Large screen display control method and control system

Abstract

The present invention provides a large-screen display control method and control system, which belongs to the field of large-screen display technology. By introducing a real-time stream processing framework and a pre-trained convolutional neural network prediction model, the system can detect changes in device status and data to be displayed in real time, and intelligently predict the optimal display layout based on historical data and real-time data. This intelligent layout adjustment mechanism not only significantly improves the agility of system response, but also greatly enhances the flexibility and adaptability of the layout, and can automatically optimize the display effect according to different scenarios and needs. At the same time, the automated control process reduces manual intervention, reduces the maintenance cost of the system, and improves the overall operating efficiency and reliability, ultimately enabling the large-screen display system to achieve significant beneficial effects in terms of intelligence, efficiency, and flexibility.

Description

Technical Field

[0001] The present invention relates to the field of large-screen display technology, and in particular to a large-screen display control method and control system. Background Art

[0002] With the rapid development of information technology, large-screen display systems have been widely used in conferences, exhibitions, advertising, monitoring and other fields due to their high definition, wide field of view and strong intuitiveness. Large-screen display systems are usually composed of multiple display modules spliced ​​together, which can present richer and more complex visual content. However, how to efficiently manage and control these large-scale display modules to optimize content display is an important challenge facing current large-screen display technology.

[0003] In the existing large-screen display control methods, most rely on manual configuration or simple preset rules to adjust the display layout. These methods have the following shortcomings:

[0004] Slow response speed: Traditional methods often require manual or preset scripts to adjust the layout after detecting data changes or device status changes, and cannot respond to complex and changing display needs in real time.

[0005] Poor flexibility: Due to reliance on preset rules, it is difficult to adapt to the diverse display needs in different scenarios, especially in situations where the layout content needs to be adjusted frequently.

[0006] Low intelligence: Lack of intelligent prediction and decision-making mechanisms, unable to automatically optimize layout configuration based on historical and real-time data, resulting in poor display effects or waste of resources.

[0007] Therefore, it is necessary to provide a large-screen display control method and a control system to solve the above technical problems. Summary of the invention

[0008] In order to solve the above technical problems, the present invention provides a large-screen display control method and control system, which solves the problems of slow response speed, poor flexibility, low intelligence and high maintenance cost in the prior art by introducing real-time stream processing, intelligent prediction and automatic control.

[0009] A large-screen display control method provided by the present invention is configured with a display controller and a large screen driven by the display controller, and the large screen includes a plurality of display modules spliced ​​together, and the control method includes the following steps:

[0010] Collecting data sets from different data sources and performing data preprocessing, wherein the data sets include device status data and data to be displayed;

[0011] Use the real-time stream processing framework to detect events on device status data and data to be displayed, and start adjusting the layout configuration when the detection results meet the preset trigger conditions;

[0012] When the adjustment of the layout configuration is started, the device status data and the data to be displayed are input into a pre-trained prediction model to predict the layout configuration and output the optimal layout, wherein the prediction model is a convolutional neural network model trained based on a historical data set;

[0013] Generating a layout adjustment instruction according to the optimal layout output by the prediction model, wherein the layout adjustment instruction includes the position and size of the layout;

[0014] The layout adjustment instruction is converted into a control signal, and the control signal is sent to all display modules through a display controller, so that all display modules adjust their respective display contents in real time according to the position and size of the layout.

[0015] Preferably, the data sets collected from different data sources and preprocessed, wherein the data sets include device status data and data to be displayed, include:

[0016] Extract data sets including device status data and data to be displayed from various data sources in real time through data access points;

[0017] The extracted device status data and the data to be displayed are preprocessed, wherein the preprocessing includes data cleaning, data conversion and data fusion.

[0018] Preferably, the use of a real-time stream processing framework to perform event detection on the device status data and the data to be displayed, and starting the adjustment of the layout configuration when the detection result meets a preset trigger condition, includes:

[0019] Using a preconfigured rule engine in a real-time stream processing framework to perform event definition and detection processing on the device status data and the data to be displayed, and obtain real-time feedback results of event triggering;

[0020] Using the preset trigger conditions to evaluate the real-time feedback results of the event trigger, and obtain the judgment result of triggering the layout configuration adjustment;

[0021] The determination result is used to make a decision and respond to the layout configuration adjustment, and an execution result of starting the layout configuration adjustment is obtained.

[0022] Preferably, the step of inputting the device status data and the data to be displayed into a pre-trained prediction model to perform layout configuration prediction and output an optimal layout comprises:

[0023] Using a data integration interface to perform standardized processing on the device status data and the data to be displayed;

[0024] Using a pre-trained prediction model to extract features from the standardized device status data and the data to be displayed to obtain an intermediate feature vector;

[0025] Taking the intermediate feature vector as input, and calculating the predicted value of the layout configuration through forward propagation of the prediction model;

[0026] The prediction values ​​are adjusted using the optimization algorithm built into the prediction model to obtain the final optimal layout.

[0027] Preferably, the generating of layout adjustment instructions according to the optimal layout output by the prediction model, wherein the layout adjustment instructions include the position and size of the layout, including:

[0028] Utilizing a layout parser to parse the predicted value of the optimal layout to obtain the position and size of the layout elements;

[0029] Use the instruction generator to generate layout adjustment instructions based on the position and size of layout elements.

[0030] Preferably, converting the layout adjustment instruction into a control signal and sending the control signal to all display modules through a display controller so that all display modules adjust their display contents in real time according to the position and size of the layout, includes:

[0031] Using an instruction compiler to convert the layout adjustment instruction into a control signal that can be recognized by a display controller;

[0032] The display controller uses a communication protocol to encapsulate the control signal and send it to each display module;

[0033] The display module receives and decodes the control signal, and updates the position and size of the data to be displayed in real time according to the optimal layout.

[0034] The present invention also provides a large-screen display control system for executing a large-screen display control method, wherein the control system is configured with a display controller and a large screen driven by the display controller, and the large screen includes a plurality of display modules spliced ​​together, and the control system includes:

[0035] A data acquisition module, used to collect data sets from different data sources and perform data preprocessing, wherein the data sets include device status data and data to be displayed;

[0036] An event detection module is used to detect events on device status data and data to be displayed respectively using a real-time stream processing framework, and to start adjusting the layout configuration when the detection result meets a preset trigger condition;

[0037] A layout prediction module, used for inputting the device status data and the data to be displayed into a pre-trained prediction model to perform layout configuration prediction and output an optimal layout when starting the adjustment of the layout configuration, wherein the prediction model is a convolutional neural network model trained based on a historical data set;

[0038] A layout instruction generation module, used to generate a layout adjustment instruction according to the optimal layout output by the prediction model, wherein the layout adjustment instruction includes the position and size of the layout;

[0039] The instruction distribution module is used to convert the layout adjustment instruction into a control signal, and send the control signal to all display modules through the display controller, so that all display modules can adjust their display contents in real time according to the position and size of the layout.

[0040] Compared with the related art, the large-screen display control method and control system provided by the present invention have the following beneficial effects:

[0041] The present invention introduces a real-time stream processing framework and a pre-trained convolutional neural network prediction model, so that the system can detect changes in device status and data to be displayed in real time, and intelligently predict the optimal display layout based on historical data and real-time data. This intelligent layout adjustment mechanism not only significantly improves the agility of system response, but also greatly enhances the flexibility and adaptability of the layout. It can automatically optimize the display effect according to different scenarios and needs. At the same time, the automated control process reduces manual intervention, reduces the maintenance cost of the system, and improves the overall operating efficiency and reliability, ultimately making the large-screen display system achieve significant beneficial effects in terms of intelligence, efficiency, and flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A flow chart of a large-screen display control method provided by the present invention;

[0043] Figure 2 This is a module structure diagram of a large-screen display control system provided by the present invention. DETAILED DESCRIPTION

[0044] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only the parts related to the present invention, rather than all structures, are shown in the accompanying drawings. In addition, the embodiments of the present invention and the features in the embodiments may be combined with each other without conflict.

[0045] It should also be noted that, for ease of description, only the part relevant to the present invention but not all the content is shown in the accompanying drawings. It should be mentioned before discussing the exemplary embodiments in more detail that some exemplary embodiments are described as the processing or method described as a flow chart. Although the flow chart describes each operation (or step) as being processed sequentially, many operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of each operation can be rearranged. When its operation is completed, the processing can be terminated, but it can also have additional steps not included in the accompanying drawings. The processing can correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.

[0046] Embodiment 1

[0047] The present invention provides a large-screen display control method, which is configured with a display controller and a large screen driven by the display controller, and the large screen includes a plurality of display modules spliced ​​together, referring to Figure 1 As shown, the control method includes the following steps:

[0048] S1: Collecting data sets from different data sources and performing data preprocessing, wherein the data sets include device status data and data to be displayed.

[0049] In this embodiment, multiple sensors and data interfaces are used to continuously collect data from the equipment operation status monitoring system and the data source of the network data stream. The data set mainly includes equipment status data (such as temperature, humidity, operation status, etc.) and data to be displayed (such as real-time monitoring screen, statistical charts, warning information, etc.). Subsequently, data preprocessing is performed on the data set to ensure the quality and consistency of the data, laying a good foundation for subsequent processing.

[0050] S2: Use the real-time stream processing framework to perform event detection on the device status data and the data to be displayed, and start adjusting the layout configuration when the detection result meets the preset trigger condition.

[0051] In this embodiment, a real-time stream processing framework is used to perform real-time analysis on the preprocessed data set, and event detection is used to identify specific patterns, such as precursors to equipment failure, performance degradation trends, or sudden changes in key indicators. Once a preset trigger condition is detected (for example, the temperature exceeds a threshold or the equipment load reaches an upper limit), the layout configuration adjustment process is immediately initiated.

[0052] S3: When starting the adjustment of the layout configuration, the device status data and the data to be displayed are input into a pre-trained prediction model to perform layout configuration prediction, and output an optimal layout, wherein the prediction model is a convolutional neural network model trained based on a historical data set.

[0053] In this embodiment, when the preset trigger conditions are met, the device status data and the data to be displayed are input into a pre-trained convolutional neural network (CNN) model. The model is trained on a large amount of historical data and can predict the optimal layout configuration based on the current input, taking into account the spatial relationship between display modules, the importance level of data, and the optimization of visual effects.

[0054] Predicting the optimal layout through deep learning technology not only reduces the need for manual intervention, but also improves the intelligence level of the layout, making the displayed content more reasonable and clear, helping to quickly convey key information while reducing the user's cognitive burden.

[0055] S4: Generate a layout adjustment instruction according to the optimal layout output by the prediction model, wherein the layout adjustment instruction includes the position and size of the layout.

[0056] In this embodiment, after the layout prediction is completed, a series of layout adjustment instructions are automatically generated, including the position and size of the layout. These instructions ensure that each display module can be accurately adjusted according to the predicted optimal layout. The automated layout instruction generation simplifies the operation process, avoids errors and delays in manual adjustment, ensures the accuracy and speed of layout adjustment, and enhances the system's degree of automation.

[0057] S5: converting the layout adjustment instruction into a control signal, and sending the control signal to all display modules through a display controller, so that all display modules adjust their display contents in real time according to the positions and sizes of the layout.

[0058] In this embodiment, the layout adjustment instructions are converted into specific control signals, which are uniformly managed and scheduled by the display controller. The control signals are sent to each display module, prompting them to update the display content in real time according to the new layout instructions to achieve seamless switching. The real-time nature of the layout adjustment is ensured by the rapid distribution and execution of the control signals, so that the display content can quickly adapt to the changing environment and needs, thereby improving the efficiency and accuracy of information transmission, and ultimately improving the overall performance of the system and user satisfaction.

[0059] Specifically, step S1 includes the following steps:

[0060] S101: extracting data sets including device status data and data to be displayed from various data sources in real time through data access points.

[0061] In this embodiment, multiple data access points are configured, and these access points are connected to the equipment operation status monitoring system and the data source of the network data stream. The data access point uses API interface, database connection, sensor reading, etc. to extract equipment status data (such as temperature, humidity, operation status, etc.) and data to be displayed (such as real-time monitoring screen, statistical charts, warning information, etc.) in real time. In order to ensure the real-time and integrity of the data, the data access point uses a polling mechanism or an event-driven mechanism to extract data regularly or immediately when the data changes.

[0062] S102: Preprocessing the extracted device status data and the data to be displayed, wherein the preprocessing includes data cleaning, data conversion and data fusion.

[0063] In this embodiment, a preprocessing operation is performed on the extracted data, specifically the following operations:

[0064] Data cleaning: Remove duplicate data, fill in missing values, detect and correct outliers to improve data quality.

[0065] Data conversion: Convert data into a unified format, such as converting timestamps into a standard time format, to facilitate subsequent processing and analysis.

[0066] Data fusion: Integrate information from different data sources to build a unified data view to ensure data consistency and integrity.

[0067] Preprocessing can significantly improve the quality and availability of data and reduce errors and deviations in subsequent processing. Data cleaning ensures the accuracy and reliability of data, data conversion promotes data compatibility and standardization, and data fusion enhances the comprehensiveness and contextual relevance of data, enabling the system to more effectively understand and respond to complex real-world scenarios.

[0068] Specifically, step S2 includes the following steps:

[0069] S201: Using a preconfigured rule engine in a real-time stream processing framework to perform event definition and detection processing on the device status data and the data to be displayed, to obtain real-time feedback results of event triggering.

[0070] In this embodiment, a rule engine is integrated into the real-time stream processing framework, and the rule engine pre-configures a series of rules for defining and detecting events in the device status data and the data to be displayed. For example, the rules include but are not limited to temperature exceeding a set threshold, humidity below a safe range, device load reaching a warning level, and abnormal fluctuations in network traffic.

[0071] The rule engine continuously monitors the data stream, and when it detects an event that matches the predefined rules, it immediately generates real-time feedback results that contain specific details of the event, such as event type, occurrence time, related data, etc.

[0072] The real-time stream processing framework combined with the application of the rule engine realizes real-time monitoring and event detection of device status and data to be displayed, greatly shortening the time interval from data generation to event response, improving the system's response speed and the timeliness of event processing. In addition, the configurability of the rule engine allows system administrators to dynamically adjust event definitions as needed, enhancing the flexibility and adaptability of the system.

[0073] S202: Using preset trigger conditions to perform conditional evaluation on the real-time feedback result triggered by the event, and obtain a determination result for triggering layout configuration adjustment.

[0074] In this embodiment, after receiving the real-time feedback results of event triggering, these events will be further evaluated according to the preset trigger conditions. The trigger conditions include the percentage of temperature exceeding the normal working range, the frequency of equipment failure precursors, and the sudden change amplitude of key indicators.

[0075] When an event meets any trigger condition, it will be determined that the layout configuration needs to be adjusted and the corresponding judgment result will be generated. This condition-based evaluation mechanism improves the pertinence and effectiveness of layout adjustment, ensures that key information can be displayed first, and enhances the urgency and importance of information transmission.

[0076] S203: Making a decision and responding to the layout configuration adjustment using the determination result, and obtaining an execution result of starting the layout configuration adjustment.

[0077] In this embodiment, based on the determination result, the layout configuration adjustment process is automatically started, and the automated decision-making and response mechanism ensures the rapid execution of the layout adjustment, reduces the need for human intervention, and reduces the risk of operational errors.

[0078] Specifically, step S3 includes the following steps:

[0079] S301: Standardizing the device status data and the data to be displayed using a data integration interface.

[0080] In this embodiment, the data integration interface is responsible for receiving device status data and data to be displayed from different sources. These data may have different formats, units and magnitudes. The data integration interface first cleans the data to remove invalid or abnormal values, then converts the data into a unified format and performs standardization, such as converting all numerical data into a normal distribution form with a mean of 0 and a standard deviation of 1 for subsequent processing.

[0081] S302: Using a pre-trained prediction model, extracting features from the standardized device status data and the data to be displayed to obtain an intermediate feature vector.

[0082] In this embodiment, the pre-trained prediction model uses a deep learning architecture of a convolutional neural network to extract features from the standardized data. The hidden layer of the prediction model can automatically learn the complex relationships and patterns in the data and convert the raw data into a series of high-dimensional intermediate feature vectors that contain information useful for layout configuration prediction.

[0083] S303: Taking the intermediate feature vector as input, and performing forward propagation calculation of the prediction model to obtain a predicted value of the layout configuration.

[0084] In this embodiment, the intermediate feature vector is input to the output layer of the prediction model, and through forward propagation calculation, the model generates predicted values ​​of the layout configuration according to the learned weight parameters. These predicted values ​​are specific layout parameters, namely, position and size.

[0085] S304: Use the built-in optimization algorithm of the prediction model to adjust the prediction value to obtain the final optimal layout.

[0086] In this embodiment, the gradient descent optimization algorithm built into the prediction model will further optimize the predicted value to find the optimal layout under specific constraints. The optimization algorithm takes into account multiple factors, including device status, display efficiency, and user experience, in order to obtain a global optimal solution.

[0087] Specifically, step S4 includes the following steps:

[0088] S401: Utilize a layout parser to parse the predicted value of the optimal layout to obtain the position and size of the layout element.

[0089] In this embodiment, the layout parser receives the optimal layout prediction values ​​generated by step S3. These prediction values ​​are a series of numerical values ​​representing the ideal position coordinates of different layout elements on the screen and their respective sizes. The layout parser is responsible for converting these abstract numerical values ​​into specific layout element definitions, that is, determining the exact position and size of each interface element, such as buttons, text boxes, icons, etc.

[0090] Through layout analysis, the predicted values ​​are visualized, allowing the system to understand how to apply these layout adjustments on the actual interface, thereby achieving a visual presentation of the optimal layout.

[0091] S402: Generate a layout adjustment instruction by using an instruction generator in combination with the position and size of the layout element.

[0092] In this embodiment, the instruction generator generates a series of layout adjustment instructions for the user interface (UI) framework based on the position and size information provided by the layout parser. These instructions can be code snippets in a specific programming language, or command sets that can be recognized by the UI framework, which are used to instruct the interface how to redraw or reposition elements according to the optimal layout. Exemplary instructions may include moving a button to a new coordinate, changing the width and height of a text box, etc.

[0093] The role of the instruction generator is to convert the results of layout parsing into commands that can be directly executed, ensuring that the UI framework can correctly perform layout adjustments and implement the optimal layout solution recommended by the prediction model.

[0094] Specifically, step S5 includes the following steps:

[0095] S501: Using an instruction compiler to convert the layout adjustment instruction into a control signal recognizable by a display controller.

[0096] In this embodiment, the instruction compiler plays the role of a translator, which receives the layout adjustment instruction generated from S402 and converts it into a control signal that the display controller can understand and execute. These control signals are usually based on a communication protocol. This conversion process ensures that the layout adjustment instruction can be accurately conveyed to the display controller, so that the latter can process subsequent display tasks according to the requirements of the optimal layout.

[0097] S502: The display controller uses a communication protocol to encapsulate the control signal and sends it to each display module.

[0098] In this embodiment, after receiving the control signals output from the instruction compiler, the display controller further encapsulates these signals so as to send them to each display module through an appropriate communication protocol. By packaging and transmitting the signals, it is ensured that each display module can receive the correct update instruction.

[0099] S503: The display module receives and decodes the control signal, and updates the position and size of the data to be displayed in real time according to the optimal layout.

[0100] In this embodiment, when the display module receives the control signals, it will immediately decode these signals and parse out the latest layout information about the location and size of the data. The display module will then adjust the content it is displaying in real time based on this information to ensure that the display of the data matches the optimal layout; this real-time update mechanism allows the display module to respond quickly to layout adjustments and provide a smooth visual experience, especially in application scenarios that require fast response, such as dynamic billboards, multi-screen interactive displays, or virtual reality environments.

[0101] The working principle of a large-screen display control method provided by the present invention is as follows: by introducing a real-time stream processing framework and a pre-trained convolutional neural network prediction model, the system can detect changes in device status and data to be displayed in real time, and intelligently predict the optimal display layout based on historical data and real-time data. This intelligent layout adjustment mechanism not only significantly improves the agility of system response, but also greatly enhances the flexibility and adaptability of the layout. It can automatically optimize the display effect according to different scenarios and needs. At the same time, the automated control process reduces manual intervention, reduces the maintenance cost of the system, and improves the overall operating efficiency and reliability, ultimately enabling the large-screen display system to achieve significant beneficial effects in terms of intelligence, efficiency, and flexibility.

[0102] Embodiment 2

[0103] The present invention also provides a large-screen display control system for executing a large-screen display control method, wherein the control system is configured with a display controller and a large screen driven by the display controller, and the large screen includes a plurality of display modules spliced ​​together. Figure 2 As shown, the control system includes:

[0104] The data acquisition module 100 is used to collect data sets from different data sources and perform data preprocessing, wherein the data sets include device status data and data to be displayed.

[0105] The event detection module 200 is used to perform event detection on the device status data and the data to be displayed respectively by using the real-time stream processing framework, and start adjusting the layout configuration when the detection result meets the preset trigger condition.

[0106] The layout prediction module 300 is used to input the device status data and the data to be displayed into a pre-trained prediction model to perform layout configuration prediction when starting the adjustment of the layout configuration, and output the optimal layout, wherein the prediction model is a convolutional neural network model trained based on a historical data set.

[0107] The layout instruction generation module 400 is used to generate a layout adjustment instruction according to the optimal layout output by the prediction model, wherein the layout adjustment instruction includes the position and size of the layout.

[0108] The instruction distribution module 500 is used to convert the layout adjustment instruction into a control signal, and send the control signal to all display modules through the display controller, so that all display modules can adjust their display contents in real time according to the position and size of the layout.

[0109] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0110] A person skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0111] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device that includes a series of elements includes not only those elements, but also other elements that are not explicitly listed, or includes elements that are inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device that includes the elements.

Claims

1. A large-screen display control method, comprising a display controller and a large screen driven by the display controller, wherein the large screen comprises a plurality of display modules spliced ​​together, characterized in that: The control method comprises the following steps: Collecting data sets from different data sources and performing data preprocessing, wherein the data sets include device status data and data to be displayed; Use the real-time stream processing framework to detect events on device status data and data to be displayed, and start adjusting the layout configuration when the detection results meet the preset trigger conditions; When the adjustment of the layout configuration is started, the device status data and the data to be displayed are input into a pre-trained prediction model to predict the layout configuration and output the optimal layout, wherein the prediction model is a convolutional neural network model trained based on a historical data set; Generating a layout adjustment instruction according to the optimal layout output by the prediction model, wherein the layout adjustment instruction includes the position and size of the layout; Converting the layout adjustment instruction into a control signal, and sending the control signal to all display modules through a display controller, so that all display modules adjust their respective display contents in real time according to the position and size of the layout; The collecting of data sets from different data sources and performing data preprocessing, wherein the data sets include device status data and data to be displayed, includes: Extract data sets including device status data and data to be displayed from various data sources in real time through data access points; Preprocess the extracted device status data and the data to be displayed, wherein the preprocessing includes data cleaning, data conversion and data fusion The method of using the real-time stream processing framework to respectively detect events on the device status data and the data to be displayed, and starting the adjustment of the layout configuration when the detection result meets the preset trigger condition, includes: Using a preconfigured rule engine in a real-time stream processing framework to perform event definition and detection processing on the device status data and the data to be displayed, and obtain real-time feedback results of event triggering; Using the preset trigger conditions to evaluate the real-time feedback results of the event trigger, and obtain the judgment result of triggering the layout configuration adjustment; Using the determination result to make a decision and respond to the layout configuration adjustment, the execution result of starting the layout configuration adjustment is obtained. The device status data includes temperature, humidity, and operating status, the data to be displayed includes real-time monitoring images, statistical charts, and warning information, and the data access point uses a polling mechanism or an event-driven mechanism to extract data regularly or immediately when the data changes; A rule engine is integrated into the real-time stream processing framework to define and detect events in device status data and data to be displayed, including temperature exceeding a set threshold, humidity falling below a safe range, device load reaching a warning level, and abnormal fluctuations in network traffic; The trigger conditions include the percentage of temperature exceeding the normal operating range, the frequency of equipment failure precursors, and the magnitude of sudden changes in key indicators.

2. A large-screen display control method according to claim 1, characterized in that: The step of inputting the device status data and the data to be displayed into a pre-trained prediction model to perform layout configuration prediction and output an optimal layout includes: Using a data integration interface to perform standardized processing on the device status data and the data to be displayed; Using a pre-trained prediction model to extract features from the standardized device status data and the data to be displayed to obtain an intermediate feature vector; Taking the intermediate feature vector as input, and calculating the predicted value of the layout configuration through forward propagation of the prediction model; The prediction values ​​are adjusted using the optimization algorithm built into the prediction model to obtain the final optimal layout.

3. A large-screen display control method according to claim 2, characterized in that: The generating of a layout adjustment instruction according to the optimal layout output by the prediction model, wherein the layout adjustment instruction includes the position and size of the layout, includes: Utilizing a layout parser to parse the predicted value of the optimal layout to obtain the position and size of the layout elements; Use the instruction generator to generate layout adjustment instructions based on the position and size of layout elements.

4. A large-screen display control method according to claim 3, characterized in that: The converting the layout adjustment instruction into a control signal, and sending the control signal to all display modules through a display controller, so that all display modules adjust their respective display contents in real time according to the position and size of the layout, includes: Using an instruction compiler to convert the layout adjustment instruction into a control signal that can be recognized by a display controller; The display controller uses a communication protocol to encapsulate the control signal and send it to each display module; The display module receives and decodes the control signal, and updates the position and size of the data to be displayed in real time according to the optimal layout.

5. A large-screen display control system, used to execute a large-screen display control method according to any one of claims 1 to 4, wherein the control system is configured with a display controller and a large screen driven by the display controller, and the large screen includes a plurality of display modules spliced ​​together, characterized in that: The control system comprises: A data acquisition module, used to collect data sets from different data sources and perform data preprocessing, wherein the data sets include device status data and data to be displayed; An event detection module is used to detect events on device status data and data to be displayed respectively using a real-time stream processing framework, and to start adjusting the layout configuration when the detection result meets a preset trigger condition; A layout prediction module, used for inputting the device status data and the data to be displayed into a pre-trained prediction model to perform layout configuration prediction and output an optimal layout when starting the adjustment of the layout configuration, wherein the prediction model is a convolutional neural network model trained based on a historical data set; A layout instruction generation module, used to generate a layout adjustment instruction according to the optimal layout output by the prediction model, wherein the layout adjustment instruction includes the position and size of the layout; The instruction distribution module is used to convert the layout adjustment instruction into a control signal, and send the control signal to all display modules through the display controller, so that all display modules can adjust their display contents in real time according to the position and size of the layout.

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