A tunnel construction command management method and system based on the Internet of Things

Abstract

The application discloses a tunnel construction command management method and system based on Internet of Things, and relates to the technical field of tunnel construction management and intelligent construction. The position data of construction personnel and equipment is collected in real time; based on a multi-dimensional rule base, the equipment gathering area, equipment behavior mode and personnel cooperation characteristics are analyzed to identify the current construction process; the start time, end time and actual time consumption of the process are recorded, and a visual progress report is generated; the actual time consumption of the process is compared with a preset standard threshold value to trigger an overtime alarm and hierarchical push; a resource optimization model is constructed to optimize and schedule the approach order of construction personnel, equipment and materials. The scheme deeply combines the Internet of Things positioning data with construction management business logic, realizes automatic identification of the tunnel construction process, real-time monitoring of the progress, active early warning of risks and intelligent cooperation of the process, and effectively improves the fine, automation and intelligent level of construction management.

Description

Technical Field

[0001] This invention relates to the field of tunnel construction management and intelligent construction technology, and in particular to a tunnel construction command and management method and system based on the Internet of Things. Background Technology

[0002] Currently, tunnel construction management mainly relies on manual records, report statistics, and regular meetings. Managers need to be physically present on-site or communicate by phone to understand construction progress, equipment usage, and the start and end times of each process. This approach has the following significant drawbacks: 1) Data is lagging and inaccurate; manual records are subject to delays, omissions, and subjective errors, failing to reflect the real situation on the construction site in a real-time and objective manner. 2) Process identification cannot automatically and accurately determine the specific construction process currently underway (such as excavation, muck removal, and support), resulting in coarse-grained management. 3) The early warning mechanism is passive; existing methods only detect significant delays, failing to proactively warn of problems in their early stages (such as minor delays in a process), missing the optimal time for management intervention. 4) Collaboration efficiency is low; existing methods rely on manual notification and scheduling for the connection between preceding and subsequent processes, and insufficient preparation can easily lead to idle work, affecting overall construction efficiency. 5) Decision support is insufficient; the lack of in-depth analysis of massive amounts of construction process data makes it difficult to form intuitive progress comparisons and trend predictions, providing managers with limited decision-making support.

[0003] With the development of the Internet of Things (IoT), edge computing, and industrial big data technologies, especially the maturity of high-precision positioning technologies (such as UWB), new technical approaches have been provided to solve the aforementioned problems. However, existing technologies mostly remain at the level of simple location tracking of personnel and equipment, with a single data collection dimension and no unified industrial data lake. Some solutions can only display the real-time location of equipment and cannot infer construction procedures based on multi-source heterogeneous industrial data (location data + equipment operating data + environmental monitoring data); a few systems with procedure identification functions have simple rules and lack adaptive algorithms, making them unable to adapt to the differences in procedure data characteristics under different tunnel types (such as drill-and-blast method and shield tunneling method) and complex geological conditions. In addition, there is a lack of early warning mechanism design based on data importance classification. All anomalies are pushed to the same management level, resulting in information overload or the neglect of key early warnings, failing to realize the layered release of industrial data value. Therefore, how to deeply integrate IoT positioning data with construction management business logic to achieve automated and intelligent management of the construction process remains a technical problem that urgently needs to be solved in this field. To this end, a tunnel construction command and management method and system based on IoT is proposed. Summary of the Invention

[0004] The main objective of this invention is to provide a tunnel construction command and management method and system based on the Internet of Things (IoT). By deeply integrating IoT positioning data with construction management business logic, it enables automatic identification of tunnel construction procedures, real-time monitoring of progress, proactive risk warning, and intelligent collaboration of procedures. This improves the level of refinement, automation, and intelligence in construction management, reduces the risk of construction delays, and enhances construction efficiency and safety control capabilities, effectively solving the problems in the background technology.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A tunnel construction command and management method based on the Internet of Things includes the following steps: The location data of construction personnel and equipment are collected in real time by deploying UWB positioning base stations and tags inside the tunnel; Based on a multi-dimensional rule base, the system analyzes equipment clustering areas, equipment behavior patterns, and personnel cooperation characteristics to automatically identify the current construction process. Record the start time, end time, and actual time spent on each process, and generate a visual progress report; The actual time taken for a process is compared with a preset standard threshold, triggering a timeout alarm and sending tiered notifications. Construct a resource optimization model to optimize the scheduling of the entry sequence of construction personnel, equipment, and materials; Based on the current process progress and historical data, predict the start time of subsequent processes and send out advance notices; The system compares actual progress with planned progress on a daily, weekly, and monthly basis, activates a tiered alarm mechanism, and implements closed-loop processing.

[0006] Furthermore, the multi-dimensional rule base includes: Equipment clustering area rules include the type, distribution, and quantity of equipment within the preset work area; Equipment behavior pattern rules: including the duration of equipment stationary or moving, movement trajectory, and coordinated actions between equipment; Personnel coordination characteristic rules: including the duration of personnel stay in a specific area and the inspection trajectory.

[0007] Furthermore, the standard thresholds are dynamically set based on tunnel type, surrounding rock grade, and construction process type, and are dynamically adjusted based on historical data and expert experience.

[0008] Furthermore, the tiered alarm mechanism includes: The alarm levels are divided into four categories based on the degree of delay. Each alarm level corresponds to different notification recipients, processing time limits, and alarm methods; Achieve closed-loop management from alarm triggering, processing feedback to effect verification.

[0009] Furthermore, the prediction of the start time of subsequent processes is determined based on the completion rate and remaining time of the current process, the planned preparation time of the subsequent process, and the correction coefficient based on historical process connection interval data. The calculation method is expressed as: Start time of subsequent process = Current time + Remaining time of current process + Preparation time of subsequent process + Correction coefficient. The remaining time is determined by the following method: Remaining time = (Actual time of the current process / Percentage of the current process completed) - Actual time of the current process.

[0010] Furthermore, the resource optimization model is constructed with minimizing equipment idle time as the optimization objective, where the optimization objective is expressed as: = ,in, For equipment idle time, For equipment number, For the collection of construction equipment, Artificially defined units of construction time For equipment In time State parameters, =1 indicates the device In time Under construction =0 indicates the device In time It is currently idle.

[0011] Furthermore, the resource optimization model is constructed using construction conditions, the uniqueness of construction equipment, the continuity of operations, equipment availability time, and equipment completion time as constraints. The construction conditions are used to constrain equipment allocation after the work process is ready, and are expressed as follows: ≤ , ,in, For equipment In time Assigned to the process The state variable, when When =1, it indicates that the device In time Assigned to the process ,when When =0, it indicates that the device In time Not assigned to a process ; In time process The state variable, when When =0, it indicates that at time process Not started, when When =1, it indicates that at time process It has started; Number the process steps; A set of construction procedures; The uniqueness of construction equipment is used to restrict that a piece of equipment can only be assigned to one work process at the same time, as shown in the following: , ; Job continuity is used to constrain equipment to operate continuously for at least one time unit after the start of a process, and is expressed as: , ; Equipment availability time is used to constrain equipment from being allocated before the earliest available time, and is expressed as: , ,in, For equipment The earliest available time; Equipment completion time is used to constrain the final completion time of the equipment, and is expressed as: , ,in, Indicates device The time required to complete the current process; For equipment In the process Standard operating time.

[0012] An Internet of Things (IoT) based tunnel construction management system includes: The IoT positioning layer includes UWB positioning base stations, positioning tags, and auxiliary sensors; The data storage layer includes real-time and historical databases, supporting high-frequency data storage and partition management; The business logic layer includes an automatic process identification module, a data recording and processing module, an alarm judgment module, an intelligent prediction module, an optimized scheduling module, and a progress comparison module; The application presentation layer provides web and mobile user interfaces that adapt to multiple roles. The external interface layer supports data integration with tunnel monitoring systems, equipment management systems, and enterprise OA systems.

[0013] The business logic layer adopts a microservice architecture, supporting the visual configuration and dynamic updates of process identification rules; The data storage layer implements data cleaning, association, and backup mechanisms, including location drift filtering, data association binding, and scheduled full / incremental backups.

[0014] The system also includes a memory, a processor, and a computer program stored in the memory, characterized in that when the program is executed by the processor, it implements an Internet of Things-based tunnel construction management method.

[0015] The present invention has the following beneficial effects: Compared with existing technologies, this solution achieves fully automated, non-intrusive identification and real-time recording of construction procedures through UWB high-precision positioning (accuracy ≤30cm) and a multi-dimensional rule engine, effectively avoiding the lag and errors of manual recording.

[0016] Compared with existing technologies, this solution transforms passive response into proactive management by using "process overtime alarm" (detecting time deviations within 10% in advance) and "subsequent process pre-notification" (reminding preparations 30 minutes in advance), effectively reducing the risk of project delays.

[0017] Compared with existing technologies, this solution provides managers with intuitive and quantitative decision support through visual reports such as dynamic Gantt charts and progress trend charts, combined with related data such as geological conditions and equipment status.

[0018] Compared with existing technologies, this solution ensures that information accurately reaches the corresponding level of responsibility by setting up a tiered alarm mechanism and a pre-notification confirmation mechanism, which can effectively avoid information overload and speed up the response to problems.

[0019] Compared to existing technologies, this solution establishes a complete management loop encompassing "data collection → intelligent analysis → status display → proactive early warning → management intervention → effect verification." All operations (such as threshold adjustment, alarm handling, and pre-notification feedback) are logged, supporting traceability. Furthermore, the historical database storing process data and reasons for schedule deviations can serve as a reference for planning and optimizing processes in subsequent similar projects, enabling the reuse of experience.

[0020] Compared with existing technologies, this solution supports different tunnel construction methods such as drill-and-blast method and shield tunneling method. It can dynamically adjust the standard threshold and identification rules according to the surrounding rock grade and process type, adapt to the construction management needs under complex geological conditions, and has strong practical value and promotion significance. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating a tunnel construction command and management method based on the Internet of Things according to the present invention. Figure 2 This is a block diagram of the dynamic scheduling logic of the technical solution of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0023] See Figures 1 to 2 This invention provides a tunnel construction command and management method based on the Internet of Things, comprising the following steps: 1) Real-time location data of construction personnel and equipment is collected by deploying UWB positioning base stations and tags inside the tunnel; 2) Based on a multi-dimensional rule base, the system analyzes equipment clustering areas, equipment behavior patterns, and personnel cooperation characteristics to automatically identify the current construction process; 3) Record the start time, end time, and actual time spent on each process, and generate a visual progress report; 4) Compare the actual time taken for the process with the preset standard threshold, trigger timeout alarms and push notifications in different tiers; 5) Construct a resource optimization model to optimize the scheduling of the entry sequence of construction personnel, equipment, and materials; 6) Based on the current process progress and historical data, predict the start time of subsequent processes and send advance notices; 7) Compare the actual progress with the planned progress on a daily, weekly, and monthly basis, activate the hierarchical alarm mechanism, and achieve closed-loop processing.

[0024] The technical solution of the present invention will now be further described in conjunction with practical application scenarios. The specific implementation steps of the technical solution of the present invention include the following steps: Step S1: Data Acquisition and Automatic Process Identification Hardware Deployment: For tunnels less than 2km in length, positioning base stations are set up at the tunnel entrance and mobile trolley base stations are set up inside the tunnel (for tunnels longer than 2km, additional positioning base stations are set up every 1.5km) to ensure that the positioning signal coverage is without blind spots; explosion-proof UWB positioning tags are installed on all key mechanical equipment (such as rock drilling rigs, arch frame installation rigs, drilling and anchoring machines, wet spraying machines, excavators, loaders, dump trucks, concrete mixer trucks, etc.) and construction personnel entering the tunnel (classified by job type, such as tunneling workers, support workers, and safety officers). Each tag has a unique ID that is bound to the equipment type and personnel information and stored in the database.

[0025] Data Acquisition: The UWB positioning base station receives the coordinate data of all positioning tags in real time (positioning accuracy ≤30cm) and transmits the data to the data storage layer via industrial Ethernet (transmission rate ≥100Mbps). The data sampling frequency is set to 1 time / second to ensure the capture of dynamic changes of equipment and personnel.

[0026] Automatic process identification: The automatic process identification module has a built-in "multi-dimensional rule base" that automatically determines the current construction process by analyzing the following characteristics: Equipment clustering areas: such as the equipment distribution in pre-defined areas like the "excavation face area", "support operation area", and "invert arch construction area"; Equipment behavior patterns: the duration of stationary / moving motion of specific equipment, movement trajectory (such as the regular trajectory of dump trucks traveling back and forth between the excavation face and the slag yard), and coordinated actions between equipment (such as the continuous action of loaders loading dump trucks). Personnel coordination characteristics: such as the length of time the support worker stays near the arch frame installation trolley and the inspection route of the safety officer in the work area.

[0027] Example 1 (Drill and blast tunnel - muck removal process): If the system detects that there are ≥1 loader and ≥3 dump trucks in the "excavation face area", and the average stationary time of the dump trucks is >5 minutes (for loading) and the movement trajectory conforms to the "excavation face → muck yard" round trip pattern, then the system will automatically identify it as the "muck removal" process.

[0028] Example 2 (Shield Tunneling - Segment Assembly Process): If the shield machine cutterhead is detected to be stationary (through the shield machine status sensor linkage), and the segment assembly machine stays in the tail area for ≥20 minutes, and 2 segment installers are stationary near the assembly machine, the system will automatically identify it as the "segment assembly" process.

[0029] Step S2: Automatic recording and visualization of process time Time Recording: The data recording and processing module automatically records the start time when a process is identified as "start" (the first time point that meets the process identification rules); when a process is identified as "end" (no longer meets the process identification rules and lasts for more than 10 minutes), it records the end time and automatically calculates the actual time spent on the process (accurate to the minute). Simultaneously, it records key data during the process: participating equipment numbers and operating status (e.g., number of failures), number of personnel, and geological conditions (e.g., surrounding rock grade).

[0030] Visualization: The system automatically generates three types of visual reports from the recorded process time data, supporting real-time viewing on both web and mobile devices. Dynamic Gantt Chart: With time as the horizontal axis and process name as the vertical axis, it uses different colors (planned time - blue, actual time - green, overtime portion - red) to show the comparison between the planned and actual progress of each process. It supports zooming to view the progress of a single day, a single week, and a single month. Clicking on a process segment can view detailed data (such as participating equipment and reasons for time deviation). Data Table: Displays information such as "Process ID, Name, Start Time, End Time, Actual Time, Planned Time, Time Deviation, Participating Equipment, and Person in Charge" for each process in list format. It supports filtering and sorting by dimensions such as "Time Deviation" and "Process Type". Progress trend chart: With time as the horizontal axis and the number of processes completed / cumulative progress as the vertical axis, it shows the progress change trend of a single day and a single week, and automatically marks the progress peak, valley and corresponding influencing factors (such as "the slag removal progress decreased on August 26 due to the failure of 1 slag truck").

[0031] Step S3: Automatic alarm for process timeout Threshold setting: Establish a "Standard Process Time Threshold Library" in the database. The thresholds are divided into three dimensions: "Tunnel Type (Drill and Blasting Method / Shield Method), Surrounding Rock Grade (II-V), and Process Type". See the table below for details:

[0032] Alarm Triggering and Push Notification: The alarm judgment module compares the actual time consumed in the process recorded in step S2 with the corresponding standard threshold in real time. If the actual time taken is less than or equal to the standard threshold: the system marks it as "normal" and does not trigger an alarm; If the actual time consumed exceeds the standard threshold: trigger an alarm signal immediately and push it in the following manner: Management platform interface: Alarm information is highlighted in the "Alarm Center" module (flashing red), including "Alarm ID, process name, current time elapsed, timeout duration, and affected area"; Audio alerts: A buzzer will be triggered in the project dispatch room and the construction site duty room (different types of alarms correspond to different frequencies, such as low frequency for process timeout and high frequency for safety risks). Mobile terminal notification: Pop-up notifications and SMS messages will be pushed to the person in charge of the process and their direct supervisor via the system APP (e.g., if the slag removal process times out, the notification will be pushed to the muck truck dispatcher and construction team leader). The notification content includes "[Process Timeout Warning] The 'slag removal' process in XX area of ​​XX tunnel has exceeded the timeout by 30 minutes. The current time is 4.8 hours, the standard threshold is 4.5 hours. Please handle it in time."

[0033] Step S4: Pre-notification of subsequent processes Time Prediction: The intelligent prediction module calculates the possible start time of subsequent processes based on the following data: Actual progress of the current process: If the current process is 70% complete, the remaining time = (actual time / percentage completed) - actual time; Planned preparation time for subsequent processes: For example, in the "scaffolding erection" process following "slag removal", arch frame materials need to be prepared and the erection trolley needs to be inspected 30 minutes in advance. The preparation time is set at 30 minutes. Historical collaborative data: For example, the average interval between the last 10 "slag removal → frame erection" processes is 20 minutes, which can be used as a correction factor.

[0034] Final predicted start time = current time + remaining time of current process + preparation time of subsequent processes + historical connection interval correction value.

[0035] Pre-notification push: The system pushes pre-notifications to relevant construction teams and responsible persons before the predicted start time of subsequent processes (which can be set according to process type, such as 30 minutes in advance for "scaffolding erection" and 20 minutes in advance for "shotcrete"). Notification recipients: Team leaders, equipment operators, and material handlers for subsequent processes (e.g., pre-notification for the "erection" process will be sent to the support team leader, erection trolley operator, and arch frame material handler). The notice reads: "[Preliminary Work Notice] The 'scaffolding erection' work in the XX area of ​​the XX tunnel is scheduled to begin at 14:30 on September 10, 2025. Please complete the following preparations in advance: 1. Check the operating status of the scaffolding trolley; 2. Ensure the arch frame material (Φ25mm) is in place; 3. Ensure the support team members are on duty." Confirmation mechanism: The recipient needs to click "Received" or "Ready to Go" in the APP to provide feedback. If no feedback is provided, the system will send a reminder again after 10 minutes.

[0036] Step S5: Construct a resource optimization model and perform optimized scheduling. With minimizing equipment idle time as the optimization objective, a resource optimization model is constructed using construction conditions, equipment uniqueness, work continuity, equipment availability time, and equipment completion time as constraints. Construction conditions constrain equipment to be allocated after the work process is ready; equipment uniqueness constrains that a single piece of equipment can only be allocated to one work process at a time; work continuity constrains that equipment must work continuously for at least one time unit after starting a work process; equipment availability time constrains that equipment cannot be allocated before its earliest available time; and equipment completion time constrains the final completion time of the equipment. This is represented as: Optimization goal: = ;

[0037] Constraints: ≤ , ; , ; , ; , ; , ; in, For equipment idle time, For equipment number, For the collection of construction equipment, Artificially defined units of construction time For equipment In time State parameters, =1 indicates the device In time Under construction =0 indicates the device In time It is currently idle. For equipment In time Assigned to the process The state variable, when When =1, it indicates that the device In time Assigned to the process ,when When =0, it indicates that the device In time Not assigned to a process ; In time process The state variable, when When =0, it indicates that at time process Not started, when When =1, it indicates that at time process It has started; Number the process steps; A set of construction procedures; For equipment The earliest available time; Indicates device The time required to complete the current process; For equipment In the process Standard operating time.

[0038] Step S6: Progress Comparison and Tiered Alarms Progress Calculation: The progress comparison module compares the current overall construction progress with the pre-stored annual, monthly, and weekly overall construction plans on a daily, weekly, and monthly basis. Progress indicators: The core indicators are "cumulative footage (meters)" and "process completion rate (number of completed processes / number of planned processes × 100%)". Comparison logic: For example, if the monthly plan is to advance 100 meters, and the current monthly cumulative advance is 80 meters, then the progress is lagging by 20 meters, with a lag rate of 20%; if the weekly plan is to complete the "slag removal" process 12 times, and the current process is completed 9 times, then the process completion rate is 75%, lagging by 3 times.

[0039] Tiered Alarm Mechanism: The system activates a four-level alarm mechanism based on the degree of progress lag (lag time, lag advance, lag rate). The alarm level is linked to the notification recipient and processing time limit. See the table below for details of the alarm mechanism:

[0040] Alarm closed-loop management: Alarm information must follow a closed-loop process of "receive - process - feedback - verification". a. After the recipient confirms receipt of the alarm, they should fill in the "handling plan" in the system (e.g., "Level 3 alarm handling plan: 1. Add one more dump truck for slag removal; 2. Extend the operation time by 2 hours"). b. During the process, update the progress in real time (e.g., "Dump trucks have been dispatched and operations have begun at 14:00"). c. After the process is completed, submit a “Rectification Completion Report” with progress recovery data (e.g., “20 meters behind schedule, 15 meters have been made up, and the remaining 5 meters are expected to be completed tomorrow”). The system automatically verifies the progress recovery status. If the status is met, it marks the alarm as "closed loop". If the status is not met, the alarm level is upgraded.

[0041] This invention also provides an IoT-based tunnel construction management system for implementing the above method, comprising a five-layer architecture: an IoT positioning layer, a data storage layer, a business logic layer, an application presentation layer, and an external interface layer. Specifically: 1) IoT Positioning Layer: Composed of a UWB positioning base station, UWB positioning tags, and auxiliary sensors (such as equipment status sensors and surrounding rock pressure sensors), it is responsible for collecting real-time location data of personnel and equipment, equipment operating parameters (such as trolley speed and fuel consumption), and on-site environmental data (such as surrounding rock temperature and humidity). The positioning base station supports PoE power supply to adapt to the complex power supply environment inside the tunnel. The positioning tags are waterproof (IP67) and explosion-proof (Ex d IIC T6 Gb) with a battery life of ≥72 hours (personnel tags) and ≥1 month (equipment tags, which support charging).

[0042] 2) Data storage layer: It consists of a "real-time database + historical database". The real-time database (using InfluxDB) stores nearly 24 hours of location data and device status data (storage period of 1 second / record), and supports high-concurrency read and write. The historical database (using MySQL) stores over 24 hours of data, including process records, alarm information, construction plans, standard thresholds, and personnel and equipment files. It employs a partitioned table design (partitioned by time, such as one partition per month) to improve query efficiency. Simultaneously, a data backup mechanism is established, with automatic full backups every morning and incremental backups every hour. Backup data is stored off-site to prevent data loss.

[0043] 3) Business logic layer: The system's core processing engine comprises five functional modules, each employing a microservice architecture that supports independent deployment and upgrades. Automatic process identification module: Built-in rule engine, supports visual configuration of process identification rules (such as dragging and dropping equipment types, setting time thresholds), and rule changes do not require system restart; Data recording and processing module: Implements data cleaning (filtering location drift data, such as judging a single coordinate change > 5 meters as drift, and correcting it with the average of the data before and after 5 seconds), data association (binding equipment location with process and personnel), and time consumption calculation; Alarm judgment module: includes threshold management, alarm triggering, and alarm push sub-modules, and supports custom alarm push methods and recipients; Intelligent forecasting module: Integrates time series forecasting algorithms (such as ARIMA model), optimizes forecast accuracy based on historical process connection data, and achieves a forecast error rate of ≤10%; The optimization scheduling module has a built-in resource optimization model that optimizes the order of personnel, equipment and materials to minimize equipment idle time, with constraints such as construction conditions, uniqueness of construction equipment, operation continuity, equipment availability time and equipment completion time. Progress Comparison Module: Supports importing construction plans in Excel format (such as Project exported files), automatically parses the plan data, and realizes automatic alignment and comparison between the plan and the actual progress.

[0044] 4) Application presentation layer: Provides personalized user interfaces for different user roles (construction workers, team leaders, project supervisors, project managers, company leaders), supporting web (compatible with Chrome and Edge browsers) and mobile app (supports Android 8.0+ and iOS 12.0+): Construction worker's interface: Displays individual location, current work process, and pre-notification information; supports feedback on preparation status. Team leader's terminal: View the process progress and alarm information of the teams under their jurisdiction, and handle pre-notification confirmation and alarm feedback; Project Manager Portal: View the overall project progress, the collaboration status of various work teams, and approve rectification plans; Project Manager Portal: View the project's entire lifecycle progress trend, tiered alarm statistics, and export monthly progress reports; Company leadership: View progress comparisons of multiple projects, major alerts (level 3 and above), and make macro-level decisions.

[0045] 5) External interface layer: Provides standardized interfaces to support integration with other systems and enable data exchange. Interface with tunnel monitoring system: Import surrounding rock pressure and displacement monitoring data, and conduct correlation analysis on the impact of geological conditions on the progress of the process (such as excessive surrounding rock displacement causing the "support process" to exceed the time limit); Integration with equipment management system: Obtain equipment fault records and maintenance plans, and automatically associate the reasons for process delays (such as "maintenance of rock drilling rig caused delays in excavation process"). Integration with the enterprise OA system: Push level 4 alarm information to the OA system to trigger the approval process; Integration with reporting systems: Supports exporting progress reports and alarm statistics reports in Excel and PDF formats, with customizable report templates.

[0046] The technical solution of this invention will be further described in detail below, taking into account the actual application of a railway tunnel (drill and blast method construction) project.

[0047] 1) Project Background The tunnel is a double-track railway tunnel with a total length of 4705m, a design speed of 250km / h, a maximum burial depth of 224m, and a rock grade distribution of: Grade II 1760m (37.0%), Grade IV 2435m (51.2%), and Grade V 565m (11.9%). It is constructed using the drill-and-blast method, and the main procedures include "drilling → charging → blasting → slag removal → frame erection → shotcreting → invert arch construction → secondary lining construction".

[0048] 2) System Deployment and Configuration 2.1) Hardware Deployment UWB positioning base stations: 1 unit is deployed every 200 meters along the tunnel axis (cross-section ≤120㎡), for a total of 24 units. They are powered by PoE and connected to the project server via industrial Ethernet. 1 unit is also deployed at the tunnel entrance / exit and the slag yard to track the trajectory of equipment entering and leaving the tunnel.

[0049] UWB positioning tags: Equipment tags were installed on 20 key pieces of equipment (2 rock drilling rigs, 2 arch frame installation rigs, 1 drilling-anchoring-grouting integrated machine, 2 wet spraying machines, 3 excavators, 4 loaders, and 6 dump trucks); personnel tags were worn on 80 construction workers (by job type: 20 tunneling workers, 30 support workers, 10 safety officers, 5 dispatchers, and 15 management personnel). The tag ID was bound to the equipment / personnel information (e.g., "Equipment tag ID001 → Rock drilling rig 1#" "Personnel tag ID101 → Support team leader Zhang San").

[0050] Auxiliary sensors: Install equipment status sensors (collecting speed, fuel consumption, and fault codes) on rock drilling rigs and dump trucks; install surrounding rock pressure sensors (collecting surrounding rock pressure values) in Class IV and V surrounding rock sections. The data is transmitted wirelessly to the positioning base station via LoRa and then aggregated to the data storage layer.

[0051] 2.2) Software Configuration Rule base configuration: In the automatic process identification module, configure rules according to the characteristics of the drilling and blasting process, for example: Slag removal process: Within the excavation area (coordinates X: 1000-1200m, Y: 0-20m), there are ≥1 loader (label type: loader) and ≥3 dump trucks (label type: dump truck), and the average stationary time of the dump trucks is >5 minutes, and the movement trajectory includes a round trip path from "excavation face → slag yard (coordinates X: 500-600m, Y: 0-20m)"; Erection process: Within the support operation area (coordinates X: 1200-1300m, Y: 0-20m), there is one arch frame installation trolley (label type: erection trolley), and ≥3 support workers (label type: support workers) stay near the trolley for more than 10 minutes, and the surrounding rock pressure sensor data is stable (pressure fluctuation < 5%).

[0052] Threshold setting: Referencing the project's construction quota and historical data of similar procedures over the past 3 months (e.g., the average time for muck removal in Class IV surrounding rock is 4 hours), set a standard threshold in the database, as shown in the example below:

[0053] Advance notification time: set according to the complexity of the process preparation: 20 minutes in advance for "drilling", 30 minutes in advance for "slag removal", 40 minutes in advance for "scaffolding erection", and 25 minutes in advance for "spraying".

[0054] Tiered alarm standards: Based on project schedule requirements (3 meters of planned daily progress), the following settings are configured: Level 1 Alarm: Daily progress lag < 0.5 meters, or process completion rate ≥ 90%; Level 2 alarm: Daily progress is lagging behind by 0.5-1 meter, or the process completion rate is 80%-89%; Level 3 alarm: Daily progress is 1-2 meters behind schedule, or the process completion rate is 70%-79%; Level 4 alarm: Daily progress is delayed by ≥2 meters, or the process completion rate is <70%.

[0055] 3) System operation process and effects 3.1) Process identification and time recording At 08:00 on September 10, 2025: The UWB base station detected that one loader (ID008) and four dump trucks (ID012, ID013, ID014, ID015) were stationary in the "excavation face area" (X:1100-1150m). The average stationary time of the dump trucks was 6 minutes, and the trajectory showed that one round trip from "excavation face → slag yard" had been completed. The automatic process identification module matched the "slag removal process" rule and recorded the start time of 08:00.

[0056] 08:00-12:30: The system records the location changes and operating status of dump trucks and loaders in real time (e.g., if dump truck ID012 is suspended due to a fault from 09:10 to 09:20, the system will automatically record "equipment fault once, time taken 10 minutes").

[0057] 12:30: The last dump truck (ID015) left the excavation area and did not return for 10 minutes. The system recognized "the muck removal process is over", recorded the end time as 12:30, and calculated the actual time as 4.5 hours (which is the same as the standard threshold of 4.5 hours and marked as "normal").

[0058] 3.2) Process timeout alarm September 11, 2025, 09:00: "Frame erection process" begins (identification rule matching), standard threshold 9 hours (Class V surrounding rock).

[0059] 18:30: The scaffolding erection process is still underway, having taken 9.5 hours, exceeding the standard threshold by 0.5 hours. The alarm judgment module has triggered a level two alarm. Web interface: The "Alarm Center" flashes red and displays "[Level 2 Alarm] The 'scaffolding' process in the Class V surrounding rock section has exceeded the time limit by 0.5 hours, and the current time is 9.5 hours. Please have the project manager handle this within 4 hours." Mobile devices: Push APP pop-ups and text messages to project supervisor (Engineer Li) and support team leader (Engineer Wang); Dispatch room: Trigger a buzzer alert (frequency 1 time / 30 seconds).

[0060] 18:40: Engineer Wang provided feedback in the APP: "Solution: Add 2 support workers to assist with the installation and check the tightness of the bolts on the erection trolley." 20:00: The scaffolding erection process is completed, with an actual time of 11 hours. Engineer Wang submits a "Rectification Completion Report". The system verifies that the progress has no subsequent impact and marks it as "Alarm Closed Loop".

[0061] 3.3) Pre-notification of subsequent processes September 12, 2025, 10:00 AM: The "slag removal process" is underway, with actual time taken 2 hours (50% complete). The intelligent prediction module calculates: Remaining time for the current process = (2 hours / 50%) - 2 hours = 2 hours; The preparation time for the subsequent "shotcrete process" is 25 minutes; The average historical connection interval is 15 minutes; Predicted start time = 10:00 + 2 hours + 25 minutes + 15 minutes = 12:40.

[0062] 12:15 (25 minutes in advance): The system sends a pre-notification to the shotcrete team leader (Engineer Zhang), wet shotcrete machine operator (Engineer Liu), and material handler (Engineer Chen): "[Shotcrete Process Pre-Notification] Expected to start at 12:40. Please complete the following: 1. Check the wet shotcrete machine pressure (≥8MPa required); 2. Ensure the concrete (C30) is in place; 3. All 5 members of the shotcrete team must be on duty." 12:20: Engineers Zhang, Liu, and Chen clicked "Received," "Equipment Check Normal," and "Materials Arrived" respectively in the APP to provide feedback; 12:40: The slag removal process was completed on time, and the shotcrete process started on time, with no delays in the connection.

[0063] 3.4) Progress Comparison and Tiered Alarms September 2025 monthly plan: 100 meters of progress, with plans to complete 30 "slag removal" processes, 25 "scaffolding" processes, and 25 "spraying" processes.

[0064] September 30: Progress comparison module calculation: The cumulative monthly progress was 85 meters, lagging behind by 15 meters; Process completion rate: slag removal 28 times (93.3%), frame erection 22 times (88%), shotcrete 23 times (92%), overall process completion rate 91%; It was determined to be "Level 2 Delay" (average daily progress lag of 0.5 meters, overall process completion rate of 88%).

[0065] October 1, 09:00: Level 2 alarm triggered, notifying project supervisor and project manager; October 1, 11:00: The project manager convened a progress analysis meeting and formulated rectification measures (such as adding one dump truck and optimizing blasting parameters to improve excavation efficiency). October 15: The monthly progress was made up by 10 meters, the lag was reduced to 5 meters, and the system alarm level was adjusted to Level 1.

[0066] 3.5) Construction scheduling optimization 3.51) Key procedures: Drilling → Charging → Blasting → Slag Removal → Frame Erection → Shotcrete → Invert Arch Construction → Secondary Lining Construction.

[0067] Optimization objective: With "minimizing equipment idle time" as the core objective, the system uses an integer programming model to dynamically optimize the entry sequence and work allocation of key equipment such as rock drilling rigs, loaders, dump trucks, and wet spraying machines, and accordingly coordinates the entry time of personnel teams and materials.

[0068] 3.52) Model Parameters and Decision Variables a) Definition of a set: Process set J: {drilling, slag removal, frame erection, shotcreting} (For simplicity, focus on the core processes within the cycle) Equipment set E: {Rock drilling rig 1#, Rock drilling rig 2#, Loader 1#, Loader 2#, Dump truck 1#, ..., Dump truck 6#, Wet shotcrete machine 1#, Wet shotcrete machine 2#} Time set T: Each time unit is 15 minutes, and a planning period is 8 hours (32 time units).

[0069] b) Key parameters: Standard operation time p ej (Unit: Time unit, i.e., 15 minutes):

[0070] Process ready parameter r jt : The drilling process is ready at t=1 (r_drilling, 1=1).

[0071] The slag removal process must be completed after the blasting is finished (assuming it is completed at t=20), i.e., r_slag removal, 20=1.

[0072] The frame erection process can only be completed after the slag removal is finished.

[0073] The shotcrete process can only be completed after the scaffolding is erected.

[0074] 3.53) Optimization Model Application Scenario Simulation Scenario: Class IV surrounding rock section, at the start of a work cycle.

[0075] Step 1: Model Input Current time t=0.

[0076] Drilling operation is ready (r_drilling, 0=1).

[0077] All devices are in a usable state.

[0078] Step 2: Model Solving and Output The model solves for the optimal equipment scheduling scheme x by minimizing the total idle time. ejt As shown below: The equipment allocation plan is as follows: Rock drilling rigs #1 and #2: Immediately begin drilling operations (x_rig #1 drilling hole 0=1), expected to be completed at t=16.

[0079] Loaders #1 and #2 and dump trucks #1-#6: The model predicts they will begin the slag removal process at t=20. To avoid idleness, the model may issue the following instructions: Loader #1 departed from the warehouse area at t=18, slowly drove to the working face, and arrived at the working face at t=20.

[0080] Dump trucks are dispatched in batches: trucks #1-#3 depart at t=19; trucks #4-#6 depart at t=25, to match the loading schedule.

[0081] Wet shotcrete machine #1: The model predicts that it will begin the shotcrete process at t=40. It is instructed to move from the maintenance area at t=38.

[0082] The personnel and material allocation plan is as follows: Personnel: The arrival time of the drilling team, slag removal team, support team, and shotcrete team will strictly correspond to the start time of the equipment for the process they are responsible for.

[0083] Example: The support team receives the instruction: "Please arrive at the erection work site at t=35". This is synchronized with the planned arrival time of the erection trolley, avoiding personnel arriving early and waiting.

[0084] Material: The explosives transport vehicle was instructed to arrive at the loading site at t=15 and complete the drilling.

[0085] The concrete mixer truck was instructed to arrive at the shotcrete work surface at t=38, simultaneously with the wet shotcrete machine.

[0086] 3.54) Comparison of Optimization Effects index Traditional experience scheduling Intelligent optimization scheduling Optimization effect Rock drilling rig idle rate Approximately 15% (waiting between processes) <5% Efficiency improvement >10% Average waiting time for dump trucks One cycle takes about 25 minutes. One cycle takes about 8 minutes Waiting time reduced by 68% Process connection interval Approximately 45 minutes on average Approximately 15 minutes on average Connection efficiency improved by 67%. Total time for a single loop Approximately 10 hours Approximately 8.5 hours Construction period shortened by 15% 4) Application effect After applying this invention, the following improvements are achieved in this project: Process identification accuracy: increased from 75% by manual judgment to 98%; Total waiting time between processes: reduced from an average of 2 hours to 30 minutes, reducing idle time by 60%; Alarm handling efficiency: Average processing time reduced from 24 hours to 8 hours; Overall construction efficiency: Monthly progress increased from 80 meters to 95 meters, representing an efficiency improvement of 18.75%; Schedule risk control: 12 delay issues were identified and resolved in advance, avoiding a total delay of 15 days.

[0087] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A tunnel construction management method based on the Internet of Things, characterized in that, Includes the following steps: The location data of construction personnel and equipment are collected in real time by deploying UWB positioning base stations and tags inside the tunnel; Based on a multi-dimensional rule base, the system analyzes equipment clustering areas, equipment behavior patterns, and personnel cooperation characteristics to automatically identify the current construction process. Record the start time, end time, and actual time spent on each process, and generate a visual progress report; The actual time taken for a process is compared with a preset standard threshold, triggering a timeout alarm and sending tiered notifications. Construct a resource optimization model to optimize the scheduling of the entry sequence of construction personnel, equipment, and materials; Based on the current process progress and historical data, predict the start time of subsequent processes and send out advance notices; The system compares actual progress with planned progress on a daily, weekly, and monthly basis, activates a tiered alarm mechanism, and implements closed-loop processing.

2. The tunnel construction management method based on the Internet of Things according to claim 1, characterized in that, The multi-dimensional rule base includes: Equipment clustering area rules include the type, distribution, and quantity of equipment within the preset work area; Equipment behavior pattern rules: including the duration of equipment stationary or moving, movement trajectory, and coordinated actions between equipment; Personnel coordination characteristic rules: including the duration of personnel stay in a specific area and the inspection trajectory.

3. The tunnel construction management method based on the Internet of Things according to claim 1, characterized in that, The standard threshold is dynamically set according to the tunnel type, surrounding rock grade, and process type, and is dynamically adjusted based on historical data and expert experience.

4. The tunnel construction management method based on the Internet of Things according to claim 1, characterized in that, The tiered alarm mechanism includes: The alarm levels are divided into four categories based on the degree of delay. Each alarm level corresponds to a different notification target, processing time limit, and alarm method; Achieve closed-loop management from alarm triggering, processing feedback to effect verification.

5. The tunnel construction management method based on the Internet of Things according to claim 1, characterized in that, The prediction of the start time of the subsequent process is determined based on the completion ratio and remaining time of the current process, the planned preparation time of the subsequent process, and the correction coefficient based on historical process connection interval data. The calculation method is expressed as: Start time of the subsequent process = Current time + Remaining time of the current process + Preparation time of the subsequent process + Correction coefficient. The remaining time is determined by the following method: Remaining time = (Actual time of the current process / Percentage of the current process completed) - Actual time of the current process.

6. The tunnel construction management method based on the Internet of Things according to claim 1, characterized in that, The resource optimization model is constructed with minimizing equipment idle time as the optimization objective, wherein the optimization objective is expressed as: = ,in, For equipment idle time, For equipment number, For the collection of construction equipment, Artificially defined units of construction time For equipment In time State parameters, =1 indicates the device In time Under construction =0 indicates the device In time It is currently idle.

7. The tunnel construction management method based on the Internet of Things according to claim 6, characterized in that, The resource optimization model is constructed using construction conditions, the uniqueness of construction equipment, operational continuity, equipment availability time, and equipment completion time as constraints. The construction status is used to constrain the allocation of equipment after the process is completed, and is expressed as follows: ≤ , ,in, For equipment In time Assigned to the process The state variable, when When =1, it indicates that the device In time Assigned to the process ,when When =0, it indicates that the device In time Not assigned to a process ; In time process The state variable, when When =0, it indicates that at time process Not started, when When =1, it indicates that at time process It has started; Number the process steps; A set of construction procedures; The uniqueness of the construction equipment is used to restrict that a single piece of equipment can only be assigned to one work process at any given time, as expressed as: , ; The work continuity requirement constrains the equipment to operate continuously for at least one time unit after the start of the process, and is expressed as: , ; The device availability time is used to constrain the device from being allocated before the earliest availability time, expressed as: , ,in, For equipment The earliest available time; The device completion time is used to constrain the final completion time of the device, and is expressed as follows: , ,in, Indicates device The time required to complete the current process; For equipment In the process Standard operating time.

8. A tunnel construction management system based on the Internet of Things, characterized in that, include: The IoT positioning layer includes UWB positioning base stations, positioning tags, and auxiliary sensors; The data storage layer includes real-time and historical databases, supporting high-frequency data storage and partition management; The business logic layer includes an automatic process identification module, a data recording and processing module, an alarm judgment module, an intelligent prediction module, an optimized scheduling module, and a progress comparison module; The application presentation layer provides web and mobile user interfaces that adapt to multiple roles. The external interface layer supports data integration with tunnel monitoring systems, equipment management systems, and enterprise OA systems.

9. A tunnel construction management system based on the Internet of Things according to claim 8, characterized in that, The business logic layer adopts a microservice architecture, which supports the visual configuration and dynamic updating of process identification rules; The data storage layer implements data cleaning, association, and backup mechanisms, including location drift filtering, data association binding, and scheduled full / incremental backups.

10. A tunnel construction management system based on the Internet of Things according to claim 9, characterized in that, The system further includes a memory, a processor, and a computer program stored in the memory, characterized in that, when the program is executed by the processor, it implements the Internet of Things-based tunnel construction management method as described in any one of claims 1-7.

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