Multi-dimensional data processing method and device, equipment and storage medium

Abstract

The invention relates to the technical field of data processing, and particularly provides a multi-dimensional data processing method, device and equipment and a storage medium, and the method comprises the following steps: receiving original data input by a user, and extracting the original data by adopting a JavaScript-based improved prototype chain technology to obtain extracted data; performing logic processing on the extracted data, converting the extracted data into multi-dimensional visual data, and storing the multi-dimensional visual data; the multi-dimensional visual data is packaged to obtain multi-dimensional packaged data, and packaging comprises data compression, data partitioning and index construction; pre-loading the multi-dimensional packaging data so as to obtain pre-loaded data; the preloaded data is displayed in a multi-dimensional visualization mode; by improving the prototype chain technology, the extraction efficiency of structured, semi-structured and non-structured original data is improved, and meanwhile, the safety of original data acquisition is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and specifically provides a multidimensional data processing method, device, equipment and storage medium. Background Art

[0002] With the development of human society, the era of big data has arrived. Faced with the explosive growth of data, how to process massive data in a timely and fast manner is an imminent challenge for researchers. At the same time, data analysis and mining the rich information behind these data to help make better decisions have also become a top priority. Faced with large amounts of data, traditional processing methods can no longer meet the requirements. Data visualization has become a powerful tool for processing big data. How to develop new visualization technologies and use existing technologies to solve practical problems and improve the processing speed and display effect of big data is an important research direction in the current big data field.

[0003] There are two major categories of existing data processing and display technologies: traditional business intelligence (BI) solutions and data visualization Web frameworks. Specifically, traditional BI solutions focus on the cleaning and conversion of raw data to the final display effect. They include upstream and downstream systems in the business, need to obtain data from multiple data sources, clean and analyze it, and then present it to end users through visualization in the form of charts and other forms. This method requires a lot of supporting software and hardware, professional analysts and data modelers to be deployed. The entire cycle is relatively long and cannot dynamically meet business needs. In addition, attention needs to be paid to data storage and calculation, and the details of the front-end display are less involved. In BI solutions, because of its complex analysis and calculation, the development of multiple back-end components, and the focus on big data storage, its deployment and development cycle is long, and the development work requires professional data analysis and data modeling personnel, and the delivery system provides services for specific business needs; with the visualization Web framework, using its customizable method, front-end personnel can quickly create visualization scenes required by the business, but the framework is non-modular, and the development and maintenance workload is large. In addition, for big data cleaning and extraction, additional components are still required to cooperate with computing tasks, which is relatively complex in deployment and maintenance. In addition, when faced with large-scale time series data and complex business scenarios, the above solutions cannot meet the needs well.

[0004] In addition, when faced with huge amounts of data, how to load it effectively and quickly has an important impact on data loading efficiency and user experience. Summary of the invention

[0005] The present invention provides a multidimensional data processing method, device, equipment and storage medium, which are used to solve the defects of slow processing speed and poor display effect of big data in the prior art, and improve the processing speed and display effect of big data.

[0006] In a first aspect, the present invention provides a multidimensional data processing method, the method comprising: receiving original data input by a user, extracting the original data using an improved prototype chain technology based on JavaScript to obtain extracted data; converting the extracted data into multidimensional visualization data through logical processing, and storing the multidimensional visualization data; encapsulating the multidimensional visualization data to obtain multidimensional encapsulated data, wherein the encapsulation includes data compression, data segmentation, and index construction; preloading the multidimensional encapsulated data to obtain preloaded data; and displaying the preloaded data in a multidimensional visualization manner.

[0007] In some implementations, the original data is structured data or unstructured data, the structured data is a SQL database, and the unstructured data includes log files and text data.

[0008] In some implementations, the original data is extracted using JavaScript prototype chain technology, including:

[0009] Determine the original data type;

[0010] Create a base class based on Array.prototype and set the properties and methods of the original data object;

[0011] Creating a subclass that inherits the base class, creating an attribute set according to different primitive data types and the attributes of the corresponding primitive data objects, and adding the attribute set to the corresponding primitive data objects;

[0012] The original data is extracted based on the original data type.

[0013] In some implementations, the extracting data is converted into multi-dimensional visualization data through logical processing, including:

[0014] By improving the prototype chain algorithm through JavaScript, the general logic of data cleaning is placed in the parent object, and the processing rules of the data type are added in the child object to realize the cleaning of the extracted data;

[0015] Converting the cleaned extracted data into multidimensional data, and performing logical processing on the multidimensional data through an advanced data analysis algorithm to obtain multidimensional visualized data, wherein the logical processing includes multidimensional data generation, calculation of derived variables, data aggregation, and normalization processing;

[0016] The multidimensional preprocessed data is loaded into a target storage.

[0017] In some implementations, preloading the multi-dimensional packaging data includes:

[0018] Set the preloading priority of the user behavior dimension to:

[0019] Y1, Y1=K1X+B1

[0020] Among them, X is the user behavior dimension, K1 is the weight value of each user behavior dimension, and B1 is the default priority of each user behavior dimension;

[0021] Set the preloading priority of the data feature dimension to:

[0022] Y2=K2X+B2

[0023] Among them, X is the data feature dimension, K2 is the weight value of each data feature dimension, and B2 is the default priority of each data feature dimension;

[0024] The preloading strategy value is calculated according to the preloading priority of the user behavior dimension and the preloading priority of the data feature dimension, and the preloading strategy value is recorded as Z, then,

[0025] Z=A1Y1+A2Y2

[0026] Among them, A1 is the weight value of the preloading priority in the user behavior dimension, and A2 is the weight value of the preloading priority in the data feature dimension;

[0027] The encapsulated multidimensional data is preloaded according to the preloading strategy value.

[0028] In some implementations, the user behavior dimensions include:

[0029] A heat map of the user's mouse movement trajectory, which is used to show the distribution of the user's clicks on the page;

[0030] The position and duration of a user's mouse cursor resting, which are used to identify the content of interest to the user;

[0031] User mouse click data, which is used to obtain the user's browsing status of the page;

[0032] User mouse scrolling information, where the mouse scrolling information is used to identify the user's focus point and area of ​​interest;

[0033] User historical access records: User historical access records are used to indicate the data information that the user has historically accessed.

[0034] In some embodiments, presenting the preloaded data in a multi-dimensional visualization manner includes:

[0035] Using the dynamic characteristics of JavaScript, the preloaded data is mapped to the visual component;

[0036] Perform multi-dimensional display of the preloaded data, including rolling up, drilling, slicing, dicing, and rotating;

[0037] The preloaded data is displayed on the data interface through pie charts, line charts, and bar charts.

[0038] In a second aspect, the present invention further provides a multidimensional data processing device, the multidimensional data processing device comprising:

[0039] An extraction module is used to receive the original data input by the user, and extract the original data using the improved prototype chain technology based on JavaScript to obtain extracted data;

[0040] A conversion module, used to convert the extracted data into multi-dimensional visualization data through logical processing, and store the multi-dimensional visualization data;

[0041] An encapsulation module, used for encapsulating the multi-dimensional visualization data to obtain multi-dimensional encapsulated data, wherein the encapsulation includes data compression, data segmentation, and index construction;

[0042] A calculation module, used for preloading the multi-dimensional encapsulation data to obtain preloaded data;

[0043] The display module is used to display the preloaded data in a multi-dimensional visual manner.

[0044] In a third aspect, the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the above-mentioned multidimensional data processing methods when executing the program.

[0045] In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program implements any of the above-mentioned multidimensional data processing methods when executed by a processor.

[0046] The multidimensional data processing method provided by the present invention receives the original data input by the user, extracts the original data by using the improved prototype chain technology based on JavaScript to obtain the extracted data; converts the extracted data into multidimensional visualization data through logical processing, and stores the multidimensional visualization data; encapsulates the multidimensional visualization data to obtain multidimensional encapsulated data, wherein the encapsulation includes data compression, data segmentation, and index construction; preloads the multidimensional encapsulated data to obtain preloaded data; and displays the preloaded data in a multidimensional visualization manner. By using the characteristics of the prototype chain in the existing JavaScript technology, and creating subclasses of inheritance relationships for different data classes, the inheritance relationship can automatically process the properties and methods of the data object, realize the generalization processing between different types of data objects, and improve the data extraction efficiency; encapsulate the data by JavaScript, compress the data and construct the index, and adopt a dynamic preloading strategy to control the display process of the multidimensional visualization data, so as to improve the data processing efficiency; the preloading priority of the user behavior dimension and the data feature dimension is set respectively, and the preloading strategy value is calculated to optimize the cached data, so as to improve the user's experience of accessing the web page.

[0047] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 It is a flowchart of the multidimensional data processing method provided by the present invention;

[0050] Figure 2 It is a structural schematic diagram of the multidimensional data processing device provided by the invention;

[0051] Figure 3 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] Combine the following Figures 1 to 3 The embodiments shown describe the technical solution of the present invention:

[0054] The embodiment of the present invention provides a multi-dimensional data processing method, such as Figure 1 As shown, the multidimensional data processing method may include:

[0055] Step S110: receiving the original data input by the user, and extracting the original data using the improved prototype chain technology based on JavaScript to obtain extracted data.

[0056] Exemplarily, the original data may be structured data or unstructured data. The structured data may be a SQL database, and the unstructured data may include log files and text data.

[0057] It should be noted that the original data can be stored locally or at an address input by the user.

[0058] In some exemplary embodiments, the extraction of raw data based on JavaScript prototype chain technology in the above step S110 may include: determining the raw data type; creating a base class based on Array.prototype, and setting the properties and methods of the raw data object; creating a subclass that inherits the base class, creating a property set according to different raw data types and the properties of the corresponding raw data objects, and adding the property set to the corresponding raw data object; extracting the raw data based on the raw data type.

[0059] Step S120: convert the extracted data into multi-dimensional visualization data through logical processing, and store the multi-dimensional visualization data.

[0060] In some exemplary embodiments, the above-mentioned step S120 converts the extracted data into multi-dimensional visualization data through logical processing, which may include: placing the general logic of data cleaning in the parent object through the improved prototype chain algorithm of JavaScript, and adding the processing rules of the data type in the child object to realize the cleaning of the extracted data; converting the cleaned extracted data into multi-dimensional data, and performing logical processing on the multi-dimensional data through the advanced data analysis algorithm to obtain multi-dimensional visualization data, wherein the logical processing may include but is not limited to multi-dimensional data generation, calculation of derived variables, data aggregation, and normalization processing; loading the multi-dimensional pre-processed data into the target storage. Among them, the logical processing including multi-dimensional data generation, calculation of derived variables, data aggregation, and data normalization processing algorithm is used for unified data processing, and the processed data is displayed in the form of a cube, supporting visualization display from different perspectives of rotation, slicing, and dicing.

[0061] The calculation of derived variables is implemented by using a built-in JavaScript algorithm library, and the data generation is implemented by using a built-in JavaScript algorithm library in combination with data to implement a method of data-derived variables, which supports user-defined methods for calculating data-derived variables.

[0062] In this embodiment, the cleaned extracted data can be converted into multidimensional data through a data processing algorithm, which may specifically include data normalization, calculation of derived variables, data aggregation, data summary function, and data reconstruction, wherein:

[0063] The purpose of data normalization is to normalize the messy data in the original data to facilitate data calculation and visualization. In the present invention, the Apriori algorithm can be used to mine frequent itemsets from the original data, and representative feature items in the original data can be filtered out from the frequent itemsets to form a normalized data set.

[0064] After normalization, it is convenient to perform unified data calculation and visualization on the data. Exemplarily, the data normalization steps may include: setting a threshold range; analyzing the item sets in the original data to obtain frequent item sets; and generating an item set list in the frequent item set according to the threshold.

[0065] The purpose of calculating derived variables is to add new dimensions to the original data set to facilitate subsequent data aggregation and data summary. In the present invention, the data derived variables use data analysis algorithms to analyze the original data, which mainly includes three parts: calculating the statistical data between attributes, calculating the weight value of the attributes and calculating the similarity between the attributes. The specific steps may include: setting the data attribute set to be calculated; calculating the statistical data between the attributes, and calculating the weight value of the attributes.

[0066] Data aggregation can specifically include calculating basic statistical values ​​of data. In multi-dimensional data visualization, data aggregation is an indispensable data processing process.

[0067] Data aggregation cleans the original data set, filters out abnormal data, and forms a new data set. The specific steps of data aggregation may include: filtering out abnormal values ​​in the table data set; filtering the abnormal values; and generating an updated data set.

[0068] Data reconstruction, using the updated data set to form a visual data set.

[0069] Step S130: encapsulate the multi-dimensional visualization data to obtain multi-dimensional encapsulated data, wherein the encapsulation may include data compression, data segmentation, and index construction.

[0070] Specifically, in the above step S130, the multi-dimensional visualization data can be encapsulated by using internal programs through data compression, data segmentation, index construction, etc., to reduce data transmission and loading time, support on-demand loading, and at the same time, speed up data query through data indexing.

[0071] Step S140: preload the multi-dimensional encapsulation data to obtain preload data.

[0072] In some exemplary embodiments, preloading the multi-dimensional packaging data in the above step S140 may include:

[0073] Set the preloading priority of the user behavior dimension to:

[0074] Y1, Y1=K1X+B1

[0075] Wherein, X may be a user behavior dimension, K1 may be a weight value of each user behavior dimension, and B1 may be a default priority of each user behavior dimension;

[0076] Set the preloading priority of the data feature dimension to:

[0077] Y2=K2X+B2

[0078] Wherein, X may be a data feature dimension, K2 may be a weight value of each data feature dimension, and B2 may be a default priority of each data feature dimension;

[0079] The preloading strategy value is calculated based on the preloading priority of the user behavior dimension and the preloading priority of the data feature dimension. The preloading strategy value is recorded as Z, then,

[0080] Z=A1Y1+A2Y2

[0081] Wherein, A1 may be a weight value of the preloading priority in the user behavior dimension, and A2 may be a weight value of the preloading priority in the data feature dimension;

[0082] The encapsulated multidimensional data is preloaded according to the preloading strategy value.

[0083] Furthermore, the user behavior dimension may include: a heat map of the user's mouse movement trajectory, the user's mouse stop position and duration, the user's mouse click data, the user's mouse scrolling information, and the user's historical access records, wherein:

[0084] The user mouse movement trajectory heat map is used to show the distribution of user clicks on the page; the user mouse stop position and duration are used to identify the user's attention content; the user mouse click data is used to obtain the user's browsing status on the page; the user mouse scrolling information is used to identify the user's focus and interest areas; the user's historical access record is used to indicate the data information that the user has visited in the past.

[0085] Step S150: display the preloaded data in a multi-dimensional visualization manner.

[0086] For example, the preloaded data can be displayed according to user behavior analysis, using dynamic display technology of HTML tags to achieve data visualization display from different perspectives, improve its display effect, and satisfy visual senses. Among them, the dynamic display technology presents data in priority order, and can display the current request data as required, and provide other required visualization data display according to user operations.

[0087] Furthermore, displaying the preloaded data in a multi-dimensional visualization manner may include: utilizing the dynamic characteristics of JavaScript to map the preloaded data to visualization components; displaying the preloaded data in multiple dimensions, which may include rolling up, drilling down, slicing, dicing, and rotating; and displaying the preloaded data on the data interface through pie charts, line charts, and bar charts.

[0088] The multidimensional data processing method provided by the present invention receives the original data input by the user, extracts the original data by using the improved prototype chain technology based on JavaScript to obtain the extracted data; converts the extracted data into multidimensional visualization data through logical processing, and stores the multidimensional visualization data; encapsulates the multidimensional visualization data to obtain multidimensional encapsulated data, wherein the encapsulation includes data compression, data segmentation, and index construction; preloads the multidimensional encapsulated data to obtain preloaded data; and displays the preloaded data in a multidimensional visualization manner. By using the characteristics of the prototype chain in the existing JavaScript technology, and creating subclasses of inheritance relationships for different data classes, the inheritance relationship can automatically process the properties and methods of the data object, realize the generalization processing between different types of data objects, and improve the data extraction efficiency; encapsulate the data by JavaScript, compress the data and construct the index, and adopt a dynamic preloading strategy to control the display process of the multidimensional visualization data, so as to improve the data processing efficiency; the preloading priority of the user behavior dimension and the data feature dimension is set respectively, and the preloading strategy value is calculated to optimize the cached data, so as to improve the user's experience of accessing the web page.

[0089] Figure 2 is a schematic diagram of the structure of a multidimensional data processing device provided by the invention. The embodiment of the present invention also provides a multidimensional data processing device, such as Figure 2 As shown, the multi-dimensional data processing device may include: an extraction module 201, a conversion module 202, a packaging module 203, a calculation module 204 and a display module 205, wherein:

[0090] The extraction module 201 is used to receive the original data input by the user, and extract the original data using the improved prototype chain technology based on JavaScript to obtain the extracted data;

[0091] The conversion module 202 is used to convert the extracted data into multi-dimensional visualization data through logical processing, and store the multi-dimensional visualization data;

[0092] The encapsulation module 203 is used to encapsulate the multi-dimensional visualization data to obtain multi-dimensional encapsulated data, wherein the encapsulation may include data compression, data segmentation, and index construction;

[0093] The calculation module 204 is used to preload the multi-dimensional packaging data to obtain preloaded data;

[0094] The display module 205 is used to display the preloaded data in a multi-dimensional visualization manner.

[0095] Those skilled in the art should understand that the division of the various modules in the embodiment is only a division of logical functions, and can be fully or partially integrated into one or more actual carriers in actual application, and these modules can be implemented entirely in the form of software called by the processing unit, or entirely in the form of hardware, or in the form of a combination of software and hardware. It should be noted that each module in the optimization device of the network attack detection model in this embodiment corresponds one-to-one to each step in the optimization method of the network attack detection model in the above-mentioned embodiment. Therefore, the specific implementation of this embodiment can refer to the implementation of the above-mentioned multidimensional data processing method, which will not be repeated here.

[0096] An embodiment of the present invention further provides an electronic device, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the multidimensional data processing method as described above when executed by the processor.

[0097] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the multidimensional data processing method as described above is implemented.

[0098] Figure 3 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 3 As shown, the electronic device may include: a processor 310, a communication interface 320, a memory 330 and a communication bus 340, wherein the processor 310, the communication interface 320 and the memory 330 communicate with each other through the communication bus 340. The processor 310 may call the logic instructions in the memory 330 to execute the optimization method of the network attack detection model, the method comprising: receiving the original data input by the user, extracting the original data by using the improved prototype chain technology based on JavaScript to obtain the extracted data; converting the extracted data into multidimensional visualization data through logical processing, and storing the multidimensional visualization data; encapsulating the multidimensional visualization data to obtain multidimensional encapsulation data, wherein the encapsulation includes data compression, data segmentation and index construction; preloading the multidimensional encapsulation data to obtain the preloaded data; and displaying the preloaded data in a multidimensional visualization manner.

[0099] In addition, the logic instructions in the above-mentioned memory 330 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0100] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the multidimensional data processing method provided by the above methods, which includes: receiving original data input by a user, extracting the original data using an improved prototype chain technology based on JavaScript to obtain extracted data; converting the extracted data into multidimensional visualization data through logical processing, and storing the multidimensional visualization data; encapsulating the multidimensional visualization data to obtain multidimensional encapsulated data, wherein encapsulation includes data compression, data segmentation, and index construction; preloading the multidimensional encapsulated data to obtain preloaded data; and displaying the preloaded data in a multidimensional visualization manner.

[0101] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it is implemented to execute the multidimensional data processing method provided by the above-mentioned methods, the method comprising: receiving original data input by a user, extracting the original data using an improved prototype chain technology based on JavaScript to obtain extracted data; converting the extracted data into multidimensional visualization data through logical processing, and storing the multidimensional visualization data; encapsulating the multidimensional visualization data to obtain multidimensional encapsulated data, wherein encapsulation includes data compression, data segmentation, and index construction; preloading the multidimensional encapsulated data to obtain preloaded data; and displaying the preloaded data in a multidimensional visualization manner.

[0102] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0103] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0104] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multidimensional data processing method, characterized in that: include: Receive the original data input by the user, and extract the original data using the improved prototype chain technology based on JavaScript to obtain extracted data; Converting the extracted data into multi-dimensional visualization data through logical processing, and storing the multi-dimensional visualization data; Encapsulating the multidimensional visualization data to obtain multidimensional encapsulated data, wherein the encapsulation includes data compression, data segmentation, and index construction; Preloading the multi-dimensional encapsulation data to obtain preloaded data; The preloaded data is displayed in a multi-dimensional visualization manner.

2. The multidimensional data processing method according to claim 1, characterized in that: The original data is structured data or unstructured data. The structured data is an SQL database, and the unstructured data includes log files and text data.

3. The multidimensional data processing method according to claim 1, characterized in that: The extracting of the original data based on JavaScript prototype chain technology includes: Determine the original data type; Create a base class based on Array.prototype and set the properties and methods of the original data object; Creating a subclass that inherits the base class, creating an attribute set according to different primitive data types and the attributes of the corresponding primitive data objects, and adding the attribute set to the corresponding primitive data objects; The original data is extracted based on the original data type.

4. The multidimensional data processing method according to claim 1, characterized in that: The extracting data is converted into multi-dimensional visual data through logical processing, including: By improving the prototype chain algorithm through JavaScript, the general logic of data cleaning is placed in the parent object, and the processing rules of the data type are added in the child object to realize the cleaning of the extracted data; Converting the cleaned extracted data into multidimensional data, and performing logical processing on the multidimensional data through an advanced data analysis algorithm to obtain multidimensional visualized data, wherein the logical processing includes multidimensional data generation, calculation of derived variables, data aggregation, and normalization processing; The multidimensional preprocessed data is loaded into a target storage.

5. The multidimensional data processing method according to claim 1, characterized in that: The preloading of the multi-dimensional encapsulation data includes: Set the preloading priority of the user behavior dimension to: Y1, Y1=K1X+B1 Among them, X is the user behavior dimension, K1 is the weight value of each user behavior dimension, and B1 is the default priority of each user behavior dimension; Set the preloading priority of the data feature dimension to: Y2=K2X+B2 Among them, X is the data feature dimension, K2 is the weight value of each data feature dimension, and B2 is the default priority of each data feature dimension; The preloading strategy value is calculated according to the preloading priority of the user behavior dimension and the preloading priority of the data feature dimension, and the preloading strategy value is recorded as Z, then, Z=A1Y1+A2Y2 Among them, A1 is the weight value of the preloading priority in the user behavior dimension, and A2 is the weight value of the preloading priority in the data feature dimension; The encapsulated multidimensional data is preloaded according to the preloading strategy value.

6. The multidimensional data processing method according to claim 5, characterized in that: The user behavior dimensions include: A heat map of the user's mouse movement trajectory, which is used to show the distribution of the user's clicks on the page; The position and duration of a user's mouse cursor resting, which are used to identify the content of interest to the user; User mouse click data, which is used to obtain the user's browsing status of the page; User mouse scrolling information, where the mouse scrolling information is used to identify the user's focus point and area of ​​interest; User historical access records: User historical access records are used to indicate the data information that the user has historically accessed.

7. The multidimensional data processing method according to claim 1, characterized in that: The presenting of the preloaded data in a multi-dimensional visualization manner includes: Using the dynamic characteristics of JavaScript, the preloaded data is mapped to the visual component; Perform multi-dimensional display of the preloaded data, including rolling up, drilling, slicing, dicing, and rotating; The preloaded data is displayed on the data interface through pie charts, line charts, and bar charts.

8. A multidimensional data processing device, characterized in that: include: An extraction module is used to receive the original data input by the user, and extract the original data using the improved prototype chain technology based on JavaScript to obtain extracted data; A conversion module, used to convert the extracted data into multi-dimensional visualization data through logical processing, and store the multi-dimensional visualization data; An encapsulation module, used for encapsulating the multi-dimensional visualization data to obtain multi-dimensional encapsulated data, wherein the encapsulation includes data compression, data segmentation, and index construction; A calculation module, used for preloading the multi-dimensional encapsulation data to obtain preloaded data; The display module is used to display the preloaded data in a multi-dimensional visual manner.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the multidimensional data processing method according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the multidimensional data processing method according to any one of claims 1 to 7 is implemented.

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