Data analysis system

Abstract

The application belongs to the technical field of computers and provides a data analysis system, comprising: a registration module for registering pages, interfaces and events; wherein the registration of the pages obtains page registration information, the page registration information comprising a page route and a page ID; a front-end collection module for collecting data generated by a client through a full-buried-point method; the data generated by the client comprising a page route; a back-end collection module for preprocessing the data generated by the client and sending the preprocessed data to a real-time data warehouse; the preprocessing steps comprising: mapping the page route to a page ID according to the page registration information; an analysis module for obtaining data from the real-time data warehouse and performing data analysis using the data obtained from the real-time data warehouse. The data collection method of the application is efficient, in addition, the application can also quickly count data and improve the accuracy of data counting.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a data analysis system. Background Technology

[0002] Data analysis platforms, also known as data analysis systems, possess rich capabilities in data collection, data management, data analysis, and information presentation, making them indispensable tools for decision support. Currently, different data analysis systems on the market each have their own shortcomings. For example, some existing data analysis systems require manual insertion of tracking code into the business system code for each scenario to collect data. This data collection method has the following two drawbacks: (1) Significant modifications: Manually inserting code requires modifying the business code for data collection, polluting the business system code and posing a risk of impacting the business system. (2) Poor scalability: For each business scenario requiring data collection, the business code needs to be manually modified, resulting in low efficiency and poor scalability. Furthermore, the functions provided by existing data analysis systems are insufficient to meet user needs. Therefore, there is an urgent need to provide a more comprehensive data analysis system. Summary of the Invention

[0003] The main purpose of this application is to provide a more comprehensive data analysis system.

[0004] In a first aspect, embodiments of this application provide a data analysis system, including:

[0005] The registration module is used to register pages, interfaces, and events; among them, registering a page yields page registration information, which includes the page route and page ID.

[0006] The front-end data collection module is used to collect data generated by the client using a full-tracking method; the data generated by the client includes page routes.

[0007] The backend data collection module is used to preprocess the data generated by the client and send the preprocessed data to the real-time data warehouse; the preprocessing steps include: mapping the page route to a page ID according to the page registration information;

[0008] The analysis module is used to acquire data from the real-time data warehouse and perform data analysis using the data acquired from the real-time data warehouse.

[0009] Furthermore, the registration module includes a page registration submodule, which includes:

[0010] The first receiving unit is used to receive page registration requests;

[0011] The first response unit is used to respond to the page registration request, open the registration page, and allow the user to enter registration information in the input boxes of the registration page; wherein, the registration page includes a page ID input box, an Android path input box, an iOS path input box, and an H5 path input box.

[0012] The second receiving unit is used to receive page registration activation requests;

[0013] The second response unit is used to respond to the page registration activation request and register the page according to the registration information.

[0014] Furthermore, the analysis module includes a user trajectory analysis submodule, which includes:

[0015] The third receiving unit is used to receive requests to open the user activity trajectory query page;

[0016] The third response unit is used to respond to the request to open the user activity trajectory query page, open the user activity trajectory query page, so that the user can enter query conditions in the input box of the user activity trajectory query page; the user activity trajectory query page includes a user ID input box and a time range input box;

[0017] The fourth receiving unit is used to receive user activity trajectory generation requests;

[0018] The fourth response unit is used to respond to the user activity trajectory generation request, obtain the corresponding user behavior data from the real-time data warehouse according to the user ID entered in the user ID input box and the time range entered in the time range input box, and generate the user activity trajectory in chronological order.

[0019] Furthermore, the analysis module includes a funnel model creation submodule, which includes:

[0020] The fifth receiving unit is used to receive funnel creation requests;

[0021] The fifth receiving unit is used to respond to the funnel creation request and open the funnel creation page; wherein the funnel creation page includes an option to add steps;

[0022] The sixth receiving unit is used to receive requests to add steps;

[0023] The sixth response unit is used to respond to the add step request and open the add step page; wherein, the add step page includes an event type input box, a page name input box, a page ID input box, a data source input box, and other input boxes corresponding to the event type;

[0024] The splicing unit is used to splice all the steps in the order they are added to generate a funnel model from the initial event to the final event.

[0025] Furthermore, the analysis module also includes a funnel analysis submodule, which includes:

[0026] The seventh receiving unit is used to receive funnel analysis requests;

[0027] The seventh response unit is used to respond to the funnel analysis request, and to perform user behavior analysis using the information entered in the input boxes of each step page in the funnel model, and generate the analysis result of the funnel shape.

[0028] Furthermore, the analysis module includes a risk location and tracking submodule, which includes:

[0029] The eighth receiving unit is used to receive requests to open the risk location tracking page;

[0030] The eighth response unit is used to respond to the request to open the risk location tracking page, and open the risk location tracking page so that the user can input tracking conditions; wherein, the risk location tracking page includes any one or more of the following: username input box, name input box, mobile phone number input box, response tag name input box, trigger frequency input box, and response type input box;

[0031] The ninth receiving unit is used to receive risk query requests;

[0032] The ninth response unit is used to respond to the risk query request, query risks that match the information entered into the input box of the risk location and tracking page, and generate a risk list.

[0033] Furthermore, each row in the risk list includes both a ban and an unban option.

[0034] Furthermore, the data analysis system also includes a display module for displaying the monitored data and issuing alarm prompts when alarm conditions are met; wherein, the monitored data includes raw data and / or data obtained through statistical analysis.

[0035] Furthermore, the real-time data warehouse includes:

[0036] The raw data layer is used to store business data synchronously uploaded by the acquisition module and the database, and to keep the data in its original state.

[0037] The dimensional data layer is used to store the data dimensions that need to be analyzed.

[0038] The data detail layer is used to store detailed data.

[0039] The data aggregation layer is used to store the indicator data that has already undergone fine-grained aggregation;

[0040] The data application layer is used to provide the data required by applications.

[0041] Furthermore, the technology stack used to build the data warehouse includes StarRocks, Apache Flink, Elasticsearch, and Apache Kafka.

[0042] This application's embodiments involve collecting data using a full-tracking method. Full-tracking refers to the SDK using the client's lifecycle functions and various event handling functions to collect pre-defined events. Because full-tracking does not require intrusion into business logic code, it solves the problems of large modifications and poor scalability associated with existing data collection methods. Data collection using full-tracking only requires embedding the SDK, greatly avoiding the complex work of re-tracking due to changes in requirements or tracking errors. Furthermore, by registering pages, interfaces, and events, the required data can be retained. Additionally, by mapping page routes to page IDs, rapid data statistics are achieved, and the accuracy of data statistics is improved. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the structure of the data analysis system provided in the embodiments of this application;

[0044] Figure 2 This is a schematic diagram of page registration provided in an embodiment of this application;

[0045] Figure 3 This is a schematic diagram of the user activity trajectory query page provided in the embodiments of this application.

[0046] Figure 4 This is a partial schematic diagram of the funnel creation page provided in an embodiment of this application;

[0047] Figure 5 This is a schematic diagram of user behavior in the funnel model provided in the embodiments of this application;

[0048] Figure 6 This is a schematic diagram of the risk location tracking page provided in an embodiment of this application;

[0049] Figure 7 This is a schematic diagram of the logical architecture of the real-time data warehouse provided in the embodiments of this application;

[0050] Figure 8 This is a schematic diagram of the technical architecture of the real-time data warehouse provided in the embodiments of this application;

[0051] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0053] Those skilled in the art will understand that, unless explicitly stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this application means the presence of features, integers, steps, operations, elements, modules, and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, modules, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any modules and all combinations of one or more associated listed items.

[0054] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0055] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0056] Please see Figure 1 This application provides a data analysis system, including:

[0057] Registration module 1 is used to register pages, interfaces, and events; wherein, registering a page yields page registration information, which includes page route and page ID;

[0058] Front-end data collection module 2 is used to collect data generated by the client using a full-tracking method; the data generated by the client includes page routes;

[0059] The backend acquisition module 3 is used to preprocess the data generated by the client and send the preprocessed data to the real-time data warehouse; the preprocessing steps include: mapping the page route to a page ID according to the page registration information;

[0060] Analysis module 4 is used to acquire data from the real-time data warehouse and perform data analysis using the data acquired from the real-time data warehouse.

[0061] As described in registration module 1 above, the page registration information needs to include page routes and page IDs. This is because the same page may exist on H5, iOS, and Android platforms, but the page routes on each platform are different. To ensure unified data processing, the page routes on all three platforms need to be bound and mapped to a unified page ID. It should be understood that the information within the same page registration information is interdependent. For example, in this embodiment, the page routes and page IDs in the page registration information are interdependent. For instance, the page routes and page IDs in page registration information A are interdependent, and the page routes and page IDs in page registration information B are interdependent.

[0062] As described above, front-end data collection module 2 collects data generated by the client using a full-tracking method, which is equivalent to collecting all pages, interfaces, and / or events on the client, thus eliminating the need for individual configuration modifications for each page. After registering the required pages, interfaces, and events, only the registered, i.e., the required data, can be retained during data cleaning (preprocessing). If all pages, interfaces, and events are registered, all data generated by the system will be retained. The front-end data collection module 2 can be a client-side tracking SDK, which can perform global click event listening, obtain information about all controls on the page, scan and monitor the events of each control, and report the required data.

[0063] As mentioned in the backend data collection module 3 above, the same page may exist on H5, iOS, and Android platforms, but the page routes on each platform are different. Therefore, in order to facilitate data statistical analysis, during data cleaning, i.e. data preprocessing, it is necessary to map the page route of each piece of data to a unified page ID according to the mapping relationship in the page registration information. Subsequent data statistics are all based on the page ID.

[0064] This application's embodiments involve collecting data using a full-tracking method. Full-tracking refers to the SDK using the client's lifecycle functions and various event handling functions to collect pre-defined events. Because full-tracking does not require intrusion into business logic code, it solves the problems of large modifications and poor scalability associated with existing data collection methods. Data collection using full-tracking only requires embedding the SDK, greatly avoiding the complex work of re-tracking due to changes in requirements or tracking errors. Furthermore, by registering pages, interfaces, and events, the required data can be retained. Additionally, by mapping page routes to page IDs, rapid data statistics are achieved, and the accuracy of data statistics is improved.

[0065] In some embodiments, the registration module 1 includes a page registration submodule, which includes:

[0066] The first receiving unit is used to receive page registration requests;

[0067] The first response unit is used to respond to the page registration request, open the registration page, and allow the user to enter registration information in the input boxes of the registration page; wherein, the registration page includes a page ID input box, an Android path input box, an iOS path input box, and an H5 path input box.

[0068] The second receiving unit is used to receive page registration activation requests;

[0069] The second response unit is used to respond to the page registration activation request and register the page according to the registration information.

[0070] In this embodiment, it should be noted that the Android path, iOS path, and H5 path of the page are all page routes. The input boxes on the registration page not only include the page ID input box, the Android path input box, the iOS path input box, and the H5 path input box, but may also include a channel name input box and an APPid input box, such as... Figure 2 As shown, Figure 2 This is a screenshot of the registration page; the Android path input box on the page corresponds to... Figure 2The Android URL input field in the image shows the Android path to the page, while the iOS path input field corresponds to... Figure 2 The iOS URL in the text corresponds to the H5 path input box on the page. Figure 2 The H5 URL in the document. It should be noted that during page registration, all information in the page registration information needs to be mutually bound, such as... Figure 2 The page ID, Android path, iOS path, channel name, and APPid are bound together to obtain the mapping relationship between these information. The channel name can be Xiaomi App Store, 360 Mobile Assistant, etc.

[0071] This application embodiment provides a page registration function, which realizes the mapping between page routes and page IDs, making data statistics more convenient.

[0072] In some embodiments, the analysis module 4 includes a user trajectory analysis submodule, which includes:

[0073] The third receiving unit is used to receive requests to open the user activity trajectory query page;

[0074] The third response unit is used to respond to the request to open the user activity trajectory query page, open the user activity trajectory query page, so that the user can enter query conditions in the input box of the user activity trajectory query page; the user activity trajectory query page includes a user ID input box and a time range input box;

[0075] The fourth receiving unit is used to receive user activity trajectory generation requests;

[0076] The fourth response unit is used to respond to the user activity trajectory generation request, obtain the corresponding user behavior data from the real-time data warehouse according to the user ID entered in the user ID input box and the time range entered in the time range input box, and generate the user activity trajectory in chronological order.

[0077] In this embodiment of the application, the generated user activity trajectory can be placed on a user activity trajectory query page, such as... Figure 3 As shown, Figure 3 This is the user activity tracking page. Figure 3 The upper half is the input box, and the lower half is the generated user activity trajectory. Figure 3 Each node in the user activity trajectory includes the time, operation event, platform type used to perform the operation, platform version number, channel, and device type. For example: 2022-02-14, Launch App, Android, 7.0.8, Xiaomi App Store, vivo.

[0078] This application provides a module that can automatically analyze user trajectories, enabling users of the data analysis system to easily obtain the activity trajectory of target users, which helps users make correct decisions quickly and improves user experience.

[0079] In some embodiments, the analysis module 4 includes a funnel model creation submodule, which includes:

[0080] The fifth receiving unit is used to receive funnel creation requests;

[0081] The fifth receiving unit is used to respond to the funnel creation request and open the funnel creation page; wherein the funnel creation page includes an option to add steps;

[0082] The sixth receiving unit is used to receive requests to add steps;

[0083] The sixth response unit is used to respond to the add step request and open the add step page; wherein, the add step page includes an event type input box, a page name input box, a page ID input box, a data source input box, and other input boxes corresponding to the event type;

[0084] The splicing unit is used to splice all the steps in the order they are added to generate a funnel model from the initial event to the final event.

[0085] In this embodiment of the application, a partial schematic diagram of the funnel creation page is shown below. Figure 4 As shown, creating a funnel requires creating analysis conditions, which consist of various steps. Users can add the necessary steps by clicking the "Add Steps" option on the funnel creation page. Event types include browsing events and click events. Other input fields for click events include a "Select Statistics Type" input field, a "Select Button" input field, and a "Button ID" input field; the statistics type can be a button, and the "Select Button" can be a payment button. It should be understood that user behavior analysis first requires creating a data funnel. Each data funnel consists of events, which are further divided into browsing events and click events. Multiple actions occur throughout the funnel path. By treating each action as a step and piecing them together into the funnel path, the funnel model from the initial event to the final event can be created.

[0086] In some embodiments, the analysis module 4 further includes a funnel analysis submodule, which includes:

[0087] The seventh receiving unit is used to receive funnel analysis requests;

[0088] The seventh response unit is used to respond to the funnel analysis request, and to perform user behavior analysis using the information entered in the input boxes of each step page in the funnel model, and generate the analysis result of the funnel shape.

[0089] It's important to note that, generally speaking, the earlier steps (pages) in a business process receive more page views than subsequent steps (pages), with later steps receiving fewer page views. This creates a funnel-shaped model, as shown below. Figure 5 As shown. This application embodiment uses a funnel model to observe the conversion and drop-off between multiple key behaviors during the user's product usage process (path), which can scientifically reflect user behavior and conversion status.

[0090] In some embodiments, the analysis module 4 includes a risk location and tracking submodule, which includes:

[0091] The eighth receiving unit is used to receive requests to open the risk location tracking page;

[0092] The eighth response unit is used to respond to the request to open the risk location tracking page, and open the risk location tracking page so that the user can input tracking conditions; wherein, the risk location tracking page includes any one or more of the following: username input box, name input box, mobile phone number input box, response tag name input box, trigger frequency input box, and response type input box;

[0093] The ninth receiving unit is used to receive risk query requests;

[0094] The ninth response unit is used to respond to the risk query request, query risks that match the information entered into the input box of the risk location and tracking page, and generate a risk list.

[0095] In this embodiment of the application, the risk list can also be placed on the risk location and tracking page, such as... Figure 6 As shown, the upper part is an input box, and the lower part is a risk list. The response label name can be something like "more than 3 orders placed by the terminal within 10 minutes," and can be designed according to the actual situation. The response type can be a warning, alarm, etc.

[0096] This application provides a module that can track and locate risks, enabling system users to promptly identify potential risks.

[0097] In some embodiments, each row of the risk list includes a blocking option and an unblocking option.

[0098] In this embodiment of the application, by setting blocking and unblocking options in each record of the risk list, such as... Figure 6As shown, risky accounts can be blocked or unblocked, thereby preventing attacks on business systems.

[0099] In some embodiments, the data analysis system further includes a display module for displaying the monitored data and issuing alarm prompts when alarm conditions are met; wherein the monitored data includes raw data and / or data obtained through statistical analysis.

[0100] This application embodiment enables data analysis users to grasp the business situation in real time by setting up a module that can display data.

[0101] In some embodiments, the real-time data warehouse includes:

[0102] The raw data layer is used to store business data synchronously uploaded by the acquisition module and the database, and to keep the data in its original state.

[0103] The dimensional data layer is used to store the data dimensions that need to be analyzed.

[0104] The data detail layer is used to store detailed data.

[0105] The data aggregation layer is used to store the indicator data that has already undergone fine-grained aggregation;

[0106] The data application layer is used to provide the data required by applications.

[0107] The logical architecture diagram of a real-time data warehouse is as follows: Figure 7 As shown, the raw data layer is also called the ODS (Operational Data Store) layer. The ODS layer is responsible for storing the business data synchronously uploaded by the acquisition module (specifically, the backend acquisition module in this embodiment) and the database, and maintaining the original data. It should be noted that the data collected by the acquisition module is embedded data. The dimensional data layer is also called the DIM (Dictionary Data Layer) layer. The DIM layer stores the data dimensions to be analyzed, such as user information, store information, organizational structure information, business relationship information, etc. The data detail layer is also called the DWD (Data Warehouse Detailed) layer. The DWD layer is responsible for storing detailed data. The detailed data is obtained by combining the raw data with the required dimensions, and then stored in the DWD layer to provide data for subsequent data analysis. The data aggregation layer is also called the DWS (Data Warehouse Summary) layer: it is responsible for storing the indicator data that has been aggregated in a fine-grained manner. The data application layer is also called the APP (Application) layer, which is responsible for providing the data required by the application. The data required by the application is a secondary processing of the fine-grained data, providing the data required by applications such as monitoring dashboards and real-time queries.

[0108] In some embodiments, the technology stack used to build the data warehouse includes StarRocks, Apache Flink, Elasticsearch, and Apache Kafka.

[0109] In this embodiment, data storage and computation utilize StarRocks, a next-generation, high-speed, full-scenario MPP (Massively Parallel Processing) database. Historical data storage and retrieval employ Elasticsearch, a distributed data retrieval and analysis engine. Data processing utilizes Apache Flink, a distributed real-time data processing and analysis engine. Message transmission uses Apache Kafka, a distributed message queue. Data caching utilizes Redis (RemoteDictionaryServer), an open-source, network-enabled, in-memory or persistent log-structured key-value database written in ANSI C.

[0110] In the embodiments of this application, such as Figure 8 As shown, Figure 8 This is a schematic diagram of the technical architecture of a real-time data warehouse. The Apache Kafka message queue receives data from the backend acquisition module; here, Apache Kafka serves as the storage medium for the ODS layer of the data warehouse. The Apache Flink program pulls data from the Apache Kafka message queue and, by associating it with dimensional data (DIM layer) in MySQL and Redis, stores the broadened detailed data into StarRocks and Elasticsearch. StarRocks and Elasticsearch form the DWD layer of the data warehouse. Data in StarRocks is used for subsequent real-time calculations in the data warehouse, while data in Elasticsearch is used for retrieving detailed data. StarRocks uses materialized views to achieve fine-grained, lightweight aggregation of data, thus creating the DWS layer of the data warehouse. Data in the DWS layer can be provided to components such as the operations platform and analytics engine in the application layer for aggregation and querying.

[0111] It should be noted that the above technology stack can be used to build a fast, massive, and reliable real-time data warehouse.

[0112] The above description is only a preferred embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural changes made based on the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A data analysis system, characterized in that, include: The registration module is used to register pages, interfaces, and events; among them, registering a page yields page registration information, which includes the page route and page ID. The front-end data collection module is used to collect data generated by the client using a full-tracking method; the data generated by the client includes page routes. The backend data collection module is used to preprocess the data generated by the client and send the preprocessed data to the real-time data warehouse; the preprocessing steps include: mapping the page route to a page ID according to the page registration information; An analysis module is used to acquire data from the real-time data warehouse and perform data analysis using the data acquired from the real-time data warehouse; The registration module includes a page registration submodule, which includes: The first receiving unit is used to receive page registration requests; The first response unit is used to respond to the page registration request, open the registration page, and allow the user to enter registration information in the input boxes of the registration page; wherein, the registration page includes a page ID input box, an Android path input box, an iOS path input box, and an H5 path input box. The second receiving unit is used to receive page registration activation requests; The second response unit is used to respond to the page registration activation request and register the page according to the registration information; The analysis module includes a funnel model creation submodule. Each data funnel consists of events, which are categorized into browsing events and click events. Multiple actions occur throughout the funnel path, and each action is treated as a step and appended to the funnel path. Specifically, the funnel model creation submodule includes: The fifth receiving unit is used to receive funnel creation requests; The fifth response unit is used to respond to the funnel creation request and open the funnel creation page; wherein the funnel creation page includes an option to add steps; The sixth receiving unit is used to receive requests to add steps; The sixth response unit is used to respond to the add step request and open the add step page; wherein, the add step page includes an event type input box, a page name input box, a page ID input box, a data source input box, and other input boxes corresponding to the event type; The splicing unit is used to splice all the steps in the order in which they are added to generate a funnel model from the initial event to the end event. The analysis module further includes a funnel analysis submodule, which includes: The seventh receiving unit is used to receive funnel analysis requests; The seventh response unit is used to respond to the funnel analysis request, and to perform user behavior analysis using the information entered in the input boxes of each step page in the funnel model, and generate the analysis result of the funnel shape.

2. The data analysis system according to claim 1, characterized in that, The analysis module includes a user trajectory analysis submodule, which includes: The third receiving unit is used to receive requests to open the user activity trajectory query page; The third response unit is used to respond to the request to open the user activity trajectory query page, open the user activity trajectory query page, so that the user can enter query conditions in the input box of the user activity trajectory query page; the user activity trajectory query page includes a user ID input box and a time range input box; The fourth receiving unit is used to receive user activity trajectory generation requests; The fourth response unit is used to respond to the user activity trajectory generation request, obtain the corresponding user behavior data from the real-time data warehouse according to the user ID entered in the user ID input box and the time range entered in the time range input box, and generate the user activity trajectory in chronological order.

3. The data analysis system according to claim 1, characterized in that, The analysis module includes a risk location and tracking submodule, which includes: The eighth receiving unit is used to receive requests to open the risk location tracking page; The eighth response unit is used to respond to the request to open the risk location tracking page, and open the risk location tracking page so that the user can input tracking conditions; wherein, the risk location tracking page includes any one or more of the following: username input box, name input box, mobile phone number input box, response tag name input box, trigger frequency input box, and response type input box; The ninth receiving unit is used to receive risk query requests; The ninth response unit is used to respond to the risk query request, query risks that match the information entered into the input box of the risk location and tracking page, and generate a risk list.

4. The data analysis system according to claim 3, characterized in that, Each line in the risk list includes both a ban and an unban option.

5. The data analysis system according to claim 1, characterized in that, The data analysis system also includes a display module for displaying the monitored data and issuing alarm prompts when alarm conditions are met; wherein, the monitored data includes raw data and / or data obtained through statistical analysis.

6. The data analysis system according to claim 1, characterized in that, The real-time data warehouse includes: The raw data layer is used to store business data synchronously uploaded by the acquisition module and the database, and to keep the data in its original state. The dimensional data layer is used to store the data dimensions that need to be analyzed. The data detail layer is used to store detailed data. The data aggregation layer is used to store the indicator data that has already undergone fine-grained aggregation; The data application layer is used to provide the data required by applications.

7. The data analysis system according to claim 6, characterized in that, The technology stack used to build the data warehouse includes StarRocks, Apache Flink, Elasticsearch, and Apache Kafka.

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