Multifunctional safety helmet management system and method based on Internet of Things

Abstract

The invention discloses a multifunctional safety helmet management system and method based on the Internet of Things, relates to the field of safety production, and solves the problems that a traditional safety helmet is single in safety management function, insufficient in data processing precision and the like. A multi-dimensional state data sequence is continuously collected through a three-axis acceleration sensor, a temperature and humidity sensor, a positioning sensor, a vital sign sensor and the like integrated on the safety helmet terminal; terminal monitoring characteristics including an acceleration module value, a position drift distance and a physical sign fluctuation index are constructed, data reliability is evaluated through weighted fusion of dynamic fluctuation values, and an abnormal terminal is isolated. And carrying out space-time calibration on the reliable data to generate a unified coordinate system trajectory, analyzing and identifying abnormal behaviors through group trajectory consistency, and constructing a regional safety risk map in combination with a signal blind area coverage rate, thereby realizing safety risk level prediction based on historical data and real-time trajectory matching. According to the invention, comprehensive real-time monitoring of the state of the operator is realized, and the intelligent level of safety production management is improved.

Description

Technical Field

[0001] The present invention relates to the field of safe production, and in particular to a multifunctional safety helmet management system and method based on the Internet of Things. Background Art

[0002] In industrial sectors like construction, mining, and power generation, worker safety is paramount. Traditional hard hats offer only physical protection and are unable to provide real-time information about workers' status, location, and working environment. This makes them unable to meet the demands of modern safety management, which demands intelligent and sophisticated management. With the rapid development of the Internet of Things (IoT), applying this technology to hard hat management has become a key area for improving safety in production.

[0003] While existing IoT-based hardhat management solutions can achieve a certain level of data collection and monitoring, they still have many shortcomings. Most solutions only collect data from a single or limited set of dimensions, such as simple positioning data or basic motion data. These solutions fail to fully and deeply reflect the actual status of workers and the complex working environment. For example, the collection of critical information such as workers' vital signs, ambient temperature and humidity, is incomplete, making it difficult for managers to accurately assess workers' safety status.

[0004] In terms of data processing and analysis, existing solutions use simplistic methods for assessing data reliability, lacking scientific and rational multi-dimensional feature construction and dynamic fluctuation analysis. This can easily lead to misjudgment or omission of abnormal data, impacting the accuracy of subsequent management decisions. Furthermore, the processing of positioning data fails to fully account for inter-device positioning deviations, resulting in inaccurate worker activity trajectories and making it difficult to effectively monitor and manage group activities.

[0005] When it comes to safety risk prediction, existing solutions often lack in-depth fusion analysis of historical and real-time data, and lack a comprehensive regional safety risk map. This makes it impossible to accurately predict safety risks based on real-time operational conditions and historical risk data, making it difficult to proactively identify potential safety hazards and implement effective preventative measures. Furthermore, existing safety helmets lack functional integration and system coordination. For example, functional modules such as wear detection, positioning, and communication lack close coordination, resulting in low data transmission and processing efficiency, impacting the performance and reliability of the entire management system. Summary of the Invention

[0006] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a multifunctional helmet management method based on the Internet of Things, comprising the following steps:

[0007] Step 1: Multi-dimensional data collection of the wearer is performed through multiple hard hat terminals deployed in the work area to obtain the real-time status data sequence of each hard hat terminal;

[0008] Step 2: Build terminal monitoring features based on the real-time status data of the helmet terminal and calculate the dynamic fluctuation value of each terminal monitoring feature. If the fluctuation value is less than the preset stability threshold, the terminal data reliability is determined to be up to standard and proceed to step 3. Otherwise, it is marked as an abnormal terminal and offline isolation is initiated.

[0009] Step 3: Perform spatiotemporal calibration on the positioning data of each reliable terminal according to the terminal's spatial coordinates to generate the wearer's activity trajectory in a unified coordinate system. Calculate the group trajectory consistency index. If the group trajectory consistency index exceeds the preset safety threshold, proceed to step 5; otherwise, proceed to step 4.

[0010] Step 4: Compare the deviation of each terminal's activity trajectory with the standard operation path, and locate and isolate the terminal with abnormal trajectory based on the deviation;

[0011] Step 5: Calculate the signal blind spot coverage rate of the current operation surface based on the distribution density and spatial location of the isolation terminals. Combined with historical blind spot coverage data for different operation scenarios, establish a regional safety risk map and store it in the cloud database.

[0012] Step 6: Predict the safety risk level of the current operator through matching analysis of the real-time operation path and risk map.

[0013] Furthermore, the real-time status data includes:

[0014] Three-dimensional motion acceleration, ambient temperature and humidity, positioning coordinates, and vital signs data are continuously collected at a set sampling frequency to form a state data sequence containing time series characteristics.

[0015] Furthermore, the method for constructing the terminal monitoring feature is:

[0016] The acceleration modulus, position drift and vital sign fluctuation index are selected as characteristic parameters, the relative change rate of each parameter in adjacent sampling periods is calculated, and the dynamic fluctuation value is generated through weighted fusion.

[0017] Furthermore, the time-space calibration includes:

[0018] The local coordinate system positioning data obtained by each terminal is unified into the reference coordinate system of the working area through the coordinate conversion matrix to eliminate positioning deviations between devices.

[0019] Furthermore, the group trajectory consistency index is calculated as follows:

[0020] The spatial overlap of each calibration trajectory in the preset path segment is counted, and the proportion of terminals that reach the overlap threshold is calculated as the consistency index.

[0021] Furthermore, the calculation of the signal blind area coverage rate includes:

[0022] The signal loss area is constructed based on the communication radius of the isolation terminal, and the proportion of the area of ​​this area to the total monitoring area of ​​the working surface is calculated.

[0023] Furthermore, the security risk prediction includes:

[0024] The real-time trajectory is dynamically time-warped and matched with the risk map, the blind spot data of the most similar historical trajectory is extracted, and the risk probability is calculated based on the current environmental parameters.

[0025] A multifunctional helmet management system based on the Internet of Things, applying the multifunctional helmet management method based on the Internet of Things, includes a wearing detection device, a positioning device, a communication device, a data transmission status detection device, a helmet management module, a workgroup grouping module and a cloud data server module;

[0026] The wearing detection device is used to detect whether the helmet is worn correctly and to monitor whether the helmet is taken off in the area;

[0027] The positioning device includes a network positioning module and an off-network positioning module for locating the position of the worker; wherein the off-network positioning module is used to locate the worker without a network; the off-network positioning includes locating the worker based on historical trajectories;

[0028] The data transmission status detection device is used to detect the data transmission status of the helmet;

[0029] The work group grouping module is used to group the work groups according to the helmet information of the workers entering the construction site;

[0030] The cloud data server is used to store helmet information;

[0031] The safety helmet management module is used to monitor the status of the safety helmet, which includes the energy status of the safety helmet and the worker information bound to the safety helmet.

[0032] The present invention has the beneficial effect of continuously collecting multi-dimensional information such as the worker's three-dimensional motion acceleration, ambient temperature and humidity, positioning coordinates, and vital signs by deploying multiple sensors on the helmet terminal. This data comprehensively reflects the worker's motion state, environmental conditions, and physical health, providing rich and accurate basic data for safe production management. This data allows managers to understand the worker's work status in real time and promptly identify abnormal conditions such as excessive fatigue or excessive ambient temperature, allowing them to take appropriate safety measures to ensure the worker's safety.

[0033] A terminal monitoring feature is constructed that includes characteristic parameters such as acceleration modulus, position drift, and vital sign fluctuation index. Dynamic fluctuation values ​​are generated by calculating the relative rate of change of each parameter within adjacent sampling periods and performing weighted fusion. This multi-feature fusion reliability assessment method more accurately determines data reliability, promptly identifies abnormal terminals and isolates them offline, avoiding management decision errors caused by data anomalies. This ensures that data entering subsequent processing is reliable, improving the stability and accuracy of the entire management system.

[0034] The coordinate transformation matrix unifies the local coordinate system positioning data of each terminal into the reference coordinate system of the operating area, eliminating positioning deviations between devices and generating accurate wearer activity trajectories. Based on this, the group trajectory consistency index is calculated to effectively monitor the consistency of the activities of the group of workers. When the consistency index exceeds the preset safety threshold, the signal blind spot coverage rate is promptly calculated and the risk map is constructed. When the consistency index is low, the deviation of the terminal activity trajectory from the standard operating path is compared to accurately locate and isolate terminals with abnormal trajectories. This precise trajectory analysis and exception handling mechanism can improve the monitoring capabilities of worker activities, promptly detect abnormal behavior of individuals or groups, and reduce the possibility of safety accidents.

[0035] The signal blind spot coverage rate for the current operation surface is calculated based on the distribution density and spatial location of isolation terminals. Historical blind spot coverage data for different operation scenarios is then used to construct a regional safety risk map. By dynamically time-warping the real-time operation path with the risk map, the safety risk level of the current operator can be accurately predicted. This risk prediction method, based on both historical and real-time data, can identify high-risk areas and operation links in advance, providing a scientific basis for managers to formulate targeted safety protection measures, and achieving a transition from passive safety management to proactive safety prevention. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 The figure is a flowchart of a multifunctional helmet management method based on the Internet of Things; Figure 2This is a schematic diagram of the principle of a multifunctional safety helmet management system based on the Internet of Things. DETAILED DESCRIPTION

[0037] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings, but the protection scope of the present invention is not limited to the following.

[0038] The features and performance of the present invention are further described in detail below with reference to the embodiments.

[0039] like Figure 1 As shown, the multifunctional helmet management method based on the Internet of Things includes:

[0040] Step 1: Multi-dimensional data collection

[0041] Multiple hard hat terminals are deployed in the work area, and each hard hat terminal is equipped with a variety of sensors, including but not limited to three-axis acceleration sensors, temperature and humidity sensors, positioning modules (such as GPS / Beidou positioning modules), and vital signs monitoring sensors (such as heart rate sensors, blood oxygen sensors, etc.). Among them, the three-axis acceleration sensor is used to collect the three-dimensional motion acceleration data of the workers to monitor the workers' motion status, such as walking, running, falling, etc.; the temperature and humidity sensor is used to obtain the temperature and humidity information of the working environment in real time, providing a basis for evaluating the comfort and safety of the working environment; the positioning module is used to obtain the positioning coordinates of the workers to track their position; the vital signs monitoring sensor is used to collect the workers' heart rate, blood oxygen and other vital signs data to monitor their physical health.

[0042] Set an appropriate sampling frequency to continuously collect various data. The sampling frequency setting needs to be determined based on the characteristics of different data and actual application requirements. For example, for three-dimensional motion acceleration data and vital signs data, in order to be able to capture subtle changes in a timely manner, the sampling frequency can be set to a higher value, such as 50Hz; for ambient temperature and humidity data and positioning coordinate data, since their changes are relatively slow, the sampling frequency can be appropriately reduced, such as 10Hz. Through continuous collection, a state data sequence containing time series characteristics is formed. Each data sequence contains various state data of the corresponding helmet terminal at different time points.

[0043] Step 2: Terminal data reliability assessment

[0044] Acceleration modulus, position drift, and vital sign fluctuation index were selected as characteristic parameters. The acceleration modulus reflects the intensity of the worker's movement and is obtained by calculating the vector modulus of three-dimensional acceleration. Position drift measures the stability of positioning data and is calculated by calculating the Euclidean distance between positioning coordinates within adjacent sampling periods. The vital sign fluctuation index assesses the fluctuation of vital sign data and is obtained by calculating the variation in vital sign data such as heart rate and blood oxygen levels within adjacent sampling periods and performing normalization.

[0045] Calculate the relative rate of change of each characteristic parameter within adjacent sampling periods. For the acceleration modulus, the relative rate of change is the difference between the acceleration modulus of the current period and the acceleration modulus of the previous period divided by the acceleration modulus of the previous period. The relative rate of change of the position drift is the difference between the position drift of the current period and the position drift of the previous period divided by the position drift of the previous period. The relative rate of change of the vital sign fluctuation index is the difference between the vital sign fluctuation index of the current period and the vital sign fluctuation index of the previous period divided by the vital sign fluctuation index of the previous period.

[0046] The dynamic fluctuation value is generated through weighted fusion. Based on the importance of each characteristic parameter in assessing data reliability, different weights are assigned to the relative rate of change of the acceleration modulus, the relative rate of change of the position drift, and the relative rate of change of the vital signs fluctuation index. For example, the weight coefficients are w1, w2, and w3, respectively, with w1 + w2 + w3 = 1. The dynamic fluctuation value is calculated as: Dynamic Fluctuation Value = w1 × Relative Rate of Change of Acceleration Modulus + w2 × Relative Rate of Change of Position Drift + w3 × Relative Rate of Change of the Vital Signs Fluctuation Index.

[0047] Reliability determination and exception handling

[0048] Compare the calculated dynamic fluctuation value with the preset stability threshold. If the fluctuation value is less than the preset stability threshold, it indicates that the terminal's data changes relatively smoothly within adjacent sampling periods, and the data reliability meets the requirements. Then proceed to step 3 for subsequent processing. If the fluctuation value is greater than or equal to the preset stability threshold, the terminal is determined to be an abnormal terminal, marked as abnormal, and offline isolation is initiated to prevent abnormal data from interfering with subsequent processing.

[0049] Step 3: Spatiotemporal calibration and group trajectory consistency analysis

[0050] The positioning data obtained by each terminal is typically based on its own local coordinate system. Due to factors such as device installation location and sensor errors, the local coordinate systems of different terminals may differ, making it difficult to directly compare and analyze positioning data. To eliminate positioning errors between devices, the positioning data of each terminal's local coordinate system must be unified into the work area reference coordinate system using a coordinate transformation matrix.

[0051] First, determine the reference coordinate system of the working area. Usually, a three-dimensional rectangular coordinate system is established with a fixed point in the working area as the origin. Then, for each hard hat terminal, coordinate calibration is performed by setting a reference point in the working area to obtain the conversion parameters between the terminal's local coordinate system and the reference coordinate system, including the rotation matrix and translation vector. The coordinate conversion process can be expressed as: Preference = R × Plocal + T, where Preference is the coordinate in the reference coordinate system, Plocal is the coordinate in the local coordinate system, R is the rotation matrix, and T is the translation vector. Through this coordinate conversion, the positioning data of all terminals are unified to the reference coordinate system, and the wearer's activity trajectory in the unified coordinate system is generated.

[0052] Calculation of group trajectory consistency index

[0053] The spatial overlap of each calibration trajectory on a preset path segment is calculated. A preset path segment is a critical path segment determined based on the actual work process and standard work path in the work area. For each preset path segment, the ratio of the length of the spatial overlap of each terminal trajectory on that path segment to the total length of the path segment is calculated as the terminal's overlap on that path segment.

[0054] The consistency index is calculated as the percentage of terminals that meet the overlap threshold. The overlap threshold is set based on actual operational requirements and safety standards. For example, if the overlap threshold is set to 80%, when the overlap of a terminal's trajectory on a preset path segment is greater than or equal to 80%, the terminal's trajectory is considered highly consistent with the group's trajectory. The consistency index is calculated as: Consistency Index = (Number of Terminals Meeting the Overlap Threshold / Total Number of Terminals) × 100%.

[0055] When the group trajectory consistency index exceeds the preset safety threshold, it indicates that the activity trajectories of the group of operators are highly consistent, and there may be potential safety risks such as signal blind spots. In this case, the system proceeds to step 5 to calculate the signal blind spot coverage rate and construct a risk map. Otherwise, it indicates that the trajectories of some terminals are inconsistent with the group trajectory. In this case, the system proceeds to step 4 to locate and isolate terminals with abnormal trajectories.

[0056] Step 4: Locating and isolating terminals with abnormal trajectories

[0057] Compare the deviation of each terminal's activity trajectory from the standard operating path. The standard operating path is a reasonable operating path pre-set according to operating procedures and safety regulations. Deviation can be calculated using methods such as Euclidean distance and dynamic time warping (DTW) to measure the degree of spatial and temporal deviation of the terminal's trajectory from the standard operating path.

[0058] Terminals with abnormal trajectories are located based on their degree of deviation. A set deviation threshold is established. When a terminal's trajectory deviates from the standard operating path by more than the threshold, it is identified as an abnormal terminal. A positioning device is then used to obtain the abnormal terminal's location information and isolate it to prevent it from impacting group trajectory analysis and safety management.

[0059] Step 5: Calculate signal blind spot coverage and construct risk map

[0060] Signal blind area coverage calculation

[0061] Construct a signal loss zone based on the communication radius of the isolated terminal. Each hardhat terminal has a certain communication radius. When the terminal is offline and isolated, a circular (in a two-dimensional plane) or spherical (in a three-dimensional space) signal loss zone is constructed with the terminal's location as the center and the communication radius as the radius.

[0062] Calculate the ratio of the area of ​​this region to the total monitoring area of ​​the working surface, which is the signal blind area coverage rate. In the case of a two-dimensional plane, the area of ​​the signal missing area is πr 2 (r is the communication radius), the total monitored area of ​​the working surface is the actual area of ​​the working area; in the three-dimensional space, the height range of the working surface needs to be considered, and the ratio of the volume of the signal-missing area to the total monitored volume is calculated.

[0063] Construction of regional security risk map

[0064] A regional safety risk map is created by combining historical blind spot coverage data from different operational scenarios. This data includes blind spot coverage at different times, during different operational stages, and under different environmental conditions. By analyzing this historical data, the safety risk levels of different regions under different circumstances are determined, such as low, medium, and high risk. This safety risk level is combined with information such as geographic coordinates and operational scenarios to create a visual regional safety risk map. This map is then stored in a cloud database, providing data support for subsequent safety risk predictions.

[0065] Step 6: Security Risk Prediction

[0066] Dynamic time warping is used to match real-time trajectories with risk maps. The dynamic time warping algorithm finds the best matching path between two time series with different lengths, thereby calculating their similarity. By performing a DTW match on the operator's real-time trajectory with the historical trajectories stored in the risk map, the blind spot data of the most similar historical trajectory is extracted.

[0067] Calculate the risk probability based on current environmental parameters (such as temperature, humidity, and work intensity). Utilize machine learning algorithms or statistical models to establish a relationship between risk probability, blind spot data, and environmental parameters. For example, a logistic regression model uses blind spot coverage, ambient temperature, and worker exercise intensity as input variables to output the current worker's safety risk probability. Based on the risk probability, a safety risk level (low, medium, or high) is determined, and timely warning signals are issued to management personnel so that appropriate safety precautions can be taken.

[0068] The multifunctional helmet based on the Internet of Things includes:

[0069] Wearing detection device

[0070] The wearing detection device is used to detect whether the helmet is worn correctly and monitor for helmet removal within the area. The device includes a pressure sensor and an infrared sensor. The pressure sensor is installed on the helmet's inner lining where it contacts the wearer's head. When the helmet is worn correctly, the pressure sensor detects a certain pressure signal; when the helmet is worn incorrectly or removed, the pressure signal disappears or weakens. The infrared sensor, installed on the front or side of the helmet, detects whether the helmet has been removed within the area. It transmits and receives infrared signals to determine whether the wearer has removed the helmet.

[0071] The wearing detection device operates as follows: When a worker puts on a helmet, the pressure sensor detects a pressure signal, indicating that the helmet is properly worn. During operation, the infrared sensor continuously monitors the surrounding environment and immediately issues a hat removal signal if it detects the wearer removing the helmet. The wearing detection device transmits the detected signal to the helmet management module for real-time monitoring and alarms.

[0072] Positioning device

[0073] The positioning device includes both a networked and offline positioning module, used to locate workers. The networked positioning module uses GPS / Beidou positioning systems to obtain high-precision coordinates in real time in areas with network coverage. The offline positioning module is used for offline positioning of workers. It uses inertial navigation technology, combined with accelerometers and gyroscopes, to locate workers based on their historical trajectories.

[0074] In an internet-connected environment, the internet-connected positioning module takes priority, obtaining positioning data in real time and transmitting it to the helmet management module. In an offline environment, it automatically switches to the offline positioning module, calculates the position of the operator through inertial navigation technology, and uses historical trajectory data for calibration and prediction, ensuring that the operator's location information can still be accurately obtained in an offline environment.

[0075] communication device

[0076] The communication device facilitates data transmission between the hardhat terminal and the cloud-based data server module, as well as other hardhat terminals. Wireless communication technologies such as Wi-Fi, Bluetooth, and 4G / 5G are used, depending on the network environment of the work area. In areas with internet access, data is uploaded to the cloud-based data server via the 4G / 5G network. In areas without internet access or with weak signal strength, Bluetooth or Wi-Fi is used to communicate with a nearby relay device, which then transmits the data to the cloud.

[0077] Data transmission status detection device

[0078] The data transmission status detection device monitors the helmet's data transmission status, including parameters such as signal strength, data transmission rate, and bit error rate. By monitoring these parameters in real time, it determines whether data transmission is normal. If data transmission anomalies are detected, such as low signal strength or high bit error rate, an alarm is promptly sent to the helmet management module, enabling appropriate measures to be taken, such as switching communication methods or reconnecting to the network, to ensure stable data transmission.

[0079] Workgroup Grouping Module

[0080] The workgroup grouping module is used to group workers into groups based on their helmet information. This information includes worker identity, job type, and task assignment. Workers entering the construction area are automatically grouped based on pre-defined grouping rules, such as those with the same job type or task assignment. Once grouped, this information is transmitted to the helmet management module and the cloud-based data server module for team management and statistical analysis.

[0081] Cloud data server module

[0082] The cloud-based data server module stores safety helmet information, including basic safety helmet terminal information (such as device number, model, and sensor parameters), operator information (such as name, job type, and health status), collected real-time status data, historical data, and regional safety risk maps. Distributed storage technology ensures data security and reliability, supporting large-scale data storage and rapid retrieval. Data interfaces are also provided to facilitate data access and analysis by managers and other systems.

[0083] Safety helmet management module

[0084] The hard hat management module is the core module of the multifunctional hard hat, used to monitor the status of the hard hat, including its energy status and the worker information associated with it. By communicating with various functional modules, it obtains real-time information such as the hard hat's wearing status, positioning data, data transmission status, and energy status. When an abnormal condition is detected, such as an improperly worn hard hat, abnormal data transmission, or insufficient energy, an alarm signal is promptly issued and the abnormal information is transmitted to the cloud data server module for management personnel to process. Simultaneously, the workgroup information generated by the workgroup grouping module is managed to achieve efficient monitoring and scheduling of the operator team.

[0085] Example: Application example of underground mine operation scenario

[0086] For underground mining operations in metal mines, the operating area includes the main tunnel (8 meters wide), the stope (15 meters high), and the shaft (500 meters deep), and 150 explosion-proof helmets are deployed. Hardware configuration optimization:

[0087] Sensor module: Add dust concentration sensor (DSM501A, sampling frequency 5Hz), temperature and humidity sensor to adapt to -20℃~60℃ environment

[0088] Positioning module: Network positioning uses a mine-specific UWB positioning system (accuracy within the tunnel ≤ 1 meter), and network-free positioning combines inertial navigation + tunnel structure map matching

[0089] Communication module: supports mine leaky cable communication system, signal coverage of the entire tunnel area, data transmission delay ≤ 200ms

[0090] The cloud server is deployed in the mining area’s self-built data center, equipped with an industrial-grade firewall, and data encryption uses the AES-256 algorithm.

[0091] (1) Multi-dimensional data collection

[0092] Special parameter collection:

[0093] Added air pressure data in the tunnel (BMP280 sensor, accuracy ±1hPa) for altitude calibration

[0094] Vital signs data adds body temperature detection (MAX30205 sensor, accuracy ±0.1℃) to monitor heat stroke risk in real time

[0095] Anti-interference design: To cope with electromagnetic interference environments, the accelerometer uses hardware filtering (80Hz low-pass filter), and the positioning data is added with a median filter algorithm to eliminate spike noise.

[0096] (2) Terminal Data Reliability Assessment

[0097] Feature parameter adjustment:

[0098] The position drift calculation uses the lane coordinate system to convert longitude and latitude into lane mileage (X-axis: along the main lane, Y-axis: perpendicular to the main lane, Z-axis: altitude). The drift threshold is dynamically adjusted according to the lane width (main lane 8 meters → threshold 4 meters, branch lane 5 meters → threshold 2.5 meters).

[0099] The body temperature change rate (ΔT / 5min) is added to the physical sign fluctuation index, and the weight is increased to 0.4 (high temperature environment is given priority attention)

[0100] (3) Space-time calibration and trajectory analysis

[0101] Three-dimensional coordinate calibration: A mine-specific three-dimensional coordinate system (X: northeast, Y: northwest, Z: depth) is established using the wellhead coordinates as the origin. UWB positioning base stations are deployed at tunnel intersections (at intervals of 50 meters) to calibrate terminal coordinates using trilateration, with an error of ≤0.8 meters.

[0102] Group trajectory consistency application: The preset path segment is "shaft → main roadway → stope." Overlap calculation considers three-dimensional space and uses a roadway centerline matching algorithm. When the consistency index is less than 60% (safety threshold 65%), trajectory deviation analysis is triggered, and a drilling team is promptly isolated for entering an unauthorized branch roadway.

[0103] (IV) Risk prediction and application effects

[0104] Signal blind spot characteristics: The underground communication radius is affected by the tunnel structure, ranging from 50 meters in straight sections to 20 meters in curves. Using a Gaussian mixture model to fit the blind spot distribution, we found that the blind spot coverage rate at the top of the stope often reaches 40%. Combined with historical data, this area is marked as a high-risk area.

[0105] Dynamic risk matching: When the real-time trajectory is matched with the risk map, ventilation status parameters (obtained through the underground ventilation system API) are added. When the blind spot coverage rate is greater than 35% and the wind speed is less than 0.5m / s, the probability of dust concentration exceeding the standard is 75%. The system automatically pushes a dust mask wearing reminder to personnel in the area.

Claims

1. A multifunctional helmet management method based on the Internet of Things, characterized in that: The steps include: Step 1: Multi-dimensional data collection of the wearer is performed through multiple hard hat terminals deployed in the work area to obtain the real-time status data sequence of each hard hat terminal; Step 2: Build terminal monitoring features based on the real-time status data of the helmet terminal and calculate the dynamic fluctuation value of each terminal monitoring feature. If the fluctuation value is less than the preset stability threshold, the terminal data reliability is determined to be up to standard and proceed to step 3. Otherwise, it is marked as an abnormal terminal and offline isolation is initiated. Step 3: Perform spatiotemporal calibration on the positioning data of each reliable terminal according to the terminal's spatial coordinates to generate the wearer's activity trajectory in a unified coordinate system. Calculate the group trajectory consistency index. If the group trajectory consistency index exceeds the preset safety threshold, proceed to step 5; otherwise, proceed to step 4. Step 4: Compare the deviation of each terminal's activity trajectory with the standard operation path, and locate and isolate the terminal with abnormal trajectory based on the deviation; Step 5: Calculate the signal blind spot coverage rate of the current operation surface based on the distribution density and spatial location of the isolation terminals. Combined with historical blind spot coverage data for different operation scenarios, establish a regional safety risk map and store it in the cloud database. Step 6: Predict the safety risk level of the current operator through matching analysis of the real-time operation path and risk map.

2. The multifunctional helmet management method based on the Internet of Things according to claim 1 is characterized in that: The real-time status data includes: Three-dimensional motion acceleration, ambient temperature and humidity, positioning coordinates, and vital signs data are continuously collected at a set sampling frequency to form a state data sequence containing time series characteristics.

3. The multifunctional helmet management method based on the Internet of Things according to claim 2 is characterized in that: The method for constructing the terminal monitoring feature is: The acceleration modulus, position drift and vital sign fluctuation index are selected as characteristic parameters, the relative change rate of each parameter in adjacent sampling periods is calculated, and the dynamic fluctuation value is generated through weighted fusion.

4. The multifunctional helmet management method based on the Internet of Things according to claim 1 is characterized in that: The time-space calibration includes: The local coordinate system positioning data obtained by each terminal is unified into the reference coordinate system of the working area through the coordinate conversion matrix to eliminate positioning deviations between devices.

5. The multifunctional helmet management method based on the Internet of Things according to claim 4 is characterized in that: The calculation method of the group trajectory consistency index is: The spatial overlap of each calibration trajectory in the preset path segment is counted, and the proportion of terminals that reach the overlap threshold is calculated as the consistency index.

6. The multifunctional helmet management method based on the Internet of Things according to claim 1 is characterized in that: The calculation of the signal blind area coverage rate includes: The signal loss area is constructed based on the communication radius of the isolation terminal, and the proportion of the area of ​​this area to the total monitoring area of ​​the working surface is calculated.

7. The multifunctional helmet management method based on the Internet of Things according to claim 1, characterized in that: The security risk prediction includes: The real-time trajectory is dynamically time-warped and matched with the risk map, the blind spot data of the most similar historical trajectory is extracted, and the risk probability is calculated based on the current environmental parameters.

8. A multifunctional helmet management system based on the Internet of Things, characterized by: The multifunctional helmet management method based on the Internet of Things according to any one of claims 1 to 7 is applied, comprising a wearing detection device, a positioning device, a communication device, a data transmission status detection device, a helmet management module, a workgroup grouping module and a cloud data server module; The wearing detection device, positioning device, communication device, data transmission status detection device, and work group grouping module are respectively connected to the helmet management module; the cloud data server is in communication connection with the communication module; The wearing detection device is used to detect whether the helmet is worn correctly and to monitor whether the helmet is taken off in the area; The positioning device includes a network positioning module and an off-network positioning module for locating the position of the worker; wherein the off-network positioning module is used to locate the worker without a network; the off-network positioning includes locating the worker based on historical trajectories; The data transmission status detection device is used to detect the data transmission status of the helmet; The work group grouping module is used to group the work groups according to the helmet information of the workers entering the construction site; The cloud data server is used to store helmet information; The safety helmet management module is used to monitor the status of the safety helmet, which includes the energy status of the safety helmet and the worker information bound to the safety helmet.

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