Big data visual display optimization control method, front-end equipment and storage medium

Abstract

The invention provides a big data visual display optimization control method, front-end equipment and a storage medium, and relates to the field of front-end data display. The method comprises the following steps: if a request is not a first request, integrating request incremental data and front-end cache data into new cache data; if the request is the first-time request, forming new cache data by full-amount request data; if the new cache data is not greater than the maximum data display quantity and is displayed for the first time, displaying based on the new cache data; if the new cache data is not greater than the maximum data display quantity and is not displayed for the first time, generating an incremental image based on the incremental data, splicing the incremental image with the original image and then displaying the spliced image; and if the new cache data is greater than the maximum data display quantity, performing point sampling on the new cache data and displaying the new cache data. According to the method, the requested data are placed in the front-end cache region, subsequent data requests only need to request incremental data, the display mode is dynamically selected by combining the data size and whether the data are displayed for the first time or not, and the data processing efficiency and the display efficiency are improved.

Description

Technical Field

[0001] This invention relates to the field of front-end data display technology, and in particular to a big data visualization display optimization control method, front-end device and storage medium. Background Technology

[0002] Users enter requests through their browsers and send them to the server. After receiving the request message from the browser, the server generates response data and sends it to the browser. The front end then updates the data and displays it.

[0003] In existing technologies, servers and front-end pages typically use a full request model. A full request means that every time the front-end requests data from the server, it obtains the complete data set, regardless of the actual amount of data required for display. This undoubtedly greatly increases the amount of data requested, thus increasing the burden on network transmission.

[0004] Because a large amount of data needs to be transmitted over the network, waiting time is significantly extended, which severely impacts user experience. Furthermore, prolonged waiting times slow down page loading, potentially causing lag or even freezing. Users cannot receive timely feedback during operations, hindering smooth interaction and greatly reducing product usability and user satisfaction. Summary of the Invention

[0005] This invention provides a big data visualization display optimization control method, front-end device, and storage medium to solve the problem of excessively long waiting time for full data requests, which affects the user experience.

[0006] In a first aspect, embodiments of the present invention provide a big data visualization display optimization control method, including: In response to the user's data viewing command, determine the maximum amount of data to be displayed on the current page; Determine if this is the first data request; If this is not the first time data is requested, incremental data is requested from the server, and the received incremental data is integrated with the cached data in the front-end cache to obtain new cached data. If it is the first time requesting data, the server will request the full amount of data, and the received full amount of data will be stored in the front-end cache to obtain new cached data; Determine whether the new cached data does not exceed the maximum data display size; If so, and this is the first time it has been displayed, then a visualization will be generated based on the new cached data; If so, and it is not the first time it has been displayed, then an incremental image is generated based on the incremental data and stitched with the visualization image on the current page to obtain a new visualization image; If not, the new cached data is sampled to obtain visualization data and generate a visualization image.

[0007] Secondly, embodiments of the present invention provide a big data visualization display optimization control method, including: In response to the user's data viewing command, determine the maximum amount of data to be displayed on the current page; Determine the sampling interval based on the maximum amount of data to be displayed; If the sampling interval is 1 and this is the first time it is displayed, then the visualization data is requested from the front-end cache and the server according to the sampling interval and a visualization image is generated. If the sampling interval is 1 and it is not the first time it is displayed, then request incremental data from the server and generate an incremental image. Then, stitch the incremental image with the visualization image on the current page to obtain a new visualization image. If the sampling interval is not 1, then the visualization data is requested from the front-end cache and the server according to the sampling interval and a visualization image is generated.

[0008] Thirdly, embodiments of the present invention provide a big data visualization display optimization control method, including: In response to the user's data viewing command, determine the maximum amount of data to be displayed on the current page; The sampling interval is determined based on the maximum amount of data to be displayed. Based on the sampling interval, determine the visual data information corresponding to the current page; Determine whether all the visualized data is in the cached data in the front-end cache area based on the visualized data information. If so, then retrieve the visualization data from the front-end cache based on the visualization data information and display it; If not, then based on the visualization data information, the existing data is extracted from the front-end cache and incremental sampling data is requested from the server. The existing sampling data and incremental sampling data are then integrated to obtain visualization data and displayed.

[0009] Fourthly, embodiments of the present invention provide a front-end device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the big data visualization display optimization control method as described in the first aspect or any possible implementation of the first aspect.

[0010] Fifthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the big data visualization display optimization control method as described in the first aspect or any possible implementation of the first aspect.

[0011] This invention provides a big data visualization display optimization control method, a front-end device, and a storage medium. The big data visualization display optimization control method includes: responding to a user's data viewing instruction, determining the maximum data display volume of the current page; determining whether it is the first data request; if not, requesting incremental data from the server and integrating the received incremental data with cached data in the front-end cache to obtain new cached data; if it is the first data request, requesting full data from the server and storing the received full data in the front-end cache to obtain new cached data; determining whether the new cached data is not greater than the maximum data display volume; if so, and it is the first display, generating a visualization image based on the new cached data; if so, and it is not the first display, generating an incremental image based on the incremental data and stitching it with the visualization image of the current page to obtain a new visualization image; if not, sampling points from the new cached data to obtain visualization data and generating a visualization image. This invention places the requested data in the front-end cache, so subsequent data requests only need to request incremental data, without needing to request all data, reducing data acquisition time, increasing page loading speed, providing timely feedback to user operations, and making front-end interaction smoother. Meanwhile, the display method is dynamically selected based on whether it is the first time it is displayed and the amount of data, which improves display efficiency and effect and enhances user experience. Attached Figure Description

[0012] Figure 1 This is an application scenario diagram of the big data visualization and optimization control method provided in the embodiments of the present invention; Figure 2 These are visualizations of the operational data provided in the embodiments of the present invention; Figure 3 This is a flowchart illustrating the implementation of a big data visualization and optimization control method provided in an embodiment of the present invention. Figure 4 This is a flowchart illustrating the implementation of another big data visualization and optimization control method provided in this embodiment of the invention. Figure 5 This is a flowchart illustrating the implementation of another big data visualization and optimization control method provided in this embodiment of the invention. Figure 6 This is a schematic diagram of the structure of a big data visualization and optimization control device provided in an embodiment of the present invention; Figure 7 This is a schematic diagram of the structure of another big data visualization and optimization control device provided in an embodiment of the present invention; Figure 8 This is a schematic diagram of the structure of another big data visualization display optimization control device provided in an embodiment of the present invention; Figure 9This is a schematic diagram of the front-end device provided in an embodiment of the present invention. Detailed Implementation

[0013] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0014] Figure 1 This diagram illustrates an application scenario of the big data visualization and optimization control method provided in this embodiment of the invention. Figure 1 As shown, this is applied to power equipment monitoring scenarios, such as monitoring power electronic devices like UPS, photovoltaic inverters, and energy storage converters. During the operation of these devices, operational data is continuously generated and stored in monitoring server 1 via data acquisition equipment. Front-end device 2 (e.g., computer equipment in the monitoring room) interacts with server 1, retrieving data from server 1 and displaying the equipment's operational data on front-end device 2. Front-end device 2, through a visual interface, can intuitively display various operational data of the equipment, such as the output voltage of each phase, the output current of each phase, and the load current of each phase. (Refer to...) Figure 2 Maintenance personnel can promptly identify potential faults by using the operational data displayed on front-end device 2. Once abnormal data fluctuations are detected, they can quickly pinpoint the problem and take effective countermeasures to prevent the fault from escalating further.

[0015] In existing technologies, server 1 and front-end device 2 typically use a full request mode, which retrieves the complete data set each time. This greatly increases the amount of data requested, thereby increasing the burden on network transmission, resulting in slower page loading speed and a poor user experience.

[0016] Based on the above, see Figure 3 This invention provides a big data visualization display optimization and control method, applied to a front-end device 2. The aforementioned big data visualization display optimization and control method includes: S101: In response to the user's data viewing command, determine the maximum amount of data to be displayed on the current page; When the data volume is small, it can be displayed directly. However, when the data volume is large, it cannot be fully displayed. Therefore, this application determines the maximum amount of data to be displayed on the current page for subsequent display method selection.

[0017] In one possible implementation, S101 may include: S1011: Determine the page zoom level based on the data viewing instructions; S1012: Determine the maximum amount of data to be displayed on the current page based on the page zoom level.

[0018] With a fixed page size, the maximum amount of data that can be displayed on the current page changes with the zoom level. For example, zooming in decreases the maximum amount of data displayed, while zooming out increases it. The page zoom level determines the maximum amount of data that can be displayed on the current page. Therefore, the page zoom level can be determined based on the data viewing command triggered by the user, thus obtaining the maximum amount of data that can be displayed on the current page.

[0019] S102: Determine if this is the first data request; S103: If this is not the first time data is requested, request incremental data from the server and integrate the received incremental data with the cached data in the front-end cache to obtain new cached data; S104: If it is the first time requesting data, request the full amount of data from the server and store the received full amount of data in the front-end cache to obtain new cached data; If there is no data in the front-end cache on the first request, then the full data needs to be requested from the server.

[0020] If it's not the first request, it means the data has been requested at least once before, and the front-end cache already stores the old data from previous requests. Therefore, only incremental data (newly generated runtime data since the last request) needs to be requested from the server. After receiving the incremental data, front-end device 2 stores it in the front-end cache and integrates it with the previously cached data to update the front-end cache and obtain the new cached data for data display. In other words, for non-first requests, each data request doesn't need to request all the data; only the new data needs to be requested. Old data doesn't need to be requested repeatedly, effectively reducing the amount of data requests, thereby reducing data processing time and data request waiting time, and also reducing the bandwidth required for concurrent requests.

[0021] It should be noted that if this is not the first time data is requested, the data requests can be made to ensure that the data is continuous and not repeated by setting a timestamp, synchronizing with the server time, or recording the exact time point each time data is requested.

[0022] S105: Determine whether the new cached data is not greater than the maximum data display size; S106: If so, and this is the first time it has been displayed, then a visualization image will be generated based on the new cached data; S107: If so, and it is not the first time it is displayed, then an incremental image is generated based on the incremental data and stitched with the visualization image on the current page to obtain a new visualization image; S108: If not, perform point sampling on the new cached data to obtain visualization data and generate a visualization image.

[0023] During the data display phase, this application also dynamically selects a display strategy based on the amount of new cached data.

[0024] The fact that the new cached data is no larger than the maximum data display size means that all the new cached data can be displayed at the current zoom level. To ensure the display effect, all data can be displayed.

[0025] For the initial display, if the current page does not have an image, a visual image will be generated directly based on all the new cached data; For subsequent displays, since the scaling ratio remains unchanged, only the incremental data needs to be rendered before being stitched together with the original page image. The original page image does not need to be re-rendered, reducing rendering pressure.

[0026] The new cached data exceeds the maximum data display size, indicating that the new cached data is too large to be fully displayed. Since the intervals between adjacent running data are small, and users are more concerned with the trend of running data, the new cached data can be sampled to reduce the data volume. This preserves the data trend while reducing the data volume, alleviating rendering pressure and preventing page lag.

[0027] Specifically, a flag can be set, with an initial value of 1 to indicate that it is the first request. After the first request is completed, the flag is set to 0. Thus, the flag can be used to determine whether it is the first request.

[0028] Based on the above, this application places the requested data in the front-end cache. Subsequent data requests only need to request incremental data, instead of requesting all data, reducing data retrieval time, increasing page loading speed, providing timely feedback to user operations, and making front-end interaction smoother. Furthermore, by dynamically selecting the display method based on whether it's the first time displaying the data and the data volume, display efficiency and effectiveness are improved, enhancing the user experience.

[0029] In one possible implementation, S108 may include: S1081: Determine the sampling interval based on the maximum amount of data to be displayed and the amount of new cached data; The maximum amount of data to be displayed is the upper limit of the amount of data that a page can reasonably display. If the total amount of data exceeds the maximum amount of data to be displayed, it cannot be fully displayed. Therefore, the sampling interval can be determined based on the maximum amount of data to be displayed. New cached data can be sampled and displayed. By using an appropriate sampling interval, the amount of data to be displayed can be reduced, ensuring that the data can be fully displayed while preserving the trend of the data as much as possible.

[0030] The sampling interval determines the frequency at which data points are extracted from new cached data. For example, if the amount of new cached data is large, but the maximum display size is relatively small, the sampling interval may be set to be large, meaning the frequency of data point extraction is low; conversely, if the amount of new cached data is small, the sampling interval may be reduced accordingly to extract more data points to ensure data integrity and visualization effects.

[0031] Based on this, in one possible implementation, S1081 may include: 1. Based on the maximum amount of data displayed and the amount of new cached data, the sampling interval is obtained using the first formula; The first formula is:

[0032] in, The sampling interval is an integer. The amount of new cached data, This represents the maximum amount of data to display.

[0033] By rounding down, the amount of sampled data is always kept within a displayable range, satisfying display limitations while preserving the data's trend characteristics to some extent. Furthermore, the sampling interval can be optimized based on factors such as data distribution characteristics and trends to ensure that the extracted data points better reflect the characteristics of the original data.

[0034] S1082: Perform point sampling on the new cached data according to the sampling interval to obtain visualization data and generate a visualization image.

[0035] This application sets a reasonable sampling interval to sample points, obtain visualization data, and generate visualization images. While ensuring that the data can accurately reflect the information, it reduces the amount of data, improves the efficiency and effect of visualization, and avoids slow loading or abnormal display of visualization images due to excessive data volume.

[0036] In one possible implementation, prior to S101, the above method may further include: S109: If the data viewing command is a zoom-out command, then execute the steps to determine the maximum amount of data to be displayed on the current page in response to the user's data viewing command; S1010: If the data viewing command is a zoom-in command, then the cached data is sampled to obtain the visualized data and generate a visualized image.

[0037] If the data viewing command is a zoom-in command, it means the user is viewing a detailed image within the currently displayed visualization, and there's no need to request new data from the server. Therefore, the visualization image can be generated by sampling the cached data according to the zoom level. If the data viewing command is a zoom-out command, requiring more data to be displayed, then steps S101 to S108 are executed to update the visualization image.

[0038] In one possible implementation, the front-end cache can be the browser's IndexDB database.

[0039] IndexDB is a low-level database API provided by browsers for storing large amounts of structured data (such as JSON objects) on the client side. It allows developers to store and retrieve data asynchronously, has transaction processing capabilities, and is suitable for scenarios where the front-end needs to persistently store large amounts of data, thus fitting the application scenario of this application.

[0040] refer to Figure 4 This invention provides yet another method for optimizing and controlling big data visualization, applied to front-end device 2. The aforementioned method includes: S201: In response to the user's data viewing command, determine the maximum amount of data to be displayed on the current page; S202: Determine the sampling interval based on the maximum amount of data to be displayed; S203: If the sampling interval is 1 and this is the first time it is displayed, then request visualization data from the front-end cache and the server according to the sampling interval and generate a visualization image; S204: If the sampling interval is 1 and it is not the first time it is displayed, request incremental data from the server and generate an incremental image. Then, stitch the incremental image with the visualization image on the current page to obtain a new visualization image. S205: If the sampling interval is not 1, then request visualization data from the front-end cache and the server according to the sampling interval and generate a visualization image.

[0041] If the sampling interval is 1 and this is the first time it is displayed, it means that there is no visual image displayed on the current page. Data is directly requested from the front-end cache and the server and displayed in full.

[0042] If the sampling interval is 1 and it is not the first time it is displayed, only incremental data can be requested to avoid repeatedly transmitting historical data; at the same time, incremental image stitching avoids full redrawing and improves the speed of interactive response.

[0043] If the sampling interval is not 1, data can be requested by sampling at intervals, that is, only the data that is needed can be requested, reducing the amount of data requested.

[0044] In one possible implementation, S205 may include: S2051: Determine if the front-end cache is empty; S2052: If not empty, retrieve the existing sampled data from the front-end buffer according to the sampling interval, and request incremental sampled data from the server; S2053: If empty, request incremental sampling data from the server according to the sampling interval, and set the existing sampling data to 0; S2054: Integrate existing sampled data with incremental sampled data to obtain visualized data and generate a visualized image.

[0045] If the front-end cache is not empty, it means that there is already data in the front-end cache. In this case, the existing sampled data is retrieved from the cache, and only data not found in the cache is requested from the server to reduce the amount of data transmitted. If the front-end cache is empty, all data is requested from the server according to the sampling interval. Finally, the existing sampled data and the incremental sampled data are integrated to ensure the continuity of the visualized image.

[0046] refer to Figure 5 This invention provides another method for optimizing and controlling big data visualization, applied to front-end device 2. The aforementioned method includes: S301: In response to the user's data viewing command, determine the maximum amount of data to be displayed on the current page; S302: Determine the sampling interval based on the maximum amount of data to be displayed; S303: Determine the visual data information corresponding to the current page based on the sampling interval; Once the sampling interval is determined, the data information for each data point that needs to be acquired can be determined based on the sampling interval. For example, if a data point is acquired every 5 minutes over the past 4 hours, the visualized data information would be the time of each data point.

[0047] S304: Determine whether all the visualized data is in the cached data in the front-end cache area based on the visualized data information; By comparing the time period of the visualized data information with the time period of the cached data, it can be determined whether new data needs to be requested from the server.

[0048] S305: If so, then extract the visualization data from the front-end cache based on the visualization data information and display it; If all the data is already stored in the front-end cache, there is no need to interact with the server. The corresponding data points can be retrieved directly from the front-end cache according to the sampling interval. Data loading and rendering are completed locally, resulting in fast response speed and avoiding loading delays or failures caused by network fluctuations.

[0049] S306: If not, then extract existing data from the front-end cache and request incremental sampling data from the server based on the visualization data information, and integrate the existing sampling data and incremental sampling data to obtain visualization data and display it.

[0050] If the data is not entirely in the cache, the system will split the data source. It will extract the data that overlaps with the current requirement from the front-end cache, and only request the missing data from the front-end cache from the server, with the request carrying the sampling interval. The server will only return the data that meets the sampling requirements, reducing the amount of data transmission and further reducing network bandwidth usage and front-end computing pressure.

[0051] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0052] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0053] Figure 6 A schematic diagram of the structure of the big data visualization display optimization control device provided in an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below: like Figure 6 As shown, the big data visualization display optimization control device includes: The first maximum display volume determination module 41 is used to determine the maximum data display volume of the current page in response to the user's data viewing command; The initial request determination module 42 is used to determine whether the data is the first request. The incremental request module 43 is used to request incremental data from the server if it is not the first time requesting data, and to integrate the received incremental data with the cached data in the front-end cache to obtain new cached data. The full request module 44 is used to request the full data from the server if it is the first time requesting data, and to store the received full data in the front-end cache to obtain new cached data; The data volume exceeding the limit judgment module 45 is used to determine whether the new cached data does not exceed the maximum data display volume; The first display module 46 is used to generate a visual image based on the new cached data if it is the first time the image is displayed. The second display module 47 is used to generate an incremental image based on incremental data and stitch it with the visualization image of the current page to obtain a new visualization image if, and if, it is not the first time it is displayed. The third display module 48 is used to sample new cached data if no, obtain visualized data and generate a visualized image.

[0054] In one possible implementation, the third display module 48 may include: The sampling interval determination unit is used to determine the sampling interval based on the maximum amount of data to be displayed and the amount of new cached data. The sampling unit is used to sample new cached data at sampling intervals to obtain visualization data and generate visualization images.

[0055] In one possible implementation, the sampling interval determination unit can be specifically used for: 1. Based on the maximum amount of data displayed and the amount of new cached data, the sampling interval is obtained using the first formula; The first formula can be:

[0056] in, The sampling interval is an integer. The amount of new cached data, This represents the maximum amount of data to display.

[0057] In one possible implementation, the above-described apparatus may further include: The first instruction judgment module is used to determine the maximum amount of data to be displayed on the current page if the data viewing instruction is a zoom-out instruction. The second instruction judgment module is used to perform point sampling on the cached data if the data viewing instruction is a zoom-in instruction, to obtain the visualization data and generate a visualization image.

[0058] In one possible implementation, the first maximum display volume determination module 41 includes: The scaling ratio determination unit is used to determine the page scaling ratio based on the data viewing instructions; The maximum display output unit is used to determine the maximum amount of data to be displayed on the current page based on the page scaling ratio.

[0059] Figure 7 A schematic diagram of another big data visualization display optimization control device provided in an embodiment of the present invention is shown. The aforementioned big data visualization display optimization control device includes: The second maximum display volume determination module 51 is used to determine the maximum data display volume of the current page in response to the user's data viewing command; The first sampling interval determination module 52 is used to determine the sampling interval based on the maximum amount of data to be displayed; The fourth display module 53 is used to request visualization data from the front-end cache and the server and generate a visualization image based on the sampling interval if the sampling interval is 1 and it is the first display. The fifth display module 54 is used to request incremental data from the server and generate an incremental image if the sampling interval is 1 and it is not the first display. The incremental image is then stitched together with the visualization image on the current page to obtain a new visualization image. The sixth display module 55 is used to request visualization data from the front-end cache and the server and generate a visualization image if the sampling interval is not 1.

[0060] In one possible implementation, the sixth display module 55 may include: The cache detection unit is used to determine whether the front-end cache is empty; The incremental request unit is used to retrieve existing sampled data from the front-end cache based on the sampling interval if it is not empty, and then request incremental sampled data from the server. The full request unit is used to request incremental sampling data from the server based on the sampling interval if it is empty, and to set the existing sampling data to 0. The integrated display unit is used to integrate existing sampled data with incremental sampled data to obtain visualized data and generate visualized images.

[0061] Figure 8 A schematic diagram of another big data visualization display optimization control device provided in an embodiment of the present invention is shown. The aforementioned big data visualization display optimization control device includes: The third maximum display volume determination module 61 is used to determine the maximum data display volume of the current page in response to the user's data viewing command; The second sampling interval determination module 62 is used to determine the sampling interval based on the maximum amount of data to be displayed. The information extraction module 63 is used to determine the visual data information corresponding to the current page based on the sampling interval; The data location determination module 64 is used to determine, based on the visualized data information, whether all the visualized data is in the cached data in the front-end cache area; The seventh display module 65 is used to extract and display the visualization data from the front-end cache based on the visualization data information if the condition is met. The eighth display module 66 is used to extract existing data from the front-end cache and request incremental sampling data from the server based on the visualization data information if no, and integrate the existing sampling data and incremental sampling data to obtain visualization data and display it.

[0062] Figure 9 This is a schematic diagram of the front-end device 2 provided in an embodiment of the present invention. Figure 9 As shown, the front-end device 2 in this embodiment includes a processor 20 and a memory 21. The memory 21 stores a computer program 22. When the processor 20 executes the computer program 22, it implements the steps in the various method embodiments described above. Alternatively, when the processor 20 executes the computer program 22, it implements the functions of each module / unit in the various device embodiments described above.

[0063] For example, computer program 22 may be divided into one or more modules / units, which are stored in memory 21 and executed by processor 20 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 22 in front-end device 2.

[0064] The front-end device 2 may include, but is not limited to, a processor 20 and a memory 21. Those skilled in the art will understand that... Figure 9 This is merely an example of front-end device 2 and does not constitute a limitation on front-end device 2. It may include more or fewer components than shown, or combine certain components, or different components. For example, front-end device 2 may also include input / output devices, network access devices, buses, etc.

[0065] The processor 20 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0066] The memory 21 can be an internal storage unit of the front-end device 2, such as a hard disk or RAM of the front-end device 2. The memory 21 can also be an external storage device of the front-end device 2, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the front-end device 2. Furthermore, the memory 21 can include both internal storage units and external storage devices of the front-end device 2. The memory 21 is used to store the computer program 22 and other programs and data required by the front-end device 2. The memory 21 can also be used to temporarily store data that has been output or will be output.

[0067] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.

[0068] This invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the methods described in the above-described method embodiments.

[0069] This invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the methods described in the above-described method embodiments.

[0070] Computer programs include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. Computer-readable media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0071] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0072] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for optimizing and controlling big data visualization, characterized in that, include: In response to the user's data viewing command, determine the maximum amount of data to be displayed on the current page; Determine if this is the first data request; If this is not the first time data is requested, incremental data is requested from the server, and the received incremental data is integrated with the cached data in the front-end cache area to obtain new cached data; If it is the first time data is requested, the server is requested to send the full amount of data, and the received full amount of data is stored in the front-end cache to obtain the new cached data; Determine whether the new cached data is not greater than the maximum data display size; If so, and this is the first time it has been displayed, then a visualization image is generated based on the new cached data; If so, and it is not the first time it has been displayed, then an incremental image is generated based on the incremental data and stitched together with the visualization image on the current page to obtain a new visualization image; If not, then the new cached data is sampled to obtain visualization data and generate a visualization image.

2. The big data visualization display optimization control method according to claim 1, characterized in that, The step of sampling the new cached data to obtain visualization data and generate a visualization image includes: The sampling interval is determined based on the maximum amount of data to be displayed and the amount of new cached data. The new cached data is sampled at the specified sampling interval to obtain the visualization data and generate a visualization image.

3. The big data visualization display optimization control method according to claim 2, characterized in that, The step of determining the sampling interval based on the maximum data display volume and the data volume of the new cached data includes: The sampling interval is obtained by combining the maximum data display volume and the data volume of the new cached data with the first formula; The first formula is: in, The sampling interval is an integer. The amount of data in the new cached data. This refers to the maximum amount of data to be displayed.

4. The big data visualization display optimization control method according to any one of claims 1 to 3, characterized in that, Before determining the maximum amount of data to be displayed on the current page in response to the user's data viewing instruction, the method further includes: If the data viewing command is a zoom-out command, then the step of responding to the user's data viewing command and determining the maximum amount of data to be displayed on the current page is executed; If the data viewing command is a zoom-in command, then the cached data is sampled to obtain visualized data and generate a visualized image.

5. The big data visualization display optimization control method according to claim 4, characterized in that, The process of responding to a user's data viewing command and determining the maximum amount of data to be displayed on the current page includes: Determine the page scaling ratio based on the data viewing instructions; The maximum amount of data to be displayed on the current page is determined based on the page scaling ratio.

6. A method for optimizing and controlling big data visualization, characterized in that, include: In response to the user's data viewing command, determine the maximum amount of data to be displayed on the current page; The sampling interval is determined based on the maximum amount of data to be displayed; If the sampling interval is 1 and this is the first time it is displayed, then the visualization data is requested from the front-end cache and the server according to the sampling interval and a visualization image is generated. If the sampling interval is 1 and it is not the first time it is displayed, then incremental data is requested from the server and an incremental image is generated. The incremental image is then stitched together with the visualization image of the current page to obtain a new visualization image. If the sampling interval is not 1, then the visualization data is requested from the front-end cache and the server according to the sampling interval and the visualization image is generated.

7. The big data visualization display optimization control method according to claim 6, characterized in that, The step of requesting visualization data from the front-end cache and the server according to the sampling interval and generating the visualization image includes: Determine whether the front-end cache is empty; If it is not empty, then according to the sampling interval, the existing sampled data is extracted from the front-end cache, and incremental sampled data is requested from the server; If empty, then according to the sampling interval, request the incremental sampling data from the server and set the existing sampling data to 0; The existing sampled data and the incremental sampled data are integrated to obtain the visualization data, and the visualization image is generated.

8. A method for optimizing and controlling big data visualization, characterized in that, include: In response to the user's data viewing command, determine the maximum amount of data to be displayed on the current page; The sampling interval is determined based on the maximum amount of data displayed. Based on the sampling interval, determine the visual data information corresponding to the current page; Based on the visualized data information, determine whether all the visualized data is in the cached data in the front-end cache area; If so, the visualization data is extracted from the front-end cache based on the visualization data information and then displayed. If not, then based on the visualization data information, existing data is extracted from the front-end cache and incremental sampling data is requested from the server. The existing sampling data and the incremental sampling data are then integrated to obtain the visualization data and displayed.

9. A front-end device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the big data visualization display optimization control method as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the big data visualization display optimization control method as described in any one of claims 1 to 8.

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