Visual large screen rapid construction system and method based on industry template library and dynamic configurable component

Abstract

The invention discloses a visual large screen rapid construction system and method based on an industry template library and a dynamic configurable component, and relates to the technical field of large screen construction, the method comprises the following steps: realizing intelligent matching of industry templates through natural language input, and automatically completing verification and instantiation of the component; generating an interaction script in combination with the decision table and performing use case verification; meanwhile, an edge scheduling mechanism is introduced to dynamically allocate rendering node resources, and the low-delay and high-frame-rate display effect is guaranteed; key indexes are monitored in real time, and sandbox isolation and thermal repair are executed when fault probe conditions are triggered so as to ensure stable operation of the system. According to the invention, one-key matching and automatic verification are realized based on an industry template library and a dynamic component, and fault isolation and hot repair are realized through rendering time delay and heartbeat packet loss feedback in combination with interactive arrangement driven by a decision table and dynamic resource scheduling perceived by a network and a load, so that a stable and reliable large-screen visualization system is constructed with high efficiency and low delay.

Description

Technical Field

[0001] This invention relates to the field of large screen construction technology, and in particular to a visual large screen rapid construction system and method based on industry template libraries and dynamically configurable components. Background Technology

[0002] With the deepening application of big data and visualization technologies, the demand for large-screen visualization displays in various industries is constantly increasing. However, existing solutions still have some shortcomings in terms of template and component library completeness, automated verification and interactive orchestration, distributed rendering performance scheduling, and fault self-healing mechanisms. First, the industry templates and component instances provided by the platform are often limited and not accurately matched, requiring developers to perform a lot of manual configuration and debugging. Second, the lack of automatic verification based on data models and rendering rules leads to frequent runtime errors or rendering anomalies. Third, the interactive logic orchestration relies on handwritten scripts, making it difficult to guarantee reliability in high-concurrency and low-latency scenarios. Fourth, the selection and resource scheduling of distributed rendering nodes are relatively static and cannot be dynamically optimized according to network latency and system load, which easily leads to frame rate drops or interactive stuttering. At the same time, the monitoring of key indicators such as rendering latency, interface call failures, and heartbeat packet loss is not comprehensive enough. Once a fault occurs, there is a lack of rapid isolation, rollback, and hot repair capabilities, which seriously affects the continuity of large-screen displays and decision-making efficiency.

[0003] Therefore, there is an urgent need for a visual dashboard rapid construction system and method based on industry template libraries and dynamically configurable components, with automated verification and intelligent orchestration, support for dynamic resource scheduling and built-in self-healing rollback mechanism, in order to improve construction efficiency, operational stability and fault recovery capabilities. Summary of the Invention

[0004] Given that current large-screen construction solutions suffer from narrow template and component library coverage and a lack of automated component instantiation and verification mechanisms, resulting in low development efficiency and a high susceptibility to errors; furthermore, interactive orchestration still requires extensive manual scripting, lacks performance verification methods, and struggles to guarantee response latency and concurrency reliability; and that rendering node selection and resource scheduling are relatively static, unable to be dynamically optimized based on real-time load and network conditions, easily leading to frame rate drops or stuttering, this invention is proposed.

[0005] Therefore, the problem to be solved by this invention is how to provide a system and method for rapidly building a visual large screen based on an industry template library and dynamically configurable components.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, embodiments of the present invention provide a method for rapidly constructing a visual large screen based on an industry template library and dynamically configurable components, including: accurately matching and recommending the optimal industry template through natural language input and semantic vectorization; automatically verifying the data fields and rendering capabilities of the components based on the preset layout and component metadata of the selected template, and instantiating the verified components into the template layout; generating interactive orchestration scripts using action mapping conditions in the decision table, and running typical interactive use cases in the background for verification; intelligently selecting the optimal rendering node and dynamically scheduling resources, and collecting network latency and load information of each node in real time through the edge scheduling module to maintain high frame rate and low latency; monitoring key indicators in real time, and performing sandbox isolation and hot repair when fault probe conditions are triggered to ensure stable system operation.

[0008] As a preferred embodiment of the method for rapidly constructing a visual large screen based on an industry template library and dynamically configurable components described in this invention, the user inputs an industry scenario description through a visual interface, the semantic processing engine performs word segmentation, entity recognition and vectorization on the input text, and matches industry templates in the knowledge graph to recommend the optimal template.

[0009] As a preferred embodiment of the method for rapidly constructing a visual large screen based on an industry template library and dynamically configurable components described in this invention, the method involves: retrieving corresponding components from the component meta-database according to the row and column division and component slots of the selected template, and automatically verifying the availability of the components based on the input / output schema and frame rate capability.

[0010] As a preferred embodiment of the method for rapidly constructing a visual large screen based on an industry template library and dynamically configurable components described in this invention, the rule engine generates an orchestration script based on a predefined decision table, and verifies the script response latency and conflict by running no less than ten typical interactive use cases in the background.

[0011] As a preferred embodiment of the method for rapidly constructing a visual large screen based on an industry template library and dynamically configurable components described in this invention, the edge scheduling module collects network latency and CPU and memory load information of each rendering node in real time, and schedules or switches rendering nodes according to the comprehensive score.

[0012] As a preferred embodiment of the method for rapidly building a visual large screen based on an industry template library and dynamically configurable components described in this invention, when the interface error rate or rendering frame rate is continuously abnormal, the fault probe triggers sandbox isolation, the system reverts to the previous stable version, and hot repair is completed in an imperceptible state.

[0013] As a preferred embodiment of the method for rapidly building a visual large screen based on an industry template library and dynamically configurable components described in this invention, the system generates an operation and maintenance report after completing a hot update. The report includes the cause of the fault, the processing time, and the performance comparison, and can be exported as a PDF document.

[0014] Secondly, to further address the problems existing in the large screen construction scheme, the present invention provides a visual large screen rapid construction system based on an industry template library and dynamically configurable components, which includes: a template matching module, used to receive the user's industry scenario description and match the optimal template in the industry template library through semantic vectorization; The component management module is used to retrieve and automatically verify the input and output fields and rendering capabilities of dynamically configurable components from the component meta database based on the layout information of the selected template, and instantiate the verified components into the template layout. The interactive orchestration module is used to generate front-end interactive scripts based on predefined decision tables and execute typical interactive use cases in the background to verify the latency and conflict of the scripts. The edge scheduling module is used to collect network latency and load information of each rendering node in real time, and intelligently select and schedule the optimal rendering node to ensure high frame rate and low latency. The monitoring and self-healing module is used to monitor key indicators such as rendering latency, number of API calls and heartbeat packet loss rate in real time. When the fault probe conditions are triggered, it performs sandbox isolation and automatic rollback and hot repair to maintain stable system operation. The Operations and Maintenance Report module is used to generate an operations and maintenance report after a hotfix is ​​completed, which includes the cause of the failure, the processing time, and a performance comparison. It also supports exporting the report as a PDF document.

[0015] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the method for rapid construction of a visual large screen based on an industry template library and dynamically configurable components as described in the first aspect of the present invention.

[0016] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the method for rapid construction of a visual large screen based on an industry template library and dynamically configurable components as described in the first aspect of the present invention.

[0017] The beneficial effects of this invention are as follows:

[0018] 1. Based on a rich industry template library and dynamically configurable components, this invention allows template selection and component instantiation to be completed in just a few minutes, greatly shortening the project startup cycle; automated data pattern verification and rendering rule verification reduce errors and rendering anomalies during development and improve the user experience.

[0019] 2. This invention introduces decision table-driven interactive orchestration and automatic background test case verification, which not only makes the writing of interactive logic more intuitive, but also ensures that the system still responds smoothly in high-concurrency scenarios; the dynamic resource scheduling algorithm can perceive network latency and node load in real time, and intelligently allocate rendering tasks, so that the frame rate is stable at a high level and the operation is smoother.

[0020] 3. This invention can monitor key indicators such as rendering latency, interface call failure and heartbeat packet loss around the clock through the built-in self-healing and rollback mechanism. When a fault occurs, it can automatically isolate the problem node and quickly roll back to a known stable version to ensure uninterrupted large screen display. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0022] Figure 1 This is a flowchart illustrating the implementation of the present invention in Example 1.

[0023] Figure 2 This is a template effect diagram of the present invention in Example 2.

[0024] Figure 3 This is a diagram of the system configuration tool of the present invention in Example 2. Detailed Implementation

[0029] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0030] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0031] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0032] Example 1

[0033] Reference Figure 1 This is the first embodiment of the present invention, which provides a method for rapidly building a visual large screen based on an industry template library and dynamically configurable components, including the following steps:

[0034] Step S1: Accurately match and recommend the optimal industry template through natural language input and semantic vectorization, which includes the following sub-steps:

[0035] S1-1: Users input natural language or keyword descriptions through a visual interface, such as "intelligent transportation" or "factory energy consumption monitoring".

[0036] S1-2: The semantic processing engine performs word segmentation, entity recognition, and vectorization on the text, outputting a 256-dimensional semantic vector.

[0037] S1-3: Calculate the cosine similarity sim(v) in the knowledge graph. i ,v t The top 5 templates are compared with a preset threshold (e.g., 0.75) and returned to the user.

[0038] Specifically, the cosine similarity calculation mentioned above can be represented by the following formula:

[0039]

[0040] In the formula, v i This represents the user scenario vector output by the semantic processing engine; v t This represents the label vector of a template for a certain industry in a knowledge graph; "·" represents the vector dot product; ||·|| represents the Euclidean norm of the vector.

[0041] For example, when sim(v i ,v t When the value is greater than or equal to 0.75, the template is considered to match the input scenario.

[0042] It should be noted that if no template meets the threshold, the system will prompt "No matching template" and suggest that the user adjust the description.

[0043] Step S2: Automatically validate and instantiate components based on the template's preset layout and component metadata to ensure data and rendering capabilities match. This includes the following sub-steps:

[0044] S2-1: Retrieve the required components from the metadata database based on the layout description (row and column division, component slots) of the selected template.

[0045] S2-2: Refer to the input / output schema, frame rate capability, and other fields in Table 2, and perform automatic verification to check the consistency of data field types and whether the rendering capability meets the template requirements.

[0046] Specifically: If the validation rule is that the field does not match or the frame rate is lower than 30fps, the validation is deemed to have failed. In this case, the rule engine will automatically recommend alternative components or prompt the user based on the decision table (see Table 3); otherwise, the validation is deemed to have passed, and the component will be instantiated and filled into the layout slot.

[0047] Step S3: Utilize the action mapping in the decision table execution conditions to generate and verify an efficient interactive orchestration script, including the following sub-steps:

[0048] S3-1: The rule engine generates orchestration scripts based on the condition-action mapping in Table 3.

[0049] S3-2: The system runs a typical set of interactive test cases (no fewer than 10) in the background, measures the response latency, and ensures the average response latency T. avg ≤100ms;

[0050] Specifically, the average response time T mentioned above avg This can be reflected by the following formula:

[0051]

[0052] In the formula, M represents the number of typical interaction use cases; T j The time taken from triggering the j-th use case to completing the rendering is recorded by the monitoring module.

[0053] S3-3: During script execution, if a conflict or timeout is detected (single script execution delay T), exec If the time exceeds 200ms, the engine will automatically downgrade the processing or split the execution.

[0054] Specifically, the single script execution latency T exec =T 结束 -T 开始 T 结束 T 开始 These represent the script's end and start timestamps, respectively.

[0055] Step S4: Intelligently select the optimal rendering node and dynamically schedule resources to maintain high frame rate and low latency on the large screen, including the following sub-steps:

[0056] S4-1: Issue a command to trigger the rendering engine and distribute the task to the optimal node;

[0057] S4-2: The edge scheduling module collects network latency, CPU and memory load of each node in real time and calculates the node's overall score. i ;

[0058] Specifically, the overall score of the above nodes can be represented by the following formula:

[0059]

[0060] In the formula, Score i This represents the overall score for the i-th rendering point, with lower scores being better. Let be the normalized value of the network delay of the i-th node; α is the normalized load value of the i-th node; α is the weighting coefficient of latency and load, which is generally 0.7 and can be adjusted according to business needs.

[0061] For example, the above parameters can be expressed as follows:

[0062]

[0063] In the formula, L i L represents the average round-trip time between the i-th node and the front end. min L max These are the minimum and maximum delays for all nodes, respectively.

[0064]

[0065] In the formula, U i The overall utilization rate (%) of the node's current CPU and memory; U min U max These represent the minimum and maximum loads for all candidate nodes, respectively.

[0066] S4-3: When the frame rate is <55fps or the network latency is >100ms, an automatic decision is triggered to temporarily increase node resources (minimum unit: 1vCPU, 2GB memory) or switch to the next highest-scoring node.

[0067] It should be noted that the node switching or resource scheduling process should be completed within 500ms to ensure a seamless experience.

[0068] Step S5: Monitor key indicators in real time, trigger sandbox isolation and hotfix, quickly roll back and update to ensure stable system operation, including the following sub-steps:

[0069] S5-1: The monitoring module collects metrics such as rendering latency, number of API calls, and heartbeat packet loss rate;

[0070] S5-2: The fault probe is triggered by the interface error rate E. rate ≥5% and lasting for 30 seconds or rendering frame rate (FPS) below 30fps and lasting for 10 seconds;

[0071] Specifically, the error rate E of the aforementioned interface rate This can be reflected by the following formula:

[0072]

[0073] In the formula, E represents the number of interface anomalies during the monitoring period; R represents the total number of interface calls during the monitoring period.

[0074] For example, a rendering frame rate (FPS) below 30fps for 10 seconds can be represented by the following formula:

[0075]

[0076] In the formula, f j The frame rate for the j-th sample is N; N is the number of samples, such as once per second, so 10s means N=10.

[0077] S5-3: After sandbox isolation is triggered, the system will revert to the previous stable version, with a rollback time of ≤2 seconds;

[0078] S5-4: The self-healing module pulls the patch package, performs hash verification and digital signature verification, and hot updates after successful verification. The entire self-healing process takes ≤10 seconds and is imperceptible to the user.

[0079] S5-5: The system generates an operation and maintenance report, which includes the cause of the fault, the processing time, and a performance comparison before and after recovery. It can be exported as a PDF document.

[0080] In summary, this invention, by constructing a rapid construction method for visual large screens based on an industry template library and dynamically configurable components, not only achieves accurate matching and one-click instantiation of templates and components in multiple scenarios, significantly reducing manual selection and configuration costs, but also effectively avoids runtime rendering anomalies and data inconsistencies by relying on data pattern verification and automatic rendering rule verification mechanisms. Through decision table-driven interactive orchestration and background test case performance verification, it can maintain stable response in high-concurrency, low-latency environments, greatly improving user experience. Combined with dynamic resource scheduling strategies based on indicators such as network latency and CPU / memory load, the system can intelligently select the optimal rendering node to ensure high frame rate and low latency. Furthermore, by utilizing a feedback mechanism that monitors rendering latency and heartbeat packet loss rate, it can achieve rapid fault isolation, hot repair, and automatic rollback, thereby ensuring the continuous availability of the visual large screen and the overall stability of the system.

[0081] Example 2 Reference Figures 2 to 3 This is the second embodiment of the present invention. Unlike the first embodiment, this embodiment also provides a method for applying a rapid visualization large-screen construction system based on an industry template library and dynamically configurable components in a panoramic perception scenario of a municipal power grid, including: Server cluster deployment mainly includes the following services and components: The knowledge graph service (deployment scale: active-active cluster, number of nodes ≥ 3) is mainly responsible for storing and updating concept nodes and relationships in multiple industry fields, and can complete efficient queries through graph databases; The semantic processing engine (deployment scale: 2 primary and 2 backup machines) is based on the lightweight Transformer model and provides text preprocessing, word segmentation, entity extraction and vectorization functions. Template and component metadata database (deployment scale: 2 primary and 2 backup machines) are used to store descriptive metadata (including input and output data structures, display methods, interaction interfaces, etc.) of industry templates and visualization components respectively. The rules engine platform (with ≥3 cluster nodes) uses a decision table to define business rules and supports one-to-many mapping between conditions and actions. The rendering and edge scheduling module can distribute rendering tasks to the cloud or nearby edge nodes and schedule them in real time through the frame rate monitoring module. The scheduling algorithm prioritizes the node with the lowest network latency, combined with the node load balancing weight (node ​​score = 0.7 × latency normalization + 0.3 × load normalization).

[0089] Also includes:

[0090] Template matching module: When maintenance personnel input "panoramic perception of power grid operation" as a scenario keyword in the system interface, the template matching module performs semantic vectorization processing on this natural language description, automatically identifies the "Power Industry - City-level Power Grid Monitoring" template, and recommends it to the user based on weight ranking. After the user selects the template, the system loads its default layout structure, including area definitions such as "Real-time Load Monitoring Area," "Power Supply Area Heat Map," and "Equipment Alarm List."

[0091] Component Management Module: Based on the selected template's regional layout, the system automatically queries the component metadata database and matches the corresponding data layer components. For the "Load Monitoring Area," a curve graph component is selected, and its supported fields, including "Power Line Number," "Current Load Value," and "Maximum Capacity Value," are verified. After confirming that the component meets the frame rate refresh requirement per second, it is instantiated into the template. Similarly, the "Device Alarm List" area loads a table component and connects to the alarm center interface to form dynamic data binding.

[0092] Interaction Orchestration Module: After loading all components, the system reads a pre-set interaction decision table, such as "Click on a heat map of a certain area → Open a device details pop-up window → Automatically locate the center point of the GIS map". The interaction orchestration module generates scripts based on these rules and performs typical interaction operation verifications in the background, including mouse drag response latency and whether pop-up window linkages conflict, to ensure smooth user operation.

[0093] Edge scheduling module: Deployed in a local data center and cloud-edge collaborative architecture, the edge scheduling module periodically collects network latency and GPU load data from the three local graphics rendering nodes and selects the node with the lowest load based on the overall score to assign layer rendering tasks. If the load on the primary node exceeds a threshold, it automatically switches to a backup node for a seamless transition, ensuring that the large screen maintains a refresh rate of over 60 frames per second during peak access periods.

[0094] Monitoring and Self-Healing Module: During system operation, the monitoring module collects key operational metrics in real time, such as rendering latency, interface response time, and heartbeat packet loss rate. When it detects that "the number of abnormal responses from the alarm module interface exceeds a preset threshold," the fault probe triggers a sandbox mechanism, temporarily isolating the component logic and rolling it back to the previous stable state. Simultaneously, the system reloads the component instance through a hot-repair mechanism, restoring normal display without user intervention.

[0095] Operations and Maintenance Report Module: After a fault is repaired, the system automatically generates an operations and maintenance report, which includes information such as: fault time, abnormal interface name, automatic repair time, frame rate comparison before and after, and the final stable node IP address. This report supports exporting in PDF format for easy archiving and subsequent inspection and analysis.

[0096] Through the above embodiment 2, it can be seen that the synergistic effect of the various modules of the present invention can quickly build an interactive, high-performance, self-healing visualization screen in typical scenarios of the power industry, significantly shortening the deployment cycle and improving operation and maintenance efficiency, and has good industry versatility and scalability. Table 1 is an example table of knowledge graph node attribute fields in Example 1. Table 2 is an example table of component metadata fields in Example 1. Table 3 is a decision example table from Example 1.

[0097] This embodiment also provides a computer device applicable to the rapid construction method of a visual large screen based on an industry template library and dynamically configurable components, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the rapid construction method of a visual large screen based on an industry template library and dynamically configurable components as proposed in the above embodiment.

[0098] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0099] This embodiment also provides a storage medium storing a computer program. When executed by a processor, the program implements the method for rapidly constructing a visual large screen based on an industry template library and dynamically configurable components, as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0100] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for rapidly constructing a visual dashboard based on an industry template library and dynamically configurable components, characterized in that: include: Step S1: Through natural language input and semantic vectorization, accurately match and recommend the best industry template; Step S2: Based on the preset layout and component metadata of the selected template, automatically verify the data fields and rendering capabilities of the components, and instantiate the verified components into the template layout. Step S3: Generate an interaction orchestration script using the action mapping conditions in the decision table, and run typical interaction test cases in the background for verification. Step S4: Intelligently select the optimal rendering node and dynamically schedule resources. The network latency and load information of each node are collected in real time through the edge scheduling module to maintain high frame rate and low latency. Step S5 involves real-time monitoring of key indicators and, when fault probe conditions are triggered, performing sandbox isolation and hot repair to ensure stable system operation.

2. The method for rapidly constructing a visual large screen based on an industry template library and dynamically configurable components as described in claim 1, characterized in that: In step S1, the user inputs an industry scenario description through a visual interface. The semantic processing engine performs word segmentation, entity recognition, and vectorization on the input text, and matches industry templates in the knowledge graph to recommend the optimal template.

3. The method for rapidly constructing a visual large screen based on an industry template library and dynamically configurable components as described in claim 1, characterized in that: In step S2, based on the row and column division of the selected template and the component slots, the corresponding components are retrieved from the component metadata database, and the availability of the components is automatically verified according to the input / output schema and frame rate capability.

4. The method for rapidly constructing a visual large screen based on an industry template library and dynamically configurable components as described in claim 1, characterized in that: In step S3, the rule engine generates an orchestration script based on a predefined decision table, and verifies the script response latency and conflict by running no less than ten typical interaction test cases in the background.

5. The method for rapidly constructing a visual large screen based on an industry template library and dynamically configurable components as described in claim 1, characterized in that: In step S4, the edge scheduling module collects network latency and CPU and memory load information of each rendering node in real time, and schedules or switches rendering nodes according to the comprehensive score.

6. The method for rapidly constructing a visual large screen based on an industry template library and dynamically configurable components as described in claim 1, characterized in that: In step S5, when the interface error rate or rendering frame rate is continuously abnormal, the fault probe triggers sandbox isolation, the system reverts to the previous stable version, and completes hot repair without the user noticing.

7. The method for rapidly constructing a visual large screen based on an industry template library and dynamically configurable components as described in claim 1, characterized in that: In step S5, after completing the hot update, the system generates an operation and maintenance report. The report includes the cause of the fault, the processing time, and the performance comparison, and can be exported as a PDF document.

8. A rapid visualization large screen construction system based on an industry template library and dynamically configurable components, based on the rapid visualization large screen construction method based on an industry template library and dynamically configurable components as described in any one of claims 1 to 7, characterized in that: include, The template matching module is used to receive the user's industry scenario description and match the optimal template in the industry template library through semantic vectorization. The component management module is used to retrieve and automatically verify the input and output fields and rendering capabilities of dynamically configurable components from the component meta database based on the layout information of the selected template, and instantiate the verified components into the template layout. The interactive orchestration module is used to generate front-end interactive scripts based on predefined decision tables and execute typical interactive use cases in the background to verify the latency and conflict of the scripts. The edge scheduling module is used to collect network latency and load information of each rendering node in real time, and intelligently select and schedule the optimal rendering node to ensure high frame rate and low latency. The monitoring and self-healing module is used to monitor key indicators such as rendering latency, number of API calls and heartbeat packet loss rate in real time. When the fault probe conditions are triggered, it performs sandbox isolation and automatic rollback and hot repair to maintain stable system operation. The Operations and Maintenance Report module is used to generate an operations and maintenance report after a hotfix is ​​completed, which includes the cause of the failure, the processing time, and a performance comparison. It also supports exporting the report as a PDF document.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the rapid construction method for visual large screens based on industry template libraries and dynamically configurable components as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the rapid construction method for visual large screens based on industry template libraries and dynamically configurable components as described in any one of claims 1 to 7.

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