Data analysis and visualization method based on low-code platform

Abstract

The present invention relates to the field of electronic digital data processing technology, and specifically to a data analysis and visualization display method based on a low-code platform. By obtaining time log data of the low-code platform, the actual usage frequency, current component set and historical component set of each component are determined according to the number of times each component is used and the corresponding usage time by several users. The component selection tendency of each user in application design is determined according to the historical component set selected by each user for each historical application design. The correlation between the application design performed at the current usage time of each user and the corresponding historical application design is determined according to the current component set and the historical component set, which is conducive to providing support for subsequent accurate recommendations. According to the correlation, component selection tendency and actual usage frequency, it is conducive to providing accurate recommendation groups and performing visualization display. New users can independently and quickly determine the components they ultimately need based on the effect of the visualization display.

Description

Technical Field

[0001] The present invention relates to the field of electronic digital data processing technology, and in particular to a data analysis and visualization method based on a low-code platform. Background Art

[0002] A low-code platform is a development platform that allows developers to create applications through a graphical interface and configuration method, and provides developers with a wealth of components and templates, allowing developers to create applications like a puzzle. Figure 1 Similarly, by dragging and configuring these components, applications can be quickly built. Compared with traditional code development, low-code development platforms have greatly lowered the development threshold, allowing non-professional developers with business logic understanding to participate in software development, which greatly expands the talent pool for software development.

[0003] In the current low-code platform, although the development threshold has been lowered, development still depends on the familiarity with the components of the low-code platform. For some users who are not familiar with the components, such as new users, the current low-code platform lacks corresponding recommendations and other advisory information, which is not conducive to the use of new users. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide a data analysis and visualization method based on a low-code platform. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present invention provides a method for data analysis and visualization based on a low-code platform, including:

[0006] Obtain time log data of the low-code platform, wherein the time log data includes the number of times a number of users use each component and the corresponding usage time;

[0007] According to the number of times each component is used by several users and the corresponding usage time, the actual usage frequency of each component is determined, the current component set selected by each user when designing an application at the current usage time is determined, and the historical component set selected by each user for each historical application design is determined;

[0008] Determine the component selection tendency of each user in application design according to the historical component set selected by each user in each historical application design, and determine the correlation between the application design performed at the current usage time of each user and the corresponding historical application design according to the current component set and the historical component set, and determine the recommended component according to the correlation, the component selection tendency and the actual usage frequency;

[0009] The recommended components are visually displayed.

[0010] In one implementation, determining the actual usage frequency of each component according to the number of times each component is used by a number of users and the corresponding usage time includes:

[0011] Determine a first total number of times each component is used within a preset time length according to the number of times each component is used by a number of users and the corresponding time of use, and sum the total number of times each component is used within the preset time length to determine a second total number of times all components are used within the preset time length;

[0012] determining a usage frequency of each component according to a ratio of the first total usage times to the second total usage times;

[0013] Determine the reusability of each component based on the number of times each component is used by a number of users and the corresponding usage time;

[0014] According to the usage frequency of each component and the corresponding repeated usage performance, the actual usage frequency of each component is determined.

[0015] In one embodiment, determining the repetitive use performance of each component according to the number of times each component is used by a plurality of users and the corresponding use time includes:

[0016] According to the number of times each component is used by a number of users and the corresponding time of use, the number of users who have used each component, the number of first times each user has designed an application, and the number of second times each user has used each component are determined;

[0017] Determine a candidate component from the components, determine a first ratio of the second number of times of the candidate component corresponding to each user to the first number of times, and determine a repetitive use performance degree of the candidate component according to a second ratio of a sum of the first ratios to the number of users of the candidate component;

[0018] Return to the step of determining candidate components from the components until the reuse performance of each component is determined.

[0019] In one embodiment, determining the actual usage frequency of each component according to the usage frequency of each component and the corresponding repeated usage performance includes:

[0020] Determine a first product of the usage frequency of each component and the corresponding reuse performance degree;

[0021] The actual usage frequency of each component is determined according to the normalized function and the first product.

[0022] In one embodiment, the method further comprises:

[0023] Arrange the components from largest to smallest based on their actual usage frequency;

[0024] A first preset number of components arranged at the front are determined as reference components.

[0025] In one embodiment, determining the component selection tendency of each user in application design according to the historical component set selected by each user in each historical application design includes:

[0026] According to the historical component sets selected by each user for each historical application design, two by two combinations are made to obtain multiple historical component combinations;

[0027] Determine the usage time difference of each historical component combination respectively, and determine the analysis attention of each user to each historical component combination according to the usage time difference;

[0028] Determine the first Jaccard similarity of each of the historical component combinations, determine a candidate user from the users, respectively determine the second product of the analysis attention of each of the historical component combinations corresponding to the candidate user and the corresponding first Jaccard similarity, and determine the component selection tendency of the candidate user in application design according to a third ratio of the sum of each of the second products to the number of combinations of the historical component combinations;

[0029] Return to the step of determining candidate users from the users until the component selection inclination of each user during application design is determined.

[0030] In one embodiment, determining the analysis attention of each user to each combination of the historical components according to the usage time difference includes:

[0031] The inverse of each of the usage time differences is determined respectively to obtain the analysis attention of each user to each of the historical component combinations.

[0032] In one embodiment, the determining, based on the current component set and the historical component set, respectively the correlation between the current usage time of each user and the corresponding historical application design includes:

[0033] Determine a candidate user from the users, and determine the second Jaccard similarity between the current component set corresponding to the candidate user and the historical component set selected for each historical application design;

[0034] determining, according to a fourth ratio of the sum of the second Jaccard similarities to the number of the second Jaccard similarities, a correlation between the application design performed at the current usage time of the candidate user and the corresponding historical application design;

[0035] Return to the step of determining candidate users from the users until the correlation between the current usage time of each user and the application design and the corresponding historical application design is determined.

[0036] In one implementation, determining the recommended component according to the association degree, the component selection tendency, and the actual usage frequency includes:

[0037] Determine candidate components corresponding to each user, and determine a target user from the users and a target candidate component from the candidate components;

[0038] Taking the association degree of the target user as a first weight, determining a third product of the first weight and the component selection tendency of the target user;

[0039] Determine a second weight according to the first weight, and determine a fourth product of the second weight and the actual usage frequency of the target candidate component corresponding to the target user;

[0040] Determining the recommendation degree of the target candidate component of the target user as the next component according to the sum of the third product and the fourth product, and returning to the step of determining the target candidate component from the candidate components until the recommendation degree of each candidate component of the target user as the next component is determined;

[0041] Arrange each of the candidate components from large to small according to the recommendation degree, and determine a second preset number of candidate components that are ranked top as the recommended components for the target user;

[0042] Return to the step of determining the target user from the users until the recommended components for each user are determined.

[0043] In one implementation, determining the candidate components corresponding to each user includes:

[0044] Determine a candidate user from the users, determine a first target historical component set including the current component set of the candidate user from the historical component set of the candidate user, and determine a second target historical component set including the current component set of the candidate user from the historical component sets of all users;

[0045] Determine a first component in the first target historical component set that is arranged after the current component set of the candidate user in terms of usage time, and determine a second component in the second target historical component set that is arranged after the current component set of the candidate user in terms of usage time;

[0046] The first component and the second component are determined as candidate components for the candidate user, and the process returns to the step of determining candidate users from the users until the candidate components corresponding to the respective users are determined.

[0047] The present invention has the following beneficial effects:

[0048] By obtaining the time log data of the low-code platform, the time log data includes the number of times several users use each component and the corresponding usage time. According to the number of times several users use each component and the corresponding usage time, the actual usage frequency of each component is determined, the current component set selected by each user when designing an application at the current usage time is determined, and the historical component set selected by each user for each historical application design is determined. According to the historical component set selected by each user for each historical application design, the component selection tendency of each user when designing an application is determined. According to the current component set and the historical component set, the correlation between the application design at the current usage time of each user and the corresponding historical application design is determined respectively, which is conducive to providing support for subsequent accurate recommendations; and according to the correlation, component selection tendency and actual usage frequency, the recommended components are comprehensively determined, and the recommended components are visualized, which is conducive to providing accurate recommended components for new users, and new users can independently and quickly determine the final components required based on the effect of the visualization display. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0050] Figure 1 A schematic diagram of the steps of a data analysis and visualization method based on a low-code platform provided by an embodiment of the present invention;

[0051] Figure 2 This is a flowchart of the steps of determining the actual usage frequency of each component according to the number of times each component is used by a number of users and the corresponding usage time in step S200 in one embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the following is a detailed description of the specific implementation method, structure, characteristics and effects of a data analysis and visualization method based on a low-code platform proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0053] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0054] In the related art, the components of the low-code platform are usually built by the platform development team based on user feedback, market demand, changes in business scenarios, etc., so as to facilitate users to quickly build components commonly used in their applications. Usually, the low-code platform will define commonly used components based on the overall frequency of use of the components on the platform, so that components with high usage frequency are often regarded as benchmark components so that new users can quickly find and use these components. However, different users have different usage preferences and application design requirements, so in actual use, the benchmark components determined in this way cannot accurately and quickly meet the needs of users, which will cause users to be unable to efficiently use the resources provided by the current platform to users. In view of this, in an embodiment of the present invention, by redefining the actual usage frequency of the components, the benchmark components are determined based on the actual usage frequency, which is more accurate and more in line with the preferences and needs of each user.

[0055] It should be noted that the term “exemplary” in the embodiments of the present invention refers to examples listed for the convenience of explanation, and other embodiments are not limited to the examples listed.

[0056] The following is a detailed description of a data analysis and visualization method based on a low-code platform provided by the present invention in conjunction with the accompanying drawings.

[0057] See also Figure 1 , which shows a flow chart of a data analysis and visualization method based on a low-code platform provided by an embodiment of the present invention. The data analysis and visualization method based on a low-code platform may at least include steps S100-S400:

[0058] S100. Obtain the time log data of the low-code platform, where the time log data includes the number of times a number of users use each component and the corresponding usage time.

[0059] S200. Determine the actual usage frequency of each component based on the number of times each component is used by several users and the corresponding usage time, determine the current component set selected by each user when designing an application during the current usage time, and determine the historical component set selected by each user for each historical application design.

[0060] S300. Determine the component selection tendency of each user in application design based on the historical component set selected by each user in each historical application design, and determine the correlation between the application design of each user's current usage time and the corresponding historical application design based on the current component set and the historical component set, and determine the recommended components based on the correlation, component selection tendency and actual usage frequency.

[0061] S400: Visually display the recommended components.

[0062] The technical solution of the embodiment of the present invention obtains the time log data of the low-code platform, and the time log data includes the number of times several users use each component and the corresponding usage time. According to the number of times several users use each component and the corresponding usage time, the actual usage frequency of each component is determined, the current component set selected by each user when designing an application at the current usage time is determined, and the historical component set selected by each user for each historical application design is determined. According to the historical component set selected by each user for each historical application design, the component selection tendency of each user when designing an application is determined. According to the current component set and the historical component set, the correlation between the application design at the current usage time of each user and the corresponding historical application design is determined, which is conducive to providing support for subsequent accurate recommendations; and according to the correlation, component selection tendency and actual usage frequency, the recommended components are comprehensively determined, and the recommended components are visually displayed, which is conducive to providing accurate recommended components for new users, and new users can independently and quickly determine the final required components based on the effect of the visual display.

[0063] In one embodiment, the low-code platform (hereinafter referred to as the platform) records the components used by the user, the number of times the components are used, the usage time of the components, etc. each time the user uses the low-code platform for application design, and records them in the time log to form time log data. Therefore, the time log data includes the number of times each component is used by several users and the corresponding usage time. In addition, through these time log data, it is possible to determine the current component set selected by each user when designing an application at the current usage time and the historical component set selected by each user for each historical application design. Both the current component set and the historical component set may include one or more components.

[0064] In one embodiment, in a low-code platform, since the platform usually defines commonly used components based on the frequency of use of the components, and components with high frequency of use are often regarded as benchmark components, the use of each component can be analyzed first; in step S200, the actual frequency of use of each component is determined according to the number of times a number of users use each component and the corresponding usage time, including steps S201-S204, such as Figure 2 As shown:

[0065] S201. Determine a first total usage count of each component within a preset time length based on the usage counts of each component by a number of users and the corresponding usage time, and sum the total usage counts of each component within the preset time length to determine a second total usage count of all components within the preset time length.

[0066] Optionally, the preset time length is exemplarily 1 month, and then the number of times a number of users use each component and the corresponding usage time within one month are analyzed to determine the first total number of times each component is used within the preset time length. , Indicates Then, the total number of times each component is used within the preset time length is summed to determine the second total number of times all components are used within the preset time length. .

[0067] S202: Determine the usage frequency of each component according to the ratio of the first total usage times to the second total usage times.

[0068] Specifically, the formula is:

[0069]

[0070] in, Indicates The frequency of use of a component.

[0071] S203: Determine the repetitive use performance of each component according to the number of times each component is used by a number of users and the corresponding use time.

[0072] It should be noted that, in the embodiment of the present invention, the determination of the benchmark component should not only consider the frequency of use, because the use of the actual component in the user's application design will fully consider the visualization effect it presents, and when the user approves of the presentation effect of the component, it will continue to be used in subsequent other application designs. Therefore, the user's secondary use of the component should also be considered here, and then the usage of each component in the platform should be comprehensively determined, thereby defining the actual usage frequency of each component.

[0073] First, determine the number of users who have used each component based on the number of times a number of users use each component and the corresponding usage time. (No. the number of users who have used the corresponding component), the number of first times each user designs the application (No. The number of times each user designs an application) and the second number of times each user uses each component (No. Users use components).

[0074] Second, determine candidate components from the components (e.g. components), determine the first ratio of the second number of times to the first number of times of the candidate component corresponding to each user: , and according to the sum of the first ratios and the second ratio of the number of users of the candidate component, the reuse performance of the candidate component is determined, so the formula is:

[0075]

[0076] Then, we return to the step of determining candidate components from the components until the reuse performance of each component is determined, i.e. Take different values ​​to get the reuse performance of each component , representing The degree of reuse of components.

[0077] S204: Determine the actual usage frequency of each component according to the usage frequency of each component and the corresponding repeated usage performance.

[0078] Optionally, determine the usage frequency of each component separately The corresponding degree of repeated use The first product of And the first product, determine the actual usage frequency of each component, the specific formula is:

[0079] in, Indicates The actual usage frequency of each component.

[0080] In one implementation, after determining the actual usage frequency of each component, the components are arranged from large to small based on the corresponding actual usage frequency, and then, illustratively, taking the first preset number as 10, the first 10 components arranged at the top, that is, the first 10 components with the largest actual usage frequency, are determined as reference components. Finally, the reference components can be visualized for use by users in application design, and the determination of the reference components is more accurate.

[0081] It should be noted that for the same user, each application design requirement will have different design habits and preferences, so the components used have certain preference tendencies. Therefore, the component selection tendency can be analyzed by analyzing the user's multiple historical application design records. Therefore, the user's component selection tendency can be defined by comparing the correlation between the components used in different application designs of the user. That is, the higher the similarity of the components used in different application designs, the higher the component selection tendency of the user in the application design.

[0082] In one embodiment, step S300 determines the component selection tendency of each user in application design according to the historical component set selected by each user in each historical application design, including steps S301-S304 (not shown in the figure):

[0083] S301. According to the historical component sets selected by each user for each historical application design, the historical component sets are combined in pairs to obtain multiple historical component combinations.

[0084] For example, if there are historical component sets A, B, and C, then they are combined in pairs to obtain multiple historical component combinations: AB, AC, and BC. For the User's history component combination, record The number of combinations of historical component combinations for a user is .

[0085] S302: Determine the usage time difference of each historical component combination respectively, and determine the analysis attention of each user to each historical component combination according to the usage time difference.

[0086] It should be noted that when conducting comparative analysis of different application designs, it is also necessary to consider the time proximity. The closer the time is, the more attention should be paid. This is because users may have obvious component usage tendencies in the short term when designing applications. Therefore, determine the usage time difference of each historical component combination separately. , for example, calculated in units of one day, Refers to the User's The usage time difference corresponding to the historical component combination ( The time difference between two historical application designs of a historical component combination). According to the usage time difference, the analysis attention of each user to each historical component combination is determined. Specifically: the inverse of each usage time difference is determined respectively to obtain the analysis attention of each user to each historical component combination. , that is, User's The analysis focus corresponding to each historical component combination, 0.01 is to prevent the denominator from being 0.

[0087] S303. Determine the first Jaccard similarity of each historical component combination, determine the candidate user from the users, determine the second product of the analysis attention of each historical component combination corresponding to the candidate user and the corresponding first Jaccard similarity, and determine the component selection tendency of the candidate user in application design based on the third ratio of the sum of each second product and the number of combinations of historical component combinations.

[0088] Optionally, determine the first Jaccard similarity for each historical component combination , determine the candidate user from the users, assuming that the For each user, determine the second product of the analysis attention of each historical component combination corresponding to the candidate user and the corresponding first Jaccard similarity , and according to the sum of the second products Number of combinations with historical components The third ratio determines the candidate user's component selection tendency when designing the application:

[0089]

[0090] in, Indicates The component selection tendency of a user (candidate user in this case) during application design.

[0091] S304: Return to the step of determining candidate users from the users until the component selection tendency of each user during application design is determined.

[0092] Specifically, return to the above step S303 to determine candidate users from the users, so that When different values ​​are taken, the component selection tendency of each user in application design is obtained .

[0093] In one embodiment, step S300 includes determining the correlation between the application design for the current usage time of each user and the corresponding historical application design according to the current component set and the historical component set, including steps S305-S306 (not shown in the figure):

[0094] S305 , determining a candidate user from the users, and determining the second Jaccard similarity between the current component set corresponding to the candidate user and the historical component set selected for each historical application design.

[0095] S306. Determine the correlation between the application design based on the current usage time of the candidate user and the corresponding historical application design based on the fourth ratio of the sum of the second Jaccard similarities to the number of second Jaccard similarities; return to the step of determining the candidate user from the users until the correlation between the application design based on the current usage time of each user and the corresponding historical application design is determined.

[0096] Optionally, a candidate user is determined from the users, and then the second Jaccard similarity between the current component set corresponding to the candidate user and the historical component set selected for each historical application design is determined. The calculation method of the Jaccard similarity is a prior art and will not be described in detail. Then, according to the fourth ratio of the sum of the second Jaccard similarities to the number of the second Jaccard similarities, that is, the average value of the second Jaccard similarities is calculated, so as to determine the correlation between the application design of the current usage time of the candidate user and the corresponding historical application design. , for example, the candidate user is user, and the result is The correlation between the current usage time of each user and the corresponding historical application design Finally, return to the step of determining candidate users from the users in S306 until the correlation between the current usage time of each user and the corresponding historical application design is determined. .

[0097] In one embodiment, the step S300 determines the recommended components according to the relevance, component selection tendency and actual usage frequency, including steps S310-S350 (not shown):

[0098] S310: Determine candidate components corresponding to each user, determine target users from the users, and determine target candidate components from the candidate components.

[0099] It should be noted that the current user can compare the current component with historical data when designing an application, so as to make recommendations and suggestions for the next component.

[0100] In one implementation, determining the candidate components corresponding to each user may include:

[0101] First, a candidate user is determined from the users, and a first target historical component set containing the candidate user's current component set is determined from the candidate user's historical component set. For example, if the current component set is A, B, and C, and a certain historical component set is A, B, C, and E, then the historical component set contains the current component set; therefore, the first target historical component set can have one or more component sets. Similarly, a second target historical component set containing the candidate user's current component set is determined from the historical component sets of all users. During the screening process, the component order of the current component set selected by the current user can be strictly followed to ensure high relevance while reducing the amount of data.

[0102] Secondly, determine the first component in the first target historical component set that is arranged after the current component set of the candidate user in terms of usage time, and determine the second component in the second target historical component set that is arranged after the current component set of the candidate user in terms of usage time, and determine the first component and the second component as candidate components for the candidate user. For example, if a certain component set in the first target historical component set is arranged as A, B, C, and E in terms of usage time, and the current component set is A, B, and C, then component E is determined as a candidate component. Similarly, the candidate components of all component sets in the first target historical component set and all component sets in the second target historical component set can be determined, and finally all candidate components of the candidate user are obtained.

[0103] Then, the process returns to the step of determining candidate users from the users until the candidate components corresponding to each user are determined.

[0104] Optionally, determine any target user from the users, such as users, and determine the target candidate components from the candidate components, such as Alternative components.

[0105] S320: Taking the relevance of the target user as the first weight, determining a third product of the first weight and the component selection tendency of the target user.

[0106] Optionally, the relevance of the target user As the first weight, determine the first weight Component selection preference of target users The third product of It should be noted that when The higher the correlation between the current application design of a user and his own history, it means that the current user can choose the appropriate component based on his previous processing experience, so a greater weight is given to the component selection tendency of the user in application design (because the component selection tendency of the user in application design reflects the user's own preference in selecting components, so the higher the correlation between the current application design and his own history, the more the next component can be determined based on his own preference).

[0107] S330. Determine a second weight according to the first weight, and determine a fourth product of the second weight and the actual usage frequency of the target candidate component corresponding to the target user.

[0108] Optionally, according to the first weight , determine the second weight , determine the second weight The actual usage frequency of target candidate components corresponding to the target users The fourth product of , For the User's The actual usage frequency of the candidate components.

[0109] S340. Determine the recommendation degree of the target candidate component of the target user as the next component based on the sum of the third product and the fourth product, and return to the step of determining the target candidate component from the candidate components until the recommendation degree of each candidate component of the target user as the next component is determined.

[0110] Then, according to the sum of the third product and the fourth product, the recommendation degree of the target candidate component of the target user as the next component is determined:

[0111]

[0112] in, Indicates the current User's The candidate components are used as the recommendation degree of the next component; then the step of determining the target candidate component from the candidate components is returned until the recommendation degree of each candidate component of the target user as the next component is determined, that is, when When taking different values, Equivalent to Each candidate component of the user is used as the recommendation degree of the next component. It should be noted that when the The smaller the correlation between the current application design of a user and his / her own history, the more attention should be paid to recommending the next component for the user through the overall data (mainly based on the actual usage frequency of the component, the higher the frequency, the more recognized it is).

[0113] S350, arranging each candidate component from large to small according to the degree of recommendation, and determining a second preset number of candidate components that are ranked top as recommended components for the target user; returning to the step of determining the target user from the users until the recommended components for each user are determined.

[0114] Optionally, each candidate component is arranged from large to small according to the degree of recommendation, and illustratively, the second preset number is 4, and the 4 candidate components with the highest ranking are determined as the recommended components for the target user, that is, the first 4 candidate components with the highest recommendation degree are used as the recommended components for the target user. Finally, return to the step of determining the target user from the users, based on the same principle as above, until the recommended components for each user are determined.

[0115] In one embodiment, in step S400, after obtaining the recommended components for each user, the user can independently select and determine the required recommended components from the recommended components according to the displayed and actual visual display effects as the final components.

[0116] The method of the embodiment of the present invention redefines the actual usage frequency of components and takes into account the component selection tendency of users when designing applications and the correlation between the currently involved components and the history, etc., so as to weigh the user's component selection preference and the accuracy of the adapted components among the alternative components, determine the recommendation degree of each component, determine the recommended components for users to choose, thereby improving and accurately meeting the user's application design needs and enhancing the user experience.

[0117] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0118] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A data analysis and visualization method based on a low-code platform, characterized in that: The method comprises: Obtain time log data of the low-code platform, wherein the time log data includes the number of times a number of users use each component and the corresponding usage time; According to the number of times each component is used by several users and the corresponding usage time, the actual usage frequency of each component is determined, the current component set selected by each user when designing an application at the current usage time is determined, and the historical component set selected by each user for each historical application design is determined; Determine the component selection tendency of each user in application design according to the historical component set selected by each user in each historical application design, and determine the correlation between the application design performed at the current usage time of each user and the corresponding historical application design according to the current component set and the historical component set, and determine the recommended component according to the correlation, the component selection tendency and the actual usage frequency; Visually display the recommended components; Determining the component selection tendency of each user in application design according to the historical component set selected by each user in each historical application design includes: According to the historical component sets selected by each user for each historical application design, two by two combinations are made to obtain multiple historical component combinations; Determine the usage time difference of each historical component combination respectively, and determine the analysis attention of each user to each historical component combination according to the usage time difference; Determine the first Jaccard similarity of each of the historical component combinations, determine a candidate user from the users, respectively determine the second product of the analysis attention of each of the historical component combinations corresponding to the candidate user and the corresponding first Jaccard similarity, and determine the component selection tendency of the candidate user in application design according to a third ratio of the sum of each of the second products to the number of combinations of the historical component combinations; Returning to the step of determining candidate users from the users, until the component selection tendency of each user during application design is determined; The determining, based on the current component set and the historical component set, respectively the correlation between the current usage time of each user and the corresponding historical application design for application design includes: Determine a candidate user from the users, and determine the second Jaccard similarity between the current component set corresponding to the candidate user and the historical component set selected for each historical application design; determining, according to a fourth ratio of the sum of the second Jaccard similarities to the number of the second Jaccard similarities, a correlation between the application design performed at the current usage time of the candidate user and the corresponding historical application design; Returning to the step of determining candidate users from the users, until the correlation between the current usage time of each user for application design and the corresponding historical application design is determined; The determining of the recommended component according to the association degree, the component selection tendency and the actual usage frequency includes: Determine candidate components corresponding to each user, and determine a target user from the users and a target candidate component from the candidate components; Taking the relevance of the target user as a first weight, determining a third product of the first weight and the component selection tendency of the target user; Determine a second weight according to the first weight, and determine a fourth product of the second weight and the actual usage frequency of the target candidate component corresponding to the target user; Determining the recommendation degree of the target candidate component of the target user as the next component according to the sum of the third product and the fourth product, and returning to the step of determining the target candidate component from the candidate components until the recommendation degree of each candidate component of the target user as the next component is determined; Arrange each of the candidate components from large to small according to the recommendation degree, and determine a second preset number of candidate components that are ranked top as the recommended components for the target user; Return to the step of determining the target user from the users until the recommended components for each user are determined.

2. The data analysis and visualization method based on the low-code platform according to claim 1 is characterized in that: Determining the actual usage frequency of each component according to the number of times each component is used by a plurality of users and the corresponding usage time includes: Determine a first total usage count of each component within a preset time length according to the usage counts and corresponding usage times of each component by a number of users, and sum the total usage counts of each component within the preset time length to determine a second total usage count of all components within the preset time length; determining a usage frequency of each component according to a ratio of the first total usage times to the second total usage times; Determine the reusability of each component based on the number of times each component is used by a number of users and the corresponding usage time; According to the usage frequency of each component and the corresponding repeated usage performance, the actual usage frequency of each component is determined.

3. The data analysis and visualization method based on the low-code platform according to claim 2 is characterized in that: Determining the repetitive use performance of each component according to the number of times each component is used by a plurality of users and the corresponding use time includes: According to the number of times each component is used by a number of users and the corresponding time of use, the number of users who have used each component, the number of first times each user has designed an application, and the number of second times each user has used each component are determined; Determine a candidate component from the components, determine a first ratio of the second number of times of the candidate component corresponding to each user to the first number of times, and determine a reuse performance degree of the candidate component according to a second ratio of a sum of the first ratios to the number of users of the candidate component; Return to the step of determining candidate components from the components until the reuse performance of each component is determined.

4. The data analysis and visualization method based on the low-code platform according to claim 2 is characterized in that: Determining the actual usage frequency of each component according to the usage frequency of each component and the corresponding repeated usage performance includes: Determine a first product of the usage frequency of each component and the corresponding reuse performance degree; The actual usage frequency of each component is determined according to the normalized function and the first product.

5. The data analysis and visualization method based on a low-code platform according to any one of claims 1 to 4, characterized in that: The method further comprises: Arrange the components from largest to smallest based on their actual usage frequency; A first preset number of components arranged at the front are determined as reference components.

6. The data analysis and visualization method based on the low-code platform according to claim 1 is characterized in that: Determining the analysis attention of each user to each of the historical component combinations according to the usage time difference includes: The inverse of each of the usage time differences is determined respectively to obtain the analysis attention of each user to each of the historical component combinations.

7. The data analysis and visualization method based on the low-code platform according to claim 1 is characterized in that: Determining the candidate components corresponding to each user includes: Determine a candidate user from the users, determine a first target historical component set including the current component set of the candidate user from the historical component set of the candidate user, and determine a second target historical component set including the current component set of the candidate user from the historical component sets of all users; Determine a first component in the first target historical component set that is arranged after the current component set of the candidate user in terms of usage time, and determine a second component in the second target historical component set that is arranged after the current component set of the candidate user in terms of usage time; The first component and the second component are determined as candidate components for the candidate user, and the process returns to the step of determining candidate users from the users until the candidate components corresponding to the respective users are determined.

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