Internet-of-things equipment loss alarm tracking method

Abstract

The invention discloses an Internet of Things equipment loss alarm tracking method, and provides a double-stage technical scheme for the problems of loss response lag, positioning technology limitation and the like of Internet of Things equipment without a GPS (Global Positioning System) environment: in the first stage, a real-time loss alarm method is realized through dynamic comparison of base station data, and connection data of equipment and a communication base station are compared in real time; once it is found that the equipment is switched to a strange base station, triggering a loss alarm; and the second stage is fused with the LBS technology to realize the trajectory tracking method. According to the scheme, through a dynamic base station comparison algorithm, in combination with acquisition of historical data of a handshake base station, equipment loss alarm is realized, passive response is changed into active response, and the response timeliness is improved; meanwhile, the method only depends on base station handshake data, energy consumption is not increased additionally, high positioning precision can be achieved in a GPS-free environment, and extra cost is not increased; and multi-dimensional analysis of a multi-layer display technology of a GIS can be fused, and space-time multi-dimensional analysis is realized through track visualization.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) device management technology, and in particular to a method for tracking and alarming lost IoT devices without the need for satellite positioning (such as GPS). Background Technology

[0002] With the rapid advancement of smart city construction, Internet of Things (IoT) devices have been deeply integrated into urban infrastructure management, covering multiple fields such as transportation, energy, and municipal pipelines. However, due to the complexity of the equipment deployment environment (such as tunnels, underground pipelines, manholes, and other areas without GPS signals) and the fact that some devices are not equipped with GPS modules, traditional positioning methods have become ineffective. At the same time, such devices are usually unattended, and once lost, they not only cause asset losses, but may also interfere with the operation of the management platform due to the continuous transmission of invalid data by the devices, forming a "silent failure" risk. That is, the devices will still report invalid data after being lost, and the backend cannot know or handle it in time. For example, if the waterlogging monitoring device leaves the flood-prone area, the backend cannot know in time, and the device may report 0 water depth forever, but the flood-prone area may already have serious water accumulation. Therefore, the current technical solutions still have the following defects: (1) Passive response defect: It relies on manual inspection or abnormal data accumulation to discover the loss, and the response is delayed; (2) Positioning technology limitation: It is difficult to effectively track the devices in an environment without GPS (or other satellite positioning), which increases the operation and maintenance costs. Summary of the Invention

[0003] This invention addresses the shortcomings of existing technologies by providing a method for tracking IoT device loss alarms that achieves real-time loss alarms through dynamic comparison of base station data and trajectory tracking through the fusion of LBS technology.

[0004] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: a method for tracking and alarming lost IoT devices, the implementation steps of which are as follows:

[0005] Phase 1: Real-time data loss alarm is achieved through dynamic comparison of base station data. The connection data between the device and the communication base station is compared in real time. Once the device is found to have switched to an unfamiliar base station, a data loss alarm is triggered.

[0006] Phase 2: Tracking based on LBS (Location Based on Base Station) positioning. New base station data is obtained through real-time device base station handshake data, and LBS positioning is integrated to track the trajectory of lost devices.

[0007] The connection data between the device and the communication base station includes LAC (Location Area Code) and / or CELLID (Base Station Unit Identifier).

[0008] In the second phase, local base station basic information database is built using real-time device base station handshake data to form positioning information, and combined with the historical database of device base station information, multi-dimensional visualization of the trajectory is achieved.

[0009] The specific steps for this IoT device loss alarm and tracking method are as follows:

[0010] SO1, IoT Devices and System Architecture

[0011] 1) The hardware components include IoT device and server. The IoT device integrates a multi-mode communication module, a built-in low-power MCU, and is equipped with a SIM card slot and a handshake protocol stack with the operator's base station. The server is equipped with a time-series database to store historical base station data and a GIS engine to realize trajectory visualization.

[0012] 2) Network Topology: The device transmits LAC / CELLID data to the server through the operator's network. The server establishes an API interface with the operator's LBS platform to obtain base station operator LBS data in real time, including latitude and longitude, coverage radius, location, and name.

[0013] S02, Base station data dynamic comparison and loss alarm module

[0014] 1) Data Acquisition and Preprocessing

[0015] (1) Real-time data acquisition: IoT devices periodically send base station handshake signals through the operator's network to collect base station data;

[0016] Data is uploaded to the cloud via the MQTT protocol, and after verification, it is stored in a time-series database, retaining records for the most recent few days, and automatically archiving expired data.

[0017] (2) Historical database construction: Establish a time-series database to store historical base station connection records, set a sliding time window, and automatically eliminate expired data;

[0018] 2) Dynamic comparison and alarm triggering

[0019] (1) Real-time comparison mechanism:

[0020] The newly collected base station data is hashed and compared with the historical database, and a dynamic threshold is set: if the device connects to an unfamiliar base station 3 times in a row, an alarm is triggered; if the connection to the original base station is not restored for 10 minutes, it is upgraded to an emergency alarm.

[0021] (2) Alarm information generation: Output structured alarm logs, including the abnormal base station location, timestamp, and last known location of the device after preliminary LBS positioning;

[0022] 3) Location-based tracking and visualization:

[0023] (1) Base station positioning data fusion

[0024] Based on the collected base station data and the integration of operator LBS data, a basic information database for base stations is constructed.

[0025] (2) GIS trajectory visualization, combined with GIS (Geographic Information System) technology to realize multi-layer display.

[0026] Historical trajectory layer: Draws the movement paths of equipment stations and base stations according to time series, and marks normal / abnormal periods with colors;

[0027] Real-time positioning layer: Displays the current distribution of base station clusters in the form of a heat map, highlighting the locations of abnormal base stations;

[0028] Geofencing layer: Customize the electronic fence range, triggering a secondary alarm when a device crosses the boundary.

[0029] In step S02, a sliding window algorithm is used to store the base station connection records of the device ID for the most recent 24*7 hours, and a hash table structure is used to achieve millisecond-level query.

[0030] In step S02, the hash calculation process is as follows: the newly collected base station data is compared with the three most recent records in the historical database. If the match fails, it is marked as an "unknown base station".

[0031] The threshold determination process is as follows:

[0032] Three consecutive connections to an unfamiliar base station → trigger a basic alarm and push it to the IoT platform;

[0033] If the connection to the original base station is not restored for 10 consecutive minutes, an emergency alarm will be triggered and pushed to the mobile APP and IoT platform.

[0034] Compared with the prior art, the present invention has the following advantages:

[0035] 1. By using a dynamic base station comparison algorithm and combining it with historical data collected from handshake base stations, device loss alarms are implemented, changing the passive response to an active response and improving the timeliness of the response; ensuring that abnormal device loss can be resolved in a timely manner and guaranteeing the reliability of data monitoring.

[0036] 2. Compared to the power consumption required by continuously keeping the GPS module active, this invention only relies on base station handshake data (LAC, CELLID), without increasing power consumption. By integrating LBS positioning technology, it can still achieve high positioning accuracy in environments without GPS, without incurring additional costs.

[0037] 3. It can integrate GIS multi-layer display technology (historical trajectory layer / real-time positioning layer / geofence layer) for multi-dimensional analysis, and realize spatiotemporal multi-dimensional analysis through trajectory visualization. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of dynamic comparison of base station data according to the present invention;

[0039] Figure 2 This is a flowchart of the device loss alarm process of the present invention;

[0040] Figure 3 This is a schematic diagram of base station positioning and tracking according to the present invention;

[0041] Figure 4 This is a flowchart of the base station positioning and tracking process of the present invention;

[0042] Figure 5 This is a flowchart of the alarm tracking process of the present invention. Detailed Implementation

[0043] The present invention will be further illustrated below through specific embodiments:

[0044] like Figures 1-5 As shown, this IoT device loss alarm tracking method adopts a two-stage solution: the first stage achieves real-time loss alarm through dynamic comparison of base station data (the principle of the method is as follows). Figure 1 As shown, by comparing the connection data between the device and the communication base station in real time (mainly LAC and CELLID), a loss alarm is triggered once the device is found to have switched to an unfamiliar base station (such as one that is outside the range of base stations the device has connected to in the previous 7 days).

[0045] The second stage is to achieve trajectory tracking based on LBS positioning (the method and principle are as follows). Figure 3 As shown, new base station data is obtained through real-time device base station handshake data, and base station location (LBS) technology is integrated to track the trajectory of lost devices. In this process, a local base station basic information database (location information) is constructed using real-time device base station handshake data, which, combined with a historical database of device base station information, enables multi-dimensional visualization of the trajectory.

[0046] The specific steps are as follows:

[0047] SO1, IoT Devices and System Architecture

[0048] 1) The hardware components include IoT device-side and server-side. The IoT device-side integrates a multi-mode communication module (supporting 2G / 3G / 4G / NB-IoT), a built-in low-power MCU (such as the STM32L4 series), and is equipped with a SIM card slot and a handshake protocol stack for operator base stations. The server-side deploys a time-series database to store historical base station data and configures a GIS engine to achieve trajectory visualization.

[0049] 2) Network Topology: The device transmits LAC / CELLID data to the server through the operator's network. The server establishes an API interface with the operator's LBS platform to obtain operator LBS data such as base station latitude and longitude, coverage radius, and location name in real time.

[0050] S02, Base station data dynamic comparison and loss alarm module

[0051] Real-time data loss alarm module, such as Figure 2 As shown, the steps are as follows:

[0052] 1) Data Acquisition and Preprocessing

[0053] (1) Real-time data acquisition: IoT devices periodically send base station handshake signals through the operator's network to collect base station data (LAC / CELLID / timestamp).

[0054] For example, the device reads the currently connected base station information through the AT command set and encapsulates it in JSON format (fields: timestamp, lac, cellid, signal_strength).

[0055] Data is uploaded to the cloud via the MQTT protocol, and after verification, it is stored in a time-series database, retaining records for the most recent 7 days. Expired data is automatically archived.

[0056] (2) Historical database construction: Establish a time-series database to store historical base station connection records of the device. A sliding time window (such as 24*7 hours) can be set to automatically eliminate expired data.

[0057] For example, using a sliding window algorithm, with the device ID as the key, the base station connection records for the most recent 24*7 hours are stored, and a hash table structure enables millisecond-level queries.

[0058] 2) Dynamic comparison and alarm triggering

[0059] (1) Real-time comparison mechanism:

[0060] The newly collected base station data is hashed and compared with the historical database. Dynamic thresholds can be set: if the device connects to an unfamiliar base station (not in the historical database) three times consecutively, an alarm is triggered; if the connection to the original base station is not restored for 10 minutes, it is escalated to an emergency alarm.

[0061] For example:

[0062] Hash calculation: The newly collected base station data (lac+cellid combination) is compared with the three most recent records in the historical database. If the match fails, it is marked as an "unknown base station".

[0063] Threshold determination:

[0064] Three consecutive connections to an unfamiliar base station → trigger a basic alarm (pushed to the IoT platform).

[0065] If the connection to the original base station is not restored for 10 consecutive minutes, an emergency alarm will be triggered (pushed to the mobile APP and IoT platform).

[0066] (2) Alarm information generation: Output structured alarm logs, including fields such as abnormal base station location (preliminary LBS location), timestamp, and last known location of the device.

[0067] Output structured logs (example):

[0068] { "alert_level": "urgent", "last_known_position": {"lac": "12345", "cellid": "67890", "estimated_position": {"lat": 31.23, "lng": 121.47}}, "timestamp": "2025-07-28T11:42:02Z"}

[0069] S03, LBS-based tracking and visualization module; the second phase implements a real-time data loss alarm module through dynamic comparison of base station data. Figure 4 As shown, the steps are as follows:

[0070] 1) Base station positioning data fusion

[0071] Based on the collected base station data (LAC / CELLID), and by integrating operator LBS data, a basic base station information database is constructed (LAC, CELLID, operator data, such as latitude and longitude, positioning radius, positioning location name, etc.).

[0072] For example, you can call the operator's LBS API (such as China Mobile's OneNET platform) to query the precise coordinates of the base station using lac+cellid.

[0073] 2) GIS trajectory visualization, which can be combined with GIS technology for multi-layer display.

[0074] Historical trajectory layer: Draws the movement paths of equipment stations and base stations according to time series, and marks normal / abnormal periods with colors.

[0075] Real-time positioning layer: Displays the current distribution of base station clusters in the form of a heat map, highlighting the locations of abnormal base stations.

[0076] Geofencing layer: Supports custom electronic fences (such as a circular area with a radius of 500 meters), triggering a secondary alarm when a device crosses the boundary.

[0077] Implementation Case Validation

[0078] In a smart water management project, approximately 4,000 electronic water level gauges and manhole covers were deployed to monitor water levels in real time. A data loss alarm method using dynamic comparison of device base station data was implemented through a water management IoT platform. The system automatically compares the connection data between the device and the communication base station, triggering an alarm if the device switches to an unfamiliar base station. This solution effectively improves equipment safety management efficiency and reduces manual inspection costs. Actual alarm data for manhole covers is shown in Table 1 below.

[0079] Table 1

[0080] Serial Number Information on the station Equipment Information Alarm information Monitoring Projects Monitoring Item Values Time of occurrence End time Duration 140 Station code: 0060509181; Station name: Helong 8th Road Station; Area: Baiyun District Equipment Manufacturer: ... Limited Company Person in Charge: Wang ... Station Type: Cellar-type Water Level Station Equipment Model: NF-WLZ-1 (Wastewater) Alarm Level: High Alarm Category: Warning Alarm Content: Abnormal Base Station Information Base station information Base station information is abnormal (LAC: 10321, CELLID: *232664904) 2025-03-0200:06:16 2025-03-0200:26:57 0 days 0 hours 20 minutes 141 Station code: 0060509181; Station name: Helong 8th Road Station; Area: Baiyun District Equipment Manufacturer: ... Limited Company Person in Charge: Wang ... Station Type: Cellar-type Water Level Station Equipment Model: NF-WLZ-1 (Wastewater) Alarm Level: High Alarm Category: Warning Alarm Content: Abnormal Base Station Information Base station information Base station information is abnormal (LAC: 10321, CELLID: *232664904) 2025-03-0200:06:10 2025-03-0200:26:57 0 days 0 hours 20 minutes

[0081] The present invention has been described in detail above. The above description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of this application. All equivalent changes and modifications made in accordance with the scope of this application should still fall within the scope of the present invention.

Claims

1. A method for tracking and alarming lost IoT devices, characterized in that: The implementation steps are as follows: Phase 1: Real-time data loss alarm is achieved through dynamic comparison of base station data. The connection data between the device and the communication base station is compared in real time. Once the device is found to have switched to an unfamiliar base station, a data loss alarm is triggered. Phase 2: Tracking based on LBS positioning. New base station data is obtained through real-time device base station handshake data, and LBS positioning is integrated to track the trajectory of lost devices.

2. The IoT device loss alarm and tracking method according to claim 1, characterized in that: The connection data between the device and the communication base station includes LAC and / or CELLID.

3. The IoT device loss alarm and tracking method according to claim 1, characterized in that: In the second phase, local base station basic information database is built using real-time device base station handshake data to form positioning information, and combined with the historical database of device base station information, multi-dimensional visualization of the trajectory is achieved.

4. The IoT device loss alarm and tracking method according to claim 1, characterized in that: The specific steps are as follows: SO1, IoT Devices and System Architecture 1) The hardware components include IoT device and server. The IoT device integrates a multi-mode communication module, a built-in low-power MCU, and is equipped with a SIM card slot and a handshake protocol stack with the operator's base station. The server is equipped with a time-series database to store historical base station data and a GIS engine to realize trajectory visualization. 2) Network Topology: The device transmits LAC / CELLID data to the server through the operator's network. The server establishes an API interface with the operator's LBS platform to obtain base station operator LBS data in real time, including latitude and longitude, coverage radius, location, and name. S02, Base station data dynamic comparison and loss alarm module 1) Data Acquisition and Preprocessing (1) Real-time data acquisition: IoT devices periodically send base station handshake signals through the operator's network to collect base station data; Data is uploaded to the cloud via the MQTT protocol, and after verification, it is stored in a time-series database, retaining records for the most recent few days, and automatically archiving expired data. (2) Historical database construction: Establish a time-series database to store historical base station connection records, set a sliding time window, and automatically eliminate expired data; 2) Dynamic comparison and alarm triggering (1) Real-time comparison mechanism: The newly collected base station data is hashed and compared with the historical database, and a dynamic threshold is set: if the device connects to an unfamiliar base station 3 times in a row, an alarm is triggered; if the connection to the original base station is not restored for 10 minutes, it is upgraded to an emergency alarm. (2) Alarm information generation: Output structured alarm logs, including the abnormal base station location, timestamp, and last known location of the device after preliminary LBS positioning; 3) Location-based tracking and visualization: (1) Base station positioning data fusion Based on the collected base station data and the integration of operator LBS data, a basic information database for base stations is constructed. (2) GIS trajectory visualization, combined with GIS technology to realize multi-layer display Historical trajectory layer: Draws the movement paths of equipment stations and base stations according to time series, and marks normal / abnormal periods with colors; Real-time positioning layer: Displays the current distribution of base station clusters in the form of a heat map, highlighting the locations of abnormal base stations; Geofencing layer: Customize the electronic fence range, triggering a secondary alarm when a device crosses the boundary.

5. The IoT device loss alarm and tracking method according to claim 4, characterized in that: In step S02, a sliding window algorithm is used to store the base station connection records of the device ID for the most recent 24*7 hours, and a hash table structure is used to achieve millisecond-level query.

6. The IoT device loss alarm and tracking method according to claim 4, characterized in that: In step S02, the hash calculation process is as follows: the newly collected base station data is compared with the three most recent records in the historical database. If the match fails, it is marked as an "unknown base station". The threshold determination process is as follows: Three consecutive connections to an unfamiliar base station → trigger a basic alarm and push it to the IoT platform; If the connection to the original base station is not restored for 10 consecutive minutes, an emergency alarm will be triggered and pushed to the mobile APP and IoT platform.

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