An interface generation method and system for mapping data using a visual interaction panel

Abstract

The application discloses an interface generation method and system for mapping data by using a visual interaction panel, and belongs to the technical field of dynamic interface rendering. The method comprises the following steps: acquiring a multi-source data stream, extracting static and dynamic behavior characteristics, and generating feature anchor points; constructing a dynamic logic topology comprising components, links and logic units on the interaction panel; translating business association into layout instructions and fusing to generate a collaborative constraint network; inputting real-time data stream for rendering to generate an interface; and collecting feedback to collaboratively optimize logic parameters and layout weights. The application adopts a shadow deduction initialization and closed-loop feedback adjustment mechanism, can realize deep decoupling and adaptive alignment of data logic and interface layout, and improves the accuracy, robustness and interactive configuration efficiency under large-scale heterogeneous data of interface generation.

Description

Technical Field

[0001] This invention relates to the field of dynamic interface rendering technology, and in particular to an interface generation method and system that utilizes a visual interactive panel to map data. Background Technology

[0002] Dynamic interface rendering technology is an important component of the information technology field, with automatic interface generation technology finding wide application in areas such as monitoring systems and data visualization. In the interface construction process, how to efficiently and flexibly generate intelligent user interfaces with dynamic interaction logic and stable layouts based on large-scale heterogeneous data streams is currently a hot research topic.

[0003] In related technologies, Chinese invention patent application CN108334387A discloses a dynamic interface rendering method and apparatus. The method involves storing component configuration information and overlay structure relationship configuration information of the target interface class on a server; dividing the content to be displayed on the interface into at least one component, with the content and interaction method of each component defined by data; upon receiving an instantiation rendering request, obtaining business data and mapping it to the data required by the component to generate a component data set; calling the component implementation code for rendering; and completing interface instantiation based on the overlay structure relationship configuration information.

[0004] Regarding the aforementioned technologies, the inventors believe that although this method can improve interface rendering efficiency through business data mapping, its component configuration and structural relationships are often preset and static, making it difficult to cope with the dynamic changes in complex business logic driven by real-time data. Furthermore, this technology lacks in-depth analysis of data characteristics and a closed-loop optimization mechanism for operational effects, resulting in difficulties in achieving an optimal adaptive balance between logical accuracy and layout stability when processing large-scale heterogeneous data, and also exhibiting poor system cold start performance. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a method and system for generating interfaces using data mapping via a visual interactive panel. It employs a technical solution that constructs a dynamic logical topology, generates a collaborative constraint network, and performs closed-loop feedback collaborative optimization. This enables the automated generation of intelligent user interfaces where both logic and layout can be dynamically adaptive.

[0006] To achieve the above objectives, this application adopts the following technical solution:

[0007] Firstly, a method for generating an interface using a visual interactive panel to map data is provided, comprising: acquiring data streams to be mapped and historical data streams from several data sources, extracting static and dynamic behavioral features of the data streams to be mapped, and generating corresponding dynamic feature anchors; based on the dynamic feature anchors, constructing a dynamic logical topology in the visual interactive panel that includes interface components, data mapping links, and logical strategy units; parsing the dynamic logical topology, translating the business relationships contained therein into layout strategy instructions, and generating a collaborative constraint network in combination with user-defined basic layout rules; inputting the real-time data stream corresponding to the data streams to be mapped into the dynamic logical topology for processing, and performing layout rendering based on the collaborative constraint network, the processed data, and the distribution characteristics of the historical data streams to generate a user interface; during the interface operation, collecting logical effect feedback and layout conflict feedback, and performing collaborative optimization of the logical strategy unit parameters in the dynamic logical topology and the strategy weights in the collaborative constraint network based on the feedback.

[0008] Based on the above technical solution, in the interface generation method using visual interactive panel mapping data provided in this application, the technical solution of constructing dynamic logical topology, generating collaborative constraint network, and performing closed-loop feedback collaborative optimization can realize the automated generation of intelligent user interface with both logic and layout dynamically adaptive.

[0009] In conjunction with the first aspect above, in one possible implementation, the step of extracting the static features and dynamic behavioral features of the data stream to be mapped and generating corresponding dynamic feature anchors includes: extracting the data type, value range, and business semantic tags of the data stream to be mapped to obtain static features; analyzing the data update frequency, data change periodicity, and inter-field correlation and coupling of the data stream to be mapped to obtain dynamic behavioral features; and fusing the static features and dynamic behavioral features to form dynamic feature anchors.

[0010] In conjunction with the first aspect above, in one possible implementation, constructing a dynamic logical topology in the visual interaction panel based on the dynamic feature anchor points, comprising interface components, data mapping links, and logical strategy units, includes: matching and recommending initial interface components from a component library for storing interface components based on the static features of the dynamic feature anchor points; matching and recommending logical strategy units from a strategy library for storing logical strategies based on the dynamic behavior features of the dynamic feature anchor points, and attaching the logical strategy units to the data mapping links; and editing the initial interface components, data mapping links, and logical strategy units in response to user adjustment operations on the visual interaction panel to form the dynamic logical topology.

[0011] In conjunction with the first aspect above, in one possible implementation, parsing the dynamic logical topology, translating the business relationships contained therein into layout strategy instructions, and generating a collaborative constraint network by combining user-defined basic layout rules includes: traversing the dynamic logical topology to identify the business relationships formed by connecting different interface components through the logical strategy units; mapping each of the business relationships into several layout strategy instructions used to define the layout behavior of interface components in a specific logical state; and merging the layout strategy instructions with the basic layout rules into a weighted set of constraint equations to generate a collaborative constraint network containing the weights of each layout strategy instruction.

[0012] In conjunction with the first aspect above, in one possible implementation, the step of collecting logical effect feedback and layout conflict feedback during interface operation includes: monitoring the operation result of the logical strategy unit and comparing it with the expected operation target of the unit to generate logical effect feedback; verifying abnormal adjustments in component position or size caused by the execution of the collaborative constraint network during interface rendering to generate layout conflict feedback.

[0013] In conjunction with the first aspect above, in one possible implementation, the collaborative optimization of the logical strategy unit parameters in the dynamic logical topology and the strategy weights in the collaborative constraint network based on the feedback includes: inputting the logical effect feedback and the layout conflict feedback to an optimizer for calculating adjustment amounts based on system-level optimization objectives; the optimizer calculates the adjustment amount for the logical strategy unit parameters and the adjustment amount for the corresponding strategy weights in the collaborative constraint network; and performs synchronous adjustments on the logical strategy unit parameters and the strategy weights according to the adjustment amounts.

[0014] In conjunction with the first aspect above, in one possible implementation, the method further includes, before generating the user interface: using historical data streams to drive the dynamic logical topology and the cooperative constraint network to perform shadow simulation; recording parameter snapshots of the dynamic logical topology and the cooperative constraint network when a stable state is reached during the shadow simulation; and using the parameter snapshots to initialize the dynamic logical topology and the cooperative constraint network.

[0015] In conjunction with the first aspect above, in one possible implementation, the logical strategy unit for matching and recommending logical strategies based on the dynamic behavior features of the dynamic feature anchor points includes: when the dynamic behavior features indicate that the data has an occasional spike pattern, recommending a dynamic threshold alarm unit in the logical strategy unit; and configuring an initial threshold range and an adaptive fine-tuning strategy for adjusting the threshold according to the target false alarm rate at runtime for the dynamic threshold alarm unit based on the historical distribution of the data stream to be mapped.

[0016] In conjunction with the first aspect above, in one possible implementation, mapping each of the aforementioned business relationships into several layout strategy instructions for defining the layout behavior of interface components under specific logical states includes: when an alarm linkage relationship is detected between two interface components, defining the interface component that triggers the alarm as the first interface component, defining the interface component affected by the alarm as the second interface component, and generating a first layout strategy instruction; the first layout strategy instruction is used to increase the layout priority of the second interface component and trigger a positioning animation when the first interface component triggers an alarm.

[0017] Secondly, a system for generating an interface using a visual interactive panel to map data is provided, comprising: a data feature extraction module for acquiring data streams to be mapped and historical data streams from several data sources, extracting static and dynamic behavioral features of the data streams to be mapped, and generating corresponding dynamic feature anchors; a topology construction module for constructing a dynamic logical topology in the visual interactive panel based on the dynamic feature anchors, including interface components, data mapping links, and logical strategy units; a constraint network generation module for parsing the dynamic logical topology, translating the business relationships contained therein into layout strategy instructions, and generating a collaborative constraint network in combination with user-defined basic layout rules; an interface rendering generation module for inputting the real-time data stream corresponding to the data streams to be mapped into the dynamic logical topology for processing, and performing layout rendering based on the collaborative constraint network, the processed data, and the distribution characteristics of the historical data streams to generate a user interface; and a closed-loop feedback optimization module for collecting logical effect feedback and layout conflict feedback during interface operation, and performing collaborative optimization of the logical strategy unit parameters in the dynamic logical topology and the strategy weights in the collaborative constraint network based on the feedback.

[0018] Compared with the prior art, the present invention has the following advantages:

[0019] This invention achieves intelligent and automated interface construction by deeply extracting static and dynamic features of data streams and recommending corresponding interface components and logical strategy units based on these features. This simplifies the development process, reduces reliance on the professional experience of developers, and improves the development efficiency of data-driven interfaces.

[0020] This invention innovatively parses and translates the business relationships contained in the dynamic logical topology into a weighted collaborative constraint network, so that the interface layout is no longer static and fixed, but can be dynamically and adaptively adjusted according to the business logic state triggered by real-time data. This ensures that key information can be given priority in visual presentation at critical moments, thereby improving the user's information perception efficiency and response speed.

[0021] This invention establishes a closed-loop feedback optimization mechanism for logical effects and layout conflicts. It can continuously collect system performance data during interface operation and adjust the logical unit parameters and layout strategy weights in a collaborative and automated manner based on this data. This enables the interface system to have the ability to learn and evolve on its own, thereby dynamically maintaining the best balance between logical accuracy and layout stability during long-term operation.

[0022] This invention introduces a shadow simulation mechanism based on historical data streams to warm up and calibrate the system before its official launch, effectively solving the cold start problem. By using a snapshot of the stable state parameters obtained from the simulation as the initial system configuration, it ensures that the user interface has high performance and robustness from the start, improving the overall reliability of the system and the user experience.

[0023] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This application provides a structural architecture diagram of an interface generation system that utilizes a visual interactive panel to map data, as provided in an embodiment of the present application.

[0026] Figure 2 A flowchart illustrating a method for generating an interface using a visual interactive panel to map data, provided in an embodiment of this application;

[0027] Figure 3 This is the Pearson correlation coefficient matrix R diagram provided in the embodiments of this application.

[0028] Figure 4 This is a shadow inference convergence characteristic curve provided in the embodiments of this application. Detailed Implementation

[0029] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" is intended to present the relevant concepts in a concrete manner.

[0030] The interface generation method for mapping data using a visual interactive panel provided in this application embodiment can be applied to, for example... Figure 1 In the interface generation system 100 that utilizes a visual interactive panel to map data, as shown, Figure 1 As shown, the system includes:

[0031] The data feature extraction module is used to acquire the data stream to be mapped and historical data streams from several data sources, and extract the static features and dynamic behavior features of the data stream to be mapped to generate corresponding dynamic feature anchor points.

[0032] The topology construction module is used to construct a dynamic logical topology, including interface components, data mapping links, and logical strategy units, in the visual interaction panel based on the dynamic feature anchor points.

[0033] The constraint network generation module is used to parse the dynamic logical topology, translate the business relationships contained therein into layout strategy instructions, and generate a collaborative constraint network in combination with user-defined basic layout rules.

[0034] The interface rendering generation module is used to input the real-time data stream corresponding to the data stream to be mapped into the dynamic logical topology for processing, and to perform layout rendering based on the distribution characteristics of the cooperative constraint network, the processed data, and the historical data stream to generate the user interface.

[0035] The closed-loop feedback optimization module is used to collect logical effect feedback and layout conflict feedback during the interface operation, and to perform collaborative optimization of the logical strategy unit parameters in the dynamic logical topology and the strategy weights in the collaborative constraint network based on the feedback.

[0036] like Figure 2 As shown in the figure, this application provides a method for generating an interface that uses a visual interactive panel to map data, including:

[0037] Acquire the data streams to be mapped and historical data streams from several data sources, and extract the static and dynamic behavioral features of the data streams to be mapped to generate corresponding dynamic feature anchors;

[0038] Based on the dynamic feature anchor points, a dynamic logical topology containing interface components, data mapping links, and logical strategy units is constructed in the visual interaction panel.

[0039] The dynamic logical topology is parsed, and the business relationships contained therein are translated into layout strategy instructions. A collaborative constraint network is then generated by combining the user-defined basic layout rules.

[0040] The real-time data stream corresponding to the data stream to be mapped is input into the dynamic logical topology for processing, and the layout rendering is performed based on the distribution characteristics of the cooperative constraint network, the processed data, and the historical data stream to generate a user interface;

[0041] During the operation of the interface, feedback on logical effects and layout conflicts are collected, and based on the feedback, the logical strategy unit parameters in the dynamic logical topology and the strategy weights in the collaborative constraint network are collaboratively optimized.

[0042] It is important to note that a complete mapping and adaptive optimization loop from data features to interface behavior is constructed. Through deep analysis of the data flow, a structured identity containing static attributes and dynamic behaviors is established—the dynamic feature anchor. Based on this anchor, a dynamic logical topology containing interface components, data links, and logical strategies is built in a visualization environment, achieving visual modeling of business logic and data flow. The business relationships inherent in this topology are translated into a set of priority-based mathematical constraints, which are then integrated with user-defined rules to form a collaborative constraint network, thereby quantifying abstract business logic into concrete layout drivers. Through a continuously running feedback mechanism, the system collects real-time data on logic execution effects and layout conflicts, and collaboratively adjusts logical parameters and layout strategy weights based on this feedback, enabling the entire interface generation and operation system to possess self-learning and dynamic evolution capabilities.

[0043] In one possible implementation of the embodiments of this application, combined with Figure 2 The step of extracting static features and dynamic behavioral features of the data stream to be mapped and generating corresponding dynamic feature anchor points includes:

[0044] Extract the data type, value range, and business semantic tags of the data stream to be mapped to obtain static features;

[0045] By analyzing the data update frequency, data change periodicity, and inter-field correlation and coupling of the data stream to be mapped, dynamic behavioral characteristics are obtained.

[0046] By integrating the static features and dynamic behavioral features, dynamic feature anchors are formed.

[0047] In some implementations, the raw, unstructured data stream is transformed into a standardized, structured descriptor containing rich contextual information. This descriptor serves as a dynamic feature anchor, providing a decision-making basis for subsequent intelligent recommendation of interface components and automatic generation of layout strategies. This process is specifically implemented in three core steps: static feature extraction, dynamic behavioral feature analysis, and feature fusion. Static feature extraction is performed on the received data stream to be mapped. The input consists of the metadata definition of the data stream to be mapped and a data sample set, such as buffered data containing 1000 consecutive data points. By parsing the schema definition provided by the data source, such as a database table structure or a message queue JSON schema, the data type, such as floating-point, integer, or string, is directly obtained. For numeric data, its value range is calculated by scanning the data sample set or querying historical data aggregation results, i.e., determining the actual minimum and maximum values ​​of the data. Simultaneously, business semantic tags are extracted. These are string identifiers predefined in metadata or configured by the user, such as "CPU_Core_1_Usage" or "Environmental_Temperature". These tags give the data business-level meaning and are crucial for subsequent component semantic matching. After extraction, static feature vectors are generated. This includes data types, value ranges, and business semantic tags. To deeply analyze the behavioral patterns of data streams over time, reveal their inherent dynamic laws and correlations, and provide a basis for recommending logical strategy units that can adapt to these dynamic changes, dynamic behavioral characteristic analysis is then performed on a representative historical data stream, such as data from the past 24 hours. The data update frequency is calculated by statistically analyzing the number of data points arriving within a unit of time, such as a 60-second window. By employing time series analysis techniques, such as performing a Fast Fourier Transform (FFT) on historical data sequences and analyzing their spectrograms, the frequency points with the highest energy concentration can be identified. The period corresponding to these frequency points represents the periodicity of the data variation. For example, a periodic fluctuation that occurs during daily business peaks. For data streams containing multiple fields, the Pearson correlation coefficient matrix between the time series of different fields is calculated. This quantifies the correlation and coupling between fields. A coefficient value close to 1 indicates a strong positive correlation between two fields, which is crucial for subsequently constructing a linked layout strategy. After analysis, dynamic behavioral feature vectors are generated. This encapsulates information such as data update frequency, data change periodicity, and the degree of coupling between fields. It integrates separate static and dynamic features into a structured data object, i.e., a dynamic feature anchor. This serves as the unique digital identity identifier for the data stream. The fusion step involves combining the static feature vectors obtained in the first two steps. With dynamic behavior feature vector This involves structured combination to form a comprehensive descriptor. This dynamic feature anchor point... A data structure or a multidimensional feature vector is defined as follows:

[0048] ;

[0049] in, It represents a static feature set, containing fields such as data type, value range, and business semantic tags. This represents a set of dynamic behavioral features, including fields such as data update frequency, data change periodicity, and correlation coupling matrix. It expresses a structural combination rather than numerical operations, ensuring that features of different dimensions can be managed and used uniformly. The generated dynamic feature anchors... This will be used as output for subsequent automated matching and recommendation of components and logical strategies. For example... Figure 3 As shown, this heatmap visually displays the Pearson correlation coefficient matrix between various fields in the data stream to be mapped. The degree of correlation and coupling is represented by the intensity of black and white, for example... and The correlation coefficient between the two reached 0.92, indicating a strong positive correlation, and a linkage layout strategy was automatically generated in subsequent steps.

[0050] For example, in the process of generating a real-time combat monitoring interface for a massively multiplayer online game (MMORPG), the system first receives a data stream of player skill casting data containing 1000 sampling points and metadata definitions. The data type is determined to be floating-point by parsing, and a scan of the sample set reveals that the player's "damage per second (DPS)" fluctuates between 12000.5 and 28000.2. This determines the value range and extracts the business semantic tag "Combat_Performance_DPS," thereby generating a static feature vector. Dynamic behavioral characteristic analysis was performed on historical combat data from the past 24 hours, and the data update frequency was determined by statistical analysis using a 60-second sliding window. The algorithm operates at 20 times per second and utilizes Fast Fourier Transform (FFT) to identify 24-hour periodicity reflecting players' day-night online patterns. If a "server latency (Ping value)" data stream also exists, the Pearson correlation coefficient between it and the DPS value is calculated. A strong negative correlation of -0.85 was used to obtain the dynamic behavior feature vector. .Will and According to the formula This data is structured and encapsulated into a unique dynamic feature anchor for the combat data stream. This provides a basis for decision-making in order to accurately recommend the "Dynamic Damage Distribution Map" component and the "Data Smoothing Processing" logic unit in the future.

[0051] In one possible implementation, combining Figure 2 Based on the aforementioned dynamic feature anchors, the dynamic logical topology constructed in the visual interaction panel, including interface components, data mapping links, and logical strategy units, includes:

[0052] From the component library used to store interface components, initial interface components are matched and recommended based on the static features of the dynamic feature anchors;

[0053] From the policy library used to store logical policies, logical policy units are matched and recommended based on the dynamic behavior features of the dynamic feature anchors, and the logical policy units are mounted onto the data mapping link.

[0054] In response to user adjustments on the visual interactive panel, the initial interface components, data mapping links, and logical strategy units are edited to form a dynamic logical topology.

[0055] In some implementations, abstract data features are transformed into a visual, user-editable logical blueprint. This blueprint includes interface elements, data flow, and processing logic, providing structured input for subsequent interface layout and rendering. This process is completed on a node-based visual interactive panel, a development environment that allows users to define program logic by dragging and connecting graphical nodes. The process includes three stages: initial component recommendation, logic strategy mounting, and user interactive editing. Based on the static attributes of the data, the most suitable initial interface components are intelligently recommended to the user, thereby accelerating the interface development process and ensuring the rationality of data presentation. The component recommendation process is initiated when the user drags a dynamic feature anchor point representing the data flow to be mapped onto the visual interactive panel. The input is this dynamic feature anchor point. static eigenvectors Access the pre-built component library, which stores various UI components. Each component has a set of metadata tags describing its applicable data types, value range characteristics, and business scenarios. A matching score algorithm is used to calculate the fit between each component and the data anchor. The scoring formula is expressed as:

[0056] ;

[0057] in, The final matching score typically ranges from 0 to 1. The matching weights are calculated based on three dimensions: data type, value range, and business semantic tag. The sum of these weights is 1. These weights can be preset by the administrator or dynamically adjusted by learning from users' historical choices. The data type matching score is 1 when the data type required by the component is exactly the same as the data type in the anchor, 0.7 when they are compatible, and 0 when they are incompatible. This represents the range matching degree, for example, 1 when the component is a gauge and the data range is bounded, and 0 otherwise. The business semantic tag similarity is calculated by the cosine similarity between the component tag and the anchor point's business semantic tag vector. The top-scoring components, such as the top three, are recommended to the user in a list format. The user selects one and places it on the panel, forming an initial interface component node in the topology. Based on the dynamic behavior of the data, intelligent recommendations and processing logic are attached to the data flow, making the interface not just a data display but an intelligent agent with real-time analysis and response capabilities. When a user drags a line from a data source node or another logical node on the visual interaction panel and connects it to an interface component node, a data mapping link is created. At this point, the dynamic feature anchor point at the source of the link is parsed. Dynamic behavior feature vectors Access the policy library, which stores various logical policy units, such as a moving average unit for handling high-frequency jitter data, or a dynamic threshold alarm unit for capturing occasional spikes. Each policy unit is also labeled, indicating the applicable dynamic data behavior pattern. Dynamic behavior features are matched with logical policy units based on a series of preset rules or a trained decision tree model. For example, when the data update frequency in the dynamic behavior feature is detected... The data changes more than a preset threshold, such as 50 times per second, and the data changes periodically. When the situation is unclear, the moving average unit is recommended first. Recommended logical strategy units appear as floating icons next to the data mapping links. Users can drag and drop them onto the links; this operation is called "mounting." After mounting, the logical strategy unit becomes a new node on the topology graph, embedded in the data flow. To give users final editing rights and ensure that the automated construction results of the recommendations can be precisely adjusted to meet complex and ever-changing specific business needs, after the initial recommendation and construction are completed, the entire dynamic logical topology is fully presented on the visual interactive panel in the form of nodes and connections. Users can perform a series of free adjustment operations. These operations include, but are not limited to, manually dragging and dropping new interface components or logical strategy units from the component library and strategy library to add to the topology; deleting any existing nodes or data mapping links; reconnecting nodes to change the data flow direction; and clicking on any node to modify its internal parameters in the pop-up attribute configuration window, such as adjusting the color and time axis range of the line chart component, or modifying the initial threshold and sensitivity parameters of the dynamic threshold alarm unit. All user adjustments are responded to in real time and updated in the underlying graph structure data. Once the user confirms the completion of editing, the complete picture on the current visual interaction panel, which includes all interface components, data mapping links, and the graph structure of logical strategy units and their configuration parameters, is solidified into the final dynamic logical topology and output in formats such as JSON or XML.

[0058] For example, when constructing the dynamic logical topology of a game real-time performance monitoring dashboard, when a user drags a dynamic feature anchor point representing the "instantaneous frame rate of player skill effects" onto the panel, the topology construction module initiates the initial component recommendation process, inputting its static feature vector. Taking the matching of the "real-time frame rate curve" component as an example, we set the weights. , , If the data types are verified to be completely consistent, then... If the frame rate range is bounded, then The semantic tag "FPS" is highly similar to the component tag. Substitute into the formula Calculate the score Based on this, it is recommended to users as the preferred component. Subsequently, in the logical strategy mounting phase, users create a mapping link from the data source to the graph component, and parse the dynamic behavior feature vector. Discover data update frequency 60 times per second and periodic If the effect is not obvious, the decision tree rule is triggered, and a "high-frequency data smoothing unit" is recommended next to the link. The user can drag and drop it onto the link to filter rendering jitter. Finally, during the user interaction editing stage, the user changes the time axis range of the curve from the default 60 seconds to 300 seconds through a pop-up window, and adjusts the sampling window parameter of the smoothing unit to 20. After confirmation, the system solidifies this complete graph structure, which includes component attributes, smoothing logic, and link configuration, into a dynamic logical topology output in JSON format.

[0059] In one possible implementation, combining Figure 2 The process involves parsing the dynamic logical topology, translating the business relationships it contains into layout strategy instructions, and generating a collaborative constraint network by combining user-defined basic layout rules, including:

[0060] Traverse the dynamic logical topology and identify the business relationships formed by connecting different interface components through the logical strategy unit;

[0061] Each of the aforementioned business relationships is mapped to several layout strategy instructions used to define the layout behavior of interface components under specific logical states;

[0062] The layout strategy instructions and basic layout rules are merged into a weighted set of constraint equations to generate a collaborative constraint network containing the weights of each layout strategy instruction.

[0063] In some implementations, the business logic and data flow relationships designed by the user on the visual interactive panel are automatically translated into a set of priority-based mathematical constraints that can be executed by the layout engine, thereby enabling the interface layout to dynamically adapt to real-time business states. This process consists of three closely linked steps: business relationship identification, layout strategy instruction mapping, and collaborative constraint network generation. To accurately identify the relationships between components that contain specific layout intentions within a complex dynamic logical topology, the input dynamic logical topology, which is essentially a directed graph, is traversed. A depth-first or breadth-first search algorithm is used, focusing on logical strategy units that connect at least two different interface component nodes. When a logical strategy unit's input comes from the output of an interface component or its bound data flow, and its output is connected to the input of another interface component, a business relationship is identified. This relationship is recorded as a tuple in engineering, containing the source component, the target component, and the type and state of the logical strategy units connecting them. The identified abstract business relationships are then concretized into a set of deterministic layout behavior rules triggered under specific conditions. For each identified business relationship, the built-in strategy instruction mapping library is queried. This library defines how different types of logical strategy units should affect the layout of their associated UI components under different logical states. For example, when a business relationship is identified connecting two UI components via a dynamic threshold alarm unit, this relationship will be mapped to one or more layout strategy instructions. A typical layout strategy instruction format is: if the logical strategy unit state is active, modify the target component's layout attributes. For example, when the alarm unit state changes from never triggered to triggered, a layout strategy instruction is generated to improve the layout hierarchy of the associated chart component and highlight its border. These instructions are conditional and form the basis for subsequent construction of dynamic constraints. To integrate all discrete layout rules, including dynamic layout strategy instructions and static basic layout rules, into a unified, weighted mathematical model, i.e., a collaborative constraint network, so that the layout rendering engine can solve for the optimal layout of all components at any given time, the generated layout strategy instructions are integrated with the global basic layout rules set by the user through the interface, such as a minimum spacing of 20 pixels between components or top alignment of all components. Each rule and instruction is transformed into one or more constraint equations or inequalities. For example, "Component A is to the left of component B" is translated as A.right is less than or equal to B.left. Crucially, each constraint is assigned a weight. Constraints derived from layout strategy instructions typically have higher weights, such as 0.8 to 1.0, to ensure that business logic-driven layout changes are prioritized. Basic layout rules have relatively lower weights, such as 0.5. Finally, all weighted constraint equations are combined to form the objective function to be optimized; this function is the co-constraint network. Its mathematical expression is a function that minimizes the weighted error:

[0064] ;

[0065] in, It is the total conflict cost of the entire layout, which is the objective that the constraint solver needs to minimize. Represents the first in the system Constraints. It is the first The strategy weight of a constraint is determined by the source of the constraint, such as alarm linkage or normal alignment. It is dimensionless and ranges from 0 to 1. It is the first The error function for a constraint quantifies the degree to which the current layout deviates from that constraint. Its dimensions are consistent with layout attributes such as pixels. For example, if a constraint is Ax equal to Bx, its error function can be the square of (Ax - Bx). Solving this function allows... The minimum set of layout parameters is needed to generate the optimal UI layout scheme that satisfies all constraints. This includes the objective function. The set of all constraint equations is the final generated collaborative constraint network.

[0066] For example, when constructing a real-time monitoring interface for boss battles in a large-scale multiplayer raid, the dynamic logic topology is first traversed to identify the business relationship between the "Boss Health Threshold Judgment Unit" connecting the "Boss Health Bar" and the "Full-Screen Execution Ultimate Button," and this relationship is mapped to a layout strategy instruction. Specifically, when the Boss's health drops below 10% and the execution logic state is triggered, the layout weight of the "Ultimate Button" is increased from the initial 0.3 to 0.95 and centered. When generating the collaborative constraint network, this instruction is integrated with the basic layout rule of "minimum spacing between components of 20 pixels" to construct the objective function. For example, setting the weight of the basic spacing constraint. Layout constraint weights driven by execution logic If the current layout is limited by screen space, causing the button position to deviate from the center coordinates by 10 pixels, i.e., an error... Furthermore, the actual distance between the bar and the health bar is only 15 pixels, deviating from the 20-pixel constraint target by 5 pixels, i.e., error. Then the layout engine calculates the current total conflict cost. By solving... The minimized set of layout parameters will automatically compress the space of low-priority interface elements, prioritizing the "execution ultimate move button" to be presented in the optimal position during critical battle moments, thereby guiding players to deliver the fatal blow.

[0067] In one possible implementation, combining Figure 2The collection of logical effect feedback and layout conflict feedback during interface operation includes:

[0068] Monitor the execution results of the logic strategy unit and compare them with the expected execution target of the unit to generate logic effect feedback;

[0069] During the verification interface rendering process, abnormal adjustments to component positions or sizes caused by the execution of the collaborative constraint network are generated, and layout conflict feedback is produced.

[0070] In some implementations, a quantitative feedback loop is established from operational performance to the system model, providing precise and quantifiable input signals for subsequent adaptive optimization. This process consists of two parallel monitoring tasks: logic performance feedback acquisition and layout conflict feedback acquisition. The deviation between the actual performance of the logic strategy unit and its design goals is quantified to determine whether the unit's parameter configuration is appropriate. The output of each logic strategy unit deployed in the dynamic logic topology is monitored in real time. Simultaneously, the expected operational goals configured during the unit's construction phase are read; these are quantifiable performance indicators. For example, for a dynamic threshold alarm unit, the expected operational goal might be set as a false alarm rate, such as no more than 0.5 times per hour. Within a preset time window, such as every 30 minutes, the actual operational results of the unit are statistically analyzed, such as the actual number of alarms generated. The actual results are compared with the expected goals to generate logic performance feedback. This feedback is a scalar value, calculated as follows:

[0071] ;

[0072] in, It is the generated logical effect feedback, which is a signed numerical value. The positive or negative sign indicates the direction of deviation, and the absolute value indicates the degree of deviation. It refers to the actual performance metrics measured during the evaluation period, such as the actual false alarm rate obtained by statistical analysis of unit output logs over several minutes to several hours. This is the pre-defined expected operating target of the logic strategy unit. This value is configured and stored in the unit's properties when constructing the dynamic logical topology. A positive value A negative value might indicate an overly sensitive alert, while a negative value indicates an overly sluggish one. This feedback value will be sent to the optimizer. To detect and quantify the extent to which contradictory constraints are not fully satisfied during layout rendering, unreasonable weight configurations in the collaborative constraint network are identified. Verification is performed immediately after each call to the layout engine to solve the collaborative constraint network. The layout engine's goal is to find a set of component layout parameters that make the objective cost function of the collaborative constraint network... Minimize. Ideally, A value of zero indicates that all constraints are satisfied. However, when conflicts exist between constraints, It will be a positive value, and the final cost will be checked. When Exceeding a very small positive threshold, for example This leads to the conclusion that a layout conflict exists. Further analysis of the composition is then necessary. The weighted errors of each individual constraint are used to identify those constraints with the largest error values. Layout conflict feedback is not a single numerical value, but a data structure containing conflict details, typically a set recording all constraints with non-negligible errors. Each record contains the constraint identifier of the conflict, the original definition of the constraint, and its actual error value in this solution. For example, a record might be: there is a spacing constraint conflict between component A and component B; the expected spacing is 20 pixels, but the actual spacing is 15 pixels, resulting in an error of 5 pixels. This structured set of layout conflict feedback will be sent as another input to the optimizer to guide the adjustment of strategy weights.

[0073] For example, during the operation of the "player network latency monitoring interface" in a competitive game, the "high-frequency Ping value judgment unit" is used to collect logical effect feedback. If the unit's preset expected false alarm rate target is met... The expected false high latency alerts are 0.1 times per hour, meaning that false high latency alerts are expected to be extremely rare. The actual false alarm rate was measured by log statistics over the past 30-minute evaluation period. If it is 0.4 times per hour, then substitute it into the formula. Calculate the logical effect feedback This positive feedback indicates that the current alarm logic is overly sensitive, causing normal fluctuations to be misinterpreted as stuttering. Simultaneously, layout conflict feedback collection is performed in parallel. When the layout engine attempts to render the "Network Status Line Graph" and the "Real-time Frame Rate Statistics Table," if a conflict is found between their positional constraints, resulting in a higher total conflict cost... achieve Exceeding the preset threshold If a layout conflict is detected, a structured set of conflict feedback is recorded. For example, if the expected value of the "spacing between the line chart and the statistics table" constraint is 20 pixels but the actual value is only 12 pixels, this indicates an actual error. Pixel; ultimately, the numerical logical feedback 0.3 and the set of structured layout conflicts containing pixel errors are synchronously sent to the optimizer as precise input for dynamically adjusting parameters and weights of the game interface.

[0074] In one possible implementation, combining Figure 2 The collaborative optimization of the logical policy unit parameters in the dynamic logical topology and the policy weights in the collaborative constraint network based on the feedback includes:

[0075] The logical effect feedback and the layout conflict feedback are input into the optimizer used to calculate the adjustment amount based on the system-level optimization target;

[0076] The optimizer calculates the adjustment amount for the parameters of the logical policy unit and the adjustment amount for the corresponding policy weights in the cooperative constraint network.

[0077] Based on the adjustment amount, the parameters of the logical strategy unit and the strategy weight are adjusted synchronously.

[0078] In some implementations, an adaptive closed-loop control system is established. This system can automatically fine-tune its internal business logic and layout rules based on the actual operating effect of the interface, thereby achieving continuous self-improvement and dynamic balance of system performance and user experience. This process involves three steps: receiving feedback, calculating adjustment amounts, and synchronous updates. To unify and integrate operational effect feedback signals from different dimensions into the system-level optimization objective function, a unified decision-making benchmark is provided for subsequent quantitative adjustments. The optimizer first receives logical effect feedback from the monitoring task. Feedback conflicts with the structured layout. The optimizer integrates these inputs into the global system cost function. The goal of this function is to simultaneously minimize logical bias and layout conflicts. A feasible cost function form is:

[0079] ;

[0080] in, The total cost of the system is the target value that the optimizer needs to minimize. and These are preset hyperparameters, serving as weighting coefficients for logic cost and layout conflict cost, used to adjust the system's preference between logic accuracy and layout stability. For example, Set it to 0.6. Set it to 0.4. It is to provide feedback on the logical effects. Transform it into a function with non-negative cost. Obtained from the logical effect feedback collection steps. Representative layout conflict feedback set, The total layout conflict cost is calculated based on this set, for example, by weighted summation of the error values ​​of all conflict constraints. Through this cost function, the optimizer unifies two seemingly unrelated problems within a multi-objective optimization framework. To accurately calculate the specific adjustment values ​​for the logical policy unit parameters and policy weights in the cooperative constraint network based on the total system cost function, so that the adjusted system state evolves in the direction of reducing the total cost, the optimizer uses a gradient descent-based algorithm to calculate the adjustment amount. The total cost function is calculated separately. The partial derivative with respect to each adjustable parameter, i.e., the gradient, indicates the direction in which parameter changes most quickly reduce the cost. This applies to the parameters of the logic policy unit. Its adjustment amount The calculation formula is:

[0081] ;

[0082] For a certain policy weight in a cooperative constraint network Its adjustment amount The calculation formula is:

[0083] ;

[0084] in, and This is the calculated adjustment amount. and The learning rate is two independent, small positive numbers, for example, between 0.001 and 0.1, used to control the step size of each adjustment and prevent system oscillation. and It is the partial derivative of the total cost with respect to the corresponding parameters, calculated using the chain rule. For example, Will pass and The product of these values ​​indicates that the optimizer evaluates the impact of minor changes in logical parameters on the final logical effect. Similarly, the optimizer also evaluates the impact of changes in policy weights on the final layout conflicts. To ensure the calculated adjustments are applied safely and consistently to the running system, and to ensure that the evolution of logical rules and layout rules remains synchronized, avoiding intermediate chaos caused by inconsistent update timing, the optimizer does not apply the adjustments to all parameters immediately after calculation. Instead, it performs synchronous adjustments as transaction batches within the next system update cycle. The relevant parts of the dynamic logical topology and cooperative constraint network are locked, and then the new parameter values ​​and weight values ​​are written. New logical policy unit parameters. Equal to old parameters Add adjustment amount New strategy weights Equal to old weight Add adjustment amount This is typically done through normalization or range pruning, for example, ensuring that the weight values ​​always remain within the range of 0 to 1. This synchronized adjustment ensures that when the behavior of the logical strategy changes due to parameter variations, the corresponding layout strategy weights also change accordingly, thereby enabling the logical performance and layout behavior of the entire interface system to evolve towards a better state in a coordinated manner.

[0085] For example, during the collaborative optimization process of the "player character attribute development screen" in a certain anime-style open-world game, the optimizer received feedback on the logical effect. This refers to a collection of feedback messages indicating excessively frequent skill value changes and layout conflicts. For example, component overlap errors caused by information overload. After each pixel, perform feedback integration. Set logical cost weights. With layout cost weight ,definition and Substitute into the formula to calculate the total system cost. The optimizer calculates the adjustment based on the gradient descent algorithm, if the current skill judgment parameters... Calculate the gradient when the value is 0.8. Set the learning rate According to the formula Similarly, if a UI layout strategy has a weight... The value is 0.9, calculated as follows During the synchronization update phase, the new parameters are added in the next frame rendering transaction. With new weights Simultaneously, by reducing logic sensitivity and fine-tuning layout weights, the game's character development interface achieves an adaptive dynamic balance between logical performance and visual presentation.

[0086] In one possible implementation, combining Figure 2 The method further includes the following steps before generating the user interface:

[0087] Historical data streams are used to drive the shadow simulation of the dynamic logical topology and the cooperative constraint network.

[0088] Record a snapshot of the parameters of the dynamic logical topology and the cooperative constraint network when a stable state is reached during the shadow deduction process;

[0089] The parameter snapshot is used to initialize the dynamic logical topology and cooperative constraint network.

[0090] In some implementations, historical data is used to warm up and calibrate the newly built system model, thereby finding a validated and better-performing initial state before official deployment and avoiding the cold start problem. The cold start problem refers to the poor initial performance of a new system due to a lack of empirical data, resulting in random or default adaptive parameters. This process is executed before the main interface is rendered and consists of three stages: shadow simulation, stable state snapshot recording, and parameter initialization. This simulates the operation under real historical data in a secure sandbox environment and triggers its adaptive optimization mechanism, causing its parameters to converge to a more reasonable state. After the user completes the construction of the dynamic logical topology and cooperative constraint network, it is not immediately used for real-time data processing. Instead, a background shadow simulation process is initiated. This process retrieves a representative historical data stream from the data source, such as data from the past 24 hours or week. This historical data stream is then input into the newly constructed dynamic logical topology at a rate several times faster than real time, for example, 10 times faster. Simultaneously, the shadow simulation process, much like in a real-world environment, collects simulated logical effect feedback and layout conflict feedback, driving the optimizer to continuously adjust the logical policy unit parameters and policy weights in the cooperative constraint network. This process is performed offline, accelerating training. During shadow simulation, the complete configuration state at the moment the system reaches optimal or near-optimal performance is identified and captured. The total system cost function calculated by the optimizer is continuously monitored throughout the shadow simulation process. The value of . When The value of is lower than the preset convergence threshold over multiple consecutive iterations, such as 100 consecutive iteration steps. For example, less than At this point, the deduction process is determined to have reached a stable state. Reaching a stable state means that the adaptive optimization process has found a local optimum, and the parameter configuration, driven by this historical data, is quite mature. At this time, a snapshot action is triggered, completely recording the internal parameters of all logical policy units in the current dynamic logical topology, as well as the values ​​of all policy weights in the cooperative constraint network, forming a parameter snapshot. This parameter snapshot is a data structure containing all adjustable parameter key-value pairs, a culmination of experience learned from historical data. The optimization results obtained from the shadow deduction are applied to the system about to go live as its initial configuration. After the parameter snapshot is successfully recorded, the shadow deduction process ends. Then, the formal interface generation process begins. At the beginning of this process, the basic structure of the previously constructed dynamic logical topology and cooperative constraint network is loaded, but its default or user-defined parameters are not used. Instead, the parameter snapshot just recorded is read and applied. Each item in the snapshot is iterated through, and the recorded logical policy unit parameter values ​​are overwritten onto the parameters of the corresponding units in the dynamic logical topology, and the recorded policy weight values ​​are applied to the corresponding constraints of the cooperative constraint network. After this initialization, the dynamic logical topology and cooperative constraint network are no longer untrained "novices," but rather "experienced" users with historical knowledge. Only then do they begin to receive the real-time data stream generated by the data stream to be mapped, perform layout rendering, and generate the user interface. This approach greatly improves the stability and logical accuracy of the user interface in the initial startup phase. Figure 4 As shown, the total cost during the shadow deduction process is... The curve exhibits a non-linear decreasing trend as the step size increases. It reaches a plateau when the rate of change falls below the convergence threshold. When a stable state is reached, a parameter snapshot is recorded, which serves as the initial configuration for generating the final interface.

[0091] For example, before the official launch of a "real-time match analysis panel" in an esports game, a shadow simulation process was executed to address the cold start issue. The background process first retrieved the historical match data stream of top professional players from the past 48 hours and input it at 10x speed into the newly constructed dynamic logic topology to simulate the performance of the "economic gap alarm" logic under real matches. During the simulation, the total system cost function was continuously monitored. Changes, when It gradually decreased from an initial value of 5.0, and its rate of change decreased over a continuous 100 iterations. All are less than the preset convergence threshold For example, only fluctuations When a stable state is reached, a snapshot is taken, recording the "dynamic sensitivity parameters" at that point. and "Contestant Comparison Component Layout Weights" A parameter snapshot is generated. When entering the formal interface generation process, this parameter snapshot is read and applied to overwrite the initial settings. This allows the analysis panel to perform accurate situation judgments and visual presentations based on the converged parameters the moment it first connects to the real-time event live data stream, ensuring extremely high logical accuracy and layout stability from the very beginning.

[0092] In one possible implementation, combining Figure 2 The logic strategy unit for matching and recommending based on the dynamic behavior features of the dynamic feature anchors includes:

[0093] When the dynamic behavior characteristics indicate that the data has an occasional spike pattern, the dynamic threshold alarm unit in the recommended logic strategy unit is recommended.

[0094] Based on the historical distribution of the data stream to be mapped, an initial threshold range and an adaptive fine-tuning strategy for adjusting the threshold according to the target false alarm rate are configured for the dynamic threshold alarm unit.

[0095] In some implementations, automated and intelligent analysis and alarm solutions are provided for a common but difficult-to-handle data pattern—intermittent spikes—using fixed thresholds. This process is triggered upon identification of specific dynamic behavioral characteristics and involves spike pattern recognition, dynamic threshold alarm unit recommendation, and intelligent configuration of initial parameters. From the dynamic behavioral characteristics of the data stream to be mapped, the system automatically identifies intermittent, non-periodic, and drastic fluctuations in the data. This is achieved by analyzing the dynamic behavioral feature vectors at dynamic feature anchor points. At that time, a spike pattern detection subroutine will be executed. This subroutine first checks the periodicity of data changes. Is it significant, for example, if there is no dominant frequency with concentrated energy in the Fast Fourier Transform result? Calculate the kurtosis coefficient of the data. This is a statistic used to measure the sharpness of a data distribution. For a given data set containing... Historical sequence of data points The calculation formula is as follows:

[0096] ;

[0097] in, These are data points in the sequence. It is the average value of the sequence. When the kurtosis coefficient... A value significantly greater than 3, such as exceeding 5.0, indicates a "thick-tailed" data distribution, meaning the existence of extreme values ​​far exceeding the normal range. When both the periodicity and kurtosis coefficient are met, the data stream to be mapped is determined to have an occasional spike pattern. After identifying the spike pattern, the most suitable logical strategy unit for handling this type of problem is accurately recommended to the user. Once the occasional spike pattern is confirmed, the strategy library is queried to find logical strategy units tagged with "spike detection" or "anomaly detection." In this scenario, dynamic threshold alarm units are prioritized for matching and recommendation. Unlike alarm units using fixed thresholds, dynamic threshold alarm units can dynamically adjust their alarm trigger boundaries based on the real-time statistical characteristics of the data, such as moving averages and standard deviations. Therefore, they can effectively adapt to baseline fluctuations and capture true abnormal spikes while suppressing false alarms caused by normal fluctuations. The recommended dynamic threshold alarm units will appear as icons on the data mapping link in the visual interactive panel, awaiting user confirmation for mounting. After a user attaches a dynamic threshold alarm unit, a set of reasonable, data-driven initial parameters is provided, rather than relying on manual guesswork, thereby improving the unit's initial availability and performance. An automated configuration process is executed immediately after the user attaches the dynamic threshold alarm unit to the data mapping link. The initial threshold range is set based on the historical distribution of the data stream to be mapped. The mean of the historical data streams is calculated. and standard deviation The initial alarm threshold is typically set to... The lower threshold is Here This is a configurable multiple, typically ranging from 3 to 5, representing the degree of abnormal deviation. Configure an adaptive fine-tuning strategy for this unit; this is an algorithm built into the alarm unit used to adjust parameters based on performance feedback during interface runtime. Set an initial target false alarm rate for this strategy, for example, 0.1 times per hour. This target value will serve as the benchmark for calculating the feedback effect of the logic in subsequent closed-loop feedback optimization. Through this series of intelligent initial configurations, the dynamic threshold alarm unit, once added to the dynamic logical topology, possesses excellent initial performance and the ability to continuously self-optimize.

[0098] For example, in the "anti-cheat data monitoring backend" of a certain competitive action game, spike mode detection is performed on the "player skill instantaneous damage" data stream to analyze and include... Damage sequence at each sampling point The mean was measured. The point, after being calculated by a subroutine, has a fourth central moment of 2,500,000 and a square of its second central moment of 250,000. Substituting these values ​​into the formula yields the kurtosis coefficient. ;because Much greater than 5 and the Fast Fourier Transform shows no significant periodicity. The system determines that the data exhibits an intermittent spike pattern and automatically recommends attaching a "dynamic threshold alarm unit" to the monitoring link. After user confirmation of attachment, the average damage is calculated based on historical distribution. and standard deviation Take the deviation multiple Automatically configure the initial alarm upper threshold to be The system is set to a target false alarm rate of 0.1 per hour. As an adaptive fine-tuning benchmark, it enables intelligent, precise, and real-time monitoring of "instantaneous numerical cheating" behavior in the game.

[0099] In one possible implementation, combining Figure 2 The mapping of each of the aforementioned business relationships into several layout strategy instructions for defining the layout behavior of interface components under specific logical states includes:

[0100] When an alarm linkage relationship is detected between two interface components, the interface component that triggers the alarm is defined as the first interface component, the interface component affected by the alarm is defined as the second interface component, and a first layout strategy instruction is generated.

[0101] The first layout strategy instruction is used to increase the layout priority of the second interface component and trigger a positioning animation when the first interface component triggers an alarm.

[0102] In some implementations, the abstract "alarm linkage" business requirement is translated into a set of specific instructions that can be executed by the layout engine. This allows the user interface to automatically highlight and focus on related UI elements when a certain metric is abnormal, guiding the user's attention. This process is executed automatically when an alarm linkage relationship is identified, involving two steps: defining associated roles and generating layout strategy instructions. In the identified alarm linkage relationships, the roles of each UI component in the linkage are clearly defined, providing clear direction for the subsequent generation of precise layout instructions. When a business relationship formed by a dynamic threshold alarm unit connecting two UI components is identified during the traversal of the dynamic logical topology, a role definition subroutine is initiated. This subroutine defines the UI component associated with the source of the alarm unit's input data as the first UI component, i.e., the trigger source of the alarm. The UI component pointed to by the output signal of the alarm unit is defined as the second UI component, i.e., the target affected by the alarm. For example, if a CPU utilization dashboard's data stream passes through an alarm unit, and its alarm output is connected to a table component displaying a list of associated processes, then the dashboard is the first interface component, and the table is the second interface component. This role definition is recorded for subsequent instruction generation. Based on the defined roles, a set of instructions is generated that can significantly change the visual presentation and layout state of the target component when an alarm occurs. After identifying the first and second interface components, one or more layout strategy instructions are generated according to preset linkage rules. Let's assume a dedicated first layout strategy instruction is generated. This instruction is a set of conditional rules, triggered when the data bound to the first interface component triggers the alarm unit, causing its logical state to change from inactive to active. When this condition is met, the instruction will execute the following series of layout behaviors: Generate a constraint to modify the layout priority of the second interface component. In the collaborative constraint network, this means the strategy weight of the layout constraints related to the second interface component. The value will be temporarily and significantly increased, for example, from 0.5 to 0.95. This ensures that in the event of a layout conflict, layout requirements related to the second UI component, such as position and size, will be prioritized. This instruction also triggers an additional rendering effect, namely a positioning animation. This typically means sending a one-time event containing the ID of the second UI component and the animation type, such as "blinking border" or "panning to center of screen." After this event, the specified animation effect will be overlaid on the component in the next frame render, typically lasting 1 to 3 seconds. Through this combination of instructions, when an alert occurs, the user's visual focus can be quickly guided to the relevant information that needs attention.

[0103] For example, in the alarm linkage scenario of a "professional league real-time broadcast screen" in an e-sports game, when the dynamic logic topology is traversed and the business association between the "economic difference sudden change alarm unit" connecting the "team gold coin trend chart" and the "player real-time first-person perspective screen" is identified, the role definition subroutine defines the trend chart as the first interface component, i.e., the trigger source, and the first-person perspective screen as the second interface component, i.e., the affected target. Based on this, a first layout strategy instruction is generated, stipulating that if a large-scale team battle causes the gold coin difference to trigger the alarm activation state, the strategy weights involving the position and size constraints of the second interface component will be applied in the cooperative constraint network. The initial value of 0.5 is temporarily increased to 0.95, and the weight increment is calculated. This ensures that in the event of a layout conflict within limited screen space, the player's exciting duel will be prioritized for rendering and magnification. Simultaneously, the command sends a one-time event containing the component ID, triggering a "golden-flashing border" positioning animation for that viewpoint for 3 seconds, thereby weighting the animation. The quantitative enhancements and the overlay of visual effects guide the director and the audience to quickly focus on the most visually appealing real-time battle scenes.

[0104] All equivalent changes and modifications made in accordance with the teachings of this invention shall still fall within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or customary techniques in the art not described herein.

Claims

1. An interface generation method for mapping data with a visual interaction panel, characterized in that, The method comprises: acquiring to-be-mapped data streams and historical data streams from a plurality of data sources, and extracting static features and dynamic behavior features of the to-be-mapped data streams to generate corresponding dynamic feature anchor points, wherein the extraction of the static features and the dynamic behavior features of the to-be-mapped data streams to generate the corresponding dynamic feature anchor points comprises: extracting data types, value range and business semantic labels of the to-be-mapped data streams to obtain static features; analyzing data update frequency, data change periodicity and inter-field coupling degree of the to-be-mapped data streams to obtain dynamic behavior features; and fusing the static features and the dynamic behavior features to form dynamic feature anchor points; based on the dynamic feature anchor points, constructing a dynamic logic topology comprising interface components, data mapping links and logic strategy units in a visual interactive panel; parsing the dynamic logic topology, translating business association relationships contained therein into layout strategy instructions, and generating a collaborative constraint network in combination with user-defined basic layout rules, wherein the parsing of the dynamic logic topology, the translation of the business association relationships contained therein into the layout strategy instructions, and the generation of the collaborative constraint network in combination with the user-defined basic layout rules comprise: traversing the dynamic logic topology to identify business association relationships formed by the logic strategy units connecting different interface components; mapping each of the business association relationships into a plurality of layout strategy instructions for defining layout behaviors of the interface components under specific logic states; and fusing the layout strategy instructions and the basic layout rules into a set of constraint equations with weights to generate a collaborative constraint network comprising weights of the layout strategy instructions; inputting real-time data streams corresponding to the to-be-mapped data streams into the dynamic logic topology for processing, and performing layout rendering according to the collaborative constraint network, the processed data and distribution characteristics of the historical data streams to generate a user interface; during interface operation, collecting logic effect feedback and layout conflict feedback, and collaboratively optimizing logic strategy unit parameters in the dynamic logic topology and strategy weights in the collaborative constraint network based on the feedback, wherein the collection of the logic effect feedback and the layout conflict feedback during the interface operation comprises: monitoring running results of the logic strategy units and comparing them with expected running targets of the units to generate the logic effect feedback; and checking abnormal adjustments of component positions or sizes caused by the execution of the collaborative constraint network during interface rendering to generate the layout conflict feedback; and the collaborative optimization of the logic strategy unit parameters in the dynamic logic topology and the strategy weights in the collaborative constraint network based on the feedback comprises: inputting the logic effect feedback and the layout conflict feedback into an optimizer for calculating adjustment amounts based on system-level optimization targets; the optimizer calculates adjustment amounts for the logic strategy unit parameters and corresponding strategy weights in the collaborative constraint network; and synchronous adjustments are performed on the logic strategy unit parameters and the strategy weights according to the adjustment amounts.

2. The interface generation method for mapping data with a visual interaction panel according to claim 1, wherein, The method further comprises the following steps before generating the user interface: driving the dynamic logic topology and the collaborative constraint network to perform shadow inference by using historical data streams; recording a parameter snapshot of the dynamic logic topology and the collaborative constraint network when a stable state is reached during the shadow inference; using the parameter snapshot to initialize the dynamic logic topology and the collaborative constraint network.

3. The method of claim 1, wherein the method further comprises: The method further comprises the following steps before generating the user interface: driving the dynamic logic topology and the collaborative constraint network to perform shadow inference by using historical data streams; recording a parameter snapshot of the dynamic logic topology and the collaborative constraint network when a stable state is reached during the shadow inference; using the parameter snapshot to initialize the dynamic logic topology and the collaborative constraint network.

4. The method of claim 2, wherein the method further comprises: The method further comprises the following steps before generating the user interface: driving the dynamic logic topology and the collaborative constraint network to perform shadow inference by using historical data streams; recording a parameter snapshot of the dynamic logic topology and the collaborative constraint network when a stable state is reached during the shadow inference; 5. The method of claim 1, wherein the method further comprises: using the parameter snapshot to initialize the dynamic logic topology and the collaborative constraint network. The method further comprises the following steps before generating the user interface: driving the dynamic logic topology and the collaborative constraint network to perform shadow inference by using historical data streams; 6. An interface generation system for mapping data with a visual interaction panel, characterized by, recording a parameter snapshot of the dynamic logic topology and the collaborative constraint network when a stable state is reached during the shadow inference; using the parameter snapshot to initialize the dynamic logic topology and the collaborative constraint network. The method further comprises the following steps before generating the user interface: driving the dynamic logic topology and the collaborative constraint network to perform shadow inference by using historical data streams; recording a parameter snapshot of the dynamic logic topology and the collaborative constraint network when a stable state is reached during the shadow inference; using the parameter snapshot to initialize the dynamic logic topology and the collaborative constraint network. The method further comprises the following steps before generating the user interface: driving the dynamic logic topology and the collaborative constraint network to perform shadow inference by using historical data streams; recording a parameter snapshot of the dynamic logic topology and the collaborative constraint network when a stable state is reached during the shadow inference; using the parameter snapshot to initialize the dynamic logic topology and the collaborative constraint network. The method further comprises the following steps before generating the user interface: driving the dynamic logic topology and the collaborative constraint network to perform shadow inference by using historical data streams; recording a parameter snapshot of the dynamic logic topology and the collaborative constraint network when a stable state is reached during the shadow inference; using the parameter snapshot to initialize the dynamic logic topology and the collaborative constraint network. The system is used for the interface generation method for mapping data by using a visual interactive panel, and comprises: a data feature extraction module configured to obtain to-be-mapped data streams and historical data streams from a plurality of data sources, and extract static features and dynamic behavior features of the to-be-mapped data streams to generate corresponding dynamic feature anchors, wherein the extraction of the static features and the dynamic behavior features of the to-be-mapped data streams to generate the corresponding dynamic feature anchors comprises: extracting data types, value range and business semantic labels of the to-be-mapped data streams to obtain the static features; analyzing data update frequency, data change periodicity and coupling degrees among fields of the to-be-mapped data streams to obtain the dynamic behavior features; and fusing the static features and the dynamic behavior features to form the dynamic feature anchors; a topology construction module configured to construct, based on the dynamic feature anchors, a dynamic logic topology comprising interface components, data mapping links and logic policy units in a visual interactive panel; The constraint network generation module is configured to parse the dynamic logical topology, translate the service association relationship contained in the dynamic logical topology into layout strategy instructions, and generate a collaborative constraint network in combination with the user-defined basic layout rules. The parsing of the dynamic logical topology, the translation of the service association relationship contained in the dynamic logical topology into layout strategy instructions, and the generation of the collaborative constraint network in combination with the user-defined basic layout rules include: traversing the dynamic logical topology, identifying the service association relationship formed by connecting different interface components by the logical strategy unit; mapping each of the service association relationships into a plurality of layout strategy instructions for defining the layout behavior of the interface components in a specific logical state; and fusing the layout strategy instructions and the basic layout rules into a set of constraint equations with weights to generate a collaborative constraint network containing the weights of the layout strategy instructions. The interface rendering generation module is configured to input the real-time data stream corresponding to the to-be-mapped data stream into the dynamic logical topology for processing, and perform layout rendering based on the collaborative constraint network, the processed data, and the distribution characteristics of the historical data stream to generate a user interface. The closed-loop feedback optimization module is configured to collect logical effect feedback and layout conflict feedback during interface operation, and perform collaborative optimization of the logical strategy unit parameters in the dynamic logical topology and the strategy weights in the collaborative constraint network based on the feedback. The collection of the logical effect feedback and the layout conflict feedback during the interface operation includes: monitoring the running result of the logical strategy unit and comparing it with the expected running target of the unit to generate the logical effect feedback; and checking the abnormal adjustment of the component position or size caused by the execution of the collaborative constraint network in the interface rendering process to generate the layout conflict feedback. The collaborative optimization of the logical strategy unit parameters in the dynamic logical topology and the strategy weights in the collaborative constraint network based on the feedback includes: inputting the logical effect feedback and the layout conflict feedback into an optimizer configured to calculate an adjustment amount based on a system-level optimization target; the optimizer calculates the adjustment amount for the logical strategy unit parameters and the adjustment amount for the corresponding strategy weights in the collaborative constraint network; and performing synchronous adjustment of the logical strategy unit parameters and the strategy weights according to the adjustment amounts.

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