Webpage data loading method and device, computer device, and storage medium

Abstract

The application relates to a webpage data loading method and device, computer equipment, a storage medium and a computer program product. Original data is modeled, the original data is sliced and layered based on a model, the data is more conducive to page loading, a data return priority index is constructed based on a slicing and layering result, data is sequentially returned / loaded in a priority mode, the data range for the first loading is reduced, and all data required by a page is gradually returned according to the priority in a subsequent loading process, so that the page loading speed is significantly improved.

Description

Technical Field

[0001] This application relates to the field of big data resource scheduling technology, and in particular to a web page data loading method, apparatus, computer equipment, storage medium and computer program product. Background Technology

[0002] Currently, many web pages need to load a huge amount of data, especially some map and chart pages, which require a lot of data support. There is a certain network data latency from the server to the front-end page. When the data volume is small, this data latency can be ignored. However, when the data volume is huge, the page loading speed will become very slow. Because the data takes longer to reach the page and has no order or logic, the page display effect is very poor, resulting in user experience problems such as blank screens, lag, or unattractive charts.

[0003] Therefore, there is an urgent need for a webpage data loading solution to improve webpage data loading speed. Summary of the Invention

[0004] Therefore, it is necessary to provide a web page data loading method, apparatus, computer device, computer-readable storage medium, and computer program product that can significantly improve the data loading capabilities, addressing the aforementioned technical problems.

[0005] Firstly, this application provides a method for loading webpage data. The method includes:

[0006] Obtain the raw data;

[0007] The original data is modeled, and the model is used to slice and layer the original data according to the return priority, so as to obtain the slice and layer results.

[0008] Based on the slice layering results, a data return priority index is established;

[0009] Data is returned according to the priority index of the data to load the web page data.

[0010] In one embodiment, the step of modeling the original data and using the model to perform slice layering based on return priority to obtain the slice layering result includes:

[0011] The raw data is sliced ​​and layered according to the returned priority using an indicator model; the sliced ​​and layered results are cached.

[0012] In one embodiment, the step of slicing and layering the raw data according to the returned priority using an indicator model includes:

[0013] Obtain the chart data display requirements; based on the chart data display requirements, determine the priority of returning raw data; slice and layer the raw data according to the return priority using a key-value indicator model.

[0014] In one embodiment, the cache slice hierarchical result includes:

[0015] The sliced ​​and layered results are cached using JSON format.

[0016] In one embodiment, establishing a data return priority index based on the slice layering result includes:

[0017] Based on the slice layering results, obtain the page rendering hierarchy; based on the page rendering hierarchy, establish a data return priority index.

[0018] In one embodiment, the step of returning data according to the data return priority index for webpage data loading includes:

[0019] Based on the data, a priority index is returned, and a B+ tree index is constructed using the B+ tree algorithm; data is then returned based on the B+ tree index to load web page data.

[0020] Secondly, this application also provides a webpage data loading device. The device includes:

[0021] The data acquisition module is used to acquire raw data;

[0022] The slicing module is used to model the original data, and to slice and layer the original data according to the return priority using the model to obtain the slicing and layering results;

[0023] An index building module is used to build a data return priority index based on the slice layering results;

[0024] The loading module is used to return data according to the data return priority index in order to load web page data.

[0025] In one embodiment, the slicing module is further configured to slice and layer the original data according to the returned priority using an index model; and cache the slicing and layering results.

[0026] In one embodiment, the slicing module is further configured to obtain chart data display requirements; obtain the priority of returning raw data based on the chart data display requirements; and slice and layer the raw data according to the return priority using a key-value indicator model.

[0027] In one embodiment, the slicing module is also used to cache the slicing layering results in JSON format.

[0028] In one embodiment, the index building module is further configured to obtain the page rendering hierarchy based on the slice layering result; and to establish a data return priority index based on the page rendering hierarchy.

[0029] In one embodiment, the index building module is further configured to return a priority index based on the data and construct a B+ tree index using the B+ tree algorithm;

[0030] Data is returned based on the B+ tree index to load web page data.

[0031] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0032] Obtain the raw data;

[0033] The original data is modeled, and the model is used to slice and layer the original data according to the return priority, so as to obtain the slice and layer results.

[0034] Based on the slice layering results, a data return priority index is established;

[0035] Data is returned according to the priority index of the data to load the web page data.

[0036] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:

[0037] Obtain the raw data;

[0038] The original data is modeled, and the model is used to slice and layer the original data according to the return priority, so as to obtain the slice and layer results.

[0039] Based on the slice layering results, a data return priority index is established;

[0040] Data is returned according to the priority index of the data to load the web page data.

[0041] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the following steps:

[0042] Obtain the raw data;

[0043] The original data is modeled, and the model is used to slice and layer the original data according to the return priority, so as to obtain the slice and layer results.

[0044] Based on the slice layering results, a data return priority index is established;

[0045] Data is returned according to the priority index of the data to load the web page data.

[0046] The aforementioned web page data loading method, apparatus, computer equipment, storage medium, and computer program product model the original data, and based on the model, slice and layer the original data to make the data more conducive to page loading. In addition, a data return priority index is constructed based on the slice and layer results, and data is returned / loaded in an orderly manner using a priority approach, which narrows the range of data loaded initially, and subsequent loading processes return all the data required by the page step by step according to priority, significantly improving page loading speed. Attached Figure Description

[0047] Figure 1 This is an application environment diagram of a web page data loading method in one embodiment;

[0048] Figure 2 This is a flowchart illustrating a webpage data loading method in one embodiment;

[0049] Figure 3 This is a flowchart illustrating a webpage data loading method in another embodiment;

[0050] Figure 4 A schematic diagram of the layering of the indicator model;

[0051] Figure 5 A diagram illustrating the creation of a priority index;

[0052] Figure 6 This is a structural block diagram of a web page data loading device in one embodiment;

[0053] Figure 7 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0055] The web page data loading method provided in this application embodiment can be applied to, for example, Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or placed on the cloud or other network servers. Terminal 102 obtains raw data from server 104; models the raw data, uses the model to slice and layer the raw data according to return priority, and obtains the sliced ​​layering results; establishes a data return priority index based on the sliced ​​layering results; and returns data according to the data return priority index for web page data loading. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can be smart speakers, smart TVs, smart air conditioners, smart vehicle devices, etc. Portable wearable devices can be smartwatches, smart bracelets, head-mounted devices, etc. Server 104 can be implemented using a standalone server or a server cluster composed of multiple servers. It should be noted that the acquisition, storage, use, and processing of data in this application's technical solution all comply with relevant national laws and regulations.

[0056] In one embodiment, such as Figure 2 As shown, a webpage data loading method is provided, which can be applied to... Figure 1 Taking the terminal in the example, the explanation includes the following steps:

[0057] S200: Obtain raw data.

[0058] Raw data refers to the data stored in the entire database before further processing. When a webpage needs to be loaded, the terminal accesses the server to retrieve the raw data from this database. Specifically, webpage loading typically involves loading chart data; therefore, the raw data here refers to the raw chart data.

[0059] S400: Model the original data, and use the model to slice and layer the original data according to the return priority, so as to obtain the slice and layer results.

[0060] Generally, in databases, raw data is stored as an entity-relationship list, which is the standard database storage model and facilitates data CRUD operations. However, in practical applications, this data storage method is not conducive to improving page loading speed. Therefore, this approach models the raw data and uses the model to slice and layer the raw data according to return priority, resulting in a sliced ​​layered result. It's understandable that the sliced ​​layered result includes the sliced ​​data set, which can be cached on the terminal and read directly from the cache when needed.

[0061] S600: Based on the slice layering results, create a data return priority index.

[0062] To ensure orderly and efficient data transmission and loading, a data return priority index needs to be established based on the data slicing hierarchy. Subsequently, when returning data, the data that needs to be returned at the current moment (based on priority) can be quickly found directly through this index. Specifically, since the slicing hierarchy is based on data return priorities, the return priority relationship of the data to be returned can be determined according to the slicing hierarchy. Furthermore, since the page is rendered sequentially according to its rendering hierarchy, the data for the earlier rendered hierarchy needs to be retrieved first. Therefore, this can be understood as establishing a data return priority index based on the page rendering hierarchy.

[0063] S800: Returns data according to the data return priority index for loading web page data.

[0064] When the current page requests data, the terminal returns data according to the data return priority index. Each time the index is queried, data of the corresponding priority is returned to load the web page data.

[0065] The above-mentioned web page data loading method models the original data and slices and layers the original data based on the model, making the data more conducive to page loading. In addition, a data return priority index is constructed based on the slice and layer results, and data is returned / loaded in an orderly manner according to priority, which narrows the range of data loaded initially, and subsequent loading processes return all the data required by the page step by step according to priority, which significantly improves the page loading speed.

[0066] like Figure 3 As shown, in one embodiment, S400 includes:

[0067] S420: The raw data is sliced ​​and layered according to the priority of the returned data using an indicator model;

[0068] S440: Cache slice layering results.

[0069] As mentioned above, the traditional method of storing data according to entity-relationship lists is detrimental to page loading speed. In this embodiment, we use an indicator model to re-slice the original data, obtaining sliced ​​and layered results. These sliced ​​and layered results are then cached for direct retrieval of the corresponding sliced ​​data during subsequent loading. Specifically, the indicator model's data slicing and layering refers to extracting the data required for the chart from the full indicator data and grouping this extracted data according to indicator type. For example, sales indicator data for various cities in Yunnan Province are selected from a table of sales indicators for different regions across the country, used to display a sales map chart for Yunnan Province.

[0070] In one embodiment, slicing and layering the raw data according to the returned priority using an indicator model includes:

[0071] Obtain the chart data display requirements; based on the chart data display requirements, determine the priority of the returned raw data; slice and layer the raw data according to the returned priority using a key-value indicator model.

[0072] Chart data display requirements include parameters such as the data type required by the terminal when displaying charts and the loading order of different data types. Specifically, the data display requirements can be obtained by analyzing the chart to be displayed. Based on these requirements, the rendering order of the chart layers during display is determined. Data corresponding to the layers requiring rendering first should be retrieved first, meaning it should be returned to the front end first. Therefore, the priority of returning raw data can be determined based on the chart data display requirements. After determining the data return priority, the raw data is sliced ​​and layered according to the return priority using an indicator model. Furthermore, the indicator model operates in a key-value pattern, describing each data point required for chart display, ensuring clear and accurate data slicing and layering. Further, the sliced ​​and layered results can be cached; preferably, they can be stored in JSON format to support data storage and retrieval in various application scenarios. Taking the requirement of displaying a sales map chart of Yunnan Province as an example, during the slicing stage, the sales indicator table for all regions across the country is filtered. After filtering out the data for cities in Yunnan Province, a key-value conversion is performed, and the data is stored in JSON format.

[0073] Overall, in the above embodiments, we re-slice the original data using an indicator model. Based on the chart display requirements, the original data is sliced ​​according to the minimum set needed for the chart, and stored in JSON format, laying the foundation for improved query efficiency. The indicator model describes each data point required for chart display in a key-value pattern, specifically as follows... Figure 4 As shown, since the chart data model is directly interfaced with the data chart visualization engine, it does not require secondary conversion processing by the browser, enabling efficient rendering.

[0074] In one embodiment, establishing a data return priority index based on the slice layering results includes:

[0075] Based on the slice layering results, obtain the page rendering hierarchy; based on the page rendering hierarchy, establish a data return priority index.

[0076] The page rendering hierarchy is primarily determined by the order in which the charts are displayed; charts displayed earlier have higher priority, and those displayed later have lower priority. As mentioned above, the slice hierarchy is determined based on the priority of the returned data. Therefore, the page rendering hierarchy can be obtained based on the slice hierarchy. A data return priority index can then be established based on the page rendering hierarchy.

[0077] In practical applications, a priority index can be established for the displayed data according to the page rendering hierarchy. Data with priority 1 is returned first, data with priority 2 is returned next, data with priority 3 is returned the next, and so on. Specifically, as follows... Figure 5 As shown, the homepage map only requires data from rows 1 and 4 to complete its initial rendering, and the homepage bar chart only requires data from rows 2, 5, and 7 to complete its initial rendering. Therefore, the priority of these two sets of data is set to 1. However, when the page carousel is running, the homepage map also needs to load data from rows 1, 2, and 3, and the homepage bar chart also needs to load data from row 7. Therefore, the priority of these two sets of data is set to 2. After the data with priority 1 is loaded, the data with priority 2 continues to be loaded. During the above process, the homepage map and the homepage bar chart are displayed simultaneously, and their priorities are the same.

[0078] In one embodiment, returning data according to a data return priority index for webpage data loading includes:

[0079] Based on the data return priority index, a B+ tree index is constructed using the B+ tree algorithm; data is returned based on the B+ tree index to load web page data.

[0080] Data is returned to the front end in an ordered manner according to data return priority index, enabling the webpage to efficiently load the data and display the necessary charts. Furthermore, the constructed B+ tree index can be stored in the terminal's memory for later retrieval.

[0081] Specifically, when the front-end page requests data, it returns data based on the priority index table. Each index query returns partial data, satisfying the basic elements of front-end view rendering. When more detailed data is needed, the interface can be called sequentially according to the index value to retrieve the returned data. Utilizing the B+ tree algorithm, a B+ tree index is constructed according to priority. Due to the short and wide characteristics of the B+ tree—few tree levels and numerous branches per node—data retrieval is faster, resulting in fewer I / O (Input / Output) operations and effectively improving query performance. Simultaneously, the index (row number) of each row of data resides in the leaf nodes, ensuring the same number of lookups per instance and more stable query speed. To further enhance performance, the priority index table is loaded into memory and remains resident there. The B+ tree structure implemented in memory further improves query performance compared to disk. The front-end requests and renders data according to priority, thus achieving a gradual loading of the page from vague to detailed, from general to comprehensive.

[0082] In practical applications, the web page data loading method of this application improves the speed of data loading on the page and reduces the system pressure on the server. On the other hand, it allows the chart page to be loaded and rendered gradually from blurry to clear and from general to specific, ensuring the effect and experience of page rendering and preventing white screens, lag, or page errors.

[0083] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0084] Based on the same inventive concept, this application also provides a web page data loading apparatus for implementing the web page data loading method described above. The solution provided by this apparatus is similar to the implementation described in the above method; therefore, the specific limitations in one or more web page data loading apparatus embodiments provided below can be found in the limitations of the web page data loading method described above, and will not be repeated here.

[0085] like Figure 6 As shown, this application also provides a webpage data loading apparatus. The apparatus includes:

[0086] Data acquisition module 200 is used to acquire raw data;

[0087] The slicing module 400 is used to model the original data, and uses the model to slice and layer the original data according to the return priority, so as to obtain the slice and layer results;

[0088] The index building module 600 is used to build a data return priority index based on the slice layering results;

[0089] Loading module 800 is used to return data according to the data return priority index in order to load web page data.

[0090] The aforementioned web page data loading device models the original data and slices and layers the original data based on the model, making the data more conducive to page loading. In addition, it constructs a data return priority index based on the slice and layer results, and uses a priority approach to return / load data in an orderly manner, narrowing the range of data loaded initially. Furthermore, the subsequent loading process returns all the data required by the page step by step according to priority, significantly improving page loading speed.

[0091] In one embodiment, the slicing module 400 is further configured to slice and layer the original data according to the returned priority using an index model; and cache the slicing and layering results.

[0092] In one embodiment, the slicing module 400 is further configured to obtain chart data display requirements; obtain the priority of the original data return based on the chart data display requirements; and slice and layer the original data according to the return priority using a key-value indicator model.

[0093] In one embodiment, the slicing module 400 is also used to cache the sliced ​​layering results in JSON format.

[0094] In one embodiment, the index building module 600 is further configured to obtain the page rendering hierarchy based on the slice layering result; and to establish a data return priority index based on the page rendering hierarchy.

[0095] In one embodiment, the index building module 600 is further configured to return a priority index based on data, construct a B+ tree index using the B+ tree algorithm, and return data based on the B+ tree index for loading web page data.

[0096] Overall, the webpage data loading method and apparatus of this application have the following significant technical advantages in practical applications:

[0097] 1. Loading Priority Index Table. Establishing a loading priority index table is the core of this invention. Through the priority index table, the range of data loaded in the first batch of pages can be minimized, while supporting normal page rendering. That is, while improving loading speed, the integrity of page rendering is ensured, without affecting page effect and user experience.

[0098] 2. Raw Data Slice Storage. Raw data slice storage is the data foundation of this invention. Starting from the goal of chart display, entity-relational database tables are transformed into data slices that are easy to use in charts through indicator models, laying the foundation for improving the loading speed of front-end pages.

[0099] 3. The data return interface uses the B+ algorithm to build a B+ tree index structure in memory according to priority. The B+ tree index structure resides in memory, and each request can quickly find the index (row number) of the required data starting from the root node of the B+ tree according to priority. Then, the corresponding data is found according to the index, integrated, and returned to the front end for display.

[0100] Each module in the aforementioned web page data loading device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0101] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 7 As shown, the computer device includes a processor, memory, communication interface, display screen, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a web page data loading method.

[0102] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0103] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0104] Obtain the raw data;

[0105] Model the original data, and use the model to slice and layer the original data according to the return priority, and obtain the slice and layer results;

[0106] Based on the slice layering results, a data return priority index is created;

[0107] Data is returned according to the data return priority index in order to load web page data.

[0108] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0109] The raw data is sliced ​​and layered according to the returned priority using an indicator model; the sliced ​​and layered results are cached.

[0110] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0111] Obtain the chart data display requirements; based on the chart data display requirements, determine the priority of the returned raw data; slice and layer the raw data according to the returned priority using a key-value indicator model.

[0112] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0113] The sliced ​​and layered results are cached using JSON format.

[0114] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0115] Based on the slice layering results, obtain the page rendering hierarchy; based on the page rendering hierarchy, establish a data return priority index.

[0116] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0117] Based on the data return priority index, a B+ tree index is constructed using the B+ tree algorithm; data is returned based on the B+ tree index to load web page data.

[0118] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0119] Obtain the raw data;

[0120] Model the original data, and use the model to slice and layer the original data according to the return priority, and obtain the slice and layer results;

[0121] Based on the slice layering results, a data return priority index is created;

[0122] Data is returned according to the data return priority index in order to load web page data.

[0123] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0124] The raw data is sliced ​​and layered according to the returned priority using an indicator model; the sliced ​​and layered results are cached.

[0125] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0126] Obtain the chart data display requirements; based on the chart data display requirements, determine the priority of the returned raw data; slice and layer the raw data according to the returned priority using a key-value indicator model.

[0127] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0128] The sliced ​​and layered results are cached using JSON format.

[0129] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0130] Based on the slice layering results, obtain the page rendering hierarchy; based on the page rendering hierarchy, establish a data return priority index.

[0131] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0132] Based on the data return priority index, a B+ tree index is constructed using the B+ tree algorithm; data is returned based on the B+ tree index to load web page data.

[0133] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0134] Obtain the raw data;

[0135] Model the original data, and use the model to slice and layer the original data according to the return priority, and obtain the slice and layer results;

[0136] Based on the slice layering results, a data return priority index is created;

[0137] Data is returned according to the data return priority index in order to load web page data.

[0138] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0139] The raw data is sliced ​​and layered according to the returned priority using an indicator model; the sliced ​​and layered results are cached.

[0140] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0141] Obtain the chart data display requirements; based on the chart data display requirements, determine the priority of the returned raw data; slice and layer the raw data according to the returned priority using a key-value indicator model.

[0142] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0143] The sliced ​​and layered results are cached using JSON format.

[0144] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0145] Based on the slice layering results, obtain the page rendering hierarchy; based on the page rendering hierarchy, establish a data return priority index.

[0146] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0147] Based on the data return priority index, a B+ tree index is constructed using the B+ tree algorithm; data is returned based on the B+ tree index to load web page data.

[0148] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0149] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0150] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0151] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for loading webpage data, characterized in that, The method includes: Obtain the raw data; the raw data refers to the original chart data. The original data is modeled, and the model is used to slice and layer the original data according to the return priority, so as to obtain the slice and layer results. Based on the slice layering results, a data return priority index is established; Data is returned according to the data return priority index to load webpage data; The original data is modeled, and the model is used to slice and layer the original data according to the return priority. The slice and layer results include: obtaining the chart data display requirements; obtaining the priority of the original data return according to the chart data display requirements; slicing and layering the original data according to the return priority using a key-value indicator model; the chart data display requirements include the data type required by the terminal when displaying the chart and the loading order parameters of different types of data; the indicator model slices and layers the original data, which means extracting the data required for the chart from the full indicator data and slicing and grouping the extracted data required for the chart according to the indicator type. The step of establishing a data return priority index based on the slice layering result includes: obtaining the page rendering layer based on the slice layering result; and establishing a data return priority index based on the page rendering layer. The step of returning data according to the data return priority index for webpage data loading includes: constructing and storing a B+ tree index based on the data return priority index using the B+ tree algorithm; and returning data according to the B+ tree index for webpage data loading.

2. The method according to claim 1, characterized in that, The step of modeling the original data and using the model to slice and layer the original data according to return priority, resulting in slice and layer results, includes: Cache the sliced ​​and layered results.

3. The method according to claim 2, characterized in that, The cache slicing hierarchy results include: The sliced ​​and layered results are cached using JSON format.

4. The method according to claim 1, characterized in that, The raw data is stored in a list format of entity-relationship.

5. A webpage data loading device, characterized in that, The device includes: The data acquisition module is used to acquire raw data; the raw data is raw chart data. The slicing module is used to model the original data, and to slice and layer the original data according to the return priority using the model to obtain the slicing and layering results; An index building module is used to build a data return priority index based on the slice layering results; The loading module is used to return data according to the data return priority index in order to load web page data; The slicing module is also used to obtain chart data display requirements; obtain the priority of returning raw data according to the chart data display requirements; slice and layer the raw data according to the return priority using a key-value indicator model; the chart data display requirements include the data type required by the terminal when displaying the chart and the loading order parameters of different types of data; the indicator model slices and layers the raw data, which means extracting the data required for the chart from the full indicator data and slicing and grouping the extracted data required for the chart according to the indicator type. The index building module is also used to obtain the page rendering hierarchy based on the slice layering result; and to establish a data return priority index based on the page rendering hierarchy. The loading module is also used to return a priority index based on the data, construct a B+ tree index using the B+ tree algorithm, and store it; and return data according to the B+ tree index to load web page data.

6. The apparatus according to claim 5, characterized in that, The slicing module is also used to cache the slicing and layering results.

7. The apparatus according to claim 6, characterized in that, The slicing module is also used to cache the sliced ​​layering results in JSON format.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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