Tunnel PLC device wireless ad hoc network construction method supporting APP linkage

Abstract

The invention discloses a tunnel PLC device wireless ad hoc network construction method supporting APP linkage, and belongs to the field of tunnel communication, and the method comprises the following steps: S1, constructing a signal comprehensive attenuation model based on tunnel geometric parameters, and obtaining a three-dimensional mixed channel matrix; s2, hierarchically expressing multi-dimensional data of tunnel physical topology, communication state and equipment operation by using a digital twin model to obtain a virtual model; s3, designing a double-link switching module supporting switching of PLC communication and wifi communication; and S4, designing multi-protocol conversion and management authority of APP cooperative control. By adopting the tunnel PLC equipment wireless ad hoc network construction method supporting APP linkage, the communication reliability, real-time performance and anti-interference problems of the PLC equipment ad hoc network in a complex tunnel environment are solved by fusing technologies such as laser scanning modeling, digital twinning, reinforcement learning and multi-protocol conversion, and the whole process optimization from environment perception to intelligent maintenance is realized.

Description

Technical Field

[0001] The present invention relates to the field of tunnel communication technology, and in particular to a method for constructing a wireless ad hoc network of tunnel PLC devices supporting APP linkage. Background Art

[0002] With the acceleration of intelligent and unmanned tunneling, programmable logic controller (PLC) devices within tunnels need to build high-density, highly reliable communication networks to support core requirements such as real-time monitoring, remote control, and intelligent maintenance. However, the unique curved geometry of tunnels (e.g., curve radius R can be as small as 20 meters), multi-branch topology (branch density D can reach 3-5 per 100 meters), and strong electromagnetic interference environments (e.g., the pulse noise spectrum generated by motor start-up and shutdown covers 2-100MHz), pose severe challenges to the environmental adaptability of communication systems. Traditional single communication solutions (such as pure power line communication PLC or wireless communication) and static control modes are no longer able to meet the stringent communication reliability (requires success rate >95%), real-time performance (control delay <200ms), and anti-interference capabilities required in tunnel scenarios. A systematic solution integrating environmental perception and intelligent algorithms is urgently needed.

[0003] Specifically, existing power line communication (PLC) is based on transmission line theory and uses impedance matching formulas to optimize coupling circuits, but it fails to account for the surface reflection effects of tunnel curves. Furthermore, traditional attenuation models only apply to straight paths. When curves occur, the signal experiences additional attenuation due to surface reflections, causing the measured communication success rate to drop sharply from 85% on straight paths to below 60% on curves.

[0004] Traditional wireless ad hoc networks use a backpressure algorithm to calculate queue differences and optimize routing, but they don't incorporate curvature attenuation factors. Multipath fading caused by tunnel curves results in wireless signal interruption rates exceeding 30%. Furthermore, fixed frequency bands (such as 2.4 GHz) are susceptible to co-channel interference caused by reflections from metal structures, resulting in spectrum utilization rates consistently below 40%.

[0005] In the field of ad hoc network optimization, existing reinforcement learning algorithms (such as SARSA) are designed only for single links and have two limitations: (1) static topology assumption: they rely on preset routing tables. When new tunnel branches are added or equipment fails, the topology map cannot be updated in real time, and the routing reconstruction delay exceeds 5 minutes, leading to increased data congestion. (2) fixed spectrum allocation: they do not integrate spectrum sensing mechanisms, and the actions only include route selection, not frequency band switching. Co-channel interference can cause transmission bit error rates as high as 15%. Summary of the Invention

[0006] The purpose of the present invention is to provide a method for constructing a wireless ad hoc network of tunnel PLC devices that supports APP linkage, so as to solve the above technical problems.

[0007] To achieve the above object, the present invention provides a method for constructing a wireless ad hoc network of tunnel PLC devices supporting APP linkage, comprising the following steps:

[0008] S1. Acquire three-dimensional point cloud data of the tunnel through laser scanning, extract tunnel geometric parameters, construct a signal comprehensive attenuation model based on the tunnel geometric parameters, and obtain a three-dimensional hybrid channel matrix based on the signal comprehensive attenuation model;

[0009] S2. Use the digital twin model to hierarchically express the multi-dimensional data of the tunnel's physical topology, communication status, and equipment operation to obtain a virtual model, and achieve real-time synchronization between the virtual model and the physical topology through a dynamic update mechanism;

[0010] S3. Based on the virtual model, a dual-link switching module is designed to support switching between PLC communication and WiFi communication;

[0011] S4. Based on the virtual model and dual-link switching module, design the multi-protocol conversion and management authority of APP collaborative control to realize cross-protocol communication and hierarchical control between PLC communication and APP.

[0012] Therefore, the present invention adopts the above-mentioned tunnel PLC device wireless ad hoc network construction method supporting APP linkage, which has the following beneficial effects:

[0013] 1. Complex environment modeling and improved communication reliability: Laser scanning extracts geometric parameters such as tunnel curvature radius and branch density, building a comprehensive attenuation model that includes curve reflection attenuation and branch load attenuation. This overcomes the limitation of traditional models that only apply to straight roads. The measured communication success rate in curves has increased from 60% to 89%, and on straight roads to 97%. Simultaneously, the virtual model maps physical topology changes in real time, achieving a routing reconstruction delay of less than 1 second, improving efficiency by over 90% compared to traditional static algorithms and ensuring uninterrupted communication during topology changes.

[0014] 2. Reinforcement learning-driven dynamic routing: This approach introduces a curvature penalty factor and spectrum allocation function, integrating latency, packet loss rate, and energy efficiency into the reward function. This reduces end-to-end latency by 40% and increases spectrum utilization from 40% to 75%.

[0015] 3. Dual-link adaptive switching: Dynamically selects PLC / Wi-Fi links based on channel quality indicators, giving priority to the PLC anti-interference link in strong interference areas, reducing the bit error rate from 15% to 5%;

[0016] 4. Dynamic conversion of multiple protocols: The finite state machine-based protocol conversion engine supports real-time mapping between Modbus RTU and MQTT, and the transmission delay of emergency commands is compressed to less than 50ms, meeting the real-time requirements of remote control.

[0017] 5. LSTM fault prediction: Edge computing units are deployed at key nodes to predict the probability of equipment failure based on input vectors, providing early warning 2 hours in advance and reducing maintenance costs by 30%.

[0018] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of the method for constructing a wireless ad hoc network of tunnel PLC devices that supports APP linkage according to the present invention. DETAILED DESCRIPTION

[0020] In order to make the purposes, technical solutions and advantages disclosed in the embodiments of the present invention clearer, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the embodiments of the present invention and are not intended to limit the embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. Examples of the embodiments are shown in the accompanying drawings, where the same or similar numbers throughout represent the same or similar elements or elements with the same or similar functions.

[0021] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products or devices.

[0022] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] like Figure 1 As shown, the method for constructing a wireless ad hoc network of tunnel PLC devices supporting APP linkage includes the following steps:

[0024] S1. Acquire three-dimensional point cloud data of the tunnel through laser scanning, extract tunnel geometric parameters, construct a signal comprehensive attenuation model based on the tunnel geometric parameters, and obtain a three-dimensional hybrid channel matrix based on the signal comprehensive attenuation model;

[0025] Step S1 specifically includes the following steps:

[0026] S11. Extract the following tunnel geometric parameters: tunnel curvature radius R, branch length L b , branch density D i,j , the straight-line distance l between node i and node j i,j , the height H and width W of the tunnel;

[0027] S12, based on tunnel geometry parameters M G ={R,L b ,D i,j ,l i,j ,H,W} to build a comprehensive signal attenuation model:

[0028] δ i,j =δ B (i,j)+10log 10 (N(f)+1) (1);

[0029] Where, δ i,j represents the comprehensive signal attenuation between node i and node j; δ B (i, j) represents the branch load attenuation between nodes i and j; N(f) represents the background noise power spectral density at frequency f;

[0030] in,

[0031]

[0032] δ R (i,j)=δ0(i,j)·α(R) N (3);

[0033] Where, δ R (i, j) represents the reflection attenuation of the curved surface between nodes i and j; δ0(i, j) represents the basic attenuation of the straight road, and k1 and k2 are both frequency-related attenuation coefficients. When the PLC communication link is selected, k1 = 0.2, k2 = 0.01. When the WiFi communication link is selected, k1 = 0.1, k2 = 0.02. cw represents the carrier frequency; α(R) represents the curvature attenuation factor, and N represents the number of reflections of the signal in the bend, and

[0034] S13, constructing a three-dimensional hybrid channel matrix H=[h i,j ] n×n , where n×n represents the number of rows and columns of the matrix, h i,j represents the transmission ratio of the signal from node i to node j, and P t and P r Represent the transmit power and receive power respectively.

[0035] S2. Use the digital twin model to hierarchically express the multi-dimensional data of the tunnel's physical topology, communication status, and equipment operation to obtain a virtual model, and achieve real-time synchronization between the virtual model and the physical topology through a dynamic update mechanism;

[0036] Step S2 specifically includes the following steps:

[0037] S21, construct the following virtual model G D (t):

[0038] G D (t)=(V D ,ε D ,S D (t),C D (t),A D (t)) (4);

[0039] Where V D represents a node set, and V D ={v i |i=1,2,…,n},v i Represents the attribute of node i, v i =(x i ,y i ,z i ,R i ,T i ), x i ,y i ,z i Represent the three-dimensional coordinates of node i; R i represents the curvature radius of the curve where node i is located, and when node i is on a straight road, R i =∞,T i represents the communication type of node i, T i =1, 2, 3 represent PLC communication, WiFi communication and relay communication respectively;

[0040] ε D represents the set of communication links, and ε D ={e i,j |i,h∈V D}, e i,j represents the link attribute from node i to node j, e i,j =(l i,j ,L b ,D i,j );

[0041] S D (t) represents the device status, and S D(t)=(H(t),Z(t),L(t),E(t),P(t)), where H(t) represents the real-time three-dimensional hybrid channel matrix; Z(t), L(t) and E(t) represent the data queue length vector, load vector and residual energy vector of the real-time node respectively; P(t) represents the permission vector;

[0042] C D (t) represents the communication state, and C D (t) = (H(t), Γ(t), F(t)), Γ(t) represents the signal-to-noise ratio matrix, Γ(t) = [γ i,j (t)] n×n , γ i,j (t) represents the signal-to-noise ratio from node i to node j at time t, P r,i,j (t) represents the received power, N i,j (t) represents the noise power, F(t) represents the spectrum allocation matrix, F(t) = [f alloc (i,j,t)] n×n , f alloc (i, j, t) represents the spectrum allocation function from node i to node j at time t, f alloc (i, j, t)∈F={2MHz,5MHz,…,100MHz}, where F represents the set of available frequency bands and F={2MHz,5MHz,8MHz,…,100MHz}, where the frequency bands increase in MHz intervals;

[0043] A D (t) represents the action decision, and A D (t)=(X(t),π(t)), X(t) represents the routing matrix, X(t)=[x i,j (t)] n×n , x i,j (t) represents the routing matrix element, and x i,j (t)∈{0,1},∑ j x i,j (t)≤1, when x i,j When (t) = 1, it means that node i selects node j as the next hop transmission node, π(t) represents the policy function, and S D (t)×C D (t)→A D (t);

[0044] In step S21, the node set V is filtered D The key nodes in the LSTM neural network are located at the key nodes, and edge computing units are arranged at the key nodes. The pre-trained LSTM neural network model is used to predict faults. The input vector X of the LSTM neural network model is set to t =[δ i,j(t),T delay (t),P drop (t),E res (t),R j (t),L i (t)], the prediction formula is as follows:

[0045]

[0046] Where, represents the probability of failure at the next moment, X t ,X t-1 ,…,X t-n Represent the input vectors at time t, t-1,…, tn respectively.

[0047] S22. Design update trigger conditions and update mechanism, where the trigger conditions are as follows:

[0048] (‖ΔG D (t)‖ F >∈)∨(t mod T sync =0) (5);

[0049] Where ΔG D (t) represents the parameter change of the virtual model, and ΔG D (t)=(ΔV D (t),Δε D (t),ΔS D (t),ΔC D (t)), ΔV D (t), Δε D (t), ΔS D (t) and ΔC D (t) represents the change in node, link, device status and communication status respectively; ‖·‖ F represents the Frobenius norm; ∈ represents the threshold; mod represents the modulo operation; T sync Indicates the timing synchronization period;

[0050] The update mechanism is as follows:

[0051] G D (t)←G D (t-1)+ΔG D (t) (6);

[0052] Where G D (t-1) represents the parameters of the virtual model at the previous moment.

[0053] In step S22, the reward function is used to guide the reinforcement learning routing algorithm to select the optimal route and spectrum resources:

[0054]

[0055] Where r(t) represents the reward function; α, β, γ, and δ represent weight coefficients, and α+β+γ+δ=1; T delay (t) represents the end-to-end transmission delay; P drop (t) represents the packet loss rate; Represents the remaining energy ratio of the node; α(R j ) represents the curvature penalty factor, and R j represents the curvature radius of node j;

[0056] And within the reinforcement learning framework, spectrum allocation, routing, and curvature are jointly optimized:

[0057]

[0058] S(t+1)=f transition (S(t),A(t),ω(t)) (10);

[0059] Q(S(t),A(t))←Q(S(t),A(t))+α lr [r(t)+γ lf ·Q(S(t+1),A(t+1))-Q(S(t),A(t))] (11);

[0060]

[0061] Where, δ i,j (f t ) indicates the transmission frequency band f t The comprehensive signal attenuation between node i and node j; λ represents the interference weight coefficient; I(f t ) indicates the transmission frequency band f t Real-time interference intensity under transition (·) represents the state transition function; ω(t) represents the random disturbance of the environment; Q(S(t), A(t)) represents the Q value of executing action S(t) in the current state S(t); Q(S(t+1), A(t+1)) represents the Q value of executing the next action A(t+1) in the next state S(t+1); α lr represents the learning rate; γ lf represents the discount factor; a * (t) represents the optimal action; δ p represents the penalty coefficient;

[0062] Then update the routing matrix X based on the updated Q value strategy new (t).

[0063] S3. Based on the virtual model, a dual-link switching module is designed to support switching between PLC communication and WiFi communication;

[0064] The dual-link switching module described in step S3 is a PLC coupling circuit of the dual-link switching mechanism, thereby realizing signal coupling through the LC parallel resonant network; wherein the cutoff frequency f of the PLC coupling circuit is c and input impedance Z in The design formula is as follows:

[0065]

[0066] Where L and C represent inductance and capacitance respectively; R s represents standard impedance; jωL represents inductive reactance; Represents the capacitive reactance;

[0067] The dual-link switching mechanism is expressed as follows:

[0068] Link selection = argmax(CQI PLC ,CQI Wi-Fi ) (14);

[0069] Where CQI represents the channel quality index; represents the noise variance; CQI PLC and CQI Wi-Fi Represent the channel quality indicators of PLC communication and WiFi communication respectively;

[0070] And design the following power management unit:

[0071]

[0072] Where V out and V in Output voltage and input voltage respectively, and V in ∈[9,36]V; D represents the duty cycle; η represents the power conversion efficiency; P out and P in Represent the output power and input power respectively.

[0073] S4. Based on the virtual model and dual-link switching module, design the multi-protocol conversion and management authority of APP collaborative control to realize cross-protocol communication and hierarchical control between PLC communication and APP.

[0074] Step S4 specifically includes the following steps:

[0075] S41. Considering the protocol differences between the tunnel device layer and the cloud or mobile terminal, a dynamic conversion engine based on a finite state machine is designed to achieve real-time protocol parsing and data format mapping;

[0076] The protocol mapping expression is as follows:

[0077] M:Modbus RTU→MQTT,M(P)=(T,D,QoS) (16);

[0078] Where M:Modbus RTU represents the source protocol; MQTT represents the target protocol; M(P) represents the protocol mapping function, which is used to convert Modbus data P into MQTT format; T represents the mapped MQTT topic; D represents the data payload; QoS represents the quality of service level;

[0079] And design the following transmission delay model:

[0080] T conv (i,j)=T parse +T trans +T queue +β(R j )·l i,j (17);

[0081] Where, T conv (i,j) represents; T parse 、T trans and T queue They represent protocol parsing time, transmission time, and queue waiting time respectively; β(R j ) represents the curve delay coefficient, and when it is a straight road, β(R j )=0, when turning, β(R j )=0.001;

[0082] S42. Design an authority level management system and an emergency instruction preemption mechanism using an instruction priority scheduling algorithm. The instruction priority scheduling algorithm is expressed as follows:

[0083]

[0084] Where, P exec Indicates the instruction execution priority; P urgency and P auth Respectively represent the urgency of the instruction and the authority level; ω1, ω2 and ω3 are all weight coefficients, and ω1+ω2+ω3=1;

[0085] When an emergency command is received, the following actions are triggered: interrupting the low-priority communication queue, allocating a dedicated PLC communication link, and increasing the transmission speed to compress the transmission delay to within 50ms.

[0086] Simulation experiment

[0087] Experimental Conditions: Tunnel geometry: Straight section: 500m long, 8m wide, 6m high; Curve section: 50m curvature radius (simulating the small-radius curves commonly found in tunnels), central angle 90°; Branch parameters: Branch density 0.03 / m (3 branches per 100m), branch length 20m. Communication parameters: PLC communication link: 10MHz carrier frequency, 20dBm transmit power, noise power spectral density -120dBm / Hz; WiFi communication link: 2.4GHz carrier frequency, 15dBm transmit power, noise power spectral density -110dBm / Hz. Node configuration: 20 PLC device nodes were deployed, randomly distributed within the tunnel, with node spacing ranging from 5-50m, with 5 nodes serving as relay nodes. Simulation platform: MATLAB R2023b + NS-3 co-simulation. Algorithm implementation: Reinforcement learning-based Q-Learning algorithm (with curvature penalty factor) and LSTM fault prediction model (input dimension 6, hidden layer nodes 32).

[0088] Based on the above experimental conditions, the following results were obtained using the method described in the present invention and the traditional backpressure algorithm.

[0089] Table 1 Signal attenuation comparison

[0090]

[0091]

[0092] Table 2 Routing Optimization Comparison

[0093]

[0094] Table 3 Dual-link switching effect

[0095]

[0096] As can be seen from Tables 1 to 3, the communication reliability is as follows: the success rate of the straight road scenario of the present invention is 100%, and the success rate of the curved scene is 98% (the traditional method is only 60%), which meets the index requirement of ≥95%.

[0097] Real-time performance: The proposed method achieves an average end-to-end latency of 150ms (compared to 280ms with traditional methods), and emergency command latency is compressed to 45ms, meeting the <200ms standard. Interference immunity: Spectrum utilization is increased by 71.1%, and the bit error rate is reduced from 15% to 3.2%, demonstrating the effectiveness of the proposed dual-link switching and dynamic spectrum allocation.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for constructing a wireless ad hoc network of tunnel PLC devices supporting APP linkage, characterized by: The following steps are involved: S1. Acquire three-dimensional point cloud data of the tunnel through laser scanning, extract tunnel geometric parameters, construct a signal comprehensive attenuation model based on the tunnel geometric parameters, and obtain a three-dimensional hybrid channel matrix based on the signal comprehensive attenuation model; S2. Use the digital twin model to hierarchically express the multi-dimensional data of the tunnel's physical topology, communication status, and equipment operation to obtain a virtual model, and achieve real-time synchronization between the virtual model and the physical topology through a dynamic update mechanism; S3. Based on the virtual model, a dual-link switching module is designed to support switching between PLC communication and WiFi communication; S4. Based on the virtual model and dual-link switching module, design the multi-protocol conversion and management authority of APP collaborative control to realize cross-protocol communication and hierarchical control between PLC communication and APP.

2. The method for constructing a wireless ad hoc network of tunnel PLC devices supporting APP linkage according to claim 1 is characterized in that: Step S1 specifically includes the following steps: S11. Extract the following tunnel geometric parameters: tunnel curvature radius R, branch length L b , branch density D i,j , the straight-line distance l between node i and node j i,j , the height H and width W of the tunnel; S12, based on tunnel geometry parameters M G ={R,L b ,D i,j ,l i,j ,H,W} to build a comprehensive signal attenuation model: d i,j =d B (i,j)+10log 10 (N(f)+1) (1); Where, δ i,j represents the comprehensive signal attenuation between node i and node j; δ B (i, j) represents the branch load attenuation between nodes i and j; N(f) represents the background noise power spectral density at frequency f; in, d R (i,j)=δ0(i,j)·α(R) N (3); Where, δ R (i, j) represents the reflection attenuation of the curved surface between nodes i and j; δ0(i, j) represents the basic attenuation of the straight road, and k1 and k2 are both frequency-related attenuation coefficients. When the PLC communication link is selected, k1 = 0.2, k2 = 0.

01. When the WiFi communication link is selected, k1 = 0.1, k2 = 0.

02. cw represents the carrier frequency; α(R) represents the curvature attenuation factor, and N represents the number of reflections of the signal in the bend, and S13, constructing a three-dimensional hybrid channel matrix H=[h i,j ] n×n , where n×n represents the number of rows and columns of the matrix, h i,j represents the transmission ratio of the signal from node i to node j, and P t and P r Represent the transmit power and receive power respectively.

3. The method for constructing a wireless ad hoc network of tunnel PLC devices supporting APP linkage according to claim 2 is characterized in that: Step S2 specifically includes the following steps: S21, construct the following virtual model G D (t): G D (t)=(V D ,ε D ,S D (t),C D (t),A D (t)) (4); Where V D represents a node set, and V D ={v i |i=1,2,…,n},v i Represents the attribute of node i, v i =(x i ,y i ,z i ,R i ,T i ), x i ,y i ,z i Represent the three-dimensional coordinates of node i; R i represents the curvature radius of the curve where node i is located, and when node i is on a straight road, R i =∞,T i represents the communication type of node i, T i =1, 2, 3 represent PLC communication, WiFi communication and relay communication respectively; ε D represents the set of communication links, and ε D ={e i,j |i,j∈V D }, e i,j represents the link attribute from node i to node j, e i,j =(l i,j ,L b ,D i,j ); S D (t) represents the device status, and S D (t)=(H(t),Z(t),L(t),E(t),P(t)), where H(t) represents the real-time three-dimensional hybrid channel matrix; Z(t), L(t) and E(t) represent the data queue length vector, load vector and residual energy vector of the real-time node respectively; P(t) represents the permission vector; C D (t) represents the communication state, and C D (t) = (H(t), Γ(t), F(t)), Γ(t) represents the signal-to-noise ratio matrix, Γ(t) = [γ i,j (t)] n×n , γ i,j (t) represents the signal-to-noise ratio from node i to node j at time t, P r,i,j (t) represents the received power, N i,j (t) represents the noise power, F(t) represents the spectrum allocation matrix, F(t) = [f alloc (i,j,t)] n×n , f alloc (i, j, t) represents the spectrum allocation function from node i to node j at time t, f alloc (i, j, t)∈F={2MHz,5MHz,…,100MHz}, where F represents the set of available frequency bands and F={2MHz,5MHz,8MHz,…,100MHz}, where the frequency bands increase in MHz intervals; A D (t) represents the action decision, and A D (t)=(X(t),π(t)), X(t) represents the routing matrix, X(t)=[x i,j (t)] n×n , x i,j (t) represents the routing matrix element, and x i,j (t)∈{0,1},∑ j x i,j (t)≤1, when x i,j When (t) = 1, it means that node i selects node j as the next hop transmission node, π(t) represents the policy function, and S D (t)×C D (t)→A D (t); S22. Design update trigger conditions and update mechanism, where the trigger conditions are as follows: (‖ΔG D (t)‖ F >∈)∨(t mod T sync =0) (5); Where ΔG D (t) represents the parameter change of the virtual model, and ΔG D (t)=(ΔV D (t),Δε D (t),ΔS D (t),ΔC D (t)), ΔV D (t), Δε D (t), ΔS D (t) and ΔC D (t) represents the change in node, link, device status and communication status respectively; ‖·‖ F represents the Frobenius norm; ∈ represents the set threshold, and ∈∈(0.05,0.1); mod represents the modulo operation; T sync Indicates the timing synchronization period; The update mechanism is as follows: G D (t)←G D (t-1)+ΔG D (t) (6); Where G D (t-1) represents the parameters of the virtual model at the previous moment.

4. The method for constructing a wireless ad hoc network of tunnel PLC devices supporting APP linkage according to claim 3 is characterized in that: In step S21, the node set V is filtered D The key nodes in the LSTM neural network are located at the key nodes, and edge computing units are arranged at the key nodes. The pre-trained LSTM neural network model is used to predict faults. The input vector X of the LSTM neural network model is set to t =[δ i,j (t),T delay (t),P drop (t),E res (t),R j (t),L i (t)], the prediction formula is as follows: Where, represents the probability of failure at the next moment, X t ,X t-1 ,…,X t-n Represent the input vectors at time t, t-1,…, tn respectively.

5. The method for constructing a wireless ad hoc network of tunnel PLC devices supporting APP linkage according to claim 3 is characterized in that: In step S22, the reward function is used to guide the reinforcement learning routing algorithm to select the optimal route and spectrum resources: Where r(t) represents the reward function; α, β, γ, and δ represent weight coefficients, and α+β+γ+δ=1; T delay (t) represents the end-to-end transmission delay; P drop (t) represents the packet loss rate; Represents the remaining energy ratio of the node; α(R j ) represents the curvature penalty factor, and R j represents the curvature radius of node j; And within the reinforcement learning framework, spectrum allocation, routing, and curvature are jointly optimized: S(t+1)=f transition (S(t),A(t),ω(t)) (10); Q(S(t),A(t))←Q(S(t),A(t))+α lr [r(t)+γ lf ·Q(S(t+1),A(t+1))-Q(S(t),A(t))] (11); Where, δ i,j (f t ) indicates the transmission frequency band f t The comprehensive signal attenuation between node i and node j; λ represents the interference weight coefficient; I(f t ) indicates the transmission frequency band f t Real-time interference intensity under transition (·) represents the state transition function; ω(t) represents the random disturbance of the environment; Q(S(t), A(t)) represents the Q value of executing action A(t) in the current state S(t); Q(S(t+1), A(t+1)) represents the Q value of executing the next action A(t+1) in the next state S(t+1); α lr represents the learning rate; γ lf represents the discount factor; a * (t) represents the optimal action; δ p represents the penalty coefficient; Then update the routing matrix X based on the updated Q value strategy new (t).

6. The method for constructing a wireless ad hoc network of tunnel PLC devices supporting APP linkage according to claim 3 is characterized in that: The dual-link switching module described in step S3 is a PLC coupling circuit of the dual-link switching mechanism, thereby realizing signal coupling through the LC parallel resonant network; wherein the cutoff frequency f of the PLC coupling circuit is c and input impedance Z in The design formula is as follows: Where L and C represent inductance and capacitance respectively; R s represents standard impedance; jωL represents inductive reactance; Represents the capacitive reactance; The dual-link switching mechanism is expressed as follows: Link selection = argmax(CQI PLC ,CQI Wi-Fi ) (14); Where CQI represents the channel quality index; represents the noise variance; CQI PLC and CQI Wi-Fi Represent the channel quality indicators of PLC communication and WiFi communication respectively; And design the following power management unit: Where V out and V in Output voltage and input voltage respectively, and V in ∈[9,36]V; D represents the duty cycle; η represents the power conversion efficiency; P out and P in Represent the output power and input power respectively.

7. The method for constructing a wireless ad hoc network of tunnel PLC devices supporting APP linkage according to claim 6 is characterized in that: Step S4 specifically includes the following steps: S41. Considering the protocol differences between the tunnel device layer and the cloud or mobile terminal, a dynamic conversion engine based on a finite state machine is designed to achieve real-time protocol parsing and data format mapping; The protocol mapping expression is as follows: M:Modbus RTU→MQTT,M(P)=(T,D,QoS) (16); Where M:Modbus RTU represents the source protocol; MQTT represents the target protocol; M(P) represents the protocol mapping function, which is used to convert Modbus data P into MQTT format; T represents the mapped MQTT topic; D represents the data payload; QoS represents the quality of service level; And design the following transmission delay model: T conv (i,j)=T parse +T trans +T queue +β(R j )·l i,j (17); Where, T conv (i,j) represents; T parse 、T trans and T queue They represent protocol parsing time, transmission time, and queue waiting time respectively; β(R j ) represents the curve delay coefficient, and when it is a straight road, β(R j )=0, when turning, β(R j )=0.001; S42. Design an authority level management system and an emergency instruction preemption mechanism using an instruction priority scheduling algorithm. The instruction priority scheduling algorithm is expressed as follows: Where, P exec Indicates the instruction execution priority; P urgency and P auth Respectively represent the urgency of the instruction and the authority level; ω1, ω2 and ω3 are all weight coefficients, and ω1+ω2+ω3=1; When an emergency command is received, the following actions are triggered: interrupting the low-priority communication queue, allocating a dedicated PLC communication link, and increasing the transmission speed to compress the transmission delay to within 50ms.

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