A network blind spot compensation system and method for open-pit mines based on WiFi Mesh networks

Abstract

The application discloses an open-pit mine scene network blind filling system and method based on a WiFi Mesh network, wherein the method comprises the following steps: based on mine area terrain three-dimensional modeling data and mine area operation planning information, a plurality of core nodes, relay nodes and mobile nodes are distributedly arranged in a mine area to form a wireless Mesh network; at least two independent wireless frequency bands are configured for the Mesh network; link state parameters and network performance parameters of each node in the Mesh network are periodically collected to form a network state data set; based on the network state data set, an optimal network topology and data forwarding path are calculated in real time through a built-in reinforcement learning decision engine; when it is monitored that the link quality of a current transmission path is lower than a preset threshold, an optimal path is automatically selected from a plurality of pre-calculated backup paths for switching; when a node fault or link interruption causes path failure, distributed routing calculation is started, an optimal forwarding tree of the whole network or a local area is regenerated, and node flow tables are synchronously updated, so that service recovery is realized.

Description

Technical Field

[0001] This invention relates to the field of wireless communication network technology, and specifically to a network blind spot compensation system and method for open-pit mines based on WiFi Mesh networks. Background Technology

[0002] Open-pit mines are mostly located in hilly or plateau areas. As mining operations progress, the excavation steps move forward daily, creating a complex landscape of spoil heaps, valleys, and elevated platforms. This necessitates frequent re-laying of traditional wired networks, resulting in long construction cycles, high costs, and the vulnerability of fiber optic cables to damage from mine trucks and blasting debris, making them ill-suited to the dynamic demands of daily mine development and weekly changes. While existing 4G / 5G base stations offer wide-area coverage, signal penetration is severely hampered by mountainous terrain and significant elevation differences, creating persistent blind spots in valleys and depressions. Furthermore, base station construction requires towers, power supply, and transmission infrastructure, with approval and construction cycles often taking months, hindering rapid relocation alongside the work area. Ordinary WiFi, limited by line-of-sight, has a coverage radius of less than 100 meters, failing to meet the continuous link requirements of low-latency, high-bandwidth services such as unmanned mining trucks, 4K video transmission, and personnel positioning. WiFi Mesh technology, with its self-organizing, self-healing, and multi-hop relay advantages, offers a new approach to filling network gaps in mines, but existing solutions still face four major challenges: 1) Node deployment is greatly restricted by terrain, with lack of mains power and pole erection conditions in mountain tops and depressions; 2) Multipath effects and obstacle blockages cause severe signal attenuation, and single frequency bands have poor stability in complex terrain; 3) Insufficient network recovery capability when nodes move or fail, topology changes often cause second-level packet loss, affecting the safety of unmanned driving; 4) Lack of QoS guarantee mechanism for special mining operations, unable to provide deterministic latency for critical applications such as remote control and blasting monitoring.

[0003] Therefore, it is necessary to provide a network blind spot compensation system and method for open-pit mines based on WiFi Mesh networks to solve the problems mentioned in the background. Summary of the Invention

[0004] To achieve the above objectives, the present invention provides the following technical solution: a network coverage method for open-pit mines based on WiFi Mesh networks, comprising: S1. Based on the 3D modeling data of the mining area terrain and the mining area operation planning information, a three-level wireless mesh network is formed by distributing several core nodes, relay nodes and mobile nodes in the mining area. The core nodes are deployed in fixed areas of the mining area to form a backbone ring network, the relay nodes are deployed in areas with weak signal coverage in the mining area to extend network coverage, and the mobile nodes are configured on personnel or mobile devices to provide mobile access, enabling communication access between mobile terminals and the Mesh network. S2. Configure at least two independent radio frequency bands for the Mesh network; one of the radio frequency bands is used to transmit network control signaling and forms an independent control plane, and the other radio frequency band is used to transmit service data and forms an independent data plane. S3. Periodically collect the link status parameters and network performance parameters of each node in the Mesh network to form a network status dataset; S4. Based on the network state dataset, the optimal network topology and data forwarding path are calculated in real time through the built-in reinforcement learning decision engine. When the link quality of the current transmission path is detected to be lower than a preset threshold, a fast roaming mechanism is triggered to automatically select the optimal path from multiple pre-calculated backup paths for switching. When a node failure or link interruption causes the path to fail, distributed routing calculation is started to regenerate the optimal forwarding tree for the entire network or a local area and update the node flow table synchronously to achieve service recovery.

[0005] Preferably, in step S4, the optimization objective function of the reinforcement learning decision engine is to minimize the end-to-end latency and packet loss rate of critical service flows, while maximizing the overall network throughput and energy efficiency.

[0006] Preferably, the independent data plane includes at least two logical sub-channels; the method further includes service-aware differentiated scheduling, which identifies the type of the service flow through deep packet inspection, marks different service quality priorities for different service flows according to the service type and preset strategy, and implements differentiated channel allocation for different types of service flows according to the service quality requirements characteristics of the service flows and the real-time channel status, and performs adaptive migration scheduling of service flows based on dynamic changes in channel quality to ensure that all types of services can obtain transmission resource guarantees that match their needs.

[0007] Preferably, the method further includes adaptive beamforming alignment adjustment of the relay node, which acquires its installation tilt angle and azimuth angle in real time through an attitude sensor integrated in the relay node; periodically scans the link quality between the relay node and each adjacent node; and dynamically adjusts the antenna beam pointing angle of the relay node through a closed-loop control algorithm with the joint optimization objective of maximizing the target link signal-to-noise ratio and minimizing co-channel interference to adjacent links, while maintaining uninterrupted service transmission during the adjustment process.

[0008] Preferably, the initial calculated value of the antenna beam pointing angle is derived based on the deployment height of the relay node, the target coverage distance, the antenna vertical beamwidth, and the relative coordinates and magnetic declination correction value of the target neighbor node, and the dynamic adjustment is performed within a preset limited angle range centered on this initial value.

[0009] Preferably, the method further includes end-to-end security enhancement steps: employing a multi-factor access control mechanism that combines device digital certificate authentication with user dynamic tokens or biometric authentication, and implementing dynamic least privilege authorization based on terminal type, geographical location, and time context; simultaneously employing differentiated encryption strategies for control plane signaling and data plane service data; wherein the control plane signaling is encrypted using national cryptographic algorithms and subjected to integrity verification, and the data plane service data is encrypted using high-strength symmetric encryption algorithms and supplemented with message authentication codes; and generating cryptographic hash values ​​for key network event logs and storing them in a blockchain or tamper-proof log system for audit traceability.

[0010] Preferably, the relay node deployed in areas without mains power adopts a hybrid power supply system combining solar photovoltaic panels and energy storage batteries, and has a low-power sleep mode.

[0011] Preferably, the method further includes a scenario-based adaptive step for special mining blasting operations: at a preset time before the start of the blasting operation, the pre-deployed emergency relay nodes are automatically activated or the transmission power and antenna mode of existing nodes are adjusted to form a temporary high-density, high-reliability network coverage within the blasting warning zone; and after the blasting operation is completed, the temporary configuration is automatically revoked and the network is restored to normal operation mode.

[0012] A network coverage system for open-pit mines based on WiFi Mesh networks includes: The network node cluster module includes several core nodes, relay nodes, and mobile nodes, and each node is connected to the other via a wireless mesh link. A network intelligent management device, deployed in the mine dispatch center, communicates with at least one of the core nodes via a backhaul link. The network intelligent management device includes: The state awareness module, which is communicatively connected to the network node cluster module, is used to perform full network state awareness and build and maintain a real-time network state database. The intelligent decision-making and self-healing module interacts with the state perception module. The intelligent decision-making and self-healing module is equipped with a reinforcement learning decision engine to execute intelligent decision-making and self-healing steps, and at the same time generate network optimization and control strategies. The strategy execution and distribution module is communicatively connected to the intelligent decision-making and self-healing module and the network node cluster module, respectively, and is used to compile the control strategy into specific configuration instructions and send them to the corresponding nodes for execution.

[0013] Preferably, the relay node further includes an antenna direction-adjustable unit, which includes a motorized gimbal and an antenna. The beam adjustment command generated by the strategy execution and distribution module is sent to the target relay node through the network node cluster module to drive the motorized gimbal of the target relay node to perform adaptive beamforming alignment.

[0014] Compared with existing technologies, this invention provides a network coverage system and method for open-pit mines based on WiFi Mesh networks, which has the following advantages: 1. Flexible Deployment and Adaptive Coverage: Based on 3D terrain modeling data and operational planning information, core nodes, relay nodes, and mobile nodes are distributed throughout the mining area to form a three-tiered wireless mesh network. This enables precise coverage of every corner of the mining area, including areas with weak signals such as depressions and slopes that are difficult for traditional networks to reach, achieving adaptive network coverage and significantly improving the overall communication quality of the mining area.

[0015] 2. Intelligent Decision-Making and Self-Healing Capabilities: An integrated reinforcement learning decision engine calculates the optimal network topology and data forwarding path in real time based on network state datasets. When link quality degradation or node failure is detected, it can automatically trigger a fast roaming mechanism or distributed routing calculation to quickly select the optimal path for switching or regenerating the forwarding tree, achieving rapid service recovery and greatly enhancing network resilience and availability.

[0016] 3. Service-Aware Differentiated Scheduling: Deep packet inspection identifies service flow types and assigns differentiated quality of service (QoS) priorities to different service flows based on service type and preset policies. Combined with real-time channel status, differentiated channel allocation and adaptive migration scheduling are implemented for different types of service flows, ensuring that all types of services receive transmission resource guarantees matching their needs, thereby improving network resource utilization efficiency and service quality.

[0017] 4. End-to-End Security Enhancement: A multi-factor access control mechanism combining device digital certificate authentication with user dynamic tokens or biometric authentication is employed, along with dynamic least privilege authorization based on terminal type, geographical location, and time context. Simultaneously, differentiated encryption strategies are used for control plane signaling and data plane service data to ensure the security and integrity of data transmission. The cryptographic hashes of critical network event logs are stored in a blockchain or tamper-proof log system for easy auditing and traceability, further enhancing network security. Attached Figure Description

[0018] Figure 1 This is a flowchart of a network blind spot filling method for an open-pit mine scenario based on WiFi Mesh network. Detailed Implementation

[0019] Please see Figure 1 This invention provides a network coverage system and method for open-pit mines based on WiFi Mesh networks, comprising: S1. Based on the 3D modeling data of the mining area terrain and the mining area operation planning information, a three-level wireless mesh network is formed by distributing several core nodes, relay nodes and mobile nodes in the mining area. Specifically, the mining area terrain data is collected by using UAV oblique photography and laser point cloud scanning technology, and a 3D model of the mining area terrain with centimeter-level accuracy is generated using 3D modeling software. At the same time, the operation planning information such as the medium and long-term mining plan, equipment scheduling table, and distribution of fixed facilities (dispatch center, crushing station, main road) of the mining area is integrated and collected to clarify the potential deployment areas of core nodes and relay nodes and the main range of mobile nodes.

[0020] The core nodes are deployed in fixed areas of the mining area to form a backbone ring network. These fixed areas include: the rooftop of the dispatch center, high points at main road intersections, and fixed crushing plant rooms—areas with no relocation requirements year-round. Each core node is housed in a 19-inch rack and incorporates a Qualcomm IPQ8074A Wi-Fi 6 chipset, providing 8×8 MU-MIMO and 4×4 OFDMA, with a total dual-band bandwidth of ≥9.6Gbps. The core nodes are interconnected via wireless links to form a backbone ring network. The core nodes are deployed at preset intervals to ensure the stability and bandwidth redundancy of the backbone links. Each core node is connected to an external 27dBi high-gain directional antenna, with coverage areas divided by sector, achieving backbone signal coverage of the main areas of the mining area.

[0021] The relay nodes are deployed in areas with weak signal coverage in the mining area to extend network coverage, and the mobile nodes are configured on personnel or mobile devices to provide mobile access, enabling communication access between mobile terminals and the Mesh network. Specifically, the areas with weak signal coverage in the mining area include valleys, slopes, spoil heaps, and elevated areas. The relay nodes utilize a pole-mounted IP67 enclosure, equipped with a dual-band independent RF module, operating across a full temperature range of -40℃ to +75℃. They feature a built-in 14dBi sector antenna and a rotatable gimbal. Their deployment density is adjusted according to terrain complexity to form relay coverage, eliminating signal blind spots in areas with weak signal coverage. The mobile nodes are either smart mining lamps with integrated Mesh functionality, secured to workers via safety helmet clips, and integrated with the lamp's power supply; or magnetic vehicle-mounted mobile nodes, attached to the top or side of the equipment's cab, and powered via a cigarette lighter, for unmanned mining trucks, inspection vehicles, and other mobile equipment. The mobile nodes automatically connect to the Mesh network constructed by surrounding core nodes or relay nodes, achieving seamless access as personnel and equipment move.

[0022] S2. Configure at least two independent wireless frequency bands for the Mesh network; one of the wireless frequency bands is used to transmit network control signaling, and it selects the 2.4GHz band (20MHz bandwidth) as the control channel, which is dedicated to carrying network control signaling such as BSSID broadcast, routing protocol messages, topology update information, and key negotiation data, forming an independent control plane; the other wireless frequency band is used to transmit service data, and it selects the 5GHz band (80MHz bandwidth) as the data channel, which is dedicated to carrying service data such as 4K video backhaul, remote control commands, personnel positioning data, and file transfer, forming an independent data plane; Among them, the node frequency bands are distributed in batches through the Network Management System (NMS) to ensure that the frequency band functions of all core nodes, relay nodes and mobile nodes are consistent, and to avoid control signaling and service data competing for channel resources.

[0023] Optionally, the Mesh network is also configured with a 6GHz band to provide ≤10 ms air interface latency for remote control.

[0024] S3. Periodically collect the link status parameters and network performance parameters of each node in the Mesh network to form a network status dataset; Specifically, each node has a built-in status acquisition module with a acquisition period of 100ms. The link status parameters include Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR), Packet Loss Rate (PER), and Link Load Rate. The network performance parameters include end-to-end latency, jitter, throughput, node CPU utilization, memory utilization, and battery level. The acquired data is processed by a 2.4GHz band network intelligent management device to form the network status dataset.

[0025] S4. Based on the network state dataset, the optimal network topology and data forwarding path are calculated in real time using the built-in reinforcement learning decision engine. When the link quality of the current transmission path is detected to be lower than a preset threshold (e.g., RSSI < -75dBm, packet loss rate > 2%), a fast roaming mechanism is triggered, and the optimal path is automatically selected from multiple pre-calculated backup paths for switching. The optimal path is the path with the fewest hops, the lowest interference, and the highest link stability. The switching is achieved through the 802.11r FastTransition protocol. When a node failure (e.g., relay node power failure, hardware damage) or link interruption (e.g., link obstruction caused by blasting, equipment collision damage) causes the path to fail, distributed routing calculation is started to regenerate the optimal forwarding tree for the entire network or a local area and update the node flow table synchronously to achieve service recovery.

[0026] Specifically, a reinforcement learning model is pre-installed within the network intelligent management device. The optimization objectives are: critical service latency <30ms, packet loss rate <0.1%, maximum network throughput, and minimum energy consumption. Parameters from the network state dataset are input as state vectors, and the optimal network topology and data forwarding paths are output. A network intelligent management system is deployed within the device, supporting 3D topology visualization, state data acquisition and analysis, and the operation of the reinforcement learning decision engine. Each node must have a built-in Mesh protocol stack to support distributed routing computation.

[0027] Furthermore, in step S4, the optimization objective function of the reinforcement learning decision engine is to minimize the end-to-end latency and packet loss rate of critical service flows, while maximizing the overall network throughput and energy efficiency. The optimization objective function... F The formula is:

[0028] In the formula: For time delay, For the time delay threshold, For packet loss rate, Actual throughput For maximum throughput, Actual energy consumption, Rated energy consumption ~ These are the weight coefficients for each objective, and the default sum of each weight coefficient is 1.

[0029] It's important to explain that when optimizing network performance, the network intelligent management device extracts real-time data from the network status dataset, including latency and packet loss rate of critical service flows (such as control commands for unmanned mining trucks and blasting monitoring data), overall network throughput, and energy consumption data of each node (such as battery consumption rate of solar relay nodes and power consumption of mains nodes), and inputs this data into the optimization function. The reinforcement learning decision engine iteratively calculates using the Q-Learning algorithm to adjust the network topology and data forwarding paths to maximize the optimization function value. For example, if a link is overloaded, causing increased latency for critical services, the engine automatically migrates some non-critical service flows to other idle links to ensure that the latency and packet loss rate of critical services meet requirements.

[0030] Furthermore, the independent data plane includes at least two logical sub-channels; the method also includes service-aware differentiated scheduling, which identifies the type of the service flow through deep packet inspection, marks different service quality priorities for different service flows according to the service type and preset strategy, and implements differentiated channel allocation for different types of service flows based on the service quality requirements of the service flows (such as latency, bandwidth, packet loss rate, jitter requirements, etc.) and the real-time channel status (referring to parameters such as received signal strength (RSSI), signal-to-noise ratio (SNR), air interface utilization, adjacent channel interference power, etc.), and performs adaptive migration scheduling of service flows based on dynamic changes in channel quality to ensure that all types of services can obtain transmission resource guarantees that match their needs.

[0031] It should be explained that the division of each sub-channel is uniformly configured through the network management system to ensure that the sub-channel parameters of all nodes are consistent. Each node has a built-in deep packet inspection module that parses the packet header information of the service flow in real time (such as port number, protocol type, and DSCP field) to identify the service flow type. Based on a preset strategy, it assigns differentiated Quality of Service (QoS) priorities to different types of service flows: remote control, blasting monitoring, and emergency calls are marked as the highest priority (EF, DSCP=46); 4K video transmission and personnel positioning are marked as high priority (AF41, DSCP=34); sensor data (such as temperature, humidity, and gas concentration) are marked as medium priority (AF31, DSCP=26); and file backup and office data transmission are marked as low priority (BE, DSCP=0).

[0032] The network intelligent management device monitors the signal quality of each logical sub-channel in real time and performs channel allocation based on the QoS requirements of service flows, as follows: The highest priority service streams are allocated to the logical sub-channels with the best channel quality and least interference, with 20% of channel resources (TXOP) reserved to ensure low-latency transmission. High-priority service streams (such as 4K video) are allocated to high-bandwidth logical sub-channels (80MHz), using 256-QAM modulation and block acknowledgment mechanism to ensure a transmission bandwidth of ≥50Mbps for each video stream. Medium-priority service streams (such as sensor data) are transmitted in 40MHz logical sub-channels using periodic polling scheduling to ensure data acquisition and uploading are completed within 40ms. Low-priority service streams (such as file backups) are transmitted only when other service streams occupy less than 30% of the channel resources to avoid impacting high-priority services.

[0033] When the channel quality changes dynamically (e.g., the interference power of a sub-channel increases and CQI < 0.4), the network intelligent management device triggers adaptive migration scheduling of service flows, migrating high-priority service flows on that sub-channel to other sub-channels with better channel quality, with service interruption time < 100ms.

[0034] Furthermore, the method also includes adaptive beamforming alignment adjustment of the relay node, which acquires its installation tilt angle and azimuth angle in real time through attitude sensors integrated into the relay node; periodically scans the link quality between the relay node and each adjacent node; and dynamically adjusts the antenna beam pointing angle of the relay node through a closed-loop control algorithm with the joint optimization objective of maximizing the target link signal-to-noise ratio and minimizing co-channel interference to adjacent links, while maintaining uninterrupted service transmission during the adjustment process.

[0035] Specifically, the relay node integrates a 6-axis MEMS attitude sensor (3-axis accelerometer + 3-axis magnetometer) to collect real-time installation tilt and azimuth data at a 10Hz frequency. The data is transmitted to the beam control module via the relay node's internal bus. Every 30 seconds, the relay node performs a link quality scan on its adjacent nodes within its coverage area, measuring and recording link quality parameters such as RSSI, SNR, and packet loss rate with each adjacent node, forming a link quality list. Then, with the joint optimization objective of maximizing the target link signal-to-noise ratio (SNR ≥ 25dB) and minimizing co-channel interference to neighboring links (interference power ≤ -85dBm), the beam control module runs a closed-loop control algorithm (such as a PID control algorithm), combining the current tilt and azimuth data collected by the attitude sensor and the link quality scan results to calculate the optimal pointing angle adjustment value for the antenna beam. Then, the beam control module sends adjustment commands to the motorized pan / tilt unit of the relay node, driving the pan / tilt unit to adjust the azimuth and downtilt angles of the antenna in 0.1° increments. After each adjustment, a 100ms wait is performed before re-collecting link quality data to determine if the optimization target has been achieved. If not, the adjustment continues until the target link SNR is ≥25dB ​​and the interference power of adjacent links is ≤-85dBm. During the adjustment process, link redundancy technology ensures uninterrupted service transmission, guaranteeing that the transmitted service stream is free of packet loss and stuttering.

[0036] Furthermore, the initial calculated value of the antenna beam pointing angle is derived based on the deployment height of the relay node, the target coverage distance, the antenna vertical beamwidth, and the relative coordinates and magnetic declination correction value of the target neighbor node. The dynamic adjustment is then performed within a preset limited angle range centered on this initial value to avoid exceeding the mechanical limits of the motorized pan-tilt unit during antenna adjustment, which could lead to equipment damage.

[0037] The antenna beam pointing angle includes the downtilt angle and the azimuth angle.

[0038] Initial value of downtilt angle The calculation formula is:

[0039] Azimuth initial value The calculation formula is:

[0040] In the formula, Deploy the altitude for the relay node. For the desired horizontal coverage distance, The vertical half-power angle of the antenna. This represents the eastward distance between the target's neighboring nodes and the relay node. This represents the northward distance of the target neighbor node relative to the relay node. This is the local magnetic declination.

[0041] Furthermore, the method also includes end-to-end security enhancement steps: employing a multi-factor access control mechanism that combines device digital certificate authentication with user dynamic tokens or biometric authentication, and implementing dynamic least privilege authorization based on terminal type, geographical location, and time context; simultaneously employing differentiated encryption strategies for control plane signaling and data plane service data; wherein the control plane signaling is encrypted using national cryptographic algorithms and subjected to integrity verification, and the data plane service data is encrypted using high-strength symmetric encryption algorithms and supplemented with message authentication codes; and generating cryptographic hash values ​​for key network event logs and storing them in a blockchain or tamper-proof log system for audit traceability.

[0042] Specifically, the device digital certificate is an X.509 v3 digital certificate issued by the manufacturer at the time of manufacture. When a node connects to the network, it sends a certificate authentication request to the network intelligent management device. The network intelligent management device verifies the validity of the certificate, and subsequent authentication can only proceed after successful verification. During the user dynamic token or biometric authentication process, for mobile nodes, in addition to device certificate authentication, user identity authentication is also required. The user dynamic token uses a one-time dynamic password generated by a mobile app (updated every 60 seconds). The user completes authentication by entering the dynamic password. Biometric authentication uses fingerprint recognition (integrated into the side button of the handheld terminal or smart mining lamp). After the user registers their fingerprint, identity verification is completed through fingerprint comparison.

[0043] The network intelligent management device combines terminal type (such as core node, relay node, smart mine lamp, vehicle-mounted node), geographical location, and time context (such as working hours, blasting period, maintenance period) to implement dynamic least privilege authorization. For example, smart mine lamp users are granted voice communication, personnel positioning, and emergency call permissions during working hours and in the work area; when a user enters the blasting warning zone, they are automatically granted permission to receive blasting warning broadcasts, etc.

[0044] The signaling data transmitted in the control plane is encrypted using the national standard SM4-CBC algorithm with a 128-bit key. The key is renegotiated every hour based on the SM2 elliptic curve algorithm. Simultaneously, the SM3 algorithm is used for integrity verification, generating a 256-bit hash value. The receiving end verifies the hash value to ensure the signaling data has not been tampered with. Service data transmitted in the data plane uses different strengths of symmetric encryption algorithms depending on the service type: critical service data is encrypted using the AES-256-GCM algorithm with an appended 96-bit IV random vector to prevent replay attacks; ordinary service data is encrypted using the AES-128-GCM algorithm. All service data is appended with an HMAC-SHA-256 message authentication code, which is verified by the receiving end to ensure data integrity and authenticity.

[0045] The critical network events include node access / exit, certificate authentication results, permission changes, and fault alarms. Recorded information includes event time, event type, involved nodes, and event details. For each critical network event log, a cryptographic hash value is generated using the SM3 algorithm to ensure the log cannot be tampered with. The hash value and the original log text are stored together in a blockchain system (using a private blockchain architecture, deployed in a server cluster at the mine scheduling center) or a tamper-proof log system (using WORM storage media, write-protected and unmodifiable). The logs are retained for at least 3 years, meeting the audit requirements of Level 3 of the Information Security Protection Standard 2.0. When auditing is required, the relevant event logs are queried through the blockchain or tamper-proof log system to verify the hash value. After confirming that the logs have not been tampered with, event tracing and analysis are performed.

[0046] Furthermore, the relay nodes deployed in areas without mains power adopt a hybrid power supply system combining solar photovoltaic panels and energy storage batteries, and have a low-power sleep mode.

[0047] Specifically, the network intelligent management device monitors the battery level and network load of each relay node in real time. When the battery level is below 30% and the network load is below 20%, a low-power sleep mode is triggered. When the relocation of the work area results in no service demand in the coverage area of ​​the relay node, the sleep mode is remotely triggered through the network management system. After entering sleep mode, the relay node shuts down some non-critical radio frequency modules, retaining only the control channel receiving module and low-power sensors to reduce power consumption. At the same time, it maintains periodic communication with neighboring nodes to ensure uninterrupted network topology awareness. When the battery level is charged to above 50%, the network load increases to above 30%, or a wake-up command is received from the network management system, the relay node automatically wakes up and resumes normal operation.

[0048] Furthermore, the method also includes scenario-based adaptive steps for special mining blasting operations: at a preset time before the start of the blasting operation, the pre-deployed emergency relay nodes are automatically activated or the transmission power and antenna mode of existing nodes are adjusted to form temporary high-density, high-reliability network coverage within the blasting warning zone; and after the blasting operation is completed, the temporary configuration is automatically revoked and the network is restored to normal operation mode.

[0049] Specifically, during mine blasting operations, the blasting plan is pre-entered through the network management system. Portable emergency relay nodes are pre-deployed within and around the blasting warning zone. At a preset time (30 minutes before blasting), the network management system automatically sends activation commands to the pre-deployed emergency relay nodes. After powering on, the emergency relay nodes automatically connect to surrounding core nodes or relay nodes, constructing a temporary mesh network. Simultaneously, the network management system adjusts the transmission power and antenna mode of existing core nodes and relay nodes around the blasting warning zone to enhance signal coverage strength and density within the warning zone. This ensures communication reliability within the blasting warning zone and improves the safety of blasting operations.

[0050] A network blind spot filling system for an open-pit mine scene based on a WiFi Mesh network for implementing the method includes: The network node cluster module includes several core nodes, relay nodes, and mobile nodes. Each node is connected via a wireless mesh link to form a three-tier network architecture of core-relay-mobile. A network intelligent management device, deployed in the mine dispatch center, communicates with at least one of the core nodes via a backhaul link. The network intelligent management device includes: The state awareness module, which is communicatively connected to the network node cluster module, is used to perform full network state awareness, build and maintain a real-time network state database; and collects link state parameters, network performance parameters and node state parameters of each node through the state awareness module.

[0051] The intelligent decision-making and self-healing module interacts with the state awareness module via an internal bus. The intelligent decision-making and self-healing module is equipped with a reinforcement learning decision engine, which calculates the optimal network topology and data forwarding path in real time based on data in the network state database. When a link quality degradation or node failure is detected, a fast roaming mechanism or distributed routing calculation is initiated, and network optimization and control strategies are generated simultaneously. The strategy execution and distribution module is communicatively connected to the intelligent decision-making and self-healing module and the network node cluster module, respectively. It is used to compile the control strategy into specific configuration instructions and send them to the corresponding nodes for execution. At the same time, it receives execution feedback from each node to confirm the effect of strategy execution. If the execution fails, it regenerates and sends the strategy again.

[0052] It should be explained that the core node adopts a 19-inch rack form factor and integrates a Qualcomm IPQ8074A Wi-Fi 6 chipset, providing 8×8 MU-MIMO, 4×4 OFDMA, and a total dual-band bandwidth of ≥9.6Gbps. The relay node adopts a pole-mounted IP67 enclosure and is equipped with a dual-band independent RF module, with 5GHz used for wireless backhaul and 2.4GHz for local access. The mobile node is equipped with a low-power Wi-Fi 6E SoC, supporting 2.4GHz / 5GHz dual-band concurrent operation. A backbone ring network is constructed through the core node, aggregating backhaul traffic from relay nodes and mobile nodes; relay nodes fill blind spots in terrain shadow areas to achieve signal relay coverage; and mobile nodes automatically connect to surrounding nodes as people or equipment move, achieving seamless roaming. Each node collects its own status data and link quality data in real time and transmits it back to the network intelligent management device through a control channel.

[0053] Furthermore, the relay node also includes an antenna direction-adjustable unit, which includes a motorized gimbal and an antenna. The beam adjustment command generated by the strategy execution and distribution module is sent to the target relay node through the network node cluster module to drive the motorized gimbal of the target relay node to perform adaptive beamforming alignment operation.

[0054] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A network coverage method for open-pit mines based on WiFi Mesh networks, characterized in that, Includes the following steps: S1. Based on the 3D modeling data of the mining area terrain and the mining area operation planning information, a three-level wireless mesh network is formed by distributing several core nodes, relay nodes and mobile nodes in the mining area. The core nodes are deployed in fixed areas of the mining area to form a backbone ring network, the relay nodes are deployed in areas with weak signal coverage in the mining area to extend network coverage, and the mobile nodes are configured on personnel or mobile devices to provide mobile access, enabling communication access between mobile terminals and the Mesh network. S2. Configure at least two independent radio frequency bands for the Mesh network; one of the radio frequency bands is used to transmit network control signaling and forms an independent control plane, and the other radio frequency band is used to transmit service data and forms an independent data plane. S3. Periodically collect the link status parameters and network performance parameters of each node in the Mesh network to form a network status dataset; S4. Based on the network state dataset, the optimal network topology and data forwarding path are calculated in real time through the built-in reinforcement learning decision engine. When the link quality of the current transmission path is detected to be lower than a preset threshold, a fast roaming mechanism is triggered to automatically select the optimal path from multiple pre-calculated backup paths for switching. When a node failure or link interruption causes the path to fail, distributed routing calculation is started to regenerate the optimal forwarding tree for the entire network or a local area and update the node flow table synchronously to achieve service recovery.

2. The method according to claim 1, characterized in that, In step S4, the optimization objective function of the reinforcement learning decision engine is to minimize the end-to-end latency and packet loss rate of critical service flows, while maximizing the overall network throughput and energy efficiency.

3. The method according to claim 2, characterized in that, The independent data plane includes at least two logical sub-channels; the method also includes service-aware differentiated scheduling, which identifies the type of the service flow through deep packet inspection, marks different service quality priorities for different service flows according to the service type and preset strategy, and implements differentiated channel allocation for different types of service flows according to the service quality requirements characteristics of the service flows and the real-time channel status, and performs adaptive migration scheduling of service flows based on dynamic changes in channel quality to ensure that all types of services can obtain transmission resource guarantees that match their needs.

4. The method according to claim 1, characterized in that, The method also includes adaptive beamforming alignment adjustment of the relay node, which acquires its installation tilt and azimuth angle in real time through attitude sensors integrated into the relay node; periodically scans the link quality between the relay node and each adjacent node; and dynamically adjusts the antenna beam pointing angle of the relay node through a closed-loop control algorithm with the joint optimization objective of maximizing the target link signal-to-noise ratio and minimizing co-channel interference to adjacent links, while maintaining uninterrupted service transmission during the adjustment process.

5. The method according to claim 4, characterized in that, The initial calculated value of the antenna beam pointing angle is derived based on the deployment height of the relay node, the target coverage distance, the antenna vertical beamwidth, and the relative coordinates and magnetic declination correction value of the target neighbor node. The dynamic adjustment is then performed within a preset finite angle range centered on this initial value.

6. The method according to claim 1, characterized in that, The method further includes end-to-end security enhancement steps: employing a multi-factor access control mechanism that combines device digital certificate authentication with user dynamic tokens or biometric authentication, and implementing dynamic least privilege authorization based on terminal type, geographical location, and time context; simultaneously employing differentiated encryption strategies for control plane signaling and data plane service data; wherein the control plane signaling is encrypted using national cryptographic algorithms and subjected to integrity verification, and the data plane service data is encrypted using high-strength symmetric encryption algorithms and supplemented with message authentication codes; and generating cryptographic hash values ​​for key network event logs and storing them in a blockchain or tamper-proof log system for audit traceability.

7. The method according to claim 1, characterized in that, The relay nodes deployed in areas without mains power use a hybrid power supply system that combines solar photovoltaic panels and energy storage batteries, and have a low-power sleep mode.

8. The method according to claim 1, characterized in that, The method also includes scenario-based adaptive steps for special mining blasting operations: at a preset time before the start of the blasting operation, the pre-deployed emergency relay nodes are automatically activated or the transmission power and antenna mode of existing nodes are adjusted to form temporary high-density, high-reliability network coverage within the blasting warning zone; and after the blasting operation is completed, the temporary configuration is automatically revoked and the network is restored to normal operation mode.

9. A network blind spot compensation system for an open-pit mine scene based on a WiFi Mesh network for implementing the method of any one of claims 1 to 8, characterized in that, include: The network node cluster module includes several core nodes, relay nodes, and mobile nodes, and each node is connected to the other via a wireless mesh link. A network intelligent management device, deployed in the mine dispatch center, communicates with at least one of the core nodes via a backhaul link. The network intelligent management device includes: The state awareness module, which is communicatively connected to the network node cluster module, is used to perform full network state awareness and build and maintain a real-time network state database. The intelligent decision-making and self-healing module interacts with the state perception module. The intelligent decision-making and self-healing module is equipped with a reinforcement learning decision engine to execute intelligent decision-making and self-healing steps, and at the same time generate network optimization and control strategies. The strategy execution and distribution module is communicatively connected to the intelligent decision-making and self-healing module and the network node cluster module, respectively, and is used to compile the control strategy into specific configuration instructions and send them to the corresponding nodes for execution.

10. The system according to claim 9, characterized in that, The relay node further includes an antenna direction-adjustable unit, which includes a motorized gimbal and an antenna. The beam adjustment command generated by the strategy execution and distribution module is sent to the target relay node through the network node cluster module to drive the motorized gimbal of the target relay node to perform adaptive beamforming alignment.

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