Data query method and device, medium and program product

Abstract

The invention discloses a data query method and device, a medium and a program product, and relates to the field of financial science and technology. The method comprises the following steps: obtaining query features and visual indication information input by a query person; according to the query feature, querying an initial query result matched with the query feature from user data of a distributed column database, the distributed column database being used for storing the user data collected from a plurality of data sources by using a real-time stream batch processing technology; processing the initial query result according to a data processing mode matched with the visual indication information to obtain a visual query result; and displaying the visual query result. According to the data query method, the real-time user data matched with the query features can be efficiently queried, the visual query result can be flexibly displayed according to the visual requirements of query personnel, and the readability of the query result is improved.

Description

Technical Field

[0001] The present invention relates to the field of distributed technology, and in particular to a data query method, device, medium and program product. Background Art

[0002] In the financial sector, as the banking industry undergoes digital transformation, users generate a large amount of user data through online banking channels. This user data includes information such as transactions, visits, inquiries, and spending. To facilitate subsequent operations such as subsequent inquiries, this user data needs to be stored in a database.

[0003] In some scenarios, a query operator needs to retrieve some user data from a database. The query process generally involves the following: the query operator enters a query request into a computer device; the computer device sends the query request to the database; and the database returns the query results to the computer device based on the query request.

[0004] However, since the databases currently storing user data are generally relational databases, the query efficiency is low, and the user data stored in the database is not real-time data. Therefore, the data query method in the above process has low query efficiency and cannot query real-time user data. Summary of the Invention

[0005] The present invention provides a data query method, device, medium and program product to solve the technical problems of low query efficiency and inability to query real-time user data in data query methods in related technologies.

[0006] According to one aspect of the present invention, a data query method is provided, the method comprising:

[0007] Obtaining query features and visual indication information input by the query operator;

[0008] According to the query characteristics, searching for initial query results that match the query characteristics from user data in a distributed column-based database; wherein the distributed column-based database is used to store user data collected from multiple data sources using real-time stream batch processing technology;

[0009] Processing the initial query result according to a data processing method that matches the visualization indication information to obtain a visualization query result;

[0010] The visual query result is displayed.

[0011] According to another aspect of the present invention, a data query device is provided, the device comprising:

[0012] An acquisition module is used to obtain the query features and visual indication information input by the query operator;

[0013] a query module configured to query, based on the query characteristics, user data in a distributed column-based database for initial query results that match the query characteristics; wherein the distributed column-based database is configured to store user data collected from multiple data sources using real-time stream batch processing technology;

[0014] A visualization processing module, configured to process the initial query result according to a data processing method matching the visualization indication information to obtain a visualization query result;

[0015] The display module is used to display the visual query results.

[0016] According to another aspect of the present invention, an electronic device is provided, comprising:

[0017] at least one processor; and

[0018] a memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores a computer program that can be executed by the at least one processor. The computer program is executed by the at least one processor so that the at least one processor can execute the data query method described in any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and the computer program is configured to enable a processor to implement the data query method according to any embodiment of the present invention when executed.

[0021] According to another aspect of the present invention, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the data query method according to any embodiment of the present invention.

[0022] The technical solution of an embodiment of the present invention includes: obtaining query features and visual indication information input by a query operator; searching for initial query results that match the query features from user data in a distributed column-based database based on the query features, wherein the distributed column-based database is used to store user data collected from multiple data sources using real-time stream batch processing technology; processing the initial query results according to a data processing method that matches the visual indication information to obtain visual query results; and displaying the visual query results. This data query method has the following technical effects: on the one hand, user data in this embodiment is stored in the distributed column-based database. Since the distributed column-based database can quickly process massive amounts of data, the initial query results can be quickly determined during the query process, thereby improving query efficiency; on the other hand, the distributed column-based database in this embodiment stores user data collected from multiple data sources using real-time stream batch processing technology, so the initial query results are real-time user data that match the query features; on the other hand, this embodiment can process the initial query results according to the visual indication information input by the query operator to obtain visual query results, thereby improving the flexibility of query result display and the readability of the query results, providing decision support for managers. Therefore, this data query method can efficiently query real-time user data that matches the query features, and can flexibly display the visual query results according to the visualization needs of the query personnel, thereby improving the readability of the query results.

[0023] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 This is a flow chart of a data query method provided by an embodiment of the present invention;

[0026] Figure 2 It is a schematic diagram that visualizes the query results;

[0027] Figure 3 is another diagram for visualizing query results;

[0028] Figure 4 is a flow chart of another data query method provided by an embodiment of the present invention;

[0029] Figure 5 This is a schematic diagram of the architecture of a data query system provided by an embodiment of the present invention;

[0030] Figure 6 This is a structural diagram of a data query device provided by an embodiment of the present invention;

[0031] Figure 7 It is a structural diagram of an electronic device for implementing the data query method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the term "comprising" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The acquisition, storage, use, processing, etc. of data in the embodiments of the present invention comply with the relevant provisions of national laws and regulations.

[0034] The following first explains the relevant terms involved in this embodiment.

[0035] Distributed column-based database: A high-performance database that uses a distributed architecture and column-based storage. It has the ability to quickly query, store, and process massive amounts of data, making it an ideal choice for handling massive amounts of banking data.

[0036] Real-time stream batch processing technology: By streaming and processing data in real time, it enables instant data analysis and decision-making. Based on real-time stream batch processing technology, data is processed the moment it enters the data stream, providing strong real-time performance and suitable for scenarios with high data processing requirements.

[0037] Data preprocessing: Clean, filter, convert and perform other operations on raw data to ensure data accuracy and consistency.

[0038] Funnel analysis: A method of analyzing conversion rates and user churn by evaluating user behavior at different stages of a specific process or path.

[0039] Personalized recommendations: personalized financial product or service recommendations generated by algorithms based on user data.

[0040] Visualization: The process of using computer graphics and image processing techniques to convert data into graphics or images and display them on the screen.

[0041] Figure 1 This is a flow chart of a data query method provided by an embodiment of the present invention. This embodiment is applicable to scenarios where user data is queried. The method can be executed by a data query device, which can be implemented in the form of hardware and / or software. The data query device can be configured in an electronic device, such as a computer device. Figure 1 As shown, the method includes the following steps 101 to 104.

[0042] Step 101: Acquire query features and visual indication information input by the query operator.

[0043] The data query method provided in this embodiment can be applied to financial services. Optionally, the querying personnel in this embodiment can be staff members of a financial institution. The query characteristics in this embodiment refer to relevant characteristics of users participating in financial activities. Financial activities in this embodiment may include financing activities, investment activities, financial market transactions, risk management, asset management, and payment and settlement.

[0044] For example, the query features in this embodiment may include: time range, transaction amount, and trading platform. For another example, the query features in this embodiment may include: time range, financial product identifier, and trading volume. For another example, the query features in this embodiment may include: user identifier and trading platform.

[0045] In order to improve the query flexibility, in addition to obtaining the query features input by the query operator, this embodiment can also obtain the visual indication information input by the query operator. The visual indication information in this embodiment is used to display at least the query results.

[0046] In one implementation, the visual indication information includes a funnel chart display method. In another implementation, the visual indication information includes a line chart, a histogram, a column chart, a pie chart, or other display methods other than the funnel chart.

[0047] Step 102: Based on the query characteristics, an initial query result matching the query characteristics is searched from user data in a distributed column-based database.

[0048] Among them, the distributed columnar database is used to store user data collected from multiple data sources using real-time stream batch processing technology.

[0049] In order to improve query efficiency, the user data in this embodiment is stored in a distributed column-based database. Based on the performance of the distributed column-based database, efficient storage and fast query of massive data can be achieved.

[0050] In this embodiment, user data refers to the relevant data generated by each user during their participation in financial activities. To improve the real-time nature of user data, this embodiment can collect real-time user data from multiple data sources based on real-time stream batch processing technology and store it in a distributed column-based database.

[0051] It can be understood that the initial query result in this embodiment includes real-time user data that matches the query characteristics.

[0052] Step 103: Process the initial query result according to the data processing method that matches the visualization instruction information to obtain a visualization query result.

[0053] The initial query result in this embodiment may be data represented in the form of a matrix, table, array, etc., which has a low degree of visualization. In step 103, the initial query result may be visualized.

[0054] After the initial query result is obtained, the initial query result is processed according to the data processing method matched with the visualization indication information to obtain a visualization query result.

[0055] In one implementation, in a scenario where the visual indication information includes a funnel plot display method, the visual query result includes a funnel plot of the initial query result. The implementation process of step 103 is: performing funnel plot processing on the initial query result according to a funnel plot processing method that matches the funnel plot display method to obtain a funnel plot of the initial query result. Figure 2 It is a diagram that visualizes the query results. Figure 2 As shown, it shows a funnel diagram 21 of the initial query results.

[0056] In another implementation, in a scenario where the visual indication information includes a line graph display mode, the visual query result includes: a line graph of the initial query result. The implementation process of step 103 is: performing line graph processing on the initial query result according to a line graph processing mode that matches the line graph display mode to obtain a line graph of the initial query result. Figure 3is another diagram to visualize the query results. Figure 3 As shown, it shows a line graph 31 of the initial query results. Figure 3 The horizontal axis is time and the vertical axis is data. The line graph 31 can represent the user's behavior trend and change.

[0057] Step 104: Display the visual query results.

[0058] After obtaining the visual query results, the visual query results can be displayed on a computer device to meet the demand for real-time decision support.

[0059] Optionally, the data query method provided in this embodiment further includes the following steps: analyzing the funnel plot of the initial query result to obtain a funnel analysis result of the funnel plot of the initial query result; and displaying the funnel analysis result. In this embodiment, the funnel analysis result refers to the result obtained after analyzing the funnel plot of the initial query result.

[0060] In one implementation, when the initial query result is a user-based query result, that is, when the initial query result represents data related to a specific user, the funnel analysis result can represent the user's behavior pattern. Based on this, personalized recommendations can be made for the user.

[0061] Furthermore, the data query method provided in this embodiment also includes the following steps: inputting the initial query results and the funnel analysis results into a pre-trained recommendation model to obtain financial products and financial services output by the recommendation model.

[0062] This implementation method can generate personalized financial product and service recommendations based on the funnel analysis results and initial query results, and according to the user's real-time characteristics and real-time behavior patterns, using machine learning and data mining algorithms, thus achieving real-time recommendations and improving the accuracy of recommendations, thereby enhancing user experience and satisfaction.

[0063] In another implementation, when the initial query result is a query result based on the process dimension, that is, when the initial query result represents the relevant data of a certain process, the funnel analysis result can represent the flow data and conversion rate of the process. Based on this, the bottlenecks and room for improvement in the process can be identified. Optionally, the process here can be, for example, the card binding process, the fund purchase process, etc. Please continue to refer to Figure 2 , Figure 2 In this case, it means the data related to each node in a process including 5 nodes. Figure 2 Analyze and get Figure 2 Related funnel analysis results.

[0064] Furthermore, the data query method provided in this embodiment further includes the following steps: inputting the initial query results and the funnel analysis results into a pre-trained process improvement model to obtain improvement suggestions output by the process improvement model. For example, the improvement suggestions in this embodiment may include merging certain process nodes, deleting certain process nodes, etc.

[0065] This implementation method can obtain process improvement suggestions based on the funnel analysis results and the initial query results using the process improvement model, thereby promoting the efficient operation of subsequent processes.

[0066] The recommendation model and process improvement model in this embodiment are artificial intelligence models trained based on machine learning algorithms.

[0067] The data query method provided by this embodiment includes: obtaining query features and visual indication information input by a query operator; searching for initial query results that match the query features from user data in a distributed column-based database based on the query features, wherein the distributed column-based database is used to store user data collected from multiple data sources using real-time stream batch processing technology; processing the initial query results according to a data processing method that matches the visual indication information to obtain visual query results; and displaying the visual query results. The data query method has the following technical effects: on the one hand, the user data in this embodiment is stored in the distributed column-based database. Since the distributed column-based database can quickly process massive amounts of data, the initial query results can be quickly determined during the query process, thereby improving query efficiency; on the other hand, the distributed column-based database in this embodiment stores user data collected from multiple data sources using real-time stream batch processing technology, so the initial query results are real-time user data that match the query features; on the other hand, this embodiment can process the initial query results according to the visual indication information input by the query operator to obtain visual query results, thereby improving the flexibility of query result display and the readability of the query results, providing decision support for managers. Therefore, this data query method can efficiently query real-time user data that matches the query features, and can flexibly display the visual query results according to the visualization needs of the query personnel, thereby improving the readability of the query results.

[0068] Figure 4 This is a flow chart of another data query method provided by an embodiment of the present invention. Figure 1 Based on the embodiment shown and various optional implementations, a detailed description is given of how to store user data in a distributed column-based database and how to query user data from the distributed column-based database based on query characteristics. Figure 4 As shown, the data query method provided by this embodiment includes the following steps 401 to 407.

[0069] Step 401: Use real-time stream batch processing technology to collect user data from multiple data sources.

[0070] The multiple data sources in this embodiment may include: a big data platform, a data lake warehouse, and a data mart, etc.

[0071] When collecting data, a real-time stream batch processing framework is used to collect user data in real time.

[0072] One possible collection process could be to create topics using stream batch processing technology, publish data from various data sources as messages to the corresponding topics, and then use consumers in real-time stream batch processing technology to subscribe to the topics and receive the real-time data stream.

[0073] Step 402: After pre-processing the collected user data, the pre-processed user data is stored in a distributed column database.

[0074] The preprocessing in this embodiment includes operations such as data cleaning, null value filling, and outlier deletion. Preprocessing the collected user data can ensure the accuracy of the user data.

[0075] The implementation process of step 402 may be to create a real-time stream batch processing application to perform pre-processing operations such as data cleaning, deduplication, and format conversion on the data stream to ensure data accuracy and consistency.

[0076] Optionally, in step 402, data features may be extracted based on the preprocessed user data. In this embodiment, data features refer to features in the user data that can be used as query features. Furthermore, data feature extraction from the preprocessed user data may be performed using a real-time stream batch processing module library.

[0077] In step 402, the pre-processed user data can be stored in a distributed column-based database in real time. In the distributed column-based database of this embodiment, data tables are defined. Each data table includes a data structure and a storage strategy to meet the needs of query and analysis. The data structure in this embodiment refers to the structure of the table, such as the table name, field name, primary key, foreign key, index, etc. The storage strategy in this embodiment includes the storage duration, the number of replicas, and the location of the replicas.

[0078] Optionally, in this embodiment, the data table is partitioned and indexed according to data characteristics and query requirements to optimize query performance.

[0079] Step 403: Acquire the query features and visual indication information input by the query operator.

[0080] The implementation process and technical principle of step 403 are similar to those of step 101 and will not be repeated here.

[0081] Step 404: Generate a target query statement including the query features according to the query features.

[0082] In this embodiment, in order to further improve query efficiency, a target query statement including the query feature can be generated based on the query feature. For example, the target query statement in this embodiment can be a query statement written in structured query language.

[0083] Optionally, in order to improve the success rate of the query, the implementation method of step 404 includes: verifying the query features according to a pre-set feature set; if the verification passes, generating a target query statement including the query features according to the query features; if the verification fails, prompting a query error message.

[0084] A possible verification process may be: determining whether the query feature is included in the feature set; if included, determining that the verification is passed; if not included, determining that the verification is failed.

[0085] In a scenario where there are multiple query features, another possible verification process may be: determining whether the query feature is included in the feature set, and determining whether the multiple query features can be combined for query based on pre-set feature combination information; if it is determined that the query feature is included in the feature set and the multiple query features can be combined for query, then determining that the verification has passed; if it is determined that the feature set does not completely include the query feature, or at least two of the multiple query features cannot be combined for query, then determining that the verification has failed.

[0086] After verifying that the query has passed, a target query statement containing the query features is generated based on the query features. This implementation method verifies the query features, avoiding scenarios where the distributed column-based database cannot successfully return data due to the generated target query statement if the query features fail verification, thereby improving the query success rate.

[0087] Step 405: Send the target query statement to the distributed column-based database, and obtain the initial query results that match the query features in the user data fed back by the distributed column-based database.

[0088] Among them, the distributed columnar database is used to store user data collected from multiple data sources using real-time stream batch processing technology.

[0089] After generating the target query statement, it is sent to the distributed column-based database, leveraging its fast query and analysis capabilities to retrieve initial query results that match the query features. After receiving the target query statement, the distributed column-based database extracts the query features and retrieves initial query results that match the query features from the stored, pre-processed user data.

[0090] Step 406: Process the initial query result according to the data processing method that matches the visualization instruction information to obtain a visualization query result.

[0091] In scenarios where visual indicators include funnel charts, you can analyze customer behavior within a specific business process based on initial query data and funnel analysis results, assessing conversion rates and user churn. Retrieve user behavior data for each stage of the process. Calculate the conversion rate for each stage to assess how users transition from one node to the next. Funnel analysis can identify bottlenecks and churn points within the process, helping to optimize business processes.

[0092] Step 407: Display the visual query results.

[0093] The implementation process and technical principles of step 406 and step 103, and step 407 and step 104 are similar, and will not be repeated here.

[0094] Further, with Figure 1 Similar to the illustrated embodiment and various optional implementations, the data query method provided in this embodiment may also include a recommendation step: generating personalized financial product and service recommendations for the user based on the initial query results and funnel analysis results. Machine learning and data mining algorithms, such as collaborative filtering and clustering, are combined with a machine learning library to generate a recommendation model. The recommendation model is used to generate personalized financial product and service recommendations based on the initial query results and funnel analysis results. Optionally, the recommendation results can be provided to a front-end application via an application programming interface (API) or other means for subsequent invocation.

[0095] Optionally, the data query method of this embodiment can adopt a distributed architecture to support horizontal expansion to adapt to the ever-increasing amount of data and user scale.

[0096] The following describes the data query method provided by this embodiment from a system perspective. Figure 5 This is a schematic diagram of the architecture of a data query system provided by an embodiment of the present invention. Figure 5 The data query method is applied to financial institutions as an example. Figure 5As shown in Figure 1, the data query system consists of a data acquisition module, a real-time stream batch processing module, a data storage module, a data analysis module, a funnel analysis module, a recommendation engine, and a visualization module. These modules work together to process and analyze user data in real time, providing decision support and personalized recommendations for financial institutions.

[0097] The data collection module adopts real-time stream batch processing technology to collect user data from multiple data sources, which is used to implement step 401.

[0098] The real-time stream batch processing module performs data preprocessing and feature extraction. Figure 5 The features proposed by the real-time stream batch processing module include participation, winning, browsing, clicking, outbound calls, credit cards, credit, and financial management.

[0099] The data storage module automatically generates table creation statements, creates local and distributed tables, and synchronizes them. It also determines replication mechanisms and data sharding rules. It uses a distributed columnar database to store massive amounts of data.

[0100] Distributed column-based databases have the following features: column-based storage, which means only necessary column data is read during data query, resulting in high query efficiency; distributed computing; multi-core parallel processing; and support for structured query language syntax.

[0101] The real-time stream batch processing module, the data storage module, and the distributed column database jointly implement step 402.

[0102] During the data query process, the front end obtains the query features and visual indication information input by the query operator, and obtains the query submission request input by the query operator to implement step 403.

[0103] The data analysis module implements parameter reading, feature verification, syntax parsing, and query statement generation, implementing step 404. The data analysis module also sends the query statement to the distributed column database and obtains the initial query results that match the query features in the user data fed back by the distributed column database, implementing step 405.

[0104] The visualization module uses visualization tools to obtain visualized query results and recommended results, implementing step 406 and providing intuitive decision support for financial institutions and users. The visualization module designs a visualization dashboard based on the needs of the query operator. Using visualization tools, the initial query results are displayed in visual formats such as charts and dashboards. Managers can view real-time data and analysis results through the visualization interface to support decision-making. The visualization module can also generate customized data reports based on the business needs of financial institutions, providing reference for financial institution managers and decision makers.

[0105] The funnel analysis module is used to perform funnel analysis on the visual query results. The recommendation engine is used to provide users with personalized financial products and financial services.

[0106] In the financial business scenario, Figure 5 The architecture of the data query system shown realizes the combination of real-time stream batch processing technology and distributed columnar database, which can process and analyze user behavior data in real time, provide personalized services to users and decision support to financial institutions, and meet the needs of financial institutions for real-time analysis and decision-making.

[0107] The data query method provided in this embodiment has the following technical effects: on the one hand, by integrating the efficient storage and query capabilities of the distributed column database with the real-time stream batch processing technology, efficient storage and fast query of real-time data are achieved; on the other hand, in the data query process, according to the query characteristics, a target query statement including the query characteristics is generated, the target query statement is sent to the distributed column database, and the initial query results matching the query characteristics in the user data fed back by the distributed column database are obtained. The initial query results are obtained from the distributed column database through the target query statement, thereby further improving the query efficiency.

[0108] Figure 6 This is a structural diagram of a data query device provided by an embodiment of the present invention. The device is set in a computer device. Figure 6 As shown, the data query device provided by this embodiment includes the following modules: an acquisition module 61 , a query module 62 , a visualization processing module 63 and a display module 64 .

[0109] The acquisition module 61 is used to acquire the query features and visual indication information input by the query operator.

[0110] The query module 62 is configured to query the user data in the distributed column-based database for initial query results that match the query characteristics based on the query characteristics.

[0111] The distributed column-based database is used to store user data collected from multiple data sources using real-time stream batch processing technology.

[0112] The visualization processing module 63 is configured to process the initial query result according to a data processing method that matches the visualization indication information to obtain a visualization query result.

[0113] The display module 64 is configured to display the visual query results.

[0114] In one embodiment, the device further includes: an acquisition module and a pre-processing module.

[0115] The acquisition module is configured to acquire user data from the plurality of data sources using real-time stream batch processing technology. The preprocessing module is configured to preprocess the acquired user data and store the preprocessed user data in the distributed column-based database.

[0116] In one embodiment, the query module 62 is specifically used to: generate a target query statement including the query feature based on the query feature; send the target query statement to the distributed column database, and obtain an initial query result that matches the query feature in the user data fed back by the distributed column database.

[0117] In one embodiment, in terms of generating a target query statement including the query feature based on the query feature, the query module 62 is specifically used to: verify the query feature according to a preset feature set; if the verification passes, generate a target query statement including the query feature based on the query feature; if the verification fails, prompt a query error message.

[0118] In one embodiment, the visual indication information includes a funnel plot display method, and the visual query result includes a funnel plot of the initial query result. The visualization processing module 63 is specifically configured to perform funnel plot processing on the initial query result according to a funnel plot processing method that matches the funnel plot display method to obtain a funnel plot of the initial query result.

[0119] In one embodiment, the device further comprises an analysis module for analyzing the funnel plot of the initial query result to obtain a funnel analysis result of the funnel plot of the initial query result. The display module 64 is further configured to display the funnel analysis result.

[0120] In one embodiment, the device further includes a first determination module, configured to input the initial query result and the funnel analysis result into a pre-trained recommendation model to obtain financial products and financial services output by the recommendation model.

[0121] In one embodiment, the apparatus further includes a second determining module configured to input the initial query result and the funnel analysis result into a pre-trained process improvement model to obtain improvement suggestions output by the process improvement model.

[0122] The data query device provided by the embodiment of the present invention can execute the data query method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0123] Figure 71 is a schematic diagram of an electronic device that implements the data query method of an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0124] like Figure 7 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0125] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0126] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors that run machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the data query method.

[0127] In some embodiments, the data query method can be implemented as a computer program that is tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the data query method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the data query method in any other appropriate manner (e.g., by means of firmware).

[0128] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0129] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0130] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0131] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0132] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0133] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0134] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the data query method provided in any embodiment of the present invention.

[0135] The computer program product may be implemented in a computer program code for performing the operations of the present invention written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​and conventional procedural programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0136] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0137] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A data query method, characterized in that: The method comprises: Obtaining query features and visual indication information input by the query operator; According to the query characteristics, searching for initial query results that match the query characteristics from user data in a distributed column-based database; wherein the distributed column-based database is used to store user data collected from multiple data sources using real-time stream batch processing technology; Processing the initial query result according to a data processing method that matches the visualization indication information to obtain a visualization query result; The visual query result is displayed.

2. The method according to claim 1, characterized in that The method further comprises: Using real-time stream batch processing technology to collect user data from the multiple data sources; After preprocessing the collected user data, the preprocessed user data is stored in the distributed column database.

3. The method according to claim 1, characterized in that The step of searching the user data in the distributed column-based database for an initial query result that matches the query feature according to the query feature includes: generating a target query statement including the query feature according to the query feature; The target query statement is sent to the distributed column database, and an initial query result matching the query feature in the user data fed back by the distributed column database is obtained.

4. The method according to claim 3, characterized in that Generating a target query statement including the query feature according to the query feature includes: Verifying the query features according to a preset feature set; If the verification passes, generating a target query statement including the query feature according to the query feature; If the verification fails, an error message will be displayed.

5. The method according to any one of claims 1 to 4, characterized in that The visual indication information includes a funnel chart display method, and the visual query results include: a funnel chart of the initial query results; The processing of the initial query result according to the data processing method matching the visualization indication information to obtain the visualization query result includes: According to a funnel chart processing method that matches the funnel chart display method, funnel chart processing is performed on the initial query result to obtain a funnel chart of the initial query result.

6. The method according to claim 5, characterized in that The method further comprises: Analyze the funnel plot of the initial query result to obtain a funnel analysis result of the funnel plot of the initial query result; The funnel analysis results are displayed.

7. The method according to claim 6, characterized in that The method further comprises: Inputting the initial query results and the funnel analysis results into a pre-trained recommendation model to obtain financial products and financial services output by the recommendation model; or The initial query result and the funnel analysis result are input into a pre-trained process improvement model to obtain improvement suggestions output by the process improvement model.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor. The computer program is executed by the at least one processor to enable the at least one processor to perform the data query method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and the computer program is used to enable a processor to implement the data query method according to any one of claims 1 to 7 when executed.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the computer program implements the data query method according to any one of claims 1 to 7.

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