Configuration item credibility dynamic processing method and device, electronic equipment, medium and product
By introducing a time decay factor and an exponential function that adaptively adjusts the data type, combined with a machine learning model, the problems of low accuracy and low operational efficiency of CMDB data were solved, enabling efficient operational decision-making and accident prevention.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA UNITED NETWORK COMM GRP CO LTD
- Filing Date
- 2026-02-13
- Publication Date
- 2026-06-05
AI Technical Summary
In existing technologies, the accuracy and operational efficiency of CMDB data are low, mainly because static rules or fixed weights assign high credibility to expired data, which fails to reflect the true data failure situation, making it difficult for operations and maintenance personnel to prioritize high-incident issues.
By introducing a time decay factor and an exponential function that adaptively adjusts the data type, a dynamic score is calculated. Combined with a machine learning model, the decay rate and conflict factor are optimized to quantify the credibility of configuration items and trigger corresponding operation and maintenance actions.
It significantly improves the accuracy of CMDB data and operational efficiency, reduces the probability of production accidents caused by data failure, and improves the accuracy and response speed of operational decisions.
Smart Images
Figure CN122152802A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of computer operation and maintenance, and in particular to a method, apparatus, electronic device, medium and product for dynamic processing of configuration item credibility. Background Technology
[0002] In the field of computer operations and maintenance, the Configuration Management Database (CMDB) is a core infrastructure supporting system stability and business continuity. CMDB centrally manages the metadata of configuration items for servers, network devices, and application services, providing a data foundation for operational tasks such as troubleshooting, capacity planning, and change management. However, with the increasing complexity of enterprise information technology (IT) architectures (such as the widespread adoption of hybrid cloud environments and microservice architectures), the accuracy of CMDB data faces significant challenges.
[0003] Currently, the relevant technologies mainly rely on static rules or fixed weights to determine the credibility of data, which results in expired data still being assigned high credibility, failing to reflect the true data failure situation. This makes it difficult for operation and maintenance personnel to prioritize high-incident issues, leading to low accuracy of CMDB data and low operation and maintenance efficiency. Summary of the Invention
[0004] This application provides a method, apparatus, electronic device, medium, and product for dynamic processing of configuration item credibility, which can improve the accuracy of CMDB data and operational efficiency.
[0005] Firstly, this application provides a method for dynamically processing the credibility of configuration items, including:
[0006] Retrieve configuration item data and its metadata, including update time and data type;
[0007] The time decay factor is calculated based on the update time and data type. The time decay factor adopts an exponential function form, and the difference in data type affects the decay rate of the exponential function.
[0008] A dynamic score is generated based on the time decay factor and preset weight parameters. The dynamic score is used to characterize the credibility of the configuration item data.
[0009] In one possible implementation, the time decay factor is calculated based on the update time and data type, including:
[0010] The time decay factor is determined using an exponential function. The decay rate of the exponential function is adaptively adjusted based on the differences in data types, which include at least one of network configuration, hardware assets, and software versions.
[0011] In one possible implementation, in the step of determining the time decay factor using an exponential function, the adaptive adjustment of the decay rate is achieved through a machine learning model that predicts the optimal decay rate for different data types based on historical data.
[0012] In one possible implementation, after the step of generating a dynamic score based on a time decay factor and preset weight parameters, the method further includes:
[0013] Determine the conflict factor, which is based on the product of the data source consistency ratio and the attribute criticality coefficient. The data source consistency ratio is the ratio of the number of consistent data sources to the total number of data sources, and the attribute criticality coefficient is preset according to the business impact of the configuration item attribute.
[0014] In one possible implementation, after the step of determining the conflict factor, the method further includes:
[0015] Determine the conflict contribution of each data source and trigger data source governance strategies based on the conflict contribution. Data source governance strategies include at least one of adding verification rules and switching the collection method.
[0016] In one possible implementation, after the step of generating dynamic scores, the method further includes:
[0017] Operational actions are triggered based on dynamic scores. These actions include at least one of freezing configuration items, generating observation work orders, and triggering calibration scans. The triggering condition for these actions is the correspondence between the dynamic scores and preset thresholds.
[0018] Secondly, this application provides a configuration item credibility dynamic processing device, comprising:
[0019] Acquisition device, used to acquire configuration item data and its metadata, the metadata including update time and data type;
[0020] The processing module is used to calculate the time decay factor based on the update time and data type. The time decay factor adopts the form of an exponential function, and the difference in data type affects the decay rate of the exponential function.
[0021] The processing module is also used to generate dynamic scores based on the time decay factor and preset weight parameters. The dynamic scores are used to characterize the credibility of the configuration item data.
[0022] In one possible implementation, the processing module is specifically used to determine the time decay factor using an exponential function, wherein the decay rate of the exponential function is adaptively adjusted according to the differences in data types, including at least one of network configuration, hardware assets, and software versions.
[0023] In one possible implementation, in the step of determining the time decay factor using an exponential function, the adaptive adjustment of the decay rate is achieved through a machine learning model that predicts the optimal decay rate for different data types based on historical data.
[0024] In one possible implementation, the processing module is further configured to determine a conflict factor, which is based on the product of the data source consistency ratio and the attribute key coefficient. The data source consistency ratio is the ratio of the number of consistent data sources to the total number of data sources, and the attribute key coefficient is preset according to the business impact of the configuration item attribute.
[0025] In one possible implementation, the processing module is further configured to determine the conflict contribution of each data source and trigger a data source governance strategy based on the conflict contribution. The data source governance strategy includes at least one of adding verification rules and switching the collection method.
[0026] In one possible implementation, the processing module is further configured to trigger maintenance actions based on the dynamic score. The maintenance actions include at least one of freezing configuration items, generating observation work orders, and triggering calibration scans. The triggering condition for the maintenance actions is the correspondence between the dynamic score and a preset threshold.
[0027] Thirdly, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;
[0028] The memory stores instructions that the computer executes;
[0029] The processor executes computer execution instructions stored in memory to implement the configuration item confidence dynamic processing method as described in the first aspect and / or any possible implementation of the first aspect.
[0030] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the configuration item reliability dynamic processing method as described in the first aspect and / or any possible implementation of the first aspect.
[0031] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements a configuration item reliability dynamic processing method as described in the first aspect and / or any possible implementation of the first aspect.
[0032] The configuration item reliability dynamic processing method, device, electronic equipment, medium, and product provided in this application solve the problem that static weight allocation cannot reflect data timeliness by introducing a time decay factor and an exponential function that adaptively adjusts the data type. This technical approach significantly improves the accuracy of data timeliness evaluation, enhances the accuracy and operational efficiency of CMDB data, and prevents expired data from being misjudged as high-reliability configuration items, thereby reducing the probability of production accidents caused by data failure. Attached Figure Description
[0033] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.
[0034] Figure 1 This is a schematic diagram of a scenario provided for an embodiment of this application;
[0035] Figure 2 A flowchart illustrating the dynamic processing method for the credibility of configuration items provided in this application. Figure 1 ;
[0036] Figure 3 A flowchart illustrating the dynamic processing method for the credibility of configuration items provided in this application. Figure 2 ;
[0037] Figure 4 A flowchart illustrating the dynamic processing method for the credibility of configuration items provided in this application. Figure 3 ;
[0038] Figure 5 A flowchart illustrating the dynamic processing method for the credibility of configuration items provided in this application. Figure 4 ;
[0039] Figure 6 A flowchart illustrating the dynamic processing method for the credibility of configuration items provided in this application. Figure 5 ;
[0040] Figure 7 A schematic diagram of the configuration item credibility dynamic processing device provided in the embodiments of this application;
[0041] Figure 8 A schematic diagram of the structure of the electronic device provided in this application.
[0042] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation
[0043] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.
[0044] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.
[0045] In the field of computer operations and maintenance (COP), the Configuration Management Database (CMDB) is a core infrastructure supporting system stability and business continuity. The CMDB centrally manages the metadata of configuration items for servers, network devices, and application services, providing a data foundation for COP operations such as troubleshooting, capacity planning, and change management. However, with the increasing complexity of enterprise information technology (IT) architectures (such as the widespread adoption of hybrid cloud environments and microservice architectures), the accuracy of CMDB data faces severe challenges. Data sources are diverse: configuration item information may be obtained through multiple channels, including automated collection, manual entry, or synchronization with third-party systems. The data quality varies significantly across different sources, exhibiting format errors, timeliness discrepancies, and even contradictions. Dynamic requirements are intensifying: high-frequency change scenarios (such as containerized deployment and elastic scaling) require configuration data to be updated in real time, but related technologies struggle to effectively identify outdated data. Timely COP response is also crucial: inaccurate configuration data can lead to IP address conflicts, incorrect service dependencies, and other problems, potentially causing business interruptions or security vulnerabilities, requiring rapid problem location and remediation. Against this backdrop, traditional data quality assessment methods based on static rules or fixed weights are no longer sufficient to meet COP needs. Currently, related technologies mainly rely on static rules or fixed weights to determine data credibility. This results in expired data still being assigned high credibility, failing to reflect the true data failure situation and making it difficult for operations and maintenance personnel to prioritize high-risk issues. For example, manually entered server IP information may be expired after 30 days, but its credibility weight remains at the initial value, leading to distorted risk assessment. When multiple data sources provide inconsistent descriptions of the same configuration item (such as conflicts between automated collection and manual entry), related technologies only mark the conflict but cannot quantify the severity of the contradiction, making it difficult for operations and maintenance personnel to prioritize high-risk issues, resulting in low accuracy of CMDB data and low operational efficiency.
[0046] Figure 1 This is a schematic diagram of a scenario provided for an embodiment of this application, such as... Figure 1As shown, the configuration item reliability dynamic processing method, apparatus, electronic device, medium, and product provided in this application obtain configuration item data and its metadata, determine the time decay factor of the metadata, and generate a dynamic score based on the time decay factor and preset weight parameters to obtain the reliability of the configuration item data. This application solves the problem that static weight allocation cannot reflect data timeliness by introducing a time decay factor and an exponential function that adaptively adjusts the data type. This technical approach significantly improves the accuracy of data timeliness evaluation, enhances the accuracy and operational efficiency of CMDB data, and avoids expired data being misjudged as high-reliability configuration items, thereby reducing the probability of production accidents caused by data failure.
[0047] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.
[0048] Figure 2 A flowchart illustrating the dynamic processing method for the credibility of configuration items provided in this application. Figure 1 ,like Figure 2 As shown, the execution flow of the dynamic processing method for configuration item credibility is as follows:
[0049] S201. Obtain configuration item data and its metadata, including update time and data type.
[0050] In the context of specific scenarios, configuration item data refers to entity information that needs to be managed in an IT system, such as server IP addresses, hardware asset models, and service dependencies. For example, the IP address "192.168.1.10" and its hardware model "Dell R750" of a server both belong to configuration item data. Metadata refers to additional information describing the attributes of configuration item data, including update time and data type. For example, the metadata for configuration item "192.168.1.10" might include an update time of "2025-04-01 10:00" and a data type of "network configuration".
[0051] S202. Calculate the time decay factor based on the update time and data type. The time decay factor adopts the form of an exponential function, and the difference in data type affects the decay rate of the exponential function.
[0052] In the context of a scenario, the time decay factor is an exponential function value that reflects the timeliness of configuration item data. Its calculation depends on the differences between the update time and the data type. For example, if the data type is "network configuration", the time decay factor might be λ=0.15 / hour; if the data type is "hardware assets", the time decay factor might be λ=0.02 / day.
[0053] S203. Generate a dynamic score based on the time decay factor and preset weight parameters. The dynamic score is used to characterize the credibility of the configuration item data.
[0054] With a scenario example, dynamic scoring refers to a quantifiable value of credibility generated by combining a time decay factor and preset weight parameters, ranging from 0 to 1. For example, the dynamic score of a server IP address might be 0.36, indicating that its credibility is low.
[0055] This example retrieves configuration item data and its metadata from the data source layer. The metadata includes update time and data type, used for subsequent calculations. Next, a time decay factor is calculated based on the differences in update time and data type. The decay rate (λ) of the exponential function is adaptively adjusted according to the data type; for example, network configurations use a high-frequency decay rate (λ = 0.15 / hour), while hardware assets use a low-frequency decay rate (λ = 0.02 / day). Finally, the time decay factor is combined with preset weight parameters to generate a dynamic score, which characterizes the reliability of the configuration item data. The entire process models data timeliness using an exponential function and adjusts the decay rate based on data type differences to ensure that the dynamic score accurately reflects the failure risk of the configuration item data, providing a basis for subsequent operation and maintenance decisions.
[0056] Based on the method provided in this example, the problem that static weight allocation cannot reflect the timeliness of data is solved by introducing a time decay factor and an exponential function that adaptively adjusts the data type. The technical principle is based on the characteristic that the risk of data failure increases non-linearly over time, through an exponential function (α=e^(-α / α)). (-λ·Δt) This method quantifies data timeliness, where Δt is the difference between the update time and the current time, and λ is dynamically adjusted based on the data type. For example, network configurations, which undergo frequent changes, require λ=0.15 / hour, while hardware assets, which undergo infrequent changes, can use λ=0.02 / day. By dynamically adjusting the λ value, the model can accurately match the failure patterns of different data types. For instance, in a containerized environment, it can automatically increase the decay rate of network configurations to adapt to the needs of frequent changes. Ultimately, this technique significantly improves the accuracy of data timeliness assessment, preventing expired data from being misjudged as high-reliability configuration items, thereby reducing the risk of production accidents caused by data failure.
[0057] Optional, Figure 3 A flowchart illustrating the dynamic processing method for the credibility of configuration items provided in this application. Figure 2 ,like Figure 3 As shown, S202 calculates the time decay factor based on update time and data type, including:
[0058] S301. The time decay factor is determined using an exponential function. The decay rate of the exponential function is adaptively adjusted according to the differences in data types, including at least one of network configuration, hardware assets, and software versions.
[0059] In the context of this example, an exponential function is a mathematical function whose expression is α = e^(-α / α). (-λ·Δt) The exponential function α = e^(-λ / t) is used to calculate the decay rate, where λ is the decay rate and Δt is the time difference. For example, if the data type is "network configuration", the decay rate λ = 0.15 / hour. The data type refers to the classification identifier of the configuration item data, used to distinguish data categories with different update frequencies and failure patterns. Examples include network configuration (e.g., IP address), hardware assets (e.g., server model), and software version (e.g., operating system version). In the step of calculating the time decay factor, the time difference Δt is first calculated based on the update time of the configuration item data and the current time, and then the corresponding decay rate λ is selected according to the data type. For example, λ = 0.15 / hour is used for network configuration due to high-frequency changes, and λ = 0.02 / day is used for hardware assets due to low-frequency changes. (-λ·Δt) Nonlinear modeling reflects the increase in data failure risk over time, ensuring that the decay rate of different data types is adaptively adjusted. This step is linked to the step of generating dynamic scores, providing a basis for evaluating the timeliness of dynamic scores.
[0060] Based on the method provided in this example, the decay rate can be adaptively adjusted using an exponential function and data type, solving the problem in related technologies that cannot distinguish the failure patterns of different data types. For example, network configurations require frequent updates due to containerized deployment, so their decay rate is automatically increased to 0.15 / hour, while hardware assets, which undergo low-frequency changes, use 0.02 / day, ensuring that timeliness assessments accurately match actual business needs. Ultimately, this technique significantly improves the scenario adaptability of the dynamic scoring model and reduces false positives and false negatives.
[0061] Optionally, in the step of determining the time decay factor using an exponential function, the adaptive adjustment of the decay rate is achieved through a machine learning model, which predicts the optimal decay rate for different data types based on historical data.
[0062] In the context of this example, a machine learning model refers to an algorithmic model used to predict parameters, such as Extreme Gradient Boosting (XGBoost) or Long Short-Term Memory (LSTM). For instance, by training on historical data (such as configuration item failure records), the optimal decay rate λ = 0.15 / hour for network configuration can be predicted. In the step of calculating the time decay factor, the adaptive adjustment of the decay rate (λ) is achieved through a machine learning model. The model input includes historical data (such as configuration item failure records and update frequency), and the output is the optimal λ value for different data types. For example, the λ value is automatically increased to 0.15 / hour due to frequent network configuration changes. This step is associated with the exponential function calculation step, improving parameter adaptability. Based on the method provided in this example, the decay rate can be dynamically optimized through a machine learning model, solving the problem that fixed parameter values cannot adapt to complex scenarios. For example, the λ value of network configuration in a containerized environment is automatically increased to adapt to frequent change requirements. Ultimately, this technique significantly improves the scenario adaptability of the scoring model and reduces false positives and false negatives.
[0063] Optional, Figure 4 A flowchart illustrating the dynamic processing method for the credibility of configuration items provided in this application. Figure 3 ,like Figure 4 As shown, after the step of generating a dynamic score based on the time decay factor and preset weight parameters, the following steps are also included:
[0064] S401. Determine the conflict factor. The conflict factor is based on the product of the data source consistency ratio and the attribute key coefficient. The data source consistency ratio is the ratio of the number of consistent data sources to the total number of data sources. The attribute key coefficient is preset according to the business impact of the configuration item attribute.
[0065] In the context of a scenario, the conflict factor γ is a numerical value that quantifies the intensity of multi-source data conflict. The formula is γ = 1 - (N-consistent / N-total)^k. Here, N-consistent represents the amount of consistent data, N-total represents the total number of data sources, and k is a preset attribute key coefficient. For example, if an IP address has three data sources, two of which are consistent, and the attribute key coefficient k = 2.0, then γ = 1 - (2 / 3)^2.0 ≈ 0.38. The attribute key coefficient is a preset parameter reflecting the impact of configuration item attributes on business operations; for example, IP address k = 2.0, software version k = 1.2. As an example, IP address conflicts may directly lead to service interruption, so the attribute key coefficient is increased to 2.0 to amplify the conflict intensity.
[0066] Following the dynamic scoring step, a new conflict factor calculation step is added. First, the consistency ratio of data sources (N-consistent / N-total) is statistically analyzed. Then, a key coefficient (k) is preset based on the business impact of configuration item attributes. For example, IP address conflicts, due to high business impact, use k=2.0, while software version conflicts, due to low business impact, use k=1.2. The conflict factor γ=1-(N-consistent / N-total)^k amplifies high-risk conflicts through the key coefficient, providing a basis for evaluating conflict intensity in subsequent dynamic scoring. This step is linked to the dynamic scoring step, forming a multi-dimensional scoring model. Based on the method provided in this example, the conflict factor quantifies the intensity of multi-source data conflicts, solving the problem of being unable to distinguish between high-risk and low-risk conflicts. For example, IP address conflicts are prioritized because of the key coefficient k=2.0, while software version conflicts are handled with low priority because of k=1.2. Ultimately, this technique significantly improves the priority judgment capability for conflict handling, reduces the waste of operational resources, and lowers the probability of missing high-risk conflicts.
[0067] Optional, Figure 5 A flowchart illustrating the dynamic processing method for the credibility of configuration items provided in this application. Figure 4 ,like Figure 5 As shown, after the step of determining the conflict factor, the following steps are also included:
[0068] S501. Determine the conflict contribution of each data source and trigger the data source governance strategy based on the conflict contribution. The data source governance strategy includes at least one of adding verification rules and switching the collection method.
[0069] In this scenario, conflict contribution refers to the proportion of a data source's contribution to the total number of conflicts. For example, an Application Programming Interface (API) might contribute 80% of IP address conflicts. Data source governance strategies refer to measures to optimize data source quality, such as adding verification rules, like adding IP address format verification rules for low-quality APIs. A data source governance step is added after the conflict factor calculation step. First, the conflict contribution of each data source is calculated (e.g., an API contributing 80% of IP address conflicts), and then governance strategies (e.g., adding verification rules) are triggered based on the contribution. This step is linked to the conflict factor calculation step, forming a closed loop for data source optimization. Based on the method provided in this example, optimizing low-quality data sources through data source governance strategies solves the problem of low calibration efficiency. For example, low-quality APIs, due to their high conflict contribution, are prioritized for governance, improving data collection stability. Ultimately, this technique significantly reduces the overall conflict handling cost and indirectly improves the accuracy of the scoring model.
[0070] Optional, Figure 6A flowchart illustrating the dynamic processing method for the credibility of configuration items provided in this application. Figure 4 ,like Figure 6 As shown, after the step of generating dynamic scores, the following steps are also included:
[0071] S601. Trigger maintenance actions based on dynamic scores. Maintenance actions include at least one of freezing configuration items, generating observation work orders, and triggering calibration scans. The triggering condition for maintenance actions is the correspondence between dynamic scores and preset thresholds.
[0072] In this scenario example, maintenance actions refer to automated operations triggered by dynamic scores, such as freezing configuration items or generating observation work orders. For instance, when the dynamic score S < 0.6, configuration items are frozen and a calibration scan is triggered. The preset threshold refers to the mapping rule between dynamic scores and maintenance actions; for example, S < 0.6 triggers freezing, and S ≥ 0.85 triggers no action. For instance, S = 0.36 triggers freezing of configuration items, and S = 0.92 triggers no action. After the step of generating the dynamic score, a new maintenance action triggering step is added. Maintenance actions are selected based on the correspondence between the dynamic score (S) and the preset threshold: for example, S < 0.6 freezes configuration items and triggers a calibration scan, 0.6 ≤ S < 0.85 generates an observation work order, and S ≥ 0.85 triggers no action. The processing object of maintenance actions is the dynamic score, forming a closed-loop relationship with the dynamic score step. Based on the method provided in this example, the problem of response processes relying on manual judgment can be solved through the mapping rule between dynamic scores and maintenance actions. For example, low-confidence configuration items (S<0.6) are automatically frozen and trigger calibration scans, avoiding delays caused by manual intervention. Ultimately, this technique significantly shortens fault repair time, reduces the probability of risk propagation, and forms a closed-loop governance process of "detection-repair-reset".
[0073] Optionally, in the step of triggering maintenance actions based on dynamic scores, the maintenance actions include at least one of freezing configuration items, generating observation work orders, and triggering calibration scans, and the triggering condition for the maintenance actions is the comprehensive evaluation result of dynamic scores and conflict factors. The comprehensive evaluation result refers to the joint judgment result of dynamic scores and conflict factors; for example, freezing configuration items is triggered when the dynamic score S=0.36 and the conflict factor γ=0.38. In the step of triggering maintenance actions, the triggering condition for the maintenance actions is the comprehensive evaluation result of dynamic scores and conflict factors. For example, a low dynamic score (S<0.6) and a high conflict factor (γ>0.3) jointly trigger freezing configuration items. This step combines dynamic scores and conflict factors to improve the accuracy of maintenance actions. Based on the method provided in this example, the problem of maintenance actions solely relying on dynamic scores is solved by jointly judging dynamic scores and conflict factors. For example, configuration items with high conflict factors (γ>0.3) are still prioritized even if the dynamic score does not fall below the threshold. Ultimately, this technical approach significantly improves the rationality of maintenance resource allocation and reduces the probability of missing key conflicts.
[0074] Optionally, the steps of calculating the time decay factor using an exponential function, calculating the conflict factor, triggering operational actions based on dynamic scoring, and triggering data source governance strategies based on conflict contribution are executed sequentially to form a closed-loop processing flow. This closed-loop process involves a "calculation-evaluation-response-optimization" loop between steps. For example, it could be: time decay factor calculation → conflict factor calculation → operational action triggering → data source governance → recalculation of the time decay factor. Based on the method provided in this example, a closed-loop processing flow can achieve collaborative optimization between the dynamic scoring model and operational governance. For instance, recalculating the time decay factor after data source governance ensures the accuracy of the scoring model and continuous improvement in data source quality. Ultimately, this technique significantly reduces the risk of production accidents caused by data inaccuracies, forming a complete CMDB data governance system.
[0075] Based on the method provided in this example, by introducing a time decay factor and an exponential function that adaptively adjusts the data type, the problem that static weight allocation cannot reflect data timeliness is solved. This technique significantly improves the accuracy of data timeliness assessment, enhances the accuracy and operational efficiency of CMDB data, and prevents expired data from being misjudged as high-reliability configuration items, thereby reducing the probability of production accidents caused by data failure.
[0076] Figure 7 This is a schematic diagram of the configuration item credibility dynamic processing device provided in the embodiments of this application, as shown below. Figure 7 As shown, it includes:
[0077] Acquisition device 71 is used to acquire configuration item data and its metadata, the metadata including update time and data type;
[0078] Processing module 72 is used to calculate the time decay factor based on the update time and data type. The time decay factor adopts the form of an exponential function, and the difference in data type affects the decay rate of the exponential function.
[0079] The processing module 72 is also used to generate a dynamic score based on the time decay factor and the preset weight parameters. The dynamic score is used to characterize the credibility of the configuration item data.
[0080] Optionally, the processing module 72 is specifically used to determine the time decay factor in the form of an exponential function. The decay rate of the exponential function is adaptively adjusted according to the differences in data types, including at least one of network configuration, hardware assets, and software versions.
[0081] Optionally, in the step of determining the time decay factor using an exponential function, the adaptive adjustment of the decay rate is achieved through a machine learning model, which predicts the optimal decay rate for different data types based on historical data.
[0082] Optionally, the processing module 72 is also used to determine the conflict factor, which is based on the product of the data source consistency ratio and the attribute key coefficient. The data source consistency ratio is the ratio of the number of consistent data sources to the total number of data sources, and the attribute key coefficient is preset according to the business impact of the configuration item attribute.
[0083] Optionally, the processing module 72 is also used to determine the conflict contribution of each data source and trigger a data source governance strategy based on the conflict contribution. The data source governance strategy includes at least one of adding verification rules and switching the collection method.
[0084] Optionally, the processing module 72 is also used to trigger maintenance actions based on the dynamic score. The maintenance actions include at least one of freezing configuration items, generating observation work orders, and triggering calibration scans. The triggering condition for the maintenance actions is the correspondence between the dynamic score and a preset threshold.
[0085] The configuration item credibility dynamic processing device provided in this embodiment can execute the configuration item credibility dynamic processing method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.
[0086] Figure 8 A schematic diagram of the structure of the electronic device provided in this application. Figure 8 As shown, the electronic device 50 provided in this embodiment includes at least one processor 501 and a memory 502. Optionally, the electronic device 50 further includes a communication component 503. The processor 501, memory 502, and communication component 503 are connected via a bus.
[0087] In a specific implementation, at least one processor 501 executes computer execution instructions stored in memory 502, causing at least one processor 501 to perform the above-described method.
[0088] The specific implementation process of processor 501 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.
[0089] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.
[0090] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.
[0091] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.
[0092] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.
[0093] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.
[0094] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.
[0095] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.
[0096] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.
[0097] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0098] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.
[0099] 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 related technologies, 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.
[0100] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.
[0101] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.
Claims
1. A method for dynamically processing the credibility of configuration items, characterized in that, include: Obtain configuration item data and its metadata, including update time and data type; A time decay factor is calculated based on the update time and data type. The time decay factor adopts an exponential function form, and the difference in data type affects the decay rate of the exponential function. A dynamic score is generated based on the time decay factor and preset weight parameters. The dynamic score is used to characterize the credibility of the configuration item data.
2. The method according to claim 1, characterized in that, The calculation of the time decay factor based on the update time and data type includes: The time decay factor is determined using an exponential function, and the decay rate of the exponential function is adaptively adjusted according to the differences in data types, including at least one of network configuration, hardware assets, and software versions.
3. The method according to claim 2, characterized in that, In the step of determining the time decay factor using an exponential function, the adaptive adjustment of the decay rate is achieved through a machine learning model, which predicts the optimal decay rate for different data types based on historical data.
4. The method according to claim 1, characterized in that, After the step of generating a dynamic score based on the time decay factor and preset weight parameters, the method further includes: The conflict factor is determined based on the product of the data source consistency ratio and the attribute key coefficient. The data source consistency ratio is the ratio of the number of consistent data sources to the total number of data sources. The attribute key coefficient is preset according to the business impact of the configuration item attribute.
5. The method according to claim 4, characterized in that, Following the step of determining the conflict factor, the method further includes: The conflict contribution of each data source is determined, and a data source governance strategy is triggered based on the conflict contribution. The data source governance strategy includes at least one of adding verification rules and switching the collection method.
6. The method according to claim 1, characterized in that, Following the step of generating dynamic scores, the following is also included: Operational actions are triggered based on dynamic scores. These actions include at least one of freezing configuration items, generating observation work orders, and triggering calibration scans. The triggering condition for each operational action is the correspondence between the dynamic score and a preset threshold.
7. A dynamic processing device for configuration item credibility, characterized in that, include: An acquisition device is used to acquire configuration item data and its metadata, the metadata including update time and data type; The processing module is used to calculate a time decay factor based on the update time and data type. The time decay factor adopts an exponential function form, and the difference in the data type affects the decay rate of the exponential function. The processing module is also used to generate a dynamic score based on the time decay factor and the preset weight parameters, and the dynamic score is used to characterize the credibility of the configuration item data.
8. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the configuration item trust dynamic processing method as described in any one of claims 1-6.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the configuration item trust dynamic processing method as described in any one of claims 1-6.
10. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements the configuration item trust dynamic processing method as described in any one of claims 1-6.