Data visualization method and device

Abstract

The embodiment of the present application provides a data visualization method and device, which relates to the field of computer technology and solves the technical problem of low efficiency of existing product quality control. The data visualization method includes: obtaining the original data of the target product, wherein the original data includes the product data of the entire life cycle of the target product; inputting the original data into a pre-trained data classification model to obtain the data classification result of the original data, wherein the data classification model is a neural network model for classifying data; and displaying the analysis result of the target product according to the data classification result.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a data visualization method and device. Background Art

[0002] With the impact of Internet finance, the business needs of enterprises themselves have shown a spurt of growth.

[0003] As business demand continues to increase, the number of products that meet customer needs is also increasing. How to ensure product quality under high-speed iteration and improve the quality control of existing products is crucial to improving the core competitiveness of enterprises. Summary of the invention

[0004] The present application provides a data visualization method and device, which solves the technical problem of low efficiency in quality control of existing products.

[0005] In order to achieve the above objectives, this application adopts the following technical solutions:

[0006] In a first aspect, a data visualization method is provided, comprising: obtaining raw data of a target product, wherein the raw data includes product data of the target product over its entire life cycle; inputting the raw data into a pre-trained data classification model to obtain a data classification result of the raw data, wherein the data classification model is a neural network model for classifying data; and displaying an analysis result of the target product based on the data classification result.

[0007] From the above, it can be seen that after obtaining the original data of the target product, the original data can be input into the pre-trained data classification model to obtain the data classification results of the original data. Subsequently, the analysis results of the target product are displayed based on the data classification results. Since the original data includes the product data of the entire life cycle of the target product, and the data classification model is a neural network model for classifying data, the analysis results of the target product can clearly and efficiently reflect the product data of the entire life cycle of the target product, so that managers can intuitively see the product quality of the target product from different data classification results, thereby improving the efficiency of product quality control.

[0008] In a second aspect, a data visualization device is provided, which includes: an acquisition unit, a processing unit and a display unit; the acquisition unit is used to acquire original data of a target product, wherein the original data includes product data of the target product over its entire life cycle; the processing unit is used to input the original data into a pre-trained data classification model to obtain a data classification result of the original data, wherein the data classification model is a neural network model for classifying data; and the display unit is used to display an analysis result of the target product based on the data classification result.

[0009] In a third aspect, a data visualization device is provided, comprising a memory and a processor. The memory is used to store computer-executable instructions, and the processor is connected to the memory via a bus. When the data visualization device is running, the processor executes the computer-executable instructions stored in the memory, so that the data visualization device performs the data visualization method described in the first aspect.

[0010] The data visualization device may be a network device, or a part of a network device, such as a chip system in the network device. The chip system is used to support the network device to implement the functions involved in the first aspect and any possible implementation thereof, for example, receiving, determining, and diverting the data and / or information involved in the above-mentioned data visualization method. The chip system includes a chip, and may also include other discrete devices or circuit structures.

[0011] In a fourth aspect, a computer-readable storage medium is provided, the computer-readable storage medium comprising computer execution instructions, and when the computer execution instructions are executed on a computer, the computer executes the data visualization method described in the first aspect.

[0012] In a fifth aspect, a computer program product is provided. When the computer program product is run on a computer, the computer is enabled to execute the data visualization method as described in the first aspect and any possible design thereof.

[0013] It should be noted that the above-mentioned computer instructions may be stored in whole or in part on the first computer storage medium. The first computer storage medium may be packaged together with the processor of the data visualization device, or may be packaged separately from the processor of the data visualization device, which is not limited in the embodiments of the present application.

[0014] The description of the second, third, fourth and fifth aspects of the present invention can refer to the detailed description of the first aspect; and the beneficial effects of the second, third, fourth and fifth aspects can refer to the beneficial effect analysis of the first aspect, which will not be repeated here.

[0015] In the embodiments of the present application, the name of the data visualization device does not limit the device or functional module itself. In actual implementation, these devices or functional modules may appear with other names. As long as the functions of each device or functional module are similar to those of the present invention, they fall within the scope of the claims of the present invention and their equivalent technologies.

[0016] These and other aspects of the present invention will become more apparent from the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1A schematic diagram of the hardware structure of a data visualization device provided in an embodiment of the present application;

[0018] Figure 2 A schematic diagram of the hardware structure of another data visualization device provided in an embodiment of the present application;

[0019] Figure 3 A flowchart of a data visualization method provided in an embodiment of the present application;

[0020] Figure 4 A flowchart of another data visualization method provided in an embodiment of the present application;

[0021] Figure 5 A flowchart of another data visualization method provided in an embodiment of the present application;

[0022] Figure 6 A flowchart of another data visualization method provided in an embodiment of the present application;

[0023] Figure 7 A schematic diagram of the structure of a device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only 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 making creative work are within the scope of protection of the present invention.

[0025] It should be noted that, in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0026] In order to facilitate a clear description of the technical solutions of the embodiments of the present application, in the embodiments of the present application, words such as "first" and "second" are used to distinguish between identical or similar items with substantially the same functions and effects. Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and order of execution.

[0027] As described in the background technology, with the increasing business demand, the number of products that meet customer needs is also increasing. How to ensure product quality under high-speed iteration and improve the quality control of existing product quality is crucial to improving the core competitiveness of enterprises.

[0028] In response to the above problems, an embodiment of the present application provides a data visualization method. After obtaining the original data of the target product, the original data can be input into a pre-trained data classification model to obtain the data classification results of the original data. Subsequently, the analysis results of the target product are displayed according to the data classification results. Since the original data includes the product data of the entire life cycle of the target product, and the data classification model is a neural network model for classifying data, the analysis results of the target product can clearly and efficiently reflect the product data of the entire life cycle of the target product, so that managers can intuitively see the product quality of the target product from different data classification results, thereby improving the quality control efficiency of the product.

[0029] The data visualization device may be a device for performing data visualization on the system to be tested, or a chip in the device, or a system on chip in the device.

[0030] Optionally, the device may be a physical machine, such as a desktop computer, also known as a desktop or desktop computer (desktopcomputer), a mobile phone, a tablet computer, a laptop computer, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA) and other terminal devices.

[0031] Optionally, the data visualization device may also implement the functions to be implemented by the data visualization device through a virtual machine (VM) deployed on a physical machine.

[0032] To facilitate understanding, the structure of the data visualization device in the embodiment of the present application is described below.

[0033] Figure 1 FIG. 1 is a schematic diagram showing a hardware structure of a data visualization device provided in an embodiment of the present application. Figure 1 As shown, the data visualization device includes a processor 11, a memory 12, a communication interface 13, and a bus 14. The processor 11, the memory 12, and the communication interface 13 may be connected via the bus 14.

[0034] The processor 11 is the control center of the data visualization device, and can be a processor or a general term for multiple processing elements. For example, the processor 11 can be a general-purpose central processing unit (CPU) or other general-purpose processors. The general-purpose processor can be a microprocessor or any conventional processor.

[0035] As an embodiment, the processor 11 may include one or more CPUs, such as Figure 1 CPU 0 and CPU 1 are shown in .

[0036] The memory 12 may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0037] In a possible implementation, the memory 12 may exist independently of the processor 11, and the memory 12 may be connected to the processor 11 via a bus 14 to store instructions or program codes. When the processor 11 calls and executes the instructions or program codes stored in the memory 12, the data visualization method provided in the embodiment of the present invention can be implemented.

[0038] In another possible implementation, the memory 12 may also be integrated with the processor 11 .

[0039] The communication interface 13 is used to connect with other devices via a communication network. The communication network may be Ethernet, wireless access network, wireless local area network (WLAN), etc. The communication interface 13 may include a receiving unit for receiving data and a sending unit for sending data.

[0040] The bus 14 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 1 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0041] It should be pointed out that Figure 1 The structure shown does not constitute a limitation of the data visualization device. Figure 1 In addition to the components shown, the data visualization device may include more or fewer components than shown, or combine certain components, or arrange the components differently.

[0042] Figure 2 Another hardware structure of the data visualization device in the embodiment of the present application is shown. Figure 2 As shown, the communication device may include a processor 21 and a communication interface 22. The processor 21 is coupled to the communication interface 22.

[0043] The function of the processor 21 may refer to the description of the processor 11. In addition, the processor 21 also has a storage function, which may refer to the function of the memory 12.

[0044] The communication interface 22 is used to provide data to the processor 21. The communication interface 22 may be an internal interface of the communication device, or an external interface of the data visualization device (equivalent to the communication interface 13).

[0045] It should be pointed out that Figure 1 (or Figure 2 ) does not constitute a limitation on the data visualization device, except Figure 1 (or Figure 2 ) In addition to the components shown in the figure, the data visualization device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0046] The data visualization method provided in the embodiment of the present application is described in detail below with reference to the accompanying drawings.

[0047] Figure 3 The following is a flow chart of a data visualization method provided in an embodiment of the present application. Figure 3 As shown, the data visualization method includes the following S301-S303.

[0048] S301. The data visualization device obtains original data of the target product.

[0049] Specifically, when analyzing a target product, it is necessary to obtain the original data of the target product, wherein the original data includes product data of the entire life cycle of the target product.

[0050] In the entire life cycle of a system or product, there are two main ways to collect data: one is based on the operation log of the target product, by deploying Flume nodes, to achieve high availability, high reliability, distributed massive log collection, aggregation and transmission. Through the incremental collection strategy, the collection performance and timeliness are improved.

[0051] The other is based on the table structure data of the target product. By building the Datax data synchronization framework, the data of any data source type of different products can be mirrored and synchronized periodically.

[0052] Optionally, the data visualization device may also obtain the original data of the target product in other ways, which is not limited in this application.

[0053] For example, the data visualization device can obtain production defect-related data at each stage of the entire life cycle of product A. The data includes: product name, organizational department, defect injection batch and time, defect source, defect severity, defect level and other information.

[0054] S302: The data visualization device inputs the original data into a pre-trained data classification model to obtain a data classification result of the original data.

[0055] Among them, the data classification model is a neural network model used to classify data.

[0056] Specifically, after acquiring the original data of the target product, the data visualization device may input the original data into a pre-trained data classification model to obtain a data classification result of the original data.

[0057] During the data analysis process, the data visualization device does not know which data in the acquired raw data belongs to which type. Therefore, after acquiring the raw data of the target product, the data visualization device can input the raw data into a pre-trained data classification model to obtain the data classification result of the raw data. In this way, the data visualization device can determine the data type of the data based on the classification result. Subsequently, the electronic device can classify and display the data according to the determined data type.

[0058] Exemplarily, the original data includes: communication time data and online development data of the target product. The above original data is input into a pre-trained data classification model to obtain data classification results of the original data: one is the data before the production of the target product, and the other is the data during the production of the target product.

[0059] Optionally, the data visualization device can also integrate and statistically analyze the original data based on customized analysis strategies of different dimensions, levels, classifications, etc., such as a vertical comparison of the fluctuations in the total number of production defects of all products in the past three years, and a vertical comparison of the statistical distribution of production defects of products in each department in the current year classified by severity level.

[0060] S303. The data visualization device displays the analysis results of the target product according to the data classification results.

[0061] Specifically, after the original data is input into a pre-trained data classification model to obtain a data classification result of the original data, the data visualization device displays the analysis result of the target product according to the data classification result.

[0062] Optionally, the data visualization device can display the analysis results on the front end in various ways such as bar graphs, line graphs, detailed data queries, etc.

[0063] Optional, such as Figure 4 As shown, the data visualization method also includes the following S401-S404.

[0064] S401. The data visualization device collects analysis results of multiple other products and product data of the entire life cycle of each other product.

[0065] Specifically, when performing data analysis on a target product, the data visualization device may collect analysis results of multiple other products and product data of each other product throughout its life cycle.

[0066] For example, when the target product is completed in January 2021, the data visualization device can collect analysis results of multiple other products in January 2021 and product data of the entire life cycle of each other product.

[0067] S402, the data visualization device uses the product data of the entire life cycle of each other product as a training set.

[0068] Specifically, after collecting analysis results of multiple other products and product data of the entire life cycle of each other product, the data visualization device may use the product data of the entire life cycle of each other product as a training set.

[0069] S403: The data visualization device uses the analysis results of multiple other products as a verification set.

[0070] Specifically, after collecting the analysis results of multiple other products and the product data of the entire life cycle of each other product, the data visualization device may use the analysis results of the multiple other products as a verification set.

[0071] S404, the data visualization device performs supervised training on the initial dual-channel neural network model on the training set until the dual-channel neural network model meets the preset convergence conditions, and generates a data classification model.

[0072] Specifically, after taking the product data of the entire life cycle of each other product as a training set and the analysis results of multiple other products as a verification set, the data visualization device performs supervised training on the initial dual-channel neural network model on the training set until the dual-channel neural network model meets the preset convergence conditions, and then generates a data classification model.

[0073] Among them, the convergence condition can be that the number of times the model is trained reaches a preset threshold, or that the degree of change in the results of the model output is small, or other convergence conditions, which are not limited in this application.

[0074] Optional, such as Figure 5 As shown, after displaying the analysis results of the target product based on the data classification results, it also includes:

[0075] S501. The data visualization device receives an analysis result display request sent by an account.

[0076] Among other things, the analysis results show that the request includes an account identifier.

[0077] Specifically, after displaying the analysis results of the target product, the data visualization device may also display different analysis result contents according to different accounts.

[0078] S502: The data visualization device determines, according to the account identifier, the target content of the analysis result of the target product corresponding to the account identifier.

[0079] Specifically, after receiving the analysis result display request sent by the account, the data visualization device may determine the target content of the analysis result of the target product corresponding to the account identifier according to the account identifier.

[0080] For example, for an account with higher authority, the data visualization device can determine the entire content of the analysis result of the target product corresponding to the account identifier based on the account identifier. For an account with lower authority, the data visualization device can determine part of the analysis result of the target product corresponding to the account identifier based on the account identifier.

[0081] S503: The data visualization device displays the target content in response to the analysis result display request.

[0082] For example, for an account with higher authority, the data visualization device may display the entire content of the analysis result in response to the analysis result display request. For an account with lower authority, the data visualization device may display part of the analysis result in response to the analysis result display request.

[0083] The data visualization device responds to the analysis result display request. In the process of displaying the target content, a user authority management mechanism can be set up to implement access authority detection and control according to the security rules or authorization functions set by the system, so that authorized users can use the authorized functions normally and legally. The most basic thing to be achieved is that people with different responsibilities should have different permissions for system operations. By setting different functional permissions and resource permissions for users and clarifying the division of responsibilities, users of different levels and roles can see and operate different data, thereby improving the security, data isolation and clear responsibilities of the application system.

[0084] Optional, combined with,3, such as Figure 6 As shown, the data visualization method also includes the following S601.

[0085] S601. The data visualization device periodically generates a quality analysis report of the target product.

[0086] Specifically, the data visualization device can periodically generate a quality analysis report of the target product.

[0087] The data visualization device can automatically generate PDF or word reports on a periodic basis on the quality of the entire production process of the target product, the product quality metrics and analysis results, so that quality analysts can evaluate the overall quality, propose improvement measures and evaluate the improvement effects.

[0088] The quality analysis report is different from the displayed analysis results. The various charts in the displayed analysis results are the elements that constitute the quality analysis report. In the process of displaying the analysis results, the data visualization device will conduct statistics on the quality-related data of various types of products or various stages of the product life cycle at different levels, categories, and dimensions according to the business situation, and will generate dozens of charts covering all product quality assessment indicators. In each chart, it supports single chart editing, graphic display mode switching, detailed data download, dimension, historical comparative analysis configuration, etc. Users can check the status of various quality indicators online in real time. The quality analysis report generation cycle sets a relevant trigger mechanism, and automatically generates PDF or word format reports regularly according to the set rules. Users can check the quality indicators to be covered in the report according to business needs to achieve parameterized customization of the report.

[0089] The embodiment of the present application provides a data visualization method. After obtaining the raw data of the target product, the raw data can be input into a pre-trained data classification model to obtain the data classification result of the raw data. Subsequently, the analysis result of the target product is displayed according to the data classification result. Since the raw data includes the product data of the entire life cycle of the target product, and the data classification model is a neural network model for classifying data, the analysis result of the target product can clearly and efficiently reflect the product data of the entire life cycle of the target product, so that managers can intuitively see the product quality of the target product from different data classification results, thereby improving the quality control efficiency of the product.

[0090] The above mainly introduces the solution provided by the embodiment of the present application from the perspective of the method. In order to realize the above functions, it includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0091] The embodiment of the present application can divide the functional modules of the data visualization device according to the above method example. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware or in the form of software functional modules. Optionally, the division of modules in the embodiment of the present application is schematic and is only a logical functional division. There may be other division methods in actual implementation.

[0092] Figure 7 A schematic diagram of the composition of a data visualization device is shown, including: an acquisition unit 701, a processing unit 702 and a display unit 703.

[0093] The acquisition unit 701 is used to acquire the original data of the target product, wherein the original data includes the product data of the entire life cycle of the target product. Figure 3 , the acquisition unit 701 is used to execute S301.

[0094] The processing unit 702 is used to input the original data into a pre-trained data classification model to obtain a data classification result of the original data, wherein the data classification model is a neural network model for classifying data. Figure 3, the processing unit 702 is used to execute S302.

[0095] The display unit 703 is used to display the analysis results of the target product according to the data classification results. Figure 3 , the display unit 703 is used to execute S303.

[0096] Optionally, the processing unit 702 is further configured to:

[0097] Collect analysis results from multiple other products and product data from each other product throughout its life cycle. Figure 4 , the processing unit 702 is used to execute S401.

[0098] The product data of each other product throughout its life cycle is used as the training set. Figure 4 , the processing unit 702 is used to execute S402.

[0099] Use the analysis results of multiple other products as validation sets. Figure 4 , the processing unit 702 is used to execute S403.

[0100] The initial two-channel neural network model is supervised and trained on the training set until the two-channel neural network model meets the preset convergence conditions, and then a data classification model is generated. Figure 4 , the processing unit 702 is used to execute S404.

[0101] Optionally, it further includes: a receiving unit 704;

[0102] The receiving unit 704 is used to receive an analysis result display request sent by an account; the analysis result display request includes an account identifier. Figure 5 , the receiving unit 704 is used to execute S501.

[0103] The processing unit 702 is configured to determine the target content of the analysis result of the target product corresponding to the account identifier according to the account identifier. Figure 5 , the processing unit 702 is used to execute S502.

[0104] The display unit 703 is further configured to display the target content in response to the analysis result display request. Figure 5 , the display unit 703 is used to execute S503.

[0105] Optionally, it further includes: a generating unit 705;

[0106] Generating unit 705 is used to periodically generate a quality analysis report of the target product. Figure 6 , the generating unit 705 is used to execute S601.

[0107] The embodiment of the present application also provides a computer-readable storage medium, which includes computer-executable instructions. When the computer-executable instructions are executed on a computer, the computer executes the steps executed by the device in the data visualization method provided in the above embodiment.

[0108] An embodiment of the present application also provides a computer program product, which can be directly loaded into a memory and contains software code. After being loaded and executed by a computer, the computer program product can implement the various steps performed by the device in the data visualization method provided in the above embodiment.

[0109] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using a software program, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loading and executing computer execution instructions on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server, data center, etc. that contains one or more servers that can be integrated with a medium. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a DVD), or a semiconductor medium (eg, a solid state disk (SSD)).

[0110] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned 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.

[0111] In several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the modules or units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The unit described as a separate component may or may not be physically separated, and the component displayed as a unit may be a physical unit or multiple physical units, that is, it can be located in one place, or it can be distributed in multiple different places. Some or all of the units can be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0112] In addition, each functional unit in each embodiment of the present invention can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. If the integrated unit is implemented in the form of 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 embodiment of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium, including several instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a U disk, a mobile hard disk, a ROM, a RAM, a disk, or an optical disk.

[0113] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A data visualization method, characterized in that: include: Obtaining the original data of the target product, including: based on the operation log of the target product, obtaining the original data by deploying a Flume node, or, based on the table structure data of the target product, obtaining the original data by building a Datax data synchronization framework, wherein the original data includes the product data of the target product throughout its life cycle; the product data of the target product throughout its life cycle includes: production defect-related data at each stage of the target product throughout its life cycle; the production defect-related data includes: the name of the product involved, the organizational department to which it belongs, the defect injection batch and time, the defect source, the defect severity, and the defect level; Inputting the original data into a pre-trained data classification model to obtain a data classification result of the original data, wherein the data classification model is a neural network model for classifying data; According to the data classification results, the analysis results of the target product are displayed, including: according to the business situation of the target product, the quality-related data of each stage of the target product's full life cycle are counted at different levels, categories, and dimensions to obtain the quality evaluation index of the target product; the analysis results include multiple charts; each chart supports single chart editing, graphic display mode switching, detailed data download, and dimension and historical comparative analysis configuration; A quality analysis report of the target product is generated periodically; the quality analysis report is different from the analysis result; each chart in the analysis result is an element constituting the quality analysis report; the quality analysis report includes a plurality of quality indicators; the plurality of quality indicators are used to generate a parameterized customized quality analysis report in response to a check configuration operation.

2. The data visualization method according to claim 1, characterized in that: Also includes: Collect analysis results of multiple other products and product data of each other product throughout its life cycle; Using product data of the entire life cycle of each other product as a training set; using the analysis results of the multiple other products as a validation set; The initial dual-channel neural network model is supervisedly trained on the training set until the dual-channel neural network model meets the preset convergence conditions, and then the data classification model is generated.

3. The data visualization method according to claim 1, characterized in that: After displaying the analysis result of the target product according to the data classification result, the method further includes: Receiving an analysis result display request sent by an account; the analysis result display request includes an account identifier; Determining, according to the account identifier, target content of the analysis result of the target product corresponding to the account identifier; In response to the analysis result display request, the target content is displayed.

4. A data visualization device, characterized in that: include: an acquisition unit, a processing unit, a display unit, and a generation unit; The acquisition unit is used to acquire the original data of the target product, including: acquiring the original data by deploying a Flume node based on the operation log of the target product, or acquiring the original data by building a Datax data synchronization framework based on the table structure data of the target product, wherein the original data includes the product data of the entire life cycle of the target product; the product data of the entire life cycle of the target product includes: the production defect related data of each stage in the entire life cycle of the target product; the production defect related data includes: the name of the product involved, the organizational department to which it belongs, the defect injection batch and time, the defect source, the defect severity, and the defect level; The processing unit is used to input the original data into a pre-trained data classification model to obtain a data classification result of the original data, wherein the data classification model is a neural network model for classifying data; The display unit is used to display the analysis results of the target product according to the data classification results, including: performing statistics of different levels, categories, and dimensions on the quality-related data of each stage of the target product's full life cycle according to the business situation of the target product, so as to obtain the quality evaluation index of the target product; the analysis results include multiple charts; each chart supports single chart editing, graphic display mode switching, detailed data downloading, and dimension and historical comparative analysis configuration; The generation unit is used to periodically generate a quality analysis report of the target product; the quality analysis report is different from the analysis result; the various charts in the analysis result are elements that constitute the quality analysis report; the quality analysis report includes multiple quality indicators; the multiple quality indicators are used to generate a parameterized customized quality analysis report in response to a check configuration operation.

5. The data visualization device according to claim 4, characterized in that: The processing unit is further used for: Collect analysis results of multiple other products and product data of each other product throughout its life cycle; Using product data of the entire life cycle of each other product as a training set; using the analysis results of the multiple other products as a validation set; The initial dual-channel neural network model is supervisedly trained on the training set until the dual-channel neural network model meets the preset convergence conditions, and then the data classification model is generated.

6. The data visualization device according to claim 4, characterized in that: Also includes: Receiving unit; The receiving unit is used to receive an analysis result display request sent by an account; the analysis result display request includes an account identifier; The processing unit is used to determine, according to the account identifier, the target content of the analysis result of the target product corresponding to the account identifier; The display unit is further configured to display the target content in response to the analysis result display request.

7. A data visualization device, characterized in that: It includes a memory and a processor; the memory is used to store computer-executable instructions, and the processor is connected to the memory via a bus; When the data visualization device is running, the processor executes the computer-executable instructions stored in the memory, so that the data visualization device performs the data visualization method according to any one of claims 1 to 3.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes computer-executable instructions. When the computer-executable instructions are executed on a computer, the computer is enabled to execute the data visualization method according to any one of claims 1 to 3.

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