Data visualization insight method and device, equipment and storage medium

Abstract

The invention discloses a data visualization insight method and device, equipment and a storage medium, and relates to the technical field of data analysis, and the method comprises the steps: obtaining to-be-processed data in a target business field and an insight demand for the to-be-processed data, performing feature detection on the to-be-processed data based on a preset data constraint item to judge whether the to-be-processed data meets a preset feature processing condition or not; if the to-be-processed data meets the preset feature processing condition, data features in the to-be-processed data are converted based on the target insight structure, the target visual structure and the insight demand to obtain an insight feature input text and a visual feature input text; and respectively inputting the insight feature input text and the visual feature input text into a preset language text generation model to obtain structured insight data and visual result data corresponding to the to-be-processed data. In this way, personalized and diversified insight generation can be realized, and specific requirements of users in different scenes are met.

Description

Technical Field

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

[0002] Visual insights are a very important part of the data analysis process. By converting data into graphs or charts, it helps people understand the stories behind the data more intuitively, and by marking the features in the charts, such as extreme values, trends, outliers, etc., it significantly enhances the explanatory power and information transmission efficiency of these charts. For example, companies use visual insights to monitor key indicators such as sales performance, market trends, and customer behavior to support strategic planning and daily operational decisions. Financial institutions use visualization tools to analyze stock price trends, changes in economic indicators, etc., to help investors assess risks and opportunities.

[0003] Current solutions fall into two categories: one is to predefine the annotation type and effect, and use a templated approach to pre-write text and reserve specific locations for each insight result to fill in the calculated results; the other is to use the model to directly generate visualization charts and their annotation insight codes based on given data. However, the insights generated by both methods cannot generate corresponding insights for different scenarios. Therefore, how to generate corresponding insights for different scenarios needs to be solved. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a data visualization insight method, device, equipment and storage medium, which can realize personalized and diversified insight generation and meet the specific needs of users in different scenarios. The specific scheme is as follows:

[0005] In a first aspect, the present application discloses a data visualization insight method, comprising:

[0006] Acquire the data to be processed in the target business field and the insight requirements for the data to be processed, and perform feature detection on the data to be processed based on preset data constraints to determine whether the data to be processed meets preset feature processing conditions;

[0007] If the data to be processed meets the preset feature processing condition, then based on the target insight structure, the target visualization structure and the insight requirement, the data features in the data to be processed are respectively transformed to obtain insight feature input text and visualization feature input text;

[0008] The insight feature input text and the visualization feature input text are respectively input into a preset language text generation model to obtain structured insight data and visualization result data corresponding to the data to be processed.

[0009] Optionally, if the data to be processed meets the preset feature processing condition, the method further includes:

[0010] Calculate the data features in the to-be-processed data based on the feature calculation targets in the preset feature library to obtain corresponding feature calculation results, and determine whether the data features meet preset visualization insight conditions based on preset feature detection items and the feature calculation results;

[0011] If the data features meet the preset visualization insight conditions, jump to the step of converting the data features in the data to be processed based on the target insight structure, the target visualization structure and the insight requirements to obtain insight feature input text and visualization feature input text.

[0012] Optionally, before performing feature detection on the data to be processed based on the preset data constraint item to determine whether the data to be processed meets the preset feature processing condition, the method further includes:

[0013] It is determined whether the data to be processed contains data of the data item type, and a preset field detection method is used to perform field detection on the data that does not contain the data item type in the data to be processed to extract corresponding data features.

[0014] Optionally, before respectively converting the data features in the to-be-processed data based on the target insight structure, the target visualization structure and the insight requirements to obtain the insight feature input text and the visualization feature input text, the method further includes:

[0015] Generate the target insight structure based on the insight structure feature requirements and the target business domain; the insight structure feature requirements include: insight characteristics, features and field meanings;

[0016] The target visualization structure is generated based on the visualization structure requirements and the target business domain; the visualization structure requirements include characteristics of visualization components and model output requirements.

[0017] Optionally, after respectively inputting the insight feature input text and the visualization feature input text into a preset language text generation model to obtain structured insight data and visualization result data corresponding to the data to be processed, the method further includes:

[0018] The structured insight data and the visualization result data are rendered using a preset engine rendering method to obtain visualization insights and description text of the data to be processed.

[0019] Optionally, the converting of data features in the to-be-processed data based on the target insight structure, the target visualization structure and the insight requirements to obtain insight feature input text and visualization feature input text includes:

[0020] Based on the target insight structure and the insight requirement, prompt splicing is performed on the data features in the data to be processed to obtain a feature prompt splicing result;

[0021] Based on the target visualization structure and the insight requirements, prompt splicing is performed on the data features in the data to be processed to obtain a visualization prompt splicing result.

[0022] Optionally, the step of inputting the insight feature input text and the visualization feature input text into a preset language text generation model to obtain structured insight data and visualization result data corresponding to the data to be processed includes:

[0023] The feature prompt splicing result and the visualization prompt splicing result are respectively input into the generative pre-training model to obtain structured insight data and visualization result data corresponding to the data to be processed.

[0024] In a second aspect, the present application discloses a data visualization insight device, comprising:

[0025] A data detection module is used to obtain the data to be processed in the target business field and the insight requirements for the data to be processed, and perform feature detection on the data to be processed based on preset data constraints to determine whether the data to be processed meets preset feature processing conditions;

[0026] A data conversion module, configured to convert the data features in the data to be processed based on the target insight structure, the target visualization structure and the insight requirements to obtain insight feature input text and visualization feature input text if the data to be processed meets the preset feature processing conditions;

[0027] The data analysis module is used to input the insight feature input text and the visualization feature input text into a preset language text generation model to obtain structured insight data and visualization result data corresponding to the data to be processed.

[0028] In a third aspect, the present application discloses an electronic device, including:

[0029] Memory, used to store computer programs;

[0030] A processor is used to execute the computer program to implement the aforementioned data visualization insight method.

[0031] In a fourth aspect, the present application discloses a computer-readable storage medium for storing a computer program, which implements the aforementioned data visualization insight method when executed by a processor.

[0032] It can be seen that in this application, the data to be processed in the target business field and the insight requirements for the data to be processed are obtained, and the data to be processed are feature detected based on the preset data constraints to determine whether the data to be processed meets the preset feature processing conditions; if the data to be processed meets the preset feature processing conditions, the data features in the data to be processed are respectively transformed based on the target insight structure, the target visualization structure and the insight requirements to obtain insight feature input text and visualization feature input text; the insight feature input text and the visualization feature input text are respectively input into the preset language text generation model to obtain structured insight data and visualization result data corresponding to the data to be processed. In this way, combining feature extraction and language text generation model transformation, firstly, the data features are transformed based on the target insight structure and the target visualization structure, and the insight requirements are taken into account in the transformation process, and the feature text corresponding to the insight requirements is generated, and the corresponding insight results are generated using the preset language text generation model, which can achieve personalized and diversified insight generation while ensuring accuracy, and meet the specific needs of users in different scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0034] Figure 1 A flow chart of a data visualization insight method disclosed in this application;

[0035] Figure 2 This is an example diagram of a specific characteristic result disclosed in this application;

[0036] Figure 3 This is a specific example diagram of the insight structure disclosed in this application;

[0037] Figure 4 This is a specific visualization structure example diagram disclosed in this application;

[0038] Figure 5 This is a specific feature prompt example diagram disclosed in this application;

[0039] Figure 6A specific visualization prompt example diagram disclosed in this application;

[0040] Figure 7 A specific example diagram of structured insight data disclosed in this application;

[0041] Figure 8 A specific example diagram of structured visual data disclosed in this application;

[0042] Fig. 9 This is an example diagram of a specific data visualization insight result disclosed in this application;

[0043] Fig.10 This is another specific data visualization insight result example diagram disclosed in this application;

[0044] Fig.11 This is a schematic diagram of the structure of a data visualization insight device disclosed in this application;

[0045] Fig.12 This is a structural diagram of an electronic device disclosed in this application. DETAILED DESCRIPTION

[0046] 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 application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0047] The existing technologies for visualizing insights into data exist as follows: the results generated each time are exactly the same, lacking diversity; the type and effect of insights depend entirely on predefined templates, for example: maximum, extreme, average, and other display methods, which can be enumerated and combined in large numbers, and predefined templates are difficult to support complex and varied display forms; users cannot specify insight types and data dimensions for display through natural language, and the large model has weak computing power, for example, there is a probability of errors when processing logical operations, clustering, and outlier operations; the model itself does not have a real component library or method library used by actual companies in its own knowledge, making the generated results difficult to apply in practice; it is difficult for the model to provide timely feedback on erroneous data, for example, when the data lacks key information, the large model will still try to generate it. Therefore, this application will specifically introduce a data visualization insight method that can solve the limitations of the above-mentioned technologies.

[0048] See also Figure 1 As shown, the embodiment of the present application discloses a data visualization insight method, including:

[0049] Step S11: Acquire the data to be processed in the target business field and the insight requirements for the data to be processed, and perform feature detection on the data to be processed based on preset data constraints to determine whether the data to be processed meets preset feature processing conditions.

[0050] In this embodiment, before the feature detection of the data to be processed based on the preset data constraint item to determine whether the data to be processed meets the preset feature processing condition, it also includes: determining whether the data to be processed contains data of the data item type, and using the preset field detection method to perform field detection on the data that does not contain the data item type in the data to be processed to extract the corresponding data features. Specifically, firstly obtain the data to be processed in the target business field sent by the user end and the insight requirements for the data to be processed. Then, the data to be processed is subjected to field detection to generate the type of each field in the data for subsequent feature calculation and visualization. If the input data already contains the type of the data item, field detection is not required. If not, field detection is required. Field detection can use existing solutions, such as data type identification, date identification, etc. Then, detect whether the current data meets the requirements for feature calculation, support custom configuration of constraint item check items, wherein the preset data constraint items may include but are not limited to requiring the number of data to be greater than 1, etc. If the requirements are not met, an error message will be returned. If all constraints are met, subsequent feature calculation and visualization are performed. The data to be processed is subjected to feature detection through preset data constraint items to determine whether the data to be processed meets preset feature processing conditions. In other words, it is to detect whether the current data meets the requirements for feature calculation.

[0051] Step S12: If the data to be processed meets the preset feature processing conditions, the data features in the data to be processed are respectively transformed based on the target insight structure, the target visualization structure and the insight requirements to obtain insight feature input text and visualization feature input text.

[0052] In this embodiment, if the data to be processed meets the preset feature processing conditions, it also includes: calculating the data features in the data to be processed based on the feature calculation targets in the preset feature library to obtain corresponding feature calculation results, and judging whether the data features meet the preset visualization insight conditions based on the preset feature detection items and the feature calculation results; if the data features meet the preset visualization insight conditions, jump to the step of converting the data features in the data to be processed based on the target insight structure, the target visualization structure and the insight requirements to obtain insight feature input text and visualization feature input text. That is, after the data to be processed meets the preset feature processing conditions, the precise features of the data are calculated, and user-defined configuration feature libraries are supported. For example, extreme value calculation, outlier calculation, trend calculation, etc. For different types of features, the data may not meet the requirements of this feature, then the calculation of this feature will be skipped, and the calculation of other features will continue. For example, when calculating the growth rate, the data value is 0. Example feature results are as follows Figure 2 Then, check whether the current features are sufficient for subsequent visualization insights. Feature check items support custom configuration, such as requiring high feature confidence (for example, the extreme value calculation series [1,100,1] and [100,101,1] have different confidences), and the number of features that can be obtained is greater than 1.

[0053] In this embodiment, before converting the data features in the data to be processed based on the target insight structure, the target visualization structure and the insight requirements to obtain the insight feature input text and the visualization feature input text, it also includes: generating the target insight structure based on the insight structure feature requirements and the target business field; the insight structure feature requirements include: the characteristics, features and field meanings of the insight; generating the target visualization structure based on the visualization structure requirements and the target business field; the visualization structure requirements include the characteristics of the visualization components and the model output requirements. That is, Figure 3 As shown in the figure, the abstract insight structure required for model output is defined, including the characteristics and features of the insight, the meaning of the fields, etc. Figure 4 As shown, an abstract visualization structure description is defined, the characteristics of the visualization components are defined, and the format of the model output is required.

[0054] In this embodiment, the data features in the data to be processed are respectively transformed based on the target insight structure, the target visualization structure and the insight requirements to obtain insight feature input text and visualization feature input text, including: prompt splicing the data features in the data to be processed based on the target insight structure and the insight requirements to obtain feature prompt splicing results; prompt splicing the data features in the data to be processed based on the target visualization structure and the insight requirements to obtain visualization prompt splicing results. Specifically, Figure 5 As shown in Figure 1, construct a prompt that contains user input, insight structure, and features. Figure 6 As shown, build a prompt that contains data and visualization structure.

[0055] Step S13: inputting the insight feature input text and the visualization feature input text into a preset language text generation model respectively to obtain structured insight data and visualization result data corresponding to the data to be processed.

[0056] In this embodiment, the insight feature input text and the visualization feature input text are respectively input into a preset language text generation model to obtain structured insight data and visualization result data corresponding to the data to be processed, including: inputting the feature prompt splicing result and the visualization prompt splicing result into a generative pre-trained model to obtain structured insight data and visualization result data corresponding to the data to be processed. That is, after obtaining the feature prompt splicing result and the visualization prompt splicing result, the feature prompt splicing result and the visualization prompt splicing result are input into a GPT model (Generative Pre-trained Transformer), so that GPT generates structured insight data that meets the requirements according to the specified content, that is, Figure 7 and Figure 8 As shown, the structured results are generated according to the given insight structure and visualization structure.

[0057] In this embodiment, after the insight feature input text and the visualization feature input text are respectively input into the preset language text generation model to obtain the structured insight data and visualization result data corresponding to the data to be processed, it also includes: using a preset engine rendering method to render the structured insight data and the visualization result data to obtain the visualization insight and description text of the data to be processed. Fig. 9 and Fig.10 As shown, the output structured insight data and structured visualization data are rendered, and finally interactive visualization insights and description texts are obtained.

[0058] It can be seen that in this embodiment, the data to be processed and the insight requirements for the data to be processed in the target business field are obtained, and the feature detection is performed on the data to be processed based on the preset data constraints to determine whether the data to be processed meets the preset feature processing conditions; if the data to be processed meets the preset feature processing conditions, the data features in the data to be processed are respectively transformed based on the target insight structure, the target visualization structure and the insight requirements to obtain insight feature input text and visualization feature input text; the insight feature input text and the visualization feature input text are respectively input into the preset language text generation model to obtain structured insight data and visualization result data corresponding to the data to be processed. In this way, combined with feature extraction and language text generation model conversion, the data features are first transformed based on the target insight structure and the target visualization structure, and the insight requirements are taken into account in the conversion process, and the feature text corresponding to the insight requirements is generated, and the corresponding insight results are generated using the preset language text generation model, which can achieve personalized and diversified insight generation while ensuring accuracy, and meet the specific needs of users in different scenarios.

[0059] refer to Fig.11 The present application also discloses a data visualization insight device, including:

[0060] The data detection module 11 is used to obtain the data to be processed in the target business field and the insight requirements for the data to be processed, and perform feature detection on the data to be processed based on preset data constraints to determine whether the data to be processed meets preset feature processing conditions;

[0061] A data conversion module 12 is used to convert the data features in the data to be processed based on the target insight structure, the target visualization structure and the insight requirements to obtain insight feature input text and visualization feature input text if the data to be processed meets the preset feature processing conditions;

[0062] The data analysis module 13 is used to input the insight feature input text and the visualization feature input text into a preset language text generation model respectively to obtain structured insight data and visualization result data corresponding to the data to be processed.

[0063] It can be seen that in this embodiment, the feature extraction and language text generation model conversion are combined. First, the features of the data are converted based on the target insight structure and the target visualization structure. The insight needs are taken into consideration during the conversion process, and feature texts corresponding to the insight needs are generated. The preset language text generation model is used to generate corresponding insight results. This can ensure accuracy while achieving personalized and diversified insight generation to meet the specific needs of users in different scenarios.

[0064] In some specific embodiments, the data visualization insight device may further include:

[0065] An insight condition judgment module, used to calculate the data features in the to-be-processed data based on the feature calculation targets in the preset feature library to obtain corresponding feature calculation results, and to judge whether the data features meet the preset visualization insight conditions based on the preset feature detection items and the feature calculation results;

[0066] The step jump module is used to jump to the step of converting the data features in the data to be processed based on the target insight structure, the target visualization structure and the insight requirements to obtain insight feature input text and visualization feature input text if the data features meet the preset visualization insight conditions.

[0067] In some specific embodiments, the data visualization insight device may further include:

[0068] The data feature extraction module is used to determine whether the data to be processed contains data of the data item type, and use a preset field detection method to perform field detection on the data that does not contain the data item type in the data to be processed to extract corresponding data features.

[0069] In some specific embodiments, the data visualization insight device may further include:

[0070] A feature generation module, used to generate the target insight structure based on insight structure feature requirements and the target business domain; the insight structure feature requirements include: insight characteristics, features and field meanings;

[0071] The visualization structure determination module is used to generate the target visualization structure based on the visualization structure requirements and the target business domain; the visualization structure requirements include the characteristics of the visualization components and the model output requirements.

[0072] In some specific embodiments, the data visualization insight device may further include:

[0073] The data rendering module is used to render the structured insight data and the visualization result data using a preset engine rendering method to obtain visualization insights and description text of the data to be processed.

[0074] In some specific embodiments, the data conversion module 12 may specifically include:

[0075] A first feature splicing unit, configured to perform prompt splicing on the data features in the to-be-processed data based on the target insight structure and the insight requirement to obtain a feature prompt splicing result;

[0076] The second feature splicing unit is used to perform prompt splicing on the data features in the data to be processed based on the target visualization structure and the insight requirements to obtain a visualization prompt splicing result.

[0077] In some specific embodiments, the data analysis module 13 may specifically include:

[0078] A data generation unit is used to input the feature prompt splicing result and the visualization prompt splicing result into the generative pre-training model respectively to obtain structured insight data and visualization result data corresponding to the data to be processed.

[0079] Furthermore, the present application also discloses an electronic device. Fig.12 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content in the diagram cannot be regarded as any limitation on the scope of use of the present application.

[0080] Fig.12 A schematic diagram of the structure of an electronic device 20 provided in an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the data visualization insight method disclosed in any of the aforementioned embodiments. In addition, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0081] In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present application, and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0082] In addition, the memory 22, as a carrier for storing resources, can be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0083] The operating system 221 is used to manage and control the hardware devices and computer programs 222 on the electronic device 20, which may be Windows Server, Netware, Unix, Linux, etc. In addition to computer programs that can be used to complete the data visualization insight method performed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 may further include computer programs that can be used to complete other specific tasks.

[0084] Furthermore, the present application also discloses a computer-readable storage medium for storing a computer program; wherein the computer program, when executed by a processor, implements the aforementioned disclosed data visualization insight method. The specific steps of the method can refer to the corresponding contents disclosed in the aforementioned embodiments, and will not be repeated here.

[0085] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0086] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0087] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0088] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0089] The technical solution provided by the present application is introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for general technicians in this field, according to the idea of ​​the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A data visualization insight method, characterized in that: include: Acquire the data to be processed in the target business field and the insight requirements for the data to be processed, and perform feature detection on the data to be processed based on preset data constraints to determine whether the data to be processed meets preset feature processing conditions; If the data to be processed meets the preset feature processing condition, then based on the target insight structure, the target visualization structure and the insight requirement, the data features in the data to be processed are respectively transformed to obtain insight feature input text and visualization feature input text; The insight feature input text and the visualization feature input text are respectively input into a preset language text generation model to obtain structured insight data and visualization result data corresponding to the data to be processed.

2. The data visualization insight method according to claim 1, characterized in that: If the data to be processed meets the preset feature processing condition, the method further includes: Calculate the data features in the to-be-processed data based on the feature calculation targets in the preset feature library to obtain corresponding feature calculation results, and determine whether the data features meet preset visualization insight conditions based on preset feature detection items and the feature calculation results; If the data features meet the preset visualization insight conditions, jump to the step of converting the data features in the data to be processed based on the target insight structure, the target visualization structure and the insight requirements to obtain insight feature input text and visualization feature input text.

3. The data visualization insight method according to claim 1, characterized in that: Before performing feature detection on the data to be processed based on the preset data constraint item to determine whether the data to be processed meets the preset feature processing condition, the method further includes: It is determined whether the data to be processed contains data of the data item type, and a preset field detection method is used to perform field detection on the data that does not contain the data item type in the data to be processed to extract corresponding data features.

4. The data visualization insight method according to claim 1, characterized in that: Before respectively transforming the data features in the to-be-processed data based on the target insight structure, the target visualization structure and the insight requirements to obtain the insight feature input text and the visualization feature input text, the method further includes: Generate the target insight structure based on the insight structure feature requirements and the target business domain; the insight structure feature requirements include: insight characteristics, features and field meanings; The target visualization structure is generated based on the visualization structure requirements and the target business domain; the visualization structure requirements include characteristics of visualization components and model output requirements.

5. The data visualization insight method according to claim 1, characterized in that: After the insight feature input text and the visualization feature input text are respectively input into a preset language text generation model to obtain structured insight data and visualization result data corresponding to the data to be processed, the method further includes: The structured insight data and the visualization result data are rendered using a preset engine rendering method to obtain visualization insights and description text of the data to be processed.

6. The data visualization insight method according to claim 1, characterized in that: The step of converting the data features in the to-be-processed data based on the target insight structure, the target visualization structure, and the insight requirements to obtain insight feature input text and visualization feature input text includes: Based on the target insight structure and the insight requirement, prompt splicing is performed on the data features in the data to be processed to obtain a feature prompt splicing result; Based on the target visualization structure and the insight requirements, prompt splicing is performed on the data features in the data to be processed to obtain a visualization prompt splicing result.

7. The data visualization insight method according to claim 6, characterized in that: The step of inputting the insight feature input text and the visualization feature input text into a preset language text generation model to obtain structured insight data and visualization result data corresponding to the data to be processed includes: The feature prompt splicing result and the visualization prompt splicing result are respectively input into the generative pre-training model to obtain structured insight data and visualization result data corresponding to the data to be processed.

8. A data visualization insight device, characterized in that: include: A data detection module is used to obtain the data to be processed in the target business field and the insight requirements for the data to be processed, and perform feature detection on the data to be processed based on preset data constraints to determine whether the data to be processed meets preset feature processing conditions; A data conversion module, configured to convert the data features in the data to be processed based on the target insight structure, the target visualization structure and the insight requirements to obtain insight feature input text and visualization feature input text if the data to be processed meets the preset feature processing conditions; The data analysis module is used to input the insight feature input text and the visualization feature input text into a preset language text generation model to obtain structured insight data and visualization result data corresponding to the data to be processed.

9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the data visualization insight method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Used to store a computer program, which, when executed by a processor, implements the data visualization insight method according to any one of claims 1 to 7.

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