A tunnel dual-energy clustering routing method based on multi-objective optimization

Abstract

A tunnel dual-energy clustering routing method based on multi-objective optimization, comprising: constructing a three-dimensional space model of a single-tube tunnel for representing the geometric structure of the tunnel; proposing a multi-objective clustering routing algorithm (TCR) and a dual-energy dynamic supply algorithm (DETCR); on the basis of a competition of tribes and cooperation of members algorithm (CTCM), proposing a multi-objective competition of tribes and cooperation of members algorithm (MOCTCM), comprehensively considering the residual energy, communication distance, energy consumption and relay node (RN) election frequency of nodes, and providing a more adaptive and globally optimized decision basis for the selection of optimal cluster heads (Optima-CH) and optimal relay nodes (Optima-RN). The application effectively solves the problems of uneven node energy consumption and limited energy supply in a dynamic and energy-limited tunnel environment, significantly prolongs the network life cycle and improves the energy efficiency level.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of Internet of Things, and relates to a tunnel dual-energy clustering routing method based on multi-target optimization. BACKGROUND

[0002] Wireless sensor network (WSN) clustering routing algorithms play an important role in improving network efficiency, prolonging network lifetime and optimizing resource utilization. According to the distributed and centralized characteristics of the algorithm, the existing research can be roughly divided into two categories. The early representative algorithm, such as LEACH (Low-energy adaptive clustering hierarchy) proposed by Heinzelman et al., adopts a distributed structure, in which each node in the network autonomously selects CH according to a certain probability, aiming to reduce energy consumption and improve network lifetime. Although the LEACH algorithm has high energy efficiency and low topology management overhead, it cannot effectively handle the energy imbalance problem in CH selection due to the lack of global information. In order to solve this problem, Heinzelman et al. further proposed a centralized routing algorithm LEACH-C (LEACH-centralized), which obtains global energy information and node distance factors through BS to optimize the selection of CH, thereby achieving better energy balance and prolonging the network lifetime.

[0003] On the basis of LEACH algorithm, more and more improved algorithms have emerged, especially the introduction of intelligent optimization algorithm, which makes multi-objective optimization an important method to improve the efficiency of clustering routing. For example, genetic algorithm, particle swarm optimization algorithm, butterfly optimization algorithm, and meerkat algorithm are used to balance energy, distance, load, and other objectives, but they simplify multi-objective into single-objective through weighted summation, and the weight setting is subjective and ignores the conflict relationship between objectives. In recent years, multi-objective optimization algorithms directly solve the Pareto front, providing a better solution set for CH and routing selection. Kotary et al. proposed DMaOWOA(distributed many-objective clustering using whale optimization algorithm) algorithm, which can provide superior clustering results in WSN through leader selection based on reference points and whale optimization technology, and perform better than traditional distributed clustering algorithms on synthetic and real data sets, but its computational complexity increases with the size of the network and data. Zhang et al. proposed MOALO-FCM(multi-objective antlion with fuzzy clustering algorithm) algorithm, which combines fuzzy clustering with multi-objective antlion optimization to improve the energy balance of RN and effectively improve the network lifetime, energy balance, and optimization stability of WSN in 2D and 3D environments. However, this method still has high time complexity and low experimental scene complexity. Singh et al. proposed a strategy combining multi-objective optimization and edge intelligence adaptation, which uses Grey Wolf Optimizer(GWO) and bird edge calculation method to generate Pareto optimal solutions to optimize the QoS(Quality of Service) management of WSN-IoT applications, balance energy and delay, and improve resource utilization and scalability. Yuan et al. proposed a multi-objective routing(MOR) protocol based on NSGA-II optimization of energy consumption, end-to-end delay, link quality, and congestion control in underwater WSN to meet the diverse needs of underwater IoT(Internet of Things) applications. Simulation results verify its feasibility and the necessity of multi-objective optimization. Sun et al. proposed a WSN security routing protocol based on multi-objective ant colony optimization, which takes node residual energy and path trust value as optimization objectives, combines improved DS(Dempster–Shafer) evidence theory and conflict preprocessing for node trust evaluation, and uses Pareto optimal solution mechanism to optimize multi-objective routing. Simulation results show that this method can effectively deal with black hole attacks. However, this method only considers limited objectives and constraints, and does not involve network reliability, failure probability, and life cycle optimization.The above studies have made significant progress in optimizing multiple objectives of WSN and IoT applications, but they mostly focus on optimization problems in two-dimensional or static environments, ignoring the complex constraints in three-dimensional space and the impact of dynamic energy supply on network performance.

[0004] In three-dimensional environments, the spatial layout, communication range, and topology of network nodes become more complex, which puts higher requirements on the coverage, connectivity, and energy efficiency of the network. At the same time, dynamic energy supply, especially in scenarios using renewable energy such as solar energy and vibration energy, is affected by environmental changes, making network energy management face more challenges. In response to this problem, recent research has gradually begun to focus on the impact of constraints and dynamic energy supply in three-dimensional environments on WSN performance and has proposed some corresponding optimization strategies. Yang et al. proposed a BWSN model based on a three-dimensional bridge space model (SM-SSB) and introduced a multi-objective decision-making CRITIC-TOPSIS clustering (CTC) routing algorithm to select Optima-CH and Optima-RN. This algorithm considers three-dimensional spatial distance, node density, and residual energy, significantly improving energy efficiency. The results show that the CTC algorithm outperforms traditional methods in three-dimensional environments. Zheng et al. proposed a multi-hop routing protocol based on the Secretary Bird Optimization Algorithm (SBOA) to optimize the network lifetime and energy distribution of three-dimensional bridge wireless sensor networks (BWSN). This protocol combines the fuzzy C-means algorithm for clustering and uses SBOA to find the best path between CH and BS, while improving energy efficiency through a re-clustering mechanism. Experimental results show that this protocol outperforms existing methods in terms of network lifetime, energy efficiency, and energy consumption balance. However, neither this method nor the CTC algorithm considers energy supply. Zhang et al. designed an ultra-wide full-band gap phononic crystal (PnC) structure to improve the performance of piezoelectric energy harvesting (PEH) and provide energy for wireless sensors. The same year, they also studied the impact of incomplete line defect size on PnC-based PEH systems to optimize energy positioning and collection performance. The results show that when the defect is located in the fourth layer of the supercell, the system performance is optimal, with an output voltage of 22.54 V and a power of 12.78 mW, providing an effective energy harvesting solution for self-powered wireless sensors. Although the above research has made important progress in optimizing PEH structures to improve energy harvesting performance, it mainly focuses on material and structure design and does not combine the collected energy with the actual application scenarios of wireless sensor nodes.To further promote the application of energy harvesting technology in WSN, Lu et al. proposed a Field Observation Instrument Network (FOIN) solar dynamic supply routing protocol based on a multi-objective mayfly optimization algorithm (MMA) (DSFOI-MMA). This protocol ensures the energy supply of low-energy nodes by designing a solar dynamic energy harvesting model and dynamically selecting Optima-CH using MMA. Simulation results show that DSFOI-MMA performs excellently in terms of network lifetime and CH quantity, enhancing the automation of monitoring data. However, this method is still based on a two-dimensional environment and does not consider the topological constraints and uneven energy distribution in three-dimensional space. In practical applications, sensor node deployment in three-dimensional environments is more complex, and energy supply is significantly affected by spatial location, so routing strategies need to be further optimized to adapt to the challenges of three-dimensional scenarios.

[0005] In recent years, with the research and application of Tunnel Structure Health Monitoring System (TSHMS), intelligent management of tunnels has gradually become possible. TSHMS collects real-time data on the state of tunnel structures by deploying various sensors such as strain sensors, crack sensors, and temperature and humidity sensors, providing scientific basis for safety assessment and early warning of tunnels. Since the 1990s, TSHMS has been widely used in major tunnel projects at home and abroad, such as Xiamen Undersea Tunnel, Hong Kong-Zhuhai-Macao Undersea Tunnel, Nanjing Yangtze River Tunnel, and English Channel Tunnel, becoming an important means to ensure the long-term safety operation of tunnels.

[0006] At the same time, the rapid development of IoT technology and WSN provides a new technical path for the deployment of TSHMS. Through TWSN, wireless collection and transmission of tunnel structure data can be realized, significantly reducing wiring costs, complexity, and maintenance difficulty. For example, traditional tunnel monitoring systems usually require a large number of cables to connect sensors, which not only complicates installation but also easily suffers from environmental interference, leading to data distortion. TWSN has the advantages of flexible deployment, low cost, and strong scalability, effectively overcoming the limitations of traditional wired systems. However, the application of TWSN in tunnel environments also faces unique challenges, such as limited node deployment space, multiple node types, and the need for manual deployment in three-dimensional space. Therefore, in the research of TWSN technology, in addition to considering the general characteristics of WSN (such as energy limitations and short transmission distances), optimization design needs to be combined with the particularity of the tunnel environment.

[0007] At present, the research on tunnel wireless sensor network is mostly based on linear or planar model to carry out node deployment and routing algorithm design, but in the process of tunnel structure characteristic research, the safety and stability of the tunnel are jointly influenced by multiple factors such as surrounding rock pressure, groundwater seepage, lining structure, construction joint, crack distribution and geological conditions. Therefore, the traditional linear or planar model is difficult to fully reflect these complex influencing factors, thus leading to the inability to meet the demand for all-around and multi-dimensional monitoring of tunnel structure. In China's tunnel engineering, mountain tunnel and urban subway tunnel account for the main part, among which mountain tunnel is mostly in complex geological environment, and urban subway tunnel is faced with the interference of dense traffic load and surrounding construction activities. It is of great significance to promote the construction of intelligent tunnel to deeply explore and analyze the tunnel structure characteristics, construct a three-dimensional wireless sensor monitoring network conforming to the three-dimensional spatial distribution characteristics of the tunnel, and optimize the design of routing algorithm. In addition, due to the limited energy of TWSN node, the core of TWSN routing algorithm design is to ensure the efficient interconnection and intercommunication of network nodes and adapt to the tunnel monitoring demand, and at the same time, how to effectively alleviate and avoid the 'hot spot' problem and 'energy hole' phenomenon caused by uneven node energy consumption, so as to prolong the network life cycle and improve the reliability of monitoring data. SUMMARY

[0008] The application provides a tunnel dual-energy clustering routing method based on multi-objective optimization, which can effectively solve the problems of uneven node energy consumption and limited energy supply in dynamic and energy-limited tunnel environment, significantly prolong the network life cycle and improve the energy efficiency level.

[0009] The technical scheme adopted by the application is:

[0010] A tunnel dual-energy clustering routing method based on multi-objective optimization, specifically comprising: constructing a three-dimensional space model of a single tube tunnel for representing the geometric structure of the tunnel; secondly, proposing a multi-objective clustering routing algorithm TunnelClustering Routing, TCR and its dual-energy dynamic supply algorithm Dual Energy-based TunnelClustering Routing, DETCR; on the basis of the Competition of Tribes and Cooperation of Members, CTCM, a Multi-Objective Competition of Tribes and Cooperation of Members, MOCTCM is designed, which comprehensively considers the residual energy, communication distance, energy consumption and Relay Node, RN election frequency factors of the node, to provide adaptive and global optimization ability for the selection of Optimal Cluster Head, Optima-CH and Optimal Relay Node, Optima-RN; the TCR algorithm uses the Pareto solution set generated by MOCTCM, selects Optima-CH through the Technique for Order Preference by Similarity to Ideal Solution, TOPSIS method, and combines the coefficient of variation method to dynamically weight each objective function to determine Optima-RN; on this basis, DETCR integrates the solar light tracking and vehicle frequency driven vibration energy collection mechanism to realize efficient energy supplement for low-energy nodes, thereby improving the network life cycle and operation stability.

[0011] Further:

[0012] The specific implementation steps of the method are as follows:

[0013] Step 1: Establish a three-dimensional space model of a single tube tunnel:

[0014] The constructed tunnel three-dimensional space model space model single tube tunnel, 3D-SM-STT model structure is abstracted as a cuboid with L*W*H, and has a semi-cylindrical cavity with a radius of R; photovoltaic panel components are arranged at the tunnel exit and entrance areas, and through electromagnetic wave penetration power supply or magnetic induction coupling energy transmission non-contact energy supply technology, stable power supply for high-power sensors embedded in the structure is effectively realized;

[0015] Step 2: Node deployment: In the tunnel construction phase, sensor nodes are deployed into the three-dimensional space model; low-difference sequence is used to initialize the position of the nodes in combination with the tunnel structure when the nodes are deployed;

[0016] Step 3: Network initialization phase, network initialization is only executed once after the completion of node deployment to ensure the normal operation of the network; this phase includes network partitioning, variable time interval light tracking and vehicle frequency driven dual energy supply model establishment; the dual energy function model includes: solar energy supply model SESM and vibratory energy model VEM;

[0017] The network partitioning adopts a partitioning strategy based on geographical division;

[0018] The solar energy model SESM adopts an energy collection method by adjusting the angle of the photovoltaic panel by tracking the solar elevation angle;

[0019] The vibratory energy model VEM adopts a high-efficiency vibratory energy collection method based on the frequency characteristics of vehicle noise and its electrical energy response under multiple factors;

[0020] Both models introduce energy distribution balancing mechanisms to improve energy utilization efficiency and prolong network life cycle;

[0021] Step 4: Setting phase, the base station BS first completes the cluster head CH and RN election task according to the information obtained in the network initialization phase; then, the cluster structure is constructed with the selected cluster head as the core, the nodes are divided into clusters to form a hierarchical network topology; on this basis, the BS further executes inter-cluster routing optimization, reasonably configures the multi-hop forwarding path between cluster heads, thereby improving data transmission efficiency and achieving balanced distribution of network energy consumption;

[0022] Step 5: Steady state phase, ordinary nodes perform data collection and transmit data to the CH according to the time division multiple address TDMA time slot allocated by the BS, and enter sleep mode during non-transmission period to reduce data conflict and energy consumption.

[0023] Further:

[0024] In step 4, the BS performs cluster structure construction, CH selection and inter-cluster routing optimization based on TCR algorithm and DETCR algorithm:

[0025] The TCR algorithm mainly includes three parts, one is decoding: providing a basis for the selection of subsequent CH and RN through decoding; two is to select Optima-CH by MOCTCM, and to determine Optima-RN based on the RN objective function; when selecting Optima-CH, the optimal solution is determined by combining each objective function: residual energy f1, the average distance between nodes in the partition f2, and the distance from the node to the RN f3 through the MOCTCM algorithm; and when selecting Optima-RN, the optimal solution is determined by calculating the objective function value of each node through each objective function: the square of the distance between RN and CH f4, energy consumption f5, and RN count control f6, and dynamically weighting each objective function by the coefficient of variation method; three is to form the optimal cluster and the optimal inter-cluster routing based on Optima-CH and Optima-RN; after Optima-CH and Optima-RN are determined, the BS broadcasts a message, and the Ordinary Node, ON, determines the cluster structure according to the received information through the distance factor; then, the BS allocates time slots for each node to complete non-conflicting data transmission; the nodes in the cluster transmit data to the CH through single-hop mode, and the CH transmits data to the BS through multi-hop mode; the DETCR algorithm adds SESM and VEM power supply models based on the TCR algorithm, so that the network lifetime is significantly improved; each objective function is as follows:

[0026] 1) Residual energy f1

[0027] After network initialization, the BS can calculate the residual energy of each node, compared with a single residual energy value, the global average energy and distribution information can be combined to dynamically adjust the weight of node energy in CH selection, adapt to the network scene with uneven energy, and the objective function is as follows:

[0028]

[0029] Wherein E i is the residual energy of node i, unit: J, E avg is the average energy of all nodes in the partition, unit: J, E max , E min are the maximum and minimum values of the residual energy in the partition, unit: J;

[0030] 2) The average distance between nodes in the partition f2

[0031] The average distance between nodes in the partition is one of the key factors affecting the data transmission in the cluster; the target function is as follows by comprehensively considering the distance, distance distribution uniformity and inverse distance compensation:

[0032]

[0033] wherein D ij represents the distance between node i and node j, unit: m, the average distance between node i and other nodes in the partition unit: m, m represents the total number of nodes in the partition;

[0034] 3) the distance f3 of the node to the RN

[0035] The distance of the node to the RN is one of the key factors affecting the inter-cluster data transmission; since the RN is not determined in the CH selection stage, and its selection range is located in the next partition, the distance of the node to the center of the next partition is defined as the distance of the node to the RN when constructing the objective function; at the same time, by combining the intra-cluster distance to dynamically adjust the weight, the node in the sparse area is avoided to become the CH, so as to improve the efficiency of multi-hop communication, and the objective function is as follows: wherein D next (i) is the distance of node i to the RN, unit: m, D max is the maximum distance between nodes in the partition, unit: m;

[0036]

[0037] 4) the square of the distance f4 of the RN to the CH

[0038] The selection of the RN plays a key role in optimizing the data transmission efficiency and network energy balance; since the node energy consumption is closely related to the transmission distance, and the transmission energy consumption is in a power relationship with the distance; therefore, the square of the distance of the RN to the CH node is taken as the optimization target, and the objective function is as follows:

[0039]

[0040] 5) energy consumption f5

[0041] Energy consumption balance is the core mechanism to ensure the sustainability and stability of the network; the objective function is constructed by combining the ratio of the residual energy E i of the node, unit: J, and the transmission energy consumption E tx (i), unit: J, and the variance thereof:

[0042]

[0043] 6) RN count control f6

[0044] Controlling the node to be frequently selected as the RN can prevent the node from failing due to excessive energy consumption; the present application sets an initial value L con (i) = 0.1 for each node, and is incremented in each subsequent round, and when the value of the node is closer to 1, the probability of being selected as the RN is greater; once the node is selected as the RN, the value is restored to the initial value in the next round, and the objective function is as follows:

[0045]

[0046] The MOCTCM algorithm introduces three key steps in the CTCM to enhance its solving ability for multi-objective problems, specifically:

[0047] First, the calculation and archive of non-dominated solutions: in the iteration process, each tribe generates a corresponding solution set according to the multi-objective fitness function; after completing a round of iteration, the fitness values of all tribes are compared, and the non-dominated solutions are selected and stored in the non-dominated solution set to maintain the superiority and diversity of the solutions;

[0048] Second, local optimal solution update based on dominance relationship: for the new fitness value generated after the iteration of the tribe, compare it with the local optimal solution before the iteration, if the new solution dominates the original local optimal solution, replace it; if it does not dominate, introduce a random function to update it with a certain probability to balance the global exploration and local development ability;

[0049] Finally, global optimal solution update based on TOPSIS: after each iteration, apply TOPSIS sorting to the non-dominated solution set, select the global optimal solution Optima-CH according to the proximity of the ideal solution, and use it to guide the next iteration to improve the optimization convergence and solution quality.

[0050] In step 3, the solar energy model SESM makes the photovoltaic panel always perpendicular to the sunlight, and the corresponding energy TE da The output is as follows:

[0051] TE da = H0S pv = E e

[0052] Where S pv represents the area of the photovoltaic panel, unit: m 2 , H0 represents the light intensity of the sunlight directly hitting the photovoltaic panel, unit: Lux, E e , represents the energy under vertical illumination, unit: J;

[0053] In the vibration energy supply model VEM, the energy obtained by the sensor when the vehicle passes through is as follows: where U represents voltage, unit: V; I represents current, unit: A; t represents time, unit: s; to evaluate the total energy acquisition, the characteristics of the vehicle flow in the tunnel need to be mastered; a Poisson distribution is used to construct the basic flow model, and Gaussian noise and accident attenuation factors are introduced to enhance the description of randomness and suddenness; the instantaneous traffic flow Q(t) at time t is as follows:

[0054] W = Pt = UIt

[0055] Q(t) = max(P(t) + G(t) - A(t), 0)

[0056] Where P(t) is a random variable obeying Poisson distribution with parameter λ(t), P(t) ~ Poisson(λ(t)), λ(t) represents the average traffic flow at a certain time, and is affected by the peak λ peak , unit: veh / min and the trough λ offpeak , unit: veh / min, as follows; G(t) is a normal distribution with mean 0 and variance σ 2 G(t) ~ N(0, σ 2 ), A(t) represents the traffic decay term caused by the incident, ζ is the incident influence factor, which represents the proportion of traffic reduction during the incident; θ is the incident decay rate, which represents the recovery speed; t a represents the time of the incident;

[0057]

[0058] The data acquisition mode in step 5 is as follows: the ordinary node ON first acquires data, and transmits data to the CH according to the TDMA time slot allocated by the BS, and enters the sleep mode during the non-transmission period, so as to reduce data conflict and energy consumption; after receiving the data of the ON, the CH first carries out fusion processing on the data, so as to reduce redundant information and improve transmission efficiency; then, the CH transmits the data to the BS through the optimal inter-cluster routing and adopts the CSMA / CA protocol, so as to ensure reliable data transmission.

[0059] According to the structural characteristics and monitoring requirements of the "single-hole tunnel", the 3D-SM-STT model is constructed, and a clustering routing algorithm DETCR of TWSN dual-energy dynamic energy supply is proposed by combining the MOCTCM algorithm. The algorithm focuses on solving the multi-objective optimization problem of Optima-CH and Optima-RN selection in the three-dimensional wireless sensor monitoring network, and how to supply energy by solar energy and vibration energy and how to balance energy distribution, so as to realize the autonomous construction of an efficient path and stable data transmission. By introducing the dual-energy supplement mechanism and the dynamic routing strategy, the DETCR algorithm significantly improves the network energy efficiency and prolongs the network life cycle, and provides an efficient and energy-saving solution for WSN in a complex tunnel environment. Specifically as follows:

[0060] (1) A three-dimensional tunnel space model 3D-SM-STT with TWSN node deployment characteristics is constructed. Compared with the traditional two-dimensional plane model, the model can more truly reflect the spatial distribution characteristics of the tunnel structure, and provide a more reasonable simulation environment for verifying the performance of the clustering routing algorithm in TWSN.

[0061] (2) A dual-energy supply model based on variable time interval light tracking and vehicle frequency driving is proposed. Among them, the solar model optimizes the angle of the photovoltaic panel to achieve vertical incidence to improve the collection efficiency; the vibration energy model is based on the frequency characteristics of vehicle noise and its electric energy response, and simulates the vehicle frequency under multiple factors to realize efficient vibration energy collection. Both models introduce energy distribution balancing mechanism to improve energy utilization efficiency and prolong network life cycle.

[0062] (3) Based on the original CTCM algorithm, MOCTCM is proposed, which integrates dual-energy dynamic energy supply mechanism and tunnel space model, and realizes joint optimization for multi-dimensional optimization objectives such as node residual energy, distance, energy consumption and the number of times selected as RN. Under the constructed multi-objective optimization framework, the performance indicators of each node are comprehensively evaluated by the BS, and Optima-CH and Optima-RN are dynamically selected in each partition to further improve the overall performance of the network. BRIEF DESCRIPTION OF DRAWINGS

[0063] Figure 1 It is a single-hole tunnel space model diagram of the present application;

[0064] Figure 2 It is a TWSN clustering routing algorithm working principle diagram of the present application;

[0065] Figure 3 It is a MOCTCM decoding diagram of the present application;

[0066] Figure 4 It is a flowchart of the present application;

[0067] Figure 5 It is a network life cycle of the present application;

[0068] Figure 6 It is an energy supply diagram of the present application;

[0069] Figure 7 It is a residual energy diagram of the present application. DETAILED DESCRIPTION

[0070] The present application will be further described in detail below in combination with the drawings and experiments.

[0071] I. Single-hole tunnel space model

[0072] In TSHMS, wireless sensor nodes can be pre-set at key structural positions such as lining layer, tunnel wall or tunnel wall body according to monitoring needs during tunnel construction phase to realize long-term monitoring of the tunnel. For example, Figure 1As shown, the 3D-SM-STT model constructed by the application abstracts its structure as a cuboid of L*W*H, and has a semi-cylindrical cavity with a radius of R (equivalent to removing a semi-cylinder with a radius of R in the cuboid of L*W*H), to realistically simulate the spatial form and layout constraints of the tunnel. In addition, photovoltaic panel assemblies are arranged at the entrance and exit areas of the tunnel, and through non-contact energy supply technologies such as electromagnetic wave penetration power supply or magnetic induction coupling energy transmission, stable power supply can be effectively realized for high-power sensors embedded in the structure. The 3D-SM-STT model not only reflects the structural complexity and spatial limitations of the tunnel environment, but also provides a unified and practically feasible modeling basis for subsequent node layout optimization, energy efficiency scheduling, and routing strategy design.

[0073] The working principle diagram of the TWSN clustering routing algorithm based on the 3D-SM-STT model is shown in Figure 2 As shown, the data transmission is optimized through hierarchical structure. To enhance the illustration clarity of the regional division, the same type of ONs distributed in Area A and Area B are marked as "ON in area A" and "ON in Area B", respectively, although their functions are consistent. This representation method is only used to distinguish the spatial area to which the nodes belong. The sensor nodes deployed at specific positions of the tunnel select CH and RN according to the preset mechanism, form a cluster structure and build a multi-hop transmission path. The ONs in the cluster transmit monitoring data to the CH, and the CH transmits the data to the BS through the RNs step by step, to realize periodic data acquisition and efficient transmission.

[0074] II. DETCR algorithm design

[0075] The DETCR algorithm includes four stages of node deployment, network initialization, setting and steady state. In the node deployment stage, the nodes are pre-installed in the three-dimensional tunnel model during the tunnel construction. In the network initialization stage, the network area is first divided, and the solar dynamic energy supply model and the vibration energy supply model are established. In this stage, the BS broadcasts inquiry messages (Inquiry-REQ) to all nodes through the CSMAMAC protocol, including BS position information, network size and instructions to activate the initial communication channel, laying the foundation for subsequent cluster construction. In the setting stage, the Candidate ClusterHead (CCH) set is first constructed. On this basis, the MOCTCM algorithm is used to realize the dynamic selection of Optima-CH. After the election of CH is completed, the Optima-RN is determined according to the RN election objective function combined with the coefficient of variation method, and the cluster structure construction is completed. Finally, the inter-cluster routing is optimized based on the selected CH and RN, and the optimal cross-cluster path is established to realize the efficiency of data transmission and the balanced distribution of network energy consumption. In this stage, the BS is responsible for the formation of the cluster, the confirmation of the CH node, the optimization of the CH to BS path, the TDMA time slot allocation and the broadcast of the control information. The broadcast control information includes ON information, CH identification, inter-cluster routing table and time slot allocation scheme. In the steady state stage, the network performs data collection and intra-cluster and inter-cluster data transmission. The nodes transmit data according to the routing table and TDMA time slot arrangement, and only in the specified active period, the communication state is maintained, and the rest of the time enters the low-power sleep mode, thereby effectively reducing the energy consumption and further improving the network energy efficiency.

[0076] 1) Residual energy (f1)

[0077] The residual energy is one of the most important factors in the CH election process. It not only affects the selection of CH, but also determines whether the node can support multiple rounds of data aggregation and forwarding tasks. Electing a node with high residual energy can improve network stability and prolong network life cycle. After network initialization, the BS can calculate the residual energy of each node. Compared with a single residual energy value, combining global average energy and distribution information can dynamically adjust the weight of node energy in CH selection, adapt to energy-unbalanced network scenarios, and the objective function is shown in equation (1).

[0078]

[0079] 2) Average distance between nodes in the partition (f2)

[0080] The average distance between nodes in the partition is one of the key factors affecting intra-cluster data transmission. The invention considers distance, distance distribution uniformity and inverse distance compensation, and the objective function is shown in equation (2).

[0081]

[0082] 3) Node-to-RN distance (f3)

[0083] The node-to-RN distance is one of the key factors affecting inter-cluster data transmission. Since the RN is not determined in the CH selection stage and its selection range is in the next partition, the present application defines the node-to-RN distance as the distance from the node to the center of the next partition when constructing the objective function. At the same time, by combining the intra-cluster distance to dynamically adjust the weight, the node in the sparse area is avoided to become the CH, thereby improving the efficiency of multi-hop communication. The objective function is shown in equation (3).

[0084]

[0085] The objective function of the above-defined CH election, such as the residual energy f1, the average distance between nodes in the partition f2, and the node-to-RN distance f3, can be expressed as equation (4):

[0086]

[0087] where t represents time, M represents the target number, X = [X1, X1,..., X N ]∈Ω, X represents the decision vector, Ω represents the decision space, and F(X, t) represents the minimized objective function at time t;

[0088] 4) RN-to-CH distance square (f4)

[0089] The selection of RN plays a key role in optimizing data transmission efficiency and network energy balance. Since the node energy consumption is closely related to the transmission distance, and the transmission energy consumption is in a power relationship with the distance. Therefore, the square of the distance from the RN to the CH node is taken as the optimization target, and the objective function is shown in equation (5).

[0090]

[0091] 5) Energy consumption (f5)

[0092] Energy consumption balance is the core mechanism to ensure the sustainability and stability of the network. The present application constructs the objective function as equation (6) by combining the ratio of the node residual energy E i and the transmission energy consumption E tx (i) and the variance thereof.

[0093]

[0094] 6) RN count control (f6)

[0095] Controlling the node to be frequently selected as RN can prevent the node from failing due to excessive energy consumption. The present application sets an initial value L con(i) = 0.1, and is incremented by 0.1 in each subsequent round, the closer the value of the node is to 1, the greater the probability of being selected as RN. Once a node is selected as RN, the value is restored to the initial value in the next round. The objective function is shown in equation (7).

[0096]

[0097] The objective function of the RN election defined above, such as the square of the distance f4 between RN and CH, energy consumption f5, and RN count control f6, can be expressed as equation (8):

[0098] minF(X,t) = ω1*f4(X,t) + ω2*f5(X,t) + ω3*f6(X,t) (8)

[0099] where ω1, ω2, ω3 represent the weights of the objective function, respectively.

[0100] (a) Network initialization phase

[0101] Network initialization is a necessary stage for normal operation of the network. In the DETCR protocol, the network initialization phase is only executed once after the node deployment is completed to ensure the normal operation of the network. This phase includes network partitioning, solar energy supply model (SESM) establishment, and vibratory energy model (VEM) establishment.

[0102] ① Network partitioning

[0103] Network partitioning is a key technology in TWSN, aiming to improve network energy efficiency, balance node load, and optimize data transmission path. Through reasonable partitioning strategy, not only can reduce the energy consumption between nodes, but also can improve the stability of data transmission, thereby prolonging the network life cycle. In view of the special constraints of tunnel environment, the present application adopts a partitioning strategy based on geographical division to ensure the uniform distribution of CH, optimize inter-cluster communication, and improve the overall network energy efficiency. After determining the partitioning distance, the number of Optima-CH is calculated to further enhance the rationality of network structure and operation efficiency.

[0104] ② Solar energy model establishment

[0105] Solar energy, as a stable and renewable energy source, can continuously power sensor nodes with low maintenance cost and good applicability. However, due to factors such as day and night and climate, energy management needs to be optimized to improve utilization efficiency. The present application improves the solar power supply model in combination with the geographical features of TWSN. Considering the sinusoidal relationship between light intensity and time, a model is designed to dynamically adjust the angle of the photovoltaic panel by tracking the solar altitude angle to achieve perpendicular incidence of light and maximize energy collection. The energy Ee Output as shown in formula (9).

[0106] E e = S pv H0 (9)

[0107] Wherein, S pv represents the area of the photovoltaic panel, H0 represents the light intensity of the sunlight directly on the photovoltaic panel, as shown in formula (10).

[0108]

[0109] In the formula, T α is the sunshine time, and the supplementary phase angle t sr represents the sunrise time of the day, and the light intensity function amplitude A is shown in formula (11).

[0110]

[0111] Wherein day represents a day in a year, the winter solstice is defined as the first day, α+o represents the latitude value relative to the Tropic of Cancer, the Tropic of Cancer is the maximum value 1, the value range of δ is [1, 2], δ=1 represents a leap year, and δ=2 represents a common year.

[0112] When the sunlight deflection angle β, the effective area S epv = S pv cosβ, so the energy collected by the solar energy supply model is shown in formula (12).

[0113]

[0114] However, the present application proposes an energy collection method for adjusting the angle of the photovoltaic panel by tracking the solar altitude angle, so that the photovoltaic panel is always perpendicular to the sunlight, and the corresponding energy TE da Output as shown in formula (13). In the energy distribution strategy, a remaining energy proportion driven distribution mechanism is adopted, and nodes with lower energy are preferentially supplemented to reduce the energy variance of the nodes and improve the network energy balance.

[0115] TE da = H0S pv = E e (13)

[0116] ③Vibration energy model establishment

[0117] Piezoelectric vibration energy harvesters have become an important solution for harvesting environmental vibration energy due to their simple structure, immunity to weather conditions, and ease of miniaturization. They convert environmental vibrations into electrical energy based on the mechanical-to-electrical energy conversion properties of piezoelectric materials. Existing research has made some progress in alleviating energy constraints in tunnels, with various energy supply models proposed from aspects such as structural design, modeling, and parameter optimization. However, most of these models are structurally complex and computationally expensive. Considering that the stable noise generated by vehicles passing through tunnels can excite structural vibrations, this invention uses this noise as an energy source to further improve energy harvesting efficiency. Specifically, the frequency of vehicle noise in tunnels is concentrated in the 100–1200 Hz range, exhibiting a bimodal characteristic, while train noise frequencies are even higher, reaching 20–5000 Hz, mainly concentrated above 800 Hz. Related research has verified the energy conversion capability of sensors in this frequency band. This invention combines existing methods to construct an energy harvesting model based on noise-induced vibrations and applies it to TWSN cluster routing optimization to enhance the network's self-powering capability and overall performance.

[0118] The energy that the sensor can acquire when a vehicle passes is shown in Equation (14). Here, U represents voltage, I represents current, and t represents time. Assessing the total energy acquisition requires understanding the traffic flow characteristics within the tunnel. Traffic flow typically exhibits diurnal fluctuations and peak / off-peak characteristics, and is affected by weather, holidays, and unforeseen events. To more accurately simulate actual traffic flow, this invention uses a Poisson distribution to construct a basic flow model and introduces Gaussian noise and an accident attenuation factor to enhance the characterization of randomness and suddenness. The instantaneous traffic flow Q(t) at time t is shown in Equation (15).

[0119]

[0120] Where P(t) is a random variable P(t) ~ Poisson(λ(t)) following a Poisson distribution with parameter λ(t), and λ(t) represents the average traffic flow at a certain moment, which is affected by the peak period λ. peak and trough period λ offpeak The influence of is shown in equation (16). G(t) is a function that follows a mean of 0 and a variance of σ. 2 The normal distribution G(t) ~ N(0,σ) 2 ), The term represents the flow reduction caused by a sudden event, where ζ is the event impact factor, representing the percentage reduction in flow during the event. t represents the accident decay rate and indicates the recovery speed. a This indicates the time of the accident. This method can more realistically reflect the traffic flow characteristics within the tunnel, improving the applicability of traffic flow simulation.

[0121]

[0122] (b) Setup phase

[0123] After the network initialization, the protocol enters the periodic operation mode, each round including the setup phase and the steady phase. In the setup phase, the BS performs the key tasks such as cluster structure construction, CH selection and inter-cluster routing optimization based on the information obtained in the network initialization phase, to ensure the efficiency of data transmission and the balanced distribution of network energy. The setup phase of the DETCR algorithm mainly includes three parts, one is decoding, two is to select Optima-CH by MOCTCM, and to determine Optima-RN based on RN objective function, three is to form the optimal cluster and the optimal inter-cluster routing based on Optima-CH and Optima-RN. DETCR protocol measures the running time of TWSN through rounds. In order to better combine the two energy supply models, SESM and VEM, with TWSN, the step size of SESM and VEM is set to 0.5h, which corresponds to a round in TWSN.

[0124] ①Decoding

[0125] Figure 3 The decoding of selecting CH and RN in TWSN is given. TWSN has M partitions, containing N sensor nodes, which communicate with BS through CH and RN. There are B CHs and K RNs in the decoding space of TWSN. Each partition in TWSN is represented by item P i , first the nodes whose residual energy is higher than the average residual energy in the partition are selected as CCH, and the CCH set is represented by item CC i , item C i represents CH, R i represents RN. The jth partition P j in TWSN contains H i nodes, HC i CH nodes, and HR i RN, where Then the HC j CH with the best fitness is selected from the CCH according to the objective function, and finally the HR j RN is selected from the non-CH nodes according to the RN objective function. The selected CH nodes and RN have better robustness than other nodes.

[0126] ②CTCM

[0127] CTCM algorithm is proposed by Chen et al., which is inspired by the behavior mechanism of ancient tribes competing for resources and members cooperating. The algorithm simulates the dynamic competition between tribes and the cooperation between members, and builds an optimization framework that takes into account global exploration and local development. CTCM performs outstandingly in convergence speed and stability, and can effectively avoid local optimal trap.

[0128] Step 1 Initialization

[0129] Assume that the population size of a primitive society is p, the number of tribes is n, and a tribe has m members. Then the initial position X of the whole society is shown in equation (17), where d represents the dimension of the solution space, and lb and ub represent the lower and upper bounds of the search space, respectively.

[0130]

[0131] Each person has a velocity vector that can determine where this person will go next. The total velocity matrix V of the primitive society is shown in equation (18).

[0132]

[0133] Step 2 Member Cooperation

[0134] In a tribe, the chief is usually responsible for managing and planning the future development of the tribe. Most members follow the plans and instructions of the chief, but each member also has his or her own personal ideas, causing their loyalty to fluctuate over time. Over time, this loyalty exhibits chaotic behavior, and as the number of tribes increases, the nature of this chaos becomes more apparent. In this case, the concept of a sine chaotic map shown in equation (19) is used to characterize the change in member loyalty.

[0135]

[0136] Each person will communicate with the chief of the tribe to get his or her instructions on what to do next. He will also combine his own experience to provide an additional source of information for the next step. The velocity update is shown in equation (20).

[0137]

[0138] where, vm,n(t+1) represents the velocity of the mth member of the nth tribe at time t+1, fmin(t) represents the best fitness position found by this member over the entire period, x(t) represents the position at time t, fmin represents the best fitness position found by this tribe over the entire period. c1 and c2 represent the parameters for the tribe members to follow their own experience and obey the instructions of the chief, respectively. and is the chaotic loyalty of each member.

[0139] Step 3 Tribe Competition

[0140] Random conflicts occur between tribes, and at this time, weaker tribes will flee, while stronger tribes will not be affected. The velocity update is shown in equation (21).

[0141]

[0142] where, is the optimal position found by the competitor, represents the optimal fitness of the nth tribe, is the optimal fitness of the competitor, represents the tribe escape coefficient, c3 is a chaotic random factor, reveals the retreat speed. At the same time, the position update of the tribe member is as formula (22). If the updated position of the tribe member exceeds the feasible region [X min ,X max ], the speed will be mirror bounced as formula (23), and the position information is corrected.

[0143]

[0144] ③MOCTCM

[0145] In order to realize multi-objective optimization, the application introduces three key steps in CTCM, expands it into MOCTCM, and enhances its solving ability for multi-objective problems.

[0146] Firstly, the calculation and archive of non-dominated solutions. In the iteration process, each tribe generates a corresponding solution set according to the multi-objective fitness function. After completing a round of iteration, the fitness values of all tribes are compared, the non-dominated solutions are screened out, and they are stored in the non-dominated solution set to maintain the superiority and diversity of the solutions.

[0147] Secondly, local optimal solution update based on dominance relationship. The new fitness value generated after the iteration of the tribe is compared with the local optimal solution before the iteration. If the new solution dominates the original local optimal solution, it is replaced; if it does not dominate, a random function is introduced to update it with a certain probability to balance the global exploration and local development ability.

[0148] Finally, global optimal solution update based on TOPSIS. After each round of iteration, TOPSIS sorting is applied to the non-dominated solution set, and the global optimal solution Optima-CH is selected according to the closeness of the ideal solution, which is used to guide the next round of iteration to improve the optimization convergence and the quality of the solution.

[0149] After the election of Optima-CH is completed, the fitness value of the non-CH node in the region is calculated, and the dynamic weighting calculation of the comprehensive fitness is carried out by using the coefficient of variation method to determine Optima-RN. Through this optimization strategy, the stability of the data transmission path is ensured, and the network load is balanced.

[0150] After CH and RN are determined, the BS supplies energy to the entire network according to the SESM and VEM, and optimizes the energy distribution strategy to improve energy utilization efficiency. To this end, the present application improves the traditional uniform distribution mode, combines the residual energy state of the node, and performs differentiated energy distribution according to a certain proportion to reduce the energy variance between nodes as much as possible and delay the problem of network imbalance failure. This strategy not only enhances the adaptive ability of the network, but also provides a more stable energy basis for subsequent iterations, further optimizing the inter-cluster communication performance.

[0151] ④Optima-CH and Optima-RN

[0152] After the BS selects Optima-CH through the MOCTCM algorithm and determines Optima-RN in combination with multiple RN target functions, the Optima-CH and Optima-RN information are broadcast to the entire network through a control message. After receiving the information, the ON selects the nearest CH to join the cluster according to the distance from each CH in the corresponding partition, thereby completing the construction of the cluster. Subsequently, the BS allocates time slots to the ON and CH based on the TDMA mechanism, and broadcasts the time slot allocation information to the ON, CH and RN to ensure the orderliness of data transmission and avoid transmission conflicts. During data transmission, the nodes in the cluster transmit data to the CH through single-hop mode, while the CH transmits data to the BS through multi-hop mode, achieving efficient information aggregation and transmission.

[0153] (c) Steady state phase

[0154] When the optimal cluster structure and optimal routing are established, the network enters the steady state phase. In this phase, the ON collects data and transmits it to the CH according to the TDMA time slots allocated by the BS, and enters sleep mode during the non-transmission period to reduce data conflicts and energy consumption.

[0155] After receiving the data from the ON, the CH first performs fusion processing on the data to reduce redundant information and improve transmission efficiency. Subsequently, the CH transmits the data to the BS through the optimal inter-cluster routing using the CSMA / CA protocol, ensuring reliable data transmission.

[0156] The steady state phase continues until the end of the current round, after which the network re-enters the setup phase of the next round to adapt to the dynamic adjustment of the topology and node energy state.

[0157] Figure 4 Flowchart of the present application.

[0158] Three, experimental verification of the present application

[0159] MATLAB R2020b is used for simulation experiments in this paper. To verify the adaptability of the algorithm under different network sizes, two experimental scenarios are designed, and the parameters are shown in Table I.

[0160] 1) Network lifetime

[0161] Network lifetime is an important indicator to measure the performance of TWSN, which can comprehensively reflect the running stability and sustainability of the network under resource-constrained conditions. A longer lifetime usually indicates that the adopted algorithm has good performance in energy consumption control and load balancing, which helps to improve network stability, guarantee the continuity of data transmission and the integrity of data collection, reduce maintenance costs, and enhance the application value and scalability of TWSN system in practical deployment environment. Figure 5 The network lifetime of five algorithms in two scenarios is shown.

[0162] From the results in the figure, the lifetime of EMOGJO, ESR-HAC and CTCM algorithms is generally shorter. The main reason is that there are deficiencies in the CH selection and optimization strategy design. For example, the number of CH in EMOGJO is determined by a random strategy, resulting in large fluctuations in network structure; the objective function of ESR-HAC is relatively simple, lacking effective integration of key indicators; CTCM does not introduce a reasonable multi-objective optimization strategy, resulting in uneven distribution of indicator weights in the objective function, making it difficult to achieve effective coordination of energy and communication performance. These problems limit the performance of the algorithm in energy balance scheduling and network stability.

[0163] Table I Experimental scenario parameter settings

[0164]

[0165] The parameters of three-dimensional TWSN network, MOCTCM, SESM and VEM model are shown in Table II.

[0166] Table II Experimental parameters

[0167]

[0168] In comparison, TCR and DETCR algorithms perform better in the lifetime indicator. TCR integrates the spatial structure characteristics of the 3D-SM-STT model in the clustering process, and improves the rationality of the cluster structure through regional division. When selecting CH and RN, the algorithm considers multiple parameters such as node distance, residual energy and historical selection times, and uses a multi-objective optimization strategy to make dynamic decisions on node roles, further optimizing the cluster structure and energy utilization efficiency, thus effectively delaying node failure and significantly improving network lifetime. Based on the framework of TCR, DETCR further introduces solar and vibration energy harvesting mechanisms to provide external energy supplement for low-energy nodes, effectively alleviating the early failure problem caused by node energy consumption differences, further enhancing the continuity and stability of network operation. Figure 6The schematic diagram of the energy supply mechanism of solar energy and vibration energy. The time step of energy supply is 0.5h. Since the solar energy supply is affected by sunrise and sunset time, solar elevation angle, etc., the daily energy supply is different and the energy supply presents periodic changes, Figure 6 (a) shows the total amount of solar energy supply throughout the life cycle. The vibration energy supply is affected by the daily traffic volume, Figure 6 (b) shows the total amount of vibration energy supply throughout the life cycle.

[0169] 2) Energy consumption

[0170] In order to evaluate the performance of each algorithm in terms of node energy consumption, statistical analysis of the residual energy of nodes in the network was conducted. Figure 7 The residual energy of the network in scenario 1 and scenario 2 for the five algorithms is shown. The results show that the energy consumption of TCR algorithm in scenario 1 is better than EMOGJO and ESR-HAC, and similar to CTCM; in scenario 2, it is overall superior to the three comparison algorithms. In addition, the energy consumption performance of DETCR algorithm throughout the life cycle is always significantly better than other algorithms. The reason why the proposed algorithm has better energy consumption performance is that: first, the Optima-CH number is calculated according to the TWSN model; second, combined with network partition, the CH and RN in each partition are reasonably allocated, thus effectively avoiding the energy consumption caused by long-distance communication. CTCM performs similarly to TCR in scenario 1 because it also uses the partition strategy and Optima-CH number setting proposed in this paper. However, due to the unreasonable weight allocation in the objective function, although the residual energy of the nodes is high, there is still a problem of early failure of some nodes.

Claims

1. A tunnel dual-energy clustering routing method based on multi-objective optimization, characterized in that, The specific implementation steps of this method are as follows: Step 1: Establish a three-dimensional spatial model of the single-tube tunnel: The constructed three-dimensional spatial model of the tunnel, the single tube tunnel (3D-SM-STT model), is abstracted as follows: A cuboid with a radius of A semi-cylindrical cavity; Photovoltaic panels are installed in the tunnel entrance and exit areas, and power is supplied through electromagnetic wave penetration or magnetic induction coupling energy transfer non-contact power supply technology, which effectively achieves stable power supply for high-power sensors embedded in the structure. Step 2: Node Deployment: During the tunnel construction phase, sensor nodes are deployed into the 3D spatial model; during node deployment, low-difference sequences are used to initialize the node positions in conjunction with the tunnel structure; Step 3: Network initialization phase. Network initialization is performed only once after node deployment is completed to ensure normal network operation. This phase includes network partitioning and the establishment of a dual-energy supply model based on variable time interval illumination tracking and vehicle frequency drive. The dual-energy supply model includes: a solar energy supply model (SESM) and a vibration energy model (VEM). The network partitioning adopts a geographically based partitioning strategy; The solar energy supply model SESM employs an energy harvesting method that adjusts the angle of photovoltaic panels by tracking the solar altitude angle. The vibration energy model (VEM) is based on the frequency characteristics of vehicle noise and its electrical response, and simulates vehicle frequencies under multiple factors to achieve efficient vibration energy acquisition. Both models introduce an energy allocation balancing mechanism to improve energy utilization efficiency and extend network lifetime; Step 4: Setup Phase. Based on the information obtained during the network initialization phase, the Base Station (BS) first completes the election of Cluster Head, Cluster Leader (CH), and Receiver (RN). Then, it constructs the cluster structure with the selected cluster head as the core, forming a hierarchical network topology by dividing the nodes into clusters. On this basis, the BS further performs inter-cluster routing optimization, rationally configuring multi-hop forwarding paths between cluster heads to improve data transmission efficiency and achieve a balanced distribution of network energy consumption. Step 5: In the steady state phase, ordinary nodes collect data and transmit data to the CH according to the Time Division Multiple Address (TDMA) time slots allocated by the BS. During non-transmission periods, they enter sleep mode to reduce data conflicts and energy consumption.

2. The tunnel dual-energy clustering routing method based on multi-objective optimization according to claim 1, characterized in that, In step 4, the BS performs cluster structure construction, CH selection, and inter-cluster routing optimization based on the TCR and DETCR algorithms: The TCR algorithm mainly consists of three parts: first, decoding: decoding provides the basis for the subsequent selection of CH and RN; second, using MOCTCM to elect Optima-CH and determining Optima-RN based on the RN objective function; during Optima-CH election, the MOCTCM algorithm combines various objective functions: remaining energy... Average distance between nodes within a partition Distance from node to RN The optimal solution is determined; and when electing the Optima-RN, the objective function is determined by the squared distance between the RN and the CH. Energy consumption RN counting control The objective function values ​​of each node are calculated, and the coefficient of variation method is used to dynamically assign weights to each objective function to determine the optimal solution. Thirdly, optimal cluster and inter-cluster routes are formed based on Optima-CH and Optima-RN. After Optima-CH and Optima-RN are determined, the BS broadcasts a message, and Ordinary Nodes (ONs) determine the cluster structure based on the received information using distance factors. Subsequently, the BS allocates time slots to each node to complete conflict-free data transmission. Nodes within a cluster send data to the CH via a single-hop method, while the CH transmits the data to the BS level by level via a multi-hop method. The DETCR algorithm adds SESM and VEM power supply models to the TCR algorithm, significantly improving network lifetime. The specific objective functions mentioned above are: 1) Remaining energy ; After network initialization, the Base Station (BS) can calculate the remaining energy of each node. Compared to a single remaining energy value, by combining the global average energy and distribution information, the weight of node energy in the selection of the Chosen Center (CH) can be dynamically adjusted to adapt to network scenarios with uneven energy distribution. The objective function is as follows: ； in It is a node Remaining energy, unit: J It is the average energy of all nodes within the partition, in J. , These are the maximum and minimum remaining energy values ​​within the partition, respectively, in J. 2) Average distance between nodes within the partition ; The average distance between nodes within a partition is one of the key factors affecting intra-cluster data transmission. This invention comprehensively considers distance, distance distribution uniformity, and inverse distance compensation, with the objective function shown below: ； in Represents a node To the node Distance between nodes, in meters (m). Average distance to other nodes within the partition Unit: m This indicates the total number of points within a partition. 3) Distance from the node to the RN ; The distance from a node to the RN is one of the key factors affecting inter-cluster data transmission. Given that the RN is not yet determined during the CH selection phase, and its selection range lies within the next partition, this invention defines the distance from a node to the center of the next partition as the distance from the node to the RN when constructing the objective function. Simultaneously, by dynamically adjusting weights based on intra-cluster distances, nodes in sparse regions are prevented from becoming CHs, thereby improving the efficiency of multi-hop communication. The objective function is shown below; where... It is a node Distance to RN, in meters. It is the maximum distance between nodes within the partition, in meters. ； 4) The squared distance between RN and CH ; The selection of the RN plays a crucial role in optimizing data transmission efficiency and network energy balance. Since node energy consumption is closely related to transmission distance, and transmission energy consumption exhibits a power-law relationship with distance, the squared distance between the RN and the CH is used as the optimization objective. The objective function is shown below, where... Represents a node Distance to CH; ； 5) Energy consumption ; Energy balance is a core mechanism for ensuring network sustainability and stability; this invention addresses this by combining the remaining energy of nodes. Unit: J and transmission energy consumption The ratio and variance of (unit: J) Construct the objective function: ； 6) RN counting control ; Frequent selection of the control node as the RN can prevent node failure due to excessive power consumption; this invention sets an initial value for each node. The value of a node increases incrementally in each subsequent round, with a higher probability of being selected as an RN as the node's value approaches 1. Once a node is selected as an RN, its value is restored to its initial value in the next round. The objective function is shown below: 。 3. The tunnel dual-energy clustering routing method based on multi-objective optimization according to claim 2, characterized in that, The MOCTCM algorithm introduces three key steps into CTCM to enhance its ability to solve multi-objective problems, specifically: First, the calculation and archiving of non-dominated solutions: During the iteration process, each tribe generates a corresponding solution set according to the multi-objective fitness function; after completing one round of iteration, the fitness values ​​of all tribes are compared, non-dominated solutions are selected and stored in the non-dominated solution set to maintain the superiority and diversity of solutions. Secondly, local optimal solution update based on dominance relationship: For the new fitness value generated after tribe iteration, it is compared with the local optimal solution before iteration. If the new solution dominates the original local optimal solution, it is replaced; if it does not dominate, a random function is introduced to update with a certain probability, so as to balance the global exploration and local development capabilities. Finally, the global optimal solution is updated based on TOPSIS: after each iteration, TOPSIS is applied to sort the non-dominated solution set, and the global optimal solution, i.e., Optima-CH, is selected based on the proximity to the ideal solution to guide the next iteration, thereby improving the optimization convergence and the quality of the solution.

4. The tunnel dual-energy clustering routing method based on multi-objective optimization according to claim 1, characterized in that, In the solar energy supply model SESM described in step 3, the photovoltaic panels are always perpendicular to the sunlight, and the corresponding energy... The output is as follows: ； in, The area of ​​the photovoltaic panel is expressed in m². 2 , This represents the intensity of sunlight directly hitting a photovoltaic panel, measured in Lux. , represents the energy under vertical illumination, unit: J; In the vibration energy model (VEM), the energy acquired by the sensor when a vehicle passes is shown below; where... Power is expressed in watts (W). Voltage, unit: V; Represents electric current, unit: A; Time is expressed in seconds (s). Assessing total energy requires understanding the traffic flow characteristics within the tunnel. A Poisson distribution is used to construct the basic flow model, and Gaussian noise and an accident attenuation factor are introduced to enhance the characterization of randomness and suddenness. Instantaneous traffic flow at the location As shown below: ； ； in, It is a parameter that follows random variables with Poisson distribution , This represents the average traffic flow at a certain moment, affected by peak hours. Units: veh / min and trough period The effect of (unit: veh / min) is shown below; It follows a mean of 0 and a variance of normal distribution , This indicates the rate of decrease in traffic caused by a sudden event. This is the accident impact factor, representing the percentage reduction in flow rate during an accident. The accident decay rate represents the recovery speed; Indicates the time of the accident; 。 5. The tunnel dual-energy clustering routing method based on multi-objective optimization according to claim 1, characterized in that, The data acquisition method described in step 5 is as follows: the ordinary node ON first acquires data and transmits data to CH according to the TDMA time slot allocated by BS, and enters sleep mode during non-transmission periods to reduce data conflicts and energy consumption; after receiving the data from ON, the CH node first performs data fusion processing to reduce redundant information and improve transmission efficiency. Subsequently, CH uses optimal inter-cluster routing and the CSMA / CA protocol to transmit data to BS via multiple hops, ensuring reliable data transmission.

Images