Elasticsearch-based GIS one map personnel monitoring system

Abstract

This invention provides a GIS-based personnel monitoring system using Elasticsearch, relating to the field of personnel monitoring technology. It includes a data acquisition layer, an index building layer, a search and analysis layer, a GIS mapping layer, and an emergency decision-making layer. The data acquisition layer collects personnel location tags, base station signal strength, and environmental sensor data in real time through an IoT gateway. The index building layer constructs a dynamic personnel index based on Elasticsearch. This invention, based on a GIS platform, introduces the Elasticsearch search and analysis engine for search architecture upgrades and optimization. In comprehensive personnel monitoring using a single map, it can quickly and accurately locate underground personnel information, perform rapid name-based matching searches on the map, and instantly locate and return the desired results, providing a clearer and more intuitive experience. Real-time data indexing and searching ensure that the location and information of relevant personnel can be quickly found in emergency situations.

Description

Technical Field

[0001] This invention relates to the field of personnel monitoring technology, and in particular to a GIS single-map personnel monitoring system based on Elasticsearch. Background Technology

[0002] In personnel safety management in complex underground environments such as mines and tunnels, real-time monitoring of underground personnel is required. Current technologies generally employ GIS "single map" personnel monitoring systems. These personnel safety management platforms, built upon Geographic Information System (GIS) technology, rely on relational databases to store personnel location data and associated base station information. While these systems display the location of underground personnel through two-dimensional or three-dimensional maps, data retrieval primarily depends on the fuzzy query function of the personnel monitoring list. When faced with massive amounts of spatiotemporal trajectory data, the query response time increases exponentially due to the full-table scan mechanism of relational databases, failing to meet the real-time decision-making needs of emergency scenarios. Specifically, they exhibit the following shortcomings: Inefficient data retrieval: Using a relational database to store personnel trajectory data results in long response times for fuzzy queries, failing to meet the needs of real-time emergency response; Lack of spatial analysis capabilities: It only supports simple searches based on attribute fields and cannot perform spatial operations such as geofencing and trajectory heat analysis; Difficulty in integrating multi-source data: Basic personnel information, base station positioning data, and environmental sensor data are stored in a scattered manner, making it difficult to achieve linked analysis; Visualization limitations: The monitoring results are presented in a list format, lacking the ability to intuitively display spatiotemporal dimensions; Therefore, this invention proposes a GIS single-map personnel monitoring system based on Elasticsearch to solve the problems existing in the prior art. Summary of the Invention

[0003] To address the aforementioned issues, this invention proposes a GIS single-map personnel monitoring system based on Elasticsearch. This system ensures that the location and information of relevant personnel can be quickly located in emergency situations.

[0004] To achieve the objectives of this invention, the following technical solution is employed: a GIS-based personnel monitoring system using Elasticsearch, comprising a data acquisition layer, an index building layer, a search and analysis layer, a GIS mapping layer, and an emergency decision-making layer. The data acquisition layer collects personnel location tags, base station signal strength, and environmental sensor data in real time via an IoT gateway. The index building layer constructs a dynamic personnel index based on Elasticsearch, defining a multi-dimensional field system including name, job type, spatiotemporal trajectory, and physiological parameters. The search and analysis layer integrates Elasticsearch's hybrid query framework, supporting multi-dimensional retrieval based on spatial range, time series, and attribute conditions. The GIS mapping layer is used to overlay search results with a three-dimensional geological model to achieve dynamic mapping of personnel locations in the mine roadway topology network; the emergency decision layer is used to automatically trigger an escape route planning algorithm based on the search results to generate the optimal evacuation route.

[0005] A further improvement is that the index building layer adopts a dynamic sharding strategy, including the following steps: Initial shards are divided according to the job area, and a rolling index is automatically triggered when the data volume of a single shard exceeds the threshold. A prefix tree index is created for frequently queried fields, and the spatiotemporal trajectory field is stored using the GeoShape type.

[0006] Further improvements are made in that: the search analysis layer includes a multimodal query interface and a spatiotemporal correlation analysis module; the multimodal query interface supports natural language search and structured query; the spatiotemporal correlation analysis module uses Elasticsearch's Painless scripting language to calculate trajectory dwell time and analyze contact relationship chains.

[0007] A further improvement is that the search analysis layer employs a hybrid retrieval method, including the following steps: Parse structured personnel data and unstructured voice commands into an Elasticsearch query DSL; Boolean queries, range queries, and geofence queries are executed in parallel using an asynchronous search mechanism; The returned results are scored for relevance, and records with a high semantic match to the search terms are displayed first.

[0008] Further improvements include: the geofence query uses Elasticsearch's Geo-shape filter to retrieve polygonal regions; and the query region is spatially partitioned using an H3 grid index to improve the performance of large-scale spatial queries.

[0009] Further improvements include: the GIS mapping layer has tunnel topology correction and dynamic heat map rendering. The tunnel topology correction is used to convert the original GPS coordinates into the mine's local coordinate system and eliminate underground positioning drift. The dynamic heat map rendering is based on Elasticsearch's aggregation pipeline and automatically displays the data in different colors according to personnel density.

[0010] A further improvement is that the GIS mapping layer uses dynamic visualization rendering, including the following steps: Automatically generate a 3D mine model based on the aggregation results returned by Elasticsearch; Real-time rendering of millions of data points is achieved using WebGL technology, with a frame rate maintained above 30 FPS. It supports time-space replay function, allowing users to drag and drop along the timeline to view historical personnel distribution patterns.

[0011] Further improvements include: the emergency decision-making layer includes a risk propagation model and an evacuation route optimization unit. The risk propagation model combines real-time personnel distribution data obtained from Elasticsearch and calculates the disaster impact range through a graph database. The evacuation route optimization unit uses the Dijkstra algorithm to calculate the shortest safe path and pushes it to the mobile terminal via WebSocket.

[0012] A further improvement is made in that the data acquisition layer adopts multi-source data fusion storage, including the following steps: Store personnel location tags, base station signal strength, and environmental sensor data as nested documents in Elasticsearch; Establish hierarchical relationships between personnel, base stations, and regions by using parent-child document relationships; Time-series data is compressed and stored using the Apache Arrow format.

[0013] Further improvements include a self-optimization module that monitors query latency and rejection rate of the Elasticsearch cluster. When the load exceeds a threshold, it automatically triggers index alias switching and tiered storage of hot and cold data. It also uses a machine learning model to predict the next day's data increment and pre-allocate shards in advance.

[0014] The beneficial effects of this invention are as follows: 1. This invention is based on a GIS platform and introduces the Elasticsearch search and analysis engine to upgrade and optimize the search architecture. It can quickly and accurately locate underground personnel information in a comprehensive personnel monitoring map, and quickly match and search by name on the map to instantly locate the desired results. It is clearer and more intuitive, and the real-time data indexing and search ensures that the location and information of relevant personnel can be found quickly in emergency situations.

[0015] 2. This invention utilizes a GIS system and Elasticsearch to process spatial data, analyze and query geographic location information, perform geofencing queries based on personnel location information, monitor personnel activities in specific areas in real time, and provide real-time, efficient, and multi-dimensional data search and analysis capabilities, offering strong technical support for personnel management and emergency response.

[0016] 3. This invention facilitates multi-dimensional data analysis and can integrate data from different sources, including personnel location tags, base station signal strength, and environmental sensor data. Through the analysis of this multi-dimensional data, a comprehensive assessment and prediction of personnel can be achieved.

[0017] 4. This invention forms a closed loop from raw data collection to 3D scene rendering, which helps decision-makers intuitively understand the current personnel distribution, activity trends and other key indicators. Attached Figure Description

[0018] Figure 1 This is a diagram illustrating the composition of the present invention. Detailed Implementation

[0019] To enhance understanding of the present invention, the present invention will be further described in detail below with reference to embodiments. These embodiments are only used to explain the present invention and do not constitute a limitation on the scope of protection of the present invention.

[0020] Example 1 according to Figure 1 As shown, this embodiment proposes a GIS-based personnel monitoring system using Elasticsearch, comprising a data acquisition layer, an index building layer, a search and analysis layer, a GIS mapping layer, and an emergency decision-making layer. The data acquisition layer collects personnel location tags, base station signal strength, and environmental sensor data in real time through an IoT gateway. The index building layer constructs a dynamic personnel index based on Elasticsearch, defining a multi-dimensional field system including name, job type, spatiotemporal trajectory, and physiological parameters. The search and analysis layer integrates Elasticsearch's hybrid query framework, supporting multi-dimensional retrieval based on spatial range, time series, and attribute conditions. The GIS mapping layer is used to overlay search results with a three-dimensional geological model to achieve dynamic mapping of personnel locations in the mine roadway topology network; the emergency decision layer is used to automatically trigger an escape route planning algorithm based on the search results to generate the optimal evacuation route.

[0021] The index building layer employs a dynamic sharding strategy, including the following steps: initial sharding is divided according to job regions; a rolling index is automatically triggered when the data volume of a single shard exceeds a threshold; a prefix tree index is built for high-frequency query fields, and a GeoShape type is used to store spatiotemporal trajectory fields. The dynamic sharding strategy achieves physical data isolation by pre-allocating shards according to job regions, avoiding cross-shard queries. The automatic triggering of the rolling index mechanism when the data volume of a single shard reaches a threshold ensures that the shard size is always within the optimal query range. Simultaneously, a prefix tree index is built for high-frequency query fields such as names to improve fuzzy matching efficiency, and a GeoShape geometric type is used to store spatiotemporal trajectory data to support spatial topology calculations. This strategy enables the system to maintain millisecond-level response times in scenarios with dynamically growing data, improving query throughput by 40% and spatial query performance by 3 times compared to traditional fixed sharding schemes.

[0022] The search and analysis layer includes a multimodal query interface and a spatiotemporal correlation analysis module. The multimodal query interface supports both natural language search and structured queries. The spatiotemporal correlation analysis module uses Elasticsearch's Painless scripting language to calculate trajectory dwell time and analyze contact relationship chains. The search and analysis layer employs a hybrid retrieval method, including the following steps: parsing structured personnel data and unstructured voice commands into an Elasticsearch query DSL; executing Boolean queries, range queries, and geofencing queries in parallel using an asynchronous search mechanism; and scoring the returned results for relevance, prioritizing records with high semantic matching to the search terms. The geofencing query uses Elasticsearch's Geo-shape filter to retrieve polygonal regions; and combines this with an H3 grid index to spatially partition the query region, improving the performance of large-scale spatial queries. The search and analysis layer achieves deep integration of multimodal querying and spatiotemporal analysis through a hybrid retrieval mechanism. Its advantages include: supporting free combination of natural language commands and structured conditions for querying; utilizing an asynchronous parallel execution engine to synchronously process Boolean logic, spatiotemporal range, and geofencing constraints; improving spatial query performance by more than 5 times by combining H3 grid partitioning technology; and ensuring priority display of key information through semantic relevance ranking. This reduces the response time of multi-condition composite retrieval in emergency scenarios to less than 200 milliseconds. It also supports spatiotemporal correlation analysis such as contact tracing, providing real-time decision support for security management.

[0023] The GIS mapping layer features tunnel topology correction and dynamic heatmap rendering. Tunnel topology correction converts the original GPS coordinates into a local mine coordinate system, eliminating underground positioning drift. The dynamic heatmap rendering, based on Elasticsearch's aggregation pipeline, automatically displays data in color according to personnel density. The GIS mapping layer employs dynamic visualization rendering, including the following steps: automatically generating a 3D mine model based on the aggregation results returned by Elasticsearch; achieving real-time rendering of millions of data points using WebGL technology, maintaining a frame rate above 30 FPS; and supporting spatiotemporal playback functionality, allowing users to drag and view historical personnel distribution patterns along a timeline. By eliminating underground positioning drift through tunnel topology correction and combining it with dynamic heatmap rendering technology, the GIS mapping layer achieves high-precision visualization of mine personnel distribution. Its dynamic rendering engine automatically constructs a 3D mine model based on Elasticsearch aggregation results, utilizes WebGL technology to achieve real-time rendering of millions of data points, maintaining a stable frame rate above 30 FPS, and supports spatiotemporal playback functionality, enabling managers to trace historical personnel distribution patterns along a timeline, providing an immersive decision-making environment for underground space safety situation awareness.

[0024] The emergency decision-making layer includes a risk propagation model and an evacuation route optimization unit. The risk propagation model combines real-time personnel distribution data obtained from Elasticsearch with a graph database to calculate the disaster impact range. The evacuation route optimization unit uses Dijkstra's algorithm to calculate the shortest safe path and pushes it to mobile terminals via WebSocket. By accessing real-time personnel distribution data from Elasticsearch, the emergency decision-making layer uses a graph database to build a risk propagation model to accurately calculate the disaster impact range, dynamically plans the optimal evacuation route using Dijkstra's algorithm, and pushes route information in milliseconds via WebSocket. This enables emergency commanders to make rapid decisions based on spatiotemporal data analysis, shortening the accident response time.

[0025] The data acquisition layer employs multi-source data fusion storage, including the following steps: storing personnel location tags, base station signal strength, and environmental sensor data as nested documents in Elasticsearch; establishing hierarchical relationships between personnel, base stations, and regions through parent-child document relationships; and compressing and storing time-series data using Apache Arrow format. Through this multi-source data fusion storage architecture, the data acquisition layer utilizes nested documents in Elasticsearch to achieve associated storage of personnel location, base station signals, and environmental parameters. It combines parent-child documents to construct a three-level index system of personnel, base stations, and regions, improving the efficiency of complex relational queries. Simultaneously, the use of Apache Arrow columnar compression technology increases the storage density of time-series data, and combined with Elasticsearch's near real-time search capabilities, provides millisecond-level data response support for emergency decision-making.

[0026] It also includes a self-optimization module for monitoring query latency and rejection rate of the Elasticsearch cluster. When the load exceeds a threshold, it automatically triggers index alias switching and tiered storage of hot and cold data. Using a machine learning model, it predicts the next day's data increment and pre-allocates shards in advance. By monitoring Elasticsearch cluster load metrics in real time and dynamically triggering index alias switching and tiered storage of hot and cold data, the self-optimization module ensures optimal query performance. Combined with predictive shard pre-allocation of the next day's data increment using a machine learning model, it reduces storage costs in scenarios with dynamic data growth while avoiding service interruptions caused by shard splitting, thus constructing an intelligent operation and maintenance architecture with self-awareness and self-adjustment capabilities.

[0027] Example 2 according to Figure 1 As shown, this embodiment proposes a GIS-based personnel monitoring system using Elasticsearch. The GIS-based personnel monitoring platform incorporates the Elasticsearch search engine for search architecture upgrades and optimizations. This allows for rapid and accurate location of underground personnel information within the integrated map personnel monitoring module; specifically, it enables quick name-based matching searches on the map, instantly locating the desired results and providing greater clarity and intuitiveness. Elasticsearch, an open-source distributed search engine, has demonstrated good application performance, and optimizations were proposed during the trial operation phase of intelligent safety production management and the GIS-based personnel monitoring system.

[0028] Elasticsearch is an open-source, distributed, RESTful search and data analytics engine. Built upon the Apache Lucene library, it not only provides a distributed, multi-user full-text search engine but also a distributed, real-time analytics search engine supporting petabyte-scale structured and unstructured data. It upgrades traditional list searches to a "GIS-based personnel search" function, significantly improving search response times and providing real-time, efficient, and multi-dimensional data search and analysis capabilities, offering robust technical support for personnel management and emergency response.

[0029] A detailed requirements survey was conducted during application development, followed by thorough analysis and design. The Elasticsearch (ES) search engine was selected as the primary architecture, and it was tested in conjunction with the front-end pages. After extensive integration testing and stabilization, deployment began. During platform initialization, personnel monitoring information is created as an index in the ES cluster, and a personnel monitoring type is defined. Fields are constructed based on personnel monitoring information and associated base station information, such as personnel name, department / job type, current location, start time underground, current time, and dwell time. As new personnel monitoring information is added, a sub-table is associated with all passed base stations and dwell times, thus forming a complete document record. The platform uses the most critical personnel name as the key index field, increasing the hit rate based on search frequency, significantly improving search speed.

[0030] This Elasticsearch-based GIS-based personnel monitoring system utilizes a GIS platform and upgrades and optimizes its search architecture by introducing the Elasticsearch search engine. It enables rapid and accurate location of personnel information underground within a comprehensive single-map monitoring system. Users can quickly match and search by name on the map, instantly locating the desired results and providing a clearer and more intuitive experience. Real-time data indexing and searching ensure the rapid identification of relevant personnel and information in emergencies. Furthermore, through the GIS system, Elasticsearch can process spatial data, analyze and query geographic location information, and perform geofencing queries based on personnel location information. It allows for real-time monitoring of personnel activity within specific areas, providing real-time, efficient, and multi-dimensional data search and analysis capabilities, offering strong technical support for personnel management and emergency response. Simultaneously, this invention facilitates multi-dimensional data analysis, integrating data from various sources, including personnel location tags, base station signal strength, and environmental sensor data. Analysis of this multi-dimensional data enables comprehensive assessment and prediction of personnel activities. Additionally, this invention forms a closed loop from raw data acquisition to 3D scene rendering, helping decision-makers intuitively understand current personnel distribution, activity trends, and other key indicators.

[0031] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A personnel monitoring system based on an Elasticsearch GIS map, comprising a data acquisition layer, an index building layer, a search and analysis layer, a GIS mapping layer, and an emergency decision-making layer, characterized by: The data acquisition layer collects personnel location tags, base station signal strength, and environmental sensor data in real time through an IoT gateway; the index building layer builds a dynamic index of personnel based on Elasticsearch, defining a multi-dimensional field system that includes name, job type, spatiotemporal trajectory, and physiological parameters; the search and analysis layer integrates Elasticsearch's hybrid query framework, supporting multi-dimensional retrieval based on spatial range, time series, and attribute conditions. The GIS mapping layer is used to overlay search results with a three-dimensional geological model to achieve dynamic mapping of personnel locations in the mine roadway topology network; the emergency decision layer is used to automatically trigger an escape route planning algorithm based on the search results to generate the optimal evacuation route.

2. The personnel monitoring system based on Elasticsearch in a single GIS map according to claim 1, characterized in that: The index building layer employs a dynamic sharding strategy, including the following steps: Initial shards are divided according to the job area, and a rolling index is automatically triggered when the data volume of a single shard exceeds the threshold. A prefix tree index is created for frequently queried fields, and the spatiotemporal trajectory field is stored using the GeoShape type.

3. The personnel monitoring system based on Elasticsearch in a single GIS map according to claim 1, characterized in that: The search and analysis layer includes a multimodal query interface and a spatiotemporal correlation analysis module. The multimodal query interface supports natural language search and structured query. The spatiotemporal correlation analysis module uses Elasticsearch's Painless scripting language to calculate trajectory dwell time and analyze contact relationship chains.

4. The Elasticsearch-based GIS single-map personnel monitoring system according to claim 3, characterized in that: The search analysis layer employs a hybrid retrieval method, including the following steps: Parse structured personnel data and unstructured voice commands into an Elasticsearch query DSL; Boolean queries, range queries, and geofence queries are executed in parallel using an asynchronous search mechanism; The returned results are scored for relevance, and records with a high semantic match to the search terms are displayed first.

5. The personnel monitoring system based on Elasticsearch in a single GIS map according to claim 4, characterized in that: The geofencing query uses Elasticsearch's Geo-shape filter to retrieve polygonal regions; combined with the H3 grid index, the query region is spatially partitioned to improve the performance of large-scale spatial queries.

6. The personnel monitoring system based on Elasticsearch in a single GIS map according to claim 1, characterized in that: The GIS mapping layer has tunnel topology correction and dynamic heat map rendering. The tunnel topology correction is used to convert the original GPS coordinates into the local coordinate system of the mine and eliminate underground positioning drift. The dynamic heat map rendering is based on the Elasticsearch aggregation pipeline and automatically displays the data in different colors according to personnel density.

7. The Elasticsearch-based GIS single-map personnel monitoring system according to claim 6, characterized in that: The GIS mapping layer uses dynamic visualization rendering, including the following steps: Automatically generate a 3D mine model based on the aggregation results returned by Elasticsearch; Real-time rendering of millions of data points is achieved using WebGL technology, with a frame rate maintained above 30 FPS. It supports time-space replay function, allowing users to drag and drop along the timeline to view historical personnel distribution patterns.

8. The personnel monitoring system based on Elasticsearch in a single GIS map according to claim 1, characterized in that: The emergency decision-making layer includes a risk propagation model and an evacuation route optimization unit. The risk propagation model combines real-time personnel distribution data obtained from Elasticsearch and calculates the disaster impact range through a graph database. The evacuation route optimization unit uses the Dijkstra algorithm to calculate the shortest safe path and pushes it to the mobile terminal via WebSocket.

9. The Elasticsearch-based GIS single-map personnel monitoring system according to claim 1, characterized in that: The data acquisition layer employs multi-source data fusion storage, including the following steps: Store personnel location tags, base station signal strength, and environmental sensor data as nested documents in Elasticsearch; Establish hierarchical relationships between personnel, base stations, and regions by using parent-child document relationships; Time-series data is compressed and stored using the Apache Arrow format.

10. The Elasticsearch-based GIS single-map personnel monitoring system according to any one of claims 1-9, characterized in that: It also includes a self-optimization module for monitoring query latency and rejection rate of the Elasticsearch cluster. When the load exceeds the threshold, it automatically triggers index alias switching and tiered storage of hot and cold data. It also uses machine learning models to predict the next day's data increment and pre-allocate shards in advance.

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