Data chart generation method and device

Abstract

The present disclosure provides a data chart generation method and device. The data chart generation method includes: responding to a data chart generation request, determining a template of a target data chart, attributes corresponding to the template, and a data source; acquiring data from the data source according to the attributes; generating and displaying the target data chart according to the template and the data of the attributes. The embodiments of the present disclosure can automatically generate data charts through simple configuration, thereby improving the generation efficiency of data charts.

Description

Background Art

[0002] With the increasingly complex external environment, when the scale of enterprises grows and the business forms become more and more complex, a management system and methodology that adapts to the business is needed, and it is implemented through technical means. JD.com proposed the Big Boss concept, which minimizes the operating units, making the organization relatively flexible and agile, greatly improving the operating ability and enhancing the overall competitiveness. In order to enhance the business awareness of the boss and make him more "goal-oriented" and "sense of achievement", JD.com created the Big Boss data dashboard, used data visualization technology to display data related to business management, and established a data decision-making system for managers that can provide a basis for business and management decisions.

[0003] Data visualization technology converts various abstract data into various figurative charts, that is, mapping data space to graphic space, to display graphical information to decision makers, so as to facilitate decision makers to discover the internal rules of the data and enable analysts to observe information from all aspects quickly, consistently and interactively, so as to achieve the purpose of in-depth understanding of the data.

[0004] In related technologies, the graphical display of various data is usually achieved by developing code. In this method, the encapsulation is relatively low-level, with high barriers to entry, complex implementation, large amount of code, low efficiency, poor flexibility, and a slight change in the data scenario requires a lot of time to modify. Moreover, this technology uses code to query and load database data. When the data source changes or the amount of data is large, the data ETL (Extract-Transform-Load) efficiency and query efficiency drop rapidly, the data processing time becomes longer, and it becomes increasingly difficult to achieve second-level queries.

[0005] Therefore, a data visualization method that can quickly process large amounts of data is needed to provide strong technical support for the establishment of the above-mentioned data decision-making system.

[0006] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0007] The purpose of the present disclosure is to provide a data chart generation method and a data chart generation device, which are used to overcome the problems of overly complex development of data charts, lack of flexibility, slow data loading, etc. caused by the limitations and defects of related technologies, at least to a certain extent.

[0008] According to a first aspect of an embodiment of the present disclosure, a data chart generation method is provided, comprising: responding to a data chart generation request, determining a template of a target data chart, attributes corresponding to the template, and a data source; acquiring data from the data source according to the attributes; generating and displaying the target data chart according to the template and the data of the attributes.

[0009] In an exemplary embodiment of the present disclosure, the process of generating the data chart generation request includes:

[0010] In response to the template dragging instruction, a template of the target data chart is newly created;

[0011] In response to a user's instruction to edit the attributes of the template or the data source, recording the attributes or the data source;

[0012] In response to the request formation instruction, the chart generation request including the template, the attributes and the data source is generated.

[0013] In an exemplary embodiment of the present disclosure, acquiring data from the data source according to the attribute includes:

[0014] Determine first information of the data according to the attribute;

[0015] When the first information meets the preset conditions, multiple data acquisition tasks are generated:

[0016] Execute the multiple data acquisition tasks through multiple task executors of distributed storage;

[0017] The output results of multiple data processing tasks are integrated to obtain the data.

[0018] In an exemplary embodiment of the present disclosure, the first information includes the data volume, data storage location, data source, and data aggregation method of the data, and the preset condition includes at least one of the data volume exceeding a preset threshold, the data storage location being multiple, the data source being multiple, and the aggregation method being multiple.

[0019] In an exemplary embodiment of the present disclosure, the data chart generation request further includes user access rights, and acquiring data from the data source according to the attribute includes:

[0020] The data is obtained according to the user access rights.

[0021] In an exemplary embodiment of the present disclosure, the acquiring data from the data source according to the attribute further includes:

[0022] If the data exists in the cache, obtain the data from the cache;

[0023] If the data does not exist in the cache, after acquiring the data from the data source, the data is stored in the cache.

[0024] In an exemplary embodiment of the present disclosure, in the template of the target data chart, the data source or the attribute is a fixed value.

[0025] According to a second aspect of an embodiment of the present disclosure, there is provided a data chart generating device, comprising:

[0026] A target determination module, configured to respond to a data chart generation request and determine a template of a target data chart, attributes corresponding to the template, and a data source;

[0027] A data acquisition module, configured to acquire data from the data source according to the attribute;

[0028] A chart generation module is configured to generate and display the target data chart based on the template and the data of the attributes.

[0029] According to a third aspect of the present disclosure, there is provided an electronic device, comprising: a memory; and a processor coupled to the memory, wherein the processor is configured to execute any one of the methods described above based on instructions stored in the memory.

[0030] According to a fourth aspect of the present disclosure, there is provided a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the data chart generating method as described in any one of the above.

[0031] The disclosed embodiment configures data sources and attributes by using a data chart template, and then uses a separately developed data acquisition process to acquire a large amount of data, thereby automatically generating a target data chart. This can greatly improve the efficiency of generating data charts, avoid the high threshold and low flexibility brought about by traditional development methods, facilitate the use of distributed systems to perform data reading tasks, and greatly improve the efficiency of generating data charts with large amounts of data.

[0032] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0034] Figure 1 is a flow chart of a data chart generating method in an exemplary embodiment of the present disclosure.

[0035] Figure 2 The present invention is a flowchart of a process for generating a data chart generation request in one embodiment of the present invention.

[0036] Figure 3 It is a sub-step flow chart of step S104 in one embodiment of the present disclosure.

[0037] Figure 4 It is a block diagram of a system for executing a data chart generation process in one embodiment of the present disclosure.

[0038] Figure 5 It is a block diagram of a data chart generating device in an exemplary embodiment of the present disclosure.

[0039] Figure 6 is a block diagram of an electronic device in an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0040] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as being limited to the examples set forth herein; on the contrary, these embodiments are provided so that the present disclosure will be more comprehensive and complete, and the concepts of the example embodiments are fully conveyed to those skilled in the art. The described features, structures, or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known technical solutions are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0041] In addition, the accompanying drawings are only schematic diagrams of the present disclosure, and the same reference numerals in the drawings represent the same or similar parts, so their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities, which do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0042] The exemplary embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0043] Figure 1 The flowchart of the data chart generation method in the exemplary embodiment of the present disclosure is schematically shown. Figure 1 , the data chart generation method 100 may include:

[0044] Step S102, responding to the data chart generation request, determining a template of a target data chart, attributes corresponding to the template, and a data source;

[0045] Step S104, acquiring data from the data source according to the attribute;

[0046] Step S106: Generate and display the target data chart based on the template and the data of the attributes.

[0047] The disclosed embodiment configures data sources and attributes by using a data chart template, and then uses a separately developed data acquisition process to acquire a large amount of data, thereby automatically generating a target data chart. This can greatly improve the efficiency of generating data charts, avoid the high threshold and low flexibility brought about by traditional development methods, facilitate the use of distributed systems to perform data reading tasks, and greatly improve the efficiency of generating data charts with large amounts of data.

[0048] Next, each step of the data graph generating method 100 is described in detail.

[0049] In step S102, in response to a data chart generation request, a template of a target data chart, attributes corresponding to the template, and a data source are determined.

[0050] In the embodiment of the present disclosure, the data chart generation request may be issued by the front end in response to a user operation.

[0051] Figure 2 The present invention is a flowchart of a process for generating a data chart generation request in one embodiment of the present invention.

[0052] refer to Figure 2 , the request generation process 200 may include:

[0053] Step S21, responding to the template dragging instruction, creating a template of the target data chart;

[0054] Step S22, responding to the user's instruction to edit the attributes of the template or the instruction to edit the data source, recording the attributes or the data source;

[0055] Step S23, in response to the request formation instruction, generating the chart generation request including the template, the attributes and the data source.

[0056] A visual operation interface can be provided at the front end for users to create new templates from the template library by clicking, dragging, etc. These data chart templates can include templates of various data charts such as umbrella charts, pie charts, dot charts, radial charts, wheel charts, bar charts, etc. These templates all have attributes and data sources. For example, when a user wants to view the company's various expenditures in the past year, the attributes of the template can be set to various expenditure items and corresponding expenditure amounts, and the data source can be set to the company's financial database.

[0057] In some embodiments, templates with fixed data sources or fixed attributes may be provided. Templates with fixed data sources have a default data source, and users can modify the attribute type, such as viewing multiple financial items of the same company; templates with fixed attributes have fixed attribute types, and users can modify the data source, such as viewing the tax status of multiple companies. There may be many types of template types, data sources, and attribute settings, and the present disclosure is not limited thereto.

[0058] After the front end generates a data chart generation request, the data chart generation device can receive the request and analyze various parameters of the request to automatically perform subsequent operations. Among them, default attributes can be set in each template. At this time, the background data acquisition module can obtain more comprehensive attribute information by acquiring the template.

[0059] By setting up a front-end that can perform visual operations, users and front-end developers do not need to pay attention to the subsequent data acquisition process. Through simple configuration, the development threshold for data chart generation can be greatly lowered, and the efficiency and flexibility of data chart generation can be improved.

[0060] In step S104, data is acquired from the data source according to the attribute.

[0061] Unlike software such as Excel, the data chart generation method provided in the embodiment of the present disclosure can automatically generate data charts with huge data volumes and complex data relationships. That is, the method itself is provided with a step for automatically integrating and processing large amounts of data from multiple sources.

[0062] Figure 3 It is a sub-step flow chart of step S104 in one embodiment of the present disclosure.

[0063] refer to Figure 3 In one embodiment, step S104 may include:

[0064] Step S1041, determining first information of the data according to the attribute;

[0065] Step S1042, generating multiple data acquisition tasks when the first information meets the preset conditions:

[0066] Step S1043, executing multiple data acquisition tasks through multiple task executors of distributed storage;

[0067] Step S1044, integrating the output results of multiple data processing tasks to obtain data.

[0068] Among them, the first information may include, for example, the data volume, data storage location, data source, and data aggregation method, and the preset conditions may include, for example, at least one of the data volume exceeding a preset threshold, multiple data storage locations, multiple data sources, and multiple aggregation methods.

[0069] That is, in the disclosed embodiment, multiple data acquisition tasks are executed simultaneously by multiple task executors of distributed storage to improve the efficiency of querying and extracting large amounts of data. For example, when the amount of data exceeds a preset threshold, the amount of each data acquisition task can be set not to exceed the preset threshold, so that data acquisition is achieved through multiple data acquisition tasks; when the data storage location is discontinuous, data located in different storage locations can be acquired through different tasks; when the data sources are different, data from different data sources can be acquired through different tasks; when the data aggregation methods are different (for example, sum\count\avg and min\max are two types of aggregation methods), different processes are used to execute different data aggregation tasks.

[0070] Multiple data acquisition tasks can be sent to the general task executor for execution. The general task executor is deployed in a distributed manner. Each machine's virtual machine can have multiple executors, which are composed of several machines. A general distributor is set up to be responsible for task distribution, status monitoring, and executor slot management.

[0071] The OLAP (Online Analytical Processing) computing engine can be used to perform specific data acquisition on the data source. The general task executor is responsible for interacting with the OLAP computing engine on specific tasks. The general task executor sends the task to the OLAP computing engine. The OLAP data query engine determines the data source type (such as MySQL, Oracle, Presto, Kylin, Clickhouse, PostgreSQL, etc.) based on the task information, and constructs the corresponding database query language to query and extract data from each data source. After selecting and executing the optimal SQL engine, the calculation result is returned to the general task executor.

[0072] After the overall task executor obtains the data returned by each subtask, it merges the data according to the task type: first, the aggregated results of each subtask are put into one thread, and then aggregated to obtain the final data (for example, the return values ​​of each subtask of the sum task are added together, and the maximum return value of each subtask of the max task is selected, etc.).

[0073] In some embodiments, in order to improve data acquisition efficiency, the data acquired each time can be stored in a cache. When receiving a new data acquisition task, the cache is first queried to see whether there is target data. If so, there is no need to interact with the data source. If not, the data acquisition task is executed and the acquired data is stored in the cache for subsequent application.

[0074] For example, you can store the data of the most recent day in redis cache or elasticsearch. Elasticsearch is a distributed, highly scalable, and highly real-time search and data analysis engine. If the query task is to query the data of the most recent day, and the template has the es cache function enabled, the query engine will first query the data in es, and there is no need to generate database query language to query the data in the database. By saving the data in the cache and querying it again under the same conditions, the query results can be directly obtained through the cache, which can greatly improve the query efficiency.

[0075] You can set an expiration policy for the cache, such as the LRU (Least Recently Used) algorithm, to prevent cache accumulation due to long-term use of the system.

[0076] In some embodiments, the data chart generation request also includes user access permission information, and data can be obtained according to the user access permission information. Multiple user access permissions can be determined by the user identifier and the stored user access permission table, where the user access permissions can include system resource permissions and data permissions. Before data acquisition, it is first verified whether the latest user access permission corresponding to the data chart generation request can obtain all data.

[0077] For example, if some of the attributes or data sources set by the user are inaccessible to the user's access rights, a prompt message can be returned to the user, or a null value can be returned. If some data exceeds the user's access rights, only the data within the user's access rights can be obtained, and the user can be prompted in the returned information that the data is partial data.

[0078] In step S106, the target data chart is generated and displayed according to the template and the data of the attributes.

[0079] Step S106 can also be implemented by the front end. For example, after acquiring the data, the data corresponding to each attribute can be determined, and the attribute and the corresponding data can be returned to the front end, so that the front end can map the target data to various visual channels of the target chart and display it in a graphical form.

[0080] Figure 4 It is a block diagram of a system for executing the entire data chart generation process in one embodiment of the present disclosure.

[0081] refer to Figure 4 The chart generation execution system 400 may include a front end 41 , a back end 42 and a data source 43 .

[0082] The front end 41 may include a user login entry 411, a template library 412, an attribute configuration entry 413, a data source configuration entry 414, and a data mapping module 415. The back end 42 may include a user authority verification module 421, a parameter extraction module 422, a task allocation module 423, a task integration module 424, a cache module 425, etc.

[0083] The user login entry 411 is connected to the user authority verification module 421, the template library 412, the attribute configuration entry 413, and the data source configuration entry 414 are connected to the parameter extraction module 422, the task integration module 424 is connected to the data mapping module 415, and the task allocation module 423 is connected to the data source 43. The data source 43 may include multiple data sources, Figure 4 These are examples only.

[0084] The chart generation execution system provided by the embodiment of the present disclosure can quickly generate data charts for complex calculations involving large amounts of data through simple operations, thereby effectively improving the efficiency and flexibility of data chart generation tasks.

[0085] Corresponding to the above method embodiments, the present disclosure also provides a data chart generating device, which can be used to execute the above method embodiments.

[0086] Figure 5 A block diagram schematically shows a data chart generating device in an exemplary embodiment of the present disclosure.

[0087] refer to Figure 5 , the data chart generating device 500 may include:

[0088] A target determination module 502 is configured to respond to a data chart generation request and determine a template of a target data chart, attributes corresponding to the template, and a data source;

[0089] A data acquisition module 505, configured to acquire data from the data source according to the attribute;

[0090] The chart generation module 506 is configured to generate and display the target data chart according to the template and the data of the attributes.

[0091] In an exemplary embodiment of the present disclosure, a request generation module 508 is further included, which is configured to:

[0092] In response to the template dragging instruction, a template of the target data chart is newly created;

[0093] In response to a user's instruction to edit the attributes of the template or the data source, recording the attributes or the data source;

[0094] In response to the request formation instruction, the chart generation request including the template, the attributes and the data source is generated.

[0095] In an exemplary embodiment of the present disclosure, the data acquisition module 505 is configured as follows:

[0096] Determine first information of the data according to the attribute;

[0097] When the first information meets the preset conditions, multiple data acquisition tasks are generated:

[0098] Execute the multiple data acquisition tasks through multiple task executors of distributed storage;

[0099] The output results of multiple data processing tasks are integrated to obtain the data.

[0100] In an exemplary embodiment of the present disclosure, the first information includes the data volume, data storage location, data source, and data aggregation method of the data, and the preset condition includes at least one of the data volume exceeding a preset threshold, the data storage location being multiple, the data source being multiple, and the aggregation method being multiple.

[0101] In an exemplary embodiment of the present disclosure, the data chart generation request also includes user access rights, and the data acquisition module 505 is configured to:

[0102] The data is obtained according to the user access rights.

[0103] In an exemplary embodiment of the present disclosure, the data acquisition module 505 is configured as follows:

[0104] If the data exists in the cache, obtain the data from the cache;

[0105] If the data does not exist in the cache, after acquiring the data from the data source, the data is stored in the cache.

[0106] In an exemplary embodiment of the present disclosure, in the template of the target data chart, the data source or the attribute is a fixed value.

[0107] Since the functions of the device 500 have been described in detail in the corresponding method embodiments, the present disclosure will not elaborate on them here.

[0108] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0109] In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

[0110] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as a system, method or program product. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as a "circuit", "module" or "system".

[0111] Refer to the following Figure 6 The electronic device 600 according to this embodiment of the present invention is described. Figure 6 The electronic device 600 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0112] like Figure 6 As shown, the electronic device 600 is in the form of a general computing device. The components of the electronic device 600 may include but are not limited to: at least one processing unit 610, at least one storage unit 620, and a bus 630 connecting different system components (including the storage unit 620 and the processing unit 610).

[0113] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above “Exemplary Method” section of this specification. For example, the processing unit 610 can perform the following steps: Figure 1 Follow the steps shown in .

[0114] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0115] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0116] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0117] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 660. As shown, the network adapter 660 communicates with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0118] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0119] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementations, various aspects of the present invention can also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" section of the present specification.

[0120] The program product for implementing the above method according to an embodiment of the present invention can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto, and in this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0121] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0122] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0123] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0124] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0125] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0126] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are to be considered exemplary only, and the true scope and concept of the present disclosure are indicated by the claims.

Claims

1. A data chart generation method, characterized in that: include: In response to a data chart generation request, determine a template of a target data chart, attributes corresponding to the template, and a data source; After determining the template, acquiring data from the data source according to attributes corresponding to the template; Automatically generate and display the target data chart according to the template, the attributes and the data; Wherein, acquiring data from the data source according to the attributes corresponding to the template includes: Determine first information of the data according to the attribute; When the first information meets the preset conditions, multiple data acquisition tasks are generated: Sending the multiple data acquisition tasks to a general task executor for execution, wherein the general task executor is deployed in a distributed manner; The output results of multiple data processing tasks are sent to the general task executor for integration. After the general task executor obtains the data returned by each data processing task, it merges the data according to the task type to obtain the data; Automatically generating the target data chart according to the template, the attributes and the data and displaying the chart includes: Determine the data corresponding to each attribute; Return the attributes and their corresponding data to the front end, so that the front end can map the data to various visual channels of the chart and present it in a graphical form.

2. The data chart generation method according to claim 1, characterized in that: The process of generating the data chart generation request includes: In response to the template dragging instruction, a template of the target data chart is newly created; In response to a user's instruction to edit the attributes of the template or the data source, recording the attributes or the data source; In response to the request formation instruction, the chart generation request including the template, the attributes and the data source is generated.

3. The data chart generation method according to claim 1, characterized in that: The first information includes the data volume, data storage location, data source, and data aggregation method of the data, and the preset conditions include at least one of the data volume exceeding a preset threshold, the data storage location being multiple, the data source being multiple, and the aggregation method being multiple.

4. The data chart generation method according to claim 1, characterized in that: The data chart generation request also includes user access rights, and obtaining data from the data source according to the attribute includes: The data is obtained according to the user access rights.

5. The data chart generation method according to claim 1, characterized in that: The acquiring data from the data source according to the attribute further comprises: If the data exists in the cache, obtain the data from the cache; If the data does not exist in the cache, after acquiring the data from the data source, the data is stored in the cache.

6. The data chart generation method according to claim 2, characterized in that: In the template of the target data chart, the data source or the attribute is a fixed value.

7. A data chart generating device, characterized in that: include: A target determination module, configured to respond to a data chart generation request and determine a template of a target data chart, attributes corresponding to the template, and a data source; A data acquisition module, configured to acquire data from the data source according to the attributes corresponding to the template after determining the template; A chart generation module, configured to automatically generate and display the target data chart according to the template, the attributes and the data; The data acquisition module is configured to: determine the first information of the data according to the attribute; generate multiple data acquisition tasks when the first information meets the preset conditions; send the multiple data acquisition tasks to the general task executor for execution, and the general task executor is distributed; send the output results of multiple data processing tasks to the general task executor for integration, and after the general task executor obtains the data returned by each data processing task, it merges the data according to the task type to obtain the data; The chart generation module is configured to: determine the data corresponding to each attribute; return the attribute and its corresponding data to the front end, so that the front end maps the data to various visual channels of the chart and presents it in a graphical form.

8. An electronic device, characterized in that: include: Memory; as well as A processor coupled to the memory, wherein the processor is configured to execute the data chart generating method according to any one of claims 1 to 6 based on instructions stored in the memory.

9. A computer-readable storage medium having a program stored thereon, wherein when the program is executed by a processor, the data chart generating method according to any one of claims 1 to 6 is implemented.

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