WEB system upgrading method and device, electronic equipment and storage medium

Abstract

The invention provides a WEB system upgrading method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of computers. The upgrading method comprises the following steps: acquiring a target report component and an online type thereof, and judging whether the online type is trial online or not; in response to the online type being trial online, determining a trial user of the target report component based on a user portrait algorithm, the user portrait algorithm including one of a data statistical algorithm and a clustering algorithm; obtaining trial data and a feedback result of the trial user, and judging whether the trial data and the feedback result meet a preset condition or not; and in response to the trial data and the feedback result meeting a preset condition, the target report component is online to upgrade the WEB system. The problems that server resources are seriously wasted, project cost is high, a data synchronization mechanism is not perfect enough, and data are prone to being lost and repeated in the prior art are at least solved. The method is suitable for system upgrading and updating scenes.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method, device, electronic device and computer-readable storage medium for upgrading a WEB system. Background Art

[0002] As an online web system, requirements are constantly changing and being added. Each time a program is developed to meet these requirements, system operations must be suspended to allow for system updates (i.e., upgrades) and for new features to take effect. During the upgrade period, the system is unable to provide external services, temporarily interrupting operations. This interruption not only results in lost revenue but also severely impacts user experience. Furthermore, some system issues and bugs cannot be detected during the testing phase and are only exposed in real-world production scenarios. Once data errors are involved, they must be located and corrected, which not only places a significant workload on system operations and maintenance but also significantly prolongs service interruptions, significantly impacting revenue and user experience.

[0003] Existing technology usually deploys two systems on the server, one is the new version of the system, and the other is the old version of the system, and the new and old versions run simultaneously. Through technical means, users can switch to the new version without noticing to complete the demand update and operation and maintenance of the WEB system.

[0004] However, the existing technology requires two sets of hardware equipment, which seriously wastes server resources and increases project costs. In addition, the data synchronization mechanism between the two systems is not perfect, which easily leads to data loss and duplication, and a series of data problems. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to address the above-mentioned deficiencies in the prior art and to provide a method, device, electronic device and computer-readable storage medium for upgrading a WEB system. The method can realize safe and convenient WEB system upgrade.

[0006] In a first aspect, the present invention provides an upgrade method for a web page WEB system, comprising: obtaining a target report component and its online type, and determining whether the online type is a trial online, wherein the online type includes one of the following: immediate online and trial online; in response to the online type being a trial online, determining the trial users of the target report component based on a user portrait algorithm, wherein the user portrait algorithm includes one of the following: a data statistical algorithm and a clustering algorithm; obtaining trial data and feedback results of the trial users, and determining whether the trial data and feedback results meet preset conditions; in response to the trial data and feedback results meeting the preset conditions, launching the target report component to upgrade the WEB system.

[0007] Preferably, obtaining the target report component and its online type specifically includes: obtaining user requirements and selecting a report component template based on the user requirements, wherein the user requirements include the report data to be displayed; generating a structured query language SQL statement corresponding to the report component template; mapping the report data to be displayed with the report component template according to the SQL statement corresponding to the report component template to generate the target report component; and determining the online type of the target report component based on the report data to be displayed.

[0008] Preferably, based on the user portrait algorithm, the trial users of the target report component are determined, specifically including: judging the user group range of the target report component according to the report data to be displayed, wherein the user group range includes one of the following: all users, target user group; in response to the user group range of the target report component being the target user group, the trial users of the target report component are determined based on the data statistical algorithm; in response to the user group range of the target report component being all users, the trial users of the target report component are determined based on the clustering algorithm.

[0009] Preferably, based on a data statistical algorithm, the trial users of the target report component are determined, specifically including: obtaining the historical access report of the target user group, and based on the historical access report, calculating the frequency of each user accessing the report data to be displayed; sorting the users in the target user group based on frequency, and determining the first N users as trial users of the target report component, where N is a positive integer.

[0010] Preferably, based on a clustering algorithm, determining trial users of a target report component specifically includes: obtaining user feature vectors of all users, wherein the user feature vector includes at least one of the following: role type, business category of the most frequently accessed application, administrative department, and professional line; determining the user feature vector of the cluster center based on the report data to be displayed; normalizing the user feature vectors of all users and the user feature vector of the cluster center respectively to obtain a first user feature vector and a second user feature vector; calculating the distance between the first user feature vector and the second user feature vector; and determining trial users of the target report component based on the distance and clustering algorithm.

[0011] Preferably, calculating the distance between the first user feature vector and the second user feature vector specifically includes: calculating the distance between the first user feature vector and the second user feature vector according to formula (1):

[0012]

[0013] Where d(a,b) represents the distance between the first user feature vector a and the second user feature vector b, a i represents the i-th feature in the first user feature vector a, bi represents the i-th feature in the second user feature vector b, and n represents the total number of all features in the first user feature vector a or the second user feature vector b.

[0014] Preferably, after determining whether the launch type is a trial launch, the WEB system upgrade method further comprises: in response to the launch type of the target report component being immediate launch, launching the target report component to upgrade the WEB system.

[0015] In the second aspect, the present invention also provides an upgrading device for a WEB system, comprising a first acquisition module, a determination module, a second acquisition module and a first online module. The first acquisition module is used to acquire a target report component and its online type, and to determine whether the online type is a trial online, wherein the online type includes the following: immediate online and trial online. The determination module is connected to the first acquisition module, and is used to determine the trial users of the target report component based on a user portrait algorithm in response to the online type being a trial online, wherein the user portrait algorithm includes the following: a data statistics algorithm and a clustering algorithm. The second acquisition module is connected to the determination module, and is used to acquire the trial data and feedback results of the trial users, and to determine whether the trial data and feedback results meet preset conditions. The first online module is connected to the second acquisition module, and in response to the trial data and feedback results meeting the preset conditions, the target report component is launched online to upgrade the WEB system.

[0016] In a third aspect, the present invention further provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to implement the WEB system upgrade method provided in the first aspect.

[0017] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for upgrading the WEB system provided in the first aspect is implemented.

[0018] The present invention provides a web system upgrade method, device, electronic device, and computer-readable storage medium. By using a collaborative clustering algorithm and a data statistical algorithm, the method ensures that the identified trial users accurately match the target report component regardless of user size. Furthermore, the clustering algorithm and the data statistical algorithm have high computational efficiency, enabling trial user determination to better adapt to sudden changes in demand for the target report component, thereby improving the efficiency and accuracy of trial user determination. Furthermore, based on the trial data and feedback results of the trial users, potential issues with the web system upgrade can be quickly and effectively located, thereby avoiding upgrade risks for the web system. Therefore, the present invention can achieve a safe and convenient web system upgrade. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flowchart of a method for upgrading a WEB system according to embodiment 1 of the present invention;

[0020] Figure 2 This is a schematic diagram of the structure of the upgrade system of the WEB system in Example 1 of the present invention;

[0021] Figure 3 This is a flowchart of configuring a personalized page of a WEB system in Example 1 of the present invention;

[0022] Figure 4 This is a flowchart of determining trial users of a target report component based on a data statistical algorithm in Example 1 of the present invention;

[0023] Figure 5 This is a flowchart of determining trial users of a target report component based on a clustering algorithm in Example 1 of the present invention;

[0024] Figure 6 This is a flowchart of the target reporting component for all users in Example 1 of the present invention;

[0025] Figure 7 This is a flowchart of configuring the target report component in Example 1 of the present invention;

[0026] Figure 8 This is a flowchart of a method for upgrading a WEB system according to embodiment 2 of the present invention;

[0027] Figure 9 This is a structural diagram of an upgrading device for a WEB system according to embodiment 3 of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0029] It should be understood that the specific embodiments and drawings described herein are only used to explain the present invention rather than to limit the present invention.

[0030] It is understood that, in the absence of conflict, the various embodiments of the present invention and the various features in the embodiments may be combined with each other.

[0031] It can be understood that, for the convenience of description, the drawings of the present invention only show parts related to the present invention, while parts unrelated to the present invention are not shown in the drawings.

[0032] It can be understood that each unit and module involved in the embodiments of the present invention may correspond to only one physical structure, or may be composed of multiple physical structures, or multiple units and modules may be integrated into one physical structure.

[0033] It will be understood that, without conflict, the functions and steps marked in the flowcharts and block diagrams of the present invention may occur in an order different from that marked in the drawings.

[0034] It is understood that the flowcharts and block diagrams of the present invention illustrate the possible architectures, functions, and operations of the systems, devices, equipment, and methods according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a unit, module, program segment, or code, which contains executable instructions for implementing the specified functions. Moreover, each box or combination of boxes in the block diagram and flowchart may be implemented using a hardware-based system that implements the specified functions, or may be implemented using a combination of hardware and computer instructions.

[0035] It can be understood that the units and modules involved in the embodiments of the present invention can be implemented by software or hardware. For example, the units and modules can be located in a processor.

[0036] Example 1:

[0037] like Figure 1 As shown, this embodiment provides a method for upgrading a WEB system.

[0038] In this embodiment, the upgrade method of the WEB system is applied to Figure 2 The upgrade system of the WEB system shown, wherein the upgrade system of the WEB system includes but is not limited to: a low-code development platform, a model algorithm module, a page display module, and a usage monitoring module. The low-code development platform is used to edit and modify the code of the WEB system to achieve functional upgrades of the WEB system. The page display module is used to render and visually present the WEB system after functional upgrades. The model algorithm module is used to intelligently determine trial users of the WEB system after functional upgrades. The trial monitoring module is used to monitor the feedback of trial users on the WEB system after functional upgrades.

[0039] The upgrade method of the WEB system includes:

[0040] S101, obtaining a target report component and its launch type, and determining whether the launch type is a trial launch, wherein the launch type includes one of the following: immediate launch and trial launch.

[0041] In this embodiment, a report component generally refers to a code file that provides a visual design interface, data binding mechanism, rendering engine, and export functionality to help developers quickly build complex reports. Immediate launch refers to directly releasing the target report component after it is generated and tested, allowing users to immediately see the target report component. Trial launch refers to pre-launching the target report component to a small number of users after it is generated and tested. Based on the feedback from these users, a decision is made on whether to fully roll out the target report component.

[0042] Specifically, obtaining the target report component and its online type includes steps S1011 to S1014:

[0043] S1011, obtaining user requirements and selecting a report component template according to the user requirements, wherein the user requirements include the report data to be displayed.

[0044] In this embodiment, since the data to be displayed or applied on each page of the WEB system is different, the report components of each page are also different accordingly. Therefore, it is necessary to use the low-code development platform to analyze the report characteristics of the report components under all pages of the WEB system, and based on the report characteristics, pre-generate multiple report component templates, where the report characteristics include but are not limited to: report type, report parameters, and visualization type.

[0045] After pre-generating a variety of report component templates based on report features, this embodiment can extract the report data required for display based on user needs. For example, if the user needs to be "store managers need to view in real time: ① sales trends for each time period of the day; ② TOP10 best-selling products (with inventory warnings); ③ member vs. non-member consumption ratios", then the report data required for display include report type: mixed (time series data + rankings + pie chart ratios), report parameters: product name, sales volume, sales revenue, current inventory, number / amount of member consumption, number / amount of non-member consumption, visualization type: line chart (time period trend), table (TOP10 products), pie chart (membership ratio). Then, the corresponding report component template is matched according to the report data required for display.

[0046] S1012: Generate a structured query language SQL statement corresponding to the report component template.

[0047] In this embodiment, the data to be displayed or applied on each page of the WEB system is different, and the SQL (Structured Query Language) statements required to load the displayed or applied data from the background database or storage unit are also different. Therefore, it is necessary to generate SQL statements corresponding to the report component template based on the report parameters: product name, sales volume, sales revenue, current inventory, number / amount of member consumption, and number / amount of non-member consumption. This embodiment does not directly generate the data call statements corresponding to the report component template on the report component template, that is, the data call statements corresponding to the report component template are generated in the XML (Extensible Markup Language) format, but instead generates the SQL statements corresponding to the report component template in the background database. Therefore, this embodiment directly links the database monitoring mechanism through SQL statements to access the latest data in real time, avoiding the data expiration problem caused by the cache layer accelerating template parsing in the XML solution, transferring the computing pressure from the application server to the database, and eliminating the need for preloading when the system is restarted, thereby solving the problem of system updates requiring restarts. In addition, this embodiment only requires developers to write SQL statements, and does not require developers to write high-level software development languages ​​​​such as Java or C++, which greatly reduces the difficulty of developing target report components corresponding to user needs, shortens the development cycle, and also realizes the ready-to-use WEB system.

[0048] It should be noted that before generating the SQL statement corresponding to the report component template, this embodiment can also determine the SQL type, database type, and SQL text description according to user needs, where the SQL type includes but is not limited to: query, modify, and delete, and the database type includes but is not limited to: RDS, Mysql, and Oralce.

[0049] S1013 , mapping the report data to be displayed with the report component template according to the SQL statement corresponding to the report component template to generate a target report component.

[0050] In this embodiment, through SQL statements, the report parameters in the report data to be displayed: product name, sales volume, sales revenue, current inventory, number / amount of member consumption, number / amount of non-member consumption can be mapped to the report parameters of the report characteristics in the report component template, so that the real data of product name, sales volume, sales revenue, current inventory, number / amount of member consumption, and number / amount of non-member consumption in the subsequent database can be dynamically displayed on the WEB system page in the form of a target report component.

[0051] It should be noted that after generating multiple target report components, this embodiment also includes: configuring a personalized page of the WEB system. Figure 3As shown, configuring the personalized page of the WEB system specifically includes: through the low-code development platform, dragging and dropping the deployment locations of multiple target report components to splice out the personalized page of the WEB system; recording the generated multiple target report component information (such as component ID), the report component template information corresponding to the target report component (such as component template name, ID), and the SQL statement corresponding to the target report component to the database for subsequent direct reference and use. The recorded SQL statement can also add Chinese comments to the fields therein and delete the fields that do not need to be displayed; verify whether the target report component has errors, wherein the errors include but are not limited to: the report parameters have paging size errors, the page data volume exceeds the preset number, and there are automatically generated parameters (such as null).

[0052] S1014: Determine the online type of the target report component based on the report data to be displayed.

[0053] In this embodiment, immediate launch is primarily intended for situations where the report data required for display is small in size or has only a small amount of changes, or when the user is eager to try out the target report component. For example, a user is eager to see the report data required for presentation to a superior. Trial launch is primarily intended for situations where the report data required for display is small in size or has only a small amount of changes, such as when the report data required for display is large in business data or when modifying process logic, where even the slightest deviation can result in data errors.

[0054] S102 , in response to the launch type being a trial launch, determining trial users of the target report component based on a user portrait algorithm, wherein the user portrait algorithm includes one of the following: a data statistical algorithm and a clustering algorithm.

[0055] In this embodiment, in response to the launch type being a trial launch, a data statistical algorithm or a clustering algorithm is selected through the model algorithm module to determine the trial users of the target report component.

[0056] Specifically, based on the user portrait algorithm, determining the trial users of the target report component includes steps S1021 to S1023:

[0057] S1021: Determine the user group scope of the target report component based on the report data to be displayed, where the user group scope includes one of the following: all users, and target user group.

[0058] In this embodiment, the business corresponding to the target report component is determined based on the report data to be displayed; it is determined whether there is a corresponding target user group for the business; if there is no corresponding target user group for the business, the user group scope of the target report component is determined to be all users; if there is a corresponding target user group for the business, the user group scope of the target report component is determined to be the target user group corresponding to the business. For example: the report data to be displayed are the real-time values ​​of soil temperature and humidity, the light intensity curve, and the water consumption of the irrigation system, and the corresponding business is the irrigation optimization business. Therefore, the user group scope of the target report component is determined to be agricultural technicians.

[0059] S1022: In response to the target user group of the target report component being the target user group, determine trial users of the target report component based on a data statistical algorithm.

[0060] Specifically, based on the data statistical algorithm, the trial users of the target report component are determined, including: obtaining the historical access report of the target user group, and based on the historical access report, calculating the frequency of each user accessing the report data to be displayed; sorting the users in the target user group based on frequency, and determining the first N users as trial users of the target report component, where N is a positive integer.

[0061] In this embodiment, Figure 4 As shown, based on the data statistical algorithm, the trial users of the target report component are determined, specifically including: obtaining the historical access report of the target user group (i.e. Figure 4 user access history table in the target report component); using the report data to be displayed as the label attribute of the target report component, calculate the proportion of label attributes in the historical access report of each user in the target user group. The larger the proportion, the higher the frequency of users accessing the report data to be displayed. For example: the target report component is the report of the social channel marketing line, and the historical access report of the social channel marketing salesperson is obtained. The personnel who frequently access the report data of the social channel marketing line also frequently access the target report component. This embodiment takes N as 3 as an example, sorts the proportion of label attributes in the historical access report of each user, and determines the users corresponding to the top 3 proportions as trial users of the target report component. This embodiment introduces data statistical technology, targets the needs of obvious user groups, and uses the statistics of the user history access system to find high-frequency users suitable for the target report component, so as to ensure the least number of users, the least trial time, and discover as many problems as possible with the newly launched functions.

[0062] It should be noted that, since this is a production system, if a problem occurs during the trial, the fewer trial users there are, the less loss there will be. Therefore, this embodiment significantly reduces the number of trial users (ie, N), thereby reducing the loss after the problem occurs.

[0063] S1023 , in response to the target report component being targeted at all users as a user group, determine trial users of the target report component based on a clustering algorithm.

[0064] Specifically, based on the clustering algorithm, the trial users of the target report component are determined, including: obtaining user feature vectors of all users, wherein the user feature vectors include at least one of the following: role type, business category of the most frequently accessed application, administrative department, and professional line; based on the report data to be displayed, determining the user feature vector of the cluster center; normalizing the user feature vectors of all users and the user feature vector of the cluster center respectively to obtain a first user feature vector and a second user feature vector; calculating the distance between the first user feature vector and the second user feature vector; and determining the trial users of the target report component based on the distance and clustering algorithm.

[0065] In this embodiment, Figure 5 As shown, based on the clustering algorithm, the trial users of the target report component are determined, specifically including: obtaining the user feature vectors of all users, and according to the formula Normalize the user feature vectors of all users and scale them to the interval [0, 1] to eliminate the dimension difference and obtain the first user feature vector, where X norm represents the first user feature vector, X represents the user feature vectors of all users, X max represents the maximum value of the user feature vector of all users, X min Represents the minimum value of the user feature vector of all users. Based on the report data to be displayed, derive the user feature vector corresponding to the target report component (i.e., role type, business category to which the most frequently accessed application belongs, administrative department, and professional line). The user feature vector corresponding to the target report component is determined as the user feature vector of the cluster center, and similarly, the user feature vectors of all users are normalized, and the user feature vector of the cluster center is also normalized to obtain the second user feature vector. Using Euler's formula, calculate the distance between the first user feature vector and the second user feature vector. Based on the distance between the first user feature vector and the second user feature vector, cluster all users and determine the trial users of the target report component. This embodiment introduces a clustering algorithm to target the needs without obvious user groups, analyzes the feature models of all users and target report components, and matches the best trial users according to the feature vectors of all users and target report components to ensure the least number of trial users, the least trial time, and discover as many new online function problems as possible.

[0066] It should be noted that in this embodiment, the clustering algorithm can also be replaced by other machine algorithms, such as decision trees, random forests, and support vector machines, to model the user feature vectors of all users and the report data to be displayed, and train a matching model for subsequent determination of the best trial users for the target report component.

[0067] Specifically, calculating the distance between the first user feature vector and the second user feature vector includes: calculating the distance between the first user feature vector and the second user feature vector according to formula (1):

[0068]

[0069] Where d(a,b) represents the distance between the first user feature vector a and the second user feature vector b, a i represents the i-th feature in the first user feature vector a, b i represents the i-th feature in the second user feature vector b, and n represents the total number of all features in the first user feature vector a or the second user feature vector b.

[0070] Optionally, after determining whether the launch type is a trial launch, the WEB system upgrade method further includes:

[0071] S105 : In response to the launch type of the target report component being immediate launch, launch the target report component to upgrade the WEB system.

[0072] S103: Obtain trial data and feedback results of the trial user, and determine whether the trial data and feedback results meet preset conditions.

[0073] S104, in response to the trial data and feedback results meeting the preset conditions, the target report component is launched online to upgrade the WEB system.

[0074] In this embodiment, the trial monitoring module monitors trial user trial data and feedback results, which serve as a basis for decision-making on blocking the release of the target report component. For example, if during the trial period of the target report component, the trial data and feedback results show problems or negative comments, the trial user can manually block the release of the target report component. If during the trial period of the target report component, the trial data and feedback results do not show any problems or negative comments, and the target report component reaches a preset number of click-through rates, the target report component will be released to all users.

[0075] It should be noted that if Figure 6As shown in the figure, launching the target report component to all users involves sending the target report component to the web system's backend. A menu corresponding to the target report component will appear in the web system's menu. To view the target report component, click the corresponding menu to open it. The web system's frontend then sends a request to the backend. Upon receiving the request, the backend executes the appropriate logic algorithm to process the frontend request, configure the target report component, and ultimately return the results to the frontend. Based on the results returned by the backend, the frontend loads and renders the target report component and corresponding data on the page. Once rendering is complete, the user's requested page is fully displayed.

[0076] like Figure 7 As shown, the corresponding logical algorithm is executed to process the front-end request to configure the target report component. Specifically, after the superclass controller receives the front-end request, it parses the front-end request parameters and stores the parameters in a Map. The core algorithm is as follows:

[0077]

[0078] returnMap.put(name,value). Extract the relevant information required to execute SQL from the Map, such as the database type. The core code obtained is as follows:

[0079]

[0080] During the configuration phase, parameters are identified by special characters. When executing SQL, special characters need to be removed and parameters replaced with corresponding constants. The core algorithm is as follows:

[0081]

[0082]

[0083] Determine the SQL type, execute the SQL, obtain the execution result, and respond to the request. The core algorithm is as follows:

[0084]

[0085] The WEB system upgrade method provided in this embodiment ensures that regardless of the size of the user base, the determined trial users are accurately matched with the target report components through a collaborative clustering algorithm and a data statistical algorithm, and utilizes the high computational efficiency of the clustering algorithm and the data statistical algorithm, so that the trial user determination can better adapt to the sudden demand changes of the target report components, thereby improving the efficiency and accuracy of the trial user determination, and then based on the trial data and feedback results of the trial users, quickly and effectively locate potential problems in the WEB system upgrade, avoid the upgrade risks of the WEB system, and achieve a safe and convenient WEB system upgrade.

[0086] Example 2:

[0087] like Figure 8 As shown, this embodiment provides a method for upgrading a WEB system. The method for upgrading a WEB system includes:

[0088] S201, obtaining user requirements and selecting a report component template based on the user requirements, wherein the user requirements include the report data to be displayed; generating a structured query language SQL statement corresponding to the report component template; mapping the report data to be displayed with the report component template according to the SQL statement corresponding to the report component template to generate a target report component.

[0089] In this embodiment, the report component template is Figure 8 The front-end components in .

[0090] S202: Determine the launch type of the target report component according to the report data to be displayed, wherein the launch type includes one of the following: immediate launch and trial launch.

[0091] S203 , in response to the launch type being a trial launch, determining trial users of the target report component based on a user portrait algorithm, wherein the user portrait algorithm includes one of the following: a data statistical algorithm and a clustering algorithm.

[0092] In this embodiment, the clustering algorithm is Figure 8 Machine learning algorithms in .

[0093] S204, obtaining trial data and feedback results of trial users, and determining whether the trial data and feedback results meet preset conditions; in response to the trial data and feedback results meeting the preset conditions, launching a target report component to upgrade the WEB system.

[0094] S205 : In response to the launch type of the target report component being immediate launch, launch the target report component to upgrade the WEB system.

[0095] The WEB system upgrade method provided in this embodiment ensures that regardless of the size of the user base, the determined trial users are accurately matched with the target report components through a collaborative clustering algorithm and a data statistical algorithm, and utilizes the high computational efficiency of the clustering algorithm and the data statistical algorithm, so that the trial user determination can better adapt to the sudden demand changes of the target report components, thereby improving the efficiency and accuracy of the trial user determination, and then based on the trial data and feedback results of the trial users, quickly and effectively locate potential problems in the WEB system upgrade, avoid the upgrade risks of the WEB system, and achieve a safe and convenient WEB system upgrade.

[0096] Example 3:

[0097] like Figure 9 As shown, this embodiment provides an upgrading device for a WEB system, including a first acquisition module 31, a determination module 32, a second acquisition module 33 and a first online module 34. The first acquisition module 31 is used to obtain a target report component and its online type, and to determine whether the online type is a trial online, wherein the online type includes the following: immediate online and trial online. The determination module 32 is connected to the first acquisition module 31, and is used to determine the trial users of the target report component based on a user portrait algorithm in response to the online type being a trial online, wherein the user portrait algorithm includes the following: a data statistics algorithm and a clustering algorithm. The second acquisition module 33 is connected to the determination module 32, and is used to obtain trial data and feedback results of trial users, and to determine whether the trial data and feedback results meet preset conditions. The first online module 34 is connected to the second acquisition module 33, and in response to the trial data and feedback results meeting the preset conditions, the target report component is launched online to upgrade the WEB system.

[0098] Specifically, the first acquisition module 31 includes: an acquisition unit 311, a generation unit 312, a mapping unit 313 and a first determination unit 314, the acquisition unit 311 is used to obtain user needs and select a report component template according to the user needs, wherein the user needs include the report data to be displayed, the generation unit 312 is used to generate a structured query language SQL statement corresponding to the report component template, the mapping unit 313 is used to map the report data to be displayed with the report component template according to the SQL statement corresponding to the report component template to generate a target report component, and the first determination unit 314 is used to determine the online type of the target report component according to the report data to be displayed.

[0099] Specifically, the determination module 32 includes: a judgment unit 321, a second determination unit 322 and a third determination unit 323. The judgment unit 321 is used to judge the user group range of the target report component based on the report data to be displayed, wherein the user group range includes one of the following: all users, target user group. The second determination unit 322 is used to determine the trial users of the target report component based on a data statistical algorithm in response to the user group range of the target report component being the target user group. The third determination unit 323 is used to determine the trial users of the target report component based on a clustering algorithm in response to the user group range of the target report component being all users.

[0100] Specifically, the second determination unit 322 includes: a first acquisition subunit and a first determination subunit, the first acquisition subunit is used to obtain the historical access report of the target user group, and based on the historical access report, calculate the frequency of each user accessing the report data to be displayed, the first determination subunit is used to sort the users in the target user group based on frequency, and determine the first N users as trial users of the target report component, where N is a positive integer.

[0101] Specifically, the third determination unit 323 includes: a second acquisition subunit, a second determination subunit, a normalization processing subunit, a calculation subunit and a third determination subunit. The second acquisition subunit is used to obtain user feature vectors of all users, wherein the user feature vector includes at least one of the following: role type, business category of the most frequently accessed application, administrative department, and professional line. The second determination subunit is used to determine the user feature vector of the cluster center based on the report data to be displayed. The normalization processing subunit is used to normalize the user feature vectors of all users and the user feature vectors of the cluster center respectively to obtain a first user feature vector and a second user feature vector. The calculation subunit is used to calculate the distance between the first user feature vector and the second user feature vector. The third determination subunit is used to determine the trial users of the target report component based on the distance and clustering algorithm.

[0102] Specifically, the calculation subunit includes: a minimum calculation unit, configured to calculate the distance between the first user feature vector and the second user feature vector according to formula (1):

[0103]

[0104] Where d(a,b) represents the distance between the first user feature vector a and the second user feature vector b, a i represents the i-th feature in the first user feature vector a, b i represents the i-th feature in the second user feature vector b, and n represents the total number of all features in the first user feature vector a or the second user feature vector b.

[0105] Optionally, the upgrading device of the WEB system further comprises: a second online module 35 for online launching the target report component in response to the online type of the target report component being immediate online, so as to upgrade the WEB system.

[0106] It can be understood that the above-mentioned WEB system upgrade device executes the WEB system upgrade method corresponding to the embodiment 1 provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects of the solution corresponding to the WEB system upgrade method of the embodiment 1 above, and will not be repeated here.

[0107] Example 4:

[0108] This embodiment provides an electronic device, including a memory and a processor. The memory stores a computer program, and the processor is configured to run the computer program to implement the WEB system upgrade method in the above-mentioned embodiment 1 or embodiment 2.

[0109] Example 5:

[0110] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for upgrading the WEB system in the above-mentioned embodiment 1 or embodiment 2 is implemented.

[0111] It will be understood that the above embodiments are merely exemplary embodiments for illustrating the principles of the present invention, and the present invention is not limited thereto. Those skilled in the art will appreciate that various modifications and improvements can be made without departing from the spirit and substance of the present invention, and such modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. A method for upgrading a web page system, characterized in that: include: Obtain a target report component and its launch type, and determine whether the launch type is a trial launch, wherein the launch type includes one of the following: immediate launch and trial launch; In response to the launch type being a trial launch, determining trial users of the target report component based on a user portrait algorithm, wherein the user portrait algorithm includes a data statistics algorithm and a clustering algorithm; Obtaining trial data and feedback results of the trial user, and determining whether the trial data and feedback results meet preset conditions; In response to the trial data and feedback results meeting the preset conditions, the target report component is launched to upgrade the WEB system.

2. The method for upgrading a WEB system according to claim 1, wherein: The target report component and its online type are obtained, specifically including: Obtain user requirements and select report component templates based on them, where user requirements include the report data to be displayed; Generate structured query language SQL statements corresponding to report component templates; According to the SQL statement corresponding to the report component template, map the report data to be displayed with the report component template to generate the target report component; Determine the launch type of the target report component based on the report data to be displayed.

3. The method for upgrading a WEB system according to claim 2, wherein: Determining trial users of the target report component based on the user portrait algorithm specifically includes: Determine the target user group scope of the target report component based on the report data to be displayed, where the target user group scope includes one of the following: all users, target user group; In response to the target user group of the target report component being the target user group, determining trial users of the target report component based on a data statistical algorithm; In response to the target report component targeting all users as its target user group, trial users of the target report component are determined based on a clustering algorithm.

4. The method for upgrading a WEB system according to claim 3, wherein: Determining trial users of the target report component based on a data statistical algorithm specifically includes: Obtain historical access reports for the target user group and, based on these reports, calculate the frequency with which each user accesses the report data to be displayed. Sort users in the target user group based on frequency, and determine the top N users as trial users of the target report component, where N is a positive integer.

5. The method for upgrading a WEB system according to claim 3, wherein: Determining trial users of the target report component based on a clustering algorithm specifically includes: Obtaining user feature vectors for all users, wherein the user feature vectors include at least one of the following: role type, business category of the most frequently accessed application, administrative department, and professional line; Determine the user feature vector of the cluster center based on the report data to be displayed; Normalizing the user feature vectors of all users and the user feature vectors of the cluster center respectively to obtain a first user feature vector and a second user feature vector; Calculating the distance between the first user feature vector and the second user feature vector; Determine trial users of the target reporting component based on distance and clustering algorithms.

6. The method for upgrading a WEB system according to claim 5, characterized in that: The calculating the distance between the first user feature vector and the second user feature vector specifically includes: According to formula (1), the distance between the first user feature vector and the second user feature vector is calculated: Where d(a,b) represents the distance between the first user feature vector a and the second user feature vector b, a i represents the i-th feature in the first user feature vector a, b i represents the i-th feature in the second user feature vector b, and n represents the total number of all features in the first user feature vector a or the second user feature vector b.

7. The method for upgrading a WEB system according to claim 1, wherein: After determining whether the online type is a trial online, the method further includes: In response to the target report component being launched immediately as the launch type, the target report component is launched to upgrade the WEB system.

8. A WEB system upgrade device, characterized in that: It includes a first acquisition module, a determination module, a second acquisition module and a first online module. The first acquisition module is used to obtain the target report component and its launch type, and determine whether the launch type is a trial launch, wherein the launch type includes the following: immediate launch and trial launch. A determination module is connected to the first acquisition module and is used to determine the trial users of the target report component based on a user portrait algorithm in response to the launch type being a trial launch, wherein the user portrait algorithm includes one of the following: a data statistical algorithm and a clustering algorithm. The second acquisition module is connected to the determination module and is used to obtain the trial data and feedback results of the trial user and determine whether the trial data and feedback results meet the preset conditions. The first online module is connected to the second acquisition module, and in response to the trial data and feedback results meeting the preset conditions, the target report component is online to upgrade the WEB system.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to implement the method for upgrading a WEB system according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for upgrading a WEB system according to any one of claims 1 to 7 is implemented.

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