Visual data stream weaving method and device for zero code platform and medium

Abstract

The invention discloses a visual data flow weaving method and device for a zero-code platform and a medium, and relates to the field of zero-code data processing.The method comprises the steps that based on triggering of a user, a data flow editor module is started and visually displayed; generating a visual component node, a data processing node and a data source node based on a dragging operation of a user; displaying a configuration area based on triggering of the visual node by a user, and configuring the visual node through the configuration area; and on the basis of triggering of a user, connecting the visual nodes through the connection piles carried in the visual nodes to generate a visual data stream. And the data flow relationship is displayed in a visual mode, so that a developer can intuitively understand the data interaction logic between the components. Input, output, events and the like of the components can be visually configured through the data flow editor, and the maintenance difficulty caused by configuration item dispersion is reduced.

Description

Technical Field

[0001] The present application relates to the field of zero-code data processing, and specifically to a visual data flow weaving method, device and medium for a zero-code platform. Background Art

[0002] With the rapid development of internet technology, a growing number of zero-code products have emerged, spawning a variety of data presentation formats. To ensure accurate data display in appropriate scenarios and enhance project interactivity, existing data and interaction configurations are relatively fragmented and complex.

[0003] Traditionally, when configuring data and interactions, data is typically obtained through interfaces, data sources, static data, and other methods, and then directly bound to component-related configurations for component display. For single components, interactions are set up with default built-in interaction events that are associated and bound to the events exposed by the component.

[0004] However, this traditional solution has the following shortcomings: 1. Configuration items are scattered, making it difficult to centrally and conveniently manage data flow logic, and making secondary development difficult.

[0005] 2. For specific data, it is easy to lose control of the data and the data flow link is unclear.

[0006] 3. For a single specific component, only the data flow of the previous and next links of the current component can be viewed, making it difficult to view the association relationship of the entire link.

[0007] In this scenario, it is particularly important to develop an easy-to-use and scalable visual data flow weaving system to centrally manage complex data and interaction logic. Summary of the Invention

[0008] To solve the above problems, this application proposes a visual data flow weaving method for the zero-code platform, including: Based on the user's trigger, the data flow editor module is started and visually displayed; the data flow editor module includes a material area, a drawing board area, and a configuration area; the material area includes a component unit, a data processing unit, and a data source unit; Based on the user's drag operation, the components in the component unit, the data processing functions in the data processing unit, and the data sources in the data source unit are added to the drawing board area respectively, and visual component nodes, data processing nodes, and data source nodes are generated respectively; Based on the user triggering the visualization node, the configuration area is displayed, and the visualization node is configured through the configuration area; the types of the visualization node include component nodes, data processing nodes, and data source nodes; the configuration area includes at least events and related attributes for the visualization node; Based on user triggering, each visualization node is connected through the connection piles carried in the visualization node to generate a visualization data flow.

[0009] In one example, the method further includes: Based on a user trigger, determining to perform a logic test on the visual data flow; Determine the verification rules that have been set, which include basic verification rules and custom verification rules; Based on the basic verification rules, the data type output by the upstream node is tested against the data type processing authority of the downstream node; Based on the user's selection, the application scenario to which the current visualized data flow belongs is determined, and according to the application scenario, the corresponding custom verification rule is selected in the corresponding rule library and added to this logic test; Based on the custom verification rules, the data content output by the upstream node is tested with the data content processing flow of the downstream node.

[0010] In one example, based on the custom verification rules, the data content output by the upstream node and the data content processing flow of the downstream node are tested, specifically including: Determine data transaction content, and generate a corresponding first data fingerprint for the data transaction content based on a security sandbox set in the data flow editor module; Entering the data transaction content with the empty fingerprint slot into the visual data stream; Based on the user-defined verification rule, and according to the configuration content of the downstream node in the configuration area, determining that the downstream node is a key node; For the downstream node, receiving the data transaction content output by the upstream node, performing a hash calculation on the data transaction content, and obtaining a second data fingerprint corresponding to the data transaction content; The second data fingerprint is matched with the first data fingerprint in the security sandbox. If the match is successful, the test passes.

[0011] In one example, based on the custom verification rules, the data content output by the upstream node and the data content processing flow of the downstream node are tested, specifically including: According to the node type and the configuration content in the configuration area, the upstream node is determined to be a data preprocessing node; For the downstream node, receiving the first data preprocessing result output by the upstream node, and determining a first data structuring degree corresponding to the first data preprocessing result; Based on the visualized data flow, determining whether there is a data preprocessing node upstream of the upstream node; If so, taking the data preprocessing node that is closest to the upstream of the upstream node as the designated node, and determining a second data structuring degree of the second data preprocessing result output by the designated node; If the first data is more structured than the second data, the test is passed.

[0012] In one example, the method further includes: Determining that there are multiple downstream nodes in the visualization data flow corresponding to the same upstream node; displaying the configuration area based on a user triggering a connection line between the plurality of downstream nodes and the same upstream node; Configure the data flow real-time display function, data flow statistics function, and data flow playback function of the connection line through the configuration area; Among them, during the actual operation of the visualized data flow, the real-time display function of data traffic is used to display the real-time traffic corresponding to each connection line at the current moment; the data traffic statistics function is used to display the total traffic corresponding to each connection line within a preset time period; and the data traffic playback function is used to display the real-time traffic corresponding to each connection line at a historical moment.

[0013] In one example, the method further includes: Based on a user trigger, display the configuration area of ​​the exception tracking sandbox, and associate the configuration area of ​​the exception tracking sandbox with the configuration area of ​​the connection line; Based on the configuration area of ​​the anomaly tracking sandbox, setting the anomaly tracking function and the data stream holographic snapshot function contained in the anomaly tracking sandbox; Among them, the abnormal tracking function is used to start when the real-time traffic corresponding to each connection line at the current moment exceeds the preset traffic, triggering the data flow holographic snapshot function; the data flow holographic snapshot function is used to obtain the corresponding data flow snapshots for the data flow of the visualization nodes and connection lines set by the user according to the frequency set by the user.

[0014] In one example, the method further includes: Obtain the functional description of the required material input by the user; Based on the functional description and all materials included in the material area, output the corresponding specified material through a large language model; In the material area, the designated material is rendered and displayed.

[0015] In one example, the method further includes: For each type of visualization node, set the corresponding node color and the corresponding connection pile setting position; According to the connection status between connected visualization nodes, set the corresponding connection line color, connection line shape, and connection line text content.

[0016] On the other hand, this application also proposes a visual data flow weaving device for the zero-code platform, including: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the visual data flow weaving method for the zero-code platform as described in any of the above examples.

[0017] On the other hand, the present application also proposes a non-volatile computer storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured to implement a visual data flow weaving method for a zero-code platform as described in any of the above examples.

[0018] The visual data flow weaving method for the zero-code platform proposed in this application can bring the following beneficial effects: 1. Visualize data flow relationships, allowing developers to intuitively understand the data interaction logic between components. Component inputs, outputs, events, etc. can all be intuitively configured through the data flow editor, reducing the maintenance difficulty caused by scattered configuration items.

[0019] 2. A modular architecture supports the expansion of new components and new event types. By centrally managing data flows and interaction logic, it avoids the fragmented configuration items found in traditional solutions, lowers the threshold for secondary development, and improves system flexibility.

[0020] 3. Provide global data state management to make data flow between components more efficient and clear. Adopt an event-driven mechanism to support dynamic interaction between components, such as data transfer and state synchronization.

[0021] 4. Optimize artboard performance through virtualized rendering technology, maintaining smoothness even when dragging multiple components. Adopt efficient data flow calculation methods to reduce unnecessary computing overhead. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 This is a flow chart of a visual data flow weaving method for a zero-code platform in an embodiment of the present application; Figure 2 This is a schematic diagram of the interface when the data flow editor module is started in one scenario in an embodiment of the present application; Figure 3 This is a schematic diagram of the interface after the data flow editor module includes a visualization node in one scenario in an embodiment of the present application; Figure 4 This is a schematic diagram of the interface when the data flow editor module displays the configuration area in one scenario in an embodiment of the present application; Figure 5 This is a schematic diagram of the interface when the data flow editor module performs node connection in one scenario in an embodiment of the present application; Figure 6 This is a schematic diagram of the interface when a node link in the data flow editor module fails in one scenario in an embodiment of the present application; Figure 7 This is a schematic diagram of the interface of the data flow editor module when a single upstream node is connected to multiple downstream nodes in one scenario in an embodiment of the present application; Figure 8 This is a schematic diagram of a visual data flow weaving device for a zero-code platform in an embodiment of the present application. DETAILED DESCRIPTION

[0023] To make the purpose, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the specific embodiments of this application and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0024] The following describes in detail the technical solutions provided by various embodiments of the present application in conjunction with the accompanying drawings.

[0025] like Figure 1 As shown, the embodiment of the present application provides a visual data flow weaving method for a zero-code platform, including: S101: Based on the user's trigger, start and visually display the data flow editor module; the data flow editor module includes a material area, a drawing board area, and a configuration area; the material area includes a component unit, a data processing unit, and a data source unit.

[0026] The data flow editor module can be set in the corresponding application. The user starts the application and starts the data flow editor module by clicking, double-clicking, long pressing, etc., selecting, dragging, etc. on the corresponding button in the application.

[0027] The data flow editor module can be built using AntV X6, providing a visual data flow configuration method based on nodes and connecting lines. Users can define the data flow and interaction logic between components by dragging and dropping, and allow dynamic creation, modification and deletion of data flow nodes, realizing flexible interaction relationship management and improving development and operation efficiency.

[0028] The material area includes component units, data processing units, and data source units. The main function of the material area is to hold corresponding materials for users to choose.

[0029] The core function of component units is to provide elements for visual display and interaction with consumers (users who use the final visual data flow to conduct corresponding business operations). These are the parts that are ultimately presented to consumers of the data flow. They are used to display processed and converted data in an intuitive and easy-to-understand form and allow consumers to perform certain operations (such as clicking and filtering).

[0030] The core function of data processing units is to provide functional modules for data conversion, processing, calculation, and logical control. These units act on raw data obtained from the data source and shape it into a format suitable for component display or to meet specific business needs.

[0031] The core role of data source units is to provide entry points for connecting and obtaining raw data. These units are responsible for interacting with external systems, services, or storage to bring data into the data flow editor.

[0032] like Figure 2 As shown in the figure, a visualization state of the data flow editor module is shown. The left side is the material area, which contains component units, data processing units, and data source units. Figure 2 The component units are displayed in the , which include buttons, maps, dashboards, tables, bar charts, gradient text, text and other components. Of course, other components can also be set based on the needs. Figure 2 The structure of the material area does not capture the entire status of the data flow editor module, but only displays the material area and part of the left side of the canvas area.

[0033] exist Figure 2In the example, the user selected a component unit, so only the component materials included in the component unit are displayed here. Furthermore, when the user selects a data processing unit or a data source unit, the material area can also display data processing materials such as custom, local data, blueprint jump, and time-to-year conversion, as well as data source materials such as test third-party components, test data components, and alarm handling status.

[0034] The right side of the component unit is the canvas area. The main function of the canvas area is to carry component layout and support real-time rendering. It can hold the materials selected by the user in the material area and add them to the canvas area as nodes. The nodes are used to generate visual data flows. Figure 2 In the drawing, the user has not yet added any nodes in the drawing area, so the drawing area is still blank.

[0035] The configuration area is used to display the configuration content of the node added to the canvas area. When the user does not select a node, the configuration area is hidden. When the user clicks the corresponding node, the configuration area corresponding to the node is displayed. The configuration area can be set to the right side of the canvas area.

[0036] The configuration area centrally manages component properties and interacts with the data flow editor module to ensure transparency and controllability of data flows.

[0037] S102: Based on the user's drag operation, the components in the component unit, the data processing functions in the data processing unit, and the data sources in the data source unit are added to the drawing board area respectively, and visual component nodes, data processing nodes, and data source nodes are generated respectively.

[0038] Based on the needs, users add materials in the component unit, data processing unit, and data source unit to the canvas area as nodes. Figure 3 As shown, users can add component nodes corresponding to component materials. Of course, they can also add data processing nodes, data source nodes, etc. based on needs.

[0039] Modularize the code to reduce redundant logic and improve maintainability. Use virtualization technology to optimize canvas rendering, allowing for smooth dragging of large numbers of components.

[0040] Among them, for each type of visualization node, the corresponding node color is set respectively, and different nodes can be quickly distinguished.

[0041] S103: Based on the user triggering the visualization node, the configuration area is displayed, and the visualization node is configured through the configuration area; the types of the visualization node include component nodes, data processing nodes, and data source nodes; the configuration area includes at least events and related attributes for the visualization node.

[0042] For the convenience of description, component nodes, data processing nodes, and data source nodes are collectively referred to as visualization nodes. Figure 4 As shown, taking the "bar chart node" in the component node as an example, when the user selects the bar chart node, the configuration area of ​​the node is displayed on the right, and the configuration area includes at least events and related properties. Still taking the bar chart node as an example, the events contained therein include input items and output events. Of course, in other nodes, events can also include other events such as data processing. The related properties of the input items include whether to display data, and the output events include whether to click or double-click. When the relevant data corresponding to the input items flows into the bar chart node, the corresponding bar chart is generated. When the user applies an action to the bar chart node through the method selected in the output event, the bar chart is displayed. Among them, Figure 2 Similarly, Figure 4 Only the right part of the canvas area and the configuration area are displayed.

[0043] Of course, for other nodes besides component nodes, such as data source nodes, the configuration content in their configuration areas may be different. For example, when the data source node is a test data component node, the corresponding configuration content events may include update intervals, input items, output items, test interfaces, etc.

[0044] S104: Based on the user's trigger, the visualization nodes are connected through the connection stubs carried in the visualization nodes to generate a visualization data flow.

[0045] like Figure 5 As shown, each visualization node carries a connection pile. For different node types, the corresponding connection pile setting position can be set to make the overall connection line smoother. Taking the custom data processing node as an example, the input position of its connection pile is set in the middle position on the left, and the output position is set in the lower position on the right. Based on the user's choice, the visualization nodes are connected to generate a visualization data flow. Of course, Figure 5 The connection relationship in the diagram is only an example of the connection effect and does not represent a real visual data flow that can be run. Figure 5 In the visualization, the business logic of the data flow is: click the button to call the interface. After the interface returns the data, the data is processed into data that meets the table data format through a custom method, and then the data is passed to the table, which displays the data.

[0046] After the user generates the visual data flow, he can save and exit the data flow editor module, and then click the preview button on the homepage to view the page that has been successfully rendered after the configuration is completed. Figure 5For example, the user can click a text button on the page to trigger the call interface according to the connection direction of the previously visualized data flow. After obtaining the background data, it goes to the custom data processing node, processes it into the data format required by the next component, and successfully displays the data in the table.

[0047] 1. Visualize data flow relationships, allowing developers to intuitively understand the data interaction logic between components. Component inputs, outputs, events, etc. can all be intuitively configured through the data flow editor, reducing the maintenance difficulty caused by scattered configuration items.

[0048] 2. A modular architecture supports the expansion of new components and new event types. By centrally managing data flows and interaction logic, it avoids the fragmented configuration items found in traditional solutions, lowers the threshold for secondary development, and improves system flexibility.

[0049] 3. Provide global data state management to make data flow between components more efficient and clear. Adopt an event-driven mechanism to support dynamic interaction between components, such as data transfer and state synchronization.

[0050] 4. Optimize artboard performance through virtualized rendering technology, maintaining smoothness even when dragging multiple components. Adopt efficient data flow calculation methods to reduce unnecessary computing overhead.

[0051] The advantages of the embodiment of the present application compared to the traditional solution are shown in Table 1 below: Table 1 Schematic diagram of actual test results

[0052] In one embodiment, after a user has edited a visual data flow, a logic test can be performed on the visual data flow based on a user trigger. This can be triggered by clicking a test button in the data flow editor module. The logic test is primarily used to test whether there are any anomalies in the data processing logic of the visual data flow. The trigger location can be within the data flow editor module or within a successfully rendered page.

[0053] Determine the validation rules that have been set. These include basic validation rules and custom validation rules. A validation rule is a process based on the validation process in the validation rule. An automated script is used to execute corresponding actions on the obtained visual data flow, thereby determining whether the visual data flow can execute the action normally. Basic validation rules are pre-set rules that apply to all scenarios, while custom validation rules are user-defined validation rules for each application scenario.

[0054] Based on basic validation rules, the data type output by the upstream node and the data type processing permissions of the downstream node are tested. Different visualization nodes can process and output different data types. For basic validation rules, the main purpose is to verify whether the data permissions are correct, which ensures the normal operation of the visualization data flow.

[0055] Based on the user's selection, the application scenario to which the current visual data flow belongs is determined, and according to the application scenario, the corresponding custom verification rules are selected in the corresponding rule library and added to this logical test. When the user has generated some custom verification rules in advance, they can be added to the corresponding rule library. In the rule library, corresponding labels can also be set for each custom verification rule. The label is used to describe the application scenario to which the custom verification rule applies. When generating a visual data flow, the user can select the currently edited application scenario, and the data flow editor module will select matching visual nodes, custom verification rules and other content with corresponding labels according to the scenario, and give priority to display, for example, improving their display order or enhancing their display method.

[0056] Based on custom validation rules, tests are performed based on the data content output by upstream nodes and the data content processing flow of downstream nodes. In addition to basic validation rules, tests can also be performed based on custom validation rules selected automatically or manually by the user.

[0057] According to the connection status between connected visualization nodes, set the corresponding connection line color, connection line shape, and connection line text content.

[0058] During the test, if the node is normal, the test passes and the normal connection line is displayed, such as Figure 6 As shown, the connection lines can be realized, dotted, of different thicknesses, different colors, etc. If the test fails, such as a node in the visualized data flow cannot process the data type output by the upstream node or cannot meet the corresponding business requirements, the connection line between the node and the upstream node can be marked with "Link Failed" or similar text.

[0059] Furthermore, application scenarios can include finance, which is often used in e-commerce platforms and payment services. The functions of visualized data flows can include: transmitting transaction data between the client and the cloud, deducting fees from user accounts based on transaction data, and generating corresponding reports based on transaction data. In financial scenarios, the data involved is often sensitive, and strict consistency of upstream and downstream data transmission is required.

[0060] Based on this, the data transaction content is determined, and a corresponding first data fingerprint is generated for the data transaction content based on the security sandbox configured in the data flow editor module. The data transaction content may include user transaction records (e.g., transaction amount, time, and items traded). The security sandbox is a virtual environment that can be used to securely isolate code or files. Its core function is to run potentially risky operations in a controlled, isolated space, preventing damage to real systems or data. In this case, the first data fingerprint is calculated in an isolated environment through sandbox isolation, preventing interference from malicious nodes.

[0061] The data transaction content with an empty fingerprint slot is entered into the visual data stream. The empty fingerprint slot is a placeholder with no actual value. The empty fingerprint slot is mainly used to store the corresponding data fingerprint in the future.

[0062] Based on custom validation rules and the configuration of downstream nodes in the configuration area, downstream nodes are identified as key nodes. Key nodes can include fund operation nodes (e.g., payment instruction generation nodes, clearing and settlement triggering nodes, cross-border remittance routing nodes), sensitive data exposure nodes (e.g., transaction report generation nodes, customer information decryption nodes), and other configurations. These configurations may include account credential fields and real-time funds export interfaces.

[0063] For the downstream node, it receives the data transaction content output by the upstream node, performs a hash calculation on the data transaction content, and obtains the second data fingerprint corresponding to the data transaction content. As a key node, the downstream node calculates the second data fingerprint itself when it receives the data transaction content output by the upstream node.

[0064] The second data fingerprint is matched with the first data fingerprint in the security sandbox. If the match is successful, the test passes. If the match fails, it is considered that there may be a node in the upstream node that has abnormal data processing during the data transmission process.

[0065] Of course, you can also temporarily add corresponding attack nodes based on needs and attack certain nodes to conduct a simple test of the anti-attack capability of the visual data flow.

[0066] In addition to financial scenarios, common application scenarios can also include data preprocessing scenarios, such as data cleaning, data structuring processing, etc.

[0067] Based on the node type and the configuration content in the configuration area, the upstream node is determined to be a data preprocessing node. When the upstream node is a data preprocessing node, its output content will include the data preprocessing results.

[0068] For the downstream node, the first data preprocessing result output by the upstream node is received, and the first data structuring degree corresponding to the first data preprocessing result is determined. The determination of the data structuring degree can be obtained by calculating the entropy ratio of the data. For example, the degree of chaos of the data is represented by Shannon entropy, and the corresponding structured score is obtained by 1-[Shannon entropy (data) / Shannon entropy (random data)]. Of course, the degree of data structuring can also be corrected by combining other dimensions such as data types. For example, for mandatory data types such as int and date, the score of the degree of data structuring can be increased, or the degree of data structuring can be increased or decreased according to whether it contains required fields or preset chaotic fields.

[0069] Based on the visualized data flow, it is determined whether there is a data preprocessing node upstream of the upstream node; if so, the data preprocessing node closest to the upstream node is used as the designated node, and the second data structuring degree of the second data preprocessing result output by the designated node is determined. At this time, the downstream node can establish a temporary data connection relationship with the designated node. Since the designated node and the upstream node of the downstream node belong to the same type of data preprocessing node, both can connect to the downstream node, and the downstream node can receive the corresponding data preprocessing result and determine the corresponding data structuring degree.

[0070] If the first data structured degree is higher than the second data structured degree, the upstream node is considered to have performed further data preprocessing on the second data preprocessing result of the specified node, and the test passes. If the first data structured degree is lower, the upstream node is considered to have not performed the data preprocessing process normally.

[0071] In one embodiment, Figure 7 As shown, during the editing process of the visual data flow and the actual data flow operation process, there may be multiple downstream nodes corresponding to the same upstream node. Figure 7 This is only an illustrative example and does not represent the visual data flow that can be achieved in real situations.

[0072] At this point, based on the user triggering the connection lines between multiple downstream nodes and the same upstream node, the configuration area is displayed. In addition to nodes, the configuration area can also configure the content of the connection lines accordingly. In addition to the shape and color of the connection lines, the corresponding functions of the connection lines can also be configured.

[0073] Use the configuration area to configure the real-time display, statistics, and playback functions for the data traffic of the connection line. When multiple downstream nodes correspond to the same upstream node, performance issues caused by parallel transmission may occur. Based on this, corresponding functions are set for the data traffic received on each connection line.

[0074] When visualizing the actual workings of data flows, the real-time data flow display function displays the current real-time flow rate of each connection line. Flow rate refers to the amount of data passing through the connection line per unit time. Selecting a connection line instead of a node is because a node may be connected to multiple nodes. If a node experiences a problem, it is difficult to determine which node on the other end has experienced the problem. However, flow monitoring on a connection line allows for the specific flow rate between the two nodes transmitting the data.

[0075] The data traffic statistics function is used to display the total traffic of each connection line within a preset time period. For example, the total traffic of the connection line in the last 1 minute can be counted.

[0076] The data traffic playback function is used to display the real-time traffic of each connection line at a historical moment. For example, it displays the real-time traffic of the connection line 10 minutes ago.

[0077] During configuration, users can choose whether to enable each function, which connection lines to monitor the corresponding traffic, as well as data traffic statistics function, the length of the preset time period in the data traffic playback function, and other functions. In this way, when consumers actually use the data flow, they can call the corresponding traffic monitoring function to visually monitor the data traffic during the actual business processing process.

[0078] Furthermore, based on traffic monitoring, further abnormality tracking functions can be configured for users so that consumers can quickly track the abnormal location in complex environments with multiple downstream nodes.

[0079] Based on user triggers, the configuration area of ​​the anomaly tracking sandbox is displayed and associated with the connection line configuration area. The association here mainly refers to which connection lines the anomaly tracking sandbox is used to track, setting the corresponding association, and obtaining the corresponding traffic parameters through the output interface provided by the relevant traffic parameters in the connection line configuration area.

[0080] Based on the configuration area of ​​the exception tracking sandbox, set the exception tracking function and data flow holographic snapshot function contained in the exception tracking sandbox.

[0081] The exception tracking function is used to trigger the data flow holographic snapshot function when the current real-time traffic corresponding to each connection line exceeds the preset traffic flow. In other words, when the real-time traffic exceeds the preset traffic flow, it is considered that there may be an anomaly in the data transmission process of the connection line, and the data flow holographic snapshot function is triggered.

[0082] The data stream holographic snapshot function is used to obtain corresponding data stream snapshots for the data streams of user-defined visualization nodes and connection lines at the user-defined frequency. A data stream snapshot refers to a snapshot of the data transmission process of the entire data stream at the current moment (of course, if the user only defines some visualization nodes, only those visualization nodes are used). The data stream snapshot can include the current node status (for example, memory data, configuration parameters, processing time), connection line status (for example, the type, size, and checksum of the data being transmitted), and environmental context (for example, external factors such as system load). This allows the consumption card to quickly identify the location of the anomaly when an actual anomaly occurs.

[0083] When setting up, users can set the preset flow that the real-time flow needs to exceed, the range of data flow snapshots, snapshot frequency, snapshot saving time, etc.

[0084] In one embodiment, since there are many materials (including components, data sources, data processing, etc.) in the material area, it may be difficult for users to quickly find the required materials.

[0085] Based on this, the corresponding AI conversation function is set up to quickly find the materials required by the user according to the user's description.

[0086] Specifically, the function description of the required material input by the user is obtained, and a corresponding dialog box is set in the material area, and the user can enter the function description in the dialog box.

[0087] First, the user's input function description is used as a keyword to search across all materials to determine whether a corresponding material exists. If not, the corresponding designated material is output using a large language model based on the function description and all materials included in the material area. A prompt word is generated based on the function description and all materials, and the large language model is used to understand the user's intent and find the desired material among all materials. The designated material is then rendered and displayed in the material area, for example, by highlighting it, increasing its display priority, or blocking other materials.

[0088] like Figure 8 As shown, the embodiment of the present application also proposes a visual data flow weaving device for the zero-code platform, including: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the visual data flow weaving method for the zero-code platform as described in any of the above embodiments.

[0089] An embodiment of the present application further proposes a non-volatile computer storage medium storing computer executable instructions, wherein the computer executable instructions are configured to implement a visual data flow weaving method for a zero-code platform as described in any of the above embodiments.

[0090] The various embodiments in this application are described in a progressive manner. Similar portions between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the device and medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simple. For relevant portions, refer to the descriptions of the method embodiments.

[0091] The devices and media provided in the embodiments of the present application correspond one-to-one to the methods. Therefore, the devices and media also have similar beneficial technical effects to their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0092] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A visual data flow weaving method for a zero-code platform, characterized in that: include: Based on user triggers, the data flow editor module is started and visually displayed; The data flow editor module includes a material area, a drawing board area, and a configuration area; the material area includes a component unit, a data processing unit, and a data source unit; Based on the user's drag operation, the components in the component unit, the data processing functions in the data processing unit, and the data sources in the data source unit are added to the drawing board area respectively, and visual component nodes, data processing nodes, and data source nodes are generated respectively; Based on the user's triggering of the visualization node, the configuration area is displayed, and the visualization node is configured through the configuration area; the types of the visualization node include component nodes, data processing nodes, and data source nodes; The configuration area includes at least events and related attributes for the visualization node; Based on user triggering, each visualization node is connected through the connection piles carried in the visualization node to generate a visualization data flow.

2. The visual data flow weaving method for the zero-code platform according to claim 1 is characterized in that: The method further comprises: Based on a user trigger, determining to perform a logic test on the visual data flow; Determine the verification rules that have been set, which include basic verification rules and custom verification rules; Based on the basic verification rules, the data type output by the upstream node is tested against the data type processing authority of the downstream node; Based on the user's selection, the application scenario to which the current visualized data flow belongs is determined, and according to the application scenario, the corresponding custom verification rule is selected in the corresponding rule library and added to this logic test; Based on the custom verification rules, the data content output by the upstream node is tested with the data content processing flow of the downstream node.

3. The visual data flow weaving method for the zero-code platform according to claim 2 is characterized in that: Based on the custom validation rules, the data content output by the upstream node and the data content processing flow of the downstream node are tested, specifically including: Determine data transaction content, and generate a corresponding first data fingerprint for the data transaction content based on a security sandbox set in the data flow editor module; Entering the data transaction content with the empty fingerprint slot into the visual data stream; Based on the user-defined verification rule, and according to the configuration content of the downstream node in the configuration area, determining that the downstream node is a key node; For the downstream node, receiving the data transaction content output by the upstream node, performing a hash calculation on the data transaction content, and obtaining a second data fingerprint corresponding to the data transaction content; The second data fingerprint is matched with the first data fingerprint in the security sandbox. If the match is successful, the test passes.

4. The visual data flow weaving method for the zero-code platform according to claim 2 is characterized in that: Based on the custom validation rules, the data content output by the upstream node and the data content processing flow of the downstream node are tested, specifically including: According to the node type and the configuration content in the configuration area, the upstream node is determined to be a data preprocessing node; For a downstream node, receiving a first data preprocessing result output by the upstream node, and determining a first data structuring degree corresponding to the first data preprocessing result; Based on the visualized data flow, determining whether there is a data preprocessing node upstream of the upstream node; If so, taking the data preprocessing node that is closest to the upstream of the upstream node as the designated node, and determining a second data structuring degree of the second data preprocessing result output by the designated node; If the first data is more structured than the second data, the test is passed.

5. The visual data flow weaving method for the zero-code platform according to claim 1 is characterized in that: The method further comprises: Determining that there are multiple downstream nodes in the visualization data flow corresponding to the same upstream node; displaying the configuration area based on a user triggering a connection line between the plurality of downstream nodes and the same upstream node; Configure the data flow real-time display function, data flow statistics function, and data flow playback function of the connection line through the configuration area; Among them, during the actual operation of the visualized data flow, the real-time display function of data traffic is used to display the real-time traffic corresponding to each connection line at the current moment; the data traffic statistics function is used to display the total traffic corresponding to each connection line within a preset time period; and the data traffic playback function is used to display the real-time traffic corresponding to each connection line at a historical moment.

6. The visual data flow weaving method for the zero-code platform according to claim 5 is characterized in that: The method further comprises: Based on the user's trigger, the configuration area of ​​the exception tracking sandbox is displayed, and the configuration area of ​​the exception tracking sandbox is associated with the configuration area of ​​the connection line; Based on the configuration area of ​​the anomaly tracking sandbox, setting the anomaly tracking function and the data stream holographic snapshot function contained in the anomaly tracking sandbox; Among them, the abnormal tracking function is used to start when the real-time traffic corresponding to each connection line at the current moment exceeds the preset traffic, triggering the data flow holographic snapshot function; the data flow holographic snapshot function is used to obtain the corresponding data flow snapshots for the data flow of the visualization nodes and connection lines set by the user according to the frequency set by the user.

7. The visual data flow weaving method for the zero-code platform according to claim 1 is characterized in that: The method further comprises: Obtain the functional description of the required material input by the user; Based on the functional description and all materials included in the material area, output the corresponding specified material through a large language model; In the material area, the designated material is rendered and displayed.

8. The visual data flow weaving method for the zero-code platform according to claim 1 is characterized in that: The method further comprises: For each type of visualization node, set the corresponding node color and the corresponding connection pile setting position; According to the connection status between connected visualization nodes, set the corresponding connection line color, connection line shape, and connection line text content.

9. A visual data flow weaving device for a zero-code platform, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the visual data flow weaving method for the zero-code platform as described in any one of claims 1 to 8.

10. A non-volatile computer storage medium storing computer executable instructions, characterized in that: The computer executable instructions are configured to implement the visual data flow weaving method for a zero-code platform as described in any one of claims 1 to 8.

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