extending from traditional physical machine operation and maintenance to containerized and cloud-native environment operation and maintenance, and from basic network monitoring to complex scenarios such as security compliance auditing and end-to-end performance tracing; on the other hand, data sources are becoming increasingly diverse, with multiple source systems such as ticketing systems, monitoring platforms, log systems, and asset management systems running in parallel, generating structured, semi-structured, and unstructured data that are intertwined and integrated, and field types, formats, and relational logic continuously adjusting as scenarios change. For example, in the server log analysis scenario of internet companies, initially only the IP address + port number field was needed to locate basic network requests, but with the advancement of containerization deployment, a new container ID field needs to be added for troubleshooting container environment faults.

[0004] In existing technologies, field matching usually relies on fixed rules configured manually or static matching methods with preset associations. The matching criteria are strongly bound to specific operation and maintenance scenarios. When the operation and maintenance scenarios change, existing solutions are difficult to adapt automatically, resulting in a decrease in the accuracy of field matching. Summary of the Invention

[0005] To address at least one of the aforementioned technical problems, this application provides a general field matching method, system, and electronic device for operation and maintenance scenarios.

[0006] Firstly, this application provides a general field matching method for operation and maintenance scenarios, adopting the following technical solution: Acquire multi-source basic data and historical field matching data for operation and maintenance scenarios; Based on historical field matching data, an initial field regular expression standard and an initial priority model are constructed. The initial field regular expression standard is a mapping relationship standard between historical field types and corresponding regular expression matching templates, and the initial priority model is a mapping relationship model between historical operation and maintenance scenario types and the priority of operation and maintenance field association keys. Feature extraction and verification are performed on multi-source basic data to obtain four core features of the operation and maintenance scenario and corresponding effective features. The four core features include scenario type features, business priority features, field metadata features, and historical interaction features. Based on effective features, the initial field regular expression standard and the initial priority model are optimized to obtain regular expression matching rules, structured association key rules and fuzzy matching threshold rules. The regular expression matching rules are the rules in the optimized initial field regular expression standard that match the field metadata features and scenario type features. The structured association key rules are the single-key or multi-key association logic rules generated by the optimized initial priority model. The fuzzy matching threshold rules are the threshold rules set by fuzzy differentiation based on business priority features and field metadata features. Regular expression matching rules, structured association key rules, and fuzzy matching threshold rules are converted into executable code and the matching task is executed to obtain real-time matching results and exception handling feedback. Based on real-time matching results and anomaly handling feedback, closed-loop optimization is performed on regular expression matching rules, structured association key rules, and fuzzy matching threshold rules to obtain field matching standards under different operation and maintenance scenarios. Collect operation and maintenance scenario data within the current time period, apply the field matching criteria to the field matching of the operation and maintenance scenario data, and obtain the field matching results corresponding to the operation and maintenance scenario data.

[0007] By adopting the above technical solution, multi-source basic data and historical field matching data of operation and maintenance scenarios are obtained, providing a comprehensive and realistic data foundation for subsequent accurate model construction. Based on the multi-source basic data and historical field matching data, initial field regularization standards and initial priority models are constructed. The initial field regularization standards clarify the mapping relationship between historical field types and corresponding regularization matching templates, providing preliminary specifications for field matching; the initial priority model determines the correspondence between historical operation and maintenance scenario types and the priority of operation and maintenance field association keys, helping to quickly locate key fields in complex scenarios. Feature extraction and verification are performed on the multi-source basic data to obtain four core features of operation and maintenance scenarios and corresponding effective features. Scenario type features clarify the category of operation and maintenance scenarios, providing scenario background for subsequent matching; business priority features reflect the importance of different businesses in operation and maintenance, helping to allocate resources rationally; field metadata features describe the basic attributes of fields and are key to accurate matching; historical interaction features record past interaction situations, providing a reference for matching. The initial field regularization standards and initial priority models are optimized based on the effective features. The initial field regular expression standard is optimized by combining field metadata features and scenario type features, making the regular expression matching rules more closely match the actual characteristics of fields in different scenarios and improving matching accuracy. For the initial priority model, the optimized structured association key rules, through single-key or multi-key association logic rules, more reasonably determine the association relationships between fields, improving the rationality of the association. The fuzzy matching threshold rules, combined with business priority features and field metadata features, are set in a fuzzy, differentiated manner, making the matching process more flexible and adaptable. The optimized regular expression matching rules, structured association key rules, and fuzzy matching threshold rules collectively improve the accuracy and comprehensiveness of field matching. The regular expression matching rules, structured association key rules, and fuzzy matching threshold rules are then converted into executable code and executed for matching tasks. During the matching task, the regular expression matching rules can quickly and accurately identify fields that conform to specific patterns; the structured association key rules can accurately find the association relationships between fields based on the set logic rules; and the fuzzy matching threshold rules can flexibly match within a certain range, handling some irregular data. This automated execution method enables rapid real-time matching results and anomaly handling feedback, significantly improving the efficiency of field matching in operational scenarios, reducing manual intervention, and lowering the probability of errors. Closed-loop optimization is performed on regular expression matching rules, structured association key rules, and fuzzy matching threshold rules based on real-time matching results and anomaly handling feedback. Real-time matching results reflect the application effect of the current rules in real-world scenarios, while anomaly handling feedback points out existing problems and shortcomings. Targeted optimization and adjustments to the rules allow them to continuously adapt to ever-changing operational scenarios.The field matching standards obtained after closed-loop optimization for different operation and maintenance scenarios are more closely aligned with actual needs, exhibiting higher accuracy and reliability. Operation and maintenance scenario data within the current time period is collected, and the field matching standards are applied to the field matching of this data. Due to the series of optimizations and adjustments made previously, the resulting field matching standards possess high accuracy and adaptability. When applied to actual operation and maintenance scenario data, they can quickly and accurately match fields in the data with the corresponding standards, obtaining field matching results corresponding to the operation and maintenance scenario data. This helps operation and maintenance personnel quickly understand the actual situation of the operation and maintenance scenario, promptly identify potential problems, and thus improve the efficiency and quality of the entire operation and maintenance work.

[0008] In one possible implementation, feature extraction and verification are performed on multi-source basic data to obtain four core features of the operation and maintenance scenario and corresponding effective features, including: The scenario description information, business identification information, field attribute information and historical interaction records in the multi-source basic data are extracted separately to obtain the initial scenario type features, initial business priority features, initial field metadata features and initial historical interaction features. Based on the preset integrity verification rules, the integrity of the initial scenario type features, initial business priority features, initial field metadata features and initial historical interaction features are verified respectively. Invalid initial features with missing key attributes are removed, and features that meet the integrity requirements are retained. If there are conflicting features among the features to be processed, if so, calculate the feature confidence level of the feature to be processed for each conflicting branch, and determine the optimal feature value corresponding to the feature to be processed based on the feature confidence level. Replace each conflicting feature data in the feature to be processed with the corresponding optimal feature value to obtain conflict-free features. The conflict-free features are categorized and organized according to scenario type features, business priority features, field metadata features, and historical interaction features, resulting in four core features of the operation and maintenance scenario and the corresponding effective features.

[0009] In one possible implementation, the feature confidence level of the feature to be processed is calculated for each conflicting branch. Based on the feature confidence level, the optimal feature value corresponding to the feature to be processed is determined. Each conflicting feature data in the feature to be processed is then replaced with its corresponding optimal feature value to obtain conflict-free features, including: Traverse the features to be processed and identify target feature items with conflicting values. Conflicts are defined as multiple different values ​​under the same feature dimension or feature values ​​that are incompatible with the scenario's business logic. Construct a feature credibility assessment system, which includes the credibility of feature data sources, the frequency of feature values, the correlation and fit between features and scenario business, and the effectiveness of feature values ​​in historical matching. We assign weight coefficients to the feature data source credibility, feature value frequency, correlation fit and effectiveness respectively, and obtain the feature credibility evaluation system after weight allocation. Based on the feature credibility evaluation system after weight allocation, the individual evaluation score of each candidate value in the target feature item is calculated to obtain the feature credibility of each candidate value; The candidate value with the highest feature confidence is taken as the optimal feature value of the target feature item, and all conflicting feature values ​​of the target feature items in the features to be processed are replaced with the corresponding optimal feature values ​​to obtain conflict-free features.

[0010] In one possible implementation, the initial field regularization criteria and initial priority model are optimized based on effective features to obtain regular expression matching rules, structured association key rules, and fuzzy matching threshold rules, including: Extract field metadata features and scenario type features from the valid features; The basic regular expression template is obtained by matching the field metadata features according to the initial field regular expression standard. Based on scenario type characteristics and business priority characteristics, the parameters of the basic regular expression template are adjusted, the boundary conditions are optimized, and the format compatibility is extended to obtain candidate rules for regular expressions. The candidate rules of the regular expression are validated, and rules whose matching error exceeds a preset threshold are removed to obtain the regular expression matching rules; Input the scenario type feature, business priority feature, and historical interaction feature from the effective features into all the initial priority models, and calculate the correlation importance weight of each field feature; Based on the ranking results of the importance weights of associations and the business complexity of the current scenario, determine the single-key association mode or the multi-key association mode, and generate structured association key rules. Business priority weights are determined based on business priority features among the effective features, field types are determined based on field metadata features, and sensitivity coefficients are assigned to different field types according to a preset sensitivity grading standard. The business priority and sensitivity coefficient are respectively input into the fuzzy matching threshold calculation formula to obtain the dynamic fuzzy threshold corresponding to each field under different operation and maintenance scenarios. Based on the correspondence between dynamic fuzzy thresholds and fields, fuzzy matching threshold rules are generated.

[0011] In one possible implementation, regular expression matching rules, structured association key rules, and fuzzy matching threshold rules are respectively converted into executable code and executed to perform the matching task, obtaining real-time matching results and exception handling feedback, including: Substitute the regular expression matching rules, structured association key rules, and fuzzy matching threshold rules into the corresponding execution templates to generate executable code; Deploy executable code to the operation and maintenance data processing node, import the data of the fields to be matched, execute the matching task, and monitor the data flow status in real time during the matching process. Record the matching success results, matching failure data and abnormal trigger events to obtain real-time matching results and abnormal handling feedback.

[0012] In one possible implementation, closed-loop optimization is performed on regular expression matching rules, structured association key rules, and fuzzy matching threshold rules based on real-time matching results and anomaly handling feedback to obtain field matching standards for different operational scenarios, including: Classify and statistically analyze the real-time matching results and anomaly handling feedback, identify the adaptation defects of the matching standards, and determine the types of rules to be adjusted and the direction of adjustment. Based on the adjustment direction, the parameters of the regular expression matching rule, the structured association key rule, and the fuzzy matching threshold rule are modified to obtain the optimized regular expression matching rule, the optimized structured association key rule, and the optimized fuzzy matching threshold rule. The optimized regular expression matching rules, optimized structural association key rules, and optimized fuzzy matching threshold rules are stored in a preset matching standard knowledge base to obtain field matching standards for different operation and maintenance scenarios.

[0013] In one possible implementation, field matching criteria are applied to field matching of the operation and maintenance scenario data to obtain field matching results corresponding to the operation and maintenance scenario data, including: Extract scenario features from operation and maintenance scenario data and the scenario feature set from historical operation and maintenance scenario data, and calculate the similarity between the scenario features and each feature vector in the scenario feature set; Based on similarity and field matching criteria, determine the field matching method corresponding to the operation and maintenance scenario data, and apply the field matching method to the operation and maintenance scenario data to obtain the field matching result corresponding to the operation and maintenance scenario data.

[0014] Secondly, this application provides a general field matching system for operation and maintenance scenarios, which adopts the following technical solution: A general field matching system for operation and maintenance scenarios includes: The data acquisition module is used to acquire multi-source basic data and historical field matching data for operation and maintenance scenarios; The initial construction module is used to build an initial field regular expression standard and an initial priority model based on historical field matching data. The initial field regular expression standard is a mapping relationship standard between historical field types and corresponding regular expression matching templates, and the initial priority model is a mapping relationship model between historical operation and maintenance scenario types and the priority of operation and maintenance field association keys. The extraction and verification module is used to extract and verify features from multi-source basic data to obtain four types of core features of the operation and maintenance scenario and the effective features corresponding to the four types of core features. The four types of core features include scenario type features, business priority features, field metadata features, and historical interaction features. The rule generation module is used to optimize the initial field regular expression standard and the initial priority model based on effective features to obtain regular expression matching rules, structured association key rules and fuzzy matching threshold rules. The regular expression matching rules are the rules in the optimized initial field regular expression standard that match the field metadata features and scenario type features. The structured association key rules are the single-key or multi-key association logic rules generated by the optimized initial priority model. The fuzzy matching threshold rules are the threshold rules set by fuzzy differentiation based on business priority features and field metadata features. The matching test module is used to convert regular expression matching rules, structured association key rules, and fuzzy matching threshold rules into executable code and execute matching tasks to obtain real-time matching results and exception handling feedback. The closed-loop optimization module is used to perform closed-loop optimization of regular expression matching rules, structured association key rules and fuzzy matching threshold rules based on real-time matching results and anomaly handling feedback, so as to obtain field matching standards under different operation and maintenance scenarios. The real-time matching module is used to collect operation and maintenance scenario data within the current time period, apply field matching standards to the field matching of the operation and maintenance scenario data, and obtain the field matching results corresponding to the operation and maintenance scenario data.

[0015] Thirdly, this application provides an electronic device that adopts the following technical solution: At least one processor; Memory; At least one application, wherein the application is stored in memory and configured to be executed by at least one processor, the application being configured to: execute a general field matching method for an operational scenario as described in any of the first aspects.

[0016] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform a general field matching method for an operational scenario, as described in any of the first aspects.

[0017] In summary, this application includes at least one of the following beneficial technical effects: By adopting the above technical solution, multi-source basic data and historical field matching data of operation and maintenance scenarios are obtained, providing a comprehensive and realistic data foundation for subsequent accurate model construction. Based on the multi-source basic data and historical field matching data, initial field regularization standards and initial priority models are constructed. The initial field regularization standards clarify the mapping relationship between historical field types and corresponding regularization matching templates, providing preliminary specifications for field matching; the initial priority model determines the correspondence between historical operation and maintenance scenario types and the priority of operation and maintenance field association keys, helping to quickly locate key fields in complex scenarios. Feature extraction and verification are performed on the multi-source basic data to obtain four core features of operation and maintenance scenarios and corresponding effective features. Scenario type features clarify the category of operation and maintenance scenarios, providing scenario background for subsequent matching; business priority features reflect the importance of different businesses in operation and maintenance, helping to allocate resources rationally; field metadata features describe the basic attributes of fields and are key to accurate matching; historical interaction features record past interaction situations, providing a reference for matching. The initial field regularization standards and initial priority models are optimized based on the effective features. The initial field regular expression standard is optimized by combining field metadata features and scenario type features, making the regular expression matching rules more closely match the actual characteristics of fields in different scenarios and improving matching accuracy. For the initial priority model, the optimized structured association key rules, through single-key or multi-key association logic rules, more reasonably determine the association relationships between fields, improving the rationality of the association. The fuzzy matching threshold rules, combined with business priority features and field metadata features, are set in a fuzzy, differentiated manner, making the matching process more flexible and adaptable. The optimized regular expression matching rules, structured association key rules, and fuzzy matching threshold rules collectively improve the accuracy and comprehensiveness of field matching. The regular expression matching rules, structured association key rules, and fuzzy matching threshold rules are then converted into executable code and executed for matching tasks. During the matching task, the regular expression matching rules can quickly and accurately identify fields that conform to specific patterns; the structured association key rules can accurately find the association relationships between fields based on the set logic rules; and the fuzzy matching threshold rules can flexibly match within a certain range, handling some irregular data. This automated execution method enables rapid real-time matching results and anomaly handling feedback, significantly improving the efficiency of field matching in operational scenarios, reducing manual intervention, and lowering the probability of errors. Closed-loop optimization is performed on regular expression matching rules, structured association key rules, and fuzzy matching threshold rules based on real-time matching results and anomaly handling feedback. Real-time matching results reflect the application effect of the current rules in real-world scenarios, while anomaly handling feedback points out existing problems and shortcomings. Targeted optimization and adjustments to the rules allow them to continuously adapt to ever-changing operational scenarios.The field matching standards obtained after closed-loop optimization for different operation and maintenance scenarios are more closely aligned with actual needs, exhibiting higher accuracy and reliability. Operation and maintenance scenario data within the current time period is collected, and the field matching standards are applied to the field matching of this data. Due to the series of optimizations and adjustments made previously, the resulting field matching standards possess high accuracy and adaptability. When applied to actual operation and maintenance scenario data, they can quickly and accurately match fields in the data with the corresponding standards, obtaining field matching results corresponding to the operation and maintenance scenario data. This helps operation and maintenance personnel quickly understand the actual situation of the operation and maintenance scenario, promptly identify potential problems, and thus improve the efficiency and quality of the entire operation and maintenance work. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a general field matching method for an operation and maintenance scenario provided in an embodiment of this application.

[0019] Figure 2 This is a schematic diagram of the structure of a general field matching system in an operation and maintenance scenario provided in an embodiment of this application.

[0020] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0021] The following is in conjunction with the appendix Figure 1-3 This application will be described in further detail.

[0022] This specific embodiment is merely an explanation of this application and is not intended to limit it. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they are within the scope of this application.

[0023] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0025] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0026] This application provides a general field matching method for operation and maintenance scenarios, executed by electronic devices, such as... Figure 1 As shown, the method includes: Step S10: Obtain multi-source basic data and historical field matching data for the operation and maintenance scenario.

[0027] In this embodiment of the application, fault work order data (structured data, including fields such as work order number, fault description, processing priority, and associated asset ID) from the work order system, server performance monitoring data (semi-structured data, including fields such as CPU utilization, memory usage, and log output path) from the monitoring platform, application operation logs (unstructured data, including information such as timestamp, request URL, IP address, and response status code) from the log system, and equipment information data (structured data, including fields such as equipment model, deployment location, and business line) from the asset management system are synchronized through the data acquisition interface to form multi-source basic data for the operation and maintenance scenario; at the same time, field matching rules (such as various regular expressions and association key configurations), matching results (such as successfully matched field pairs and failed matching records) and manual correction records (such as corrected rule content and correction reason annotations) under different scenarios (such as network fault troubleshooting, security compliance auditing, and performance optimization scenarios) in the past 6 months are extracted from the historical operation and maintenance database to form historical field matching data.

[0028] Step S11: Construct initial field regular expression standards and initial priority models based on historical field matching data.

[0029] Among them, the initial field regular expression standard is the mapping relationship standard between historical field types and corresponding regular expression matching templates, and the initial priority model is the mapping relationship model between historical operation and maintenance scenario types and the priority of operation and maintenance field association keys.

[0030] In this embodiment, scenario type, business priority identifier (high / medium / low), field association frequency, and association matching success rate are extracted from historical data to construct a model training dataset, in which the training set accounts for 70% and the test set accounts for 30%. The gradient boosting tree (XGBoost) algorithm is used to train the model, with the field association matching success rate as the optimization objective. The learning rate (0.1), tree depth (6), and other parameters are iteratively adjusted. Training stops when the prediction accuracy of the test set reaches 92%, and the initial priority model is obtained. The input features of this model are scenario type, business priority, and field metadata, and the output is the priority ranking result of the field association key.

[0031] Step S12: Perform feature extraction and verification on the multi-source basic data to obtain four types of core features of the operation and maintenance scenario and the effective features corresponding to the four types of core features.

[0032] Among them, the four core features include scenario type features, business priority features, field metadata features, and historical interaction features.

[0033] Specifically, scenario description information, business identification information, field attribute information, and historical interaction records are extracted from multi-source basic data to obtain initial scenario type features, initial business priority features, initial field metadata features, and initial historical interaction features. Based on preset integrity verification rules, integrity verification is performed on the initial scenario type features, initial business priority features, initial field metadata features, and initial historical interaction features respectively, eliminating invalid initial features with missing key attributes and retaining features that meet the integrity requirements. Conflicting features are detected among the features to be processed. If they exist, the feature credibility of each conflicting branch is calculated, and the optimal feature value corresponding to the feature to be processed is determined based on the feature credibility. Each conflicting feature data in the features to be processed is replaced with the corresponding optimal feature value to obtain conflict-free features. The conflict-free features are then categorized and organized according to scenario type features, business priority features, field metadata features, and historical interaction features to obtain four core features of the operation and maintenance scenario and corresponding effective features.

[0034] When performing conflict feature detection, the features to be processed are traversed to identify target feature items with conflicting values. Conflicts occur when multiple different values ​​exist within the same feature dimension, or when the feature value is incompatible with the scenario's business logic. A feature credibility evaluation system is constructed, which includes the credibility of the feature data source, the frequency of feature values, the correlation and fit between the feature and the scenario's business logic, and the effectiveness of the feature value in historical matching. Weight coefficients are assigned to the feature data source credibility, feature value frequency, correlation and fit, and effectiveness, resulting in a weighted feature credibility evaluation system. Based on this weighted system, a single evaluation score is calculated for each candidate value in the target feature item, yielding the feature credibility of each candidate value. The candidate value with the highest credibility is selected as the optimal feature value for the target feature item, and all conflicting feature values ​​in the features to be processed are replaced with the corresponding optimal feature value, resulting in conflict-free features.

[0035] In this embodiment, the initial scenario type features include scenarios such as server performance monitoring, system fault alarms, operation and maintenance work order processing, and security vulnerability scanning. The initial business priority features are divided into four levels: P0 (urgent), P1 (high), P2 (medium), and P3 (low) based on the scope of business impact and processing time requirements. The initial field metadata features include field name, data type, field length, value range, and whether it is a key field. The initial historical interaction features include matching success rate, number of manual corrections, and field association frequency.

[0036] Pre-defined integrity verification rules: Scenario type features must include scenario category and core scenario elements; business priority features must include three parameters: impact scope, processing time limit, and business importance; field metadata features must include field name, data type, and value range; and historical interaction features must include matching frequency and matching success rate. For example, based on these rules, initial features are verified and it is found that the initial historical interaction features are missing the "percentage of correction reasons" parameter. However, this parameter is not a critical attribute, so all initial features are retained as features to be processed.

[0037] By traversing the features to be processed, it was found that in the metadata features of the "encryption protocol version" field, the data type recorded by the log system was "character", while the data type recorded synchronously by the asset management system was "numeric", which is inconsistent. This feature item was determined to be the target feature item. A feature credibility evaluation index system was constructed, setting four evaluation indicators: data source credibility (work order system 0.9, monitoring platform 0.85, log system 0.8, asset management system 0.75), value occurrence frequency (character value appears 86 times, numeric value appears 14 times), relevance and fit with the scenario business (semantic matching value of encryption protocol version as character and network fault troubleshooting scenario is 0.82, numeric is 0.31), and historical matching effectiveness (historical matching effectiveness of character value is 92%, numeric is 65%). The following weighting coefficients are assigned to each indicator based on the Analytic Hierarchy Process (AHP): Data Source Credibility 0.3, Value Occurrence Frequency 0.25, Relevance Fit 0.3, and Historical Matching Efficiency 0.15. The feature credibility of character-type values ​​is calculated as follows: Log System × Data Source Credibility + (Percentage of Character Values) × Value Occurrence Frequency + Semantic Matching Value Fit × Relevance Fit + Historical Matching Efficiency of Character-Type Values ​​× Historical Matching Efficiency = 0.839. The feature credibility of numerical-type values ​​is calculated as follows: Asset Management System × Data Source Credibility + (Percentage of Numerical Values) × Value Occurrence Frequency + Numerical Fit × Relevance Fit + Numerical Matching Efficiency × Historical Matching Efficiency = 0.4505. Character-type values ​​are determined as the optimal feature values, replacing contradictory values ​​of the target feature items to obtain conflict-free features. These conflict-free features are then categorized and organized according to scenario type features, business priority features, field metadata features, and historical interaction features to obtain effective features.

[0038] Step S13: Optimize the initial field regularity standard and the initial priority model based on effective features to obtain regular expression matching rules, structured association key rules and fuzzy matching threshold rules.

[0039] Among them, the regular expression matching rule is the rule in the optimized initial field regular expression standard that matches the field metadata features and scenario type features; the structured association key rule is the single-key or multi-key association logic rule generated by the optimized initial priority model; and the fuzzy matching threshold rule is the threshold rule set by combining business priority features and field metadata features in a fuzzy differentiation.

[0040] Specifically, the process involves extracting field metadata features and scenario type features from the effective features. These features are then matched against initial field regularization standards to obtain a basic regularization template. Based on scenario type features and business priority features, the basic regularization template is adjusted for parameters, optimized for boundary conditions, and extended for format compatibility to obtain candidate regular expression rules. These candidate rules are then validated for effectiveness, and rules with matching errors exceeding a preset threshold are removed to obtain the final regular expression matching rules. The scenario type features, business priority features, and historical interaction features from the effective features are input into the initial priority model. The association importance weight of each field feature is calculated. Based on the ranking of association importance weights and the current scenario's business complexity, a single-key association mode or a multi-key association mode is determined, generating structured association key rules. Business priority weights are determined based on the business priority features from the effective features. Field types are determined based on field metadata features, and sensitivity coefficients are assigned to different field types according to a preset sensitivity grading standard. The business priority and sensitivity coefficients are input into the fuzzy matching threshold calculation formula to obtain the dynamic fuzzy threshold corresponding to each field under different operational scenarios. Based on the correspondence between the dynamic fuzzy threshold and the field, fuzzy matching threshold rules are generated.

[0041] In this embodiment, three core parameters of the business priority feature are extracted: "scope of impact," "processing timeliness requirement," and "business association level." The specific values ​​corresponding to these parameters in the effective feature are "medium-range impact" (affecting 2-5 business subsystems), "urgent handling" (requiring a response within 1 hour), and "core business association" (associating with the core transaction system of the enterprise). These three parameters are then input into a priority weight correction model constructed based on the Analytic Hierarchy Process (AHP), and the weight values ​​corresponding to the effective feature are output.

[0042] The construction and calculation method of the priority weight correction model are as follows: A hierarchical model is established, with the target layer being "determining the final business priority weights", the criteria layer consisting of the three extracted core parameters (scope of impact, processing time requirements, and business association level), and the solution layer consisting of preset priority weight ranges (high priority [0.7, 0.9], medium priority [0.4, 0.6], and low priority [0.1, 0.3]). Construct a judgment matrix and compare the importance of the three parameters of the criterion layer pairwise to determine the judgment matrix: the importance of processing timeliness requirement (urgent) is higher than that of the scope of influence (medium range), with a weight of 40%; the importance of business relevance level (core) is equivalent to that of processing timeliness requirement, with a weight of 35%; the weight of the scope of influence (medium range) is 25%. The consistency test of the judgment matrix is ​​passed (consistency ratio CR = 0.04 < 0.1). The weight calculation and correction process involves weighting and summing the weights of each parameter in the criteria layer with the corresponding parameter values ​​(0.5 for medium-range impact, 0.9 for emergency handling, and 0.8 for core business association) to obtain an initial weight value of 0.58. Then, it is combined with the preset medium-priority weight range [0.4, 0.6] for boundary correction, and finally the priority weight is determined to be 0.6 (this value falls within the medium-priority range and conforms to the logic of adjusting the weight towards the upper limit of the interval in the core business association scenario).

[0043] In this embodiment, the weighted summation formula is: Impact Scope Weight × Impact Scope Score + Processing Timeliness Weight × Processing Timeliness Score + Business Association Weight × Business Association Score = 0.25 × 0.5 + 0.4 × 0.9 + 0.35 × 0.8 = 0.125 + 0.36 + 0.28 = 0.58. The calculated initial value of 0.58 falls within the medium priority range of [0.4, 0.6] and is close to the upper limit of the range (0.6). Considering the attributes associated with core businesses (the priority corresponding to core businesses should appropriately approach the upper limit of the range to better meet the actual needs of operation and maintenance scenarios), 0.58 is finally corrected to 0.6.

[0044] Sensitivity coefficients are assigned: the types of field metadata features are divided into fixed-format character types (IP address, container ID), variable-format character types (fault description), numeric types (response time), date types (processing time), and boolean types (whether encrypted); the preset sensitivity grading standards are: 0.7 for fixed-format character types, 0.3 for variable-format character types, 0.8 for numeric types, 0.5 for date types, and 0.2 for boolean types.

[0045] In this embodiment of the application, the formula for calculating the fuzzy matching threshold is: Dynamic fuzzy threshold = Baseline threshold × (Priority weight + Sensitivity coefficient). The calculated fuzzy matching threshold is 0.84 × (0.6 + 0.703) = 0.84 × 1.303 ≈ 1.094, thus forming the fuzzy matching threshold rule.

[0046] Baseline threshold: From historical field matching data, 86 valid matching records under "Containerized Environment Network Fault Troubleshooting Scenario" and "Fixed Format Character Field" were selected. 6 records with failed matches or marked as rule errors were removed, leaving 80 valid records. The fuzzy matching threshold values ​​of the 80 records were statistically analyzed, and the arithmetic mean was calculated to be 0.85, which was used as the initial baseline threshold. The standard deviation was calculated using the 3σ principle to be 0.06. 2 outliers exceeding the range of "0.85±3×0.06" (i.e., [0.67, 1.03]) were removed. Based on the remaining 78 records, the arithmetic mean was recalculated to be 0.84, which was used as the final baseline threshold.

[0047] Step S14: Convert the regular expression matching rules, structured association key rules, and fuzzy matching threshold rules into executable code and execute the matching task to obtain real-time matching results and exception handling feedback.

[0048] Specifically, regular expression matching rules, structured association key rules, and fuzzy matching threshold rules are substituted into the corresponding execution templates to generate executable code. The executable code is deployed to the operations and maintenance data processing node, imports the data to be matched, executes the matching task, and monitors the data flow status in real time during the matching process, recording successful matching results, failed matching data, and abnormal trigger events to obtain real-time matching results and abnormal handling feedback.

[0049] An automatic code generation template library is built, including regular expression matching execution templates (using the `re` module in Python), association key matching execution templates (using the `PandasMerge` function call framework), and fuzzy matching execution templates (using the `ratio` function call framework from the `FuzzyWuzzy` library). The generated regular expression matching rules, structured association key rules, and fuzzy matching threshold rules are substituted into the corresponding templates, parameters are filled in, and executable Python code is generated. The code is deployed to the real-time operation and maintenance processing node, importing the data to be matched (including 1000 server log fields and work order fields from the historical operation and maintenance cycle) to execute the matching task. The matching process is monitored in real time, recording 786 successfully matched field pairs, 43 failed matches, and 3 abnormal events (2 due to incompatible field formats and 1 due to an empty matching result), generating real-time matching results and abnormal handling feedback.

[0050] Step S15: Based on the real-time matching results and anomaly handling feedback, perform closed-loop optimization of the regular expression matching rules, structured association key rules, and fuzzy matching threshold rules to obtain field matching standards under different operation and maintenance scenarios.

[0051] Specifically, the real-time matching results and anomaly handling feedback are categorized and statistically analyzed to identify the adaptation defects of the matching standards, determine the types of rules to be adjusted and the direction of adjustment, and modify the parameters of the regular expression matching rules, structured association key rules, and fuzzy matching threshold rules based on the adjustment direction. This results in optimized regular expression matching rules, optimized structured association key rules, and optimized fuzzy matching threshold rules. These optimized rules are then stored in a preset matching standard knowledge base to obtain field matching standards for different operation and maintenance scenarios.

[0052] Step S16: Collect operation and maintenance scenario data within the current time period, apply the field matching standard to the field matching of the operation and maintenance scenario data, and obtain the field matching results corresponding to the operation and maintenance scenario data.

[0053] Specifically, the scenario features of the operation and maintenance scenario data and the scenario feature set of historical operation and maintenance scenario data are extracted, and the similarity between the scenario features and each feature vector in the scenario feature set is calculated. Based on the similarity and field matching criteria, the field matching method corresponding to the operation and maintenance scenario data is determined, and the field matching method is applied to the operation and maintenance scenario data to obtain the field matching result corresponding to the operation and maintenance scenario data.

[0054] 15,000 operational scenario data entries were collected within the current time period (1 hour), covering 4 types of operational scenarios. Scenario features (e.g., "scene_type: server performance monitoring, priority_level: P1") were extracted from this data. The similarity between these data and each feature vector in the scenario feature set was calculated (using Euclidean distance) to determine the corresponding field matching method (e.g., "regular expression matching + multi-key association matching"). The field matching standard was applied to the field matching of this operational scenario data, yielding the following results: 14,325 successful matches, a success rate of 95.5%, an average matching latency of 80ms, and no abnormal triggering events. The matching results can be directly used for server performance analysis, alarm association processing, and other operational tasks, effectively improving the efficiency of operational data processing.

[0055] This application provides a general field matching method for operation and maintenance scenarios. It acquires multi-source basic data and historical field matching data for operation and maintenance scenarios, providing a comprehensive and realistic data foundation for subsequent accurate model construction. Based on the multi-source basic data and historical field matching data, an initial field regularization standard and an initial priority model are constructed. The initial field regularization standard clarifies the mapping relationship between historical field types and corresponding regularization matching templates, providing a preliminary specification for field matching. The initial priority model determines the correspondence between historical operation and maintenance scenario types and the priority of operation and maintenance field association keys, helping to quickly locate key fields in complex scenarios. Feature extraction and verification are performed on the multi-source basic data to obtain four core features of the operation and maintenance scenario and corresponding effective features. The scenario type feature clarifies the category of the operation and maintenance scenario, providing scenario background for subsequent matching; the business priority feature reflects the importance of different businesses in operation and maintenance, helping to allocate resources rationally; the field metadata feature describes the basic attributes of the field and is key to accurate matching; the historical interaction feature records past interaction situations, providing a reference for matching. The initial field regularization standard and the initial priority model are optimized based on the effective features. The initial field regular expression standard is optimized by combining field metadata features and scenario type features, making the regular expression matching rules more closely match the actual characteristics of fields in different scenarios and improving matching accuracy. For the initial priority model, the optimized structured association key rules, through single-key or multi-key association logic rules, more reasonably determine the association relationships between fields, improving the rationality of the association. The fuzzy matching threshold rules, combined with business priority features and field metadata features, are set in a fuzzy, differentiated manner, making the matching process more flexible and adaptable. The optimized regular expression matching rules, structured association key rules, and fuzzy matching threshold rules collectively improve the accuracy and comprehensiveness of field matching. The regular expression matching rules, structured association key rules, and fuzzy matching threshold rules are then converted into executable code and executed for matching tasks. During the matching task, the regular expression matching rules can quickly and accurately identify fields that conform to specific patterns; the structured association key rules can accurately find the association relationships between fields based on the set logic rules; and the fuzzy matching threshold rules can flexibly match within a certain range, handling some irregular data. This automated execution method enables rapid real-time matching results and anomaly handling feedback, significantly improving the efficiency of field matching in operational scenarios, reducing manual intervention, and lowering the probability of errors. Closed-loop optimization is performed on regular expression matching rules, structured association key rules, and fuzzy matching threshold rules based on real-time matching results and anomaly handling feedback. Real-time matching results reflect the application effect of the current rules in real-world scenarios, while anomaly handling feedback points out existing problems and shortcomings. Targeted optimization and adjustments to the rules allow them to continuously adapt to ever-changing operational scenarios.The field matching standards obtained after closed-loop optimization for different operation and maintenance scenarios are more closely aligned with actual needs, exhibiting higher accuracy and reliability. Operation and maintenance scenario data within the current time period is collected, and the field matching standards are applied to the field matching of this data. Due to the series of optimizations and adjustments made previously, the resulting field matching standards possess high accuracy and adaptability. When applied to actual operation and maintenance scenario data, they can quickly and accurately match fields in the data with the corresponding standards, obtaining field matching results corresponding to the operation and maintenance scenario data. This helps operation and maintenance personnel quickly understand the actual situation of the operation and maintenance scenario, promptly identify potential problems, and thus improve the efficiency and quality of the entire operation and maintenance work.

[0056] The following describes a general field matching system for operation and maintenance scenarios provided by an embodiment of this application. The general field matching system for operation and maintenance scenarios described below can be referred to in conjunction with the general field matching method for operation and maintenance scenarios described above. Figure 2 , Figure 2 This is a schematic diagram of the structure of a general field matching system 20 for operation and maintenance scenarios provided in an embodiment of this application, including: Data acquisition module 21 is used to acquire multi-source basic data and historical field matching data for operation and maintenance scenarios; The initial construction module 22 is used to build an initial field regular expression standard and an initial priority model based on historical field matching data. The initial field regular expression standard is a mapping relationship standard between historical field types and corresponding regular expression matching templates, and the initial priority model is a mapping relationship model between historical operation and maintenance scenario types and the priority of operation and maintenance field association keys. The extraction and verification module 23 is used to extract and verify features from multi-source basic data to obtain four types of core features of the operation and maintenance scenario and effective features corresponding to the four types of core features. The four types of core features include scenario type features, business priority features, field metadata features and historical interaction features. The rule generation module 24 is used to optimize the initial field regular expression standard and the initial priority model based on effective features to obtain regular expression matching rules, structured association key rules and fuzzy matching threshold rules. The regular expression matching rules are the rules that match the field metadata features and scenario type features in the optimized initial field regular expression standard. The structured association key rules are the single-key or multi-key association logic rules generated by the optimized initial priority model. The fuzzy matching threshold rules are the threshold rules set by fuzzy differentiation based on business priority features and field metadata features. The matching test module 25 is used to convert regular expression matching rules, structured association key rules and fuzzy matching threshold rules into executable code and execute matching tasks to obtain real-time matching results and exception handling feedback. Closed-loop optimization module 26 is used to perform closed-loop optimization of regular expression matching rules, structured association key rules and fuzzy matching threshold rules based on real-time matching results and anomaly handling feedback, so as to obtain field matching standards under different operation and maintenance scenarios. The real-time matching module 27 is used to collect operation and maintenance scenario data within the current time period, apply the field matching standard to the field matching of the operation and maintenance scenario data, and obtain the field matching result corresponding to the operation and maintenance scenario data.

[0057] In one possible implementation of this application embodiment, when the extraction and verification module 23 performs feature extraction and verification on multi-source basic data to obtain four types of core features of the operation and maintenance scenario and the effective features corresponding to the four types of core features, it is specifically used for: The scenario description information, business identification information, field attribute information and historical interaction records in the multi-source basic data are extracted separately to obtain the initial scenario type features, initial business priority features, initial field metadata features and initial historical interaction features. Based on the preset integrity verification rules, the integrity of the initial scenario type features, initial business priority features, initial field metadata features and initial historical interaction features are verified respectively. Invalid initial features with missing key attributes are removed, and features that meet the integrity requirements are retained. If there are conflicting features among the features to be processed, if so, calculate the feature confidence level of the feature to be processed for each conflicting branch, and determine the optimal feature value corresponding to the feature to be processed based on the feature confidence level. Replace each conflicting feature data in the feature to be processed with the corresponding optimal feature value to obtain conflict-free features. The conflict-free features are categorized and organized according to scenario type features, business priority features, field metadata features, and historical interaction features, resulting in four core features of the operation and maintenance scenario and the corresponding effective features.

[0058] Another possible implementation in this application embodiment is that when the extraction and verification module 23 calculates the feature confidence level of the feature to be processed corresponding to each conflicting branch, determines the optimal feature value corresponding to the feature to be processed based on the feature confidence level, and replaces each conflicting feature data in the feature to be processed with the corresponding optimal feature value to obtain conflict-free features, it is specifically used for: Traverse the features to be processed and identify target feature items with conflicting values. Conflicts are defined as multiple different values ​​under the same feature dimension or feature values ​​that are incompatible with the scenario's business logic. Construct a feature credibility assessment system, which includes the credibility of feature data sources, the frequency of feature values, the correlation and fit between features and scenario business, and the effectiveness of feature values ​​in historical matching. We assign weight coefficients to the feature data source credibility, feature value frequency, correlation fit and effectiveness respectively, and obtain the feature credibility evaluation system after weight allocation. Based on the feature credibility evaluation system after weight allocation, the individual evaluation score of each candidate value in the target feature item is calculated to obtain the feature credibility of each candidate value; The candidate value with the highest feature confidence is taken as the optimal feature value of the target feature item, and all conflicting feature values ​​of the target feature items in the features to be processed are replaced with the corresponding optimal feature values ​​to obtain conflict-free features.

[0059] In another possible implementation of this application embodiment, when the rule generation module 24 optimizes the initial field regularity standard and the initial priority model based on effective features to obtain regular expression matching rules, structured association key rules, and fuzzy matching threshold rules, it is specifically used for: Extract field metadata features and scenario type features from the valid features; The basic regular expression template is obtained by matching the field metadata features according to the initial field regular expression standard. Based on scenario type characteristics and business priority characteristics, the parameters of the basic regular expression template are adjusted, the boundary conditions are optimized, and the format compatibility is extended to obtain candidate rules for regular expressions. The candidate rules of the regular expression are validated, and rules whose matching error exceeds a preset threshold are removed to obtain the regular expression matching rules; Input the scenario type feature, business priority feature, and historical interaction feature from the effective features into all the initial priority models, and calculate the correlation importance weight of each field feature; Based on the ranking results of the importance weights of associations and the business complexity of the current scenario, determine the single-key association mode or the multi-key association mode, and generate structured association key rules. Business priority weights are determined based on business priority features among the effective features, field types are determined based on field metadata features, and sensitivity coefficients are assigned to different field types according to a preset sensitivity grading standard. The business priority and sensitivity coefficient are respectively input into the fuzzy matching threshold calculation formula to obtain the dynamic fuzzy threshold corresponding to each field under different operation and maintenance scenarios. Based on the correspondence between dynamic fuzzy thresholds and fields, fuzzy matching threshold rules are generated.

[0060] In another possible implementation of this application embodiment, when the matching test module 25 converts the regular expression matching rules, structured association key rules, and fuzzy matching threshold rules into executable code and executes the matching task to obtain real-time matching results and exception handling feedback, it is specifically used for: Substitute the regular expression matching rules, structured association key rules, and fuzzy matching threshold rules into the corresponding execution templates to generate executable code; Deploy executable code to the operation and maintenance data processing node, import the data of the fields to be matched, execute the matching task, and monitor the data flow status in real time during the matching process. Record the matching success results, matching failure data and abnormal trigger events to obtain real-time matching results and abnormal handling feedback.

[0061] Another possible implementation in this application embodiment is that, when the closed-loop optimization module 26 performs closed-loop optimization on the regular expression matching rules, structured association key rules, and fuzzy matching threshold rules based on real-time matching results and anomaly handling feedback to obtain field matching standards under different operation and maintenance scenarios, it is specifically used for: Classify and statistically analyze the real-time matching results and anomaly handling feedback, identify the adaptation defects of the matching standards, and determine the types of rules to be adjusted and the direction of adjustment. Based on the adjustment direction, the parameters of the regular expression matching rule, the structured association key rule, and the fuzzy matching threshold rule are modified to obtain the optimized regular expression matching rule, the optimized structured association key rule, and the optimized fuzzy matching threshold rule. The optimized regular expression matching rules, optimized structural association key rules, and optimized fuzzy matching threshold rules are stored in a preset matching standard knowledge base to obtain field matching standards for different operation and maintenance scenarios.

[0062] In another possible implementation of this application embodiment, when the real-time matching module 27 applies the field matching standard to the field matching of the operation and maintenance scenario data and obtains the field matching result corresponding to the operation and maintenance scenario data, it is specifically used for: Extract scenario features from operation and maintenance scenario data and the scenario feature set from historical operation and maintenance scenario data, and calculate the similarity between the scenario features and each feature vector in the scenario feature set; Based on similarity and field matching criteria, determine the field matching method corresponding to the operation and maintenance scenario data, and apply the field matching method to the operation and maintenance scenario data to obtain the field matching result corresponding to the operation and maintenance scenario data.

[0063] This application provides an electronic device, such as... Figure 3 As shown, Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 3The illustrated electronic device 300 includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in practical applications, the transceiver 304 is not limited to one type, and the structure of this electronic device 300 does not constitute a limitation on the embodiments of this application.

[0064] Processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in connection with the embodiments of this application. Processor 301 may also be a combination that implements computing functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0065] Bus 302 may include a pathway for transmitting information between the aforementioned components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 302 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0066] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0067] The memory 303 is used to store application code that executes the scheme of the embodiments of this application, and its execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the foregoing method embodiments.

[0068] Among them, electronic devices include, but are not limited to: mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 3 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0069] The following describes a computer-readable storage medium provided by an embodiment of this application. The computer-readable storage medium described below can be referred to in correspondence with the method described above.

[0070] This application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the steps of the general field matching system in the above-described operation and maintenance scenario.

[0071] Since the embodiments of the computer-readable storage medium portion correspond to the embodiments of the method portion, please refer to the description of the embodiments of the method portion for the embodiments of the computer-readable storage medium portion.

[0072] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0073] The above are only some embodiments of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A general field matching method for operation and maintenance scenarios, characterized in that, include: Acquire multi-source basic data and historical field matching data for operation and maintenance scenarios; Based on the historical field matching data, an initial field regular expression standard and an initial priority model are constructed. The initial field regular expression standard is a mapping relationship standard between historical field types and corresponding regular expression matching templates, and the initial priority model is a mapping relationship model between historical operation and maintenance scenario types and the priority of operation and maintenance field association keys. Feature extraction and verification are performed on the multi-source basic data to obtain four types of core features of the operation and maintenance scenario and effective features corresponding to the four types of core features. The four types of core features include scenario type features, business priority features, field metadata features and historical interaction features. Based on the effective features, the initial field regular expression standard and the initial priority model are optimized to obtain regular expression matching rules, structured association key rules, and fuzzy matching threshold rules. The regular expression matching rules are the rules in the optimized initial field regular expression standard that match the field metadata features and the scenario type features. The structured association key rules are the single-key or multi-key association logic rules generated by the optimized initial priority model. The fuzzy matching threshold rules are the threshold rules set by combining the business priority features and the field metadata features with fuzzy differentiation. The regular expression matching rules, structured association key rules, and fuzzy matching threshold rules are respectively converted into executable code and the matching task is executed to obtain real-time matching results and exception handling feedback; Based on the real-time matching results and the anomaly handling feedback, the regular expression matching rules, structured association key rules and fuzzy matching threshold rules are optimized in a closed loop to obtain field matching standards under different operation and maintenance scenarios. Collect operation and maintenance scenario data within the current time period, apply the field matching criteria to the field matching of the operation and maintenance scenario data, and obtain the field matching results corresponding to the operation and maintenance scenario data.

2. The general field matching method for operation and maintenance scenarios according to claim 1, characterized in that, The feature extraction and verification of the multi-source basic data yields four core features of the operation and maintenance scenario and corresponding effective features, including: The scene description information, business identification information, field attribute information and historical interaction records in the multi-source basic data are extracted separately to obtain the initial scene type features, initial business priority features, initial field metadata features and initial historical interaction features; Based on the preset integrity verification rules, the initial scenario type feature, initial business priority feature, initial field metadata feature and initial historical interaction feature are verified for integrity respectively. Invalid initial features with missing key attributes are removed, and features that meet the integrity requirements are retained. If there are conflicting features among the features to be processed, if so, the feature confidence level of the feature to be processed is calculated for each conflicting branch, and the optimal feature value corresponding to the feature to be processed is determined based on the feature confidence level. Each conflicting feature data in the feature to be processed is replaced with the corresponding optimal feature value to obtain a conflict-free feature. The conflict-free features are categorized and organized according to scenario type features, business priority features, field metadata features, and historical interaction features to obtain four core features of the operation and maintenance scenario and the effective features corresponding to the four core features.

3. The general field matching method for operation and maintenance scenarios according to claim 2, characterized in that, The process of calculating the feature confidence level of the feature to be processed corresponding to each conflicting branch, determining the optimal feature value corresponding to the feature to be processed based on the feature confidence level, and replacing each conflicting feature data in the feature to be processed with the corresponding optimal feature value to obtain conflict-free features includes: Traverse the features to be processed and identify target feature items with conflicting values. The conflicting values ​​are those that have multiple different values ​​under the same feature dimension or those that are incompatible with the scenario business logic. Construct a feature credibility evaluation system, which includes the credibility of feature data sources, the frequency of feature values, the correlation and fit between features and scenario business, and the effectiveness of feature values ​​in historical matching. Weight coefficients are assigned to the feature data source credibility, feature value frequency, correlation fit and effectiveness respectively to obtain a feature credibility evaluation system after weight allocation; Based on the feature credibility evaluation system after weight allocation, calculate the individual evaluation score of each candidate value in the target feature item to obtain the feature credibility of each candidate value; The candidate value with the highest feature confidence is taken as the optimal feature value of the target feature item, and all conflicting feature values ​​of the target feature items in the features to be processed are replaced with the corresponding optimal feature values ​​to obtain conflict-free features.

4. The general field matching method in an operation and maintenance scenario according to claim 1, characterized in that, The optimization of the initial field regularization standard and the initial priority model based on the effective features yields regular expression matching rules, structured association key rules, and fuzzy matching threshold rules, including: Extract the field metadata features and scene type features from the effective features; The basic regular expression template is obtained by matching the field metadata features according to the initial field regular expression standard. Based on the scenario type characteristics and business priority characteristics, the parameters of the basic regular expression template are adjusted, the boundary conditions are optimized, and the format compatibility is extended to obtain candidate regular expression rules. The regular expression candidate rules are validated, and rules with matching errors exceeding a preset threshold are removed to obtain the regular expression matching rules; The scenario type feature, business priority feature, and historical interaction feature from the effective features are input into all the initial priority models, and the correlation importance weight of each field feature is calculated. Based on the ranking results of the importance weights of the associations and the business complexity of the current scenario, determine the single-key association mode or the multi-key association mode, and generate structured association key rules. The business priority weight is determined based on the business priority feature among the effective features, the field type is determined based on the field metadata feature, and sensitivity coefficients are assigned to different field types according to the preset sensitivity grading standard. The business priority and the sensitivity coefficient are respectively input into the fuzzy matching threshold calculation formula for calculation to obtain the dynamic fuzzy threshold corresponding to each field under different operation and maintenance scenarios; Based on the correspondence between the dynamic fuzzy threshold and the field, a fuzzy matching threshold rule is generated.

5. A general field matching method for operation and maintenance scenarios as described in claim 1, characterized in that, The process of converting the regular expression matching rules, structured association key rules, and fuzzy matching threshold rules into executable code and executing the matching task to obtain real-time matching results and exception handling feedback includes: Substitute the regular expression matching rules, structured association key rules, and fuzzy matching threshold rules into the corresponding execution templates to generate executable code; The executable code is deployed to the operation and maintenance data processing node, the data of the fields to be matched is imported to execute the matching task, and the data flow status during the matching process is monitored in real time. The matching success results, matching failure data and abnormal triggering events are recorded to obtain real-time matching results and abnormal handling feedback.

6. A general field matching method for operation and maintenance scenarios as described in claim 1, characterized in that, The closed-loop optimization of the regular expression matching rules, structured association key rules, and fuzzy matching threshold rules based on the real-time matching results and the anomaly handling feedback yields field matching standards for different operation and maintenance scenarios, including: The real-time matching results and anomaly handling feedback are classified and statistically analyzed to identify the adaptation defects of the matching standards and determine the types of rules to be adjusted and the direction of adjustment. Based on the adjustment direction, the parameters of the regular expression matching rule, the structured association key rule, and the fuzzy matching threshold rule are modified to obtain the optimized regular expression matching rule, the optimized structured association key rule, and the optimized fuzzy matching threshold rule. The optimized regular expression matching rules, optimized structural association key rules, and optimized fuzzy matching threshold rules are stored in a preset matching standard knowledge base to obtain field matching standards for different operation and maintenance scenarios.

7. The general field matching method for operation and maintenance scenarios according to claim 1, characterized in that, The step of applying the field matching criteria to the field matching of the operation and maintenance scenario data to obtain the field matching results corresponding to the operation and maintenance scenario data includes: Extract the scene features of the operation and maintenance scene data and the scene feature set of historical operation and maintenance scene data, and calculate the similarity between the scene features and each feature vector in the scene feature set; Based on the similarity and the field matching criteria, a field matching method corresponding to the operation and maintenance scenario data is determined, and the field matching method is applied to the operation and maintenance scenario data to obtain a field matching result corresponding to the operation and maintenance scenario data.

8. A general field matching system for operation and maintenance scenarios, characterized in that, include: The data acquisition module is used to acquire multi-source basic data and historical field matching data for operation and maintenance scenarios; An initial construction module is used to construct an initial field regular expression standard and an initial priority model based on the historical field matching data. The initial field regular expression standard is a mapping relationship standard between historical field types and corresponding regular expression matching templates, and the initial priority model is a mapping relationship model between historical operation and maintenance scenario types and the priority of operation and maintenance field association keys. The extraction and verification module is used to extract and verify the features of the multi-source basic data to obtain four types of core features of the operation and maintenance scenario and effective features corresponding to the four types of core features. The four types of core features include scenario type features, business priority features, field metadata features and historical interaction features. The rule generation module is used to optimize the initial field regular expression standard and the initial priority model based on the effective features to obtain regular expression matching rules, structured association key rules, and fuzzy matching threshold rules. The regular expression matching rules are the rules in the optimized initial field regular expression standard that match the field metadata features and the scenario type features. The structured association key rules are single-key or multi-key association logic rules generated by the optimized initial priority model. The fuzzy matching threshold rules are threshold rules set by combining the business priority features and the field metadata features with fuzzy differentiation. The matching test module is used to convert the regular expression matching rules, structured association key rules and fuzzy matching threshold rules into executable code and execute matching tasks to obtain real-time matching results and exception handling feedback. The closed-loop optimization module is used to perform closed-loop optimization of the regular expression matching rule, the structured association key rule and the fuzzy matching threshold rule based on the real-time matching result and the anomaly handling feedback, so as to obtain the field matching standard under different operation and maintenance scenarios. The real-time matching module is used to collect operation and maintenance scenario data within the current time period, apply the field matching criteria to the field matching of the operation and maintenance scenario data, and obtain the field matching results corresponding to the operation and maintenance scenario data.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; Memory; At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: execute a general field matching method for an operation and maintenance scenario according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, include: The computer program is stored that can be loaded by a processor and executed as described in any one of claims 1-7, representing a general field matching method for an operational scenario.

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