Application upgrading method, system, device and storage medium based on low-code platform

Abstract

The application provides a low-code platform-based application upgrading method, system and device and a storage medium, and relates to the technical field of computers.The method comprises the following steps: performing multi-dimensional comprehensive evaluation according to current version information and target version information of a target application to obtain a comprehensive evaluation index; comparing the comprehensive evaluation index with a comprehensive evaluation threshold value; if the comprehensive evaluation index is greater than the comprehensive evaluation threshold value, performing an automatic upgrading operation, otherwise adjusting an automatic upgrading scheme; obtaining historical upgrading information and real-time log information in an upgrading process, performing risk prediction on the upgrading process by using a random forest risk prediction model to obtain a prediction result; and continuing to perform the automatic upgrading operation or performing a safe rollback operation according to the prediction result.The application processes the application upgrading and rollback process by using multi-dimensional comprehensive evaluation and a random forest-based risk prediction algorithm, and ensures that the application always maintains high availability and stability.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of computers, in particular to an application upgrading method and system based on a low-code platform, a device and a storage medium. BACKGROUND

[0002] In the digital era, enterprises have strong demands for rapid construction and iteration of application programs to quickly respond to market changes. A low-code platform can greatly simplify processes and shorten cycles by virtue of a visual development interface, without the need for a large amount of code writing, and can quickly develop and deploy applications through drag-and-drop components and configuration parameters. The low-code platform is widely used in enterprise digital transformation and has become the preferred choice of many enterprises and developers.

[0003] However, the adoption of a low-code platform by a business needs to be continuously upgraded to introduce new functions and optimize performance, and the system needs to be quickly rolled back to a stable version when an upgrade fails. However, upgrade rollback involves complex operations such as database migration and configuration update. In complex business scenarios, module coupling and data correlation are close, and compatibility problems are likely to occur, making new functions unable to operate normally. Performance may also be affected due to resource allocation problems, affecting user experience, and even security vulnerabilities, threatening system security. Existing solutions mostly rely on manual or simple scripts, and it is difficult to ensure the security and stability of applications using a low-code platform during the upgrade rollback process. SUMMARY

[0004] In view of the above deficiencies of the prior art, the application provides an application upgrading method and system based on a low-code platform, a device and a storage medium, which effectively solve the problem of insufficient security and stability of applications using a low-code platform during the upgrade rollback process.

[0005] In a first aspect, the application provides an application upgrading method based on a low-code platform, which comprises:

[0006] Performing multi-dimensional comprehensive evaluation according to the current version information and the target version information of the target application to obtain a comprehensive evaluation index;

[0007] Comparing the comprehensive evaluation index with a comprehensive evaluation threshold value, if the comprehensive evaluation index is greater than the comprehensive evaluation threshold value, performing an automatic upgrade operation, otherwise adjusting an automatic upgrade scheme;

[0008] Obtaining historical upgrade information and real-time log information during the upgrade process, performing risk prediction on the upgrade process through a random forest risk prediction model to obtain a prediction result;

[0009] Continuing to perform the automatic upgrade operation or performing a safe rollback operation according to the prediction result.

[0010] In an optional embodiment, the multi-dimensional comprehensive evaluation according to the current version information of the target application and the target version information comprises:

[0011] calculating a function compatibility index according to the first function set of the current version and the second function set of the target version;

[0012] calculating a performance change rate index according to the first performance index data of the current version and the second performance index data of the target version, the performance change rate index at least comprising a response time change rate and a processing capacity change rate;

[0013] obtaining vulnerability set information of the target version, and calculating a security risk index according to the vulnerability set information;

[0014] calculating a resource bottleneck index according to demand information of the target version for system resources and threshold information of the system resources, the resource bottleneck index at least comprising a CPU index, a memory index and a disk space index;

[0015] comprehensively calculating the function compatibility index, the performance change rate index, the security risk index and the resource bottleneck index to obtain the comprehensive evaluation index.

[0016] In an optional embodiment, the calculation formula of the comprehensive evaluation index is as follows:

[0017]

[0018] In the above formula, S represents the comprehensive evaluation index, C func represents the function compatibility index, R T represents the response time change rate, R Q represents the processing capacity change rate, R sec represents the security risk index, max ( R sec represents the maximum security risk index recorded in the historical upgrade process, B cpu represents the CPU index, B mem represents the memory index, B disk represents the disk space index, 、 、 、 and respectively represent a first weight coefficient, a second weight coefficient, a third weight coefficient, a fourth weight coefficient, and a fifth weight coefficient, and the sum of each weight coefficient is equal to 1.

[0019] In an optional implementation, the historical upgrade information and the real-time log information in the upgrade process are acquired, a risk prediction of the upgrade process is performed by using a random forest risk prediction model, a prediction result is obtained, and the prediction result comprises:

[0020] The historical upgrade information and the real-time log information are preprocessed to obtain a sample data set;

[0021] A random forest algorithm is used to construct and train a risk prediction model according to the sample data set, and the random forest risk prediction model is obtained;

[0022] Real-time sample data in the upgrade process is acquired, the real-time sample data is input into the random forest risk prediction model to perform a risk prediction of the upgrade process, and the prediction result is obtained.

[0023] In an optional implementation, the historical upgrade information and the real-time log information are preprocessed to obtain a sample data set, and the preprocessing comprises:

[0024] The historical upgrade information and the real-time log information are cleaned to obtain a first data set, and the cleaning at least comprises missing value processing, abnormal value processing, and data consistency checking;

[0025] The first data set is converted to obtain a second data set, and the conversion at least comprises standardization processing, classification feature coding, and time feature processing;

[0026] Features of the second data set are extracted to obtain the sample data set, and the extraction at least comprises feature extraction based on business logic, feature extraction based on a statistical method, and real-time log information fusion feature extraction.

[0027] In an optional implementation, the automatic upgrade operation is continued to be performed or a safety rollback operation is performed according to the prediction result, and the continuing to perform the automatic upgrade operation or the performing the safety rollback operation comprises:

[0028] If the prediction result is low risk, the automatic upgrade operation is continued to be performed;

[0029] If the prediction result is medium risk, the automatic upgrade operation is performed after manual intervention is judged or the safety rollback operation is performed;

[0030] If the prediction result is high risk, the automatic upgrade operation is suspended to be performed and the safety rollback operation is performed.

[0031] In an optional implementation, the automatic upgrade operation comprises:

[0032] Backup and verify the application data, configuration data and code and metadata of the target application to obtain a backup file;

[0033] After compatibility check and resource assessment on the target version, deploy the target version to a server and migrate a database;

[0034] Start a service of the target version and perform an automated test, the automated test at least including a core function check and a key interface response state check;

[0035] The execution of the safety rollback operation includes:

[0036] Real-time monitoring of target indicators of the target application to obtain real-time monitoring indicators;

[0037] Trigger an automatic rollback condition according to the real-time monitoring indicators or the automated test results;

[0038] Stop the service of the target version, perform data recovery according to the backup file and start a service of a current version.

[0039] In a second aspect, the application provides an application upgrade system based on a low-code platform, the system comprising:

[0040] A comprehensive evaluation module is configured to perform multi-dimensional comprehensive evaluation according to current version information and target version information of a target application to obtain a comprehensive evaluation indicator;

[0041] An automatic upgrade module is configured to compare the comprehensive evaluation indicator with a comprehensive evaluation threshold value, and if the comprehensive evaluation indicator is greater than the comprehensive evaluation threshold value, perform an automatic upgrade operation, otherwise adjust an automatic upgrade scheme;

[0042] A risk prediction module is configured to obtain historical upgrade information and real-time log information in an upgrade process, perform risk prediction on the upgrade process through a random forest risk prediction model, and obtain a prediction result;

[0043] A safety rollback module is configured to continue performing the automatic upgrade operation or perform a safety rollback operation according to the prediction result.

[0044] In a third aspect, the application provides an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor executing the computer program to implement the application upgrade method based on a low-code platform as described in the first aspect of the application.

[0045] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the application upgrade method based on a low-code platform according to the first aspect of the present application.

[0046] The application upgrade method, system, device and storage medium based on a low-code platform provided by the present application process the application upgrade and rollback process through multi-dimensional comprehensive evaluation and a random forest-based risk prediction algorithm. The multi-dimensional evaluation comprehensively analyzes the upgrade impact from multiple key aspects such as application performance, compatibility, and business logic, identifies potential risk points, and provides detailed basis for decision-making. The random forest risk prediction algorithm accurately predicts potential problems and anticipates possible failures and abnormalities in the upgrade process with powerful data processing and pattern recognition capabilities. It realizes automatic intelligent decision-making without manual intervention, quickly and accurately decides the upgrade or rollback operation, greatly improves the upgrade efficiency, shortens the business interruption time, and effectively avoids the risks caused by manual misjudgment. Through the automated and secure upgrade and rollback process, the application always maintains high availability and stability, providing a solid guarantee for the continuous and stable operation of the low-code platform application. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0048] Figure 1 is the first schematic diagram of the application upgrade method based on a low-code platform provided by the embodiments of the present application;

[0049] Figure 2 is the second schematic diagram of the application upgrade method based on a low-code platform provided by the embodiments of the present application;

[0050] Figure 3 is the third schematic diagram of the application upgrade method based on a low-code platform provided by the embodiments of the present application;

[0051] Figure 4 is the fourth schematic diagram of the application upgrade method based on a low-code platform provided by the embodiments of the present application;

[0052] Figure 5 is the structure schematic diagram of the application upgrade system based on a low-code platform provided by the embodiments of the present application;

[0053] Figure 6 is the structure schematic diagram of an electronic device provided by the embodiments of the present application.

[0054] Main element symbol explanation:

[0055] 200, application upgrading system based on low-code platform; 210, comprehensive evaluation module; 220, automatic upgrading module; 230, risk prediction module; 240, safe rollback module; 300, electronic device; 310, processor; 320, communication interface; 330, memory; 340, communication bus. DETAILED DESCRIPTION

[0056] To make the objects, technical solutions, and advantages of the present application clearer, the technical solutions will be further described below with reference to the accompanying drawings of the embodiments of the present application. It should be noted that the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0057] In addition, the terms "first", "second" are only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise explicitly specified.

[0058] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application.

[0059] In the current rapid business development, enterprises continue to increase their demand for applications using low-code platforms, and applications need to be upgraded to introduce novel functions and optimize overall performance. Upgrade rollback involves fine and complex operations such as database migration and configuration update. In complex business scenarios, modules are tightly coupled and data associations are complex. Once a problem occurs in a certain link, it is easy to cause compatibility failures, resulting in new functions not running as expected, and making the business chaotic. At the same time, if the resource allocation is unreasonable during the upgrade process, it may cause a sharp decline in system performance, slow page loading, and slow operation response, which seriously affects user experience and reduces user satisfaction. More seriously, the upgrade process may also hide security vulnerabilities, threatening the data security and stable operation of the system. However, the existing solutions mostly rely on manual operation or simple scripts, lack of systematicness and intelligence, and are difficult to fully cope with various problems in complex scenarios, and cannot effectively guarantee the security and stability of low-code platform applications during the upgrade rollback process.

[0060] Embodiment 1

[0061] The embodiment of the application provides an application upgrading method based on a low-code platform, and effectively solves the problems of insufficient safety and stability of an application using a low-code platform in a rollback process of upgrading. Figure 1 is a first schematic diagram of a method for upgrading an application based on a low-code platform provided by the embodiment of the application, as shown in the figure, the method comprises the following steps: Figure 1 as shown in the figure, the method comprises the following steps:

[0062] S100, multi-dimensional comprehensive evaluation is performed according to current version information and target version information of the target application, and a comprehensive evaluation index is obtained.

[0063] In the embodiment of the application, the application upgrading based on the low-code platform can be comprehensively evaluated from four dimensions of functional compatibility, performance influence, security risk and system resource occupation, thereby providing a quantitative basis for the upgrading decision. Figure 2 is a second schematic diagram of a method for upgrading an application based on a low-code platform provided by the embodiment of the application, as shown in the figure, the multi-dimensional comprehensive evaluation comprises the following steps: Figure 2 as shown in the figure, the method comprises the following steps:

[0064] S110, a functional compatibility index is calculated according to a first function set of the current version and a second function set of the target version.

[0065] The function set will change due to the application type and the version iteration target, if the first function set of the current version is F current ={ f 1, f 2,…, f n} and the second function set of the target version is F target ={ g 1, g 2,…, g n}, in the embodiment of the application, the functional compatibility index is defined to measure the backward compatibility of the new version function, that is, the matching degree of the function of the current version and the function of the target version, and the calculation formula of the functional compatibility index is as follows:

[0066]

[0067] In the above formula, C func denotes the functional compatibility index, F current denotes the first function set of the current version, F targetThe second function set representing the target version, |·| represents the number of elements in the set.

[0068] In the embodiments of the present application, C func The closer the value of the compatibility rate is to 1, the better the compatibility of the functions of the target version with the current version is, and when C func When the value of the compatibility rate is less than a preset compatibility threshold, it is considered that there is a compatibility problem in the functions of the target version, and the upgrade risk is high. Optionally, the preset compatibility threshold can be set in combination with business requirements, historical data, statistical analysis and manual experience.

[0069] S120, calculating a performance change rate index according to the first performance index data of the current version and the second performance index data of the target version, the performance change rate index at least including a response time change rate and a processing capacity change rate.

[0070] In the embodiments of the present application, the performance index data of the current version and the target version under the same test environment is collected, the performance index data including but not limited to the response time and the processing capacity, wherein the response time refers to the total time from the user initiating a request to returning a result, and the processing capacity represents the number of requests processed per unit time. Correspondingly, the performance change rate index at least includes the response time change rate and the processing capacity change rate, which are used to evaluate the influence of the target version on the system performance, and the calculation formula of the response time change rate is as follows:

[0071]

[0072] In the above formula, R T represents the response time change rate, T current represents the response time of the current version, T target represents the response time of the target version.

[0073] The calculation formula of the processing capacity change rate is as follows:

[0074]

[0075] In the above formula, R Q represents the processing capacity change rate, Q current represents the processing capacity of the current version, Q target represents the processing capacity of the target version.

[0076] In the embodiments of the present application, when R T is less than a preset response time threshold or RQ If the response time threshold exceeds the preset processing capacity threshold, the target version is considered to have a significant impact on system performance, requiring further analysis and optimization. Optionally, the preset response time threshold and preset processing capacity threshold can be set based on business needs, historical data, statistical analysis, and human experience. For example, assuming a preset response time threshold of -10% and a preset processing capacity threshold of 15%, if the response time change rate is -25%, which is less than the preset response time threshold, it indicates a significant improvement in response time. However, if it is a positive change, i.e., a response time change rate of 25%, which is greater than the preset response time threshold, it indicates a significant deterioration in response time. If the processing capacity change rate is 20%, which is greater than the preset processing capacity threshold, it indicates a significant improvement in processing capacity.

[0077] S130. Obtain vulnerability set information for the target version and calculate security risk indicators based on the vulnerability set information.

[0078] In this embodiment of the invention, a vulnerability scanning tool is used to perform a comprehensive scan of the target version to obtain a vulnerability set. V ={ v 1, v 2,…, v k For each vulnerability v i Define a risk weight W i Risk weights can be determined based on factors such as the severity and difficulty of exploitation of vulnerabilities. Security risk indicators can be calculated based on vulnerability set information, using the following formula:

[0079]

[0080] In the above formula, R sec Indicators representing security risk. i Indicates the vulnerability number. k This indicates the total number of vulnerabilities. W i Indicates the first i Risk weights for each vulnerability.

[0081] In an embodiment of the present invention, R sec A higher value indicates a higher security risk for the target version. R sec If the risk level exceeds the preset security risk threshold, the target version is considered to have a high security risk and needs to be patched before upgrading. Optionally, the preset security risk threshold can be set based on business needs, historical data, statistical analysis, and human experience.

[0082] S140, calculating a resource bottleneck index according to the demand information of the target version for system resources and the threshold information of the system resources, the resource bottleneck index at least including a CPU index, a memory index and a disk space index.

[0083] In the embodiments of the present application, the demand information of the target version for system resources is analyzed, including but not limited to CPU occupancy rate, memory occupancy rate and disk space occupancy rate, and the threshold information of the corresponding system resources is obtained, including but not limited to preset CPU available rate, memory available rate and disk space available rate. A resource bottleneck index is defined to determine whether the target version will cause a resource bottleneck, the resource bottleneck index including but not limited to a CPU index, a memory index and a disk space index, and the calculation formulas are as follows:

[0084]

[0085] In the above formula, B cpu represents the CPU index, U cpu represents the CPU occupancy rate of the target version, A cpu represents the preset CPU available rate of the system resources. B cpu If the value is 1, it means that the CPU is overloaded, and expansion or optimization is needed, B cpu If the value is 0, it means that the CPU is not overloaded and is in a normal state.

[0086]

[0087] In the above formula, B mem represents the memory index, U mem represents the memory occupancy rate of the target version, A mem represents the preset memory available rate of the system resources. B mem If the value is 1, it means that the memory is overloaded, which may trigger memory overflow or performance degradation, B mem If the value is 0, it means that the memory is not overloaded and is in a normal state.

[0088]

[0089] In the above formula, B disk represents the disk space index, U disk represents the disk space occupancy rate of the target version, A diskDisk space available rate representing system resource preset. B disk If 1, it indicates that the disk space is insufficient, which may affect the writing or logging, B disk If 0, it indicates that the disk space is sufficient, which is in a normal state.

[0090] In the embodiment of the application, when B cpu = 1 or B mem = 1 or B disk = 1, it is considered that the target version may cause a corresponding resource bottleneck, and resource expansion or optimization of application code needs to be performed in advance.

[0091] S150, comprehensive calculation is performed according to the function compatibility index, the performance change rate index, the security risk index and the resource bottleneck index to obtain a comprehensive evaluation index.

[0092] In the embodiment of the application, the multi-dimensional comprehensive evaluation index is further calculated according to the function compatibility index, the performance change rate index, the security risk index and the resource bottleneck index, so that comprehensive and meticulous evaluation of application upgrade is realized. The calculation formula of the comprehensive evaluation index is as follows:

[0093]

[0094] In the above formula, S represents the comprehensive evaluation index, C func represents the function compatibility index, R T represents the response time change rate, R Q represents the processing capacity change rate, R sec represents the security risk index, max R sec represents the maximum security risk index recorded in the historical upgrade process, which is used to normalize R sec , B cpu represents the CPU index, B mem represents the memory index, B disk represents the disk space index, 、 、 、 and ​These represent the first, second, third, fourth, and fifth weight coefficients, respectively. The sum of all weight coefficients equals 1, and each weight coefficient can be adjusted according to the actual application scenario and business needs.

[0095] In this embodiment of the invention, functional compatibility assessment can identify potential functional conflicts that may arise from upgrades in advance, ensuring stable application operation; performance impact analysis can accurately pinpoint changes in performance indicators such as response time and processing capacity caused by upgrades, helping to optimize upgrade plans; security risk assessment can identify potential security vulnerabilities, protecting user data and system security; and system resource usage assessment can rationally plan resources, avoiding resource exhaustion due to upgrades. A comprehensive quantitative assessment across these four dimensions provides a scientific and comprehensive basis for application upgrade decisions, reducing upgrade risks and increasing upgrade success rates.

[0096] S200. Compare the comprehensive evaluation index with the comprehensive evaluation threshold. If the comprehensive evaluation index is greater than the comprehensive evaluation threshold, then execute the automatic upgrade operation; otherwise, adjust the automatic upgrade plan.

[0097] Optionally, preset comprehensive evaluation thresholds can be set by combining business needs, historical data, statistical analysis, and human experience. If the comprehensive evaluation indicators... S If the value exceeds the preset comprehensive evaluation threshold, the application upgrade is considered feasible, and an automatic upgrade operation is performed, which includes the following process:

[0098] First, the application data, configuration data, code, and metadata of the target application are backed up and verified to obtain backup files. Application data includes, but is not limited to, all tables, table data, and file storage corresponding to the database application. Configuration data includes, but is not limited to, environment variables, permission rules, API keys, and workflow definitions. Code and metadata include, but is not limited to, code generated by the low-code platform, page layouts, and logic scripts. Specifically, when an automatic upgrade operation is triggered, a full automatic backup is triggered simultaneously. Different methods are used for different data types. The full database files of the application are exported from the database. For file storage, the file directories that the application depends on are packaged. For configuration and code, the platform configuration JSON files are exported and the generated code directory is packaged. After collecting the backup content, the backup files are encrypted and signed. AES-256 encryption can be used, and a SHA-256 signature is generated to prevent the backup files from being tampered with. The backup files are then stored off-site, such as via Alibaba Cloud OSS off-site backup, with file naming following the format "application_full_backup_YYYYMMDD_HHMMSS.tar.gz.enc". Finally, backup verification is performed. Through automated checks, the backup file size is verified to be greater than 0 to avoid empty files. After decrypting the backup file, the database SQL file is verified to ensure that it can be successfully imported into the test environment, thus ensuring that the backup is valid and usable.

[0099] Then, after compatibility check and resource assessment of the target version, the target version is deployed to the server and the database is migrated. The compatibility check needs to confirm that the target version can be compatible with the old data structure, such as the database table field is not deleted, so as to avoid application errors caused by data mismatch after upgrading. The resource assessment needs to check whether the server disk space is sufficient, because not only the space for backup files needs to be reserved during the upgrading process, but also the code package of the target version and other upgrading files will occupy additional space, and insufficient space may interrupt the upgrading. When upgrading, in order to ensure data security and integrity, the current version service needs to be stopped first to prevent conflicts caused by new data writing. Subsequently, the target version deployment work is carried out, the target version code package is uploaded to the specified location of the server, and in the process of covering the configuration file, the key parameters such as database connection are retained to avoid the service from being unable to run normally due to configuration loss. If the target version involves database structure changes, the SQL migration script provided by the new version needs to be executed to complete the database upgrade step by step according to the script requirements, so as to ensure that the database is compatible with the target version code, thereby ensuring that the entire application system can be smoothly and successfully upgraded to the target version and put into use.

[0100] Finally, the service of the target version is started, and the automatic test is carried out, which at least includes core function check and key interface response state check. Specifically, the automatic test needs to check the core functions one by one, for example, the user login function needs to test whether different types of users can normally log in the system, and the data query function needs to verify whether various types of data can be accurately and quickly displayed and the query result is complete and correct. At the same time, professional tools or scripts can be used to initiate a request to the key interface, and the interface response status code is carefully checked to ensure that the interface can normally receive and process the request.

[0101] If the comprehensive evaluation index S is less than or equal to the preset comprehensive evaluation threshold value, it indicates that the upgrading scheme needs to be further optimized or adjusted, and the corresponding problems can be further located according to the function compatibility index, the performance change rate index, the security risk index and the resource bottleneck index. For example, if the function compatibility index is less than the preset compatibility threshold value, it indicates that the function of the target version has compatibility problems, and the upgrading risk is high, so the process is prevented from entering the application upgrading and risk prediction and intelligent decision-making stage, and the compatibility problem is solved first.

[0102] S300, historical upgrading information and real-time log information in the upgrading process are acquired, a random forest risk prediction model is used to predict the risk of the upgrading process, and a prediction result is obtained.

[0103] In the embodiment of the application, the historical upgrading information and the real-time log information in the upgrading process are used to construct a random forest risk prediction model to predict the potential risks in the automatic upgrading process.Figure 3 is a third schematic diagram of a low-code platform-based application upgrade method provided by an embodiment of the application, as shown in Figure 3 The specific steps of risk prediction are as follows:

[0104] S310, data preprocessing is performed on the historical upgrade information and the real-time log information to obtain a sample data set.

[0105] In the embodiment of the application, the data preprocessing includes three steps of data cleaning, data conversion and feature extraction, wherein:

[0106] The purpose of data cleaning is to remove noise, error values and inconsistencies in the historical upgrade information and the real-time log information to obtain a first data set, and to ensure the quality and accuracy of the data and to provide a reliable basis for subsequent data conversion and feature extraction. Data cleaning includes but is not limited to missing value processing, outlier processing and data consistency check.

[0107] The missing value processing checks whether each feature of each sample in the historical upgrade information and the real-time log information has a missing value. For example, it is checked whether the features of "upgrade time", "function compatibility indicator" and "performance change rate indicator" have null values. If a sample has too many missing features, or the missing features are crucial for analysis and cannot be reasonably filled, the sample can be deleted. For example, if a sample is missing both "upgrade time" and "performance change rate indicator", and these two features are very important in subsequent analysis, the sample can be deleted. For samples with fewer missing values, a filling method can be used. For example, for numerical features, the average, median or mode of the feature can be used for filling, and for classification features, the category with the highest frequency can be used for filling.

[0108] Outlier handling uses statistical methods or visualization tools to detect outliers in the data. For example, for numerical features, box plots can be used to identify outliers, and for categorical features, the presence of obviously unreasonable categories can be checked. If outliers are due to data entry or measurement errors, these values ​​can be corrected. For example, if the "Performance Change Rate" metric is recorded as +150%, while the performance change rates of other samples are mostly between ±20%, it is likely a data entry error and can be corrected to a reasonable value. If outliers cannot be corrected and would significantly impact the analysis results, samples containing outliers can be deleted. For example, if a sample's "Resource Bottleneck" metric shows "Memory > 500%", this is clearly unrealistic and cannot be corrected, so the sample can be deleted. In some cases, outliers may contain important information; these samples can be retained but marked for special attention in subsequent analyses. For example, if a sample's "Security Risk" is abnormally high, it may be due to a severe system attack; retaining and marking this sample helps in analyzing specific security risk scenarios.

[0109] Data consistency checks ensure that all data is formatted consistently and check for logical inconsistencies. For example, "upgrade time" should be standardized to a specific date and time format (e.g., YYYY-MM-DD HH:MM:SS), and category names for categorical features should be standardized to avoid spelling errors or inconsistent representations. The "functional compatibility index" should be between 0 and 1; values ​​greater than 1 or less than 0 indicate logical errors in the data that require correction.

[0110] The purpose of data transformation is to convert the raw data in the first dataset into a form suitable for subsequent analysis and modeling, including but not limited to standardization, categorical feature encoding, and temporal feature processing.

[0111] Standardization primarily targets numerical features. Specifically, for numerical features such as "functional compatibility indicators," "performance change rate indicators," "security risk indicators," and "number of error logs," it is necessary to determine the minimum and maximum values ​​for each feature. Then, based on these minimum and maximum values, a standardization formula is applied to each numerical feature. The standardization formula is as follows:

[0112]

[0113] In the above formula, y ij Represents the standardized first i The first sample j One standard eigenvalue, x ij Indicates the first i The first samplej the minimum value and the maximum value of the i-th feature, respectively. For example, for the "function compatibility index" value of sample 1 is 0.85, assuming the minimum value of "function compatibility index" is 0.5 and the maximum value is 1, the normalized value is 0.7. min x j max x j j

[0114] Encoding of categorical features mainly aims at categorical features. First, determine the categorical features in the data, for example, "resource bottleneck" may take values such as "CPU>80%", "insufficient memory" and "tight disk space" and other features, and "risk level" may take values such as "high", "medium" and "low" and other features. Then you can choose one-hot encoding to encode unordered categorical features. For example, for the "resource bottleneck" feature, if there are three possible values, you can use three binary bits to represent them, each binary bit corresponds to a value, only one binary bit is 1 and the others are 0. You can choose label encoding to encode ordered categorical features. For example, for the "risk level" feature, you can encode it as 0, 1 and 2, corresponding to low, medium and high respectively.

[0115] Time feature processing mainly aims at time features. For time features such as "upgrade time", you can extract time information such as year, month, day, hour, minute, etc. as new features. For example, from "2025-07-20 10:30", you can extract the year 2025, month 7, day 20, hour 10, minute 30, etc. as new features. Optionally, if you need to analyze the relationship between upgrade time and other event time, you can calculate the time difference, for example, calculate the time interval between two upgrades.

[0116] The purpose of feature extraction is to extract more meaningful and representative features from the second data set to improve the effect of subsequent analysis and modeling. Feature extraction includes but is not limited to business logic-based feature extraction, statistical method-based feature extraction and real-time log information fusion feature extraction.

[0117] ​​​​​Feature extraction based on business logic is used to extract features related to functional changes, performance, and security. According to features such as "functional compatibility" and "number of functional changes", some features related to functional changes can be extracted. For example, the impact range of functional changes can be calculated by measuring the degree of change in functional compatibility, or the frequency of functional changes can be calculated by counting the number of functional changes in a period of time. Combined with the "performance change rate index" and other related features, more comprehensive performance-related features are extracted. For example, the stability of performance changes can be measured by calculating the standard deviation of performance change rates over a period of time, or the trend features of performance changes can be extracted by time series analysis of performance change rates. According to features such as "security risk" and "error log number", security-related features are extracted. For example, the severity index of security risk can be calculated by considering the value of security risk and the number of error logs, or the frequency feature of security events can be extracted by counting the number of security events (such as upgrades of high security risk) in a period of time.

[0118] Feature extraction based on statistical methods is used to extract aggregated features and correlation features. Aggregate operations are performed on historical upgrade data to extract some aggregated features. For example, the average, maximum and minimum values of the number of upgrades in each time period (such as every day and every week) are calculated; the average performance change rate and average security risk of each functional module are calculated. Analyze the correlation between various features to extract feature combinations with strong correlation. For example, if a strong positive correlation is found between "error log number" and "security risk", they can be combined into a new feature to represent the overall risk status of the system.

[0119] Real-time log information fusion feature extraction is used to extract real-time error features and real-time performance features. Real-time error features are extracted from real-time log information, such as the number of real-time error logs, error type distribution, etc. These real-time error features can be fused with related features in historical upgrade data, for example, the change rate of real-time error log quantity in a period of time before and after the upgrade. Real-time performance features are extracted, such as real-time response time and throughput of the system. These real-time performance features can be compared and analyzed with performance change rate features in historical upgrade data to extract more meaningful performance features, for example, the deviation degree of real-time performance from historical average performance.

[0120] After the above data cleaning, data conversion and feature extraction steps, a sample data set containing multiple samples D ={ d 1, d 2,…, d n} can be obtained, where each sample di The plurality of features include data processing and feature extraction. For example, a sample can include the following features: standardized upgrade time related features, standardized function compatibility indicators, standardized performance change rate indicators, encoded security risks, encoded resource bottlenecks, standardized error log numbers, and other extracted related features (such as function change influence range and performance change stability).

[0121] S320, constructing and training a risk prediction model according to the sample data set using a random forest algorithm to obtain a random forest risk prediction model.

[0122] In the embodiment of the application, a random forest algorithm is used to construct and train a risk prediction model to obtain a random forest risk prediction model to predict potential risks in the upgrade process. The random forest algorithm improves the accuracy and stability of the model by constructing a plurality of decision trees and combining their prediction results. The training steps of the risk prediction model are as follows:

[0123] First, a self-sampling method is used to extract n samples from the sample data set with replacement to construct n training subsets. For each training subset, a decision tree is constructed. In the construction process of the decision tree, for each node split, randomly select m features from all features to obtain a feature subset, where m is much smaller than the total number of features.

[0124] Then, the optimal feature is selected from the randomly selected feature subset according to the information gain and Gini index to split the node, so that the decision tree can better divide the data and improve the classification or prediction ability of the model.

[0125] In the embodiment of the application, the information gain is used to measure the reduction of information uncertainty after dividing the data set using a certain feature. The greater the information gain, the better the feature is for data division, and the more suitable it is as a selection for splitting nodes. The calculation steps of the information gain are as follows:

[0126] First, the information entropy of the sample data set is calculated. The information entropy is an indicator to measure the purity of the sample data set. The more chaotic the sample data set, the greater the information entropy, and vice versa. The calculation formula of the information entropy is as follows:

[0127]

[0128] In the above formula, H(D) represents the sample data set Dinformation entropy of the sample data set, K denotes the number of classes in the sample data set, p k denotes the proportion of the class sample in the sample data set k . D

[0129] Then, the conditional entropy after dividing the sample data set using the target feature is calculated, and the conditional entropy represents the uncertainty of the sample data set after knowing the value of the target feature. Assuming that the target feature A has V different values a 1, a 2,…, a v , the sample data set A is divided into D subsets V 1, D 2,…, D 2, Dv , and the calculation formula of the conditional entropy is:

[0130]

[0131] In the above formula, denotes the conditional entropy of the sample data set A after dividing using the target feature, D denotes the number of subsets, V denotes the number of samples in the subset v , and D denotes the number of samples in the sample data set, denotes the number of samples in the sample data set, D denotes the information entropy of the subset v . D

[0132] Finally, the information entropy of the sample data set is subtracted from the conditional entropy after dividing the sample data set using the target feature, and the information gain of the target feature is obtained, that is:

[0133]

[0134] If there are multiple target features, the information gain is calculated in the same way, the information gain of each target feature is compared, and the target feature with the largest information gain is selected as the splitting feature of the current node.

[0135] ​​For example, a sample dataset for predicting upgrade risks contains 10 samples, categorized into "high risk" and "low risk." There are 4 "high risk" samples and 6 "low risk" samples. The dataset is partitioned using the feature of "error log count," which has two values: "more" and "less." "More" represents 3 or more error log entries, and "less" represents fewer than 3. The information gain of the "error log count" is calculated as follows:

[0136] The information entropy of the sample dataset is:

[0137]

[0138] The sample dataset was divided based on the number of error logs. There were 3 samples with a "high" number of error logs, including 2 "high-risk" samples and 1 "low-risk" sample. The subset with a "high" number of error logs... D The information entropy of 1 is:

[0139]

[0140] There are 7 samples with "few error log entries", of which 2 are "high-risk" and 5 are "low-risk". This represents a subset of samples with "few error log entries". D The information entropy of 2 is:

[0141]

[0142] The conditional entropy after dividing the sample dataset according to the "number of error logs" is:

[0143]

[0144] The information gain of "error log count" is:

[0145]

[0146] Optionally, the Gini index can also be used as a measure of the purity of a sample dataset; a larger Gini index indicates a more chaotic dataset. In a decision tree, the target feature that minimizes the Gini index is selected as the splitting feature. The steps for calculating the Gini index are as follows:

[0147] First, calculate the Gini coefficient of the sample dataset using the following formula:

[0148]

[0149] In the above formula, Gini ( D ) represents the sample dataset D The Gini coefficient, Kthe number of classes in the sample data set, p k the number of classes in the sample data set, k the proportion of samples of the class in the sample data set, D

[0150] Then the Gini index of the sample data set divided by the target feature is calculated. Assuming the target feature has A different values, V 1, a 2,…, a a v the sample data set is divided into A subsets according to the target feature, D 1, V 2,…, D D Dv The formula for calculating the Gini index is as follows:

[0151]

[0152] In the above formula, Gini ( D,A ) represents the Gini index of the sample data set divided by the target feature, A n represents the number of subsets, D ni represents the number of samples in subset i, V ni represents the number of samples in the sample data set, gi represents the Gini coefficient of subset i. D v D D v

[0153] Finally, if there are multiple target features, the Gini index of each target feature is calculated in the same way, and the target feature with the smallest Gini index is selected as the split feature of the current node, because the smaller the Gini index, the higher the purity of the divided sample data set.

[0154] For example, also taking a sample data set of 10 samples related to upgrade risk prediction as an example, the Gini index of the sample data set divided by the "error log number" feature is calculated as follows:

[0155] The Gini coefficient of the sample data set is:

[0156]

[0157] According to the "error log number" feature, the sample data set is divided into subsets and the Gini coefficient of each subset is calculated as follows: ​​​​​​​​​

[0158] Subset of 1 where the number of "error logs" is "many" D 1 where there are 2 "high risk" samples and 1 "low risk" sample D The Gini coefficient of 1 is:

[0159]

[0160] Subset of 1 where the number of "error logs" is "few" D 2 where there are 2 "high risk" samples and 5 "low risk" samples D The Gini coefficient of 2 is:

[0161]

[0162] The Gini index after using the number of "error logs" to divide the sample dataset is:

[0163]

[0164] Finally, for the T training subsets, repeat the above steps to select the optimal features from the randomly selected feature subsets to split the nodes according to indicators such as information gain and Gini index, and construct T decision trees. n After forming the random forest, a random forest risk prediction model is obtained. n

[0165] S330, real-time sample data in the upgrading process is obtained, and the real-time sample data is input into the random forest risk prediction model to predict the risk of the upgrading process, and a prediction result is obtained.

[0166] In the embodiment of the present application, the real-time log information in the upgrading process is subjected to data preprocessing such as data cleaning, data conversion and feature extraction to obtain real-time sample data. The real-time sample data is input into the trained random forest risk prediction model to predict the risk of the upgrading process, and a prediction result is obtained. The prediction result of the random forest risk prediction model is the voting result of the prediction result of each decision tree. In the embodiment of the present application, the risk of the upgrading process is divided into three levels of low risk, medium risk and high risk, and the random forest risk prediction model outputs the probability of each level P low , P medium and P high . According to the maximum probability principle, the final risk level is determined, that is, if P high is the largest, the prediction result is high risk, if P medium is the largest, the prediction result is medium risk, and if P low ​Maximum, the prediction result is low risk.

[0167] In the embodiment of the application, by continuously collecting historical upgrade information and real-time log information, the trained random forest risk prediction model is used to predict potential risks in the upgrade process. Even if the multi-dimensional comprehensive evaluation process has passed and it is considered that the upgrade is feasible at the macro level, the risk prediction and intelligent decision-making process can still accurately predict potential problems that may occur in the upgrade process from a more microscopic and dynamic perspective, combined with historical upgrade data and real-time log information.

[0168] S400, continue to perform the automatic upgrade operation or perform the safe rollback operation according to the prediction result.

[0169] In the embodiment of the application, according to the prediction result of the random forest risk prediction model, a corresponding processing operation is performed, Figure 4 is the fourth schematic diagram of the application upgrade method process based on the low-code platform provided by the embodiment of the application, as Figure 4 shown, the corresponding processing operation according to the prediction result specifically includes the following steps:

[0170] S410, if the prediction result is low risk, continue to perform the automatic upgrade operation.

[0171] In the embodiment of the application, if the prediction result is low risk, the automatic upgrade operation is continued according to step S200. The key indicators are monitored in real time during the upgrade process, including but not limited to database connection number, API request error rate, server CPU usage and memory usage, etc.

[0172] S420, if the prediction result is medium risk, perform the automatic upgrade operation or perform the safe rollback operation after manual intervention.

[0173] In the embodiment of the application, if the prediction result is medium risk, an early warning information is sent, and whether to continue to perform the automatic upgrade operation or perform the safe rollback operation is decided after manual intervention. Specifically, the multi-dimensional comprehensive evaluation result and the real-time log information generated during the upgrade process can be comprehensively judged.

[0174] S430, if the prediction result is high risk, suspend the automatic upgrade operation and perform the safe rollback operation.

[0175] In the embodiment of the application, if the prediction result is high risk, it means that the upgrade will fail or an upgrade exception will occur, at which time the automatic upgrade operation is suspended and the safe rollback operation is performed. The safe rollback operation specifically includes the following steps:

[0176] First, the target indicators of the target application are monitored in real time to obtain real-time monitoring indicators, which include but are not limited to database connection number, API request error rate, server CPU usage and memory usage, etc.

[0177] Then, an automatic rollback condition is triggered according to the real-time monitoring indicators or the automatic test results. Specifically, a corresponding threshold is set for each real-time monitoring indicator, and if a real-time monitoring indicator exceeds the corresponding threshold and the duration reaches a preset time threshold, the automatic rollback condition is triggered. At the same time, after starting the service of the target version, an automatic test is performed, and if the automatic test failure rate exceeds a preset threshold, the automatic rollback condition is also triggered.

[0178] Finally, the service of the target version is stopped, data recovery is performed according to the backup file, and the service of the current version is started. Specifically, first, the service of the target version is stopped to avoid affecting user operations and data consistency. Then, data recovery is performed. For a database, the backup file is executed in a secure environment to overwrite the current database. For file storage, the backup file is decompressed and accurately overwritten in the application directory to ensure file integrity. In the configuration link, the old version config.json is restored to the specified position. Carefully check whether all data and configurations are accurate, and finally start the service of the current version, closely observe the starting state and running situation of the service of the current version, and ensure that the business quickly recovers to normal.

[0179] In the embodiment of the application, even if the multi-dimensional comprehensive evaluation considers that the upgrade is feasible, if the random forest risk prediction model predicts that there is a high risk in the upgrade process according to the increase in the number of error logs in the real-time log information, the automatic upgrade operation is paused and the safe rollback operation is triggered, thereby providing additional safety protection for application upgrade and further ensuring the safety and stability of the system during the upgrade process.

[0180] The application upgrade method based on the low-code platform provided in the embodiment of the application processes the application upgrade and rollback process through multi-dimensional comprehensive evaluation and a random forest-based risk prediction algorithm. The multi-dimensional evaluation comprehensively analyzes the upgrade impact from multiple key aspects such as application performance, compatibility and business logic, identifies potential risk points, and provides detailed basis for decision-making. The random forest risk prediction algorithm accurately predicts potential problems and anticipates possible faults and abnormalities in the upgrade process by virtue of its powerful data processing and pattern recognition capabilities. The automatic intelligent decision-making realizes quick and accurate decision-making on upgrade or rollback operations without manual tedious intervention, which not only greatly improves the upgrade efficiency and shortens the business interruption time, but also effectively avoids the risks caused by manual misjudgment.

[0181] Embodiment 2

[0182] Based on the same technical concept as embodiment 1, the application embodiment provides a low-code platform-based application upgrading system. Figure 5 is a low-code platform-based application upgrading system structure diagram provided by the application embodiment, as Figure 5 shown, the low-code platform-based application upgrading system 200 includes:

[0183] The comprehensive evaluation module 210 is configured to perform multi-dimensional comprehensive evaluation according to the current version information and the target version information of the target application, and obtain a comprehensive evaluation index.

[0184] The automatic upgrading module 220 is configured to compare the comprehensive evaluation index with a comprehensive evaluation threshold, and if the comprehensive evaluation index is greater than the comprehensive evaluation threshold, perform an automatic upgrading operation, otherwise adjust the automatic upgrading scheme.

[0185] The risk prediction module 230 is configured to obtain historical upgrading information and real-time log information in the upgrading process, perform risk prediction on the upgrading process through a random forest risk prediction model, and obtain a prediction result.

[0186] The safe rollback module 240 is configured to continue to perform the automatic upgrading operation or perform a safe rollback operation according to the prediction result.

[0187] Optionally, the low-code platform-based application upgrading system provided by the application embodiment adopts a hierarchical architecture design, including a data layer, a service layer and an application layer, and the layers communicate with each other through interfaces to realize data interaction and function calling. The data layer is responsible for storing application version data, backup data and log information, etc., the service layer includes core services such as comprehensive evaluation, automatic upgrading, risk prediction and intelligent decision-making, and safe rollback, and the application layer provides a user interface to facilitate users to perform upgrading operation and view system status.

[0188] The low-code platform-based application upgrading system provided by the application embodiment ensures that the application always maintains high availability and stability through the automatic and safe upgrading and rollback process, and provides a solid guarantee for the continuous and stable operation of the low-code platform application.

[0189] It can be understood that the implementation of the low-code platform-based application upgrading method described in embodiment 1 is also applicable to this embodiment and can achieve the same technical effect, so it will not be described here.

[0190] Embodiment 3

[0191] Based on the same concept, the application embodiment also provides an electronic device, Figure 6 is a structure diagram of an electronic device provided by the application embodiment, as Figure 6As shown, the electronic device 300 can include a processor 310, a communication interface 320, a memory 330, and a communication bus 340, wherein the processor 310, the communication interface 320, and the memory 330 complete communication with each other through the communication bus 340. The processor 310 can invoke the logical instructions in the memory 330 to execute the steps of the application upgrade method based on the low-code platform in each of the above embodiments. For example, it includes:

[0192] S100, performing multi-dimensional comprehensive evaluation according to the current version information and the target version information of the target application, and obtaining a comprehensive evaluation index;

[0193] S200, comparing the comprehensive evaluation index with a comprehensive evaluation threshold value, if the comprehensive evaluation index is greater than the comprehensive evaluation threshold value, performing an automatic upgrade operation, otherwise adjusting an automatic upgrade scheme;

[0194] S300, obtaining historical upgrade information and real-time log information in the upgrade process, performing risk prediction on the upgrade process through a random forest risk prediction model, and obtaining a prediction result;

[0195] S400, continuing to perform the automatic upgrade operation or performing a safe rollback operation according to the prediction result.

[0196] The processor 310 can be a central processing unit (CPU). The processor can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above-mentioned chips or the like.

[0197] Further, the logic instructions in the memory 330 described above can be implemented in the form of software functional units and sold or used as independent products, and can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application or parts of the present application that essentially contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0198] The memory 330 can include a program storage area and a data storage area. The program storage area can store an operating system and at least one application required by a function; and the data storage area can store data created by the processor and the like. In addition, the memory can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory can optionally include a memory disposed remotely with respect to the processor, and these remote memories can be connected to the processor through a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0199] Embodiment 4

[0200] Based on the same concept, the embodiments of the present application also provide a computer readable storage medium storing a computer program, the computer program including at least one code executable by a host device to control the host device to implement the steps of the application upgrade method based on the low-code platform as described in the above embodiments. For example, including:

[0201] S100, performing multi-dimensional comprehensive evaluation according to the current version information and the target version information of the target application, to obtain a comprehensive evaluation index;

[0202] S200, comparing the comprehensive evaluation index with a comprehensive evaluation threshold value, if the comprehensive evaluation index is greater than the comprehensive evaluation threshold value, performing an automatic upgrade operation, otherwise adjusting an automatic upgrade scheme;

[0203] S300, obtaining historical upgrade information and real-time log information in an upgrade process, performing risk prediction on the upgrade process through a random forest risk prediction model, to obtain a prediction result;

[0204] S400, continue to perform the automatic upgrade operation or perform the safe rollback operation according to the prediction result.

[0205] Based on the same technical concept, the embodiment of the application further provides a computer program for realizing the method embodiment described above when the computer program is executed by a host device.

[0206] The computer program can be stored in a computer readable storage medium packaged together with the processor or in a memory not packaged together with the processor.

[0207] Based on the same technical concept, the embodiment of the application further provides a processor for realizing the method embodiment described above. The processor can be a chip.

[0208] In summary, the application provides a low-code platform-based application upgrade method, system, device and storage medium. The application upgrade and rollback process are processed by multi-dimensional comprehensive evaluation and random forest-based risk prediction algorithm. Multi-dimensional evaluation analyzes the upgrade impact from multiple key aspects such as application performance, compatibility, and business logic, and identifies potential risk points to provide detailed basis for decision-making. The random forest risk prediction algorithm accurately predicts potential problems and anticipates possible failures and abnormalities in the upgrade process. It realizes automatic intelligent decision-making without manual intervention, quickly and accurately decides to upgrade or rollback operation, greatly improves the upgrade efficiency, shortens the business interruption time, and effectively avoids the risks caused by manual misjudgment. Through the automated and secure upgrade and rollback process, the application always maintains high availability and stability, providing a solid guarantee for the continuous and stable operation of the low-code platform application.

[0209] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of one another. As will be apparent to those of ordinary skill in the art, embodiments described herein can be combined with other embodiments.

[0210] The above-described embodiments only express several implementation manners of the application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the application. It should be pointed out that, for those skilled in the art, without departing from the concept of the application, several modifications and improvements can be made, which are all within the protection scope of the application.

[0211] It should be pointed out finally that the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit the same; and although the present application has been described in detail with reference to the foregoing embodiments, it should be appreciated by those skilled in the art that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features thereof can be replaced equivalently; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for upgrading an application based on a low-code platform, characterized in that, The method comprises: performing multi-dimensional comprehensive evaluation according to the current version information and the target version information of the target application to obtain a comprehensive evaluation index; comparing the comprehensive evaluation index with a comprehensive evaluation threshold value, and if the comprehensive evaluation index is greater than the comprehensive evaluation threshold value, performing an automatic upgrade operation, otherwise adjusting an automatic upgrade scheme; obtaining historical upgrade information and real-time log information in an upgrade process, performing risk prediction on the upgrade process by using a random forest risk prediction model to obtain a prediction result; continuing to perform the automatic upgrade operation or performing a safe rollback operation according to the prediction result; wherein the multi-dimensional comprehensive evaluation according to the current version information and the target version information of the target application to obtain a comprehensive evaluation index comprises: calculating a function compatibility index according to a first function set of the current version and a second function set of the target version; calculating a performance change rate index according to first performance index data of the current version and second performance index data of the target version, the performance change rate index at least including a response time change rate and a processing capacity change rate; obtaining vulnerability set information of the target version, and calculating a security risk index according to the vulnerability set information; calculating a resource bottleneck index according to demand information of the target version for system resources and threshold value information of the system resources, the resource bottleneck index at least including a CPU index, a memory index and a disk space index; comprehensively calculating the function compatibility index, the performance change rate index, the security risk index and the resource bottleneck index to obtain the comprehensive evaluation index.

2. The low-code platform based application upgrade method of claim 1, wherein, The calculation formula of the comprehensive evaluation index is as follows: In the above formula, S represents a comprehensive evaluation index, C func represents a function compatibility index, R T represents a response time change rate, R Q represents a processing capacity change rate, R sec represents a security risk index, max ( R sec ) represents the maximum security risk index recorded in the historical upgrade process, B cpu represents a CPU index, B mem represents a memory index, B disk represents a disk space index, 、 、 、 and respectively represent a first weight coefficient, a second weight coefficient, a third weight coefficient, a fourth weight coefficient and a fifth weight coefficient, and the sum of each weight coefficient is equal to 1.

3. The low-code platform based application upgrade method of claim 1, wherein, The obtaining of the historical upgrade information and the real-time log information in the upgrade process, the risk prediction on the upgrade process by using the random forest risk prediction model to obtain the prediction result comprises: performing data preprocessing on the historical upgrade information and the real-time log information to obtain a sample data set; constructing and training a risk prediction model by using a random forest algorithm according to the sample data set to obtain the random forest risk prediction model; obtaining real-time sample data in the upgrade process, inputting the real-time sample data into the random forest risk prediction model to perform risk prediction on the upgrade process, and obtaining the prediction result.

4. The low-code platform based application upgrade method of claim 3, wherein, The data preprocessing on the historical upgrade information and the real-time log information to obtain a sample data set comprises: performing data cleaning on the historical upgrade information and the real-time log information to obtain a first data set, the data cleaning at least including missing value processing, abnormal value processing and data consistency checking; performing data conversion on the first data set to obtain a second data set, the data conversion at least including standardization processing, classification feature coding and time feature processing; performing feature extraction on the second data set to obtain the sample data set, the feature extraction at least including business logic-based feature extraction, statistical method-based feature extraction and real-time log information fusion feature extraction.

5. The low-code platform based application upgrade method of claim 1, wherein, The continuing to perform the automatic upgrade operation or performing the safe rollback operation according to the prediction result comprises: If the prediction result is low risk, continue to perform the automatic upgrade operation; If the prediction result is medium risk, perform the automatic upgrade operation or the safe rollback operation after manual intervention; If the prediction result is high risk, suspend the automatic upgrade operation and perform the safe rollback operation.

6. The low-code platform based application upgrade method of claim 5, wherein, The execution of the automatic upgrade operation comprises: Backup and verification of application data, configuration data and code and metadata of the target application to obtain a backup file; Compatibility check and resource assessment of the target version, deployment of the target version to a server and migration of a database; Starting of a service of the target version and performance of an automated test, the automated test comprising at least core function check and key interface response state check; The execution of the safe rollback operation comprises: Real-time monitoring of target indicators of the target application to obtain real-time monitoring indicators; Triggering of an automatic rollback condition according to the real-time monitoring indicators or the automated test result; Stopping of the service of the target version, data recovery according to the backup file and starting of a service of a current version.

7. A low-code platform-based application upgrading system, characterized in that, The system comprises: A comprehensive evaluation module configured to perform multi-dimensional comprehensive evaluation according to current version information and target version information of a target application to obtain a comprehensive evaluation indicator; An automatic upgrade module configured to compare the comprehensive evaluation indicator with a comprehensive evaluation threshold value, and if the comprehensive evaluation indicator is greater than the comprehensive evaluation threshold value, perform an automatic upgrade operation, otherwise adjust an automatic upgrade scheme; A risk prediction module configured to obtain historical upgrade information and real-time log information in an upgrade process, perform risk prediction of the upgrade process through a random forest risk prediction model to obtain a prediction result; A safe rollback module configured to continue to perform the automatic upgrade operation or perform a safe rollback operation according to the prediction result. The multi-dimensional comprehensive evaluation according to the current version information and the target version information of the target application to obtain the comprehensive evaluation indicator comprises: Calculating a function compatibility indicator according to a first function set of a current version and a second function set of a target version; Calculating a performance change rate indicator according to first performance indicator data of the current version and second performance indicator data of the target version, the performance change rate indicator comprising at least response time change rate and processing capacity change rate; Obtaining vulnerability set information of the target version, and calculating a security risk indicator according to the vulnerability set information; Calculating a resource bottleneck indicator according to demand information of the target version for system resources and threshold value information of the system resources, the resource bottleneck indicator comprising at least CPU indicator, memory indicator and disk space indicator; Comprehensive calculation according to the function compatibility indicator, the performance change rate indicator, the security risk indicator and the resource bottleneck indicator to obtain the comprehensive evaluation indicator.

8. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the application upgrade method based on the low-code platform as claimed in any one of claims 1-6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the application upgrade method based on the low-code platform as claimed in any one of claims 1-6.

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