Component linkage method, device and electronic equipment in visualization platform

Abstract

The application relates to a component linkage method and device in a visualization platform and electronic equipment, and comprises the following steps: traversing components in the visualization platform; the components have corresponding data models; determining feature vectors of the components according to the data models corresponding to the components; determining vector similarities between the components according to the feature vectors of the components; establishing an association relationship between the components according to the vector similarities between the components; when a target component in the visualization platform changes, determining components associated with the target component as associated components according to the association relationship of the target component, and linking the associated components according to the change of the target component. According to the application, the intelligent establishment of the association relationship between the components can be realized through the feature vectors of the data models corresponding to the components in the visualization platform, so that the coding workload of the front-end developers on the component linkage logic is greatly reduced, and the development efficiency of the front-end developers is improved.

Description

Technical Field

[0001] This application belongs to the field of development, and specifically relates to a component linkage method, device and electronic device in a visualization platform. Background Technology

[0002] A visualization dashboard is a monitoring and analysis system that displays business data such as monitoring data, sales figures, user volume, and order volume on large display devices using components provided by a visualization platform, such as chart components and filter components. Each component is a reusable and configurable independent functional module within the visualization platform, used to carry specific data display or interactive functions on the visualization dashboard.

[0003] In practice, some components are related. For example, if one component changes, the related components need to change accordingly. However, there are usually many components in the visualization platform, and the components may be adjusted or new components may be added. Front-end developers need to re-associate the components, which increases the workload of front-end developers in coding the component linkage logic and thus reduces the development efficiency of front-end developers. Summary of the Invention

[0004] The purpose of this application is to provide a component linkage method, apparatus, and electronic device in a visualization platform, so as to overcome or at least partially solve the above problems.

[0005] To solve the above-mentioned technical problems, this application is implemented as follows: A method for linking components in a visualization platform, the method comprising: Traverse the components in the visualization platform; each component has a corresponding data model. The feature vector of the component is determined based on the data model corresponding to the component. The vector similarity between the components is determined based on the feature vectors of the components; The association between the components is established based on the vector similarity between the components; When a target component in the visualization platform changes, the components associated with the target component are identified as associated components based on the association relationship of the target component, and the associated components are linked according to the change of the target component.

[0006] In this embodiment, the components in the visualization platform are traversed, and each component has a corresponding data model. The feature vectors of the components are determined based on their corresponding data models. This allows for the determination of vector similarity between components, and the automatic establishment of relationships between components based on this vector similarity. Subsequently, when a target component in the visualization platform changes, components associated with the target component can be identified as related components based on their relationships, and these related components can be linked in response to changes in the target component. This embodiment intelligently establishes relationships between components using the feature vectors of the data models corresponding to the components in the visualization platform. This significantly reduces the coding workload of front-end developers in handling component linkage logic and improves their development efficiency.

[0007] In one embodiment of this application, the data model includes fields, and determining the feature vector of the component based on the data model corresponding to the component includes: Principal component analysis is performed on the fields of the data model corresponding to the component to obtain the first vector; The semantic features of the field in the data model are extracted by calling a preset word vector model as a second vector; The model feature vector of the data model is constructed based on the first vector and the second vector; The feature vector of the component is determined based on the model feature vector of the data model corresponding to the component.

[0008] In this embodiment, the fields of the component's data model are analyzed using principal component analysis and word vector modeling to obtain a first vector and a second vector. This not only reveals hidden correlations between fields but also helps to understand the business meaning of the fields. As a result, the model feature vector of the data model obtained by fusion is more accurate and comprehensive. By fusing the first vector and the second vector, the model feature vector of the data model is obtained. Finally, the feature vector of the component can be determined based on the model feature vector of the component's data model. Furthermore, the presence of correlations between components can be better analyzed and determined based on the more accurate and comprehensive feature vector.

[0009] In one embodiment of this application, determining the feature vector of the component based on the model feature vector of the data model corresponding to the component includes: Determine the weight coefficients of each data model corresponding to the component; The feature vector of the component is determined based on the model feature vector and the weight coefficient of the data model corresponding to the component.

[0010] In this embodiment, the model feature vectors and weight coefficients corresponding to each data model of the component can be determined, and the feature vector of the component can be obtained by weighted calculation. By determining the weight coefficients of each data model of the component, the influence of the data model with a greater influence on the component's feature vector is also greater. Thus, for a more complex component that binds multiple data models, the accurate feature vector of the component can be generated by comprehensively considering the model feature vectors and weight coefficients of each data model of the component. This allows for better analysis and determination of whether there is a correlation between components based on more accurate feature vectors, thereby improving the accuracy of automatic linkage components.

[0011] In one embodiment of this application, determining the weight coefficients of each data model corresponding to the component includes: Obtain the relevance and integrity contribution of each data model corresponding to the component; the relevance is the relevance between the data model and the component, and the integrity contribution is the integrity contribution of the data model to the component; The weight coefficients of the data models are determined based on their relevance and integrity contribution.

[0012] In this embodiment, the relevance and integrity contribution of each data model bound to the component can be obtained to dynamically calculate the weight coefficient of each data model. Thus, when a component is bound to multiple data models, the influence of each data model on the component can be determined by the weight coefficient, thereby enabling the calculated feature vector of the component to accurately reflect its actual business value and data completeness, and improving the accuracy of automatic linkage components.

[0013] In one embodiment of this application, establishing the association relationship between the components based on the vector similarity between the components includes: When the vector similarity between the components is higher than a preset threshold, an association relationship is established between the components; the vector similarity includes at least cosine similarity and Euclidean distance.

[0014] In this embodiment, whether to establish a relationship between components can be automatically determined by whether the vector similarity between components is higher than a preset threshold. This can quickly and accurately determine the relationship between components and improve the efficiency of automatic association between components.

[0015] In one embodiment of this application, the method further includes: In response to user operations on the visualization platform, the components in the visualization platform are adjusted, including at least adjusting the data model of the components, adjusting the type of the components, adjusting the relationships between the components, and / or establishing relationships between the components.

[0016] In this embodiment, users are allowed to make adjustments to the visualization platform to adapt the components within the platform, making it easier for users to expand and maintain the components. This makes the components in the visualization platform more in line with user or business needs, thereby enabling the rapid generation of large visualization screens based on the components of the visualization platform.

[0017] In one embodiment of this application, the visualization platform includes a state management library, in which shared states are defined, and components with associated relationships are bound to the same shared state; when a target component in the visualization platform changes, components associated with the target component are determined as associated components according to the association relationship of the target component, and the associated components are linked according to the change of the target component, including: When a target component in the visualization platform changes, the components associated with the target component are identified as associated components based on the association relationships of the target component. Obtain the shared state that the target component and the associated component are bound to in the state management library; Update the shared state according to the changes in the target component; In response to the update of the shared state, the associated component is updated according to the updated shared state.

[0018] In this embodiment, the linkage between components is achieved through the state management library of the visualization platform. This avoids direct correlation between changes in components. When a component in the visualization platform changes, it is only necessary to update the shared state of that component in the state management library. Subsequently, all other components associated with the changed component will obtain the shared state from the state management library and update accordingly, thus realizing the linkage transformation of components. At the same time, since there is no need for direct communication between components, the coupling between components can be reduced, which facilitates the maintenance of the components of the visualization platform.

[0019] A component linkage device in a visualization platform, the device comprising: The component traversal module is used to traverse the components in the visualization platform; each component has a corresponding data model. A feature vector determination module is used to determine the feature vector of the component based on the data model corresponding to the component. A vector similarity determination module is used to determine the vector similarity between the components based on the feature vectors of the components; A relationship establishment module is used to establish relationship between components based on the vector similarity between the components; The component linkage module is used to determine the components associated with the target component as associated components based on the association relationship of the target component when the target component changes in the visualization platform, and to link the associated components according to the changes in the target component.

[0020] An electronic device includes: a processor; and a memory for storing processor-executable instructions. The processor is configured to execute the instructions to implement the component linkage method in the visualization platform described above.

[0021] A computer-readable storage medium, when the instructions in the storage medium are executed by the processor of a mobile terminal, enables the mobile terminal to execute the component linkage method in the visualization platform described above.

[0022] The embodiments of this application have at least the following beneficial effects: In this embodiment, the components in the visualization platform are traversed, and each component has a corresponding data model. The feature vectors of the components are determined based on their corresponding data models. This allows for the determination of vector similarity between components, and the automatic establishment of relationships between components based on this vector similarity. Subsequently, when a target component in the visualization platform changes, components associated with the target component can be identified as related components based on their relationships, and these related components can be linked in response to changes in the target component. This embodiment intelligently establishes relationships between components using the feature vectors of the data models corresponding to the components in the visualization platform. This significantly reduces the coding workload of front-end developers in handling component linkage logic and improves their development efficiency. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating the steps of a component linkage method in a visualization platform provided in this application embodiment; Figure 2 This is a schematic diagram illustrating a process where a target component triggers an update of an associated component, as provided in an embodiment of this application. Figure 3 This is a flowchart illustrating automated inter-component linkage for a large visual screen based on a visualization platform, as provided in this application embodiment. Figure 4 This is a schematic diagram of the structure of a component linkage device in a visualization platform provided in this application embodiment; Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0024] The embodiments of this application will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be understood that the preferred embodiments are only for illustrating this application and are not intended to limit the scope of protection of this application.

[0025] In relevant technical solutions, large-screen visualizations typically employ manual hard-coding to implement interactive linkages between components. This leads to chaotic binding relationships between components and significant difficulties in expansion and maintenance. Specific problems with hard-coding include: 1) Component linkages are hard-coded, making modification difficult; 2) Configurable and automated linkages between components cannot be achieved; 3) As the number of components increases, the complexity of manually managing relationships rises sharply; 4) Modifying component logic (such as data models) requires extensive code modifications, which can easily cause problems.

[0026] To address the aforementioned issues, the embodiments of this application can automate the configuration of relationships between components, thereby reducing the complexity caused by hard coding and truly realizing the configurability and automation of component logic. This significantly reduces the coding workload of front-end developers in component linkage logic and improves their development efficiency.

[0027] Reference Figure 1 The flowchart illustrates the steps of a component linkage method in a visualization platform provided in this application embodiment, specifically including the following steps: Step 101: Traverse the components in the visualization platform; each component has a corresponding data model.

[0028] In a specific implementation, the visualization platform can be a low-code development platform based on Vue. Vue is a progressive framework for building user interfaces that can be applied layer by layer from the bottom up. The visualization platform in this embodiment is a platform that generates data visualization interfaces by visually dragging and dropping chart components and other components on top of the low-code development platform. Components can be generated or adjusted through visual drag-and-drop and input methods, such as adjusting the relationships between components. Furthermore, the interaction logic of the components can be manually configured by the user, such as manually configuring and establishing relationships between components.

[0029] The visualization platform offers a variety of components. These components are reusable, configurable, and independent functional modules used to support specific data display or interactive functions on the visualization dashboard. Specifically, components encapsulate functions such as data processing, visual rendering, and / or user interaction logic. For example, the visualization platform may provide various components such as chart components and filter components. Chart components can transform data into graphics, and filter components can be used for data filtering. Users can build a complete visualization dashboard interface to display business data by dragging and configuring these components.

[0030] In this embodiment, on a low-code development platform, the data models corresponding to all components in the visualization dashboard are traversed. These data models are structured data bound to the components, generated based on data sources and customized modeling. The data models can transform irregular data sources into standardized data formats for provision to the corresponding components. Specifically, the data models can access the original data source through file uploads, database connections, or configuring HTTP (Hypertext Transfer Protocol) interfaces. Then, customized queries are performed on the accessed data source using SQL (Structured Query Language) views or table views to obtain the corresponding data, thereby obtaining the data model corresponding to the component.

[0031] Step 102: Determine the feature vector of the component based on the data model corresponding to the component.

[0032] Step 103: Determine the vector similarity between the components based on the feature vectors of the components.

[0033] Step 104: Establish the association relationship between the components based on the vector similarity between the components.

[0034] In this embodiment of the application, after obtaining the data model corresponding to each component in the visualization platform, feature vectors corresponding to the data model of each component can be generated by calculating the dimensions and indicators according to the data model corresponding to each component through dimensional analysis algorithms or other methods. Then, the feature vectors between components can be compared one by one to determine the vector similarity between components. Then, it can be determined whether it is necessary to establish a relationship between components based on the vector similarity. If it is determined that a relationship can be established between components, these components can be automatically bound, thereby establishing a relationship (linkage relationship) between the components.

[0035] Step 105: When the target component in the visualization platform changes, the component associated with the target component is determined as the associated component according to the association relationship of the target component, and the associated component is linked according to the change of the target component.

[0036] In this embodiment, when a component in the visualization platform undergoes a change, that component can be designated as the target component. Then, components associated with and bound to the target component can be identified as related components based on their relationships. These related components can then be automatically notified to follow the target component's corresponding changes. For example, if a dropdown menu component selects a new option, it becomes the target component, and the bar chart component associated with it will automatically update its data and re-render. Similarly, if a date selection component selects a new date, it becomes the target component, and the line chart component associated with it will automatically load the corresponding new data.

[0037] In some embodiments of this application, during the process of a user operating components on a visualization platform to form a large visual screen, when the user operates a target component, the components bound to the target component can be visually indicated on the visualization platform through visual identifiers, such as highlighting or adding different colors, to prompt the user that these components are bound to the target component being operated by the user. Thus, the user can determine how to operate the target component based on the associated components. Optionally, different visual identifiers or visual identifiers with different display intensities can be used to prompt the components bound to the target component according to the different degrees of association, so that the user can operate the target component accordingly based on the components associated with it, thereby better realizing the linkage between components.

[0038] In this embodiment, the components in the visualization platform are traversed, and each component has a corresponding data model. The feature vectors of the components are determined based on their corresponding data models. This allows for the determination of vector similarity between components, and the automatic establishment of relationships between components based on this vector similarity. Subsequently, when a target component in the visualization platform changes, components associated with the target component can be identified as related components based on their relationships, and these related components can be linked in response to changes in the target component. This embodiment intelligently establishes relationships between components using the feature vectors of the data models corresponding to the components in the visualization platform. This significantly reduces the coding workload of front-end developers in handling component linkage logic and improves their development efficiency.

[0039] In one embodiment of this application, the data model may include fields, and determining the feature vector of the component based on the data model corresponding to the component may include: Principal component analysis is performed on the fields of the data model corresponding to the component to obtain the first vector; The semantic features of the field in the data model are extracted by calling a preset word vector model as a second vector; The model feature vector of the data model is constructed based on the first vector and the second vector; The feature vector of the component is determined based on the model feature vector of the data model corresponding to the component.

[0040] Principal component analysis (PCA), also known as principal component analysis technique, is a dimensionality reduction algorithm that aims to transform multiple indicators into a few comprehensive indicators by using the idea of ​​dimensionality reduction.

[0041] In this embodiment, the data model may include fields, and principal component analysis (PCA) is performed on each field xi in the component's data model to obtain a first vector zi. PCA can reveal hidden correlations between fields, such as the numerical correlation pattern between sales revenue and advertising investment, but it may obscure business-irrelevant statistical coincidences.

[0042] For example, the formula for calculating the first vector zi can be as follows: zi = ω1xi1 + ω2xi2 + ... + ωnxin Where ωn is the weight coefficient of the nth principal component, and xi represents a field of the data model.

[0043] The semantic features of field xi in the data model are extracted using a pre-defined word vector model and used as the second vector word2vec(xi). While the word vector model can reveal the business meaning of the field (e.g., the semantic relationship between city and region), it cannot reveal the data distribution patterns. Subsequently, the first and second vectors can be fused to construct the model feature vector yi of the data model. Since the model feature vector yi is generated by fusing the first and second vectors obtained from principal component analysis and the word vector model, it can both discover hidden correlations between fields and understand their business meaning. Therefore, the fused model feature vector of the data model is more accurate and comprehensive.

[0044] For example, the formula for calculating the model feature vector yi of the data model can be as follows: yi = zi + word2vec(xi) Where yi represents the model feature vector of the data model, zi represents the first vector, and word2vec(xi) represents the second vector of the data model.

[0045] Finally, the feature vector of the component can be determined based on the model feature vector of the component's data model.

[0046] In this embodiment, the fields of the component's data model are analyzed using principal component analysis and word vector modeling to obtain a first vector and a second vector. This not only reveals hidden correlations between fields but also helps to understand the business meaning of the fields. As a result, the model feature vector of the fused data model is more accurate and comprehensive. By fusing the first and second vectors to obtain the model feature vector of the data model, the feature vector of the component can be determined based on the model feature vector of the component's data model. This allows for a better analysis and determination of whether there are any relationships between components based on the more accurate and comprehensive feature vector, thus improving the accuracy of the automatic linkage components.

[0047] In one embodiment of this application, determining the feature vector of the component based on the model feature vector of the data model corresponding to the component may include: Determine the weight coefficients of each data model corresponding to the component; The feature vector of the component is determined based on the model feature vector and the weight coefficient of the data model corresponding to the component.

[0048] In this embodiment, a component can be bound to one or multiple data models. When calculating the model feature vector corresponding to the data model of the component, the different data models bound to the component can be traversed to obtain the model feature vector and weight coefficient for each data model. The weight coefficient reflects the degree of influence of the corresponding data model on the component; the larger the weight coefficient, the greater the influence of the data model on the component, and vice versa. Then, the model feature vector and weight coefficient of the data model are vector-weighted to obtain the feature vector of the component.

[0049] For example, the vector weighting formula for calculating the model feature vector of a component can be as follows: Yj = ∑ wi * yi Where Yj is the feature vector of component j, yi is the model feature vector of data model i bound to component j, and wi is the weight coefficient of data model i.

[0050] In this embodiment, the model feature vectors and weight coefficients corresponding to each data model of the component can be determined, and the feature vector of the component can be obtained by weighted calculation. By determining the weight coefficients of each data model of the component, the influence of the data model with a greater influence on the component's feature vector is also greater. Thus, for a more complex component that binds multiple data models, the accurate feature vector of the component can be generated by comprehensively considering the model feature vectors and weight coefficients of each data model of the component. This allows for better analysis and determination of whether there is a correlation between components based on more accurate feature vectors, thereby improving the accuracy of automatic linkage components.

[0051] In one embodiment of this application, determining the weight coefficients of each data model corresponding to the component may include: Obtain the relevance and integrity contribution of each data model corresponding to the component; the relevance is the relevance between the data model and the component, and the integrity contribution is the integrity contribution of the data model to the component; The weight coefficients of the data models are determined based on their relevance and integrity contribution.

[0052] Here, relevance refers to the degree of correlation between the data model and the component. Specifically, relevance reflects the degree of matching between the data model and the component in terms of business intent. Based on relevance, it can be determined whether the data model provides the fields necessary for the component to achieve its function. For example, a map component has a very high relevance to a data model that includes latitude and longitude or administrative division fields. Completeness contribution refers to the data model's contribution to the completeness of the component. Specifically, completeness contribution reflects the sufficiency of the data model's fields in satisfying the content that the component needs to display. For example, a sales analysis component requires multi-dimensional information such as time, product, and region. The more comprehensively the fields provided by the data model cover the multi-dimensional information required by the sales analysis component, the higher the completeness contribution.

[0053] In this embodiment, the relevance and integrity contribution of each data model bound to the component can be obtained. Then, the weight coefficient of the data model can be determined based on the relevance and integrity contribution of each data model. Thus, the model feature vector and weight coefficient of the data model can be vector-weighted to obtain the feature vector of the component.

[0054] For example, the formula for calculating the weighting coefficient can be as follows: wi = α * relevance(mi) + β * completeness(mi) Among them, relevance(mi) represents the relevance between the data model mi and the component j, completeness(mi) represents the contribution degree of the data model mi to the information of the component j, and α and β are coefficients.

[0055] In some embodiments, the operation data of the components by the user on the visualization platform can be continuously collected, such as manually associating components, or disassociating components, which components are frequently operated, etc. Then, the word vector model can be retrained according to the operation data, or the coefficients of the vector weighting calculation formula can be adjusted, so that when automatically establishing the association relationship of the components, the operations of the user on the components on the visualization platform can be integrated, and the automatically associated components conform to the user's usage habits or business.

[0056] In the embodiments of the present application, the relevance and the contribution degree of completeness of each data model bound to the component can be obtained to dynamically calculate the weight coefficients of each data model. In this way, when multiple data models are bound to the component, the influence degree of each data model on the component can be determined through the weight coefficients, so that the calculated feature vector of the component can accurately reflect its actual business value and data completeness, and the accuracy of automatically linked components is improved.

[0057] In an embodiment of the present application, establishing the association relationship between the components according to the vector similarity between the components includes: When the vector similarity between the components is higher than a preset threshold, establish the association relationship between the components; the vector similarity at least includes cosine similarity and Euclidean distance.

[0058] In the embodiments of the present application, all components of the visualization platform are traversed, and the feature vectors between different components are compared one by one. If the vector similarity between the feature vectors of the components is higher than the preset threshold, the association relationship between the components can be automatically established. Of course, if the vector similarity between the feature vectors of the components is lower than or equal to the preset threshold, the association relationship between the components does not need to be established.

[0059] Exemplarily, the vector similarity can be cosine similarity and Euclidean distance. Among them, the calculation formula of the cosine similarity cosθ can be: cosθ = Y1·Y2 / |Y1||Y2|, and the calculation formula of the Euclidean distance d(Y1,Y2) can be: d(Y1,Y2)= , where Y1 and Y2 represent the feature vectors of the components, Set a preset threshold T. If cosθ>T or d(Y1,Y2)<T, it is determined that the similarity of the two feature vectors Y1 and Y2 is high, and the association relationship of the components corresponding to Y1 and Y2 will be automatically established.

[0060] In this embodiment, whether to establish a relationship between components can be automatically determined by whether the vector similarity between components is higher than a preset threshold. This can quickly and accurately determine the relationship between components and improve the efficiency of automatic association between components.

[0061] In one embodiment of this application, the method may further include: In response to user operations on the visualization platform, the components in the visualization platform are adjusted, including at least adjusting the data model of the components, adjusting the type of the components, adjusting the relationships between the components, and / or establishing relationships between the components.

[0062] In practice, there may be instances where components in the visualization platform do not match the data model properly, or there may be additional requirements such as adding interactive features between components. Therefore, the visualization platform allows users to modify it according to their needs.

[0063] In this embodiment, the visualization platform provides an interface where users can adaptively modify and adjust the data model of components, adjust the type of components, adjust the relationships between components, and / or establish relationships between components by dragging, inputting, and other operations on the interface. In this embodiment, users are allowed to make adjustments to the visualization platform to adapt the components within the platform, making it easier for users to expand and maintain the components. This makes the components in the visualization platform more in line with user or business needs, thereby enabling the rapid generation of large visualization screens based on the components of the visualization platform.

[0064] In one embodiment of this application, the visualization platform may include a state management library, in which shared states are defined, and components with associated relationships are bound to the same shared state; when a target component in the visualization platform changes, components associated with the target component are determined as associated components according to the association relationship of the target component, and the associated components are linked according to the change of the target component, which may include: When a target component in the visualization platform changes, the components associated with the target component are identified as associated components based on the association relationships of the target component. Obtain the shared state that the target component and the associated component are bound to in the state management library; Update the shared state according to the changes in the target component; In response to the update of the shared state, the associated component is updated according to the updated shared state.

[0065] The visualization platform can be a low-code development platform based on Vue. The visualization platform can include the state management library Pinia. Specifically, the state management library Pinia can enable the sharing and linkage between different components. The state management library Pinia allows the management of application state outside the components and synchronizes the state between components through two-way binding.

[0066] In this embodiment, the state management library Pinia defines shared states. Components with related relationships are bound to the same shared state. When a target component in the visualization platform changes, for example, when a drop-down menu component selects a new option, the components with related relationships to the drop-down menu component can be used as associated components. The shared state bound to the drop-down menu component is obtained from the state management library Pinia, and the shared state in the state management library Pinia is updated accordingly based on the change of the drop-down menu component. Subsequently, in response to the update of the shared state in the state management library Pinia, the updated shared state is updated to the associated components associated with the drop-down menu component, so that the associated components can change in tandem with the changes of the drop-down menu component.

[0067] For example, refer to Figure 2 This is a schematic diagram of a process in which a target component triggers an update of an associated component, as provided in an embodiment of this application. It is assumed that the visualization platform Vue includes a dropdown menu component A and a bar chart component B that are related. The state management library Pinia is used to manage the shared state of the components. The shared state interactParams is defined in the store of the state management library Pinia. The dropdown menu component A and the bar chart component B are bidirectionally bound to the shared state interactParams. The visualization platform listens for changes in the shared state and automatically triggers the components to make corresponding changes.

[0068] Specifically, a change in the trigger component (drop-down menu component A) modifies the shared state interactParams (interactParamsB) bound to the state management library Pinia. The controlled component (bar chart component B) watches and listens for changes in the shared state interactParamsB, responding to the changes in the shared state interactParamsB to achieve real-time linkage.

[0069] In some embodiments, the listener code is shown in the figure: / / Listener component linkage watch ()=>targetComponent.interactParams, (val)=>{ / / Update view (component) freshView(val) immediate: false, deep:true Specifically, after a user interacts with the target component, the shared state interactParams is updated. The associated component can detect the change in the shared state interactParams through the watch function, and then execute the function refreshView(val) to refresh the view of the associated component based on the new parameter val, where the new parameter val is the updated shared state interactParams.

[0070] In this embodiment, the linkage between components is achieved through the state management library of the visualization platform. This avoids direct correlation between changes in components. When a component in the visualization platform changes, it is only necessary to update the shared state of that component in the state management library. Subsequently, all other components associated with the changed component will obtain the shared state from the state management library and update accordingly, thus realizing the linkage transformation of components. At the same time, since there is no need for direct communication between components, the coupling between components can be reduced, which facilitates the maintenance of the components of the visualization platform.

[0071] To enable those skilled in the art to better understand the embodiments of this application, specific examples are used for illustration below. (Refer to...) Figure 3 This document provides a flowchart for automated inter-component linkage of a visualization dashboard based on a visualization platform, as provided in this application embodiment. The specific process may include: traversing the components of the visualization dashboard; obtaining the corresponding data model; calculating the feature vector of the data model; comparing the vector similarity between the feature vectors of the components; if the similarity is higher than a preset threshold, automatically establishing an association; if the similarity is not higher than the preset threshold, manually configuring linkage; implementing component linkage; triggering the modification of the store; adding a lifecycle watch function to monitor the global store, so that components with a binding relationship can achieve linkage changes based on the relationship.

[0072] This application proposes a method for automating the linkage configuration of visual large screen components based on a low-code development platform. This method can achieve intelligent and automated establishment of linkage relationships between components by automatically matching the feature vectors of the component's data model. Furthermore, the Pinia state management library can be used to manage and visualize the platform, enabling real-time state sharing and automated linkage of components. This significantly reduces the coding work of front-end developers on component linkage logic and improves development efficiency.

[0073] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of this application are not limited to the described order of actions, because according to the embodiments of this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily necessary for the embodiments of this application.

[0074] Reference Figure 4 The diagram illustrates a structural block diagram of a component linkage device in a visualization platform provided in this application embodiment. The device may specifically include the following modules: Component traversal module 401 is used to traverse components in the visualization platform; each component has a corresponding data model. Feature vector determination module 402 is used to determine the feature vector of the component according to the data model corresponding to the component; Vector similarity determination module 403 is used to determine the vector similarity between the components based on the feature vectors of the components; The association establishment module 404 is used to establish the association relationship between the components based on the vector similarity between the components; The component linkage module 405 is used to determine the components associated with the target component as associated components according to the association relationship of the target component when the target component in the visualization platform changes, and to link the associated components according to the change of the target component.

[0075] In one embodiment of this application, the data model includes fields, and the feature vector determination module 402 is used for: Principal component analysis is performed on the fields of the data model corresponding to the component to obtain the first vector; The semantic features of the field in the data model are extracted by calling a preset word vector model as a second vector; The model feature vector of the data model is constructed based on the first vector and the second vector; The feature vector of the component is determined based on the model feature vector of the data model corresponding to the component.

[0076] In one embodiment of this application, the feature vector determination module 402 is used for: Determine the weight coefficients of each data model corresponding to the component; The feature vector of the component is determined based on the model feature vector and the weight coefficient of the data model corresponding to the component.

[0077] In one embodiment of this application, the feature vector determination module 402 is used for: Obtain the relevance and integrity contribution of each data model corresponding to the component; the relevance is the relevance between the data model and the component, and the integrity contribution is the integrity contribution of the data model to the component; The weight coefficients of the data models are determined based on their relevance and integrity contribution.

[0078] In one embodiment of this application, the association establishment module 404 is used for: When the vector similarity between the components is higher than a preset threshold, an association relationship is established between the components; the vector similarity includes at least cosine similarity and Euclidean distance.

[0079] In one embodiment of this application, the device further includes: an adjustment module, which is always In response to user operations on the visualization platform, the components in the visualization platform are adjusted, including at least adjusting the data model of the components, adjusting the type of the components, adjusting the relationships between the components, and / or establishing relationships between the components.

[0080] In one embodiment of this application, the visualization platform includes a state management library, in which shared states are defined, and components with associated relationships are bound to the same shared state; the component linkage module 405 is used for: When a target component in the visualization platform changes, the components associated with the target component are identified as associated components based on the association relationships of the target component. Obtain the shared state that the target component and the associated component are bound to in the state management library; Update the shared state according to the changes in the target component; In response to the update of the shared state, the associated component is updated according to the updated shared state.

[0081] In this embodiment, the components in the visualization platform are traversed, and each component has a corresponding data model. The feature vectors of the components are determined based on their corresponding data models. This allows for the determination of vector similarity between components, and the automatic establishment of relationships between components based on this vector similarity. Subsequently, when a target component in the visualization platform changes, components associated with the target component can be identified as related components based on their relationships, and these related components can be linked in response to changes in the target component. This embodiment intelligently establishes relationships between components using the feature vectors of the data models corresponding to the components in the visualization platform. This significantly reduces the coding workload of front-end developers in handling component linkage logic and improves their development efficiency.

[0082] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment. This application also provides an electronic device, such as... Figure 5 As shown, it includes a processor 1001, a device interface 1002, a memory 1003, and a bus 1004; Memory 1003 is used to store computer programs; The processor 1001 executes the above steps when executing the program stored in the memory 1003.

[0083] The bus mentioned in the above terminal can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0084] The memory may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0085] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0086] This application also provides a storage medium that, when the instructions in the storage medium are executed by the processor of an electronic device, enables the electronic device to execute the component linkage method in the visualization platform of the foregoing embodiments.

[0087] The algorithms and displays provided herein are not inherently related to any particular computer, virtual device, or other equipment. The structure required to construct such a device is obvious from the above description. Furthermore, this application is not directed to any particular programming language. It should be understood that the content of this application described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of this application.

[0088] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0089] Similarly, it should be understood that, in order to simplify this application and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of this application, various features of this application are sometimes grouped together into a single embodiment, figure, or description thereof. However, this method of disclosure should not be construed as reflecting an intention that the claimed application requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of this application.

[0090] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0091] The various component embodiments of this application can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the sequencing device according to this application. This application can also be implemented as a device or apparatus program for performing part or all of the methods described herein. Such an implementation of this application can be stored on a computer-readable medium, or can take the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0092] It should be noted that the above embodiments are illustrative of this application and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0093] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0094] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.

[0095] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0096] It should be noted that the various data-related processes in the embodiments of this application are carried out in compliance with the relevant data protection laws and policies of the country where the location is located, and with the authorization granted by the owner of the corresponding device.

Claims

1. A method for linking components in a visualization platform, characterized in that, The method includes: Traverse the components in the visualization platform; each component has a corresponding data model. The feature vector of the component is determined based on the data model corresponding to the component. The vector similarity between the components is determined based on the feature vectors of the components; The association between the components is established based on the vector similarity between the components; When a target component in the visualization platform changes, the components associated with the target component are identified as associated components based on the association relationship of the target component, and the associated components are linked according to the change of the target component.

2. The method according to claim 1, characterized in that, The data model includes fields, and determining the feature vector of the component based on the data model corresponding to the component includes: Principal component analysis is performed on the fields of the data model corresponding to the component to obtain the first vector; The semantic features of the field in the data model are extracted by calling a preset word vector model as a second vector; The model feature vector of the data model is constructed based on the first vector and the second vector; The feature vector of the component is determined based on the model feature vector of the data model corresponding to the component.

3. The method according to claim 2, characterized in that, Determining the feature vector of the component based on the model feature vector of the data model corresponding to the component includes: Determine the weight coefficients of each data model corresponding to the component; The feature vector of the component is determined based on the model feature vector and the weight coefficient of the data model corresponding to the component.

4. The method according to claim 3, characterized in that, Determining the weight coefficients of each data model corresponding to the component includes: Obtain the relevance and integrity contribution of each data model corresponding to the component; the relevance is the relevance between the data model and the component, and the integrity contribution is the integrity contribution of the data model to the component; The weight coefficients of the data models are determined based on their relevance and integrity contribution.

5. The method according to claim 1, characterized in that, The step of establishing the association relationship between the components based on the vector similarity between the components includes: When the vector similarity between the components is higher than a preset threshold, an association relationship is established between the components; the vector similarity includes at least cosine similarity and Euclidean distance.

6. The method according to claim 1, characterized in that, The method further includes: In response to user operations on the visualization platform, the components in the visualization platform are adjusted, including at least adjusting the data model of the components, adjusting the type of the components, adjusting the relationships between the components, and / or establishing relationships between the components.

7. The method according to claim 1, characterized in that, The visualization platform includes a state management library, which defines shared states. Components with associated relationships are bound to the same shared state. When a target component in the visualization platform changes, components associated with the target component are identified as associated components based on the target component's relationships. The associated components are then linked in response to changes in the target component, including: When a target component in the visualization platform changes, the components associated with the target component are identified as associated components based on the association relationships of the target component. Obtain the shared state that the target component and the associated component are bound to in the state management library; Update the shared state according to the changes in the target component; In response to the update of the shared state, the associated component is updated according to the updated shared state.

8. A component linkage device in a visualization platform, characterized in that, The device includes: The component traversal module is used to traverse the components in the visualization platform; each component has a corresponding data model. A feature vector determination module is used to determine the feature vector of the component based on the data model corresponding to the component. A vector similarity determination module is used to determine the vector similarity between the components based on the feature vectors of the components; A relationship establishment module is used to establish relationship between components based on the vector similarity between the components; The component linkage module is used to determine the components associated with the target component as associated components based on the association relationship of the target component when the target component changes in the visualization platform, and to link the associated components according to the changes in the target component.

9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to execute the instructions to implement the component linkage method in the visualization platform as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor of the mobile terminal, the mobile terminal is able to execute the component linkage method in the visualization platform as described in any one of claims 1 to 7.

Images