Subway station location service platform and service method based on 5G public and private network positioning system

Abstract

The application provides a subway station location service platform and service method based on a 5G public and private network positioning system, and belongs to the technical field of intelligent transportation and location-based service (LBS). The method comprises the following steps: performing public and private network dual-mode dynamic access configuration on a positioning terminal in a subway station, and generating dynamic network switching strategy data; performing adaptive switching control of the public network and the private network on the positioning terminal through the dynamic network switching strategy data according to the signal strength and the service priority in the station area, and constructing a high-reliability data backhaul network; through the public and private network dual-mode dynamic access technology, the subway station location service system can intelligently switch the network according to the signal environment in the station, greatly improving the reliability and stability of data backhaul, and reducing the positioning failure risk caused by network interruption.

Description

Technical Field

[0001] This invention proposes a subway station location service platform and service method based on a 5G public and private network positioning system, belonging to the field of intelligent transportation and location service (LBS) technology. Background Technology

[0002] In modern urban rail transit operations, subway stations, as core scenarios with high passenger and freight traffic, place stringent demands on the real-time performance, reliability, and intelligence of location services. Traditional 5G-based positioning systems generally suffer from rigid network utilization, with public and private networks operating independently and unable to dynamically switch based on the station's signal environment. This leads to positioning interruptions and data loss in areas with private network coverage blind spots or public network congestion, seriously threatening operational safety. Furthermore, existing positioning platforms are functionally limited, providing only basic coordinate displays and static fence alerts. They lack the ability to deeply mine massive amounts of spatiotemporal data and cannot automatically execute complex business logic such as attendance statistics and risk warnings, requiring significant manual intervention to transform location data into operational value. In addition, location services are disconnected from other subway business systems (such as emergency command and material dispatch), resulting in prominent data silos and hindering synergy. Although some solutions have improved positioning accuracy by reusing 5G networks, deploying MEC edge computing, and using UTDOA / RTT algorithms, they have not yet overcome the technical bottleneck of the isolated "network-platform-service" triad, failing to meet the full-chain requirements of smart subways for "intelligent perception-intelligent decision-making-intelligent execution." Therefore, there is an urgent need for an innovative approach that deeply integrates dynamic access to public and private networks, intelligent platform analysis, and automatic business linkage to build a highly reliable, intelligent, and high-value subway station location service system. Summary of the Invention

[0003] This invention provides a subway station location service platform and service method based on a 5G public / private network positioning system to solve the problems mentioned in the background art above: This invention proposes a subway station location service method based on a 5G public / private network positioning system, the method comprising: S1. Configure the positioning terminals in the subway station to access the public and private networks in a dual-mode dynamic manner and generate dynamic network switching strategy data; based on the signal strength and service priority in the station area, use the dynamic network switching strategy data to perform adaptive switching control between the public and private networks for the positioning terminals and build a highly reliable data backhaul network. S2. Multi-source location data is collected through a high-reliability data backhaul network to obtain raw location data streams and terminal status timestamp data; the raw location data streams are aggregated to the intelligent location service platform (iLSP) deployed on the MEC edge node, and the raw location data streams are stored and preprocessed in real time through a high-concurrency spatiotemporal data lake to generate a standardized spatiotemporal dataset; S3. Real-time analysis of standardized spatiotemporal datasets is performed through a dynamic geofencing engine to generate dynamic fence trigger event data. Based on the dynamic fence trigger event data and combined with a preset business rule library, advanced business logic such as attendance check-in, boundary crossing alarm, and low battery warning are automatically executed through a multi-dimensional business linkage hub to generate business linkage instruction data. S4. Automatically update the subway station attendance system, emergency command system and material management system through business linkage command data to generate business collaboration result data; feed the business collaboration result data back to the high-concurrency spatiotemporal data lake for closed-loop optimization, and provide real-time location services to the outside world through standardized API interfaces. S5. Generate intelligent decision support reports based on business collaboration results data. Through intelligent decision support reports, dynamically optimize dynamic network switching strategy data, preset business rule base and dynamic geofence engine parameters to form an evolvable subway station location service system.

[0004] The present invention proposes a subway station location service platform based on a 5G public and private network positioning system. The platform includes: one or more processors; and a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any one of the above-mentioned methods.

[0005] The beneficial effects of this invention are as follows: Through dual-mode dynamic access technology between public and private networks, the subway station location service system can intelligently switch networks according to the signal environment within the station, significantly improving the reliability and stability of data transmission and reducing the risk of location failure due to network interruptions. The Intelligent Location Service Platform (iLSP) integrates a high-concurrency spatiotemporal data lake and a dynamic geofencing engine, which not only enhances the system's ability to process massive amounts of spatiotemporal data but also automates advanced business processes such as attendance and early warning, reducing the need for manual intervention and improving operational efficiency. Simultaneously, the system seamlessly connects with other subway business systems through standardized API interfaces, avoiding data silos and enabling location information to be quickly transformed into business value, thus improving overall collaborative effectiveness. Furthermore, the system supports a dynamic optimization mechanism, adapting to constantly changing operational needs while continuously optimizing positioning accuracy and business logic, preventing service quality degradation due to technological lag and providing strong support for the refined management of smart subways. Attached Figure Description

[0006] Figure 1 This is a diagram illustrating the steps of the method described in this invention. Detailed Implementation

[0007] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention. Example 1

[0008] One embodiment of the present invention, such as Figure 1 As shown, a subway station location service method based on a 5G public / private network positioning system is disclosed. The method includes: S1. Configure the positioning terminals in the subway station to access the public and private networks in a dual-mode dynamic manner and generate dynamic network switching strategy data; based on the signal strength and service priority in the station area, use the dynamic network switching strategy data to perform adaptive switching control between the public and private networks for the positioning terminals and build a highly reliable data backhaul network. S2. Multi-source location data is collected through a high-reliability data backhaul network to obtain raw location data streams and terminal status timestamp data; the raw location data streams are aggregated to the intelligent location service platform (iLSP) deployed on the MEC edge node, and the raw location data streams are stored and preprocessed in real time through a high-concurrency spatiotemporal data lake to generate a standardized spatiotemporal dataset; S3. Real-time analysis of standardized spatiotemporal datasets is performed through a dynamic geofencing engine to generate dynamic fence trigger event data. Based on the dynamic fence trigger event data and combined with a preset business rule library, advanced business logic such as attendance check-in, boundary crossing alarm, and low battery warning are automatically executed through a multi-dimensional business linkage hub to generate business linkage instruction data. S4. Automatically update the subway station attendance system, emergency command system and material management system through business linkage command data to generate business collaboration result data; feed the business collaboration result data back to the high-concurrency spatiotemporal data lake for closed-loop optimization, and provide real-time location services to the outside world through standardized API interfaces. S5. Generate intelligent decision support reports based on business collaboration results data. Through intelligent decision support reports, dynamically optimize dynamic network switching strategy data, preset business rule base and dynamic geofence engine parameters to form an evolvable subway station location service system.

[0009] The working principle of the above technical solution is as follows: First, it traverses all locations in the subway platform, station hall, track area, equipment room, and transfer passage, collecting 5G public network and private network pRRU signal strength and establishing a full-area signal coverage database. It continuously monitors link latency, jitter, and packet loss probability at each location and statistically analyzes the stability of communication links, assigning independent transmission weights and classifying transmission levels for personnel positioning, material tracking, emergency calls, and back-end management services. Then, it analyzes the signal coverage database to generate public and private network signal distribution characteristic data, quantifies link stability indicators to generate communication link quality data, normalizes service transmission weights to generate service transmission priority data, and weights and fuses these three types of data to form a multi-dimensional feature fusion dataset. It performs scenario matching calculations on the dataset to output the preferred network access type, performs boundary condition calculations to output the switching trigger threshold, integrates the above content, encapsulates and verifies it to generate dynamic network switching strategy data. The positioning terminal reads the current location signal indicators in real time and compares them with the strategy data, automatically completing the public and private network access selection. During the switching process, it maintains continuous transmission of positioning data and verifies data integrity, continuously maintaining a low-latency and high-stability transmission state across the entire area, ultimately constructing a highly reliable data backhaul network. The 5G positioning tags at the perception layer report UTDOA uplink time difference of arrival and RTT round-trip time measurements at fixed intervals, forming a continuous location measurement information stream. Simultaneously, parameters such as remaining battery power, operating temperature, SOS status, and transmit / receive power are collected to form complete terminal status information. A microsecond-level timestamp is added to each data set to mark the time sequence, resulting in the original positioning data stream and terminal status timestamp data. The high-reliability data backhaul network directly forwards this data to the MEC edge node, bypassing the remote core network. After receiving the data, the intelligent location service platform performs numerical range detection, message duplication detection, and field integrity detection, removing invalid data such as abnormal values, duplicate messages, and missing fields to clean the data. The cleaned data is then standardized in format and reporting cycle, reconstructed into a spatiotemporal relational structure, and validated to achieve data format standardization. Data is categorized and stored according to three dimensions: personnel / material identification, time series, and local coordinates within the station. The final output is a standardized spatiotemporal dataset that can be directly used for business calculations. The dynamic geofence engine loads the spatial coordinates of the station's track area, high-voltage room, equipment room, and restricted access passages to establish a full-domain electronic fence spatial model. It reads the real-time coordinates, movement speed, movement direction, and dwell start time of targets from standardized spatiotemporal data, comparing target locations point-by-point with the fence space to determine boundary crossing and excessively long-staying behaviors. Simultaneously, it monitors terminal battery status, records trigger type, location, time, target identity, and on-site status, and generates dynamic fence trigger event data. It retrieves the corresponding event's processing method, notification recipient, alarm method, and response level from a preset business rule library. The multi-dimensional business linkage hub drives the automatic operation of personnel attendance, safety alarms, and status reminder processes according to the rules, encapsulating the business execution content, execution recipient, execution time, and execution method to generate business linkage instruction data.Business linkage command data is written to the attendance system, including personnel arrival location, arrival time, and on-duty duration, automatically updating attendance records. Alarm locations, target information, on-site status, and alarm levels are pushed to the emergency command system, synchronously updating the command dashboard. Material locations, movement trajectories, and distribution density are pushed to the materials management system, updating ledgers and location views in real time. The system statistically analyzes the command reception status, processing time, and number of completed commands from the attendance, emergency command, and materials management systems. It compares feedback data with original command data to generate matching degree data, integrating reception status, response time, completion, and data matching information to form business collaboration evaluation data. It calculates execution success rate, response latency, and data consistency, integrating them into business collaboration result data. This business collaboration result data is written to a high-concurrency spatiotemporal data lake. Using target identifiers and time segments as search criteria, it matches original spatiotemporal data and establishes associated indexes. Based on the collaboration results, it corrects spatiotemporal data errors, supplements missing fields, and optimizes storage structure to complete closed-loop optimization. Simultaneously, real-time location, historical trajectory, alarm information, and statistical results are encapsulated into a common interface format and pushed externally through standardized API interfaces. Core indicators such as location success rate, alarm accuracy, network switching latency, terminal online rate, and system response time are extracted from business collaboration results data. These indicators are categorized and statistically analyzed according to different areas, time periods, and terminal types within the station to create a multi-dimensional service quality profile. The correlation between regional public and private network switching effects and location stability is analyzed, as well as the coverage and effectiveness of business rule execution and the accuracy and false alarm rate of the geofencing engine. Weaknesses and optimization directions in network policies, business rules, and the geofencing engine are identified. The statistical analysis, correlation analysis, and problem localization are integrated to generate an intelligent decision support report that includes current status assessment, problem analysis, and optimization directions. Network policy optimization directions are extracted from the report, updating the switching thresholds and priority network configurations in the dynamic network switching policy data. Business rule optimization directions are extracted, expanding the preset business rule library scenarios and conditions, and adjusting alarm levels and processing flows. Engine optimization directions are extracted, calibrating the spatial coordinate range and dwell time determination parameters of the dynamic geofencing engine. A full-domain iteration of network policies, business rules, and geofencing engine parameters is completed to continuously improve the system's positioning accuracy and business adaptability, ultimately forming a self-evolving and continuously optimizing subway station location service system.

[0010] The effects of the above technical solution are as follows: Using 5G public and private network converged positioning ensures stable transmission of positioning data throughout the station, improving the real-time nature and accuracy of personnel and material location awareness. Adaptive switching between public and private networks enhances network adaptability, reduces the impact of signal fluctuations on positioning services, and minimizes data interruptions and transmission delays. Edge node preprocessing accelerates data processing, reduces core network load, and minimizes invalid data consuming system resources. Geofencing and multi-service linkage improve the speed of safety supervision response, increase the efficiency of station operation management, and reduce manual inspections and alarm omissions. Multi-system collaborative updates improve data interoperability, reduce cross-platform data inconsistencies, and avoid security risks caused by information lag. Closed-loop iterative optimization continuously optimizes system operation, enhances overall service stability, meets daily positioning supervision needs, and enables rapid response to emergency scenarios, comprehensively improving the operational management level of subway stations.

[0011] In one embodiment of the present invention, S1 includes: S11. Traverse all locations in the subway platform, station hall, track area, equipment room, and transfer passage to collect the downlink reference signal reception power of the 5G public network and the pRRU signal reception power of the private network, forming a full-domain signal coverage database. S12. Continuously monitor the transmission delay, jitter, and packet loss probability of public and private network links at each location, and statistically analyze the stability of communication links over a continuous period of time. S13. Assign independent transmission weights to personnel positioning, material tracking, emergency calls, and back-end management services to distinguish the data transmission levels of routine and emergency services. S14. Perform fusion calculations on the signal coverage database, link stability indicators, and service transmission weights to output the preferred network types and handover trigger thresholds for different scenarios; encapsulate the fusion calculation results into a callable configuration file to generate dynamic network handover strategy data. S15. The positioning terminal reads the current location signal indicators in real time, compares them with the threshold in the dynamic network switching strategy data, and automatically selects to access the public network or private network; during the switching process, the positioning data stream is transmitted without interruption, and the integrity and continuity of the data before and after the switching are verified; the positioning data in the entire area of ​​the station is continuously maintained with low latency and high stability, and a highly reliable data backhaul network is built.

[0012] The working principle and effects of the above technical solution are as follows: By collecting signal power data from all locations within the subway station, the coverage distribution of both public and private networks can be fully understood, improving the suitability of network configuration. Continuous monitoring of link transmission latency jitter and packet loss probability accurately reflects communication quality and enhances data transmission stability. Assigning independent transmission weights to different services prioritizes emergency service transmission needs, improving response speed in critical scenarios. Generating network switching strategies through multi-dimensional data fusion calculations improves scenario adaptation accuracy and reduces service fluctuations caused by unreasonable switching. Automatic comparison of thresholds and selection of access networks by the terminal maintains continuous transmission of positioning data, preventing positioning interruptions due to signal fluctuations. Verifying data integrity during the switching process reduces the probability of data loss and improves the overall reliability of the backhaul network. This solution ensures stable operation of routine services while supporting efficient transmission of emergency services, providing stable data transmission support for subway station positioning services.

[0013] In one embodiment of the present invention, step S14 includes: S141. Analyze the global field strength values ​​in the signal coverage database to generate global public and private network signal distribution characteristic data; quantify the latency jitter and packet loss probability in the link stability indicators to generate global communication link quality data. S142. Normalize the hierarchical values ​​in the service transmission weight to generate full-domain service transmission priority data; integrate signal distribution characteristic data, communication link quality data and service transmission priority data to generate a multi-dimensional feature fusion dataset. S143. Perform scene matching operation on the multi-dimensional feature fusion dataset and output the network type that is preferentially accessed in different scenarios; perform boundary condition operation on the multi-dimensional feature fusion dataset and output the switching trigger threshold in different scenarios. S144. Integrate the network types that are prioritized for access and the handover trigger thresholds to generate a scenario-based network configuration parameter set; encapsulate the scenario-based network configuration parameter set into a configuration file format that the platform can read; S145. Perform parameter integrity and rationality verification on the encapsulated configuration file; store the verified configuration file in the system configuration directory and generate dynamic network switching strategy data.

[0014] The working principle and effects of the above technical solution are as follows: By analyzing the full-domain field strength values ​​to generate signal distribution characteristic data, the network coverage status within the site can be fully presented, improving the accuracy of network judgment. By quantifying link latency jitter and packet loss probability to generate communication quality data, transmission performance can be accurately reflected, enhancing the stability of data transmission. By normalizing service weights to generate transmission priority data, network resources can be rationally allocated, improving the guarantee of emergency services. By fusing multi-dimensional data to generate feature datasets, the system can fully match on-site operating conditions, reducing decision-making bias caused by single data. By using scenario matching and boundary condition calculations to output network type and switching thresholds, policy adaptability can be improved, avoiding invalid and frequent switching. By integrating, encapsulating, and verifying parameters to generate configuration files, policy invocation efficiency can be improved, reducing the risk of configuration errors. It can adapt to stable operation in normal areas and respond flexibly to switching in complex scenarios, providing reliable network decision support for location data backhaul.

[0015] In one embodiment of the present invention, S143 includes: Extract multi-dimensional features and fuse spatial distribution features within the dataset to generate scene spatial feature data; extract multi-dimensional features and fuse communication quality features within the dataset to generate scene link feature data; Extract multi-dimensional features and fuse business-level features within the dataset to generate scenario business feature data; combine scenario spatial feature data, scenario link feature data and scenario business feature data to complete scenario feature combination; Perform scene matching operations on the combined scene features and output the network type that is preferentially accessed in different scenes; extract multi-dimensional features and fuse the signal change amplitude features in the dataset to generate boundary discrimination data; Extract multi-dimensional features and fuse link fluctuation amplitude features within the dataset to generate critical state data; fuse boundary discrimination data and critical state data to complete boundary condition construction; perform boundary condition operations on the constructed boundary conditions and output switching trigger thresholds under different scenarios.

[0016] The working principle and effects of the above technical solution are as follows: By extracting multi-dimensional features in a layered manner and fusing spatial communication and service features from the dataset, the on-site operating status can be fully restored, improving the comprehensiveness of scenario judgment. Through multi-feature combination operations, the operating conditions of different areas within the station can be accurately matched, enhancing the rationality of network selection. By outputting the preferred network type through scenario matching operations, network adaptation efficiency can be improved and resource waste reduced. By extracting signal and link fluctuation features to construct boundary conditions, the switching critical interval can be accurately divided, improving the scientific nature of threshold setting. By outputting the switching trigger threshold through boundary condition operations, the timing of network switching can be stably controlled, avoiding frequent or delayed switching caused by signal mutations. Through refined feature processing and operations throughout the process, the probability of interruption in positioning data transmission can be reduced, ensuring continuous data backhaul. It can adapt to stable operation in ordinary areas and quickly adjust to complex scenarios, providing a reliable basis for adaptive switching of terminals between public and private networks.

[0017] In one embodiment of the present invention, S2 includes: S21. The 5G positioning tags in the perception layer report the UTDOA uplink arrival time difference measurement value and RTT round trip time measurement value at fixed intervals to form a continuous location measurement information stream. S22. Synchronously collect the remaining battery percentage, internal operating temperature, SOS button status, and signal transmission and reception power parameters of the positioning terminal to form complete terminal status information; add microsecond-level time stamps to each set of location measurement values ​​and terminal status information to preserve the data generation time sequence relationship and obtain the original positioning data stream and terminal status timestamp data. S23. The high-reliability data backhaul network forwards the original positioning data stream and terminal status timestamp data to the MEC edge node without passing through the remote core network. S24. After receiving the data, the intelligent location service platform first removes invalid data such as abnormal values, duplicate messages and missing fields to complete the data cleaning; and then converts the reported data of different formats and frequencies into a spatiotemporal data structure that the platform can recognize to complete the data format standardization. S25. Classify and store the data according to three dimensions: personnel and material identification, time series, and local coordinates within the station; after completing the storage and format unification, output a standardized spatiotemporal dataset that can be directly used for business calculations.

[0018] The working principle and effects of the above technical solution are as follows: By reporting positioning measurement values ​​at fixed intervals using positioning tags, a stable flow of location information can be continuously generated, improving the continuity of location awareness. By synchronously collecting multiple operating parameters of the terminal and adding microsecond-level time stamps, the temporal relationship of the data can be completely preserved, enhancing the accuracy of subsequent trajectory and status analysis. By directly forwarding data to edge nodes, the transmission path can be shortened, data return latency can be reduced, and data lag caused by core network congestion can be avoided. By cleaning and standardizing the data, invalid information can be filtered out, the data structure can be unified, and the system's computational load can be reduced. By storing data in a multi-dimensional classification, the efficiency of data retrieval and retrieval can be improved, and the speed of business processing can be increased. It can provide a reliable data source for real-time monitoring and retain complete records for post-event traceability, fully supporting the stable operation of subway station positioning services.

[0019] In one embodiment of the present invention, S24 includes: Receive the raw location data stream and terminal status timestamp data forwarded by the high-reliability data return network; perform numerical range detection on the received data and filter out abnormal values ​​that exceed the normal range; Perform message duplication detection on the received data to filter out redundant messages that are sent repeatedly; perform field integrity detection on the received data to filter out invalid data with missing key information; Remove duplicate messages and missing field data from the filtered abnormal values ​​to complete data cleaning; perform format conversion on the cleaned data to standardize the data field structure. The cleaned data undergoes a frequency uniform conversion to align with the data reporting cycle; the data with the uniform format and frequency are reconstructed into a spatiotemporal correlation structure; and the reconstructed data undergoes field and format validation to complete data format standardization.

[0020] The working principle and effects of the above technical solution are as follows: By receiving raw positioning and status data forwarded from the edge side, the data processing link can be shortened, and the overall response speed can be improved. By performing multi-layer detection of numerical duplication and integrity on the data, abnormal redundancy and missing information can be accurately identified, reducing the interference of invalid data on the system. By cleaning up unqualified data, the usability of the data can be improved, and business judgment deviations caused by erroneous information can be avoided. By unifying the data format and reporting frequency, the rhythm of information flow can be standardized, reducing the processing difficulty of subsequent calculations. By reconstructing the spatiotemporal correlation structure and performing dual verification, the standardization of data can be strengthened, and the reliability of location calculation can be enhanced. Through a standardized processing flow throughout the process, data integration compatibility can be improved, and cross-module call barriers can be reduced. This not only ensures the data quality of real-time business but also provides standard materials for historical analysis, consolidating the data foundation of the subway station positioning system.

[0021] In one embodiment of the present invention, S3 includes: S31. The dynamic geofence engine loads the spatial coordinate range of the track area, high-voltage room, equipment room, and restricted access passage within the station, and establishes a full-domain electronic fence spatial model; it reads the real-time coordinates, movement speed, movement direction, and dwell start time of the target in the standardized spatiotemporal data. S32. Compare the target location information with the fence space model point by point to determine whether the target has entered the restricted area or exceeded the permitted activity range. S33. Calculate the duration of the target's stay in the non-operation area and determine whether it exceeds the allowed stay limit; read the remaining battery level of the terminal and determine whether it is below the safe operating threshold. S34. Record the trigger type, trigger location, trigger time, target identity information and on-site status, and generate dynamic fence trigger event data; retrieve the corresponding event processing method, notification object, alarm method and response level from the preset business rule library; S35, the multi-dimensional business linkage hub drives the automatic operation of personnel attendance, security alarms, and status reminder processes according to rules; it encapsulates the automatically executed business content, execution object, execution time, and execution method to generate business linkage instruction data.

[0022] The working principle and effects of the above technical solution are as follows: By loading the coordinates of key areas within the station to establish a full-area electronic fence model, the entire security supervision scope can be fully covered, improving the comprehensiveness of area control. By comparing the target location with the fence boundary point by point, unauthorized entry can be detected in a timely manner, enhancing on-site security. By statistically analyzing dwell time and monitoring terminal battery levels, abnormal states can be identified in advance, reducing the time safety hazards remain. By fully recording event information and matching it with business rules, the accuracy of alarm response can be improved, avoiding false alarms and missed alarms. By automatically driving process operation through a multi-dimensional business linkage hub, the speed of event handling can be accelerated, reducing the lag of manual intervention. By encapsulating standardized business linkage instructions, cross-system collaboration efficiency can be improved, enabling both daily attendance and status monitoring, as well as rapid response to security alarms, comprehensively enhancing the intelligent management level of subway stations.

[0023] In one embodiment of the present invention, step S4 includes: S41. Business linkage instruction data writes the personnel's arrival location, arrival time and on-duty duration into the attendance system, and automatically completes the attendance record update. S42. Business linkage command data pushes alarm location, target information, on-site status and alarm level to the emergency command system, and updates the information on the emergency command screen simultaneously. S43. Business linkage command data pushes the current location, movement trajectory and distribution density of materials to the material management system, and updates the material ledger and location view in real time; collects the reception results, execution status and feedback information of the attendance system, emergency command system and material management system; S44. Summarize the information of the entire process of multi-system collaborative execution, and generate business collaboration result data including execution success rate, response latency, and data consistency; write the business collaboration result data into a high-concurrency spatiotemporal data lake and establish an association index with the original spatiotemporal data; S45. Correct spatiotemporal data errors based on business collaboration results data, supplement missing fields, optimize data storage structure, and complete data closed-loop optimization; encapsulate real-time location, historical trajectory, alarm information, and statistical results into a general interface format, and push data services to external systems through standardized API interfaces.

[0024] The working principle and effects of the above technical solution are as follows: Automatically updating attendance system data through business linkage commands eliminates the manual registration step, improves attendance statistics efficiency, and avoids errors caused by manual recording. Synchronously pushing alarm information to the emergency command system shortens the time for transmitting on-site information and enhances the response speed of emergency handling. Real-time updating of the materials management system data allows for accurate understanding of materials distribution status and reduces the difficulty of materials management. Aggregating execution information from multiple systems to generate business collaboration results comprehensively reflects the system's operational effectiveness and improves overall operational transparency. Writing collaboration results to a spatiotemporal data lake and establishing associated indexes improves the data traceability chain and reduces data gaps. Closed-loop correction of data errors and optimization of storage structure improve data quality and ensure stable operation of subsequent businesses. Providing services externally through standardized interfaces enhances system compatibility, meeting the needs of multi-business collaboration within the station and supporting efficient integration with external systems.

[0025] In one embodiment of the present invention, S44 includes: The system tracks the instruction reception status of the attendance system, emergency command system, and material management system, generating multi-system reception status data; it also tracks the business processing time and feedback interval of each system, generating multi-system response time data. The number of instructions completed in each system is counted against the total number of instructions, generating multi-system execution completion data; the feedback data from each system is compared with the original instruction data, generating multi-system data matching degree data; Integrate received status data, response time data, execution completion data, and data matching degree data to generate business collaboration evaluation data; calculate business execution success rate based on business collaboration evaluation data to generate execution success rate data; Calculate the average response latency of the business based on the business collaboration assessment data to generate response latency data; calculate the consistency of business data based on the business collaboration assessment data to generate data consistency data; integrate the execution success rate data, response latency data and data consistency data to generate business collaboration result data. The business collaboration result data is written to a high-concurrency spatiotemporal data lake to complete data storage; target identifiers and time segments are extracted from the business collaboration result data to generate associated search conditions; the associated search conditions are used to match the original spatiotemporal data in the high-concurrency spatiotemporal data lake; and an associated index is established between the business collaboration result data and the matched original spatiotemporal data.

[0026] The working principle and effects of the above technical solution are as follows: By statistically analyzing the reception and response of instructions from multiple systems, the execution status of business linkage can be fully grasped, improving the detail of collaborative supervision. By statistically analyzing the completion rate of instruction execution, the effectiveness of business implementation can be intuitively reflected, reducing the difficulty of operation and maintenance troubleshooting. By comparing feedback data with the original instruction content, data deviations can be detected in a timely manner, avoiding information transmission distortion. By integrating multiple indicators to generate business collaboration results, the system's operational level can be comprehensively quantified, enhancing the reference value for management decisions. By writing the collaboration results into a spatiotemporal data lake, the data retention system can be improved, reducing the loss of key information. By establishing a correlation index between the result data and the original spatiotemporal data, data traceability can be quickly achieved, improving the efficiency of problem location. Through full-process quantitative evaluation and data correlation, the business collaboration process can be monitored, and a real basis can be provided for subsequent system optimization, making the operation of multiple systems within the site more stable and efficient.

[0027] In one embodiment of the present invention, step S5 includes: S51. Extract core indicators such as location success rate, alarm accuracy rate, network switching latency, terminal online rate, and system response time from business collaboration result data. S52. Classify and statistically analyze core indicators according to different regions, time periods, and terminal types within the station to form a multi-dimensional service quality profile; analyze the correlation between the switching effect of public and private networks and positioning stability in different regions to identify weak links in network strategies; analyze the coverage and effectiveness of business rule execution to identify rule redundancy and missing points. S53. Analyze the accuracy and false alarm rate of the geofencing engine, locate the optimization points of the spatial model and judgment logic; integrate the statistical indicators, correlation analysis, and problem location content to form an intelligent decision support report that includes current status assessment, problem analysis, and optimization direction; S54. Use the intelligent decision support report to update the handover threshold and priority network configuration in the dynamic network handover strategy data; use the intelligent decision support report to expand the content of the preset business rule base and adjust the alarm level and processing flow. S55. Use intelligent decision support reports to calibrate the spatial coordinate range and dwell time determination parameters of the dynamic geofence engine; complete the full-domain iteration of strategies, rules, and engine parameters to continuously improve the system's positioning accuracy and business adaptability, forming an evolvable subway station location service system.

[0028] The working principle and effects of the above technical solution are as follows: By extracting core operational indicators from business collaboration results, the system service level can be fully reflected, improving the accuracy of operation and maintenance monitoring. Through multi-dimensional classification and statistics to form a service quality profile, the operational differences between regions, time periods, and terminals can be clearly presented, enhancing the comprehensiveness of problem identification. By correlating and analyzing network strategies and positioning effects, weak links can be quickly located, reducing the blindness of optimization adjustments. By integrating and analyzing content to form an intelligent decision support report, a clear direction can be provided for system iteration, avoiding ineffective adjustments. By synchronously updating network strategy business rules and engine parameters, full-domain collaborative optimization can be achieved, reducing system operational deviations. Through continuous iteration of parameter configuration, positioning accuracy and business adaptability can be continuously improved. This allows the system to adapt to complex on-site environments and continuously upgrade with operational needs, ultimately forming a self-improving subway station location service system.

[0029] In one embodiment of the present invention, S54 includes: Extract network strategy optimization directions from the intelligent decision support report to generate a basis for network parameter adjustment; and correct the switching threshold in the dynamic network switching strategy data based on the network parameter adjustment basis. Based on network parameters, adjust the priority network configuration in the updated dynamic network switching strategy data; store the adjusted dynamic network switching strategy data and generate a new version of the network strategy configuration; extract the business rule optimization direction from the intelligent decision support report and generate the basis for rule content adjustment. Adjust the coverage scenarios and judgment conditions of the preset business rule library based on the rule content; adjust the alarm level classification in the preset business rule library based on the rule content. Adjust the processing path in the preset business rule library according to the rule content; store the expanded and adjusted preset business rule library and generate a new version of business rule configuration.

[0030] The working principle and effects of the above technical solution are as follows: By extracting network optimization directions from decision reports to generate adjustment criteria, parameter corrections can be made to match actual operating conditions, improving the adaptability of network switching strategies. By correcting switching thresholds and updating priority network configurations, terminal access status can be stabilized, reducing transmission interruptions caused by signal fluctuations. By storing the new version of network policy configurations, optimization results can be preserved, improving the consistency of system operation. By extracting rule optimization directions to generate adjustment criteria, business rules can be made to match on-site needs, improving the rationality of alarm and handling processes. By expanding rule scenarios and adjusting alarm levels, more operating conditions can be covered, avoiding missed and false judgments. By updating processing flow paths and storing the new version of rule configurations, business linkage efficiency can be improved, and response latency can be reduced. This ensures that network configurations continuously match the site environment and business rules continuously adapt to management needs, maintaining the efficient and stable operation of the entire positioning service system.

[0031] One embodiment of the present invention provides a subway station location service platform based on a 5G public / private network positioning system, the platform comprising: One or more processors; Memory, used to store one or more programs; Wherein, when the one or more programs are executed by the one or more processors, the one or more processors are made to implement the method described in any one of the above. Example 2

[0032] In one embodiment of the present invention, during the execution of dynamic network handover strategy data, the trend of network quality change, terminal mobility status, location data continuity and service urgency are introduced to predict the location stability benefits after handover, and the location terminal is determined to perform immediate handover, delayed handover or maintain the current network based on the prediction results, thereby avoiding location data interruption, trajectory breakage and service mis-triggering caused by frequent handover.

[0033] Specifically, this embodiment includes the following steps S1-S8.

[0034] S1. Configure dual-mode dynamic access of public and private networks for positioning terminals in subway stations and generate dynamic network switching strategy data; based on the signal strength and service priority in the station area, use the dynamic network switching strategy data to perform adaptive switching control between public and private networks for positioning terminals and build a highly reliable data backhaul network.

[0035] S2. Multi-source location data is collected through a high-reliability data backhaul network to obtain raw location data streams and terminal status timestamp data; the raw location data streams are aggregated to the intelligent location service platform deployed on the MEC edge node, and the raw location data streams are stored and preprocessed in real time through a high-concurrency spatiotemporal data lake to generate a standardized spatiotemporal dataset.

[0036] S3. The standardized spatiotemporal dataset is analyzed in real time by the dynamic geofencing engine to generate dynamic fence trigger event data. Based on the dynamic fence trigger event data and combined with the preset business rule library, the business logic of attendance check-in, boundary crossing alarm and low battery warning is automatically executed through the multi-dimensional business linkage hub to generate business linkage instruction data.

[0037] S4. Automatically update the subway station attendance system, emergency command system, and material management system through business linkage command data to generate business collaboration result data; feed the business collaboration result data back to the high-concurrency spatiotemporal data lake for closed-loop optimization, and provide real-time location services to the outside world through standardized API interfaces.

[0038] S5. Generate intelligent decision support reports based on business collaboration results data. Through intelligent decision support reports, dynamically optimize dynamic network switching strategy data, preset business rule base and dynamic geofence engine parameters to form an evolvable subway station location service system.

[0039] S6. When the positioning terminal is in a cross-regional mobile state and the quality of the public network and private network links fluctuates alternately, the intelligent location service platform extracts the network quality change trend, terminal mobile state and positioning data continuity within a continuous time window from the high-concurrency spatiotemporal data lake to generate a stable positioning benefit value after the switch.

[0040] Specifically, the intelligent location service platform uses the public or private network currently accessed by the positioning terminal as the current network and other accessible networks as candidate networks. Within a continuous time window, the platform statistically analyzes the signal reception power, transmission delay, jitter, and packet loss probability of both the current and candidate networks. Based on the continuous improvement of the candidate networks relative to the current network, it generates a candidate network quality advantage trend. This trend is not a difference in signal strength at a single moment, but rather reflects whether the candidate network consistently outperforms the current network within a continuous time window and whether it possesses the ability to stably carry the positioning data stream.

[0041] Meanwhile, the intelligent location service platform generates the stability of the terminal's movement direction based on the continuous coordinates, movement speed, movement direction, and positional relationship between the terminal and the boundary of the station area, using standardized spatiotemporal data. If the terminal continuously moves towards an area with more stable candidate network coverage, and the movement direction is consistent with the boundary of the station area, the stability of the terminal's movement direction is high; if the terminal moves back and forth, pauses briefly, or frequently changes its movement direction near the boundary of the platform, transfer passage, or equipment room, the stability of the terminal's movement direction is low.

[0042] Furthermore, based on the original positioning data stream and terminal status timestamp data, the intelligent location service platform calculates the timestamp interval between adjacent positioning points, the continuity of positioning data packet sequence numbers, the spatial jump amplitude of trajectory points, and the stability of the matching between the UTDOA uplink arrival time difference measurement and the RTT round-trip time measurement, thus generating the positioning data continuity maintenance level. If the positioning data stream timestamps are continuous, the data packet sequence numbers are complete, the trajectory point changes are smooth, and the matching between positioning measurements is stable, then the positioning data continuity maintenance level is high; if there are timestamp breaks, missing data packets, abrupt changes in trajectory points, or inconsistencies in positioning measurements, then the positioning data continuity maintenance level is low.

[0043] When generating the post-switching location stability benefit value, the intelligent location service platform comprehensively evaluates the candidate network quality advantage trend, the stability of terminal movement direction, and the degree of continuity of location data. Specifically, the candidate network quality advantage trend determines whether the candidate network is truly superior to the current network; the stability of terminal movement direction determines whether the positioning terminal has actually entered an area more suitable for coverage by the candidate network; and the degree of continuity of location data determines whether the current network state has affected the continuity of the positioning trajectory. If the candidate network is consistently superior to the current network, the positioning terminal steadily moves towards the candidate network's advantageous area, and the current location data continuity has decreased, then the post-switching location stability benefit value is high. Conversely, if the candidate network is only occasionally superior to the current network for a short period, or the positioning terminal is in a state of back-and-forth movement at the area boundary, or the current location data continuity remains good, then the post-switching location stability benefit value is low.

[0044] Through this step, the intelligent location service platform can combine three factors: whether the candidate network has improved, whether the positioning terminal has actually moved across regions, and whether the current positioning data needs to be stabilized through switching, thus avoiding triggering the switching between public and private networks based solely on instantaneous signal strength.

[0045] Based on the stable location benefit value obtained after the handover, the S7 Intelligent Location Service Platform further combines the network quality change trend, terminal mobility status and service urgency to generate a handover timing priority value.

[0046] Specifically, the intelligent location service platform identifies the business type corresponding to the current positioning terminal from the preset business rule base, dynamic fence trigger event data, and business linkage instruction data. If the current positioning terminal corresponds to emergency calls, boundary crossing alarms, high-risk area personnel positioning, track area personnel supervision, or important material tracking, its business urgency level is determined to be high; if the current positioning terminal only corresponds to ordinary attendance, back-end management, low-risk area material statistics, or routine trajectory recording, its business urgency level is determined to be low.

[0047] In this step, the urgency of the service does not directly replace network switching judgment, but is used to adjust the switching timing. Specifically, when the candidate network quality advantage trend is obvious, the terminal's movement direction is relatively stable, and the current service urgency is high, the intelligent location service platform increases the switching timing priority value, enabling the positioning terminal to perform network switching faster while meeting stable revenue conditions, so as to prioritize the continuous data transmission of services such as emergency calls, boundary crossing alarms, and high-risk area operations. When the candidate network quality advantage trend is unstable, the terminal's movement direction is relatively stable, or the current service is only a low-urgency service such as ordinary attendance or back-end management, the intelligent location service platform decreases the switching timing priority value, so that the positioning terminal does not switch immediately due to short-term network fluctuations, but enters a continued observation state.

[0048] When generating the handover timing priority value, the intelligent location service platform continues to utilize the candidate network quality advantage trend and terminal movement direction stability obtained in S6, and further incorporates the current service urgency. The candidate network quality advantage trend and terminal movement direction stability participate in the judgments of both S6 and S7, ensuring that the handover benefit judgment and handover timing judgment share a common network state and movement state basis. Meanwhile, the continuity of location data is primarily used to determine whether the handover is beneficial for restoring trajectory stability, and the service urgency is primarily used to determine whether the handover waiting time needs to be shortened. Thus, the post-handover location stability benefit value and the handover timing priority value are both interconnected and correspond to different control objectives.

[0049] Through this step, the intelligent location service platform can distinguish between the two levels of issues: "should switch" and "when should switch". This avoids equating network quality improvement directly with immediate switching and also avoids delays in location data transmission due to excessive waiting in emergency business scenarios.

[0050] S8, the intelligent location service platform generates a network handover execution decision based on the stable positioning benefit value and the handover timing priority value after the handover, and feeds the network handover execution decision back to the dynamic network handover strategy data, so that the positioning terminal can perform immediate handover, delayed handover, or maintain the current network.

[0051] Specifically, the intelligent location service platform pre-sets stable revenue judgment conditions, immediate switching judgment conditions, and delayed observation judgment conditions. The stable revenue judgment condition is used to determine whether the candidate network can improve the stability of the location data stream after switching; the immediate switching judgment condition is used to determine whether the current network status, terminal mobility status, and service urgency have reached the requirements for immediate switching; the delayed observation judgment condition is used to determine whether it is necessary to postpone the switching and continue to observe the changing trends of the candidate network and the current network.

[0052] When the stable revenue value of the location after the switch meets the stable revenue judgment condition, and the priority value of the switch timing meets the immediate switch judgment condition, the intelligent location service platform determines that the candidate network not only has the revenue of stably carrying the location data stream, but also requires timely switching in the current business scenario or mobile state. The location terminal performs an immediate switch, and after the switch is completed, it continues to verify the sequence number of the location data packets, the terminal status timestamp data, and the continuity of the trajectory points before and after the switch.

[0053] When the stable return value of the location after switching meets the stable return judgment condition, but the switching timing priority value does not meet the immediate switching judgment condition but meets the delayed observation judgment condition, the intelligent location service platform does not immediately control the positioning terminal to switch networks. Instead, it places the positioning terminal in a delayed switching observation state. In the delayed switching observation state, the intelligent location service platform continues to monitor the candidate network quality advantage trend, the stability of the terminal's movement direction, the continuity of positioning data, and the urgency of the business in subsequent continuous judgment periods. If the candidate network continues to maintain its advantage in subsequent judgment periods, and the terminal's movement direction continues to point to a more stable area covered by the candidate network, the positioning terminal will switch from the delayed switching observation state to the immediate switching state. If the candidate network advantage disappears or the positioning terminal returns to the stable area covered by the current network, the switching will be canceled, and the current network will be maintained.

[0054] If the stable return value after a switch does not meet the stable return judgment condition, or the priority value of the switch timing is lower than the delay observation judgment condition, the intelligent location service platform determines that there is no need to switch at present, and the positioning terminal maintains the current network. At this time, even if the signal strength of the candidate network is higher than that of the current network at a certain moment, a network switch will not be triggered immediately, thereby avoiding frequent back-and-forth switching between the public network and the private network by the positioning terminal.

[0055] This step generates three types of network handover decisions: The first is immediate handover, where the location terminal switches to a candidate network; the second is delayed handover, where the location terminal enters the observation window and the handover is temporarily suspended; and the third is maintaining the current network, where the location terminal continues to transmit location data back through the current network. All three types of results are written into the dynamic network handover strategy data and linked with the corresponding original location data stream, terminal status timestamp data, service linkage instruction data, and service collaboration result data using an index. This allows for subsequent dynamic optimization of the network handover strategy data through intelligent decision support reports.

[0056] The working principle of the above technical solution is as follows: First, in step S6, the trend of candidate network quality advantages, the stability of terminal movement direction, and the continuity of positioning data are combined to determine whether the handover can bring about stable positioning benefits. Then, in step S7, the trend of candidate network quality advantages, the stability of terminal movement direction, and the urgency of the service are combined to determine whether the handover waiting time should be shortened or extended. Finally, in step S8, the post-handover positioning stability benefit value and the handover timing priority value are combined to convert into an execution decision of immediate handover, delayed handover, or maintaining the current network. Therefore, the handover between public and private networks is no longer solely based on the selection of communication link quality, but is coupled with the cross-regional movement status of the positioning terminal, the continuity of positioning data, and the risk of service triggering.

[0057] The above technical solution achieves the following effects: By introducing the candidate network quality advantage trend within a continuous time window, it can reduce the interference of instantaneous signal fluctuations on handover judgment and reduce invalid handovers between public and private networks; by introducing the stability of terminal movement direction, it can identify whether the positioning terminal truly crosses the boundary of the station area, avoiding frequent handovers triggered when the positioning terminal moves back and forth in boundary areas such as platforms, transfer passages, and equipment rooms; by introducing the degree of continuity of positioning data, it can link network handover with the integrity of positioning trajectory, avoiding timestamp breaks, loss of positioning data packets, and trajectory point jumps caused by network handover; by introducing the degree of business urgency, it can prioritize the continuous transmission of positioning data in scenarios such as emergency calls, boundary crossing alarms, and positioning of personnel in high-risk areas, while extending the observation time in scenarios such as ordinary attendance, back-end management, and routine material statistics, reducing the probability of ping-pong handovers. Ultimately, this embodiment can improve the stability of positioning data transmission in the complex wireless environment of subway stations, reduce trajectory breaks and false triggering of dynamic geofences, and provide a more stable data foundation for the high-reliability data transmission network, dynamic geofence engine, and multi-dimensional business linkage hub.

[0058] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for providing subway station location services based on a 5G public / private network positioning system, characterized in that, The method includes: S1. Configure the positioning terminals in the subway station to access the public and private networks in a dual-mode dynamic manner and generate dynamic network switching strategy data; based on the signal strength and service priority in the station area, use the dynamic network switching strategy data to perform adaptive switching control between the public and private networks for the positioning terminals and build a highly reliable data backhaul network. S2. Multi-source location data is collected through a high-reliability data backhaul network to obtain raw location data streams and terminal status timestamp data; the raw location data streams are aggregated to the intelligent location service platform deployed on the MEC edge node, and the raw location data streams are stored and preprocessed in real time through a high-concurrency spatiotemporal data lake to generate a standardized spatiotemporal dataset; S3. Real-time analysis of standardized spatiotemporal datasets is performed through a dynamic geofencing engine to generate dynamic fence trigger event data. Based on the dynamic fence trigger event data and combined with a preset business rule library, advanced business logic such as attendance check-in, boundary crossing alarm, and low battery warning are automatically executed through a multi-dimensional business linkage hub to generate business linkage instruction data. S4. Automatically update the subway station attendance system, emergency command system and material management system through business linkage command data to generate business collaboration result data; feed the business collaboration result data back to the high-concurrency spatiotemporal data lake for closed-loop optimization, and provide real-time location services to the outside world through standardized API interfaces. S5. Generate intelligent decision support reports based on business collaboration results data. Through intelligent decision support reports, dynamically optimize dynamic network switching strategy data, preset business rule base and dynamic geofence engine parameters to form an evolvable subway station location service system.

2. The subway station location service method based on a 5G public / private network positioning system according to claim 1, characterized in that, S1 includes: S11. Traverse all locations in the subway platform, station hall, track area, equipment room, and transfer passage to collect the downlink reference signal reception power of the 5G public network and the pRRU signal reception power of the private network, forming a full-domain signal coverage database. S12. Continuously monitor the transmission delay, jitter, and packet loss probability of public and private network links at each location, and statistically analyze the stability of communication links over a continuous period of time. S13. Assign independent transmission weights to personnel positioning, material tracking, emergency calls, and back-end management services to distinguish the data transmission levels of routine and emergency services. S14. Perform fusion calculations on the signal coverage database, link stability indicators, and service transmission weights to output the preferred network types and handover trigger thresholds for different scenarios; encapsulate the fusion calculation results into a callable configuration file to generate dynamic network handover strategy data. S15. The positioning terminal reads the current location signal indicators in real time, compares them with the threshold in the dynamic network switching strategy data, and automatically selects to access the public network or private network; during the switching process, the positioning data stream is transmitted without interruption, and the integrity and continuity of the data before and after the switching are verified; the positioning data in the entire area of ​​the station is continuously maintained with low latency and high stability, and a highly reliable data backhaul network is built.

3. The subway station location service method based on a 5G public / private network positioning system according to claim 2, characterized in that, S14 includes: S141. Analyze the global field strength values ​​in the signal coverage database to generate global public and private network signal distribution characteristic data; quantify the latency jitter and packet loss probability in the link stability indicators to generate global communication link quality data. S142. Normalize the hierarchical values ​​in the service transmission weight to generate full-domain service transmission priority data; integrate signal distribution characteristic data, communication link quality data and service transmission priority data to generate a multi-dimensional feature fusion dataset. S143. Perform scene matching operation on the multi-dimensional feature fusion dataset and output the network type that is preferentially accessed in different scenarios; perform boundary condition operation on the multi-dimensional feature fusion dataset and output the switching trigger threshold in different scenarios. S144. Integrate the network types that are prioritized for access and the handover trigger thresholds to generate a scenario-based network configuration parameter set; encapsulate the scenario-based network configuration parameter set into a configuration file format that the platform can read; S145. Perform parameter integrity and rationality verification on the encapsulated configuration file; store the verified configuration file in the system configuration directory and generate dynamic network switching strategy data.

4. The subway station location service method based on a 5G public / private network positioning system according to claim 3, characterized in that, S143 includes: Extract multi-dimensional features and fuse spatial distribution features within the dataset to generate scene spatial feature data; extract multi-dimensional features and fuse communication quality features within the dataset to generate scene link feature data; Extract multi-dimensional features and fuse business-level features within the dataset to generate scenario business feature data; combine scenario spatial feature data, scenario link feature data and scenario business feature data to complete scenario feature combination; Perform scene matching operations on the combined scene features and output the network type that is preferentially accessed in different scenes; extract multi-dimensional features and fuse the signal change amplitude features in the dataset to generate boundary discrimination data; Extract multi-dimensional features and fuse link fluctuation amplitude features within the dataset to generate critical state data; fuse boundary discrimination data and critical state data to complete boundary condition construction; perform boundary condition operations on the constructed boundary conditions and output switching trigger thresholds under different scenarios.

5. The subway station location service method based on a 5G public / private network positioning system according to claim 1, characterized in that, The S2 includes: S21. The 5G positioning tags in the perception layer report the UTDOA uplink arrival time difference measurement value and RTT round trip time measurement value at fixed intervals to form a continuous location measurement information stream. S22. Synchronously collect the remaining battery percentage, internal operating temperature, SOS button status, and signal transmission and reception power parameters of the positioning terminal to form complete terminal status information; add microsecond-level time stamps to each set of location measurement values ​​and terminal status information to preserve the data generation time sequence relationship and obtain the original positioning data stream and terminal status timestamp data. S23. The high-reliability data backhaul network forwards the original positioning data stream and terminal status timestamp data to the MEC edge node without passing through the remote core network. S24. After receiving the data, the intelligent location service platform first removes invalid data such as abnormal values, duplicate messages and missing fields to complete the data cleaning; and then converts the reported data of different formats and frequencies into a spatiotemporal data structure that the platform can recognize to complete the data format standardization. S25. Classify and store the data according to three dimensions: personnel and material identification, time series, and local coordinates within the station; after completing the storage and format unification, output a standardized spatiotemporal dataset that can be directly used for business calculations.

6. The subway station location service method based on a 5G public / private network positioning system according to claim 1, characterized in that, The S3 includes: S31. The dynamic geofence engine loads the spatial coordinate range of the track area, high-voltage room, equipment room, and restricted access passage within the station, and establishes a full-domain electronic fence spatial model; it reads the real-time coordinates, movement speed, movement direction, and dwell start time of the target in the standardized spatiotemporal data. S32. Compare the target location information with the fence space model point by point to determine whether the target has entered the restricted area or exceeded the permitted activity range. S33. Calculate the duration of the target's stay in the non-operation area and determine whether it exceeds the allowed stay limit; read the remaining battery level of the terminal and determine whether it is below the safe operating threshold. S34. Record the trigger type, trigger location, trigger time, target identity information and on-site status, and generate dynamic fence trigger event data; retrieve the corresponding event processing method, notification object, alarm method and response level from the preset business rule library; S35, the multi-dimensional business linkage hub drives the automatic operation of personnel attendance, security alarms, and status reminder processes according to rules; it encapsulates the automatically executed business content, execution object, execution time, and execution method to generate business linkage instruction data.

7. The subway station location service method based on a 5G public / private network positioning system according to claim 1, characterized in that, The S4 includes: S41. Business linkage instruction data writes the personnel's arrival location, arrival time and on-duty duration into the attendance system, and automatically completes the attendance record update. S42. Business linkage command data pushes alarm location, target information, on-site status and alarm level to the emergency command system, and updates the information on the emergency command screen simultaneously. S43. Business linkage command data pushes the current location, movement trajectory and distribution density of materials to the material management system, and updates the material ledger and location view in real time; collects the reception results, execution status and feedback information of the attendance system, emergency command system and material management system; S44. Summarize the information of the entire process of multi-system collaborative execution and generate business collaboration result data; write the business collaboration result data into a high-concurrency spatiotemporal data lake and establish an association index with the original spatiotemporal data; S45. Correct spatiotemporal data errors based on business collaboration results data, supplement missing fields, optimize data storage structure, and complete data closed-loop optimization; encapsulate real-time location, historical trajectory, alarm information, and statistical results into a general interface format, and push data services to external systems through standardized API interfaces.

8. The subway station location service method based on a 5G public / private network positioning system according to claim 7, characterized in that, S44 includes: The system tracks the instruction reception status of the attendance system, emergency command system, and material management system, generating multi-system reception status data; it also tracks the business processing time and feedback interval of each system, generating multi-system response time data. The number of instructions completed in each system is counted against the total number of instructions, generating multi-system execution completion data; the feedback data from each system is compared with the original instruction data, generating multi-system data matching degree data; Integrate received status data, response time data, execution completion data, and data matching degree data to generate business collaboration evaluation data; calculate business execution success rate based on business collaboration evaluation data to generate execution success rate data; Calculate the average response latency of the business based on the business collaboration assessment data to generate response latency data; calculate the consistency of business data based on the business collaboration assessment data to generate data consistency data; integrate the execution success rate data, response latency data and data consistency data to generate business collaboration result data. The business collaboration result data is written to a high-concurrency spatiotemporal data lake to complete data storage; target identifiers and time segments are extracted from the business collaboration result data to generate associated search conditions; the associated search conditions are used to match the original spatiotemporal data in the high-concurrency spatiotemporal data lake; and an associated index is established between the business collaboration result data and the matched original spatiotemporal data.

9. The subway station location service method based on a 5G public / private network positioning system according to claim 1, characterized in that, The S5 includes: S51. Extract core indicators such as location success rate, alarm accuracy rate, network switching latency, terminal online rate, and system response time from business collaboration result data. S52. Classify and statistically analyze core indicators according to different regions, time periods, and terminal types within the station to form a multi-dimensional service quality profile; analyze the correlation between the switching effect of public and private networks and positioning stability in different regions to identify weak links in network strategies; analyze the coverage and effectiveness of business rule execution to identify rule redundancy and missing points. S53. Analyze the accuracy and false alarm rate of the geofencing engine triggering, locate the optimization points of the spatial model and judgment logic; integrate the statistical indicators, correlation analysis, and problem location content to form an intelligent decision support report. S54. Use the intelligent decision support report to update the handover threshold and priority network configuration in the dynamic network handover strategy data; use the intelligent decision support report to expand the content of the preset business rule base and adjust the alarm level and processing flow. S55. Use intelligent decision support reports to calibrate the spatial coordinate range and dwell time determination parameters of the dynamic geofence engine; complete the full-domain iteration of strategies, rules, and engine parameters to continuously improve the system's positioning accuracy and business adaptability, forming an evolvable subway station location service system.

10. A subway station location service platform based on a 5G public / private network positioning system, characterized in that, The platform includes: One or more processors; Memory, used to store one or more programs; Wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1 to 9.

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