Wireless communication based intelligent monitoring and management system for highway infrastructure

Abstract

The application relates to the technical field of intelligent traffic and infrastructure monitoring, and particularly discloses a highway infrastructure intelligent monitoring and management system based on wireless communication. The system comprises a wireless sensor node cluster, a relay coordination node and a network management server. The system combines a hybrid routing mechanism of distributed greedy forwarding and centralized optimal path calculation, and dynamically adjusts a routing strategy according to data service priorities, so that balanced distribution of network load and energy consumption is realized, low-delay and high-reliability transmission guarantee is provided for high-priority service data packets, the network life is prolonged, and the timeliness of key information reporting is improved. The system combines a hybrid routing mechanism of distributed local decision and centralized global optimization, and fundamentally solves the problems of energy consumption hotspots and network segmentation caused by static networking.

Description

Technical Field

[0001] This invention belongs to the field of intelligent transportation and infrastructure monitoring technology, specifically relating to an intelligent monitoring and management system for highway infrastructure based on wireless communication. Background Technology

[0002] In the field of intelligent transportation infrastructure, this invention utilizes Internet of Things (IoT) technology to deploy a large number of sensor nodes, enabling real-time perception and data collection of key information such as highway structural health, traffic flow, and environmental conditions. This provides crucial support for highway operation and maintenance management and safety early warning. The intelligent monitoring and management system for highway infrastructure based on wireless communication aims to aggregate dispersed monitoring data to a central platform for processing and analysis by constructing a stable and efficient data transmission network.

[0003] Existing technologies typically employ static or semi-static wireless sensor network topology strategies, with nodes periodically reporting data and maintaining routes at a fixed power level. This approach faces significant challenges in applications where nodes are linearly distributed along highways and have limited energy: the relatively fixed network topology makes it difficult to adapt to changes caused by nodes dynamically failing due to battery depletion or the addition of replacement nodes. This leads to relay nodes on some critical paths prematurely exhausting their energy due to excessive forwarding tasks, resulting in network fragmentation and monitoring blind spots. Furthermore, all nodes adhere to a uniform communication and sleep strategy, making it impossible to dynamically adjust based on their actual remaining energy, data priority, and local network load, resulting in uneven energy distribution across the network and accelerating network lifespan degradation.

[0004] Furthermore, highway monitoring operations are characterized by diverse data. Different sensors generate structural stress, crack images, or temperature and humidity data, each with varying requirements for real-time and reliable transmission. Existing systems lack intelligent sensing and differentiated processing mechanisms for the characteristics of service data packets, still employing a non-discriminatory data transmission strategy. This not only fails to guarantee low-latency reporting of critical early warning information but also exacerbates the inefficient consumption of network resources.

[0005] Therefore, there is an urgent need for an intelligent networking method that can achieve energy balance and service self-adaptation, in order to extend the overall lifespan of the monitoring network and improve the guarantee capability of critical data transmission. Summary of the Invention

[0006] This invention provides an intelligent monitoring and management system for highway infrastructure based on wireless communication, which solves the problems of uneven energy consumption and short network life caused by static networking strategies in existing wireless sensor networks, as well as the insufficient ability to guarantee critical business information due to indiscriminate data transmission strategies.

[0007] The technical solution of this invention is a smart monitoring and management system for highway infrastructure based on wireless communication. This system includes a cluster of wireless sensor nodes deployed along the highway, relay coordination nodes deployed within the monitoring area, and a network management server located at the monitoring center. By constructing a wireless self-organizing network with dynamic energy sensing and service adaptive routing capabilities, this system achieves reliable, efficient, and differentiated transmission of monitoring data.

[0008] The wireless sensor node cluster consists of multiple heterogeneous monitoring nodes, each integrating a sensing module, a microprocessor, a wireless communication module, and an energy management module. The sensing module collects structural health data, traffic flow data, or environmental status data for highway infrastructure. The microprocessor incorporates a node status management unit and a data preprocessing unit. The node status management unit continuously monitors and records the node's remaining energy percentage, geographical location information, and link quality indicators with its neighboring nodes. The data preprocessing unit extracts features and prioritizes the raw data collected by the sensing module, marking service data packets as high-priority or ordinary-priority service data packets based on the service type of the data and whether its value exceeds a preset security threshold.

[0009] The relay coordination nodes are distributed within the sensor node cluster. Their hardware configuration is superior to that of ordinary monitoring nodes, possessing stronger data processing capabilities and a more abundant energy supply. Each relay coordination node operates a regional network coordinator, which periodically broadcasts beacon frames containing its own identity and current load status, and maintains a dynamic local network topology table. The local network topology table records the identity, remaining energy percentage, geographical location, and most recently reported link quality of all reachable monitoring nodes within its communication coverage area.

[0010] The network management server connects to the relay coordination nodes via a wide area communication network and runs a global network optimization engine and a service policy manager. The global network optimization engine receives and integrates local network topology tables from all relay coordination nodes to construct a global network energy situation map. The service policy manager has pre-set transmission quality requirement policies for service data packets of different service types, including maximum allowable end-to-end latency, minimum received signal strength requirements, and maximum number of retransmissions.

[0011] The core of the system lies in its dynamic routing decision-making mechanism, which is jointly implemented by a distributed routing algorithm deployed in the microprocessor of each monitoring node and a centralized path calculation service deployed in the network management server. For a service data packet to be sent, the source node first calls its local distributed routing algorithm to perform initial path selection.

[0012] The distributed routing algorithm is executed as follows: The source node reads the priority flag of the service data packet to be sent. If the service data packet is a normal priority service data packet, the algorithm starts a greedy forwarding strategy based on local information. The source node selects the neighbor node with the highest remaining energy percentage and link quality above a preset threshold from its neighbor table as the next-hop relay target. The neighbor table is updated periodically through neighbor discovery and link probe protocols. If the service data packet is a high-priority service data packet, or if the source node finds that the remaining energy of all neighbor nodes is below the energy alarm threshold when executing the greedy forwarding strategy, the source node sends a path calculation request to the nearest relay coordinating node. This path calculation request encapsulates the identity of the source node and the destination node, the priority of the service data packet, and the real-time status information of the source node.

[0013] Upon receiving a path calculation request, the relay coordinating node forwards it to the network management server. The network management server's global network optimization engine responds to this request and performs centralized optimal path calculation.

[0014] The centralized optimal path calculation process is as follows: The global network optimization engine uses the current global network energy situation map as its base data. The calculation process employs an improved Dijkstra's shortest path algorithm, but reconstructs the definition of path cost from the traditional hop count or latency to a multi-dimensional weighted cost function. The multi-dimensional weighted cost function consists of three linearly weighted components: the first component is the sum of the reciprocals of the remaining energy of all relay nodes on the path, used to guide the path to avoid low-energy nodes; the second component is the estimated end-to-end transmission latency of the path, which is estimated based on historical link quality data and the size of service data packets; the third component is the hop count of the path.

[0015] For high-priority service packets, the weight coefficient of the second component in the multi-dimensional weighted cost function, namely the estimated latency, is set to the maximum value, while the weight coefficient of the first component, the energy factor, is correspondingly reduced to ensure that the calculated path meets the low-latency requirements. For ordinary-priority service packets or energy-assisted requests, the weight coefficient of the first component, the energy factor, is set to the maximum value to prioritize the balanced use of network energy. After the global network optimization engine calculates one or more candidate paths that satisfy the minimum cost function, it encapsulates the optimal path information in the path calculation response and sends it to the source node via the relay coordination node.

[0016] After receiving the path information, the source node strictly follows the path sequence for multi-hop forwarding of service data packets. During forwarding, each relay node at each hop must embed its current remaining energy value into a specific field in the service data packet header before forwarding the data packet. After receiving the service data packet, the destination node reports the remaining energy information of each node along the path to the network management server along with the service data packet for real-time updates of the global network energy situation map.

[0017] Furthermore, the system's energy management module integrates a dynamic power consumption control strategy. This strategy dynamically adjusts the operating mode and transmit power of the wireless communication module based on the node's current network role and service load. When a node is selected as a path node for high-priority service data packets, its wireless communication module maintains a high-power state of continuous listening and uses standard transmit power to ensure link reliability. When a node is not selected or is only processing ordinary services, its wireless communication module adopts a periodic sleep-wake mechanism with an extremely low duty cycle. After waking up, it dynamically calculates and uses the minimum transmit power that just meets the link quality requirements based on the actual distance to the next-hop node, thereby significantly reducing the energy consumption caused by idle listening and power redundancy.

[0018] In one embodiment of the present invention, the relay coordination node also undertakes local data fusion tasks. For ordinary priority service data packets with spatiotemporal correlation from multiple monitoring nodes within its coverage area, such as environmental temperature and humidity data of adjacent road segments, the regional network coordinator calls a data fusion algorithm to aggregate the data before forwarding it upstream, removing redundant information and only reporting the fused feature values ​​or abnormal data, thereby effectively reducing the total amount of data transmission in the network.

[0019] As one embodiment of the present invention, the business policy manager of the network management server supports dynamic policy injection. The monitoring center administrator can issue temporary business priority remapping rules to the entire network through the business policy manager based on different stages of highway operation or special weather events. For example, during a rainstorm warning, the data priority of all hydrological sensors can be temporarily increased to high priority to ensure that disaster warning information can be reported immediately through a low-latency path.

[0020] Furthermore, the system introduces a reputation-based node cooperation incentive mechanism. The network management server maintains a cooperation reputation value for each monitoring node. This cooperation reputation value is initially set to 100. Each time a node successfully relays a service data packet for another node, its cooperation reputation value increases by 2. If a node refuses a forwarding request when it has the capability to forward, or if its forwarding failure rate exceeds a preset threshold, its cooperation reputation value decreases by 5. The greedy forwarding strategy in the distributed routing algorithm uses the node's cooperation reputation value as a positive weighting factor when selecting the next hop. Nodes with higher reputation values ​​are more likely to be selected as the next hop under the same remaining energy and link quality conditions. This mechanism encourages nodes to participate in cooperative forwarding, suppresses selfish behavior, and thus enhances the overall robustness and lifespan of the network.

[0021] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0022] 1. This invention fundamentally solves the energy hotspots and network segmentation problems caused by static networking through a hybrid routing mechanism that combines distributed local decision-making with centralized global optimization. The system can perceive the energy status of the entire network in real time and dynamically guide data traffic from low-energy areas to high-energy areas through a path cost function with the remaining energy of nodes as the core weighting factor. This achieves a uniform spatial distribution of network load and energy consumption, significantly delaying network performance degradation caused by premature failure of key relay nodes, thereby greatly extending the effective lifespan of the entire highway monitoring wireless sensor network.

[0023] 2. This invention innovatively couples business data prioritization with routing calculation, establishing a business-adaptive differentiated transmission guarantee system. The system can identify and provide low-latency, highly reliable routing paths for high-priority critical early warning data, while prioritizing energy balance for ordinary monitoring data. This intelligent resource allocation based on business needs ensures that, under limited network resource conditions, the most important safety information can be transmitted to the monitoring center preferentially and quickly, greatly improving the timeliness and reliability of early warning of safety risks to highway infrastructure.

[0024] 3. This invention implements a refined dynamic power consumption control strategy at the node level, precisely matching transmission power and operating status according to actual communication needs, effectively eliminating the energy waste caused by traditional fixed power strategies. Simultaneously, the local data fusion function introduced through relay coordination nodes significantly reduces redundant data transmission in the network, lowering the overall network communication energy consumption at its source. These two measures work synergistically to further optimize the system's energy efficiency from both single-point energy saving and overall network load reduction perspectives.

[0025] 4. The reputation-based node cooperation incentive mechanism introduced in this invention links a node's forwarding behavior with its long-term interests, constructing a positive-cycle network cooperation ecosystem. This node cooperation incentive mechanism automatically rewards cooperative nodes and punishes selfish nodes through algorithms, making participation in data forwarding a rational choice for nodes, thereby spontaneously maintaining network connectivity and service capabilities, and enhancing the system's autonomous management and long-term stable operation capabilities in heterogeneous node and energy-constrained environments. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the overall technical solution architecture of the present invention;

[0027] Figure 2 This is a schematic diagram illustrating the core principle framework of the dynamic routing decision mechanism (hybrid routing) in this invention;

[0028] Figure 3 This is a flowchart illustrating the data processing and status management logic of the monitoring nodes in this invention.

[0029] Figure 4 This is a logical flowchart of the network management server performing centralized optimal path calculation in this invention;

[0030] Figure 5 This is a schematic diagram of the interaction relationship and data flow of the multi-level system (monitoring node, relay coordination node, network management server) in this invention. Detailed Implementation

[0031] Example 1: Please refer to the appendix Figure 1 To be continued Figure 5 This invention provides an intelligent monitoring and management system for highway infrastructure based on wireless communication, the overall architecture of which is as follows: Figure 1 As shown, the system consists of three main parts: a cluster of wireless sensor nodes deployed along the highway, relay coordination nodes distributed within the monitoring area, and a network management server located in the monitoring center. By constructing a wireless self-organizing network with dynamic energy sensing and service adaptive routing capabilities, it achieves efficient, reliable, and differentiated collection and transmission of multi-dimensional information such as the structural health of highway infrastructure, traffic flow status, and environmental parameters.

[0032] Please refer to the attached document. Figure 1The wireless sensor node cluster consists of several heterogeneous monitoring nodes, each integrating a sensing module, a microprocessor, a wireless communication module, and an energy management module. The sensing module, depending on the functional requirements of its deployment location, can be configured as a structural strain sensor, vibration accelerometer, temperature and humidity sensor, road surface water film thickness detector, or traffic flow radar, etc., to collect real-time structural health data, traffic flow data, or environmental status data for critical infrastructure such as highway bridges, slopes, tunnels, and roadbeds. The microprocessor, as the core control unit of the monitoring node, incorporates a node status management unit and a data preprocessing unit. The node status management unit continuously monitors and records the node's remaining energy percentage, geographical location information obtained through the GPS module, and link quality indicators established with neighboring nodes through a periodic link probing protocol. The link quality indicator is quantified using a weighted average of the received signal strength indicator and packet reception rate, with a value ranging from 0 to 100; a higher value indicates a more stable link.

[0033] The data preprocessing unit performs feature extraction and priority assignment on the raw data collected by the sensing module. Feature extraction includes sliding window statistics, frequency domain transformation, or outlier detection on time-series data; priority assignment is based on the service type of the data and whether its value exceeds a preset safety threshold. For example, when the bridge strain value exceeds 95% of the design safety limit, the service data packet is marked as a high-priority service data packet; while normal environmental temperature and humidity readings within the normal range are marked as ordinary-priority service data packets. All service data packets, when encapsulated, include a priority flag field, source node identifier, destination node identifier (usually the nearest relay coordination node or network management server), and a timestamp in their header.

[0034] Each monitoring node also maintains a dynamically updated neighbor table. The neighbor table is maintained through a periodically executed neighbor discovery and link probe protocol, with a protocol period set to 30 seconds. During each probe period, a node broadcasts a probe request frame to all potential neighbors within its wireless communication coverage area. Receiving a response, a neighboring node returns its identity, current remaining energy percentage, and link quality feedback. The monitoring node updates the corresponding entry in the neighbor table accordingly and removes entries from nodes that have not responded for three consecutive periods. Each record in the neighbor table includes the neighboring node's identity, remaining energy percentage, geographical coordinates, link quality metrics, and a timestamp of the most recent successful communication.

[0035] Please continue to refer to the appendix. Figure 1The relay coordination nodes are distributed within the sensor node cluster. Their hardware configuration is significantly higher than that of ordinary monitoring nodes. Specifically, they employ embedded processors with a main frequency of no less than 800 MHz, are equipped with no less than 512 megabytes of RAM, feature a dual-band wireless communication module (supporting 2.4 GHz and 5.8 GHz bands), and are connected to a rechargeable lithium battery pack with a capacity of no less than 20 Ah, thus possessing stronger data processing capabilities and more abundant energy supply. Each relay coordination node operates an area network coordinator, which broadcasts a beacon frame containing its own identity and current load status every 10 seconds. The load status is calculated by combining the length of the currently processed service data packet queue, CPU utilization, and wireless channel utilization, with a value ranging from 0 to 100.

[0036] The regional network coordinator also maintains a dynamic local network topology table. This table records the identity, remaining energy percentage, geographical coordinates, and most recently reported link quality indicators of all reachable monitoring nodes within its communication coverage area. The update mechanism for the local network topology table is as follows: when a relay coordinating node receives a service data packet or probe response reported by any monitoring node, it extracts the status information and updates the corresponding entry. If a monitoring node fails to report actively for five consecutive beacon cycles and does not respond to an active query initiated by the relay coordinating node, it is marked as "suspected offline," but is still retained in the topology table for subsequent path calculation reference.

[0037] The network management server is deployed in a dedicated server room at the highway monitoring center, establishing stable connections with all relay coordination nodes via fiber optic or 4G / 5G wide area communication networks. The network management server runs two core software modules: a global network optimization engine and a business policy manager. The global network optimization engine receives and integrates local network topology tables from all relay coordination nodes to construct a global network energy situation map. This global network energy situation map is based on a geographic information system, mapping each monitoring node and relay coordination node to vertices in the map. Vertex attributes include their remaining energy percentage, geographical location, cooperative reputation value, and current role status; edges represent effective communication links between nodes, with edge attributes including link quality indicators and estimated transmission latency. The business policy manager has pre-defined transmission quality requirement policies for different types of business data packets, which are stored in the server database as a policy rule base.

[0038] The policy rule base defines the transmission quality parameters for various services (such as structural safety early warning, traffic congestion alert, meteorological disaster early warning, and routine environmental monitoring), including the maximum permissible end-to-end latency (in milliseconds), the minimum received signal strength requirement (in decibels and milliwatts), and the maximum number of retransmissions (ranging from 1 to 5). For example, the maximum permissible end-to-end latency for structural safety early warning services is set at 500 milliseconds, the minimum received signal strength requirement is -85 decibels and milliwatts, and the maximum number of retransmissions is 3; while the maximum permissible end-to-end latency for routine environmental monitoring services can be relaxed to 5000 milliseconds, the minimum received signal strength requirement is -95 decibels and milliwatts, and the maximum number of retransmissions is 1.

[0039] The system's dynamic routing decision-making mechanism is its core technology. This mechanism is achieved collaboratively by a distributed routing algorithm deployed in the microprocessor of each monitoring node and a centralized path calculation service deployed in the network management server. Its core principle framework is as follows: Figure 2 As shown. For a service data packet to be sent, the source node first calls its local distributed routing algorithm to select an initial path.

[0040] The execution process of the distributed routing algorithm strictly follows the following logical flow. The source node first reads the priority flag field in the header of the service data packet to be sent. If the service data packet is marked as a normal priority service data packet, the algorithm initiates a greedy forwarding strategy based on local information. Under this strategy, the source node traverses its neighbor table, filtering out a set of candidate neighbor nodes with a remaining energy percentage higher than 20% and a link quality index higher than 70. If this set of candidate neighbor nodes is not empty, the node with the highest remaining energy percentage is selected as the next-hop relay target; if multiple nodes have the same remaining energy percentage, their cooperative reputation values ​​are further compared, and the one with the highest reputation value is selected. If the set of candidate neighbor nodes is empty, meaning all neighbor nodes have a remaining energy percentage lower than 20% or the link quality is substandard, the source node determines that reliable forwarding cannot be completed locally and immediately sends a path calculation request to the nearest relay coordinating node. The encapsulation format of this path calculation request includes the source node's identity, the destination node's identity (usually a network management server), the service data packet priority, the source node's current remaining energy percentage, geographical coordinates, and a snapshot of the neighbor table.

[0041] If a pending service data packet is marked as a high-priority service data packet, the source node will directly send a path calculation request to the nearest relay coordinating node, regardless of the status of neighboring nodes. Here, "nearest" is determined by calculating the Euclidean distance between the source node and each relay coordinating node. The source node has already cached the geographical location information of all visible relay coordinating nodes from the received beacon frames during the initialization phase.

[0042] Upon receiving a path calculation request, the relay coordinating node first verifies the request's validity, including checking if the source node is registered in its local network topology table and whether the integrity checksum of the requested service data packet matches. If verification is successful, the relay coordinating node forwards the path calculation request to the network management server via the wide area network. This forwarding process employs a reliable transmission control protocol to ensure that the request is not lost.

[0043] Upon receiving a path calculation request, the global network optimization engine of the network management server immediately responds and executes centralized optimal path calculation. Its logical flow is as follows: Figure 4 As shown. The calculation process uses the current global network energy situation map as the sole data source. The global network optimization engine employs an improved Dijkstra shortest path algorithm, but reconstructs the definition of path cost from the traditional single dimension to a multi-dimensional weighted cost function. This multi-dimensional weighted cost function consists of three linearly weighted components, and its mathematical expression is as follows:

[0044] ;

[0045] in, This represents the total cost of the entire path; Indicates the first path The remaining energy percentage of each relay node, ranging from 1 to 100, is used to avoid division by zero errors when... If the value is less than 1, treat it as 1; It is the sum of the reciprocals of the remaining energy of all relay nodes on the path, and the component is used to guide the path to avoid low-energy nodes; This is the estimated end-to-end transmission delay for the path, in milliseconds. This estimated end-to-end transmission delay is calculated based on historical link quality data, current link quality metrics, service data packet size, and wireless channel bandwidth. The estimation formula is:

[0046] ;

[0047] in, For the first The theoretical bandwidth of the hop link. Its link quality indicators, For the first Average processing latency of skip nodes; This represents the number of hops in the path. These are the weighting coefficients for the three components, and the sum of the three is always 1.

[0048] The weighting coefficient allocation strategy is dynamically adjusted strictly based on the priority of service data packets. For high-priority service data packets, It was set to 0.7. Set to 0.2, Set to 0.1 to ensure the calculated path meets low latency requirements. For ordinary priority service data packets or path calculation requests triggered by energy alarms, It was set to 0.7. Set to 0.2, Set to 0.1 to prioritize the balanced use of network energy. During the calculation process, the global network optimization engine will exclude nodes with less than 10% of remaining energy, as these are considered to be nodes on the verge of failure and are not suitable for relay tasks.

[0049] After the calculation is completed, the engine selects the option that satisfies the cost function. The path with the lowest cost is selected as the optimal path. If multiple paths with the same cost exist, the one with the fewest hops is chosen. The optimal path information is encapsulated in the path calculation response, which includes a complete sequence of nodes (identification of each hop from the source node to the destination node), the estimated transmission delay for each hop, and the suggested transmit power. This path calculation response is then sent to the source node via the relay coordination node returned from the original path.

[0050] After receiving the path information, the source node strictly follows the path sequence to perform multi-hop forwarding of service data packets. During forwarding, each relay node, before forwarding the service data packet, must embed its current remaining energy value into a specific field in the service data packet header. This specific field reserves 4 bytes of space to store the remaining energy percentage value from 0 to 100 in unsigned integer form. After receiving the service data packet, the destination node (usually a relay coordination node or network management server) parses the remaining energy information of each node along the path and reports this information along with the service data packet to the network management server. The network management server uses this real-time feedback data to incrementally update the global network energy situation map, ensuring that it always reflects the true state of the network.

[0051] Furthermore, the system's energy management module integrates a dynamic power consumption control strategy, the execution logic of which is as follows: Figure 3As shown. This dynamic power consumption control strategy dynamically adjusts the operating mode and transmit power of the wireless communication module based on the node's current network role and service load. When a node is selected as a path node for high-priority service data packets, its wireless communication module will maintain a high-power state of continuous listening, with an operating current of approximately 80 mA, and will use a standard transmit power (e.g., 19 dBmW) to ensure link reliability. When a node is not selected or is only processing ordinary services, its wireless communication module adopts a periodic sleep-wake mechanism with a duty cycle of 1%, that is, it wakes up for 10 milliseconds every 1000 milliseconds to listen for the channel. After waking up, if data needs to be transmitted, the node dynamically calculates and uses the minimum transmit power that just meets the link quality requirements based on the actual distance to the next hop node. The transmit power is calculated based on the free space path loss model, and the formula is:

[0052] ;

[0053] in, For the required transmission power, For receiving sensitivity, The distance between nodes. For operating frequency, This provides an environmental attenuation margin. Through this strategy, nodes can dynamically adjust their transmit power from a fixed 19 dBmW to between 10 and 19 dBmW, thereby significantly reducing energy consumption from idle listening and power redundancy.

[0054] In one embodiment of the present invention, the relay coordination node also undertakes the task of local data fusion. Before forwarding upstream, the regional network coordinator aggregates ordinary priority service data packets with spatiotemporal correlation from multiple monitoring nodes within its coverage area. For example, for five temperature and humidity sensors deployed on the same road segment, if their reported data are synchronized in time (timestamp difference less than 5 seconds), spatially adjacent (distance less than 50 meters), and the numerical differences are less than preset thresholds (temperature difference less than 1 degree Celsius, humidity difference less than 5%), the regional network coordinator calls the data fusion algorithm to calculate their arithmetic mean as the fused feature value, and only reports the feature value and standard deviation. If the data from any sensor exceeds the normal range or differs significantly from other sensors, it is considered abnormal data, reported separately, and marked as abnormal. This mechanism can compress the original five service data packets into one or two, effectively reducing the total amount of data transmission in the network.

[0055] In another embodiment of the present invention, the business policy manager of the network management server supports dynamic policy injection. Administrators at the monitoring center can create temporary business priority remapping rules in the business policy manager through a graphical user interface. These rules specify that within a specific time period (e.g., the next 24 hours), the priority of all business data packets from a certain type of sensor (such as water level sensors deployed in low-lying road sections) will be temporarily elevated to high priority. After the rule takes effect, the business policy manager generates a policy update instruction, which is broadcast to the entire network through relay coordination nodes. Upon receiving the instruction, each monitoring node updates its local priority determination logic to ensure that during heavy rain warnings, data from all hydrological sensors can be reported via low-latency paths, thereby improving the response speed of disaster warnings.

[0056] Furthermore, the system introduces a reputation-based node cooperation incentive mechanism. The network management server maintains a cooperation reputation value for each monitoring node, which is stored in the node attributes of the global network energy situation map. The initial value of the cooperation reputation value is 100, and its update rule is as follows: whenever a node successfully relays a service data packet for another node, and the service data packet is acknowledged as received by the destination node, its cooperation reputation value increases by 2; if a node refuses a forwarding request when it is capable of forwarding (i.e., its remaining energy percentage is higher than 30% and its wireless module is active), or if its forwarding failure rate (number of failures / total number of forwards) as a relay node exceeds 10% in the last 100 forwards, its cooperation reputation value decreases by 5. The range of the cooperation reputation value is limited to between 0 and 150. The greedy forwarding strategy in the distributed routing algorithm uses the node's cooperation reputation value as a positive weighting factor when selecting the next hop. Specifically, when calculating the comprehensive score of candidate neighbor nodes, the score is... ,in The percentage of remaining energy. The normalized value of the collaborative reputation score ( Nodes with high reputation scores have a significantly higher probability of being selected as the next hop, given the same remaining energy and link quality, due to their higher overall score. This mechanism automatically rewards cooperative nodes and punishes selfish nodes through an algorithm, making participation in data forwarding a rational choice for nodes, thereby spontaneously maintaining network connectivity and service capabilities.

[0057] The multi-level interaction relationships and data flow of the entire system are as follows: Figure 5As shown, the monitoring nodes are responsible for raw data acquisition, local preprocessing, and initial routing decisions; the relay coordination nodes are responsible for local topology maintenance, data fusion, path request forwarding, and path response distribution; and the network management server is responsible for global view construction, centralized path calculation, policy management, and reputation value maintenance. This three-layer architecture works closely together through standardized data interfaces and communication protocols to form a closed-loop, adaptive intelligent monitoring and management network.

[0058] Example 2: Building upon the previous examples, this example further optimizes the deployment strategy and local data fusion algorithm for relay coordination nodes to adapt to highway monitoring scenarios with complex terrain conditions. In mountainous or canyon areas, due to severe terrain obstruction and limited wireless signal propagation, the traditional method of uniformly deploying relay coordination nodes may result in insufficient coverage in some areas. Therefore, this example introduces a relay coordination node location algorithm based on a digital elevation model.

[0059] Before system deployment, high-precision digital elevation model (DEM) data of the monitoring area is first acquired, with a spatial resolution of 5 meters. Based on this DEM, network planning software, combined with a ray tracing algorithm for radio wave propagation, simulates the signal coverage of relay coordination nodes deployed at different candidate locations. Candidate locations are preferentially selected from ridges or commanding heights with high altitudes and open views. The goal of the site selection algorithm is to minimize the total number of relay coordination nodes deployed, while ensuring that at least 95% of the monitoring nodes in the entire area are covered by relay coordination nodes. The final deployment plan will guide on-site construction personnel in accurately installing the relay coordination nodes.

[0060] Regarding data fusion, this embodiment addresses the unique characteristics of structural health monitoring data by employing a fusion algorithm based on a physical model. For example, in bridge monitoring, data from multiple strain sensors are not simply averaged but must conform to the deformation compatibility conditions of structural mechanics. The regional network coordinator incorporates a simplified finite element model. When strain data from multiple measuring points on the same cross-section are received, the model is used for consistency verification. If the deviation between the measured data and the model prediction exceeds 15%, it is determined to be a local structural anomaly. In addition to reporting the original data, a structural damage index is also added. This method enhances the physical meaning and early warning accuracy of data fusion.

[0061] Furthermore, this embodiment enhances the granularity of the dynamic power consumption control strategy. The wireless communication module no longer operates only in two modes: "high-power continuous monitoring" and "low duty cycle sleep," but introduces three power states: State 1 is high-power continuous monitoring (80 mA current), used for high-priority path nodes; State 2 is medium-power periodic monitoring (10% duty cycle, 20 mA current), used for active nodes that may be selected as path nodes in the near future; State 3 is low-power deep sleep (1% duty cycle, 5 mA current), used for nodes with no service for extended periods. Node state switching is dynamically assigned by the relay coordination node based on local service load predictions, with the prediction model based on the service arrival rate and type distribution over the past hour. This refined strategy makes energy allocation more precise, further extending network lifetime.

[0062] The above embodiments together constitute the complete technical solution of the present invention. Through multi-level and multi-dimensional innovative design, the monitoring network for highway infrastructure has achieved a comprehensive improvement in energy efficiency, business assurance, adaptability and long-term stability.

Claims

1. A highway infrastructure intelligent monitoring and management system based on wireless communication, characterized in that, include: A wireless sensor node cluster deployed along a highway, the wireless sensor node cluster consisting of multiple heterogeneous monitoring nodes, each monitoring node integrating a sensing module, a microprocessor, a wireless communication module and an energy management module; Relay coordination nodes are deployed within the monitoring area, and each relay coordination node runs an area network coordinator; The network management server located in the monitoring center is connected to the relay coordination node through a wide area communication network and runs a global network optimization engine and a service policy manager. The monitoring node's microprocessor is equipped with a distributed routing algorithm, and the network management server's global network optimization engine is equipped with a centralized path calculation service. The two work together to form a dynamic routing decision mechanism. The relay coordination node is used to forward the path calculation request to the network management server after receiving it. The monitoring node, as the source node for data transmission, strictly follows the path sequence to perform multi-hop forwarding of business data packets after receiving the transmitted path information. During the forwarding process, each relay node needs to embed its current remaining energy value into a specific field in the header of the service data packet before forwarding the service data packet. After receiving the service data packet, the destination node will report the remaining energy information of each node along the path to the network management server along with the service data packet for real-time updates of the global network energy situation map. The global network optimization engine is used to receive and integrate local network topology tables from all relay coordination nodes to construct a global network energy situation map. The service policy manager has pre-set transmission quality requirement policies for service data packets of different service types; For the business data packets to be sent, the source monitoring node first calls its local distributed routing algorithm to select the initial path; The execution process of the distributed routing algorithm is as follows: the source node reads the priority flag of the service data packet to be sent; If the service data packet is a normal priority service data packet, the algorithm starts a greedy forwarding strategy based on local information. The source node selects the neighbor node with the highest remaining energy percentage and link quality above the preset threshold from its neighbor table as the next hop relay target. If the service data packet is a high-priority service data packet, or if the source node finds that the remaining energy of all neighboring nodes is lower than the energy alarm threshold when executing the greedy forwarding strategy, the source node sends a path calculation request to the nearest relay coordinating node. The path calculation request encapsulates the identity identifiers of the source node and the destination node, the priority of the service data packet, and the real-time status information of the source node. The global network optimization engine of the network management server is used to respond to this request and perform centralized optimal path calculation; The centralized optimal path calculation process is as follows: The global network optimization engine uses the current global network energy status map as the basic data, adopts the improved Dijkstra shortest path algorithm, and reconstructs the definition of path cost into a multi-dimensional weighted cost function; The multidimensional weighted cost function is composed of three linearly weighted components: The first component is the sum of the reciprocals of the remaining energy of all relay nodes on the path; The second component is the estimated end-to-end transmission delay of the path; The third component is the number of hops in the path; For high-priority service data packets, the weight coefficient of the second component in the multidimensional weighted cost function is set to the maximum value; For ordinary priority service data packets or energy assistance requests, the weight coefficient of the first component is set to the maximum value; After the global network optimization engine calculates the candidate path that minimizes the cost function, it encapsulates the optimal path information in the path calculation response and sends it to the source node via the relay coordination node. The energy management module integrates a dynamic power consumption control strategy. The dynamic power consumption control strategy is used to dynamically adjust the working mode and transmission power of the wireless communication module according to the network role and service load currently undertaken by the node. When a node is selected as the path node for high-priority service data packets, its wireless communication module maintains a high-power state of continuous listening and uses standard transmit power. When a node is not selected or is only handling ordinary services, its wireless communication module adopts a periodic sleep-wake mechanism with an extremely low duty cycle. After waking up, it dynamically calculates and uses the minimum transmit power that just meets the link quality requirements based on the actual distance to the next hop node. The regional network coordinator of the relay coordination node is also used to undertake local data fusion tasks. For ordinary priority service data packets with spatiotemporal correlation from multiple monitoring nodes within its coverage area, the regional network coordinator calls a data fusion algorithm to aggregate the data before forwarding it upstream, removing redundant information and only reporting the fused feature values ​​or abnormal data. The greedy forwarding strategy in the distributed routing algorithm uses the cooperative reputation value of the node as a positive weighting factor when selecting the next hop. Given the same remaining energy and link quality, nodes with high reputation scores are more likely to be selected as the next hop.

2. The wireless communication based highway infrastructure intelligent monitoring and management system as claimed in claim 1, wherein, The sensing module is used to collect structural health data, traffic flow data, or environmental status data of highway infrastructure. The microprocessor has a built-in node status management unit and a data preprocessing unit; The node status management unit is used to continuously monitor and record the node's remaining energy percentage, geographical location information, and link quality indicators of its neighboring nodes. The data preprocessing unit is used to extract features and assign priorities to the raw data collected by the sensing module. Based on the service type of the data and whether its value exceeds the preset security threshold, the service data packets are marked as high-priority service data packets or ordinary-priority service data packets. The regional network coordinator is used to periodically broadcast beacon frames containing its own identity and current load status, and to maintain a dynamic local network topology table. The local network topology table is used to record the identity, remaining energy percentage, geographical location, and most recently reported link quality of all reachable monitoring nodes within its communication coverage area.

3. The wireless communication based highway infrastructure intelligent monitoring and management system as claimed in claim 1, wherein, The network management server's business policy manager supports dynamic policy injection; The monitoring center administrator can issue temporary business priority remapping rules to the entire network through the business policy manager based on different stages of highway operation or special weather events, so as to temporarily increase the priority of specific types of sensor business data packets; The network management server also maintains a collaborative reputation value for each monitoring node; The initial value of the collaboration reputation value is 100, and its update rule is as follows: Each time a node successfully relays a service data packet for another node, its cooperative reputation value increases by 2; If a node refuses a forwarding request when it is capable of forwarding, or if its forwarding failure rate exceeds a preset threshold, its collaboration reputation value is reduced by 5.

4. The wireless communication based highway infrastructure intelligent monitoring and management system as claimed in claim 1, wherein, The monitoring node also maintains a dynamically updated neighbor table; The neighbor table is maintained through a periodically executed neighbor discovery and link detection protocol. Each record in the neighbor table contains the identity of the neighbor node, the remaining energy percentage, the geographical coordinates, the link quality index, and the timestamp of the most recent successful communication. The relay coordination node has a higher hardware configuration than ordinary monitoring nodes. Specifically, it uses an embedded processor with a main frequency of no less than 800 MHz, is equipped with no less than 512 megabytes of running memory, is equipped with a dual-band wireless communication module, and is connected to a rechargeable lithium battery pack with a capacity of no less than 20 amp-hours.

5. The wireless communication based highway infrastructure intelligent monitoring and management system as claimed in claim 1, wherein, The transmission quality requirements preset by the service policy manager include the maximum allowed end-to-end delay, the minimum received signal strength requirement, and the maximum number of retransmissions.

6. The wireless communication based highway infrastructure intelligent monitoring and management system as claimed in claim 1, wherein, The estimated end-to-end transmission latency involved in the global network optimization engine's calculation of path cost is estimated based on historical link quality data, current link quality indicators, service data packet size, and wireless channel bandwidth.

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