Index data processing method and device, computer equipment and storage medium

Abstract

The invention belongs to the technical field of data processing, and relates to an index data processing method, which comprises the following steps: acquiring behavior data of a target system in a running process; cleaning the behavior data to obtain target behavior data; performing feature extraction on the target operation data based on a preset extraction strategy to obtain feature data; performing index calculation processing on the feature data based on a preset atomic dimension to obtain a calculation index; performing aggregation calculation processing on the calculation index based on a preset derived dimension to obtain an application index; summarizing the calculation index and the application index to obtain a target index; and performing display processing on the target index based on a preset display interface. The invention further provides an index data processing device, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the target index can be stored in a block chain. According to the method, the processing efficiency of generation processing of the index data of production analysis is improved, and the accuracy of the generated target index is ensured.

Description

Technical Field

[0001] The present application relates to the fields of data processing technology and financial technology, and in particular to methods, devices, computer equipment and storage media for processing indicator data. Background Art

[0002] In the financial sector, system commissioning analysis is a key step to ensure that new systems or new functions are successfully deployed and achieve the expected results. This process is similar to the commissioning analysis in the field of software engineering, which aims to verify whether the system meets the established business needs and design goals by comprehensively evaluating the system's performance, stability, security, user feedback and other multi-dimensional indicators.

[0003] However, the traditional production analysis method has significant efficiency problems. Specifically, this process often requires manual collection of a large amount of system data, including but not limited to system logs, user behavior data, etc. These data are usually scattered in different databases, log files or third-party services, so the collection process is cumbersome and time-consuming. In addition, after collecting enough data, analysts are required to perform complex indicator calculations and analyses, which not only requires analysts to have solid professional skills, but also requires a lot of time and effort to ensure the accuracy and reliability of the calculations. Therefore, the existing data processing methods for production analysis have the problem of low processing efficiency when dealing with large-scale, highly complex financial systems. Summary of the invention

[0004] The purpose of the embodiments of the present application is to propose a method, device, computer equipment and storage medium for processing indicator data to solve the technical problem of low processing efficiency in the existing data processing method for production analysis.

[0005] In order to solve the above technical problems, the present application embodiment provides a method for processing indicator data, which adopts the following technical solution:

[0006] Obtain the behavior data of the target system during operation;

[0007] Cleaning the behavior data to obtain corresponding target behavior data;

[0008] Extracting features from the target operation data based on a preset extraction strategy to obtain corresponding feature data;

[0009] Performing index calculation processing on the characteristic data based on a preset atomic dimension to obtain a corresponding calculation index;

[0010] Based on the preset derived dimensions, the calculation indicators are aggregated and calculated to obtain corresponding application indicators;

[0011] Summarizing the calculation index and the application index to obtain a corresponding target index;

[0012] The target indicator is displayed based on a preset display interface.

[0013] Furthermore, the step of extracting features from the target operation data based on a preset extraction strategy to obtain corresponding feature data specifically includes:

[0014] Performing characterization processing on the target operation data to extract corresponding key fields;

[0015] Numerically processing the key field to obtain corresponding first field data;

[0016] Normalizing the first field data to obtain corresponding second field data;

[0017] The second field data is used as the characteristic data.

[0018] Furthermore, the step of performing index calculation processing on the feature data based on the preset atomic dimension to obtain the corresponding calculation index specifically includes:

[0019] Obtaining an atomic index calculation formula corresponding to the atomic dimension;

[0020] Call the preset calculation engine;

[0021] Based on the calculation engine, the characteristic data is calculated and processed using the atomic index calculation formula to obtain the corresponding atomic index;

[0022] The atomic index is used as the calculation index.

[0023] Furthermore, the step of performing aggregation calculation processing on the calculation indicators based on the preset derived dimensions to obtain corresponding application indicators specifically includes:

[0024] Acquire multiple processing dimensions corresponding to the calculation indicators;

[0025] Merging all the processing dimensions to obtain corresponding derived dimensions;

[0026] Get the preset aggregation strategy;

[0027] Based on the derived dimension, the calculation indicator is aggregated and calculated using the aggregation strategy to obtain a corresponding derived indicator;

[0028] The derived index is used as the application index.

[0029] Furthermore, the step of cleaning the behavior data to obtain corresponding target behavior data specifically includes:

[0030] Call the preset cleaning script;

[0031] Cleaning the behavior data based on the cleaning script to obtain corresponding first behavior data;

[0032] Performing outlier repair processing on the first behavior data to obtain corresponding second behavior data;

[0033] The second behavior data is used as the target behavior data.

[0034] Furthermore, after the step of displaying the target indicator based on the preset display interface, the method further includes:

[0035] Determining whether a data selection operation triggered by a user on the display interface is received;

[0036] If so, obtaining a specified indicator and a specified dimension corresponding to the data selection operation;

[0037] Based on the specified dimension, a preset calculation strategy is called to calculate an entropy static state value and an entropy transfer state value corresponding to the specified indicator;

[0038] Filter out a matching designated management policy based on the entropy value static state value and the entropy value transfer state value;

[0039] The specified management policy is sent to the user.

[0040] Furthermore, after the step of aggregating the calculation index and the application index to obtain the corresponding target index, the method further includes:

[0041] Call the preset indicator library;

[0042] Determining a designated storage location corresponding to the target indicator in the indicator library;

[0043] Based on the designated storage location, the target indicator is stored in the indicator library.

[0044] In order to solve the above technical problems, the embodiment of the present application further provides an indicator data processing device, which adopts the following technical solution:

[0045] The first acquisition module is used to acquire the behavior data of the target system during operation;

[0046] A cleaning module, used for cleaning the behavior data to obtain corresponding target behavior data;

[0047] An extraction module, used to extract features from the target operation data based on a preset extraction strategy to obtain corresponding feature data;

[0048] A first calculation module, used to perform index calculation processing on the feature data based on a preset atomic dimension to obtain a corresponding calculation index;

[0049] A second calculation module is used to perform aggregate calculation processing on the calculation indicators based on a preset derived dimension to obtain corresponding application indicators;

[0050] A summary module, used to summarize the calculation index and the application index to obtain the corresponding target index;

[0051] The display module is used to display the target indicator based on a preset display interface.

[0052] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0053] Obtain the behavior data of the target system during operation;

[0054] Cleaning the behavior data to obtain corresponding target behavior data;

[0055] Extracting features from the target operation data based on a preset extraction strategy to obtain corresponding feature data;

[0056] Performing index calculation processing on the characteristic data based on a preset atomic dimension to obtain a corresponding calculation index;

[0057] Based on the preset derived dimensions, the calculation indicators are aggregated and calculated to obtain corresponding application indicators;

[0058] Summarizing the calculation index and the application index to obtain a corresponding target index;

[0059] The target indicator is displayed based on a preset display interface.

[0060] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0061] Obtain the behavior data of the target system during operation;

[0062] Cleaning the behavior data to obtain corresponding target behavior data;

[0063] Extracting features from the target operation data based on a preset extraction strategy to obtain corresponding feature data;

[0064] Performing index calculation processing on the characteristic data based on a preset atomic dimension to obtain a corresponding calculation index;

[0065] Based on the preset derived dimensions, the calculation indicators are aggregated and calculated to obtain corresponding application indicators;

[0066] Summarizing the calculation index and the application index to obtain a corresponding target index;

[0067] The target indicator is displayed based on a preset display interface.

[0068] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0069] The present application first obtains the behavior data of the target system during operation; and cleans the behavior data to obtain the corresponding target behavior data; then, based on a preset extraction strategy, features are extracted from the target operation data to obtain the corresponding feature data; then, based on a preset atomic dimension, indicator calculation is performed on the feature data to obtain the corresponding calculation indicator; subsequently, based on a preset derived dimension, the calculation indicator is aggregated and calculated to obtain the corresponding application indicator; the calculation indicator and the application indicator are further aggregated to obtain the corresponding target indicator; finally, the target indicator is displayed based on a preset display interface. Different from the manual data processing method of production analysis adopted in the existing production analysis method, the present application obtains the behavior data of the target system during operation, cleans the behavior data, obtains the target behavior data, and then extracts the features of the target operation data based on the use of extraction strategies to obtain feature data, and then automatically performs indicator calculation processing on the feature data based on the use of atomic dimensions to obtain calculation indicators, and automatically aggregates the calculation indicators based on the use of derived dimensions to obtain application indicators, and then summarizes the calculation indicators and application indicators, so as to realize automatic, efficient and accurate calculation of the target indicators corresponding to the production analysis of the target system, effectively improving the processing efficiency of the generation and processing of the indicator data of the production analysis, and ensuring the accuracy of the generated target indicators. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0071] Figure 1is an exemplary system architecture diagram to which the present application may be applied;

[0072] Figure 2 A flowchart of an embodiment of a method for processing indicator data according to the present application;

[0073] Figure 3 is a structural schematic diagram of an embodiment of an indicator data processing device according to the present application;

[0074] Figure 4 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0075] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0076] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0077] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0078] like Figure 1 As shown, the system architecture 100 may include a terminal device 101, a network 102 and a server 103. The terminal device 101 may be a laptop 1011, a tablet computer 1012 or a mobile phone 1013. The network 102 is used to provide a medium for a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0079] The user can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0080] The terminal device 101 can be any electronic device with a display screen and supporting web browsing. In addition to the laptop computer 1011, tablet computer 1012 or mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV), a laptop computer, a desktop computer, etc.

[0081] The server 103 may be a server that provides various services, such as a background server that provides support for a web page displayed on the terminal device 101 .

[0082] It should be noted that the indicator data processing method provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the indicator data processing device is generally set in the server / terminal device.

[0083] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to the implementation requirements.

[0084] Continue to refer Figure 2 , shows a flowchart of an embodiment of a method for processing indicator data according to the present application. According to different requirements, the order of the steps in the flowchart can be changed, and some steps can be omitted. The method for processing indicator data provided in the embodiment of the present application can be applied to any scenario where indicator processing is required, and the method for processing indicator data can be applied to products in these scenarios, for example, indicator generation in the financial insurance field. The method for processing indicator data includes the following steps:

[0085] Step S201, obtaining the behavior data of the target system during operation.

[0086] In this embodiment, the method for processing indicator data is executed on an electronic device (eg Figure 1The server / terminal device shown in the figure) can obtain the behavior data of the target system during operation through a wired connection or a wireless connection. It should be noted that the above-mentioned wireless connection methods may include but are not limited to 3G / 4G / 5G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future. The execution subject of this application may be a system with an indicator processing function. The behavior data (or operation data) of the target system during operation can be collected in real time through a specific API or interface, including user behavior data, system logs, etc. User behavior data refers to the data generated by the user when using the system or interacting with the large model, such as click behavior, input content, dwell time, etc. These data help to understand user preferences, behavior patterns, and system usage. System log refers to the log information automatically recorded during the operation of the system, including system startup, stop, exception handling, resource allocation, etc. System logs are essential for diagnosing system problems, optimizing system performance, and understanding system health. Among them, operation data is the basic data source for production relationship analysis, providing a comprehensive view of system operation status. By analyzing the operation data, we can understand the performance bottlenecks, user behavior patterns, and potential problems of the system or model. Based on the operation data, various performance indicators and derived indicators can be calculated to evaluate the performance of the system. These indicators help to quantify the improvement effect of the system and provide a basis for the formulation of optimization strategies. o In the production relationship analysis, operation data is an important basis for evaluating the relationship between input (such as resources, time, cost) and output (such as performance improvement, user growth, and revenue increase). By analyzing the operation data, the relationship between input and output can be quantified to provide decision support for resource allocation, cost control, and benefit evaluation. In addition, in the production analysis scenario in the financial field, the target system may include insurance systems, payment systems, claims systems, and so on. The production relationship is reflected in the relationship between the original behavior data and the calculated indicators. These indicators are processed to form a higher-level metric for evaluating system performance or user behavior.

[0087] Step S202, cleaning the behavior data to obtain corresponding target behavior data.

[0088] In this embodiment, the above-mentioned specific implementation process of cleaning the behavior data to obtain the corresponding target behavior data will be further described in detail in subsequent specific embodiments of the present application, and will not be elaborated on here.

[0089] Step S203: extracting features from the target operation data based on a preset extraction strategy to obtain corresponding feature data.

[0090] In this embodiment, the above-mentioned characteristic data refers to the quantifiable characteristics generated in the behavior performance of the measurement entity, such as the number of test defects found by a tester on a system. Among them, the measurement entity refers to the individual that generates the measurement characteristics under the measurement target: such as the members of the quality team. The measurement target refers to the application object of the measurement practice, such as the quality team of the company's IT.

[0091] The specific implementation process of extracting features from the target operation data based on the preset extraction strategy to obtain the corresponding feature data will be further described in detail in the subsequent specific embodiments of this application, and will not be elaborated on here. In addition, the obtained feature data can also be further labeled for subsequent classification and clustering analysis.

[0092] Step S204: performing index calculation processing on the feature data based on a preset atomic dimension to obtain a corresponding calculation index.

[0093] In this embodiment, the atomic dimension refers to the original dimension of the small particles corresponding to the above-mentioned characteristic data. The specific implementation process of the above-mentioned index calculation processing of the characteristic data based on the preset atomic dimension to obtain the corresponding calculation index will be further described in detail in the subsequent specific embodiments of this application, and will not be elaborated here.

[0094] Step S205: performing aggregation calculation processing on the calculation indicators based on the preset derived dimensions to obtain corresponding application indicators.

[0095] In this embodiment, the above-mentioned specific implementation process of performing aggregation calculation processing on the calculation indicators based on the preset derived dimensions to obtain the corresponding application indicators will be further described in detail in the subsequent specific embodiments of this application, and will not be elaborated in detail here.

[0096] Step S206, summarizing the calculation index and the application index to obtain a corresponding target index.

[0097] In this embodiment, all calculated calculation indicators and application indicators are aggregated to obtain aggregated indicators, which are used as the target indicators.

[0098] Step S207: display the target indicator based on a preset display interface.

[0099] In this embodiment, the summarized target indicators can be displayed in the above display interface in an appropriate format (such as tables and charts) to facilitate viewing and analysis by testers and managers. Among them, a data visualization library can be used to develop a visualization interface, i.e., the above display interface. The system can obtain indicator data through an API interface, and can display charts and data analysis results in a visualization interface. It also provides user interaction functions, such as circling operations, filter condition settings, etc.

[0100] The present application first obtains the behavior data of the target system during operation; and cleans the behavior data to obtain the corresponding target behavior data; then, based on a preset extraction strategy, features are extracted from the target operation data to obtain the corresponding feature data; then, based on a preset atomic dimension, indicator calculation is performed on the feature data to obtain the corresponding calculation indicator; subsequently, based on a preset derived dimension, the calculation indicator is aggregated and calculated to obtain the corresponding application indicator; the calculation indicator and the application indicator are further aggregated to obtain the corresponding target indicator; finally, the target indicator is displayed based on a preset display interface. Different from the manual data processing method of production analysis adopted in the existing production analysis method, the present application obtains the behavior data of the target system during operation, cleans the behavior data, obtains the target behavior data, and then extracts the features of the target operation data based on the use of extraction strategies to obtain feature data, and then automatically performs indicator calculation processing on the feature data based on the use of atomic dimensions to obtain calculation indicators, and automatically aggregates the calculation indicators based on the use of derived dimensions to obtain application indicators, and then summarizes the calculation indicators and application indicators, so as to realize automatic, efficient and accurate calculation of the target indicators corresponding to the production analysis of the target system, effectively improving the processing efficiency of the generation and processing of the indicator data of the production analysis, and ensuring the accuracy of the generated target indicators.

[0101] In some optional implementations, step S203 includes the following steps:

[0102] The target operation data is characterized and corresponding key fields are extracted.

[0103] In this embodiment, the target operation data obtained after cleaning can be characterized and key fields can be extracted.

[0104] The key field is numerically processed to obtain corresponding first field data.

[0105] In this embodiment, the corresponding first field data can be obtained by digitizing the obtained key fields, such as level, discovery time, discovery stage, personalized service tag, etc.

[0106] The first field data is normalized to obtain corresponding second field data.

[0107] In this embodiment, the first field data may be normalized by using a tool with a normalization function, thereby obtaining structured feature data.

[0108] The second field data is used as the characteristic data.

[0109] The present application performs characterization processing on the target operation data to extract the corresponding key fields; then performs numerical processing on the key fields to obtain the corresponding first field data; then performs normalization processing on the first field data to obtain the corresponding second field data; and subsequently uses the second field data as the feature data. The present application performs characterization processing on the target operation data to extract the corresponding key fields, and then performs numerical processing and normalization processing on the key fields, so as to achieve fast and accurate feature extraction processing of the target operation data, improve the processing efficiency of feature extraction, and ensure the accuracy and standardization of the obtained feature data.

[0110] In some optional implementations of this embodiment, step S204 includes the following steps:

[0111] Obtain an atomic index calculation formula corresponding to the atomic dimension.

[0112] In this embodiment, the atomic dimension refers to the original dimension of the small particle corresponding to the above-mentioned characteristic data. The atomic indicator refers to the value obtained after collecting the actual behavior data of the entity characteristics in the measurement target and digitizing and discretizing the relevant behavior fields. Exemplarily, if the above-mentioned characteristic data is test defect data, the atomic dimension corresponding to the characteristic data is the defect level. The atomic indicator corresponding to the characteristic data is the number of high-order defects. Among them, according to actual business needs, calculation formulas for atomic indicators corresponding to different atomic dimensions are pre-defined.

[0113] Call the preset calculation engine.

[0114] In this embodiment, the calculation engine is a processing engine having the function of performing index calculation processing.

[0115] Based on the calculation engine, the characteristic data is calculated and processed using the atomic index calculation formula to obtain corresponding atomic indexes.

[0116] In this embodiment, the above-mentioned calculation engine can be used to perform index calculation on the characteristic data according to the above-mentioned atomic index calculation formula, so as to obtain the corresponding atomic index.

[0117] The atomic index is used as the calculation index.

[0118] For example, the simple calculation of atomic dimensions is illustrated by calculating the number of high-risk defects found by testers. High-risk defects (DefectLevel>=L2) are screened out according to the defect level (DefectLevel). For each tester (TesterID), the number of high-risk defects found by him is calculated. For example, the number of high-risk defects found by tester 1 = SUM (the number of defects found by all testers with DefectLevel>=L2).

[0119] The present application obtains the atomic indicator calculation formula corresponding to the atomic dimension; then calls a preset calculation engine; then, based on the calculation engine, uses the atomic indicator calculation formula to calculate and process the feature data to obtain the corresponding atomic indicator; and subsequently uses the atomic indicator as the calculation indicator. The present application obtains the atomic indicator calculation formula corresponding to the atomic dimension, and then, based on the use of the calculation engine, calculates and processes the feature data according to the obtained atomic indicator calculation formula, so that the calculation indicator corresponding to the feature data can be calculated quickly and accurately, thereby improving the generation efficiency of the calculation indicator and ensuring the accuracy of the obtained calculation indicator.

[0120] In some optional implementations, step S205 includes the following steps:

[0121] A plurality of processing dimensions corresponding to the calculation indicator are obtained.

[0122] In this embodiment, the processing dimensions are dimensions that need to be merged and are predetermined according to actual business needs, such as version dimensions, system dimensions, business domain dimensions, and the like.

[0123] All the processing dimensions are combined to obtain corresponding derived dimensions.

[0124] In this embodiment, different processing dimensions may be combined to generate corresponding derived dimensions. For example, the version dimension and the system dimension may be combined to generate a derived dimension whose corresponding content is "a certain system under a certain version".

[0125] Get the preset aggregation strategy.

[0126] In this embodiment, the strategy content of the above aggregation strategy includes: summing the calculation indicators under the same derived dimension. For example, the total number of high-risk defects found by all testers in a certain version, a certain system, and a certain business field = SUM (the number of high-risk defects found by all testers under this dimension).

[0127] Based on the derived dimension, the calculation indicator is aggregated and calculated using the aggregation strategy to obtain a corresponding derived indicator.

[0128] In this embodiment, according to the above-mentioned derived dimensions, the calculation indicators can be aggregated and calculated using the policy content corresponding to the aggregation policy, so as to generate corresponding derived indicators, namely, application indicators.

[0129] The derived index is used as the application index.

[0130] The present application obtains multiple processing dimensions corresponding to the calculation index; then merges all the processing dimensions to obtain corresponding derived dimensions; then obtains a preset aggregation strategy; subsequently, based on the derived dimensions, the aggregation strategy is used to aggregate the calculation index to obtain the corresponding derived indicators, and the derived indicators are used as the application indicators. The present application obtains derived dimensions by merging all processing dimensions corresponding to the calculation index, and then, based on the derived dimensions, the aggregation strategy is used to aggregate the calculation index, so that the corresponding application indicators can be calculated quickly and accurately, thereby improving the generation efficiency of the application indicators and ensuring the accuracy of the obtained calculation indicators.

[0131] In some optional implementations, step S202 includes the following steps:

[0132] Call the preset cleaning script.

[0133] In this embodiment, the cleaning script is a pre-written and generated automated script with data cleaning function.

[0134] The behavior data is cleaned based on the cleaning script to obtain corresponding first behavior data.

[0135] In this embodiment, a cleaning script may be run to identify and remove irrelevant information, such as user avatar links, etc., so as to obtain corresponding first behavior data.

[0136] Perform outlier repair processing on the first behavior data to obtain corresponding second behavior data.

[0137] In this embodiment, the first behavior data may be detected for outliers and the detected outliers may be repaired, such as deleted, to obtain the corresponding second behavior data. For example, a missing defect level may be marked as unknown or filled.

[0138] The second behavior data is used as the target behavior data.

[0139] The present application calls a preset cleaning script; then performs cleaning processing on the behavior data based on the cleaning script to obtain the corresponding first behavior data; then performs outlier repair processing on the first behavior data to obtain the corresponding second behavior data; and then uses the second behavior data as the target behavior data. The present application performs cleaning processing on the behavior data based on the use of the cleaning result to obtain the first behavior data, and then performs outlier repair processing on the first behavior data, so that the cleaning processing of the behavior data can be completed quickly and accurately, ensuring the integrity, accuracy and consistency of the generated target behavior data.

[0140] In some optional implementations of this embodiment, after step S207, the electronic device may further perform the following steps:

[0141] Determine whether a data selection operation triggered by the user on the display interface is received.

[0142] In this embodiment, after the generated target indicator is displayed on the above-mentioned display interface, the user can select the indicator to be analyzed and the dimension corresponding to the indicator through interface operation.

[0143] If so, obtain the specified indicator and specified dimension corresponding to the data selection operation.

[0144] In this embodiment, the above-mentioned designated indicators and designated dimensions refer to the data to be analyzed circled by the user in the display interface.

[0145] Based on the specified dimension, a preset calculation strategy is called to calculate the entropy static state value and the entropy transfer state value corresponding to the specified indicator.

[0146] In this embodiment, the static entropy value of the indicator under each dimension can be calculated according to the dimension selected by the user (such as time, system, business field, etc.). The static entropy value reflects the uniformity of the indicator distribution under the dimension. The transfer entropy value of the indicator between adjacent time points or time periods can also be calculated. Among them, the transfer entropy value reflects the degree of change of the indicator between different time points or time periods.

[0147] Specifically, the process of calculating the static state value of entropy includes: 1) Data grouping. Divide the data set into multiple groups according to the dimensions selected by the user (such as time, system, business field, etc.). Each group represents a specific category or interval under this dimension. 2) Calculate frequency. For each group, calculate the frequency of the indicator value in the group. Frequency refers to the ratio of the number of times the indicator value appears in the group to the total number of times. 3) Calculate probability. Convert the frequency of each group to probability. Probability is a normalized representation of frequency, that is, the sum of the probabilities of all groups is 1. 4) Apply the entropy formula. Use the entropy formula to calculate the static entropy value (entropy static state value). The entropy formula is: Among them, H is the entropy value, n is the number of groups, and pi is the probability of the i-th group. The higher the static entropy value, the more evenly the indicator is distributed in this dimension; the lower the static entropy value, the more unevenly the indicator is distributed, and some groups may dominate.

[0148] The process of calculating the entropy value transition state value includes: 1) Time series data preparation. Make sure that the data set is arranged in time series, that is, the data points are arranged in time order. 2) Define the transition probability. For each time point t in the time series, calculate the probability of indicator value transition from time point t-1 to time point t. This can be achieved by counting the frequency of indicator value changes between adjacent time points. 3) Construct a transition matrix. According to the transition probability, construct a transition matrix. Each row of the transition matrix represents the indicator value state at a time point t-1, and each column represents the indicator value state at a time point t. The elements in the matrix are transition probabilities. 4) Calculate the transition entropy. Calculate the transition entropy value using the formula of the transition matrix and entropy. This usually involves applying the entropy formula to the rows or columns of the transition matrix to measure the uncertainty of the change of the indicator value between different time points. Among them, a specific algorithm or tool is used to calculate the transfer entropy. In addition, the higher the transfer entropy value, the more uncertain the change of the indicator between different time points, that is, the greater the degree of change; the lower the transfer entropy value, the more stable or predictable the indicator change. In addition, when calculating entropy, you need to ensure that the data set is large enough to accurately estimate the probability and transition probability. For static entropy, if the probability of a group is very small (close to 0), it may cause numerical instability when calculating the logarithm. In this case, a small positive number (such as 10-9) can be used to smooth the probability value. The calculation of transition entropy may involve advanced statistical methods or machine learning algorithms, especially when dealing with complex time series data. In practical applications, it may be necessary to use professional statistical software or tools to perform calculations.

[0149] A matching designated management policy is selected based on the entropy value static state value and the entropy value transfer state value.

[0150] In this embodiment, the state of the indicator in different dimensions can be analyzed according to the calculated entropy static state value and entropy transfer state value. For example, a high static entropy value may indicate that the indicator distribution is relatively uniform, while a high transfer entropy value may indicate that the indicator changes greatly. Then, according to the specific entropy state, the numerical range of the entropy static state value and the specified management policy that matches the numerical range of the entropy transfer state value are extracted from the preset management policy table. For example, in the case of a surge in test defects, it may be necessary to increase the number of testers, transfer personnel or train personnel. Among them, the above-mentioned management policy table is a data table that stores the numerical range of multiple entropy states (including static and transfer states) and the management policies corresponding to each entropy state in advance according to actual business needs. Exemplarily, the recommended management strategy may include: if a system has a surge in test defects between May and June, the surge factors fed back by the manager in the system, and the test defect status of other systems in the same business field, provide knowledge related to personnel supplementation or transfer, and training surge defects.

[0151] The specified management policy is sent to the user.

[0152] In this embodiment, the designated management policy may be sent to the user by email, SMS, interface display, or the like.

[0153] The present application determines whether a data selection operation triggered by a user on the display interface is received; if so, obtains the specified indicator and specified dimension corresponding to the data selection operation; then, based on the specified dimension, calls a preset calculation strategy to calculate the entropy static state value and the entropy transfer state value corresponding to the specified indicator; then, based on the entropy static state value and the entropy transfer state value, filters out a matching specified management strategy; and subsequently sends the specified management strategy to the user.

[0154] In some optional implementations of this embodiment, clustering calculations can also be performed on the measurement objects, such as two-dimensional feature dimension clustering or hyperplane clustering analysis, to provide analysis factors for the specified management strategy of the recommendation analysis. Specifically, the process of cluster analysis includes: 1) Selecting clustering dimensions. According to user needs and business scenarios, select the dimensions that need to be clustered. For example, time, system, business field, etc. can be selected as clustering dimensions. 2) Perform clustering calculations. Perform two-dimensional feature dimension or even hyperplane clustering analysis on measurement objects (such as systems, testers, etc.). Cluster analysis can classify similar measurement objects into one category, which is convenient for subsequent analysis and recommendation. 3) Analyze the clustering results. By understanding the characteristics and differences between different clusters. This helps to identify potential problems and improvement points.

[0155] Furthermore, the results of entropy analysis, management strategy extraction and cluster analysis can be combined to recommend practical solutions. The recommended solutions should be targeted at specific problems and propose feasible solutions and improvement measures. The recommended solutions should be presented to users in the form of reports. The reports should include the analysis process, reasons for recommendations, specific solutions, etc., to facilitate user understanding and implementation.

[0156] Take, for example, a system with a surge in test defects between May and June:

[0157] Step 1, data set preparation: The user selects a data set containing test defect data, with a time range of May-June.

[0158] Step 2, entropy calculation: Calculate the static entropy and transfer entropy of the number of test defects during the time period. It is found that the static entropy is low, indicating that the defect distribution is uneven; the transfer entropy is high, indicating that the number of defects changes greatly.

[0159] Step 3, management strategy extraction: According to the entropy value status, management strategies are extracted. It is considered necessary to increase the number of testers or transfer personnel to cope with the surge in defects.

[0160] Step 4, cluster analysis: select dimensions such as system and business field for cluster analysis. It is found that the number of defects in this system is significantly increased compared with other systems in the same business field, which belongs to abnormal clustering.

[0161] Step 5, Practice plan recommendation: Based on the results of entropy analysis, management strategy extraction and cluster analysis, practice plan recommendations are given. It is recommended to increase the number of testers, or transfer some testers from other systems to this system, and conduct relevant training to improve test efficiency and quality.

[0162] In some optional implementations of this embodiment, after step S206, the electronic device may further perform the following steps:

[0163] Call the preset indicator library.

[0164] In this embodiment, the indicator library is a pre-built database for storing indicator data.

[0165] A designated storage location corresponding to the target indicator is determined in the indicator library.

[0166] In this embodiment, according to actual business needs, a designated file for storing the above target indicators can be predefined in the indicator library and used as the above designated storage location.

[0167] Based on the designated storage location, the target indicator is stored in the indicator library.

[0168] In this embodiment, the target indicator may be stored in a designated storage location in the designated library, thereby completing the storage process for the target indicator.

[0169] This application calls a preset indicator library; then determines a designated storage location corresponding to the target indicator in the indicator library; and subsequently stores the target indicator in the indicator library based on the designated storage location. After the calculation indicator and the application indicator are aggregated to obtain the target indicator, this application will also intelligently determine a designated storage location corresponding to the target indicator in the called indicator library, and then store the target indicator in the indicator library based on the designated storage location, thereby improving the storage intelligence and standardization of the target indicator, ensuring the data security of the target indicator, and facilitating the subsequent rapid extraction of relevant indicators from the indicator library for display and analysis according to indicator requirements.

[0170] In some optional implementations, the user information obtained is subject to the user's consent and complies with relevant laws and policies.

[0171] In addition, the non-Company software tools or components that appear in the embodiments of the present application are merely examples and do not represent actual use.

[0172] In addition, this application constructs a production index system in the field of software engineering, and visualizes data applications under the guidance of real-time calculation and modeling of relevant indicators, and based on the unfavorable factors of the entropy value static state value of the indicator and the entropy value transfer state value and user feedback, from the perspective of the overall business domain, gives corresponding practical solutions. This application starts with data collection, and after key steps such as feature extraction, indicator calculation, data analysis, production data application visualization and solution recommendation, it finally completes the system's online analysis application and solution recommendation for the relevant metrics of the production relationship application. Each step is connected by data flow and control flow to form a complete closed-loop system. In the feature recognition step, the features of the behavioral entity of the metric will be extracted, and atomic indicators will be extracted, and finally aggregated and calculated into application indicators. In the presentation process of application data, user operations will be collected, and real-time solution recommendations will be given based on user analysis operations. Help users extract management strategies and multiple analysis perspectives based on web visualization.

[0173] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0174] It should be emphasized that in order to further ensure the privacy and security of the above target indicators, the above target indicators can also be stored in a node of a blockchain.

[0175] The blockchain referred to in this application is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0176] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0177] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0178] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0179] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0180] Further references Figure 3 , as a response to the above Figure 2 In order to realize the method shown in the figure, the present application provides an embodiment of a device for processing indicator data, and the device embodiment is similar to Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0181] like Figure 3 As shown, the indicator data processing device 300 described in this embodiment includes: a first acquisition module 301, a cleaning module 302, an extraction module 303, a first calculation module 304, a second calculation module 305, a summary module 306 and a display module 307. Among them:

[0182] The first acquisition module 301 is used to acquire the behavior data of the target system during operation;

[0183] A cleaning module 302 is used to clean the behavior data to obtain corresponding target behavior data;

[0184] The extraction module 303 is used to extract features from the target operation data based on a preset extraction strategy to obtain corresponding feature data;

[0185] A first calculation module 304 is used to perform index calculation processing on the feature data based on a preset atomic dimension to obtain a corresponding calculation index;

[0186] The second calculation module 305 is used to perform aggregation calculation processing on the calculation indicators based on the preset derived dimensions to obtain corresponding application indicators;

[0187] A summarizing module 306 is used to summarize the calculation index and the application index to obtain a corresponding target index;

[0188] The display module 307 is used to display the target indicator based on a preset display interface.

[0189] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the method for processing indicator data in the above-mentioned implementation mode, and will not be described in detail here.

[0190] In some optional implementations of this embodiment, the extraction module 303 includes:

[0191] The first processing submodule is used to perform characterization processing on the target operation data and extract corresponding key fields;

[0192] A second processing submodule is used to perform numerical processing on the key field to obtain corresponding first field data;

[0193] A third processing submodule, configured to perform normalization processing on the first field data to obtain corresponding second field data;

[0194] The first determining submodule is configured to use the second field data as the characteristic data.

[0195] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the method for processing indicator data in the above-mentioned implementation mode, and will not be described in detail here.

[0196] In some optional implementations of this embodiment, the first calculation module 304 includes:

[0197] A first acquisition submodule is used to acquire an atomic index calculation formula corresponding to the atomic dimension;

[0198] The first calling submodule is used to call a preset computing engine;

[0199] A first calculation submodule, configured to calculate and process the characteristic data based on the calculation engine using the atomic index calculation formula to obtain corresponding atomic indexes;

[0200] The second determination submodule is used to use the atomic indicator as the calculation indicator.

[0201] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the method for processing indicator data in the above-mentioned implementation mode, and will not be described in detail here.

[0202] In some optional implementations of this embodiment, the second calculation module 305 includes:

[0203] A second acquisition submodule is used to acquire multiple processing dimensions corresponding to the calculation index;

[0204] A merging submodule, used for merging all the processing dimensions to obtain corresponding derived dimensions;

[0205] The third acquisition submodule is used to obtain a preset aggregation strategy;

[0206] A second calculation submodule, configured to perform aggregation calculation on the calculation indicator based on the derived dimension using the aggregation strategy to obtain a corresponding derived indicator;

[0207] The third determination submodule is used to use the derived indicator as the application indicator.

[0208] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the method for processing indicator data in the above-mentioned implementation mode, and will not be described in detail here.

[0209] In some optional implementations of this embodiment, the cleaning module 302 includes:

[0210] The second calling submodule is used to call the preset cleaning script;

[0211] A cleaning submodule, configured to clean the behavior data based on the cleaning script to obtain corresponding first behavior data;

[0212] A repair submodule, used for performing outlier repair processing on the first behavior data to obtain corresponding second behavior data;

[0213] The fourth determining submodule is configured to use the second behavior data as the target behavior data.

[0214] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the method for processing indicator data in the above-mentioned implementation mode, and will not be described in detail here.

[0215] In some optional implementations of this embodiment, the indicator data processing device further includes:

[0216] A judgment module, used to judge whether a data selection operation triggered by a user on the display interface is received;

[0217] A second acquisition module, configured to acquire a specified indicator and a specified dimension corresponding to the data selection operation;

[0218] A third calculation module is used to call a preset calculation strategy to calculate the entropy static state value and the entropy transfer state value corresponding to the specified indicator based on the specified dimension;

[0219] A screening module, used for screening out matching designated management policies based on the entropy value static state value and the entropy value transfer state value;

[0220] A sending module is used to send the specified management policy to the user.

[0221] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the method for processing indicator data in the above-mentioned implementation mode, and will not be described in detail here.

[0222] In some optional implementations of this embodiment, the indicator data processing device further includes:

[0223] Calling module, used to call the preset indicator library;

[0224] A determination module, used to determine a designated storage location corresponding to the target indicator in the indicator library;

[0225] A storage module is used to store the target indicator in the indicator library based on the designated storage location.

[0226] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the method for processing indicator data in the above-mentioned implementation mode, and will not be described in detail here.

[0227] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0228] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 4 with components 41-43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0229] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0230] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk equipped on the computer device 4, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions of the processing method of indicator data, etc. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0231] The processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run computer-readable instructions or process data stored in the memory 41, such as computer-readable instructions for running the method for processing the indicator data.

[0232] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0233] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0234] In an embodiment of the present application, the target behavior data is obtained by acquiring the behavior data of the target system during operation and cleaning the behavior data, and then feature extraction is performed on the target operation data based on the use of the extraction strategy to obtain feature data, and then the feature data is automatically subjected to indicator calculation processing based on the use of atomic dimensions to obtain calculation indicators, and the calculation indicators are automatically aggregated and calculated based on the use of derived dimensions to obtain application indicators, and the calculation indicators and application indicators are subsequently aggregated and processed, so that the target indicators corresponding to the production analysis of the target system can be automatically, efficiently and accurately calculated, which effectively improves the processing efficiency of the generation and processing of the indicator data of the production analysis and ensures the accuracy of the generated target indicators.

[0235] The present application also provides another embodiment, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the indicator data processing method as described above.

[0236] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0237] In an embodiment of the present application, the target behavior data is obtained by acquiring the behavior data of the target system during operation and cleaning the behavior data, and then feature extraction is performed on the target operation data based on the use of the extraction strategy to obtain feature data, and then the feature data is automatically subjected to indicator calculation processing based on the use of atomic dimensions to obtain calculation indicators, and the calculation indicators are automatically aggregated and calculated based on the use of derived dimensions to obtain application indicators, and the calculation indicators and application indicators are subsequently aggregated and processed, so that the target indicators corresponding to the production analysis of the target system can be automatically, efficiently and accurately calculated, which effectively improves the processing efficiency of the generation and processing of the indicator data of the production analysis and ensures the accuracy of the generated target indicators.

[0238] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0239] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A method for processing indicator data, characterized in that: The steps include: Obtain the behavior data of the target system during operation; Cleaning the behavior data to obtain corresponding target behavior data; Extracting features from the target operation data based on a preset extraction strategy to obtain corresponding feature data; Performing index calculation processing on the characteristic data based on a preset atomic dimension to obtain a corresponding calculation index; Performing aggregate calculation processing on the calculation indicators based on the preset derived dimensions to obtain corresponding application indicators; Summarizing the calculation index and the application index to obtain a corresponding target index; The target indicator is displayed based on a preset display interface.

2. The method for processing indicator data according to claim 1, characterized in that: The step of extracting features from the target operation data based on a preset extraction strategy to obtain corresponding feature data specifically includes: Performing characterization processing on the target operation data to extract corresponding key fields; Numerically processing the key field to obtain corresponding first field data; Normalizing the first field data to obtain corresponding second field data; The second field data is used as the characteristic data.

3. The method for processing indicator data according to claim 1, characterized in that: The step of performing index calculation processing on the feature data based on the preset atomic dimension to obtain the corresponding calculation index specifically includes: Obtaining an atomic index calculation formula corresponding to the atomic dimension; Call the preset calculation engine; Based on the calculation engine, the characteristic data is calculated and processed using the atomic index calculation formula to obtain the corresponding atomic index; The atomic index is used as the calculation index.

4. The method for processing indicator data according to claim 1, characterized in that: The step of performing aggregation calculation processing on the calculation indicators based on the preset derived dimensions to obtain corresponding application indicators specifically includes: Acquire multiple processing dimensions corresponding to the calculation indicators; Merging all the processing dimensions to obtain corresponding derived dimensions; Get the preset aggregation strategy; Based on the derived dimension, the calculation indicator is aggregated and calculated using the aggregation strategy to obtain a corresponding derived indicator; The derived index is used as the application index.

5. The method for processing indicator data according to claim 1, characterized in that: The step of cleaning the behavior data to obtain corresponding target behavior data specifically includes: Call the preset cleaning script; Cleaning the behavior data based on the cleaning script to obtain corresponding first behavior data; Performing outlier repair processing on the first behavior data to obtain corresponding second behavior data; The second behavior data is used as the target behavior data.

6. The method for processing indicator data according to claim 1, characterized in that: After the step of displaying the target indicator based on the preset display interface, the method further includes: Determining whether a data selection operation triggered by a user on the display interface is received; If so, obtaining a specified indicator and a specified dimension corresponding to the data selection operation; Based on the specified dimension, a preset calculation strategy is called to calculate an entropy static state value and an entropy transfer state value corresponding to the specified indicator; Filter out a matching designated management policy based on the entropy value static state value and the entropy value transfer state value; The specified management policy is sent to the user.

7. The method for processing indicator data according to claim 1, characterized in that: After the step of aggregating the calculation index and the application index to obtain the corresponding target index, the method further includes: Call the preset indicator library; Determining a designated storage location corresponding to the target indicator in the indicator library; Based on the designated storage location, the target indicator is stored in the indicator library.

8. A device for processing index data, characterized in that: include: The first acquisition module is used to acquire the behavior data of the target system during operation; A cleaning module, used for cleaning the behavior data to obtain corresponding target behavior data; An extraction module, used to extract features from the target operation data based on a preset extraction strategy to obtain corresponding feature data; A first calculation module, used to perform index calculation processing on the feature data based on a preset atomic dimension to obtain a corresponding calculation index; A second calculation module is used to perform aggregate calculation processing on the calculation indicators based on a preset derived dimension to obtain corresponding application indicators; A summary module, used to summarize the calculation index and the application index to obtain the corresponding target index; The display module is used to display the target indicator based on a preset display interface.

9. A computer device, characterized in that: It comprises a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the method for processing indicator data as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the method for processing indicator data according to any one of claims 1 to 7 are implemented.

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