Mine water disaster emergency linkage escape guiding system based on internet of things and intelligent terminal

Abstract

The application discloses a mine water disaster emergency linkage escape guiding system based on an internet of things and an intelligent terminal, relates to the technical field of mine safety, and sequentially connects a water regime sensing module, a Mesh communication module and an emergency guiding module; the water regime sensing module is used for collecting water immersion state, pressure and liquid level data; the Mesh communication module is used for performing distributed information transmission on the water immersion state, pressure and liquid level data; and the emergency guiding module is used for processing the water immersion state, pressure and liquid level data and dynamically indicating an escape path according to a processing result. The application integrates water regime sensing, Mesh ad hoc network communication and dynamic path planning functions, is suitable for water inrush accident emergency rescue scenes of various underground mines, improves communication response speed, reduces disaster report sending delay, improves path planning accuracy and successfully avoids a dynamic submerged area.

Description

Technical Field

[0001] This invention relates to the field of mine safety technology, and more specifically to a mine water hazard emergency response and escape guidance system based on the Internet of Things and smart terminals. Background Technology

[0002] Existing mine water hazard emergency systems generally suffer from the following technical bottlenecks: First, they rely on wired backbone communication networks, which are easily damaged during water inrush accidents, leading to communication interruptions and delays in disaster reporting exceeding 10 minutes. Second, escape route indications are mostly static markers, unable to be dynamically adjusted according to real-time water level changes, posing a risk of misleading information. Third, they lack distributed autonomous decision-making capabilities, resulting in system-wide paralysis if the central server fails. For example, while existing mine water hazard early warning systems achieve water level monitoring, they lack self-organizing network communication and dynamic path planning, resulting in low emergency response efficiency.

[0003] Therefore, in view of the shortcomings of existing technologies, how to provide a mine water hazard emergency linkage escape guidance system based on the Internet of Things and smart terminals is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] In view of this, the present invention provides a mine water hazard emergency linkage escape guidance system based on the Internet of Things and smart terminals, which integrates water sentiment perception, Mesh self-organizing network communication and dynamic path planning functions, and is applicable to emergency rescue scenarios of water inrush accidents in various underground mines.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a mine water hazard emergency linkage escape guidance system based on the Internet of Things and smart terminals, comprising: a water sentiment perception module, a Mesh communication module and an emergency guidance module connected in sequence;

[0006] The water sensing module is used to collect water immersion status, pressure, and liquid level data;

[0007] The Mesh communication module is used for distributed information transmission of the water immersion status, pressure, and liquid level data.

[0008] The emergency guidance module is used to process the water immersion status, pressure, and liquid level data, and dynamically indicate the escape route based on the processing results.

[0009] Preferably, the Mesh communication module adopts a dual-mode Mesh self-organizing network protocol of LoRaWAN and Bluetooth.

[0010] Preferably, the emergency guidance module includes a data processing unit, which is used to perform fusion processing, dynamic weight calculation, and path planning calculation on the water immersion status, pressure, and liquid level data.

[0011] Preferably, the fusion processing of the water immersion status, pressure, and liquid level data includes:

[0012] The water immersion status, pressure, and liquid level data are preprocessed, and the data from different sensors are aligned based on timestamps and a unified coordinate system transformation is performed.

[0013] Kalman filtering is used for noise reduction, state equations and observation equations are established, and the optimal estimate is updated iteratively.

[0014] Based on the optimal estimate, water level distribution is predicted using the ARIMA model.

[0015] Preferably, the dynamic weight calculation incorporates three weighting factors: water level rise rate, tunnel slope, and refuge chamber capacity. The weighting coefficients of each factor are obtained through the analytic hierarchy process (AHP).

[0016] Preferably, the path planning operation uses an improved A algorithm. algorithm;

[0017] The improvement A The heuristic function of the algorithm is defined as f(n) = g(n) + α h(n);

[0018] Where g(n) is the actual cost from the starting point to node n, h(n) is the Manhattan distance estimate from node n to the target point, and α is the dynamic weight coefficient;

[0019] The improvement A The algorithm has a time complexity of O, where E is the number of edges and V is the number of nodes. The path update adopts a combination of event-triggered and periodic update mode, and updates immediately when the water level change rate exceeds the preset threshold or the tunnel status changes.

[0020] Preferably, a water immersion sensor is used to collect the water surge vibration wave;

[0021] Based on the aforementioned water inrush vibration wave, a triangulation algorithm combined with time difference is used to locate the disaster source. The distance difference is calculated based on the signal arrival time difference, and a hyperbolic equation system is established to solve for the coordinates of the water inrush point.

[0022] Preferably, the node coordinates of the water immersion sensor are set as S1(x1, y1), S2(x2, y2), and S3(x3, y3), the signal propagation speed is v, and the signal arrival time difference is Δt. 12 , Δt 13 ;

[0023] The distance difference equation is: |P-S1|-|P-S2|=v·Δt 12 |P-S1|-|P-S3|=v·Δt 13The nonlinear equations are solved using the Chan algorithm; where P is the coordinate of the water inrush point.

[0024] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a mine water hazard emergency linkage escape guidance system based on the Internet of Things and smart terminals. When the water immersion sensor detects that the water level exceeds the threshold (configurable from 5-50cm), it immediately broadcasts disaster information (including location, water level, and rate of rise) through the Mesh network. Adjacent nodes receive the information and automatically forward it, forming a distributed information dissemination network. The guidance light box constructs a tunnel topology map based on real-time data, calculates the escape path after excluding the flooded area, and dynamically indicates the direction through arrow lights. The system supports the function of resuming transmission after network interruption, and maintains the autonomous operation of the local Mesh network for ≥72 hours when the backbone network is interrupted.

[0025] The present invention has the following technical effects:

[0026] (1) Communication response speed is improved by 90%, and the disaster information reporting delay is reduced from 10 minutes in the traditional system to 2 seconds; (2) Path planning accuracy reaches 98%, successfully avoiding dynamic flooding areas; (3) It can work continuously for more than 3 days without backbone network support, and reliability is improved by 600%. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0028] Figure 1 An architecture diagram of a mine water hazard emergency linkage escape guidance system based on the Internet of Things and smart terminals is provided for an embodiment of the present invention;

[0029] Figure 2 This is a diagram of the Mesh network topology provided in an embodiment of the present invention;

[0030] Figure 3 This is a flowchart of the self-organizing Mesh network provided in an embodiment of the present invention;

[0031] Figure 4 This is a flowchart of the Mesh network self-healing process provided in an embodiment of the present invention;

[0032] Figure 5 A flowchart for obtaining the comprehensive evaluation function provided in an embodiment of the present invention.

[0033] Figure 6 This is a schematic diagram of the path update process provided in an embodiment of the present invention. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] This invention discloses a mine water hazard emergency linkage escape guidance system based on the Internet of Things and smart terminals, comprising: a water situation perception module, a Mesh communication module and an emergency guidance module connected in sequence;

[0036] The water sensing module is used to collect water immersion status, pressure, and liquid level data;

[0037] The Mesh communication module is used for distributed information transmission of the water immersion status, pressure, and liquid level data.

[0038] The emergency guidance module is used to process the water immersion status, pressure, and liquid level data, and dynamically indicate the escape route based on the processing results.

[0039] Specifically, the embodiments of the present invention adopt an integrated architecture of "water sentiment perception - network communication - emergency guidance", and the system architecture is as follows: Figure 1 As shown:

[0040] The water sensing module uses an IP68-rated water immersion sensor (model: SEN-WTR-01), integrating a pressure sensor (range 0-1MPa, accuracy ±0.2%FS) and a level sensor (accuracy ±0.5cm). It is deployed every 50 meters on the side wall of the underground roadway (1.2m above the floor), with a sampling frequency of 1Hz. The battery is a lithium thionyl chloride battery (capacity 3.6V / 19Ah), with a sleep current ≤10μA and a battery life ≥6 months.

[0041] The Mesh communication module adopts a LoRaWAN (470-510MHz band) + Bluetooth 5.0 dual-mode protocol. The lightbox node (model: MESH-LBX-02) has a communication radius of ≥300 meters, LoRaWAN hop count ≤5 levels, network self-healing time ≤10 seconds, and supports cascading of up to 200 nodes. The Bluetooth module is used for short-range device pairing (distance ≤10 meters), with a data transmission rate ≥1Mbps. The network topology is as follows: Figure 2 As shown:

[0042] Protocol design details: The dual-mode Mesh protocol adopts a layered frame structure, as follows:

[0043] (1) Physical layer: LoRaWAN physical layer - frequency band 470-510MHz, spread factor SF=7-12 adjustable, bandwidth 125kHz / 250kHz selectable, transmit power 14dBm±2dB, receive sensitivity -137dBm@SF12 / 125kHz; Bluetooth physical layer - operating frequency band 2.4GHz ISM, modulation method GFSK, transmit power 0dBm~+10dBm adjustable, receive sensitivity -97dBm;

[0044] (2) MAC layer: Defines three frame types: data frames (length ≤ 256 bytes), control frames (including network topology update instructions, frame header 0x01 identifier), and heartbeat frames;

[0045] (3) Node network entry process: adopts three steps: "pre-association-handshake-authentication", with pre-association timeout ≤ 5 seconds and authentication using AES-128 encryption;

[0046] (4) Self-healing protocol: Based on the distributed spanning tree algorithm, when a node detects a link interruption (three consecutive heartbeat frames are lost), it automatically triggers neighbor discovery (broadcast period 200ms), recalculates the optimal path, and the topology table update time is ≤5 seconds.

[0047] In the emergency guidance module, the smart lightbox has a built-in ARM Cortex-M4 processor (80MHz main frequency, 128KB RAM), a 128×64 dot matrix LED arrow screen (brightness ≥5000cd / m², response time ≤10ms), and supports red / green dual-color dynamic indication; the voice module uses an explosion-proof speaker (decibel ≥90dB), and the vibration module has an amplitude ≥1.5g.

[0048] Specifically, the Mesh communication module adopts a dual-mode Mesh self-organizing network protocol using LoRaWAN and Bluetooth. The Mesh network self-organization process and Mesh network self-healing process are as follows: Figure 3 As shown.

[0049] The implementation process of the core functional technology in this invention embodiment is as follows:

[0050] Specifically, a water immersion sensor is used to collect the vibration waves from the sudden water flow;

[0051] Based on the aforementioned water inrush vibration wave, a triangulation algorithm combined with time difference is used to locate the disaster source. The distance difference is calculated based on the signal arrival time difference, and a hyperbolic equation system is established to solve for the coordinates of the water inrush point.

[0052] Specifically, the node coordinates of the water immersion sensor are set as S1(x1, y1), S2(x2, y2), and S3(x3, y3), the signal propagation speed is v, and the signal arrival time difference is Δt. 12 , Δt 13 ;

[0053] The distance difference equation is: |P-S1|-|P-S2|=v·Δt 12 |P-S1|-|P-S3|=v·Δt 13 The nonlinear equations are solved using the Chan algorithm; where P is the coordinate of the water inrush point.

[0054] Specifically, the emergency guidance module includes a data processing unit, which is used to perform fusion processing, dynamic weight calculation, and path planning calculation on the water immersion status, pressure, and liquid level data.

[0055] Specifically, the data on water immersion status, pressure, and liquid level are fused, including:

[0056] The water immersion status, pressure, and liquid level data are preprocessed, and the data from different sensors are aligned based on timestamps and a unified coordinate system transformation is performed.

[0057] Kalman filtering is used for noise reduction, state equations and observation equations are established, and the optimal estimate is updated iteratively.

[0058] Based on the optimal estimate, water level distribution is predicted using the ARIMA model.

[0059] Specifically, the dynamic weight calculation introduces three weighting factors: water level rise rate, tunnel slope, and refuge chamber capacity. The weighting coefficients of each factor are obtained through the analytic hierarchy process.

[0060] Specifically, the path planning operation uses an improved A... algorithm;

[0061] The improvement A The heuristic function of the algorithm is defined as f(n) = g(n) + α h(n);

[0062] Where g(n) is the actual cost from the starting point to node n, h(n) is the Manhattan distance estimate from node n to the target point, and α=F is the dynamic weight coefficient, which is calculated by the comprehensive evaluation function;

[0063] The improvement A The algorithm has a time complexity of O(ElogV), where E is the number of edges and V is the number of nodes. Path updates employ a combination of event-triggered and periodic updates, updating immediately when the water level change rate exceeds a preset threshold or the roadway state changes. The time complexity O(ElogV) is a function that measures the algorithm's execution time as the input size increases, typically represented by Big O notation. O(ElogV) indicates that the algorithm's time complexity is proportional to the logarithmic product of the number of edges E and the number of nodes V.

[0064] Specifically, the stress data processing and application process is as follows:

[0065] 1. Data Conversion: Pressure value d2 (unit MPa) is converted using the formula h=ρgh (ρ is the density of water, 1000 kg / m³). 3 g is the acceleration due to gravity, 9.8 m / s². 2 The data is converted to water level depth, and after accuracy correction, the error is ≤±1cm, thus achieving physical quantity consistency with the liquid level sensor data.

[0066] 2. Multi-source fusion verification: The pressure-converted water level and the liquid level value d3 from the level sensor are cross-validated. When the deviation between the two exceeds 3cm, a data validity judgment is triggered. A Kalman filter is used to dynamically adjust the weights (initial confidence value of 0.4 for pressure data, 0.6 for liquid level data, dynamically adjusted according to the deviation). The liquid level value d3 directly acquired by the level sensor is the water level height data measured by the level sensor (usually in cm or m). By analyzing the deviation between the pressure-converted water level (indirect measurement) and the liquid level value d3 directly acquired by the level sensor (direct measurement), combined with the Kalman filter dynamic adjustment weights, data reliability verification is achieved.

[0067] 3. Emergency guidance application:

[0068] (1) Disaster source location: The pressure gradient change rate (Δd2 / Δt) is used to help determine the location of the water inrush point. When the pressure value of a certain node rises sharply by more than 0.2MPa within 2 seconds, it is marked as the core area of ​​potential water inrush. Combined with the state jump (0→1) of the water immersion state quantity d1 of the water immersion sensor and the rising rate of the liquid level value d3, the disaster source location accuracy is improved to ±2m. Among them, the rising rate of the liquid level value d3 refers to the change of d3 per unit time (such as cm / s), which is calculated by continuously sampling d3 and used to determine the rising trend of water level.

[0069] (2) Path planning: Pressure data participates in dynamic weight calculation. When the pressure value of a certain section of the tunnel exceeds 0.5MPa (corresponding to a water level of about 50m), the risk coefficient of the section is automatically increased by 200%, triggering the path detour mechanism. Combined with the liquid level prediction data (ARIMA model results of d3), the priority of the escape route is dynamically adjusted.

[0070] (3) Linkage alarm threshold: When the pressure change rate is >0.1MPa / s and the water immersion state d1=1, the smart light box immediately switches to red warning mode, the voice module triggers the highest level alarm (95dB continuous beeping), and broadcasts the coordinates of the pressure abnormal node to the Mesh network.

[0071] In a specific embodiment of the present invention, the specific working process of a mine water hazard emergency linkage escape guidance system based on the Internet of Things and smart terminals includes:

[0072] (1) Disaster source location: Three water immersion sensor nodes receive the water inrush vibration wave (velocity 340m / s). A triangulation algorithm combined with time difference of arrival (TDOA) is used to calculate the distance difference based on the signal arrival time difference, and a hyperbolic equation system is established to solve for the coordinates of the water inrush point. Let the coordinates of the sensor nodes be S1(x1, y1), S2(x2, y2), and S3(x3, y3), the signal propagation speed be v (approximately 340m / s in the underground roadway), and the signal arrival time difference be Δt. 12 , Δt 13 Then the distance difference equation is: |P-S1|-|P-S2|=v·Δt 12 |P-S1|-|P-S3|=v·Δt 13 Where P(x, y) are the coordinates of the water inrush point. The Chan algorithm is used to solve the nonlinear equations, and Taylor series expansion is introduced for linearization. The positioning accuracy is ≤5 meters (plane error ≤ ±3m), the time synchronization accuracy is ≤1ms, and the response time is ≤2 seconds.

[0073] (2) Path planning: Based on improved A The algorithm (introducing a weighting factor for the rate of water level rise) dynamically calculates the optimal path to the refuge chamber. Specifically, it includes water level data fusion, dynamic weight calculation, and a path update mechanism. The path planning algorithm flow is as follows: Figure 5 and Figure 6 As shown, where, Figure 5 Flowchart for obtaining the comprehensive evaluation function; Figure 6 Here is a flowchart illustrating the path update process based on the comprehensive evaluation function:

[0074] The specific steps for water level data fusion are as follows:

[0075] ① Multi-source data acquisition: Simultaneously acquire raw data from water immersion sensors, pressure sensors, and liquid level sensors (sampling frequency 1Hz) to establish a three-dimensional data matrix D=[d1,d2,d3] T d1 is the water immersion state quantity, d2 is the pressure value, and d3 is the liquid level value;

[0076] ② Outlier removal: The 3σ criterion is used to filter out outlier readings that exceed the range of (μ±3σ), where μ=E(D) is the mean, σ= The standard deviation is ≤0.5% for outlier removal rate;

[0077] ③ Spatiotemporal registration: Based on timestamps, data from different sensors are aligned (time synchronization accuracy ≤ 1ms). Spatial coordinate transformation is achieved by using an affine transformation matrix T = [[a,b,c],[d,e,f],[0,0,1]] to realize a unified coordinate system transformation.

[0078] ④ Kalman filtering for noise reduction, establishing the state equation xk =Ax k-1 +w k With the observation equation z k =Hx k +v k , where x k Let w be the state estimate at time k, A be the state transition matrix (A=[[1,Δt],[0,1]]), and w be the state estimate at time k. k For process noise (covariance Q=0.01), z k v represents the observed values, H is the observation matrix (H=[1,0]), and v k To observe the noise (covariance R=0.1), iteratively update the optimal estimate (the signal-to-noise ratio is improved by ≥15dB after filtering).

[0079] ⑤ Trend prediction: The filtered data is used to predict the water level distribution for the next 5 minutes using the ARIMA(2,1,1) model. The model parameters are p=2 (autoregressive term), d=1 (difference order), and q=1 (moving average term). The mean absolute error is ≤0.3cm, and the prediction accuracy is ≥98%.

[0080] The dynamic weighting calculation incorporates three core factors: water level rise rate, tunnel slope, and refuge chamber capacity. The weight coefficients of each factor are determined using the Analytic Hierarchy Process (AHP).

[0081] ① Construct a judgment matrix (1-9 scale method) and invite 5 mine safety experts to conduct pairwise comparisons of the importance of factors;

[0082] The specific matrix construction process includes: using water level rise rate (A), roadway slope (B), and refuge chamber capacity (C) as evaluation indicators, a 3×3 judgment matrix is ​​constructed using the 1-9 scaling method. Five mine safety experts are invited to conduct pairwise comparisons and scores, and the geometric mean is taken as the final matrix element. The result is as follows: M=[[1,3,5],[1 / 3,1,3],[1 / 5,1 / 3,1]], where M[i][j] represents the importance of indicator i relative to indicator j.

[0083] ② Calculate the eigenvectors and the maximum eigenvalue λmax, and perform a consistency test (CR<0.1).

[0084] The specific calculation steps are as follows:

[0085] 1) Eigenvector calculation (sum method):

[0086] - Matrix column normalization: M'[i][j]=M[i][j] / ∑M[k][j] (k=1,2,3);

[0087] Normalized matrix: [[0.652,0.6,0.556],[0.217,0.2,0.333],[0.130,0.2,0.111]];

[0088] - Summing by rows yields the vector:

[0089] W=[0.652+0.6+0.556,0.217+0.2+0.333,0.130+0.2+0.111]=[1.808,0.750,0.441];

[0090] - Normalized eigenvectors:

[0091] W=W / ∑W=[1.808 / 3.0,0.750 / 3.0,0.441 / 3.0]=[0.603,0.250,0.147].

[0092] 2) Consistency check:

[0093] - Calculate the maximum eigenvalue λmax: λmax = the average value of (M·W)[i] / W[i], M·W = [1×0.603+3×0.250+5×0.147,1 / 3×0.603+1×0.250+3×0.147,1 / 5×0.603+1 / 3×0.250+1×0.147] = [1.848,0.765,0.449], λmax≈(3.065+3.060+3.054) / 3≈3.059;

[0094] - Consistency index CI = (λmax - n) / (n - 1) = (3.059 - 3) / 2 = 0.0295;

[0095] - Random consistency index RI (RI=0.58 when n=3);

[0096] - The consistency ratio CR = CI / RI = 0.0295 / 0.58 ≈ 0.051 < 0.1, thus passing the consistency test.

[0097] ③ After normalization, the weighting coefficients are obtained (water level rise rate 0.6, tunnel slope 0.3, refuge chamber capacity 0.1).

[0098] Comprehensive evaluation function: F=0.6×v+0.3×s+0.1×c, where v is the rate of water level rise, taken from the ARIMA model prediction result in step ⑤ of water level data fusion; s is the tunnel slope; and c is the remaining capacity coefficient of the refuge chamber.

[0099] Improvement A Algorithm heuristic function: f(n) = g(n) + α·h(n), where g(n) is the actual cost from the starting point to node n; h(n) is the Manhattan distance estimate from node n to the target point; and α = F is the dynamic weight coefficient.

[0100] The algorithm has a time complexity of O(ElogV), where E is the number of edges and V is the number of nodes, and the path planning response time is ≤300ms.

[0101] The path update mechanism adopts a combination of event triggering (water level change rate > 0.5 cm / s or roadway status change) and periodic updating (every 5 seconds) to ensure that the path planning results are synchronized with the development of the disaster in real time.

[0102] (3) Linkage guidance: The light box and the explosion-proof mobile APP synchronize data through the Mesh network (interaction delay ≤50ms), support voice + vibration dual alarm, and maintain the operation of the local Mesh network for ≥72 hours when the network is disconnected.

[0103] System technical parameters:

[0104] Operating temperature -20℃~+60℃, humidity 95%RH (non-condensing), explosion-proof rating ExibIMb, conforming to GB3836.1-2010 standard; disaster reporting delay ≤2 seconds, path planning accuracy ≥98%, network survivability improved by 600%.

[0105] The system workflow of this invention embodiment is as follows:

[0106] When the water immersion sensor detects that the water level exceeds the threshold (configurable from 5-50cm), it immediately broadcasts disaster information (including location, water level, and rate of rise) through the Mesh network. Neighboring nodes receive the information and automatically forward it, forming a distributed information dissemination network. The guide light box constructs a tunnel topology map based on real-time data, calculates escape routes after excluding flooded areas, and dynamically indicates directions through arrow lights. The system supports the function of resuming transmission after network interruption, maintaining the autonomous operation of the local Mesh network for ≥72 hours when the backbone network is interrupted.

[0107] The positive effects and technical parameters of the embodiments of the present invention are as follows:

[0108] (1) Communication response speed is improved by 90%, and disaster reporting delay is reduced from 10 minutes in the traditional system to 2 seconds;

[0109] (2) The path planning accuracy reached 98%, successfully avoiding the dynamically flooded area;

[0110] (3) It can work continuously for more than 3 days without backbone network support, with a reliability improvement of 600%.

[0111] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0112] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A mine water hazard emergency response and escape guidance system based on the Internet of Things and smart terminals, characterized in that, include: The water sentiment sensing module, Mesh communication module, and emergency guidance module are connected in sequence. The water sensing module is used to collect water immersion status, pressure, and liquid level data; The Mesh communication module is used for distributed information transmission of the water immersion status, pressure, and liquid level data. The emergency guidance module is used to process the water immersion status, pressure, and liquid level data, and dynamically indicate the escape route based on the processing results; The emergency guidance module includes a data processing unit, which is used to first perform fusion processing on the water immersion status, pressure and liquid level data, perform dynamic weight calculation based on the fusion processing result, and perform path planning calculation based on the dynamic weight calculation result. The path planning calculation uses an improved A algorithm; The improvement A The heuristic function of the algorithm is defined as f(n) = g(n) + α h(n); Where g(n) is the actual cost from the starting point to node n, h(n) is the Manhattan distance estimate from node n to the target point, and α is the dynamic weight coefficient; The improvement A The algorithm has a time complexity of O, where O is determined by ElogV, where E is the number of edges and V is the number of nodes. The path update adopts a combination of event-triggered and periodic update mode. The event triggering is based on the water level change rate and the tunnel status. When the water level change rate exceeds the preset threshold or the tunnel status changes, the update is performed immediately. A water immersion sensor is used to collect the vibration waves from the sudden water flow. Based on the aforementioned water inrush vibration wave, a triangulation algorithm combined with time difference is used to locate the disaster source; Among them, the distance difference is calculated based on the signal arrival time difference, and a hyperbolic equation system is established to solve for the coordinates of the water inrush point; The node coordinates of the water immersion sensor are set as S1(x1, y1), S2(x2, y2), and S3(x3, y3), the signal propagation speed is v, and the signal arrival time difference is Δt. 12 , Δt 13 ; The distance difference equation is: |P-S1|-|P-S2|=v·Δt 12 |P-S1|-|P-S3|=v·Δt 13 ; The Chan algorithm is used to solve the nonlinear equation system. Where P is the coordinate of the water inrush point.

2. The mine water hazard emergency linkage escape guidance system based on the Internet of Things and smart terminals according to claim 1, characterized in that, The Mesh communication module adopts a dual-mode Mesh self-organizing network protocol of LoRaWAN and Bluetooth.

3. The mine water hazard emergency linkage escape guidance system based on the Internet of Things and smart terminals according to claim 1, characterized in that, The data on water immersion status, pressure, and liquid level are fused, including: The raw data from the water immersion sensor, pressure sensor, and liquid level sensor are acquired, the raw data are preprocessed, and the data from different sensors are aligned based on the timestamp. Spatial coordinate transformation is performed using an affine transformation matrix to achieve a unified coordinate system transformation. Kalman filtering is used for noise reduction, state equations and observation equations are established, and the optimal estimate is updated iteratively. Based on the optimal estimate, the water level change trend is predicted using the ARIMA model.

4. The mine water hazard emergency linkage escape guidance system based on the Internet of Things and smart terminals according to claim 3, characterized in that, The dynamic weight calculation incorporates three weighting factors: water level rise rate, tunnel slope, and refuge chamber capacity. The weighting coefficients of each factor are obtained through the analytic hierarchy process (AHP).

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