Index management method, system and equipment based on micro-service architecture and medium

Abstract

The invention relates to the technical field of power system index management, and discloses an index management method, system, equipment and medium based on a micro-service architecture, and the method comprises the steps: building a first index library through obtaining first historical business data of a target power grid, and comprehensively covering various indexes such as original, derivative and composite indexes; and more comprehensive and deep power grid state analysis is realized. The second index calculation logic is combined with the constant value representation and the non-constant value preset calculation logic, so that the index numerical calculation is accurate and flexible, and different scene requirements are met. The preset third index judgment logic can monitor the abnormality of the index calculation result in real time, discover problems in time, and improve the reliability and stability of index management. The first index library, the second index calculation logic and the third index judgment logic are integrated into the index management micro-service architecture, modularization, servitization and intelligentization of index management are achieved, the management efficiency is improved, and system expansion and maintenance are facilitated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system index management, and in particular to an index management method, system, device and medium based on a micro-service architecture. BACKGROUND

[0002] With the continuous progress of the power system and the significant improvement in the level of intelligence, the role played by index management in the process of power grid operation is increasingly important. Under the background of the increasing complexity and intelligence of the power system, the traditional index management method gradually exposes many shortcomings, such as low efficiency in data processing and insufficient flexibility of the system itself.

[0003] These drawbacks make it difficult for the traditional method to effectively cope with the higher requirements of modern power grids for index management, especially in terms of real-time performance, accuracy and scalability. The traditional method is not up to the task, and it is urgent to introduce more advanced and efficient management means to improve the stability and reliability of power grid operation. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides an index management method, system, device and medium based on a micro-service architecture, which can solve the deficiencies of traditional index management methods in terms of data processing efficiency, system flexibility, and real-time performance, accuracy and scalability.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides an index management method based on a micro-service architecture, comprising:

[0008] Obtaining first historical business data under a target power grid, and establishing a first index library based on the first historical business data;

[0009] The first index library includes several different target power grid original indexes, target power grid derivative indexes and target power grid composite indexes;

[0010] Establishing a second index calculation logic for the first index library, the second index calculation logic being used to calculate the numerical values of all indexes in the first index library;

[0011] The second index calculation logic includes a constant value representation calculation logic and a non-constant value pre-design calculation logic;

[0012] A third index judgment logic is preset, the third index judgment logic being used to judge whether the output of the second index calculation logic is abnormal;

[0013] An index management microservice architecture integrating a first index library, a second index calculation logic, and a third index judgment logic is established.

[0014] First real-time business data under a target power grid is acquired, and index management of the target power grid is performed in combination with the index management microservice architecture.

[0015] As a preferred scheme of the index management method based on the microservice architecture, the method further comprises:

[0016] A fourth dispatching configuration logic is established for abnormal indexes.

[0017] The output of the second index calculation logic of the abnormal index is inversed.

[0018] Several index adjustment operations after inversion are generated.

[0019] The fourth dispatching configuration logic is configured to integrate and configure the several index adjustment operations.

[0020] The index management microservice architecture further integrates the fourth dispatching configuration logic.

[0021] This preferred scheme can quickly respond to and handle index abnormal conditions, and improve the flexibility and efficiency of index management. By inverting the output of the second index calculation logic of the abnormal index, the problem can be accurately located, and targeted index adjustment operations can be generated. The fourth dispatching configuration logic can integrate and optimize these adjustment operations, ensuring the accuracy and consistency of index management. This preferred scheme not only enhances the automation of index management, but also improves the stability and safety of power grid operation.

[0022] As a preferred scheme of the index management method based on the microservice architecture, the second index calculation logic further comprises:

[0023] The original indexes of the target power grid are divided into constant value indexes and non-constant value indexes.

[0024] Any constant value index in the original indexes of the target power grid and a composite index of the target power grid are represented.

[0025] Any non-constant value index in the original indexes of the target power grid and a derived index of the target power grid are pre-designed with a calculation logic.

[0026] As a preferred scheme of the index management method based on the microservice architecture, the third index judgment logic comprises:

[0027] A threshold judgment condition of the same number as the number of indexes in the first index library is preset.

[0028] The threshold judgment condition includes a numerical value type judgment condition, a text type judgment condition, and a time sequence judgment condition.

[0029] The output of the second index calculation logic is combined with the threshold judgment condition to determine an abnormal index.

[0030] This preferred scheme can comprehensively cover different types of index abnormal conditions, and improve the accuracy and flexibility of index judgment. The numerical value type judgment condition can accurately judge the index with a clear numerical value range; the text type judgment condition is suitable for text matching and analysis of index description or state; and the time sequence judgment condition can consider the trend and pattern of the index change over time, so as to more accurately identify potential problems. This comprehensive judgment logic not only enhances the intelligent level of index management, but also provides more reliable and comprehensive protection for power grid operation.

[0031] As a preferred scheme of the index management method based on the micro-service architecture, the fourth scheduling configuration logic includes:

[0032] The abnormal indexes are prioritized, and the priority ranking is based on the severity of the abnormal index on the power grid operation, the historical occurrence frequency of the abnormal index, and the adjustability of the abnormal index.

[0033] According to the priority ranking result, the output of the second index calculation logic of the abnormal index is reversed in sequence to generate a number of index adjustment operations after reversal.

[0034] The number of index adjustment operations is converted into a number of scheduling configuration operations, and the number of scheduling configuration operations is integrated to obtain the fourth scheduling configuration logic.

[0035] As a preferred scheme of the index management method based on the micro-service architecture, the reversal includes:

[0036] According to the output of the second index calculation logic of the abnormal index, the factors or preconditions leading to the abnormality are reversely deduced;

[0037] The abnormal reason of the index abnormality is determined through simulation operation or backtracking analysis.

[0038] The abnormal reason is analyzed to generate an index adjustment operation based on the abnormal reason.

[0039] As a preferred scheme of the index management method based on the micro-service architecture, the pre-design calculation logic includes a calculation logic for numerical value type indexes, a calculation logic for text type indexes, and a processing logic for time type indexes.

[0040] In a second aspect, the present application provides an index management system based on a micro-service architecture, comprising:

[0041] The data acquisition and processing module is used to acquire the first historical business data under the target power grid and establish a first indicator library based on the first historical business data.

[0042] The first indicator library includes several different original indicators of the target power grid, derived indicators of the target power grid, and composite indicators of the target power grid;

[0043] The indicator calculation module is used to establish a second indicator calculation logic for the first indicator library. The second indicator calculation logic is used to calculate the values ​​of all indicators in the first indicator library.

[0044] The second indicator calculation logic includes fixed value representation calculation logic and non-fixed value preset calculation logic;

[0045] The indicator judgment module is used to preset the third indicator judgment logic, which is used to judge whether the output of the second indicator calculation logic is abnormal.

[0046] The microservice architecture creation module is used to create an indicator management microservice architecture that integrates the first indicator library, the second indicator calculation logic, and the third indicator judgment logic.

[0047] The indicator management module is used to obtain the first real-time business data under the target power grid and manage the indicators of the target power grid in combination with the indicator management microservice architecture.

[0048] Thirdly, the present invention provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0049] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0050] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention proposes a microservice architecture-based indicator management method. By acquiring the first historical business data under the target power grid and establishing a first indicator library, this invention can comprehensively cover various indicators of the target power grid, including original indicators, derived indicators, and composite indicators, thereby providing a more comprehensive and in-depth power grid status analysis. The design of the second indicator calculation logic, especially the combination of the fixed value representation calculation logic and the non-fixed value preset calculation logic, ensures the accuracy and flexibility of indicator numerical calculation, adapting to the indicator needs under different scenarios. The preset third indicator judgment logic can monitor anomalies in indicator calculation results in real time, promptly identify problems, and improve the reliability and stability of indicator management. By integrating the first indicator library, the second indicator calculation logic, and the third indicator judgment logic into the indicator management microservice architecture, this invention achieves modularization, service-orientation, and intelligence in indicator management, not only improving management efficiency but also facilitating system expansion and maintenance. Attached Figure Description

[0051] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a flowchart illustrating a microservice architecture-based metrics management method according to an embodiment of the present invention.

[0053] Figure 2 This is an internal structure diagram of an electronic device that provides a microservice architecture-based indicator management method according to an embodiment of the present invention. Detailed Implementation

[0054] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0055] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a metric management method based on a microservice architecture, including:

[0056] Existing technologies suffer from several problems, such as low data processing efficiency, insufficient system flexibility, and challenges in terms of real-time performance, accuracy, and scalability.

[0057] This invention provides a method that can effectively solve the problems mentioned above. The following will describe in detail how to implement this indicator management method based on microservice architecture with reference to several embodiments.

[0058] Figure 1 A flowchart illustrating a metric management method based on a microservice architecture is shown, including:

[0059] S101, Obtain the first historical business data under the target power grid, and establish a first indicator library based on the first historical business data;

[0060] It should be noted that, under the current technological background, the collection and organization of indicator data often relies on traditional centralized database systems. However, with the expansion of the power grid and the increase in business complexity, this centralized management approach has gradually exposed problems such as high data access latency and poor system scalability. To solve these problems, this invention proposes to adopt a microservice architecture, which breaks down the indicator management function into multiple independent service modules, achieving loose coupling and high configurability of services, thereby improving the system's flexibility and scalability.

[0061] It should be noted that the management of indicators in the target domain requires finding relevant patterns or underlying logic based on historical business data, so it is necessary to obtain historical business data first.

[0062] In this embodiment of the invention, the first historical business data comes from the historical records of the target power grid, covering various parameters and status information during the operation of the power grid.

[0063] In some specific implementations, the first historical business data can also come from other reliable data sources, such as meteorological data and load forecast data, which can provide a more comprehensive reference and basis for indicator management.

[0064] In other specific implementations, the first historical business data may include historical records of key parameters such as voltage, current, power factor, and load, as well as information on the operating status and fault history of power grid equipment. This data, after being cleaned, organized, and standardized, forms a structured dataset, providing a solid foundation for the subsequent establishment of the first indicator library.

[0065] It should be noted that this invention does not limit the method of obtaining the first historical business data or the specific parameters included therein; those skilled in the art can obtain it according to their actual needs.

[0066] It should also be noted that redundant first historical business data not only increases the complexity of data processing but may also introduce noise, affecting the accuracy and efficiency of indicator management. Therefore, it is necessary to preprocess the first historical business data and establish an indicator library based on the preprocessed data. Before establishing the first indicator library, this embodiment of the invention further includes preprocessing the first historical business data to remove redundant data and ensure the accuracy and representativeness of the data.

[0067] It should be noted that data cleaning, deduplication, and normalization are employed to provide a high-quality data foundation for the subsequent establishment of the indicator library. By establishing the first indicator library, this invention can systematically organize and store various types of indicator data, facilitating subsequent indicator calculations and judgments.

[0068] In some specific implementations, the first index library may include different types of indexes, such as voltage stability indexes, current load factor indexes, power factor indexes, etc. These indexes can comprehensively reflect the operating status and performance of the power grid.

[0069] In other specific implementations, the first indicator library may also include some indicators related to power grid operating efficiency and economy, such as line loss rate, transformer load rate, capacitor commissioning rate, etc.

[0070] In this embodiment of the invention, in order to achieve indicator management, it is necessary to classify different types of indicators. However, the two indicator libraries mentioned above cannot effectively realize the interrelation between indicators. Therefore, this invention designs a first indicator library, which includes several different target power grid original indicators, target power grid derived indicators, and target power grid composite indicators.

[0071] It's important to note that regardless of the methods used to establish the primary indicator database, the specific definitions and calculation methods for the target power grid's original indicators, derived indicators, and composite indicators must first be determined. These can be flexibly configured based on the actual needs and business scenarios of the target power grid. For example, the original indicators of the target power grid may directly correspond to certain basic measurements in the power grid, such as voltage and current. Derived indicators, on the other hand, may be obtained through mathematical operations or logical reasoning of the original indicators, reflecting certain specific states or performance characteristics of the power grid, such as power factor and line loss rate. Composite indicators, however, may be a combination of multiple original or derived indicators, used to comprehensively assess the overall operating status of the power grid, such as power reliability and comprehensive power quality index. These composite indicators can more comprehensively reflect the operating status of the power grid, helping managers to identify problems promptly and take appropriate measures.

[0072] In some specific implementations, the first indicator library can be implemented using a big data analytics platform. The specific steps for establishing the first indicator library using a big data analytics platform are as follows:

[0073] Step 1.1: After determining the original indicators, derived indicators, and composite indicators, select a suitable big data analytics platform, such as Apache Hadoop or Spark, to process and store large-scale historical business data.

[0074] Step 1.2: Extract historical power grid business data from different data sources through the ETL process. The ETL process includes extracting, transforming, and loading the data.

[0075] Step 1.3: Clean and preprocess the extracted data to remove erroneous values, duplicate records, etc., and store the processed data in the big data analysis platform after standardization.

[0076] Step 1.4: Establish the first indicator library based on the processed data, including original indicators, derived indicators, and composite indicators.

[0077] In some other specific implementations, the first indicator library can be implemented through Internet of Things (IoT) device integration. The specific steps for establishing the first indicator library using IoT device integration are as follows:

[0078] Step 2.1: After determining the original indicators, derived indicators, and composite indicators, deploy smart meters and other sensor devices at key nodes of the power grid to monitor and record key parameters such as voltage, current, and power factor in real time.

[0079] Step 2.2: Use protocols such as MQTT or CoAP to ensure that these IoT devices can reliably transmit the collected data to the data center.

[0080] Step 2.3: Perform real-time analysis and processing on the received data stream, identify and mark any abnormal situations.

[0081] Step 2.4: Classify and organize the processed data to form a structured dataset, and build the first indicator library based on it.

[0082] In this embodiment of the invention, the first indicator library can be implemented using cloud computing services. The specific steps for establishing the first indicator library using cloud computing services are as follows:

[0083] Step 3.1: After determining the original metrics, derived metrics, and composite metrics, select a reliable cloud service platform, such as Alibaba Cloud, AWS, or Azure, and utilize its database services and computing capabilities.

[0084] Step 3.2: Automatically synchronize historical business data of the target power grid from different sources to the cloud database through API interfaces or tools provided by cloud services.

[0085] Step 3.3: Use machine learning algorithms on the cloud platform to perform in-depth analysis of the imported data and extract valuable information for optimizing indicator management.

[0086] Step 3.4: Based on the above analysis results, construct a first indicator library containing various power grid indicators, and achieve efficient management and rapid response through a microservice architecture.

[0087] It should be noted that these indicators can further enrich the dimensions of the indicator library, helping managers to gain a more comprehensive understanding of the power grid's operational status, thereby enabling them to formulate more scientific and reasonable operation and maintenance strategies and optimization plans. By flexibly configuring different types of indicators, this invention can achieve comprehensive monitoring and management of the power grid's operational status.

[0088] It should also be noted that acquiring the first historical business data of the target power grid and establishing a first indicator database based on this data provides a comprehensive and systematic perspective for examining the power grid's operational status. As a core component of indicator management, the first indicator database not only covers the basic parameters and status information of power grid operation but also delves deeper into the patterns and correlations behind the data through derived and composite indicators, providing managers with richer and more in-depth insights. Furthermore, the establishment of the first indicator database provides a solid foundation for subsequent indicator calculations and judgments, making the indicator management process more efficient, accurate, and reliable.

[0089] S102, Establish a second indicator calculation logic for the first indicator library. The second indicator calculation logic is used to calculate the values ​​of all indicators in the first indicator library.

[0090] It should be noted that after obtaining the first indicator library, the relevant indicators to be calculated during the operation of the power system are determined. The next step is to calculate the corresponding indicator values ​​in the first indicator library based on the actual data.

[0091] In some specific implementations, the second indicator calculation logic can be implemented by integrating some common mathematical operation functions, logical reasoning rules, and machine learning algorithms to ensure the accuracy and efficiency of indicator calculation. These mathematical operation functions may include addition, subtraction, multiplication, division, exponential operations, logarithmic operations, etc., used to process raw data and calculate derived indicators; logical reasoning rules are used to derive new indicator values ​​based on preset conditions and rules; and machine learning algorithms can automatically optimize the indicator calculation process by analyzing and learning from historical data, thereby improving the accuracy and efficiency of the calculation.

[0092] In other specific implementations, the second indicator calculation logic can also employ different calculation methods depending on the type of indicator. For example, for the voltage stability indicator, the voltage stability index can be calculated using statistical methods by monitoring voltage fluctuations at key nodes in the power grid; for the current load factor indicator, the actual current load factor can be calculated by collecting current data in real time and combining it with the rated capacity of power grid equipment. Furthermore, the calculation of composite indicators may require integrating data from multiple original or derived indicators, using methods such as weighted averaging and fuzzy evaluation to obtain a comprehensive evaluation result. These calculation logics ensure the accuracy and reliability of the indicator values, providing strong support for subsequent indicator judgment.

[0093] However, the calculation logic for the aforementioned second indicator is implemented by integrating the calculation method or logic into a large server or a unified computing module. This approach suffers from low computational efficiency and high resource consumption when processing large-scale data. To address these issues, this invention proposes using distributed computing technology within a microservice architecture. By breaking down the computational task into multiple subtasks and assigning them to different service modules for processing, parallelization and high efficiency of computation are achieved. Simultaneously, the high configurability of the microservice architecture allows the computational logic to be flexibly adjusted and optimized according to actual needs, improving the accuracy and flexibility of the calculation.

[0094] In this embodiment of the invention, the second index calculation logic includes a fixed value representation calculation logic and a non-fixed value preset calculation logic;

[0095] In this embodiment of the invention, the second indicator calculation logic further includes:

[0096] The original indicators of the target power grid are divided into fixed-value indicators and non-fixed-value indicators;

[0097] Characterize any fixed-value index and composite index of the target power grid in the original indexes;

[0098] Pre-set calculation logic for any non-fixed value index in the original indexes of the target power grid and the derived indexes of the target power grid.

[0099] In this embodiment of the invention, the preset calculation logic includes calculation logic for numerical indicators, calculation logic for text indicators, and processing logic for time indicators.

[0100] It should be noted that for constant-value indicators, such as the rated capacity and fixed parameters of equipment, their characteristics are that the values ​​are relatively stable and do not change with time or operating status. For these indicators, the main task is characterization, that is, confirming the accurate values ​​of these indicators and using them as the basis for calculating other derived or composite indicators.

[0101] Specifically, these fixed values ​​can be read directly from the database and applied to subsequent calculations as needed. For example, to calculate the load rate of a transformer, you first need to know the transformer's rated capacity (a fixed-value indicator).

[0102] It should be noted that for non-constant indicators, such as current, voltage, and power factor, which change in real time, a set of calculation logic needs to be designed in advance to process these dynamic data because they change with time or the operating status of the power grid.

[0103] In some specific implementations, the calculation logic for numerical indicators can be as follows: for example, for indicators with a clearly defined numerical range, their values ​​can be directly calculated using mathematical formulas. For instance, the power factor can be calculated as the ratio between active power and reactive power.

[0104] In some specific implementations, the calculation logic for text-based metrics can be as follows, applicable to descriptive or status-based data. This may involve text matching, classification, or natural language processing techniques to analyze text information and convert it into quantifiable metrics. For example, device status reports may exist in text form, and key performance indicators can be extracted through text analysis.

[0105] In some specific implementations, the time series processing logic can be as follows: for indicators that change over time, such as load curves, time series analysis methods, such as moving averages and seasonal decomposition techniques, are usually required to process and predict future trends.

[0106] For example, in a power grid, a transformer has a fixed rated capacity (in kilovolt-amperes, kVA), which is the maximum power limit that the transformer can safely operate at.

[0107] Suppose a transformer has a rated capacity of 5000kVA. This value is a constant indicator. Using the constant value representation calculation logic, this data can be directly read from the database and used for subsequent calculations, such as calculating the load factor, which requires knowing the transformer's rated capacity.

[0108] For example, some equipment in a power system has a fixed maintenance cycle, such as a full inspection every 6 months.

[0109] By using fixed-value representations to represent calculation logic, these fixed time intervals can be used as a basis to determine whether it is time for the next maintenance, combined with the current time. For example, if the last maintenance was completed on January 1st, then according to the 6-month maintenance cycle, the next maintenance should be scheduled for July 1st.

[0110] It should be noted that voltage levels in the power grid are constantly changing, thus falling under the category of non-constant values. Voltage stability can be calculated by collecting real-time voltage data from multiple key nodes and applying statistical methods, such as standard deviation and fluctuation range, to assess voltage stability. This involves non-constant value preset calculation logic, as it requires processing data that changes over time and performing dynamic analysis.

[0111] It's also worth noting that establishing a second indicator calculation logic for the first indicator library can significantly improve the efficiency and accuracy of indicator calculations. By distributing the calculation logic within a microservice architecture, different service modules can process calculation tasks in parallel, significantly reducing computation time. Simultaneously, each service module can focus on processing specific types of indicators, making the calculation logic more specialized and efficient. Furthermore, the high scalability of the microservice architecture means that more computing resources can be easily added according to actual needs to meet the challenges of large-scale data processing and complex computing scenarios.

[0112] S103, preset the third indicator judgment logic, the third indicator judgment logic is used to determine whether the output of the second indicator calculation logic is abnormal;

[0113] In some specific implementations, the third indicator judgment logic can be determined through preset thresholds or ranges. For example, for the voltage stability indicator, a reasonable voltage fluctuation range can be set. If the calculated voltage stability index exceeds this range, the voltage stability is considered abnormal. Similarly, for the current load rate indicator, a maximum load rate threshold can be set. When the actual load rate exceeds this threshold, it is determined to be an overload state.

[0114] In other specific implementations, the third indicator judgment logic can also combine historical data and trend analysis for anomaly detection. By analyzing historical indicator data, normal change patterns and ranges of the indicators can be established. When the real-time monitored indicator data deviates from these normal patterns, it may indicate an anomaly in the power grid's operating status. For example, if the power factor suddenly drops, and a similar situation has not occurred in the same historical period, this may be an abnormal signal requiring further analysis and handling by management personnel.

[0115] In other specific implementations, the third indicator judgment logic can also utilize machine learning algorithms to automatically optimize the judgment rules. Through training and learning from a large amount of historical data, the machine learning model can identify complex correlations and potential patterns between indicators, thereby more accurately determining whether an indicator is abnormal. This intelligent judgment method not only improves the accuracy and efficiency of judgment but also adapts to the dynamic changes in the power grid's operating status, providing managers with more timely and reliable decision support.

[0116] In this embodiment of the invention, the third indicator judgment logic includes:

[0117] A threshold condition is set to be the same as the number of indicators in the first indicator library.

[0118] Threshold judgment conditions include numerical judgment conditions, text judgment conditions, and time series judgment conditions;

[0119] Based on the output of the second indicator calculation logic, abnormal indicators are determined in conjunction with threshold judgment conditions.

[0120] Specifically, a threshold judgment condition is preset with the same number of indicators as the number of indicators in the first indicator library. For each indicator, a corresponding threshold judgment condition needs to be set. For example, if there are 50 different indicators in the first indicator library, then corresponding threshold judgment conditions need to be set for each of these 50 indicators.

[0121] Numerical judgment conditions are applicable to indicators with clearly defined numerical ranges. For example, voltage stability indicators can be set to a normal range of 95% to 105%, and values ​​outside this range are considered abnormal.

[0122] Text-based judgment conditions are suitable for descriptive or status-based data. For example, device status reports may exist in text form, and text matching technology can be used to identify keywords such as "fault" and "warning" to determine whether the device is malfunctioning.

[0123] For time series analysis, the criteria apply to indicators that change over time. For example, load curves can be analyzed using methods such as moving averages and seasonal decomposition to determine historical trends, with a reasonable fluctuation range set as a threshold. If the actual data deviates significantly from the expected trend, it is considered an anomaly.

[0124] The results calculated based on the second indicator are compared with a pre-set threshold. If the calculated result of an indicator exceeds its corresponding threshold range, the indicator is marked as abnormal.

[0125] For example, suppose there is a power factor metric, ideally above 0.9. The power factor threshold is set between 0.85 and 1.0. A value below 0.85 is considered abnormal.

[0126] When the power factor output by the second indicator calculation logic is 0.8, according to the set threshold judgment condition, this value is less than 0.85, so it is judged as abnormal.

[0127] Now suppose a substation in the power grid periodically generates maintenance reports, which include descriptions of the equipment's operating status. Define certain keywords such as "fault" and "emergency repair" to indicate abnormal states.

[0128] If the latest maintenance report contains the phrase "urgent repair required", the system will identify this as an abnormal situation based on text-based judgment conditions and trigger the corresponding alarm or processing procedure.

[0129] Furthermore, assuming that the power grid load varies with time and season, it usually follows certain patterns. Through historical data analysis, a daily average load forecasting model for the summer peak period is established, allowing a fluctuation range of ±10% as a normal threshold.

[0130] If the actual load on a certain day exceeds the ±10% fluctuation range set by the forecast model, for example, the forecast value is 1000MW, but the actual load reaches 1150MW, it will be marked as abnormal according to the time series judgment conditions, prompting further investigation into the cause.

[0131] It should be noted that the preset third-indicator judgment logic enables real-time monitoring and early warning of the power grid's operating status. It can also automatically trigger corresponding processing procedures based on abnormal situations, thereby improving the automation level and response speed of power grid operation and maintenance. By setting reasonable threshold judgment conditions, the third-indicator judgment logic can accurately identify abnormal indicators in the power grid, providing managers with clear anomaly information. This allows managers to quickly locate the problem and take appropriate measures, avoiding power grid failures or accidents caused by delayed handling.

[0132] S104, Establish an indicator management microservice architecture that integrates the first indicator library, the second indicator calculation logic, and the third indicator judgment logic;

[0133] In some specific implementations, the metrics management microservice architecture can utilize containerization technology to deploy and manage its various microservice components. Each microservice component encapsulates specific functionalities, such as storing metric libraries, calculating metric values, and handling anomalies, achieving modularity of functionality and loose coupling of services. Containerization technology ensures the consistency and stability of each microservice component across different environments, while also facilitating service expansion and upgrades.

[0134] In other specific implementations, the various microservice components in the metrics management microservice architecture interact through lightweight communication protocols, such as RESTful APIs or gRPC. These protocols support not only synchronous communication but also asynchronous messaging, enabling flexible data exchange and event notification between microservice components. Furthermore, the microservice architecture provides mechanisms such as service discovery and load balancing to ensure that requests are efficiently routed to the correct service instances, improving system availability and scalability.

[0135] It should be noted that in the indicator management microservice architecture, the first indicator library serves as the data foundation, storing various indicator information related to the power grid operation. The second indicator calculation logic then performs numerical calculations on the indicators in the first indicator library according to actual needs, deriving various indicator values ​​reflecting the power grid's operating status. These indicator values ​​are subsequently passed to the third indicator judgment logic for anomaly detection. If an abnormal indicator is detected, the system will automatically trigger the corresponding processing flow, such as sending an alarm to notify management personnel or activating a preset emergency response mechanism.

[0136] In other specific implementations, the indicator management microservice architecture also supports integration with other systems or platforms, such as energy management systems and data acquisition and monitoring systems. By providing standardized interfaces and protocols, indicator data sharing and exchange can be achieved, promoting collaborative work and data fusion between different systems. This allows managers to gain a more comprehensive understanding of the power grid's operational status and make more accurate decisions.

[0137] In other specific implementations, the indicator management microservice architecture also exhibits high scalability and flexibility. As the power grid expands and operational needs evolve, new microservice components can be easily added or the functionality of existing components extended to accommodate new indicator management and decision-making requirements. This scalability and flexibility enable the indicator management microservice architecture to continuously support the safe, stable, and efficient operation of the power grid.

[0138] It should be noted that establishing a microservice architecture for indicator management that integrates the first indicator library, the second indicator calculation logic, and the third indicator judgment logic can significantly improve the efficiency and accuracy of indicator management. By modularizing different functional components and integrating them into a unified microservice architecture, centralized management, efficient calculation, and intelligent judgment of indicators are achieved. This not only reduces the tediousness and errors of manual operations but also improves the automation and intelligence level of power grid operation and maintenance.

[0139] S105: Obtain the first real-time business data under the target power grid, and perform target power grid indicator management in conjunction with the indicator management microservice architecture.

[0140] In this embodiment of the invention, a fourth scheduling configuration logic is established for abnormal indicators;

[0141] Inversion is performed based on the output of the second indicator calculation logic of the anomaly indicator;

[0142] Several index adjustments were performed after the inversion.

[0143] The fourth scheduling configuration logic is used to integrate and configure several indicator adjustment operations;

[0144] The indicator management microservice architecture also integrates a fourth scheduling and configuration logic.

[0145] In this embodiment of the invention, the fourth scheduling configuration logic includes:

[0146] Abnormal indicators are prioritized based on factors such as the severity of their impact on power grid operation, their historical frequency of occurrence, and their adjustability.

[0147] Based on the priority ranking results, the output of the second indicator calculation logic of the abnormal indicators is inverted in turn to generate several indicator adjustment operations after inversion.

[0148] Several indicator adjustment operations are transformed into several scheduling configuration operations, and these operations are integrated to obtain the fourth scheduling configuration logic.

[0149] In this embodiment of the invention, the inversion includes:

[0150] Based on the output of the second indicator calculation logic of the abnormal indicator, the factors or preconditions that caused the abnormality are deduced in reverse.

[0151] Determine the cause of abnormal indicators through simulation or backtracking analysis;

[0152] Analyze the causes of anomalies and generate indicator adjustment actions based on these causes.

[0153] In some specific implementations, prioritization can be based on a comprehensive consideration of multiple factors. For example, for abnormal indicators that directly affect the stability and security of the power grid, such as voltage stability or power factor indicators, their priority should be set to the highest. Abnormalities in these indicators may directly lead to power grid faults or accidents, and therefore require prompt handling. For abnormal indicators with less impact or those that can be compensated for through other means, their priority can be appropriately reduced. Furthermore, the historical frequency of occurrence of abnormal indicators is also an important reference for prioritization. If a certain abnormal indicator occurs frequently and has a sustained impact on power grid operation, its priority should be increased accordingly so that management personnel can focus on and resolve the problem.

[0154] In other specific implementations, the detected abnormal indicators are prioritized. The prioritization criteria may also include the following:

[0155] Step 4.1: The magnitude of the impact of abnormal indicators on power grid operation. For example, an abnormal voltage stability may be more urgent than a minor fault in a specific piece of equipment.

[0156] Step 4.2: Has this abnormal indicator occurred frequently in the past? Frequent occurrences of this abnormality may indicate a systemic problem that requires priority attention.

[0157] Step 4.3: Determine the feasibility of adjusting this indicator based on the current power grid conditions and resource availability.

[0158] In this embodiment of the invention, for each high-priority anomaly indicator, an inversion analysis is performed based on the output of its second indicator calculation logic:

[0159] Step 5.1: Based on the values ​​of the abnormal indicators, work backward to deduce the factors or conditions that may have caused the abnormality. For example, if the power factor is below the normal range, it may be due to insufficient capacitor compensation or an unbalanced load.

[0160] Step 5.2: Use simulation software or historical data analysis tools to reproduce the specific circumstances that led to the anomaly in order to determine the most likely cause. This step can help eliminate random factors and focus on the root cause.

[0161] Step 5.3: Based on the above analysis results, propose specific adjustment suggestions. For example, if the low power factor is caused by insufficient capacitor compensation, it can be suggested to increase the number of capacitors or adjust the settings of existing capacitors.

[0162] In this embodiment of the invention, several indicator adjustment operations are transformed into actual scheduling and configuration operations. For example, if the adjustment suggestion is to increase the number of capacitors, a detailed implementation plan needs to be developed, including selecting appropriate capacitor models, installation locations, and time schedules. All related scheduling and configuration operations are integrated into a complete plan to ensure coordination and consistency among operations and avoid conflicts. For example, when scheduling multiple maintenance tasks within the same time period, factors such as human resources and equipment availability need to be considered.

[0163] For example, suppose the current load rate of a critical line reaches 90%, which is close to the safety limit.

[0164] Given that this line connects multiple critical facilities, an excessively high load rate could trigger a chain reaction of failures, hence it is classified as a high priority line.

[0165] Analysis of historical load curves revealed significant load fluctuations during peak hours, and the recent addition of large new users.

[0166] It is recommended to redistribute the load or transfer some of the load to a backup line.

[0167] Plan specific load transfer schemes, including selecting appropriate time periods (such as off-peak hours at night) and adjusting protection device parameters to ensure a smooth and error-free switching process.

[0168] In summary, this invention proposes a microservice-based indicator management method. By acquiring the first historical business data of the target power grid and establishing a first indicator library, this invention can comprehensively cover various indicators of the target power grid, including original indicators, derived indicators, and composite indicators, thereby providing a more comprehensive and in-depth analysis of the power grid status. The design of the second indicator calculation logic, especially the combination of the fixed-value representation calculation logic and the non-fixed-value preset calculation logic, ensures the accuracy and flexibility of indicator numerical calculation, adapting to indicator requirements in different scenarios. The preset third indicator judgment logic can monitor anomalies in indicator calculation results in real time, promptly identify problems, and improve the reliability and stability of indicator management. By integrating the first indicator library, the second indicator calculation logic, and the third indicator judgment logic into the indicator management microservice architecture, this invention achieves modularization, service-orientation, and intelligence in indicator management, not only improving management efficiency but also facilitating system expansion and maintenance.

[0169] Example 2, in a preferred embodiment, the specific steps for breaking down the computational task into multiple sub-tasks can be as follows:

[0170] Step 6.1: Determine the decomposition strategy for computational tasks. This requires identifying which computational tasks can be broken down based on the specific needs of power grid indicator management and analyzing the dependencies between them.

[0171] For example, the present invention can divide the entire calculation process into: raw data preprocessing, which may include cleaning, deduplication, standardization, etc.; basic index calculation, such as direct calculation of basic parameters such as voltage and current; derived index calculation, which is an index further derived from the basic index, such as power factor, load rate, etc.; and composite index calculation, which is a comprehensive evaluation result obtained by combining multiple basic or derived indexes.

[0172] After clarifying these categories, define clear service interfaces for each subtask to ensure that the data exchange format between services is consistent. For example, the data acquisition service is responsible for collecting raw data from different sources, the data cleaning service performs data cleaning, deduplication, standardization and other operations, the basic indicator calculation service provides corresponding calculation logic for different types of basic indicators, the derived indicator calculation service calculates derived indicators according to preset formulas or algorithms, and the composite indicator calculation service integrates multiple types of indicator data to generate comprehensive evaluation results.

[0173] Step 6.2: Implement a distributed computing framework to support large-scale data processing. In this process, existing distributed computing tools such as Apache Spark or Hadoop are used to build the environment. For task scheduling, distributed task schedulers such as Kubernetes or Mesos can be used to manage and distribute tasks across different computing nodes.

[0174] At the same time, the dataset is divided reasonably according to the size of the data and the computational complexity, so that each subtask can be completed within a limited time. This is called data partitioning.

[0175] In addition, parallel computing can be triggered through message queues such as RabbitMQ or Kafka, or RESTful API calls, thereby achieving parallel execution, which can significantly improve processing speed and efficiency.

[0176] Step 6.3: Integrate and test all components to ensure normal system operation. This step requires integration testing between the various subtasks to verify that the interaction between service modules is normal and that data flow is smooth. Performance testing is also essential, used to evaluate the system's response speed and stability under high concurrency conditions.

[0177] In addition, setting up appropriate error handling mechanisms as a fault tolerance measure ensures that the failure of some subtasks will not affect the operation of the overall system, which is the key to ensuring system stability.

[0178] Step 6.4: After deploying the system, continuously monitor its operation and adjust and optimize it according to the actual situation. Use tools such as Prometheus and Grafana for real-time monitoring and regularly review log files to find potential problems. Continuously adjust computing strategies and service configurations based on feedback to improve efficiency and accuracy. This iterative improvement approach helps maintain the system's high-efficiency operation in the long term.

[0179] Example 3, referring to Figure 2 This embodiment also provides a metric management system based on a microservice architecture, including:

[0180] The data acquisition and processing module is used to acquire the first historical business data under the target power grid and establish a first indicator library based on the first historical business data;

[0181] The first indicator library includes several different original indicators of the target power grid, derived indicators of the target power grid, and composite indicators of the target power grid.

[0182] The indicator calculation module is used to establish the second indicator calculation logic for the first indicator library. The second indicator calculation logic is used to calculate the values ​​of all indicators in the first indicator library.

[0183] The second indicator calculation logic includes fixed value representation calculation logic and non-fixed value preset calculation logic;

[0184] The indicator judgment module is used to preset the third indicator judgment logic. The third indicator judgment logic is used to determine whether the output of the second indicator calculation logic is abnormal.

[0185] The microservice architecture creation module is used to create an indicator management microservice architecture that integrates the first indicator library, the second indicator calculation logic, and the third indicator judgment logic.

[0186] The indicator management module is used to obtain the first real-time business data under the target power grid and manage the indicators of the target power grid in combination with the indicator management microservice architecture.

[0187] The above-mentioned unit modules can be embedded in the processor of the electronic device in hardware form or independent of it, or they can be stored in the memory of the electronic device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0188] This embodiment also provides an electronic device, which can be a terminal, and its internal structure diagram can be as follows: Figure 2 As shown, the electronic device includes a processor, memory, communication interface, display screen, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a microservices-based metrics management method. The display screen can be an LCD screen or an e-ink screen. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse.

[0189] This embodiment also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, it performs the following steps:

[0190] Acquire the first historical business data under the target power grid and establish a first indicator library based on the first historical business data;

[0191] The first indicator library includes several different original indicators of the target power grid, derived indicators of the target power grid, and composite indicators of the target power grid.

[0192] Establish a second indicator calculation logic for the first indicator library. The second indicator calculation logic is used to calculate the values ​​of all indicators in the first indicator library.

[0193] The second indicator calculation logic includes fixed value representation calculation logic and non-fixed value preset calculation logic;

[0194] A third indicator judgment logic is preset, which is used to determine whether the output of the second indicator calculation logic is abnormal.

[0195] Establish a microservice architecture for indicator management that integrates the first indicator library, the second indicator calculation logic, and the third indicator judgment logic;

[0196] Obtain the first real-time business data under the target power grid, and combine it with the indicator management microservice architecture to manage the indicators of the target power grid.

[0197] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0198] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0199] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A metric management method based on a microservice architecture, characterized in that, include: Acquire the first historical business data under the target power grid, and establish a first indicator library based on the first historical business data; The first indicator library includes several different original indicators of the target power grid, derived indicators of the target power grid, and composite indicators of the target power grid; Establish a second indicator calculation logic for the first indicator library. The second indicator calculation logic is used to calculate the values ​​of all indicators in the first indicator library. The second indicator calculation logic includes fixed value representation calculation logic and non-fixed value preset calculation logic; A third indicator judgment logic is preset, which is used to determine whether the output of the second indicator calculation logic is abnormal; Establish a microservice architecture for indicator management that integrates the first indicator library, the second indicator calculation logic, and the third indicator judgment logic; Obtain the first real-time business data under the target power grid, and combine it with the indicator management microservice architecture to manage the indicators of the target power grid.

2. The indicator management method based on microservice architecture as described in claim 1, characterized in that, Also includes: A fourth scheduling configuration logic is established to address abnormal indicators; Inversion is performed based on the output of the second indicator calculation logic of the anomaly indicator; Several index adjustments were performed after the inversion. The fourth scheduling configuration logic is used to integrate and configure several indicator adjustment operations; The indicator management microservice architecture also integrates a fourth scheduling and configuration logic.

3. The indicator management method based on microservice architecture as described in claim 2, characterized in that, The calculation logic for the second indicator also includes: The original indicators of the target power grid are divided into fixed-value indicators and non-fixed-value indicators; Characterize any fixed-value index and composite index of the target power grid in the original indexes; Pre-set calculation logic for any non-fixed value index in the original indexes of the target power grid and the derived indexes of the target power grid.

4. The indicator management method based on microservice architecture as described in claim 3, characterized in that, The third indicator judgment logic includes: A threshold condition is set that has the same number of indicators as the number of indicators in the first indicator library. The threshold judgment conditions include numerical judgment conditions, text judgment conditions, and time series judgment conditions; Based on the output of the second indicator calculation logic, abnormal indicators are determined in conjunction with threshold judgment conditions.

5. The indicator management method based on microservice architecture as described in claim 4, characterized in that, The fourth scheduling configuration logic includes: Abnormal indicators are prioritized based on factors such as the severity of their impact on power grid operation, their historical frequency of occurrence, and their adjustability. Based on the priority ranking results, the output of the second indicator calculation logic of the abnormal indicators is inverted in turn to generate several indicator adjustment operations after inversion. Several indicator adjustment operations are transformed into several scheduling configuration operations, and these operations are integrated to obtain the fourth scheduling configuration logic.

6. The indicator management method based on microservice architecture as described in claim 5, characterized in that, The inversion includes: Based on the output of the second indicator calculation logic of the abnormal indicator, the factors or preconditions that caused the abnormality are deduced in reverse. Determine the cause of abnormal indicators through simulation or backtracking analysis; Analyze the causes of anomalies and generate indicator adjustment actions based on these causes.

7. The indicator management method based on microservice architecture as described in claim 6, characterized in that, The preset calculation logic includes calculation logic for numerical indicators, calculation logic for textual indicators, and processing logic for time-related indicators.

8. A metrics management system based on a microservice architecture, employing the method described in any one of claims 1 to 7, characterized in that, include: The data acquisition and processing module is used to acquire the first historical business data under the target power grid and establish a first indicator library based on the first historical business data. The first indicator library includes several different original indicators of the target power grid, derived indicators of the target power grid, and composite indicators of the target power grid; The indicator calculation module is used to establish a second indicator calculation logic for the first indicator library. The second indicator calculation logic is used to calculate the values ​​of all indicators in the first indicator library. The second indicator calculation logic includes fixed value representation calculation logic and non-fixed value preset calculation logic; The indicator judgment module is used to preset the third indicator judgment logic, which is used to judge whether the output of the second indicator calculation logic is abnormal. The microservice architecture creation module is used to create an indicator management microservice architecture that integrates the first indicator library, the second indicator calculation logic, and the third indicator judgment logic. The indicator management module is used to obtain the first real-time business data under the target power grid and manage the indicators of the target power grid in combination with the indicator management microservice architecture.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the indicator management method based on microservice architecture according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the indicator management method based on microservice architecture according to any one of claims 1 to 7.

Images