A large-scale map data dynamic sparsification and fusion rendering system and method

Abstract

The present application relates to a kind of large-scale map data dynamic thinning and fusion rendering system and method, wherein the system includes: data perception and event trigger module, for real-time acquisition map data and multi-source data, generate standardized change event by change detection, update event queue based on change event;Dynamic thinning and scheduling module, based on change event and map data generation rendering scheduling scheme;Data distribution and push module, event queue and rendering scheduling scheme are encapsulated as incremental data package, and according to terminal role, multi-end real-time synchronization is differentially pushed;Fusion rendering engine module, receive and integrate the rendering scheduling scheme in incremental data package, execute map rendering based on rendering scheduling scheme;And the rendering result is judged, update rendering scheduling scheme based on the determination result.The method provided is realized based on the above-mentioned system.Compared with prior art, the present application can perceive task logic, fuse multi-source real-time information, and support multi-end differentiating rendering.

Description

Technical Field

[0001] This invention relates to the fields of Geographic Information Systems (GIS), computer graphics, and emergency dispatch, and in particular to a system and method for dynamic thinning and fusion rendering of large-scale map data. Background Technology

[0002] In the fields of Geographic Information Systems (GIS) and emergency dispatch and command, a core technical challenge lies in achieving smooth and clear rendering of massive geographic data (often exceeding 200,000 features) generated during the operation and maintenance of infrastructure such as power, municipal, and communications systems. Directly loading and rendering all data in its entirety leads to severe performance bottlenecks, causing interface lag and interactive delays, significantly impacting command and decision-making efficiency. Therefore, dynamically thinning and optimizing the rendering of massive geographic features has become an inevitable choice for achieving an efficient visualization platform. Existing technologies include several solutions that aim to adapt map rendering strategies by evaluating the hardware performance of terminal devices, such as CPU and GPU capabilities. For example, map data can be reduced, vertex thinning can be performed, and texture resolution can be adjusted to achieve a relatively smooth display on devices with varying performance. Specifically, Chinese patent application CN119206026A provides a map rendering method that prioritizes evaluating the hardware performance parameters of the target device and then reduces or thins the map data based on the evaluation results, thereby dynamically adjusting the map rendering strategy.

[0003] However, such solutions have significant drawbacks, especially in meeting the demands of high-requirement emergency collaborative scheduling scenarios. First, their thinning strategies are mechanical and static, simplifying data indiscriminately based solely on equipment performance, lacking awareness of business logic and data semantics. This easily leads to high-priority emergency task points being treated the same as ordinary data, resulting in the aggregation or hiding of critical information, failing to meet the command and dispatch's need for precise information visualization. Second, existing solutions primarily target static or slowly changing map base data. Their architecture cannot effectively handle the multi-source, high-frequency updated real-time data streams of field personnel trajectories, task status, and on-site multimedia information in emergency scenarios, resulting in high information synchronization latency and an inability to achieve event-driven, multi-terminal, second-level real-time response. Furthermore, existing technologies adopt a holistic rendering model, failing to provide differentiated, on-demand rendering content and granularity for terminals with different roles within the system, making it difficult to support efficient cross-terminal business collaboration.

[0004] Therefore, existing technologies have significant shortcomings in terms of business relevance, real-time performance, and collaboration in map thinning and rendering. There is an urgent need for a new technical solution that can perceive task logic, integrate multi-source real-time information, and support multi-terminal differentiated rendering. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a large-scale map data dynamic thinning and fusion rendering system and method. It realizes the thinning of map elements on demand, real-time push of important event changes, and display after efficient rendering and fusion on multiple terminals, while ensuring data integrity.

[0006] The objective of this invention can be achieved through the following technical solutions: According to a first aspect of the present invention, a large-scale map data dynamic thinning and fusion rendering system is provided, comprising: The data perception and event triggering module, located on the server, is used to collect map data and multi-source data in real time, generate standardized change events through change detection, and update the event queue based on the change events; the map data includes viewport range, zoom level, and geographic feature data; the multi-source data includes personnel data and task data; The dynamic thinning and scheduling module, located on the server, receives the change events and generates a rendering scheduling scheme based on the change events and map data. The data distribution and push module, located on the server side, encapsulates the event queue and rendering scheduling scheme into incremental data packets and performs differentiated pushes to multiple terminals in real time according to terminal roles. The fusion rendering engine module, located at the front end, receives and integrates the rendering scheduling scheme in the incremental data packet, performs map rendering based on the rendering scheduling scheme, and determines the rendering result, updating the rendering scheduling scheme based on the determination result.

[0007] As a preferred technical solution, the data perception and event triggering module adopts a management mechanism that combines batch loading and local caching when collecting data. The management mechanism is as follows: During the initial data collection, the data perception and event triggering module loads the map data in batches according to spatial partitions or types, and stores the loaded map data in IndexedDB after structuring. During subsequent data collection, it executes a timestamp comparison mechanism, scheduling operation logs to assist in verification, and differential data merging to perform differential updates and cache synchronization on the map data and multi-source data. The timestamp comparison mechanism is as follows: the data perception and event triggering module compares the collected data of two adjacent timestamps each time data is collected, and only collects change events; The aforementioned scheduling operation log auxiliary verification involves comparing the scheduling operation log with the data stored in IndexedDB to verify data consistency and quickly locate change events. The differential data merging refers to the incremental merging of the change events with the data stored in IndexedDB.

[0008] As a preferred technical solution, the method for generating the aforementioned change event is as follows: Change detection is performed on the multi-source data and map data through field-level comparison, state machine transitions, and threshold changes. Change events are generated based on the change detection results. The storage format of the change events is: event type, change field, old and new values, version number and timestamp. The event types include field personnel location updates, check-in / check-out, task status changes and media upload data.

[0009] As a preferred technical solution, when the map undergoes viewport loading, movement, or scaling, the dynamic thinning and scheduling module is activated and performs the following steps to generate the rendering scheduling scheme: The system parses the change information in the event queue and simultaneously obtains the current map data and the business priority of each geographic element; the geographic element data includes point elements and line elements; the point elements include equipment, facilities, personnel locations, and task points; the line elements include trajectories, roads, and pipelines; Based on the viewport range and zoom level, the viewport is divided into several equal-sized sub-regions. The point density and line density within each sub-region are calculated to construct a density matrix. The global maximum density and average density are obtained based on the density matrix, the density threshold is determined based on the global maximum density and average density, and the maximum amount of rendering data is determined based on the performance of the device. If the total amount of data within the viewport is greater than or equal to the maximum amount of rendering data, and the density of each sub-region in the corresponding density matrix is ​​greater than or equal to the density threshold, then a dynamic thinning algorithm is triggered to obtain the thinning result. Based on the thinning result, the principle of preserving all original data, and the map data generation rendering schedule, the rendering schedule includes geographic feature data rendering, personnel data rendering, and task data rendering. If the total amount of data within the viewport and the density of each sub-region in the density matrix are both lower than the density threshold, then the initial rendering scheduling scheme for generating the map data is based on the principle of preserving all original data and the aforementioned map data. The rendering result of the previous rendering scheduling scheme executed by the fusion rendering engine module is obtained, and it is determined whether the effect meets the requirements. If it does not meet the requirements, the initial rendering scheduling scheme is modified based on the rendering result to obtain the final rendering scheduling scheme; otherwise, the initial rendering scheduling scheme is directly used as the final rendering scheduling scheme.

[0010] As a preferred technical solution, the dynamic thinning is based on the business priority to select thinning objects. Geographic elements with high business priority retain their original accuracy, while geographic elements with ordinary business priority are thinned according to element category to obtain the thinning result.

[0011] As a preferred technical solution, the method for obtaining the thinning result of the point features includes: Using a quadtree spatial index structure, an index is created for all point features. Based on the index, the dynamic thinning and scheduling module quickly retrieves candidate point features in each sub-region and constructs a candidate point set. When the point density in a sub-region is lower than the density threshold, the original map data and multi-source data are retained; otherwise, a sub-region-based aggregation algorithm is used and a hysteresis mechanism is introduced to calculate the weighted center point and generate an aggregation symbol. The aggregation symbol includes the center coordinates and statistical attributes of the aggregation point. The point feature thinning results are generated based on the aforementioned aggregation symbol, sub-region size, and density threshold.

[0012] As a preferred technical solution, the method for obtaining the thinning result of the line features is as follows: Based on the quadtree spatial index structure, the spatial index structure of line features and surfaces is established, and the candidate line feature set within the viewport range is quickly retrieved based on the index. Line features and surfaces that exceed the viewport are clipped to obtain line sets and surface sets. Calculate the viewport overlap between line features and faces in the line set and face set, where the viewport overlap for line features is the overlap length ratio and the viewport overlap for faces is the overlap area ratio. Based on the viewport overlap, total length of line features or total area of ​​surfaces, and business priority, an importance score is calculated, and lines or surfaces with an importance score greater than a first score threshold are used as rendering objects. For the rendered object, geometric node thinning is performed based on the scaling level. For line features, the Douglas-Peucker algorithm is used to simplify nodes while retaining key inflection points. For faces, node precision is adjusted according to the area threshold and scaling level, and the nodes are downgraded to centroid points or hotspot labels for display. When the number of rendering objects exceeds the maximum number of rendering data, the dynamic thinning and scheduling module merges collinear or adjacent line features into skeleton lines, performs topological merging of adjacent or similar faces to form aggregated faces, and treats lines or faces with importance scores less than or equal to the second score threshold as hidden objects. Based on the rendering object, key inflection points, centroid points or hot zone labels, skeleton lines, aggregation surfaces, and hidden objects, a thinning result is generated.

[0013] As a preferred technical solution, the following steps are performed in the data distribution and push module to achieve differentiated push: Based on the event queue, the latest status of each terminal role is obtained and the historical status of the previous moment is compared. The latest status and the historical status are compared and all changes are extracted. The changes include personnel location, task status, task image, task time frequency, task audio file and changes in task dispatch process. Determining the push timing based on the aforementioned changes includes calculating the location offset for the personnel location change; if the location offset is greater than the offset threshold, then pushing the personnel location change item to the corresponding terminal role; merging multiple changes from adjacent time periods into a single frame and pushing it to the corresponding terminal role.

[0014] As a preferred technical solution, the terminal roles include an operator terminal, a large screen terminal, and a mobile app terminal, and the data distribution and push module and the data push to each terminal role are implemented based on SignalR.

[0015] According to a second aspect of the present invention, a method for dynamic thinning and fusion rendering of large-scale map data is provided, the method comprising: The system collects map data and multi-source data in real time, generates standardized change events through change detection, and updates the event queue based on these change events. The map data includes viewport range, zoom level, and geographic feature data; the multi-source data includes personnel data and task data. Based on the event queue and identifying change information, a rendering scheduling scheme is generated based on the change information and map data. The event queue and rendering scheduling scheme are encapsulated into incremental data packets, and the rendering data in the incremental data packets is integrated to perform map rendering based on the rendering data; and the rendering scheduling scheme is updated based on the rendering results.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1) Existing solutions simplify the data indiscriminately based solely on equipment performance, leading to the aggregation or hiding of high-priority emergency task points. To address this technical issue, this invention comprehensively considers the business priorities of change information, map data, and geographic elements when generating the rendering scheduling scheme. Specifically, based on viewport division and other large sub-regions, a density matrix is ​​constructed by calculating point density and line density, and a density threshold is determined by combining the global maximum density and average density. Simultaneously, a maximum amount of rendering data is set according to equipment performance. When the data volume exceeds the limit or the density is too high, a dynamic thinning algorithm is triggered to prioritize the preservation of the original precision of high-priority business elements and thin out ordinary priority elements by category. This ensures that critical information such as emergency task points is not blindly aggregated, but is accurately presented according to actual business needs, meeting the visualization requirements of command and dispatch for high-priority data.

[0017] 2) In the system provided by this invention, the data perception and event triggering module performs change detection through field-level comparison, state machine transitions, and threshold changes, generates standardized change events, and updates the event queue. Simultaneously, it employs a batch loading and local cache management mechanism, combined with timestamp comparison, scheduling operation log-assisted verification, and differential data merging, to achieve differential data updates and cache synchronization, reducing redundant processing. Furthermore, the data distribution and push module encapsulates the event queue and rendering scheduling scheme into incremental data packets, utilizing SignalR technology to achieve real-time multi-terminal push, supporting event-driven second-level synchronization. Through these modules, the system can quickly respond to real-time events such as field personnel location updates and task status changes, ensuring efficient data stream processing and low-latency multi-terminal synchronization, thus improving the real-time performance of emergency collaboration.

[0018] 3) This invention provides a synchronous differentiated rendering method that pushes different content to different terminal roles, avoiding the problem that when using overall rendering, it is difficult to support efficient cross-terminal business collaboration because it is impossible to provide differentiated, on-demand rendering content and granularity to terminals with different roles in the system. Attached Figure Description

[0019] Figure 1 This is a system structure diagram of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0021] Example 1 To address the problems existing in the prior art, this invention provides a large-scale map data dynamic thinning and fusion rendering system, the framework of which is as follows: Figure 1 As shown, the system is divided into a server, a front-end, and a terminal. The server integrates a data perception and event triggering module, a dynamic thinning and scheduling module, and a data distribution and push module. The front-end embeds a fusion rendering engine module. The terminal includes various roles, such as operator terminal, large screen terminal, and mobile app terminal. In detail, the data distribution and push module establishes communication channels with the fusion rendering engine module and the terminal respectively, and the data push between the data distribution and push module and each terminal role is implemented based on SignalR, thereby ensuring millisecond-level synchronization across multiple terminals.

[0022] The specific functions of each module are as follows: (a) Data perception and event triggering module.

[0023] This module is used to collect map data and multi-source data in real time. It generates standardized change events through change detection and updates the event queue based on these change events. Map data includes viewport range, zoom level, and geographic feature data. Multi-source data includes personnel data and task data. In detail, personnel data includes personnel location, check-in, images, and videos. The collection process involves the system locating and collecting real-time locations through the field personnel's App, reporting the collected locations to the server, storing them in the database, generating location change events, and finally pushing them to the front end via SignalR, where the front end updates the personnel locations on the map. Task data includes operations from the scheduling end such as dispatch, reassignment, completion, and cancellation.

[0024] Furthermore, when collecting data, this module adopts a management mechanism that combines batch loading with local caching. The management mechanism is as follows: during the first data collection, the data perception and event triggering module loads the map data in batches according to spatial partitions or types, and stores the loaded map data in IndexedDB after structuring. During subsequent data collection, it executes a timestamp comparison mechanism, schedule operation logs to assist in verification, and differential data merging to perform differential updates and cache synchronization of map data and multi-source data.

[0025] Specifically, the timestamp comparison mechanism is as follows: the data perception and event triggering module compares the collected data of two adjacent timestamps each time data is collected, and only collects change events; the scheduling operation log auxiliary verification is as follows: the scheduling operation log is compared with the data stored in IndexedDB to verify data consistency and quickly locate change events; the differential data merging is as follows: the change events are incrementally merged with the data stored in IndexedDB.

[0026] In this module, changes are detected by comparing multi-source data and map data at the field level, state machine transitions, and threshold changes. Change events are generated based on the change detection results. The storage format of change events is: event type, changed field, new and old values, version number and timestamp. Event types include field personnel location updates, check-in, task status changes and media upload data.

[0027] (b) Dynamic thinning and scheduling module.

[0028] This module is used to receive change events. It is activated when the map viewport is loaded, moved, or zoomed. It uses the change events and map data to generate a rendering schedule, and the corresponding process is as follows.

[0029] T1 parses the change information in the event queue and simultaneously obtains the current map data and the business priority of each geographic feature; geographic feature data includes point features and line features; point features include equipment, facilities, personnel locations, and task points; line features include trajectories, roads, and pipelines.

[0030] T2. Based on the viewport range and zoom level, divide the viewport into several equal-sized sub-regions, calculate the point density and line density within each sub-region, and construct a density matrix.

[0031] T3. Obtain the global maximum density and average density based on the density matrix, determine the density threshold based on the global maximum density and average density, and determine the maximum amount of rendering data based on the device performance.

[0032] T4. Determine whether to trigger the thinning algorithm based on the density threshold and the maximum amount of rendered data.

[0033] If the total amount of data within the viewport is greater than or equal to the maximum amount of data to be rendered, and the density of each sub-region in the corresponding density matrix is ​​greater than or equal to the density threshold, then the dynamic thinning algorithm is triggered to execute steps T401~T416 to obtain the thinning results. Based on the thinning results, the principle of preserving all original data, and the map data, a rendering schedule is generated. The rendering schedule includes rendering of geographic feature data, rendering of personnel data, and rendering of task data.

[0034] Dynamic thinning filters thinning objects based on business priority. Geographic features with high business priority retain their original precision, while those with ordinary business priority are thinned according to feature category to obtain the thinning results. Specifically, thinning is performed on point and line features, with the following steps: T40, obtain the thinning results for point elements.

[0035] T401. Using a quadtree spatial index structure, an index is created for all point features. Based on the index dynamic thinning and scheduling module, candidate point features in each sub-region are quickly retrieved, and a candidate point set is constructed.

[0036] T402. When the point density in a sub-region is lower than the density threshold, the original map data and multi-source data are retained. Otherwise, a sub-region-based aggregation algorithm is used and a hysteresis mechanism is introduced to calculate the weighted center point and generate an aggregation symbol. The aggregation symbol includes the center coordinates and statistical attributes of the aggregation point.

[0037] T403. Based on the aggregation symbol, sub-region size, and density threshold, generate point feature thinning results, wherein the thinning results include the aggregation symbol, sub-region size, density threshold, rendering retained points, and exception points.

[0038] T41, obtain the thinning results for line features.

[0039] T411. Based on the quadtree spatial index structure, establish a spatial index structure for line features and polygons, and quickly retrieve the candidate line feature set within the viewport range based on the index. Then, perform boundary clipping on the line features and polygons that exceed the viewport to obtain the line set and polygon set.

[0040] T412. Calculate the viewport overlap between line features and surfaces in line sets and surface sets. For line features, the viewport overlap is the overlap length ratio, and for surfaces, the viewport overlap is the overlap area ratio.

[0041] T413. Calculate the importance score based on viewport overlap, total length of line features or total area of ​​surfaces, and business priority. Use lines or surfaces with an importance score greater than the first score threshold as rendering objects.

[0042] T414. For rendered objects, perform geometric node thinning based on the scaling level. For line features, use the Douglas-Peucker algorithm to simplify nodes and retain key inflection points. For polygons, adjust node precision according to area threshold and scaling level, and downgrade them to centroid points or hotspot labels. Key inflection points and centroid points (or hotspot labels) are collectively referred to as retained features.

[0043] T415. When the number of rendered objects exceeds the maximum number of rendered data, the dynamic thinning and scheduling module merges collinear or adjacent line features into skeleton lines, performs topological merging of adjacent or similar faces to form aggregated faces, and treats lines or faces with importance scores less than or equal to the second score threshold as hidden objects, retaining reference information in the data layer.

[0044] T416 generates a thinning result based on the rendering object, key inflection points, centroid points or hotspot labels, skeleton lines, aggregate surfaces, and hidden objects. The thinning result includes: preserved features and their simplified geometry, merged features and their membership relationships (i.e., skeleton lines and aggregate surfaces), a list of hidden feature identifiers, and simplified parameter and style hints for each type of feature.

[0045] Conversely, if the total amount of data within the viewport and the density of each sub-region in the density matrix are both below the density threshold, then the initial rendering schedule is generated based on the principle of preserving all original data and the map data.

[0046] T5. Obtain the rendering result of the previous rendering scheduling scheme executed by the fusion rendering engine module, determine whether the effect meets the requirements, if not, modify the initial rendering scheduling scheme based on the rendering result to obtain the final rendering scheduling scheme; otherwise, directly use the initial rendering scheduling scheme as the final rendering scheduling scheme.

[0047] In addition, the front end monitors the frame rate and load during the rendering process and sends performance feedback back to the scheduling module, thereby enabling adaptive adjustment of the thinning parameters and forming a dynamic optimization closed loop.

[0048] (c) Data distribution and push module.

[0049] This module encapsulates the event queue and rendering scheduling scheme into incremental data packets, enabling differentiated push notifications across multiple devices in real-time, based on terminal roles. The following steps are executed within this module to achieve differentiated push notifications: i. Based on the event queue, obtain the latest status of each terminal role and the historical status of the previous moment, compare the latest status and the historical status, and extract all changes; the changes include personnel location, task status, task image, task time frequency, task audio file, and changes in task dispatch process.

[0050] ii. Determine the push timing based on changes, including calculating the location offset for personnel location changes. If the location offset is greater than the offset threshold, push the personnel location change item to the corresponding terminal role. For multiple changes in adjacent time periods, merge them into a single frame and push them to the corresponding terminal role. Specifically, the system sets different data subscription rules for different terminal roles, as follows: Operator terminal (Web): pulls data related to their tasks, supporting complete interaction and task management; Large screen terminal: only pulls globally aggregated data, highlighting key tasks and hot areas; Mobile App terminal: finely synchronizes user-related tasks, navigation paths, reminder information, etc.

[0051] In addition, when pushing information to terminal roles, real-time source information fusion is also performed. The specific process is as follows: A1. This module aligns timestamps for events from different sources, such as tasks, personnel, media, and dispatch processes. If multiple events affect the same task entity, the events are merged to retain the latest status. In this process, a task priority queue mechanism ensures that critical events, such as alarms and anomalies, are pushed first.

[0052] A2. Based on the data processing results of A1, a unified multi-source fusion result dataset (Merged Data Set) is output, which includes task status, personnel location, media resources and corresponding time identifiers. Logically, it represents the smallest snapshot of the current world state of the system and is used for subsequent data distribution and rendering.

[0053] A3. Integrate the multi-source fusion result dataset with the incremental data packet, and push it to the front end via SignalR as push data. After the front end renders, it can feed back the rendering performance and event latency to the server to achieve dynamic optimization of information fusion. The push data includes the location of field personnel, the status changes of each task (in progress, completed, abnormal, etc.), task images, videos and audio files, changes in the dispatch process (who receives the task, changes in the handler, etc.), and the rendering scheduling scheme.

[0054] Through this step, the system achieves real-time integration of field location, task status, multimedia content, and dispatch process, ensuring that users on multiple devices receive a consistent geographic information view and task progress visualization results at the same time.

[0055] (d) Fusion rendering engine module.

[0056] This module receives and integrates the rendering scheduling scheme in the incremental data packet, performs map rendering based on the rendering scheduling scheme, and judges the rendering result and updates the rendering scheduling scheme based on the judgment result.

[0057] The above describes the detailed structure of the large-scale map data dynamic thinning and fusion rendering system provided by this invention. This system supports smooth thinning and rendering of 200,000-level point / line data in the map, with high performance; it supports second-level synchronization of task status, personnel trajectory, and images and videos, with high real-time performance; and it can realize automatic recognition of rendering needs by multiple terminals, saving resources, improving response, and having high collaboration; it is suitable for emergency scenarios in multiple industries such as power, municipal administration, communications, and transportation, and has strong versatility.

[0058] Example 2 This embodiment provides a method for dynamic thinning and fusion rendering of large-scale map data. This method is based on the system provided in Embodiment 1 and includes: S1. Real-time acquisition of map data and multi-source data; generation of standardized change events through change detection; and updating of the event queue based on change events; map data includes viewport extent, zoom level, and geographic feature data; multi-source data includes personnel data and task data.

[0059] S2. Based on the event queue and identify change information, generate a rendering scheduling scheme based on the change information and map data.

[0060] S3. Encapsulate the event queue and rendering schedule into an incremental data packet, integrate the rendering data in the incremental data packet, perform map rendering based on the rendering data, and update the rendering schedule based on the rendering results.

[0061] The present invention also provides an electronic device including a central processing unit (CPU) that can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). The RAM may also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0062] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0063] The processing unit executes the various methods and processes described above, such as methods S1 to S3. For example, in some embodiments, methods S1 to S3 may be implemented as computer software programs tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of methods S1 to S3 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute methods S1 to S3 by any other suitable means (e.g., by means of firmware).

[0064] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0065] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0066] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0067] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A large-scale map data dynamic thinning and fusion rendering system, characterized in that, include: The data perception and event triggering module, located on the server, is used to collect map data and multi-source data in real time, generate standardized change events through change detection, and update the event queue based on the change events; the map data includes viewport range, zoom level, and geographic feature data; the multi-source data includes personnel data and task data; The dynamic thinning and scheduling module, located on the server, receives the change events and generates a rendering scheduling scheme based on the change events and map data. The data distribution and push module, located on the server side, encapsulates the event queue and rendering scheduling scheme into incremental data packets and performs differentiated pushes to multiple terminals in real time according to terminal roles. The fusion rendering engine module, located at the front end, receives and integrates the rendering scheduling scheme in the incremental data packet, and performs map rendering based on the rendering scheduling scheme. The rendering result is then evaluated, and the rendering scheduling scheme is updated based on the evaluation result.

2. The large-scale map data dynamic thinning and fusion rendering system according to claim 1, characterized in that, The data perception and event triggering module employs a management mechanism combining batch loading and local caching when collecting data. This management mechanism is as follows: During the initial data collection, the data perception and event triggering module loads the map data in batches according to spatial partitions or types, and stores the loaded map data in IndexedDB after structuring. During subsequent data collection, it executes a timestamp comparison mechanism, scheduling operation logs to assist in verification, and differential data merging to perform differential updates and cache synchronization on the map data and multi-source data. The timestamp comparison mechanism is as follows: the data perception and event triggering module compares the collected data of two adjacent timestamps each time data is collected, and only collects change events; The aforementioned scheduling operation log auxiliary verification involves comparing the scheduling operation log with the data stored in IndexedDB to verify data consistency and quickly locate change events. The differential data merging refers to the incremental merging of the change events with the data stored in IndexedDB.

3. The large-scale map data dynamic thinning and fusion rendering system according to claim 1, characterized in that, The method for generating the aforementioned change event is as follows: Change detection is performed on the multi-source data and map data through field-level comparison, state machine transitions, and threshold changes. Change events are generated based on the change detection results. The storage format of the change events is: event type, change field, old and new values, version number and timestamp. The event types mentioned include field personnel location updates, attendance tracking, task status changes, and media data uploads.

4. The large-scale map data dynamic thinning and fusion rendering system according to claim 1, characterized in that, When the map viewport is loaded, moved, or zoomed, the dynamic thinning and scheduling module is activated and performs the following steps to generate the rendering scheduling scheme: The system parses the change information in the event queue and simultaneously obtains the current map data and the business priority of each geographic element; the geographic element data includes point elements and line elements; the point elements include equipment, facilities, personnel locations, and task points; the line elements include trajectories, roads, and pipelines; Based on the viewport range and zoom level, the viewport is divided into several equal-sized sub-regions. The point density and line density within each sub-region are calculated to construct a density matrix. The global maximum density and average density are obtained based on the density matrix, the density threshold is determined based on the global maximum density and average density, and the maximum amount of rendering data is determined based on the performance of the device. If the total amount of data within the viewport is greater than or equal to the maximum amount of rendering data, and the density of each sub-region in the corresponding density matrix is ​​greater than or equal to the density threshold, then a dynamic thinning algorithm is triggered to obtain the thinning result. Based on the thinning result, the principle of preserving all original data, and the map data generation rendering schedule, the rendering schedule includes geographic feature data rendering, personnel data rendering, and task data rendering. If the total amount of data within the viewport and the density of each sub-region in the density matrix are both lower than the density threshold, then the initial rendering scheduling scheme for generating the map data is based on the principle of preserving all original data and the aforementioned map data. The rendering result of the previous rendering scheduling scheme executed by the fusion rendering engine module is obtained, and it is determined whether the effect meets the requirements. If it does not meet the requirements, the initial rendering scheduling scheme is modified based on the rendering result to obtain the final rendering scheduling scheme. Conversely, the initial rendering scheduler will be used as the final rendering scheduler.

5. A large-scale map data dynamic thinning and fusion rendering system according to claim 4, characterized in that, The dynamic thinning is based on the business priority to select thinning objects. Geographic elements with high business priority retain their original precision, while geographic elements with ordinary business priority are thinned according to element category to obtain the thinning result.

6. A large-scale map data dynamic thinning and fusion rendering system according to claim 5, characterized in that, The methods for obtaining the thinning results for the point features include: Using a quadtree spatial index structure, an index is created for all point features. Based on the index, the dynamic thinning and scheduling module quickly retrieves candidate point features in each sub-region and constructs a candidate point set. When the point density in a sub-region is lower than the density threshold, the original map data and multi-source data are retained; otherwise, a sub-region-based aggregation algorithm is used and a hysteresis mechanism is introduced to calculate the weighted center point and generate an aggregation symbol. The aggregation symbol includes the center coordinates and statistical attributes of the aggregation point. The point feature thinning results are generated based on the aforementioned aggregation symbol, sub-region size, and density threshold.

7. A large-scale map data dynamic thinning and fusion rendering system according to claim 5, characterized in that, The method for obtaining the thinning results of the aforementioned line features is as follows: Based on the quadtree spatial index structure, the spatial index structure of line features and surfaces is established, and the candidate line feature set within the viewport range is quickly retrieved based on the index. Line features and surfaces that exceed the viewport are clipped to obtain line sets and surface sets. Calculate the viewport overlap between line features and faces in the line set and face set, where the viewport overlap for line features is the overlap length ratio and the viewport overlap for faces is the overlap area ratio. Based on the viewport overlap, total length of line features or total area of ​​surfaces, and business priority, an importance score is calculated, and lines or surfaces with an importance score greater than a first score threshold are used as rendering objects. For the rendered object, geometric node thinning is performed based on the scaling level. For line features, the Douglas-Peucker algorithm is used to simplify nodes while retaining key inflection points. For faces, node precision is adjusted according to the area threshold and scaling level, and the nodes are downgraded to centroid points or hotspot labels for display. When the number of rendering objects exceeds the maximum number of rendering data, the dynamic thinning and scheduling module merges collinear or adjacent line features into skeleton lines, performs topological merging of adjacent or similar faces to form aggregated faces, and treats lines or faces with importance scores less than or equal to the second score threshold as hidden objects. Based on the rendering object, key inflection points, centroid points or hot zone labels, skeleton lines, aggregation surfaces, and hidden objects, a thinning result is generated.

8. A large-scale map data dynamic thinning and fusion rendering system according to claim 1, characterized in that, The following steps are performed in the data distribution and push module to achieve differentiated push: Based on the event queue, the latest status of each terminal role is obtained and the historical status of the previous moment is compared. The latest status and the historical status are compared and all changes are extracted. The changes include personnel location, task status, task image, task time frequency, task audio file and changes in task dispatch process. Determining the push timing based on the aforementioned changes includes calculating the location offset for the personnel location change; if the location offset is greater than the offset threshold, then pushing the personnel location change item to the corresponding terminal role; merging multiple changes from adjacent time periods into a single frame and pushing it to the corresponding terminal role.

9. A large-scale map data dynamic thinning and fusion rendering system according to claim 1, characterized in that, The terminal roles include operator terminal, large screen terminal, and mobile App terminal, and the data distribution and push module and the data push of each terminal role are implemented based on SignalR.

10. A method for dynamic thinning and fusion rendering of large-scale map data, characterized in that, The method includes: The system collects map data and multi-source data in real time, generates standardized change events through change detection, and updates the event queue based on these change events. The map data includes viewport range, zoom level, and geographic feature data; the multi-source data includes personnel data and task data. Based on the event queue and identifying change information, a rendering scheduling scheme is generated based on the change information and map data. The event queue and rendering scheduling scheme are encapsulated into incremental data packets, and the rendering data in the incremental data packets is integrated to perform map rendering based on the rendering data; and the rendering scheduling scheme is updated based on the rendering results.

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