Visualization system capable of synchronously displaying multiple types of data according to time

Abstract

The invention provides a visualization system capable of synchronously displaying multiple types of data according to time. Comprising a distributed service cluster composed of a micro-service, a user management micro-service, a data transmission micro-service, a data storage management micro-service, a data extraction micro-service, a data visualization display micro-service, a data visualization template management micro-service and a resource monitoring micro-service; the micro-services are packaged in a containerized manner and interact by adopting a lightweight communication protocol, and automatic deployment and dynamic expansion are realized through a container arrangement tool; the system is provided with a basic parameter set with customizable micro-service configuration and a graphical configuration interface which are used for cooperatively generating a system operation environment configuration strategy. The user management micro-service establishes a multi-dimensional authority system based on a role access control model, and records an operation log to realize audit tracking; the data transmission micro-service integrates a plurality of heterogeneous data access modes, carries out transmission process verification and carries out secure channel transmission through the service gateway.

Description

Technical Field

[0001] The present invention belongs to the field of data visualization, and in particular relates to a visualization system capable of synchronously displaying multiple types of data in time. Background Art

[0002] At present, with the rapid development of big data and informatization, massive amounts of data with rich types are constantly emerging in various industries and fields, such as transaction flows in the financial field (csv format), medical images (png format), and scientific research experimental data compression packages (zip format). Traditional data visualization tools are often only targeted at specific types of data or lack efficient multi-source data integration and time-synchronized display capabilities. In actual application scenarios, users need to simultaneously analyze the correlation and changing trends of different types of data in time series, but existing technologies cannot meet this demand, resulting in low data analysis efficiency and lack of comprehensive and accurate data support for decision-making. In addition, with the continuous growth of data scale, the performance bottlenecks of data storage, transmission and extraction have become increasingly prominent, and the scalability and maintainability of the system have become key factors restricting the effective use of data. Therefore, it is crucial to develop an innovative visualization system that can display multiple types of data synchronously in time. Summary of the Invention

[0003] The present invention proposes a visualization system that can synchronously display multiple types of data in time. This system solves the problem of collaborative processing and synchronous display of multi-source heterogeneous data in the time dimension, and overcomes the technical bottlenecks of traditional visualization systems such as low storage efficiency across data types, insufficient real-time synchronization accuracy, delayed dynamic resource scheduling, and limited interactive flexibility.

[0004] The technical solution of the present invention is implemented as follows: a visualization system capable of synchronously displaying multiple types of data in time, characterized by comprising a distributed service cluster consisting of a microservice, a user management microservice, a data transmission microservice, a data storage management microservice, a data extraction microservice, a data visualization display microservice, a data visualization template management microservice, and a resource monitoring microservice;

[0005] Each microservice is encapsulated in a container and interacts using a lightweight communication protocol, and is automatically deployed and dynamically expanded through container orchestration tools. The system sets up a customizable basic parameter set and a graphical configuration interface for microservice configuration to collaboratively generate system operating environment configuration policies. The user management microservice establishes a multi-dimensional permission system based on a role-based access control model and records operation logs to achieve audit tracking. The data transmission microservice integrates multiple heterogeneous data access methods, implements transmission process verification, and transmits data through a secure channel through a service gateway. The data storage management microservice adopts a hybrid storage architecture, establishes distributed column family storage for structured data, constructs metadata index storage for image data, implements decompression-free storage for compressed data, and provides multi-dimensional combination retrieval capabilities.

[0006] The data extraction microservice includes a paging query engine, a parallel image processor, and a decompression-free extractor. The paging query engine combines memory cache and prefetching strategies to optimize access paths, the parallel image processor uses a multi-threaded pipeline architecture to decompose tasks, and the decompression-free extractor establishes a multi-process concurrent reading mechanism based on file structure indexes.

[0007] The data visualization display microservice builds a multi-window synchronous display framework with a built-in time synchronization controller and a unified timing scheduler to ensure accurate and synchronous presentation of timelines across data types, and integrates interactive visualization operation components. The data visualization template management microservice provides a visual editing interface and a version management system that supports drag-and-drop configuration of the layout and style of multimodal display modules. The version management system uses differential storage technology to achieve template backtracking and switching.

[0008] The resource monitoring microservice collects system resource indicators in real time, establishes a dynamic early warning model based on time series analysis, and triggers the container orchestration tool to perform elastic scaling operations to form a closed-loop resource control system;

[0009] Each microservice is uniformly scheduled through a service gateway, which integrates service discovery mechanism, load balancing algorithm and security authentication module to establish request routing and flow control strategies; the data storage management microservice and data extraction microservice achieve efficient data positioning through pre-built index structure, and the data visualization template management microservice and data visualization display microservice maintain display consistency through template version mapping mechanism.

[0010] System Architecture and Module Composition: Based on the microservices architecture concept, the system is divided into multiple independent microservices, such as system setup microservice, user management microservice, data transmission microservice, data storage microservice, data extraction microservice, data visualization microservice, template management microservice, and resource monitoring microservice. Each microservice can be independently developed, deployed, upgraded, and expanded, and collaborate with each other through lightweight communication protocols such as RESTful APIs or gRPC. Microservices are encapsulated using containerization technology and combined with container orchestration tools to achieve automated deployment, load balancing, dynamic scaling, and service discovery. This ensures high availability and scalability in a distributed environment, effectively addressing the needs of high concurrency and large-scale data processing.

[0011] Front-end interaction module: A concise and intuitive user interface is carefully crafted, including a data retrieval page so that users can efficiently and accurately query the required data; a visual configuration page that supports users to flexibly customize data display formats and chart styles according to their own needs, achieving intuitive presentation and in-depth insights into data; a system configuration page that can be used to personalize and manage various basic parameters and functional modules of the system; a data upload page that provides users with a convenient way to import various data files to ensure that data can be smoothly connected to the system. Each page collaborates with each other to jointly build a smooth user interaction experience.

[0012] System Settings: This module manages basic system configuration parameters, including the system language, time format, data storage path, network communication protocol, and security encryption level. It provides an intuitive graphical settings interface, facilitating parameter adjustments and system initialization. This module also works closely with other modules to ensure the consistency and stability of the system's operating environment. For example, during data transmission, data is encapsulated and encrypted according to the configured network protocol and encryption level. During data storage, various system configuration files and metadata are stored in designated paths.

[0013] User Management Module: Builds a user information database to store user accounts, passwords, role permissions, operation records, and other information. A role-based access control (RBAC) model assigns different users corresponding operational permissions and data access levels. For example, standard users can only upload data and perform basic data visualization operations, while administrators can perform advanced operations such as system settings, user management, and template management. It supports user registration, login, and password retrieval, and records detailed user operation logs for audit tracking and security management, ensuring the security of system data and the compliance of user operations.

[0014] Gateway module: As the front-end and back-end communication hub, it implements service registration discovery and request forwarding, load balancing, and security authentication to ensure reliable and secure communication.

[0015] Data transmission module: Integrates multiple data submission methods to meet the diverse needs of different users and data sources. In terms of web front-end uploading, a simple and easy-to-use upload interface is developed, which supports batch uploading, breakpoint resumption and real-time data format verification functions, and intelligently limits the size of uploaded files and provides clear prompts. For FTP upload, a stable and efficient FTP client function is implemented, which is compatible with multiple FTP server types and transmission modes, supports encryption and error reconnection mechanisms during transmission, and ensures the reliability of data transmission. In the SDK code calling embedded upload, rich and detailed development documentation and sample code are provided to facilitate third-party application developers to easily integrate data upload functions into their applications and achieve seamless data access. The received data is transmitted to the data storage management module via a secure channel, and the upload results are promptly fed back to the user to ensure the integrity and accuracy of data transmission.

[0016] Data storage management module: adopts a distributed storage architecture, combined with NoSQL database and object storage technology, to optimize storage for different data types. For structured CSV data, it is stored in a distributed column family database, taking advantage of its efficient read and write performance and flexible table structure design to facilitate data retrieval and analysis; PNG image data is stored in the object storage system, and indexes are established by extracting image metadata (such as shooting time, resolution, color mode, etc.) to achieve fast query access; zip compressed files are stored in a specific storage area in their original format, and file directories and content indexes are constructed to achieve fast retrieval without decompression when extracting data. This module also provides powerful search and query functions, supporting query conditions based on multi-dimensional combinations such as time range, data type, and keywords. It uses distributed indexing and caching technology to quickly locate and return query results, providing efficient data support for the data extraction module.

[0017] System log module: records multi-level logs, including system errors, user operations, security events, and workflow execution trajectories, to help operations and maintenance troubleshoot problems, audit tracking, and optimize the system.

[0018] Data Extraction Module (Core): Designs efficient extraction algorithms for different data types. For CSV data, a paging query mechanism is constructed based on the timestamp column, combined with memory caching and prefetching technology to effectively reduce disk I / O operations and quickly obtain data pages within a specified time range. When processing PNG data, multi-threaded parallel processing technology is used to scale, crop, and convert images based on display requirements and image time attributes, improving processing efficiency. For ZIP data, the internal structure index of the ZIP file and multi-coroutine concurrency technology are utilized to directly extract the target data without decompression, avoiding the resource consumption and time delay caused by decompressing the entire file. After preliminary processing and format conversion, the extracted data is transferred to the data visualization display module for display.

[0019] Data visualization display module (core): Builds a framework that can display multiple modules on the same screen, supporting the simultaneous display of various types of information such as charts, images, videos, data tables, etc. Users can display data by selecting predefined templates or custom templates. During the display process, after the user selects a specified time and clicks the play button, the system obtains data from the data extraction module at a preset time interval and ensures that each display window synchronously displays data at the same time point, achieving accurate synchronous display of data in the time dimension. It provides rich interactive functions, such as hovering the mouse to prompt detailed information, clicking to zoom in to view, data filtering and sorting, etc., to enhance users' understanding and analysis capabilities of data and improve data visualization effects.

[0020] Data visualization template management module (core): provides a visual template editing interface, and users can freely configure different display modules by dragging and dropping. Each module is highly customizable, and parameters such as display content, style, and layout can be set. For example, in the chart module, users can choose from a variety of types such as bar charts, line charts, and pie charts, and flexibly set axis labels, data series colors, legend positions, etc.; in the picture module, image size, position, transparency, rotation angle, etc. can be adjusted; in the data table module, column names, data formats, sorting rules, and cell styles can be customized. Template version management is supported, and users can create, save, modify, and delete templates, and view historical versions of templates to facilitate backtracking and reuse, thereby improving work efficiency.

[0021] Resource Monitoring Module: This module monitors the system's hardware resource usage in real time, including key metrics such as CPU utilization, memory usage, disk I / O rate, and network bandwidth. Resource usage is displayed on a visual dashboard, and a threshold alarm mechanism is set. When resource usage exceeds the preset threshold, administrators are promptly notified via email, SMS, or system pop-up window to facilitate resource allocation or troubleshooting. Resource usage history is also recorded to provide data support for system performance optimization and capacity planning, ensuring stable system operation under high loads.

[0022] Traditional data visualization systems face significant technical bottlenecks when processing multimodal data. First, at the data storage level, existing systems often use a single storage architecture, which makes it difficult to adapt to the differentiated characteristics of structured data, image data, and compressed data, resulting in low storage efficiency and insufficient retrieval performance. For example, the lack of a time-series sharding strategy for structured data leads to high query latency, and imperfect image metadata indexing results in low retrieval accuracy. The need for compressed files to be decompressed wastes storage resources and increases I / O load. Secondly, in the data extraction link, existing technologies lack optimization mechanisms for different data types. Structured data paging queries frequently trigger disk accesses because they are not combined with cache prefetching. Image processing has low parallel efficiency due to thread competition. Compressed data extraction is forced to fully decompress because no file structure index is established, which seriously affects processing timeliness.

[0023] In terms of visualization, existing systems struggle to achieve precise synchronization of timelines across data types. Traditional timing control methods suffer from the problem of accumulated clock deviations, and the lack of a unified scheduling mechanism during multi-window collaborative display can lead to data tearing. Furthermore, the static configuration mode of visualization templates cannot meet users' dynamic adjustment needs, and the lack of template version management leads to a chaotic iteration process. At the system architecture level, monolithic systems have poor scalability and struggle to cope with dynamic load changes in high-concurrency scenarios. Insufficient coordination between resource monitoring and container orchestration leads to delayed scaling responses, and the single flow control strategy of the service gateway can easily lead to service avalanches. Furthermore, existing system security controls are weak, the coarse granularity of the permission model leads to the risk of unauthorized access, and an imperfect verification mechanism during transmission can introduce dirty data. Together, these issues present the system with core difficulties such as low storage efficiency, high processing latency, poor synchronization accuracy, limited scalability, and a fragmented user experience when processing massive amounts of multimodal data.

[0024] As a preferred implementation, in the hybrid storage architecture of the data storage management microservice, a dynamic partitioning strategy is adopted for the distributed column family storage established for structured data, logical shards are generated according to the data timestamp, and a secondary inverted index is established; the metadata index storage of image data extracts EXIF ​​attributes through the object storage engine to construct a spatiotemporal composite index, and a hash mapping relationship is established to accelerate retrieval; for the decompression-free storage of compressed data, a hierarchical pointer table is established based on the file directory tree index, and the internal structure topology of the compressed package is generated by pre-scanning the file header information to realize on-demand positioning of the target file physical offset.

[0025] As a preferred embodiment, the paging query engine in the data extraction microservice adopts a heterogeneous data encapsulation strategy, establishes a bidirectional linked list mapping between the timestamp column and the data block, and combines the sliding window mechanism to realize the dynamic replacement of cached data; the parallel image processor builds a pipelined task distribution architecture, separates image parsing and processing operations through a decoding thread pool, and uses an instruction reordering optimizer to eliminate thread contention; the decompression-free extractor builds a memory-resident index table based on the ZIP file directory tree, establishes a multi-channel concurrent reading pipeline through an asynchronous I / O channel, and uses a circular buffer to reduce the overhead of coroutine switching.

[0026] As a preferred implementation, the time synchronization controller of the data visualization display microservice adopts an event-driven architecture, broadcasts the timing reference signal through a publish-subscribe model, each display window has a built-in timing calibrator to compare the local clock deviation, and uses an interpolation compensation algorithm to eliminate timing jitter; the interactive visualization operation component integrates a rendering instruction queue, dynamically generates progressive rendering instructions based on user interaction events, and realizes atomic updates of visualization elements through WebGL shader precompilation technology to maintain interface response consistency.

[0027] As a preferred implementation, the dynamic early warning model of the resource monitoring microservice adopts multi-dimensional indicator fusion analysis, collects resource utilization feature vectors through a sliding time window, applies a spectral clustering algorithm to identify abnormal patterns, and generates a container scaling priority queue; the elastic scaling operation adopts a hierarchical response strategy, dynamically selects horizontal expansion or vertical expansion mode according to the early warning level, maintains service continuity through container snapshot hot migration technology, and establishes a rollback transaction log for the scaling operation.

[0028] After adopting the above technical solution, the beneficial effect of the present invention is: this system realizes a comprehensive improvement in the collaborative processing and visual display of multimodal data through microservice architecture and technological innovation. In terms of architectural design, containerized encapsulation and lightweight communication protocols ensure the high cohesion and low coupling characteristics of services. Combined with the elastic scaling capabilities of container orchestration tools, the horizontal scalability and service stability of the system in high-concurrency scenarios are significantly enhanced. The data storage management module adopts a hybrid storage architecture, improves the efficiency of structured data queries through distributed column family storage, optimizes image retrieval speed based on metadata index storage strategy, reduces compressed data processing overhead through decompression-free storage mechanism, and realizes efficient correlation analysis across data types through multi-dimensional combined retrieval function.

[0029] The data extraction module reduces disk I / O bottlenecks through memory cache optimization within the paging query engine. The pipeline architecture of the parallel image processor eliminates thread contention and improves throughput. The multi-coroutine mechanism of the decompression-free extractor enables precise and rapid location of files within compressed packages, improving overall data processing timeliness. The visualization display module, through the collaboration of a time synchronization controller and a unified timing scheduler, employs event-driven and interpolation compensation algorithms to eliminate time deviations across display windows, ensuring consistent presentation of multimodal data along the timeline. The interactive operation component ensures smooth interface responsiveness through progressive rendering and atomic update technology.

[0030] The template management module's drag-and-drop configuration and differential storage technology give users a high degree of layout customization capabilities, while the version backtracking function ensures the controllability of the iteration process. The resource monitoring module uses a dynamic early warning model based on time series analysis to identify resource anomaly patterns. Combined with hierarchical scaling strategies and container hot migration technology, it ensures optimal resource utilization while ensuring service continuity. The multi-dimensional security authentication and intelligent traffic control strategies integrated in the service gateway effectively prevent service overload and unauthorized access, and the refined design of the permission system meets the security and compliance requirements in complex business scenarios. Through deep collaboration and technological innovation among modules, the overall system achieves comprehensive optimization of storage efficiency, processing performance, display accuracy, system elasticity and user experience, providing an efficient and reliable platform support for real-time collaborative analysis of multimodal data. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0032] Figure 1 This is a system block diagram of the present invention;

[0033] Figure 2 Set up a flow chart for the system of the present invention;

[0034] Figure 3 Flowchart for the implementation of the present invention. DETAILED DESCRIPTION

[0035] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0036] Example:

[0037] like Figures 1 to 3 As shown, a visualization system that can display multiple types of data synchronously according to time is characterized by including a distributed service cluster consisting of microservices, user management microservices, data transmission microservices, data storage management microservices, data extraction microservices, data visualization display microservices, data visualization template management microservices and resource monitoring microservices;

[0038] Each microservice is encapsulated in a container and interacts using a lightweight communication protocol, and is automatically deployed and dynamically expanded through container orchestration tools. The system sets up a customizable basic parameter set and a graphical configuration interface for microservice configuration to collaboratively generate system operating environment configuration policies. The user management microservice establishes a multi-dimensional permission system based on a role-based access control model and records operation logs to achieve audit tracking. The data transmission microservice integrates multiple heterogeneous data access methods, implements transmission process verification, and transmits data through a secure channel through a service gateway. The data storage management microservice adopts a hybrid storage architecture, establishes distributed column family storage for structured data, constructs metadata index storage for image data, implements decompression-free storage for compressed data, and provides multi-dimensional combination retrieval capabilities.

[0039] The data extraction microservice includes a paging query engine, a parallel image processor, and a decompression-free extractor. The paging query engine combines memory cache and prefetching strategies to optimize access paths, the parallel image processor uses a multi-threaded pipeline architecture to decompose tasks, and the decompression-free extractor establishes a multi-process concurrent reading mechanism based on file structure indexes.

[0040] The data visualization display microservice builds a multi-window synchronous display framework with a built-in time synchronization controller and a unified timing scheduler to ensure accurate and synchronous presentation of timelines across data types, and integrates interactive visualization operation components. The data visualization template management microservice provides a visual editing interface and a version management system that supports drag-and-drop configuration of the layout and style of multimodal display modules. The version management system uses differential storage technology to achieve template backtracking and switching.

[0041] The resource monitoring microservice collects system resource indicators in real time, establishes a dynamic early warning model based on time series analysis, and triggers the container orchestration tool to perform elastic scaling operations to form a closed-loop resource control system;

[0042] Each microservice is uniformly scheduled through a service gateway, which integrates service discovery mechanism, load balancing algorithm and security authentication module to establish request routing and flow control strategies; the data storage management microservice and data extraction microservice achieve efficient data positioning through pre-built index structure, and the data visualization template management microservice and data visualization display microservice maintain display consistency through template version mapping mechanism.

[0043] The visualization system proposed in this application document realizes the full life cycle management of multimodal data based on the microservice architecture. Its working principle and implementation process revolve around the five core links of data collection, storage optimization, efficient extraction, synchronous display and resource regulation. During the system initialization phase, the system setting microservice receives the basic parameters defined by the administrator through a graphical configuration interface, including the data storage path, network protocol type and security encryption level, generates a global configuration policy and synchronizes it to each microservice node. The user management microservice creates a role permission matrix based on the RBAC model, dynamically binds user operation permissions to the data access level, and builds an operation log audit chain to achieve full-process traceability of user behavior. The data transmission microservice starts the heterogeneous data access engine, deploys data verification rules for the three modes of web front-end upload, FTP transmission and SDK embedded access, implements two-way identity authentication through the security authentication module of the service gateway, establishes an encrypted transmission channel and performs data integrity verification, and distributes the data stream that passes the verification to the data storage management microservice according to the preset routing strategy.

[0044] During the data storage phase, the hybrid storage architecture implements differentiated processing strategies based on data type: Structured data is logically sharded by timestamp using a dynamic partitioning algorithm, and a distributed column family storage structure is constructed using a secondary inverted index to achieve rapid location in high-concurrency scenarios. For image data, the object storage engine parses EXIF ​​metadata to construct a spatiotemporal composite index and a hash map. Prefetching strategies are used to improve retrieval efficiency for frequently accessed data. For compressed data, directory tree indexing technology is used to create a hierarchical pointer table. Pre-scanning file headers generates a topological map of the compressed package's internal structure, enabling precise location of physical offsets without decompression. Multi-dimensional combined search indexes are generated simultaneously during storage, supporting joint queries based on multiple criteria, such as time range, data type, and keywords.

[0045] When the data extraction phase begins, the paging query engine locates the target data shard based on a pre-built distributed index. It then establishes a logical association between timestamp columns and data blocks using a bidirectional linked list mapping mechanism. A sliding window algorithm dynamically manages the memory cache, prioritizing hot data to reduce disk I / O operations. The parallel image processor employs a multi-stage pipeline architecture, breaking down image decoding, resizing, and format conversion operations into independent subtasks. An instruction reordering optimizer eliminates thread contention and leverages GPU acceleration for batch image processing. The decompression-free extractor constructs a memory-resident index table based on the ZIP file directory tree, creates an asynchronous I / O channel for multi-channel concurrent reading, and uses circular buffer technology to reduce coroutine switching overhead, extracting the target file directly from the compressed archive's physical location.

[0046] During the data visualization display stage, the multi-window synchronous display framework receives user interaction instructions through an event-driven architecture. The time synchronization controller publishes a timing reference signal to each display window. The window's built-in timing calibrator compares local clock deviations in real time and uses an interpolation compensation algorithm to eliminate timing jitter, ensuring the accuracy of timeline alignment across data types. The interactive visualization operation component converts user-triggered zoom and filtering operations into a rendering instruction queue, and uses WebGL shader pre-compilation technology to achieve atomic updates of visualization elements, maintaining a stable interface rendering frame rate. The visualization template management microservice receives user configuration instructions through a drag-and-drop editing interface, encodes layout parameters and style attributes into a template description file, and the version management system uses differential storage technology to record template iteration trajectories to ensure that historical versions can be quickly traced back and switched.

[0047] During system operation, the resource monitoring microservice continuously collects indicators such as CPU utilization, memory occupancy, and network throughput, extracts resource utilization feature vectors through a sliding time window algorithm, and applies a spectral clustering algorithm to identify abnormal patterns and generate a container scaling priority queue. When it is detected that the resource load exceeds the preset threshold, the dynamic early warning model triggers a hierarchical response strategy, performs horizontal or vertical scaling operations through the container orchestration tool, uses container snapshot hot migration technology to ensure service continuity, and establishes scaling transaction logs to support operation rollbacks. The service gateway monitors the communication traffic between microservices in real time, dynamically adjusts the request distribution weight through an adaptive load balancing algorithm, and combines the circuit breaker mechanism with the current limiting strategy to prevent service overload. The security authentication module implements token-based access control and request signature verification to ensure system communication security.

[0048] Collaboration between microservices is achieved through predefined interaction protocols: the data storage management microservice and the data extraction microservice establish an efficient data location channel based on a distributed index structure. The data visualization template management microservice ensures consistency between template configuration and real-time display through a version mapping mechanism. The resource monitoring microservice and the container orchestration tool form a closed-loop control link. The entire system achieves functional decoupling and elastic scalability through a microservices-based architecture, containerized deployment ensures environmental consistency, and lightweight communication protocols reduce interaction latency. Ultimately, the system optimizes the entire process of multimodal data, from access storage, efficient processing, to precise and synchronized display, meeting business requirements for high concurrency, low latency, and strong consistency.

[0049] The entire working steps of this program are as follows:

[0050] S1: User management settings. First, the administrator logs in. After logging in, he can set system configuration information, configure user permissions, configure user roles, add or delete users, etc.

[0051] S2: User Registration and Login: Users access the system through the front-end interface and register, filling in their username, password, email address, and other information. The user management module verifies and stores the registration information. After successful registration, the user logs in to the system using their account and password. The user management module authenticates the user and assigns permissions based on their role, allowing the user to enter the system's main interface.

[0052] S3: Data Upload: The logged-in user selects an appropriate data transmission method based on the data source and their needs, such as web front-end upload, FTP upload, or SDK embedded upload. During the upload process, the data transmission module performs format verification, size check, and encryption on the data before transferring the data to the data storage management module for storage. The data storage management module stores the data in the corresponding location based on the data type and characteristics and updates the data index.

[0053] S4: Template creation and management: In the data visualization template management module, users create or edit presentation templates by dragging and dropping. Users can add presentation modules such as charts, images, videos, and data tables according to their presentation needs, and set the display content, style, layout, and other parameters for each module in detail. After completing the template design, users can save the template and perform version management operations, such as viewing historical versions, restoring old versions, and deleting unused versions.

[0054] S5: Data extraction and display: The user selects the data to be displayed and the corresponding template in the data visualization display module, sets the display time range and time interval, and clicks the play button. The data extraction module extracts the corresponding data from the data storage management module according to the user settings, and processes it using extraction algorithms for different data types, such as time-based paging extraction of CSV data, multi-threaded processing of PNG data, and decompression-free extraction of ZIP data. After processing, the extracted data is transferred to the data visualization display module and displayed according to the selected template, realizing the synchronous display of multiple types of data at specified time intervals, and providing rich interactive functions for users to analyze data;

[0055] S6: System Monitoring and Maintenance: During system operation, the resource monitoring module monitors the system hardware resource usage in real time, visualizes the monitoring data, and compares it with preset thresholds. If resource usage exceeds the threshold, the administrator is notified immediately.

[0056] In daily work scenarios, the hybrid storage architecture optimizes the storage and retrieval efficiency of multimodal data through differentiated strategies. For structured data (such as time series logs, sensor data, etc.), the dynamic partitioning strategy automatically divides the logical shards according to the data timestamp, and combines the secondary inverted index to achieve efficient queries based on the time range. For example, in the industrial Internet of Things scenario, the time series data generated by the equipment is stored in hourly shards, and the fault period data is quickly located through the timestamp index; for image data (such as monitoring video frames, remote sensing images), the object storage engine parses the shooting time, geographic location and other information in the EXIF ​​attributes, builds a spatiotemporal composite index, supports "time + region" joint retrieval, and can quickly retrieve monitoring images of specific time periods and regions in urban security scenarios; compressed data (such as batch report compression packages) generates a directory tree index by pre-scanning the file header and establishes a hierarchical pointer table. In the financial data analysis scenario, specific transaction record files in the compressed package can be directly located to avoid storage waste and time delays caused by full decompression. During the storage process, the multi-dimensional combined retrieval function supports correlation analysis across data types. For example, in an environmental monitoring scenario, users can simultaneously query meteorological sensor data (structured), satellite cloud maps (images), and historical report compression packages, and analyze environmental change trends through timeline correlation.

[0057] During data extraction, the paging query engine dynamically manages the memory cache by mapping timestamp columns to data blocks in a bidirectional linked list, combined with a sliding window mechanism. This allows rapid loading of designated time pages of user behavior logs during major e-commerce promotions, reducing performance bottlenecks caused by frequent disk access. The parallel image processor uses a pipeline architecture to decouple image decoding and processing. In medical imaging analysis, a thread pool concurrently handles 3D reconstruction and lesion annotation tasks for CT images. An instruction reordering optimizer eliminates GPU resource competition and improves batch processing efficiency. The decompression-free extractor builds a memory-resident index table based on the ZIP directory tree. In scientific research data management, researchers can concurrently extract raw data files for specific experimental groups directly from large experimental data compression packages. Asynchronous I / O channels and circular buffer technology ensure high-throughput reading, avoiding the storage pressure associated with decompressing the entire compressed package. This complete mechanism ensures efficient extraction of heterogeneous data, meeting the needs of real-time analysis.

[0058] In visualization scenarios, the time synchronization controller broadcasts a timing reference signal through a publish-subscribe model. In a multi-screen command system for military exercises, each tactical map, sensor data panel, and real-time video stream window receives a unified timing signal. The timing calibrator compensates for network transmission delays in real time, and the interpolation compensation algorithm eliminates time jitter in multi-source data, ensuring global synchronization of battlefield situations. In smart city large-screen displays, the progressive rendering command queue of the interactive operation component can decompose user area zoom operations into multi-level detail rendering tasks. WebGL shader precompilation enables smooth transitions in traffic flow heat maps, and atomic update technology avoids interface flickering, ensuring visual coherence of multi-dimensional data linkage. In educational and training scenarios, when teachers drag the timeline to play multimodal data on historical events, each display window strictly aligns with the time label, and combined with mouse hover interaction, event details are displayed in real time, enhancing the immersiveness of teaching demonstrations.

[0059] In burst traffic scenarios (such as the peak in access to government systems after the release of policies), the resource monitoring microservice continuously collects CPU, memory, and network metrics through a sliding time window, applies a spectral clustering algorithm to identify abnormal load patterns (such as memory leaks or DDoS attacks), and generates a priority queue for scaling. When it detects that the load of the Web service cluster exceeds the threshold, the dynamic early warning model triggers the horizontal scaling strategy. The container orchestration tool automatically deploys new instances and injects them into the service registry. At the same time, the existing sessions are seamlessly transferred to the new node through the container snapshot hot migration technology to ensure that the user experience is imperceptible. If the database service encounters a disk I / O bottleneck, the vertical scaling strategy is activated to dynamically adjust the container resource quota. The scaling transaction log fully records the operation trajectory. After the e-commerce flash sale event ends, the system can quickly roll back to the initial resource configuration state based on the log to avoid idle resources and waste. The entire mechanism achieves the optimal balance between resource utilization and service quality, ensuring the stable operation of the system under high load.

[0060] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A visualization system that can display multiple types of data synchronously according to time, characterized in that: A distributed service cluster consisting of microservices, user management microservices, data transmission microservices, data storage management microservices, data extraction microservices, data visualization display microservices, data visualization template management microservices, and resource monitoring microservices; Each microservice is encapsulated in a container and interacts using lightweight communication protocols. Automated deployment and dynamic expansion are achieved through container orchestration tools. The system sets up a customizable set of basic parameters and a graphical configuration interface for microservice configuration, which are used to collaboratively generate system operating environment configuration strategies; The user management microservice establishes a multi-dimensional permission system based on the role-based access control model and records operation logs for audit tracking. The data transmission microservice integrates multiple heterogeneous data access methods, implements transmission process verification, and transmits data through a secure channel through a service gateway. The data storage management microservice adopts a hybrid storage architecture, establishes distributed column family storage for structured data, constructs metadata index storage for image data, implements decompression-free storage for compressed data, and provides multi-dimensional combination retrieval capabilities. The data extraction microservice includes a paging query engine, a parallel image processor, and a decompression-free extractor. The paging query engine combines memory cache and prefetching strategies to optimize access paths, the parallel image processor uses a multi-threaded pipeline architecture to decompose tasks, and the decompression-free extractor establishes a multi-process concurrent reading mechanism based on file structure indexes. The data visualization display microservice builds a multi-window synchronous display framework with a built-in time synchronization controller and a unified timing scheduler to ensure accurate and synchronous presentation of timelines across data types, and integrates interactive visualization operation components. The data visualization template management microservice provides a visual editing interface and a version management system that supports drag-and-drop configuration of the layout and style of multimodal display modules. The version management system uses differential storage technology to achieve template backtracking and switching. The resource monitoring microservice collects system resource indicators in real time, establishes a dynamic early warning model based on time series analysis, and triggers the container orchestration tool to perform elastic scaling operations to form a closed-loop resource control system; Each microservice is centrally scheduled through a service gateway that integrates service discovery mechanisms, load balancing algorithms, and security authentication modules to establish request routing and flow control strategies. The data storage management microservice and the data extraction microservice achieve efficient data positioning through a pre-built index structure, and the data visualization template management microservice and the data visualization display microservice maintain display consistency through a template version mapping mechanism.

2. A visualization system capable of synchronously displaying multiple types of data in time according to claim 1, characterized in that: In the hybrid storage architecture of the data storage management microservice, the distributed column family storage established for structured data adopts a dynamic partitioning strategy, generates logical shards based on data timestamps, and establishes a secondary inverted index; For metadata index storage of image data, the object storage engine extracts EXIF ​​attributes to construct a spatiotemporal composite index, and establishes a hash mapping relationship to accelerate retrieval; for decompression-free storage of compressed data, a hierarchical pointer table is established based on the file directory tree index, and the internal structure topology of the compressed package is generated by pre-scanning the file header information to achieve on-demand positioning of the target file's physical offset.

3. The visualization system capable of synchronously displaying multiple types of data according to time according to claim 1, characterized in that: The paging query engine in the data extraction microservice adopts a heterogeneous data encapsulation strategy, establishes a bidirectional linked list mapping between the timestamp column and the data block, and combines it with a sliding window mechanism to realize dynamic replacement of cached data; the parallel image processor builds a pipelined task distribution architecture, separates image parsing and processing operations through a decoding thread pool, and uses an instruction reordering optimizer to eliminate thread competition; the decompression-free extractor builds a memory-resident index table based on the ZIP file directory tree, establishes a multi-channel concurrent reading pipeline through an asynchronous I / O channel, and uses a circular buffer to reduce the overhead of coroutine switching.

4. The visualization system capable of displaying multiple types of data synchronously in time according to claim 1, characterized in that: The time synchronization controller of the data visualization display microservice adopts an event-driven architecture, broadcasts the timing reference signal through a publish-subscribe model, and each display window has a built-in timing calibrator to compare the local clock deviation and uses an interpolation compensation algorithm to eliminate timing jitter; the interactive visualization operation component integrates a rendering instruction queue, dynamically generates progressive rendering instructions based on user interaction events, and realizes atomic updates of visualization elements through WebGL shader precompilation technology to maintain interface response consistency.

5. The visualization system capable of synchronously displaying multiple types of data according to time according to claim 1, characterized in that: The dynamic early warning model of the resource monitoring microservice adopts multi-dimensional indicator fusion analysis, collects resource utilization feature vectors through a sliding time window, applies a spectral clustering algorithm to identify abnormal patterns, and generates a container scaling priority queue; the elastic scaling operation adopts a hierarchical response strategy, dynamically selects horizontal expansion or vertical expansion mode according to the early warning level, maintains service continuity through container snapshot hot migration technology, and establishes a rollback transaction log for scaling operations.

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