Data visualization processing method and apparatus, and storage medium

Abstract

The application provides a data visualization processing method and device and a storage medium, relates to the field of data processing, and is used for improving the effect of data visualization. The method comprises the following steps: acquiring a plurality of high-dimensional data. The plurality of high-dimensional data are classified, a first data set is determined, and the first data set comprises high-dimensional data corresponding to a target type. The first data set is split according to a decision tree algorithm and a plurality of preset features, a plurality of feature sets are determined, one preset feature corresponds to one feature set, and the plurality of preset features are features of the high-dimensional data in the first data set. A target set is determined from the plurality of feature sets according to a target feature, the target set is a feature set in the plurality of feature sets, and the matching degree of the target feature is greater than a preset similarity threshold. The target set is rendered and processed.

Description

Technical Field

[0001] This application relates to the field of data processing, and more particularly to a data visualization processing method, apparatus, and storage medium. Background Technology

[0002] The rapid development of big data has brought numerous conveniences to people's lives and work. To further improve data usability and ensure clear and effective information delivery, data visualization is an effective technique. Data visualization can present relatively dry data in a way that is easier for users to understand and accept.

[0003] In data visualization, the server can acquire a dataset containing a large amount of data. Then, the server can classify the data in the dataset according to requirements, obtaining various data types. Next, the server renders the data of the target type that meets the requirements, forming an image suitable for the needs. However, current technical solutions obtain the required data through only one classification, which may result in low accuracy of the data and affect the visualization effect. Summary of the Invention

[0004] This application provides a data visualization processing method, apparatus, and storage medium to improve the data visualization effect.

[0005] To achieve the above objectives, this application adopts the following technical solution:

[0006] Firstly, this application provides a data visualization processing method. In this method, a data visualization processing device (hereinafter referred to as a "processing device") acquires multiple high-dimensional data sets. The processing device can classify the multiple high-dimensional data sets to determine a first dataset, which includes high-dimensional data corresponding to a target type. Then, the processing device can split the first dataset according to a decision tree algorithm and multiple preset features to determine multiple feature sets, where each preset feature corresponds to one feature set, and the multiple preset features are features of the high-dimensional data in the first dataset. Afterwards, the processing device can determine a target set from the multiple feature sets based on the target feature, where the target set is the set of features from the multiple feature sets whose matching degree with the target feature is greater than a preset similarity threshold. Finally, the processing device can perform rendering processing on the target set.

[0007] Optionally, the above method further includes: the processing device determining a first information entropy based on multiple preset features and a first dataset, wherein the first information entropy indicates the degree of matching between high-dimensional data in the first dataset and target features. Then, the processing device determining multiple second information entropies based on multiple preset features and multiple feature sets, wherein the second information entropies indicate the degree of matching between feature sets and target features, with one feature set corresponding to one second information entropy. Next, the processing device determining multiple information gains based on the first information entropy and multiple second information entropies, wherein the information gains indicate the degree of matching between feature sets and target features. The above method of "determining a target set from multiple feature sets" includes: the processing device comparing multiple information gains to determine a target gain, wherein the target gain is the largest information gain among the multiple information gains. Then, the processing device using the feature set corresponding to the target gain as the target set.

[0008] Optionally, the above method further includes: the processing device can determine rendering task information corresponding to the target set, the rendering task information including: the number of rendering tasks and the task duration of each rendering task. The above method of "rendering the target set" includes: if the number of rendering tasks is less than a first preset threshold and the task duration of each rendering task is less than a second preset threshold, then the processing device can perform rendering processing on the target set.

[0009] Optionally, the target set includes a second dataset and a third dataset. The method further includes: if the number of rendering tasks exceeds a first preset threshold, and the duration of each rendering task exceeds a second preset threshold, the processing device can perform rendering processing on the second dataset. Then, the processing device can send a rendering message to a second server, the rendering message including the third dataset, the rendering message instructing the second server to render the third dataset. Afterwards, the processing device can receive rendering data from the second server, the rendering data being the data rendered from the third dataset.

[0010] Secondly, this application provides a data visualization processing apparatus, which includes an acquisition module and a processing module.

[0011] The module comprises three sub-modules: an acquisition module for acquiring multiple high-dimensional data sets; a processing module for classifying these high-dimensional data sets to determine a first dataset, which includes high-dimensional data corresponding to the target type; a splitting module for the first dataset based on a decision tree algorithm and multiple preset features to determine multiple feature sets, with each preset feature corresponding to a feature set; and a set of preset features representing the features of the high-dimensional data in the target set. The processing module further determines a target set from these feature sets based on the target features; the target set consists of features from the multiple feature sets whose matching degree with the target features is greater than a preset similarity threshold. Finally, the processing module renders the target set.

[0012] Optionally, the processing module is specifically used to determine a first information entropy based on multiple preset features and a first dataset. The first information entropy indicates the degree of matching between high-dimensional data in the first dataset and the target features. The processing module is also used to determine multiple second information entropies based on multiple preset features and multiple feature sets. The second information entropies indicate the degree of matching between feature sets and target features; one feature set corresponds to one second information entropy. Then, the processing module is further used to determine multiple information gains based on the first information entropy and the multiple second information entropies. The information gains indicate the degree of matching between feature sets and target features. Then, the processing module is specifically used to compare the multiple information gains and determine a target gain, which is the largest information gain among the multiple information gains. The processing module is also used to use the feature set corresponding to the target gain as the target set.

[0013] Optionally, the processing module is specifically used to determine the rendering task information corresponding to the target set. The rendering task information includes the number of rendering tasks and the task duration of each rendering task. The processing module is also used to perform rendering processing on the target set if the number of rendering tasks is less than a first preset threshold and the task duration of each rendering task is less than a second preset threshold.

[0014] Optionally, the processing module is specifically configured to perform rendering processing on the second dataset if the number of rendering tasks exceeds a first preset threshold and the task duration of each rendering task exceeds a second preset threshold. The processing module is also configured to send a rendering message to the second server, the rendering message including a third dataset, the rendering message instructing the second server to render the third dataset. Subsequently, the processing module is further configured to receive rendering data from the second server, the rendering data being the data rendered from the third dataset.

[0015] Thirdly, this application provides a data visualization processing apparatus, comprising a processor and a memory. The processor and the memory are coupled. The memory stores one or more programs, which include computer-executable instructions. When the data visualization processing apparatus is running, the processor executes the computer-executable instructions stored in the memory to implement the data visualization processing method as described in the first aspect and any possible implementation thereof.

[0016] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the data visualization processing method described in the first aspect and any possible implementation thereof.

[0017] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, causes the computer to implement the data visualization processing method as described in the first aspect and any possible implementation thereof.

[0018] The technical problems that the data visualization processing device, computer equipment, computer storage medium or computer program product can solve and the technical effects it can achieve can be found in the technical problems and technical effects solved in the first aspect above, and will not be repeated here.

[0019] The technical solution provided in this application offers at least the following beneficial effects: The server can acquire and classify multiple high-dimensional data sets to determine a first dataset, which includes high-dimensional data corresponding to the target type. Then, the server can split the first dataset using a decision tree algorithm and multiple preset features to determine multiple feature sets, with each preset feature corresponding to one feature set. These preset features are the features of the high-dimensional data in the first dataset. In other words, the server can split the first dataset, reducing the amount of data processing. Furthermore, the server can use a decision tree algorithm for splitting, further improving data accuracy. Next, the server can determine a target set from the multiple feature sets based on the target features. The target set is the feature set whose matching degree with the target features is greater than a preset similarity threshold. That is, the high-dimensional data in the target set has higher accuracy. Finally, the server can render the target set. Thus, due to the improved data accuracy, the target set can better meet the requirements (similar to the target type), thereby improving the data visualization effect. Attached Figure Description

[0020] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application, and do not constitute an undue limitation of this application.

[0021] Figure 1 This is a schematic diagram of the structure of a server according to an exemplary embodiment;

[0022] Figure 2 This is a flowchart illustrating a data visualization processing method according to an exemplary embodiment;

[0023] Figure 3 This is a flowchart illustrating another data visualization processing method according to an exemplary embodiment;

[0024] Figure 4 This is a flowchart illustrating another data visualization processing method according to an exemplary embodiment;

[0025] Figure 5 This is a flowchart illustrating another data visualization processing method according to an exemplary embodiment;

[0026] Figure 6 This is a structural block diagram of a data visualization processing apparatus according to an exemplary embodiment;

[0027] Figure 7 This is a schematic diagram illustrating the structure of a data visualization processing apparatus according to an exemplary embodiment;

[0028] Figure 8 This is a conceptual partial view of a computer program product according to an exemplary embodiment. Detailed Implementation

[0029] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0030] In this article, the character " / " generally indicates that the objects before and after it are in an "or" relationship. For example, A / B can be understood as A or B.

[0031] The terms “first” and “second” in the specification and claims of this application are used to distinguish different objects, rather than to describe a specific order of objects.

[0032] Furthermore, the terms "comprising" and "having," and any variations thereof, used in the description of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or modules is not limited to the steps or modules listed, but may optionally include other steps or modules not listed, or may optionally include other steps or modules inherent to such process, method, product, or device.

[0033] Furthermore, in the embodiments of this application, the words "exemplary" or "for example" are used to indicate that they are examples, illustrations, or descriptions. Any embodiment or design that is described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design options. Specifically, the use of the words "exemplary" or "for example" is intended to present concepts in a concrete manner.

[0034] For ease of understanding, the terminology used in the embodiments of this application will be introduced below.

[0035] High-dimensional data refers to data that exhibits patterns and has a high degree of dimension. Pattern-based data refers to data with a clearly defined data type, facilitating user searching; high-dimensional data refers to data with a vast number of dimensions, even with a clearly defined data type.

[0036] Before providing a detailed description of the data visualization processing method of this application embodiment, the implementation environment and application scenarios of this application embodiment will be introduced first.

[0037] In data visualization, the server acquires a dataset containing a large amount of data. The server then categorizes the data in the dataset according to requirements, resulting in various data types. Next, the server renders the data of the target type that meets the requirements, creating an image suitable for that purpose. However, current data visualization systems require staff to select the target data type after data categorization. Therefore, current data visualization systems consume significant human, material, and time resources. Furthermore, since current systems obtain the required data through only one categorization, the accuracy of the data may be low, affecting the visualization effect.

[0038] To address the aforementioned issues, this application provides a data visualization processing method. A server can acquire and classify multiple high-dimensional data sets to determine a first dataset, which includes high-dimensional data corresponding to a target type. Then, the server can partition the first dataset based on the characteristics of the high-dimensional data, determining multiple feature sets. Next, the server can determine a target set from these feature sets based on the target features, ensuring that the target set adequately meets the requirements. Finally, the server can render the target set. Thus, due to improved data accuracy, the target set better meets the requirements, thereby enhancing the data visualization effect.

[0039] The implementation environment of the embodiments of this application is described below.

[0040] Figure 1 This is a schematic diagram of the structure of a server applying the method provided in this disclosure, as provided in an embodiment of the present disclosure. The server 10 includes a processor 101 and a memory 102.

[0041] The processor 101 may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor 101 may include an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU). Different processing units may be independent devices or integrated into one or more processors.

[0042] Memory 102 may include one or more computer-readable storage media, which may be non-transitory. Memory 102 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In one possible implementation, the non-transitory computer-readable storage media in memory 102 is used to store at least one instruction, which is executed by processor 101 to implement the data visualization processing method provided in the embodiments of this disclosure.

[0043] In one possible implementation, server 10 may further include a peripheral device interface 103 and at least one peripheral device. The processor 101, memory 102, and peripheral device interface 103 can be connected via a bus or signal lines. Each peripheral device can be connected to peripheral device interface 103 via a bus, signal lines, or a circuit board. Specifically, the peripheral device includes at least one of the following: radio frequency circuitry 104, display screen 105, camera assembly 106, audio circuitry 107, positioning assembly 108, and power supply 109.

[0044] Peripheral interface 103 can be used to connect at least one I / O (Input / Output) related peripheral device to processor 101 and memory 102. In one implementation, processor 101, memory 102, and peripheral interface 103 are integrated on the same chip or circuit board; in some other embodiments, any one or two of processor 101, memory 102, and peripheral interface 103 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0045] The radio frequency (RF) circuit 104 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 104 communicates with communication networks and other communication devices via electromagnetic signals. The RF circuit 104 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals back into electrical signals. Optionally, the RF circuit 104 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The RF circuit 104 can communicate with other servers via at least one wireless communication protocol. This wireless communication protocol includes, but is not limited to: metropolitan area networks (MANs), various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks (WLANs), and / or Wi-Fi (Wireless Fidelity) networks. In one possible implementation, the RF circuit 104 may also include circuitry related to NFC (Near Field Communication), which is not limited in this disclosure.

[0046] Display screen 105 is used to display a UI (User Interface). This UI may include graphics, text, icons, videos, and any combination thereof. When display screen 105 is a touch screen, it also has the ability to collect touch signals on or above its surface. These touch signals can be input as control signals to processor 101 for processing. In this case, display screen 105 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In one possible implementation, display screen 105 can be a single unit, serving as the front panel of server 10; display screen 105 can be made of materials such as LCD (Liquid Crystal Display) or OLED (Organic Light-Emitting Diode).

[0047] The camera assembly 106 is used to acquire images or videos. Optionally, the camera assembly 106 includes a front-facing camera and a rear-facing camera. Typically, the front-facing camera is located on the front panel of the server, and the rear-facing camera is located on the back of the server. The audio circuit 107 may include a microphone and a speaker. The microphone is used to acquire sound waves from the user and the environment, and convert the sound waves into electrical signals that are input to the processor 101 for processing, or input to the radio frequency circuit 104 for voice communication. For stereo acquisition or noise reduction purposes, there may be multiple microphones, each located at a different location on the server 10. The microphone may also be an array microphone or an omnidirectional acquisition microphone. The speaker is used to convert electrical signals from the processor 101 or the radio frequency circuit 104 into sound waves. The speaker may be a conventional film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can convert electrical signals not only into sound waves that are audible to humans, but also into sound waves that are inaudible to humans for purposes such as ranging. In one possible implementation, the audio circuit 107 may also include a headphone jack.

[0048] The positioning component 108 is used to locate the current geographic location of the server 10 to enable navigation or LBS (Location Based Service). The positioning component 108 can be a positioning component based on the US GPS (Global Positioning System), China's BeiDou system, Russia's Granas system, or the European Union's Galileo system.

[0049] The power supply 109 is used to power the various components in the server 10. The power supply 109 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When the power supply 109 includes a rechargeable battery, the rechargeable battery can support wired or wireless charging. The rechargeable battery can also be used to support fast charging technology.

[0050] In one possible implementation, server 10 also includes one or more sensors 1010. These sensors 1010 include, but are not limited to, accelerometers, gyroscopes, pressure sensors, fingerprint sensors, optical sensors, and proximity sensors.

[0051] An accelerometer can detect the magnitude of acceleration along the three axes of a coordinate system established with respect to server 10. A gyroscope sensor can detect the orientation and rotation angle of server 10; the gyroscope sensor can work in conjunction with the accelerometer to acquire 3D motions of the user on server 10. A pressure sensor can be located on the side bezel of server 10 and / or on the lower layer of display screen 105. When the pressure sensor is located on the side bezel of server 10, it can detect the user's grip signal on server 10. A fingerprint sensor is used to acquire the user's fingerprint. An optical sensor is used to acquire ambient light intensity. A proximity sensor, also known as a distance sensor, is typically located on the front panel of server 10. The proximity sensor is used to acquire the distance between the user and the front of server 10.

[0052] The data visualization processing method disclosed herein can be executed by a data visualization processing device, which can be... Figure 1 The server shown is an example. The execution device can also be the server's central processing unit (CPU) or a control module within the server for processing data. This application embodiment uses a server executing a data visualization processing method as an example to illustrate the data visualization processing method provided in this application embodiment.

[0053] In one feasible approach, the server is used to provide voice and / or data connectivity services to the user. The server can have different names, such as UE (User Equipment), terminal unit, terminal station, mobile station, mobile station, remote station, remote terminal, mobile device, wireless communication device, vehicle user equipment, terminal agent, or terminal device, etc.

[0054] Optionally, the server can be various handheld devices, in-vehicle devices, wearable devices, or computers with communication capabilities, and this disclosure does not limit this in any way. For example, a handheld device can be a smartphone. An in-vehicle device can be an in-vehicle navigation system. A wearable device can be a smart bracelet. A computer can be a personal digital assistant (PDA) computer, a tablet computer, or a laptop computer.

[0055] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0056] like Figure 2 As shown, this application provides a data visualization processing method, which includes:

[0057] S201, The first server acquires multiple high-dimensional data.

[0058] In one possible implementation, a first server may acquire at least one image, each image including at least one object. The first server may then perform image recognition processing on each of the at least one image to determine multiple objects and the corresponding high-dimensional data for each object.

[0059] For example, if the image contains houses, trees, and cars, the first server can identify multiple objects, including houses, trees, and cars. Thus, the first server can determine the high-dimensional data corresponding to the houses (such as height, width, and length), the high-dimensional data corresponding to the trees (such as tree height, trunk thickness, and leaf color), and the high-dimensional data corresponding to the cars (such as car logo, color, and length).

[0060] It should be noted that the image recognition processing described in this application is not limited. For example, the image recognition processing can be a neural network image recognition technique. Another example is a nonlinear dimensionality reduction image recognition technique. Yet another example is a traditional image recognition technique.

[0061] In another possible implementation, the first server can send a first request message to other devices, which requests multiple high-dimensional data. These other devices are any devices other than the first server. Subsequently, the first server can receive the multiple high-dimensional data from the other devices.

[0062] S202. The first server classifies multiple high-dimensional data to determine the first dataset.

[0063] The first dataset includes high-dimensional data corresponding to the target type.

[0064] In one possible design, the target type is the type of the target dimension of the target object. That is, the high-dimensional data corresponding to the target type can be the data of the target dimension of the target object.

[0065] For example, if the target object is a car and the target dimension is color, then the first dataset can include the car's color data. If the target object is a house and the target dimension is height, then the first dataset can include the house's height data.

[0066] In one possible implementation, a first server can classify multiple high-dimensional datasets based on multiple objects to determine multiple fourth datasets. Each fourth dataset includes high-dimensional data of one object type, and the multiple fourth datasets include a fifth dataset, which includes high-dimensional data corresponding to the target object. Then, the first server can determine the fifth dataset based on the multiple fourth datasets and the target object. Finally, the first server can determine the first dataset based on the fifth dataset and the target dimension.

[0067] For example, if multiple high-dimensional data include: the height, width, and length of a house; the height, trunk width, and leaf color of a tree; and the logo, color, and length of a car, then multiple fourth datasets include: high-dimensional data corresponding to houses (height, width, and length), high-dimensional data corresponding to trees (height, trunk width, and leaf color), and high-dimensional data corresponding to cars (logo, color, and length). If the target type is the color of a car, then the target object is a car, and the target dimension is color. The first server can then determine that the fifth dataset includes high-dimensional data corresponding to cars, and the first dataset includes high-dimensional data corresponding to the color of cars.

[0068] S203. The first server splits the first dataset according to the decision tree algorithm and multiple preset features to determine multiple feature sets.

[0069] In this dataset, one preset feature corresponds to one feature set, and multiple preset features are features of high-dimensional data in the first dataset.

[0070] In one possible implementation, the first server can determine multiple feature spaces corresponding to multiple preset features, where each preset feature corresponds to one feature space, and each feature space includes multiple feature points. Then, the first server can use the feature points in the multiple feature spaces as the node splitting criteria for the decision tree to split the first dataset and determine multiple feature sets.

[0071] For example, if the first dataset includes high-dimensional data of leaves with the color green, light green, pale green, golden, yellow, yellowish-green, brown, tan, and hardwood, and if multiple preset features are green, yellow, and brown, then multiple feature spaces include: the feature space corresponding to the preset feature green (green, light green, pale green), the feature space corresponding to the preset feature yellow (gold, yellow, yellowish-green), and the feature space corresponding to the preset feature brown (brown, tan, hardwood). Subsequently, the first server can determine multiple feature sets, including: the feature set corresponding to the preset feature green (high-dimensional data of green leaves, high-dimensional data of light green leaves, and high-dimensional data of weak green leaves), the feature set corresponding to the preset feature yellow (high-dimensional data of golden leaves, high-dimensional data of yellow leaves, and high-dimensional data of yellow-green leaves), and the feature set corresponding to the preset feature brown (high-dimensional data of brown leaves, high-dimensional data of tan leaves, and high-dimensional data of hardwood leaves).

[0072] S204. The first server determines the target set from multiple feature sets based on the target characteristics.

[0073] The target set is the set of features that match the target feature more than a preset similarity threshold from among multiple feature sets.

[0074] For example, suppose multiple feature sets are high-dimensional data of leaves with a light green color, high-dimensional data of leaves with a yellowish-green color, and high-dimensional data of leaves with a brown color. If the target feature is grass green, then the matching degree between light green and the target feature is 70%, between yellowish-green and the target feature is 90%, and between brown and the target feature is 40%. If the preset similarity threshold is 80%, then the target set is high-dimensional data of leaves with a yellowish-green color.

[0075] It should be noted that the preset similarity threshold is not limited in the embodiments of this application. For example, the preset similarity threshold can be 90%. Another example is that the preset similarity threshold can be 80%. Yet another example is that the preset similarity threshold can be 85%.

[0076] In one possible implementation, the first server can determine the matching degree between each feature set and the target feature based on multiple preset features, a first dataset, and multiple feature sets. Then, the first server can compare the matching degree between each feature set and the target feature, and take the feature set with a matching degree greater than a preset similarity threshold as the target set.

[0077] In the embodiments of this application, such as Figure 3 As shown, prior to S204, the data visualization processing method may also include: S301-S303.

[0078] S301. The first server determines the first information entropy based on multiple preset features and the first dataset.

[0079] The first information entropy is used to indicate the degree of matching between the high-dimensional data in the first dataset and the target features.

[0080] In one possible implementation, after the first server determines multiple preset features, the first server can determine a first ratio based on the number of high-dimensional data corresponding to each preset feature and the number of high-dimensional data in the first dataset.

[0081] In one possible design, the first ratio can be expressed by Formula 1.

[0082]

[0083] Where, p k This is used to represent the ratio of the number of high-dimensional data corresponding to the k-th preset feature among y preset features to the number of high-dimensional data in the first dataset, k∈{1,2,...,y}, S k M is used to represent the number of high-dimensional data corresponding to the k-th preset feature among y preset features, and M is used to represent the number of high-dimensional data in the first dataset. y, k and M are all positive integers.

[0084] Then, the first server can determine the first information entropy based on the first ratio.

[0085] In one possible design, the first information entropy can be represented by Equation 2.

[0086]

[0087] Where Ent(D) represents the first information entropy, and D represents the first dataset.

[0088] It should be noted that, in this embodiment, the matching degree between the high-dimensional data and the target features in the first dataset refers to the purity of the high-dimensional data of the target type in the first dataset. The smaller the information entropy, the fewer types of high-dimensional data correspond to in dataset D, and the higher the purity of dataset D, i.e., the higher the purity of the first dataset. When the information entropy is at its minimum value of 0, it means that there is only one type of data in dataset D, i.e., there is only one type of high-dimensional data in the first dataset.

[0089] It should be noted that, in this embodiment, the information entropy is inversely proportional to the matching degree between the high-dimensional data in the first dataset and the target feature. That is, the higher the information entropy, the lower the matching degree between the high-dimensional data in the first dataset and the target feature; the lower the information entropy, the higher the matching degree between the high-dimensional data in the first dataset and the target feature.

[0090] S302. The first server determines multiple second information entropies based on multiple preset features and multiple feature sets.

[0091] The second information entropy is used to indicate the degree of matching between the feature set and the target feature, with one second information entropy corresponding to one feature set.

[0092] In one possible implementation, for each second information entropy, the first server can determine each second information entropy according to a first operation. The first operation may include: the first server splitting the first feature set according to the feature points in the feature space corresponding to the first preset feature to obtain a split dataset, where the first preset feature is any one of multiple preset features, and the first feature set is the feature set corresponding to the first preset feature among multiple feature sets. Then, the first server can determine a first sub-information entropy based on the number of feature points in the feature space corresponding to the first preset feature, the number of high-dimensional data in each dataset in the split dataset, and the information entropy of the split dataset. The first sub-information entropy is the information entropy of the first feature set.

[0093] In one possible design, the second information entropy can be represented by Equation 3.

[0094]

[0095] Where T represents the first sub-information entropy, X represents the number of feature points in the feature space corresponding to the first preset feature, and v represents the v-th feature point in the feature space corresponding to the first preset feature. Ent(D) is used to represent the ratio of the number of high-dimensional data in the split dataset corresponding to the v-th feature point to the number of high-dimensional data in the first dataset. v X is used to represent the information entropy of the split dataset corresponding to the v-th feature point, where X and v are both positive integers.

[0096] S303. The first server determines multiple information gains based on the first information entropy and multiple second information entropies.

[0097] In this context, one information gain corresponds to one feature set, and the information gain is used to indicate the degree of matching between the feature set and the target feature.

[0098] In one possible implementation, for each information gain, the first server can determine each information gain according to a second operation. The second operation may include: the first server can determine the first information gain based on the first information entropy and the first sub-information entropy, where the first information gain is the information gain corresponding to the first feature set.

[0099] In one possible design, the first information gain can satisfy Equation 4.

[0100]

[0101] Wherein, Gain(D, a) represents the information gain corresponding to the first preset feature, and a is the first preset feature.

[0102] In the embodiments of this application, such as Figure 3 As shown, S204 may include S304 and S305.

[0103] S304. The first server compares multiple information gains to determine the target gain.

[0104] Among them, the target gain is the largest information gain among multiple information gains.

[0105] For example, if multiple information gains include 15, 20, and 10, the first server can determine that the target gain is 20. It should be noted that in this embodiment, the information gain is directly proportional to the matching degree between the feature set and the target feature. That is, the larger the information gain, the higher the matching degree between the feature set and the target feature; the smaller the information gain, the lower the matching degree between the feature set and the target feature.

[0106] S305. The first server uses the feature set corresponding to the target gain as the target set.

[0107] For example, if the feature set corresponding to the target gain is high-dimensional data of leaves with a yellow-green color, then the first server can determine that the target set is high-dimensional data of leaves with a yellow-green color.

[0108] Understandably, the first server can determine a first information entropy based on multiple preset features and a first dataset. This first information entropy indicates the degree of matching between high-dimensional data in the first dataset and the target features. Then, the first server can determine multiple second information entropies based on multiple preset features and multiple feature sets. These second information entropies indicate the degree of matching between feature sets and target features; one feature set corresponds to one second information entropy. Next, the first server can determine multiple information gains based on the first information entropy and the multiple second information entropies. These information gains indicate the degree of matching between feature sets and target features. The first server can then compare these multiple information gains to determine the target gain, which is the largest information gain among them. Finally, the first server can use the feature set corresponding to the target gain as the target set. Since a larger information gain indicates a higher degree of matching between the feature set and the target feature, the target set corresponding to the target gain has a relatively high degree of matching with the target feature, resulting in higher data accuracy and a target set that meets the requirements.

[0109] S205. The first server performs rendering processing on the target set.

[0110] It should be noted that the rendering process is not limited in the embodiments of this application. For example, the rendering process can be scanline rendering. Another example is indirect lighting. Yet another example is ray tracing.

[0111] Understandably, the first server can acquire and classify multiple high-dimensional data sets to determine the first dataset, which includes high-dimensional data corresponding to the target type. Then, the first server can split the first dataset using a decision tree algorithm and multiple preset features, determining multiple feature sets. Each preset feature corresponds to one feature set, and these preset features are the features of the high-dimensional data in the first dataset. In other words, the first server can partition the first dataset based on the features of the high-dimensional data, reducing the amount of data processing. Furthermore, the splitting using the decision tree algorithm can further improve data accuracy. Next, the first server can determine the target set from the multiple feature sets based on the target features. The target set consists of features from the multiple feature sets whose matching degree with the target features is greater than a preset similarity threshold. That is, the high-dimensional data in the target set has higher accuracy. Finally, the first server can render the target set. Thus, due to the improved data accuracy, the target set can better meet the requirements, thereby improving the data visualization effect.

[0112] like Figure 4As shown, prior to S205, the data visualization processing method provided in this application embodiment may further include:

[0113] S401. The first server determines the rendering task information corresponding to the target set.

[0114] The rendering task information includes the number of rendering tasks and the duration of each rendering task.

[0115] In one possible implementation, the first server can determine the duration of each rendering task based on the amount of high-dimensional data in the target set, the number of rendering tasks, and the amount of high-dimensional data in each rendering task.

[0116] For example, if the target set contains 50 high-dimensional data points, each rendering task can render 5 high-dimensional data points, and the first server takes 12 minutes to render one high-dimensional data point, then the rendering task information includes: the number of rendering tasks is 10, and the task duration of each rendering task is 1 hour.

[0117] S402. The first server determines whether the number of rendering tasks is less than a first preset threshold and whether the task duration of each rendering task is less than a second preset threshold.

[0118] In some embodiments, if the number of rendering tasks is less than a first preset threshold and the task duration of each rendering task is less than a second preset threshold, then S205 is executed.

[0119] For example, if the rendering task information corresponding to the target set includes: the number of rendering tasks is 4, the task duration of each rendering task is 50 minutes, the first preset threshold is 5, and the second preset threshold is 1 hour, then the first server can perform rendering processing on the target set.

[0120] It should be noted that, in this embodiment, the first preset threshold and the second preset threshold are not limited. The first preset threshold and the second preset threshold are the processing range of the data rendering module in the first server. For example, the first preset threshold can be 5, and the second preset threshold can be 1 hour. Another example is that the first preset threshold can be 10, and the second preset threshold can be 2 hours. Yet another example is that the first preset threshold can be 7, and the second preset threshold can be 1.5 hours.

[0121] In one possible implementation, the first server may include multiple rendering systems. The first server can obtain load information from the multiple rendering systems, which reflects the load on the rendering systems. The first server can then allocate multiple rendering tasks to the multiple rendering systems based on the rendering task information and the load information of the multiple rendering systems.

[0122] For example, if there are 6 rendering tasks and multiple rendering systems including rendering system A, rendering system B, and rendering system C, with rendering system A having a load of 10%, rendering system B having a load of 50%, and rendering system C having a load of 25%, then the first server can allocate 3 rendering tasks to rendering system A, 1 rendering task to rendering system B, and 2 rendering tasks to rendering system C.

[0123] In other words, the first server can allocate rendering tasks based on the load information of multiple rendering systems. This avoids excessively high or low load on the rendering systems, ensures load balance, and improves the rendering efficiency of the first server.

[0124] In some embodiments, if the number of rendering tasks is less than a first preset threshold and the duration of each rendering task is less than a second preset threshold, then S205 is executed. If the number of rendering tasks is greater than the first preset threshold and the duration of each rendering task is greater than the second preset threshold, then S501-S504 are executed.

[0125] like Figure 5 As shown in the embodiment of this application, a data visualization processing method is provided, which may include:

[0126] S501, The first server divides the target set into the second dataset and the third dataset.

[0127] The target set includes the second dataset and the third dataset.

[0128] In one possible implementation, the first server can divide the target set according to a first preset threshold and a second preset threshold to determine the second dataset and the third dataset.

[0129] For example, if the rendering task information corresponding to the target set includes: the number of rendering tasks is 15, the task duration of each rendering task is 2.5 hours, the first preset threshold is 5, and the second preset threshold is 1 hour, then the first server can determine that the rendering task information corresponding to the high-dimensional data in the second dataset includes: the number of rendering tasks is 5, the task duration of each rendering task is 1 hour, and the rendering task information corresponding to the high-dimensional data in the third dataset includes: the number of rendering tasks is 10, and the task duration of each rendering task is 1.5 hours.

[0130] S502, The first server performs rendering processing on the second dataset.

[0131] It should be noted that for the description of the first server's rendering process for the second dataset, please refer to the description of the first server's rendering process for the target set in S205, which will not be repeated here.

[0132] S503, The first server sends a rendering message to the second server.

[0133] The rendering message includes a third dataset, which is used to instruct the second server to render the third dataset.

[0134] In this embodiment, after the first server sends a rendering message to the second server, the second server can receive the rendering message from the first server. Then, the second server can perform rendering processing on the third dataset to obtain rendered data, which is the data rendered from the third dataset. Afterwards, the second server can send the rendered data back to the first server.

[0135] In one possible design, the second server is equipped with a cloud rendering system, which can render the third dataset and determine the rendering data.

[0136] In another possible design, the second server can be a blockchain server, which hosts multiple cloud rendering systems. After receiving rendering messages from the server, the second server can distribute the third dataset to the multiple cloud rendering systems for rendering, thus determining the rendering data.

[0137] In another possible design, the second server can be a blockchain server, which houses multiple cloud rendering systems. These cloud rendering systems on the second server can compete for resources to render the third dataset and determine the rendering data.

[0138] S504, The first server receives rendering data from the second server.

[0139] The rendered data refers to the data rendered from the third dataset.

[0140] In this embodiment of the application, the data after rendering the target set includes: the data rendered by the first server on the second dataset and the rendered data.

[0141] Understandably, the first server can determine the rendering task information corresponding to the target set. This information includes the number of rendering tasks and the duration of each task. If the number of rendering tasks is less than a first preset threshold, and the duration of each task is less than a second preset threshold, the first server can render the target set. If the number of rendering tasks is greater than the first preset threshold, and the duration of each task is greater than the second preset threshold, the first server can render the second dataset. Afterward, the first server can send a rendering message to the second server. This message includes a third dataset and instructs the second server to render it. The target set includes both the second and third datasets. The first server can receive rendering data from the second server, which is the data rendered from the third dataset. In other words, when rendering tasks are heavy, the first and second servers can jointly render the target set. This reduces the number of tasks and the rendering time for the first server, thereby improving rendering efficiency.

[0142] The foregoing primarily describes the solutions provided in the embodiments of this application from the perspective of computer devices. It is understood that, in order to achieve the aforementioned functions, the computer device includes corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, based on the data visualization processing method steps described in conjunction with the embodiments disclosed in this application, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed through hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0143] This application also provides a data visualization processing apparatus. This data visualization processing apparatus can be a computer device, a CPU within the aforementioned computer device, a processing module within the aforementioned computer device for processing data visualization, or a client within the aforementioned computer device for processing data visualization.

[0144] This application embodiment can divide the data visualization processing into functional modules or functional units based on the above method examples. For example, each function can be divided into its own functional modules or functional units, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module or functional unit. The module or unit division in this application embodiment is illustrative and represents only one logical functional division; other division methods may be used in actual implementation.

[0145] like Figure 6 The diagram shown is a structural schematic of a data visualization processing apparatus provided in an embodiment of this application. The data visualization processing apparatus is used to perform... Figure 2 , Figure 3 , Figure 4 and Figure 5 The data visualization processing method shown is illustrated. The data visualization processing apparatus may include an acquisition module 601 and a processing module 602.

[0146] The acquisition module 601 is used to acquire multiple high-dimensional data sets. The processing module 602 is used to classify the multiple high-dimensional data sets to determine a first dataset, which includes high-dimensional data corresponding to the target type. The processing module 602 is also used to split the first dataset according to a decision tree algorithm and multiple preset features to determine multiple feature sets, where each preset feature corresponds to one feature set, and the multiple preset features are features of the high-dimensional data in the target set. The processing module 602 is also used to determine a target set from the multiple feature sets based on the target features; the target set is the set of features from the multiple feature sets whose matching degree with the target features is greater than a preset similarity threshold. The processing module 602 is also used to perform rendering processing on the target set.

[0147] Optionally, processing module 602 is specifically used to determine a first information entropy based on multiple preset features and a first dataset. The first information entropy indicates the degree of matching between high-dimensional data in the first dataset and the target features. Processing module 602 is also used to determine multiple second information entropies based on multiple preset features and multiple feature sets. The second information entropies indicate the degree of matching between feature sets and target features, with one second information entropy corresponding to one feature set. Then, processing module 602 is further used to determine multiple information gains based on the first information entropy and the multiple second information entropies. The information gains indicate the degree of matching between feature sets and target features. Then, processing module 602 is specifically used to compare the multiple information gains and determine a target gain, which is the largest information gain among the multiple information gains. Processing module 602 is also used to use the feature set corresponding to the target gain as the target set.

[0148] Optionally, the processing module 602 is specifically used to determine the rendering task information corresponding to the target set. The rendering task information includes the number of rendering tasks and the task duration of each rendering task. The processing module 602 is also used to perform rendering processing on the target set if the number of rendering tasks is less than a first preset threshold and the task duration of each rendering task is less than a second preset threshold.

[0149] Optionally, processing module 602 is specifically configured to perform rendering processing on the second dataset if the number of rendering tasks exceeds a first preset threshold and the task duration of each rendering task exceeds a second preset threshold. Processing module 602 is further configured to send a rendering message to the second server, the rendering message including a third dataset, the rendering message instructing the second server to render the third dataset. Subsequently, processing module 602 is further configured to receive rendering data from the second server, the rendering data being the data rendered from the third dataset.

[0150] Figure 7 This is a schematic diagram of the hardware structure of a data visualization processing apparatus according to an exemplary embodiment. The data visualization processing apparatus may include a processor 701, which executes application code to implement the data visualization processing method of this application.

[0151] The processor 701 may be a central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits used to control the execution of the program of the present application.

[0152] like Figure 7 As shown, the data visualization processing apparatus may further include a memory 702. The memory 702 stores the application code that executes the scheme of this application, and its execution is controlled by the processor 701.

[0153] Memory 702 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. Memory 702 may exist independently and be connected to processor 701 via bus 704. Memory 702 may also be integrated with processor 701.

[0154] like Figure 7 As shown, the data visualization processing apparatus may further include a communication interface 703, wherein the processor 701, memory 702, and communication interface 703 may be coupled to each other, for example, through a bus 704. The communication interface 703 is used for information interaction with other devices, such as enabling information interaction between the data visualization processing apparatus and other devices.

[0155] It should be pointed out that, Figure 7 The device structure shown does not constitute a limitation on the processing device for this data visualization, except... Figure 7 In addition to the components shown, the data visualization processing apparatus may include more or fewer components than those shown, or combine certain components, or have different component arrangements.

[0156] In actual implementation, the functions implemented by processing module 602 can be derived by... Figure 7 The processor 701 shown calls the program code in memory 702 to implement this.

[0157] This application also provides a computer-readable storage medium storing instructions that, when executed by a processor of a computer device, enable the computer to perform the data visualization processing method provided in the embodiments described above. For example, the computer-readable storage medium may be a memory 702 including instructions, which may be executed by a processor 701 of a computer device to complete the method. Optionally, the computer-readable storage medium may be a non-transitory computer-readable storage medium, such as a ROM, RAM, CD-ROM, magnetic tape, floppy disk, or optical data storage device.

[0158] Figure 8 A conceptual partial view of a computer program product provided in an embodiment of this application is shown schematically. The computer program product includes a computer program for executing computer processes on a computing device.

[0159] In one embodiment, the computer program product is provided using a signal bearer medium 800. The signal bearer medium 800 may include one or more program instructions that, when executed by one or more processors, can provide the above-mentioned... Figure 2 , Figure 3 , Figure 4 and Figure 5 The described function or part of the function. Therefore, for example, refer to... Figure 2 In the embodiment shown, one or more features of S201 to S205 can be fulfilled by one or more instructions associated with the signal carrying medium 800. Furthermore, Figure 8The program instructions in the document also describe example instructions.

[0160] In some examples, the signal carrying medium 800 may include a computer-readable medium 801, such as, but not limited to, a hard disk drive, a compact disc (CD), a digital video disc (DVD), a digital magnetic tape, a memory, a read-only memory (ROM), or a random access memory (RAM), etc.

[0161] In some implementations, the signal carrying medium 800 may include a computer recordable medium 802, such as, but not limited to, a memory, a read / write (R / W) CD, a R / W DVD, and so on.

[0162] In some implementations, the signal carrying medium 800 may include a communication medium 803, such as, but not limited to, digital and / or analog communication media (e.g., fiber optic cables, waveguides, wired communication links, wireless communication links, etc.).

[0163] The signal-bearing medium 800 can be transmitted by a wireless communication medium 803. One or more program instructions can be, for example, computer-executable instructions or logical implementation instructions.

[0164] In some examples, such as targeting Figure 6 The described data visualization processing apparatus can be configured to provide various operations, functions, or actions in response to one or more program instructions via a computer-readable medium 801, a computer-recordable medium 802, and / or a communication medium 803.

[0165] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0166] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0167] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the constituent units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0168] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0169] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, essentially, or the part that contributes to the prior art, or a complete or partial classification of the technical solution, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0170] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A data visualization processing method, characterized in that, Applied to a first server, the method includes: Acquire multiple high-dimensional data; The multiple high-dimensional data are classified to determine a first dataset, which includes high-dimensional data corresponding to the target type. The first dataset is split according to the decision tree algorithm and multiple preset features to determine multiple feature sets. Each preset feature corresponds to one feature set. The multiple preset features are features of high-dimensional data in the first dataset. Based on the plurality of preset features and the first dataset, a first information entropy is determined, wherein the first information entropy is used to indicate the degree of matching between the high-dimensional data in the first dataset and the target features; Based on the plurality of preset features and the plurality of feature sets, a plurality of second information entropies are determined. The second information entropy is used to indicate the matching degree between the feature set and the target feature. One feature set corresponds to one second information entropy. Based on the first information entropy and the plurality of second information entropies, a plurality of information gains are determined, wherein the information gains are used to indicate the degree of matching between the feature set and the target feature; The plurality of information gains are compared to determine a target gain, which is the largest information gain among the plurality of information gains. The feature set corresponding to the target gain is taken as the target set. The target set is the feature set among the plurality of feature sets whose matching degree with the target feature is greater than a preset similarity threshold. The target set is then rendered.

2. The method according to claim 1, characterized in that, Before rendering the target set, the method further includes: Determine the rendering task information corresponding to the target set, wherein the rendering task information includes: the number of rendering tasks and the task duration of each rendering task; The rendering process for the target set includes: If the number of rendering tasks is less than a first preset threshold, and the duration of each rendering task is less than a second preset threshold, then the target set is rendered.

3. The method according to claim 2, characterized in that, The target set includes a second dataset and a third dataset; the method further includes: If the number of rendering tasks is greater than the first preset threshold, and the duration of each rendering task is greater than the second preset threshold, then the second dataset is rendered. Send a rendering message to a second server, the rendering message including the third dataset, the rendering message being used to instruct the second server to render the third dataset; Receive rendering data from the second server, wherein the rendering data is the data rendered from the third dataset.

4. A data visualization processing device, characterized in that, Applied to a first server, the device includes: The acquisition module is used to acquire multiple high-dimensional data. The processing module is used to classify the multiple high-dimensional data and determine a first dataset, wherein the first dataset includes high-dimensional data corresponding to the target type; The processing module is further configured to split the first dataset according to the decision tree algorithm and multiple preset features to determine multiple feature sets, wherein one preset feature corresponds to one feature set, and the multiple preset features are features of high-dimensional data in the first dataset; The processing module is specifically used to determine a first information entropy based on the plurality of preset features and the first dataset. The first information entropy is used to indicate the degree of matching between the high-dimensional data in the first dataset and the target features. The processing module is specifically used to determine multiple second information entropies based on the multiple preset features and the multiple feature sets. The second information entropy is used to indicate the matching degree between the feature set and the target feature, and one feature set corresponds to one second information entropy. The processing module is specifically used to determine multiple information gains based on the first information entropy and the multiple second information entropies, wherein the information gains are used to indicate the degree of matching between the feature set and the target feature; The processing module is specifically used to compare the plurality of information gains and determine a target gain, wherein the target gain is the largest information gain among the plurality of information gains. The processing module is specifically used to take the feature set corresponding to the target gain as the target set, wherein the target set is the feature set among the plurality of feature sets whose matching degree with the target feature is greater than a preset similarity threshold; The processing module is also used to perform rendering processing on the target set.

5. The apparatus according to claim 4, characterized in that, The processing module is specifically used to determine the rendering task information corresponding to the target set, wherein the rendering task information includes: the number of rendering tasks and the task duration of each rendering task; The processing module is further configured to perform rendering processing on the target set if the number of rendering tasks is less than a first preset threshold and the task duration of each rendering task is less than a second preset threshold.

6. The apparatus according to claim 5, characterized in that, The target set includes a second dataset and a third dataset; The processing module is specifically used to perform rendering processing on the second dataset if the number of rendering tasks is greater than the first preset threshold and the task duration of each rendering task is greater than the second preset threshold. The processing module is further configured to send a rendering message to the second server, the rendering message including the third dataset, the rendering message being used to instruct the second server to render the third dataset; The processing module is further configured to receive rendering data from the second server, wherein the rendering data is data rendered from the third dataset.

7. A data visualization processing device, characterized in that, include: Processor and memory; The processor and the memory are coupled; The memory is used to store one or more programs, which include computer-executable instructions. When the data visualization processing device is running, the processor executes the computer-executable instructions stored in the memory to cause the data visualization processing device to perform the data visualization processing method as described in any one of claims 1-3.

8. A computer-readable storage medium storing instructions, characterized in that, When the computer executes the instruction, the computer performs the data visualization processing method as described in any one of claims 1-3.

9. A computer program product, comprising a computer program, characterized in that, When a computer program is executed by a processor, it implements the data visualization processing method according to any one of claims 1-3.

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