Power transmission line signal coverage enhancement method based on RIS and WAPI fusion

Abstract

The invention discloses a power transmission line signal coverage enhancement method based on RIS and WAPI fusion, and relates to the technical field of power transmission lines. According to the technical key points, detection points are arranged in a differentiated manner according to interference intensity and terminal distribution density of different areas along a power transmission line, and position characteristics of weak signal coverage areas and dynamic environment factors such as electromagnetic interference and meteorological change are captured; on the basis, interference frequency distribution is firstly analyzed by combining an interference avoidance strategy and a compensation strategy of the intelligent reflecting surface, a strong interference frequency band is avoided, an optimal working frequency band is selected, and then adverse effects of various environmental factors on signal transmission are effectively counteracted by inputting future weather and presetting signal compensation parameters for special scenes; and meanwhile, through a multi-node collaborative deployment mode, equipment arrangement is performed, a certain coverage overlapping rate is guaranteed, a dynamic resource allocation strategy is matched, reflection resources are allocated according to differentiation of terminals and coverage requirements, and full-line dead-corner-free coverage of the power transmission line is ensured.

Description

Technical Field

[0001] This invention relates to the field of power transmission line technology, specifically to a method for enhancing signal coverage of power transmission lines based on the fusion of RIS and WAPI. Background Technology

[0002] With the deepening of smart grid construction, transmission lines, as the core backbone of power energy transmission, have become crucial supports for ensuring the reliability, security, and coverage integrity of their communication links, enabling automated grid dispatching, equipment status monitoring, and rapid fault location. Modern transmission lines generally traverse vast areas with complex and diverse environments along their routes. They not only face static challenges such as electromagnetic interference from industrial plants, frequency band conflicts between urban and rural civilian communications, and natural terrain obstruction, but also dynamic risks such as signal attenuation caused by meteorological disasters like icing, strong winds, and rainstorms, as well as obstruction by temporary construction machinery. This places stringent requirements on the stability and anti-interference capabilities of signal transmission.

[0003] The idea is to expand signal coverage by adding communication base stations or wireless relay equipment along power transmission lines. This approach is based on fixed power and coverage radius deployment, attempting to fill coverage gaps by increasing hardware density. However, in practice, the load-bearing capacity of power transmission line towers is limited, and adding a large number of base stations would exceed structural safety limits. Furthermore, the deployment, power supply, and maintenance costs of these devices are extremely high. Simultaneously, base station signal propagation is easily affected by terrain and obstructions, creating coverage dead zones in complex areas such as mountains and forests, making it impossible to achieve full coverage without dead zones along the entire line. Additionally, while preset reflection parameters optimize signal direction for fixed scenarios, this approach only focuses on signal strength enhancement without considering the security requirements of power transmission line data transmission. It lacks encryption protection mechanisms for monitoring data and control commands, making it vulnerable to security risks such as data tampering and man-in-the-middle attacks. Moreover, reflection parameters are often fixed configurations or manually adjusted, failing to adapt to dynamic environments such as electromagnetic interference and weather changes in real time, resulting in large fluctuations in signal quality and a sharp decline in enhancement effects under special scenarios such as icing and strong winds.

[0004] Moreover, most existing solutions adopt a fixed parameter configuration mode, that is, the equipment operating parameters are set according to the initial scenario and remain unchanged for a long time, lacking a dynamic adjustment mechanism during implementation. This results in the equipment being unable to adapt to dynamic changes such as interference frequency jumps and temporary blockages, and the parameters becoming disconnected from environmental requirements. At the same time, the effect evaluation only focuses on single indicators such as signal receiving power and transmission rate, ignoring the balance between safety level and energy efficiency. Some solutions consume excessive energy in pursuit of signal strength, or sacrifice real-time transmission to ensure safety, resulting in insufficient practicality and economy. To address this, we propose a transmission line signal coverage enhancement method based on the integration of RIS and WAPI. Summary of the Invention

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for enhancing signal coverage of transmission lines based on the fusion of RIS and WAPI is proposed, and the steps of the enhancement method are as follows: S1. Obtain full-dimensional basic data of the transmission line scenario. The full-dimensional basic data includes transmission line path-related information, dynamic environmental interference information along the line, current signal coverage strength information, equipment deployment location information, and historical line operation status and fault association information. Among them, dynamic environmental interference information includes real-time electromagnetic interference, line icing thickness, and temporary obstruction conditions. S2. Based on the above comprehensive basic data, combined with the scenario-based signal reflection characteristics of the intelligent reflector and the dynamic security adaptation characteristics of the wireless LAN authentication and confidentiality infrastructure, the signal reflection parameters of the intelligent reflector, the security transmission configuration parameters of the wireless LAN authentication and confidentiality infrastructure, and the signal coverage enhancement execution scheme are determined through a pre-trained real-time perception environment state and dynamic matching device parameter mapping model. The mapping model is trained and generated through historical interference and attenuation correlation data, device response time data, and power transmission line special scenario parameter compensation data. S3. According to the above implementation plan, deploy intelligent reflective surface devices with multi-scenario signal compensation capabilities, enable the dynamic secure transmission mechanism of the wireless local area network authentication and confidentiality infrastructure, and achieve collaborative work through the two-way predictive data interaction channel to perform power transmission line signal coverage enhancement operation; S4. Collect enhanced signal coverage data, equipment operation data, and real-time environmental data along the route, input them into the above mapping model for iterative optimization, and obtain a three-dimensional effect evaluation result including signal quality indicators, safety level indicators, and energy efficiency indicators. S5. Based on the 3D effect evaluation results, determine whether the signal coverage meets the preset requirements. If not, optimize the parameters of the two types of equipment simultaneously based on the scenario-based pre-adjustment parameters output by the mapping model. If it meets the requirements, determine the final solution and store it in the transmission line signal enhancement scenario database to provide parameter templates for similar scenarios.

[0007] Furthermore, the specific steps for obtaining full-dimensional basic data are as follows: S101. Extract the starting and ending tower locations, tower spacing, line direction, and tower material parameters of the transmission line through the geographic information system of the transmission line, and combine them with the line sag data of the UAV inspection to form transmission line path-related information containing spatial topology and structural characteristics; S102. Along the transmission line, detection points are set up according to regional differences. Three types of data are collected continuously for 72 hours using multi-functional dynamic monitoring equipment: electromagnetic interference data covering the 100 kHz to 6 GHz frequency band and including the type and frequency pattern of interference sources; meteorological data including temperature, humidity, wind speed, ice thickness and precipitation intensity; and temporary obstruction data including the location and size of dynamic obstructions. S103. Collect the signal receiving power, signal-to-noise ratio, transmission rate, bit error rate and end-to-end delay of each detection point through signal comprehensive analysis equipment to form current signal coverage strength information with spatiotemporal dimensions; S104. Based on the tower load-bearing calculation results, determine the maximum installation weight of the intelligent reflective surface equipment, the deployment height limit of the wireless LAN authentication and confidentiality infrastructure access point, and statistically analyze the coverage area of ​​the deployed communication base stations and the distribution density of monitoring terminals to form equipment deployment location information; S105. Collect signal interruption fault records along the line, establish a database linking fault causes, environmental parameters and signal attenuation, and form information on the correlation between line operation status and faults; S106. Standardize the above types of data to obtain full-dimensional basic data.

[0008] Furthermore, the specific steps for determining the signal reflection parameters of the smart reflector are as follows:

[0009] S201. Based on the transmission line path information and the current signal coverage strength information, a multi-dimensional weighted clustering analysis method is used to identify areas with weak signal coverage, and the priority of the target coverage area of ​​the intelligent reflector is determined according to the urgency. S202. Based on the interference frequency distribution of dynamic environmental interference information along the line, a dual strategy combining interference avoidance and power compensation is adopted to select the working frequency band of the intelligent reflector. When the strong interference frequency band is concentrated in 800 to 900MHz, the 920 to 940MHz frequency band is selected and 5dBm power compensation is set. S203. Based on the target coverage area and equipment deployment location, and combined with the tower spatial coordinates, calculate the three-dimensional distance and angle between the intelligent reflector and the signal transmitter and receiver, and determine the initial angle of the reflector unit; S204. Input the weather forecast data for the next 24 hours through the mapping model, calculate the scene-based reflection parameter compensation value, that is, for every 1 mm increase in ice thickness, the reflection intensity is compensated by 0.8 dB; when the wind speed exceeds 15 m / s, the angle of the reflection unit is finely adjusted by 0.5 degrees to offset the effect of wind deflection; S205. Through on-site dynamic testing and verification, ensure that the signal reception power of the target coverage area is stably higher than the preset threshold by more than 3 dB and the fluctuation range is ≤2 dB under special scenarios, and finally determine the intelligent reflective surface signal reflection parameters including working frequency band, reflective unit angle, reflection intensity and scene compensation coefficient.

[0010] Furthermore, the specific steps for determining the wireless LAN authentication and confidentiality infrastructure and secure transmission configuration parameters are as follows: S211. Based on the safety level requirements of transmission lines, select an identity authentication method that supports four-layer peer-to-peer authentication of terminals, access points, authentication servers, and cloud platforms, and use elliptic curve cryptography combined with the SM2 algorithm to achieve key negotiation and resist man-in-the-middle attacks. S212. Based on the interference fluctuation period and intensity of the dynamic environmental interference information along the route, establish the correspondence between interference frequency and key update period. That is, when the interference is frequent and the intensity is >-60dBm, the key update period is set to 10 minutes; when the interference is mild or the intensity is ≤-60dBm, it is set to 60 minutes. S213. Combining the signal transmission rate of the intelligent reflector with the data type of the monitoring terminal, establish a four-level encryption and rate adaptation relationship, namely, for a signal-to-noise ratio ≥30 dB, use 128-bit SM4 encryption plus a 100 Mbps threshold; for 25 to 30 dB, use 96-bit SM4 encryption plus an 80 Mbps threshold; for 20 to 25 dB, use 64-bit SM4 encryption plus a 50 Mbps threshold; and for <20 dB, use 48-bit SM4 encryption plus a 30 Mbps threshold. S214. An interference adaptive identification mechanism is added, which shortens the identification timeout to 500 milliseconds when sudden narrowband interference is detected, ultimately forming a secure transmission configuration parameter that includes a four-layer peer-to-peer identification method, a key update cycle, a four-level encryption and rate adaptation relationship, and an interference adaptive mechanism.

[0011] Furthermore, the specific steps for developing and implementing a signal coverage enhancement plan are as follows: S301. Based on the signal reflection parameters of the intelligent reflector and the equipment deployment location, combined with the tower load-bearing calculation, determine the installation height of the intelligent reflector, the fixing method of the anti-vibration and anti-corrosion angle steel, and the safe distance from the high-voltage conductor, and form an intelligent reflector deployment plan; S302. Based on the security configuration parameters of the wireless LAN authentication and confidentiality infrastructure, a relay gateway integrating edge computing is deployed on the transmission line tower. The gateway uses fiber optic and microwave dual-link backhaul to the north and achieves 120-degree sector coverage to the south through a smart antenna. The relay equipment adopts multi-hop cascading between towers and dynamic power control to form an activation scheme. S303. Integrate two types of solutions and clarify the pre-start, collaborative verification and dynamic adjustment process. That is, the intelligent reflective surface starts up and initializes the compensation parameters 3 minutes in advance. After the access point detects that the reflected signal meets the standard, it triggers a secure connection. The two share status data once every 50 milliseconds. S304. Complete the equipment installation and commissioning according to the plan, analyze environmental data in real time through the edge computing module, dynamically adjust the collaborative strategy, and realize the cyclical control of environmental changes, parameter adjustments and effect verification.

[0012] Furthermore, the specific steps to obtain the three-dimensional effect evaluation results are as follows:

[0013] S401. Set up collection points every 20 meters in areas with weak signal coverage and every 50 meters in normal areas to collect enhanced signal quality data, security level data, and energy efficiency data.

[0014] S402. Calculate the compliance rates of various indicators, namely, the received power meets the standard of ≥-85dBm, the signal-to-noise ratio is ≥20dB, the transmission rate meets the level 4 encryption and rate adaptation relationship, and the bit error rate is ≤10. -6 The identification success rate is ≥99.9%, and the power consumption per unit of data is ≤50 milliwatt-hours / gigabyte;

[0015] S403. The three-dimensional effect evaluation results are calculated by using a combination of hierarchical analysis and entropy weighting for weight allocation;

[0016] S404. Compare the evaluation results with the preset threshold of ≥90 points, and record the changes in indicators under extreme scenarios such as icing ≥10 mm and wind speed ≥20 m / s, as constraints for parameter optimization.

[0017] Furthermore, the specific steps for optimizing the parameters are as follows:

[0018] S501. If the evaluation result is lower than the threshold, the reason for failure is deduced by the mapping model. That is, if the power is not up to standard, the reflection intensity of the smart reflector is adjusted and interference is avoided; if the rate is not up to standard, the frequency band is switched and the encryption level is lowered; if the security is not up to standard, the identification parameters are updated and the key cycle is shortened; if the energy consumption is not up to standard, the smart reflector sleep mechanism is activated.

[0019] S502. After adjustment, re-execute the enhancement operation, collect data to calculate the new evaluation results, until the standard is met and the extreme scenario index fluctuation is ≤5%;

[0020] S503. Store environmental parameters, adjustment strategies, and evaluation results in the database, iteratively optimize the mapping model, and reduce the response time for parameter adjustment in similar scenarios from 100 milliseconds to 40 milliseconds, and reduce the number of adjustments by 60%.

[0021] Furthermore, the specific steps for collecting dynamic environmental interference data along the line are as follows:

[0022] S1021. Deploy multi-sensor fusion monitoring equipment at each detection point to simultaneously collect electromagnetic interference data, meteorological data, and dynamic occlusion data;

[0023] S1022. Perform time-frequency analysis and source identification on electromagnetic interference data, namely, industrial interference is concentrated in 1 to 30 MHz, civilian communication interference is concentrated in 800 MHz to 6 GHz, and the interference of the line itself is 50 / 60 Hz harmonics, and establish a correlation model between interference source, frequency and influence radius;

[0024] S1023. Establish special scenario and signal attenuation models, namely, icing attenuation = icing thickness × 0.3 dB / mm, strong wind deviation = wind speed × 0.2 degrees / m / s, and obstruction attenuation = cross-sectional area of ​​obstruction × 0.1 dB / m².

[0025] S1024. Integrate the above data and models to form dynamic environmental disturbance data containing real-time values, predicted values, and compensation strategies, providing input for the mapping model.

[0026] Furthermore, the specific steps for the two-way advance prediction and collaborative operation of the intelligent reflective surface and the wireless LAN authentication and security infrastructure are as follows:

[0027] S3031. Both types of devices maintain time synchronization through a dual-mode BeiDou and GPS clock, and the intelligent reflective surface adjusts its reflection parameters in advance based on weather forecasts for the next 10 minutes.

[0028] S3032. The access point collects the signal-to-noise ratio and bit error rate of the reflected signal every 50 milliseconds. When it predicts that the signal-to-noise ratio and bit error rate of the reflected signal will be lower than the threshold within 1 second, it sends an adjustment request to the intelligent reflector. The intelligent reflector completes the fine-tuning within 30 milliseconds.

[0029] S3033. When a monitoring terminal connects, the authentication server verifies the identity through a four-layer peer-to-peer mechanism and selects anti-interference coding or frequency hopping mode according to the type of interference in the reflected signal.

[0030] S3034. Two types of equipment shall establish a three-link redundant channel of primary, backup and emergency to ensure that the data transmission interruption time is ≤1.5 seconds.

[0031] Furthermore, the enhancement method also includes the following specific steps for multi-node collaborative enhancement:

[0032] S601. Deploy multiple sets of equipment according to the transmission line length and terminal density to form a hybrid star and chain multi-hop enhancement network;

[0033] S602. It adopts intelligent beamgrouping and dynamic resource allocation, that is, it identifies the terminal access method and divides it into unassisted group, first-level assisted group and second-level assisted group, and allocates reflection resources differently;

[0034] S603. Allocate working frequency bands with an interval of ≥80MHz and support frequency hopping to adjacent access points. For overlapping areas, select access points based on signal strength ≥-75dBm and access delay ≤10ms.

[0035] S604. Establish a distributed collaborative control center to receive device data through a 5G private network. In normal scenarios, unified scheduling reduces energy consumption by 30%, and in fault scenarios, distributed resource reconstruction ensures coverage recovery time is ≤5 seconds.

[0036] S605. The control center establishes a health assessment model based on equipment operation data, predicts faults 72 hours in advance, and triggers preventive maintenance to reduce the failure rate by 40%.

[0037] This invention provides a method for enhancing signal coverage of transmission lines based on the fusion of RIS and WAPI, which has the following beneficial effects: 1. By employing comprehensive basic data acquisition technology, and based on the interference intensity and terminal distribution density in different areas along the transmission line, detection points are strategically deployed to accurately capture the location characteristics of areas with weak signal coverage, as well as dynamic environmental factors such as electromagnetic interference, weather changes, and temporary obstructions. Building upon this, a dual strategy of interference avoidance and compensation using intelligent reflective surfaces is employed. First, the frequency distribution of interference along the line is analyzed to avoid strong interference bands and select the optimal operating frequency. Then, by inputting future weather forecast data, signal compensation parameters are preset for special scenarios such as icing and strong winds, effectively offsetting the adverse effects of various environmental factors on signal transmission. Simultaneously, through a multi-node collaborative deployment mode, equipment is arranged at reasonable intervals to ensure a certain coverage overlap rate. Combined with a dynamic resource allocation strategy, reflective resources are allocated differently based on terminal access methods and coverage requirements to ensure seamless coverage of the entire transmission line. 2. A four-layer peer-to-peer authentication mechanism based on a wireless LAN authentication and confidentiality infrastructure is adopted, integrating terminal devices, access point devices, authentication servers, and cloud platforms into a unified verification system. Key negotiation is completed using elliptic curve cryptography and the national standard SM2 algorithm, forming end-to-end security protection from identity verification to data encryption, fundamentally resisting security risks such as man-in-the-middle attacks and data tampering. Addressing the dynamic changes in interference intensity along transmission lines, a dynamic correlation mechanism between interference frequency and key update cycle is established. In areas with strong interference, the key update cycle is proactively shortened to ensure data transmission security remains unaffected under complex interference environments. Simultaneously, a multi-level encryption and transmission rate adaptation relationship is constructed. Encryption levels are flexibly switched based on signal quality detection results and data type differences, meeting the high security requirements of sensitive information such as control data while avoiding transmission efficiency loss due to excessive encryption of ordinary data, achieving a dynamic balance between data transmission security and efficiency. 3. Relying on a pre-trained real-time environmental perception and dynamic matching device parameter mapping model, the solution possesses the ability to quickly respond to dynamic environmental changes. The intelligent reflector, by accessing future weather forecast data, adjusts the reflection angle and intensity in advance, proactively avoiding signal attenuation caused by meteorological factors such as icing and strong winds. The wireless LAN authentication and security infrastructure monitors changes in electromagnetic interference in real time, dynamically switching operating frequency bands and authentication strategies to address issues such as interference frequency jumps and sudden narrowband interference. A two-way predictive data exchange channel is established between the two, sharing operating status data and signal quality data at fixed intervals. When the wireless LAN authentication and security infrastructure detects that the reflected signal quality is about to fall below a preset threshold, it sends a parameter adjustment request to the intelligent reflector in advance. The intelligent reflector completes fine-tuning of reflection parameters in a very short time, achieving preventative optimization. Even under extreme weather conditions with thick icing and high wind speeds, the system can maintain stable signal coverage and data transmission without human intervention, completely solving the shortcomings of traditional fixed-parameter solutions that cannot adapt to dynamic environmental changes. Attached Figure Description

[0038] Figure 1 This is an overall flowchart of an embodiment of the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] Example: Please refer to Figure 1 This embodiment provides a method for enhancing the signal coverage of transmission lines based on the fusion of RIS and WAPI. The steps of this enhancement method are as follows:

[0041] S1. Obtain full-dimensional basic data of the transmission line scenario, including the transmission line path, dynamic environmental interference along the line, current signal coverage strength, equipment deployment location, and historical line operation status and fault association information. Among them, the dynamic environmental interference information includes real-time electromagnetic interference, line icing thickness, and temporary obstructions.

[0042] S2. Based on full-dimensional basic data, combined with the RIS scenario-based signal reflection characteristics and WAPI dynamic security adaptation characteristics, the signal reflection parameters of RIS, the security transmission configuration parameters of WAPI, and the signal coverage enhancement execution scheme are determined through a pre-trained environment perception and parameter mapping model.

[0043] S3. Deploy a RIS with multi-scenario signal compensation capabilities according to the execution plan, enable the WAPI dynamic secure transmission mechanism, and work collaboratively through a two-way predictive data interaction channel to perform transmission line signal coverage enhancement operations;

[0044] S4. Collect enhanced signal coverage data, equipment operation data, and real-time environmental data along the route, input them into the mapping model for iterative optimization, and obtain a three-dimensional effect evaluation result including signal quality indicators, safety level indicators, and energy efficiency indicators;

[0045] S5. Based on the three-dimensional effect evaluation results, determine whether the signal coverage meets the preset requirements. If not, optimize the parameters of the two types of equipment synchronously based on the scenario-based pre-adjustment parameters output by the mapping model. Otherwise, determine the final solution and store it in the transmission line signal enhancement scenario storage module to provide parameter templates for similar scenarios.

[0046] As shown in steps S1 to S5 above, comprehensive basic data is the prerequisite for subsequent data analysis, covering five categories of data: transmission line path, dynamic environmental interference, current signal coverage, equipment deployment location, and historical operation and fault correlation. Among them, the operating environment of transmission lines is highly dynamic. Factors such as industrial electromagnetic interference, seasonal icing, and temporary construction blockages can directly lead to signal attenuation or distortion. Single-dimensional data cannot support accurate equipment parameter configuration. Only by comprehensively capturing the static structural characteristics of the line and the dynamic environmental changes can we ensure that the parameters of the two types of equipment meet the actual needs of the scenario.

[0047] Dynamic parameter adaptation is achieved through a mapping model, which establishes a non-linear relationship between environmental parameters and equipment parameters. This relationship is first generated through joint training using historical interference and attenuation correlation data, equipment response time data, and parameter compensation data for specific scenarios; that is, P out =f(X) env (W), where P out The output is a set of integrated parameters, including reflection parameters such as the operating frequency band, reflection angle, and reflection intensity of the smart reflector, as well as security configuration parameters such as the encryption level, key update cycle, and authentication timeout of the wireless LAN authentication and confidentiality infrastructure; X env The input is a full-dimensional environmental parameter vector, including key environmental indicators such as regional interference intensity, ice thickness, wind speed, cross-sectional area of ​​obstructions, and terminal distribution density; W is the weight matrix iteratively optimized during model training, used to quantify the influence of different environmental indicators on equipment parameters; f(·) is a nonlinear mapping function built based on a neural network, which fits the complex relationship between the environment and parameters through a multilayer perceptron structure, with the design goal of achieving real-time adaptation requirements.

[0048] The scene-specific signal reflection characteristics of the intelligent reflector are achieved through a dual strategy of interference avoidance and compensation. The interference avoidance strategy targets concentrated frequency bands such as industrial interference and civilian communication interference, selecting interference-free or weakly interfering frequency bands as the operating frequency band to avoid signal conflicts. The compensation strategy addresses signal attenuation or offset caused by environmental factors such as icing and strong winds by dynamically adjusting the reflection intensity or angle through preset compensation coefficients to offset the negative environmental impacts. The dynamic security adaptation characteristics of the wireless LAN authentication and confidentiality infrastructure balance transmission security and efficiency through a three-layer mechanism of four-layer peer-to-peer authentication, dynamic key update, and multi-level encryption and rate adaptation. The four-layer authentication ensures the legitimacy of access devices, the dynamic key update addresses security risks in interference environments, and the multi-level encryption and rate adaptation are flexibly adjusted according to data security requirements and signal quality to avoid rate loss caused by excessive encryption. The integration of the two is not a simple functional superposition, but rather achieves status synchronization and early warning through a two-way predictive data interaction channel, ensuring that the signal enhancement effect and security protection strategy are consistent.

[0049] The value of the solution is quantified using a three-dimensional effect evaluation system, with the three-dimensional effect evaluation value being S=αS. q +βS s +γS e , among which, S q The signal quality score encompasses five core metrics: received power, signal-to-noise ratio, transmission rate, bit error rate, and end-to-end delay, reflecting the stability and reliability of signal coverage. s The security level score covers three categories of indicators: identity authentication success rate, key anti-cracking capability, and data transmission integrity, adapting to the sensitive data transmission requirements of power transmission lines; S e The energy efficiency score covers the operating energy consumption and unit data transmission energy consumption of two types of equipment, taking into account the economics of the solution; α, β, and γ are the weight coefficients of signal quality score, security level score, and energy efficiency score, respectively, and satisfy α+β+γ=1. They are determined by the analytic hierarchy process and entropy weight combination method, which takes into account the emphasis of expert experience on core indicators and optimizes the weight allocation based on actual data characteristics, avoiding the one-sidedness of traditional single-dimensional evaluation.

[0050] The purpose of the transmission line signal enhancement scenario storage module is to form a parameter template library of mature solutions. When encountering similar scenarios, the templates can be directly called and fine-tuned, which greatly improves implementation efficiency and reflects the practicality and scalability of the solution.

[0051] Taking a 50-kilometer 220 kV high-voltage transmission line as an example, the line crosses three typical terrain types: industrial plant area, mountainous area, and farmland. It has problems such as concentrated industrial electromagnetic interference in the 800 to 900 MHz frequency band with an interference intensity greater than -60 dB / mW, winter icing thickness of up to 10 mm, frequent tree obstruction and temporary obstruction by construction machinery.

[0052] Static path parameters such as tower location, spacing, and material were extracted using a geographic information system for power transmission lines. Drones equipped with lidar were used to fly low along the lines to acquire dynamic data on line sag. 300 monitoring points were strategically placed according to regional differences, continuously collecting 72 hours of data on electromagnetic interference, meteorological conditions (temperature, humidity, wind speed, icing thickness, and temporary obstruction). A spectrum analyzer was used to collect signal indicators such as received power and signal-to-noise ratio at each point. Tower load-bearing calculation software, combined with structural parameters, determined the maximum installation weight of the intelligent reflector to be 50 kg, and the minimum deployment height of the wireless LAN authentication and security infrastructure access point to be 15 meters. Records of 12 signal interruption faults over the past five years were collected to establish a correlation database of fault causes, environmental parameters, and signal attenuation. Finally, after outlier removal, spatiotemporal alignment, and Min-Max normalization, a full-dimensional dataset X was formed. env =[x1, x2, x3, x4, x5], where x1 is the interference intensity, x2 is the icing thickness, x3 is the wind speed, x4 is the cross-sectional area of ​​the obstruction, and x5 is the terminal distribution density; input the dataset into the trained mapping model, which is based on the strong interference characteristics of industrial plants in the 800 to 900 MHz range. The model outputs the following: initial reflection intensity of the intelligent reflector in the 920 to 940 MHz operating frequency band: 18 dB / mW; icing compensation coefficient: 0.8 dB / mm; wind speed fine-tuning: 0.5 degrees / m / s; effective when the wind speed is greater than 15 m / s; outputs the wireless LAN authentication and confidentiality infrastructure key update cycle: 10 minutes; four-level encryption rate adaptation relationship: 128-bit SM4 encryption corresponding rate threshold: 80 Mbps, etc.; and simultaneously generates device... A complete solution for deployment location, installation specifications, and collaborative execution processes; 167 sets of intelligent reflectors and wireless LAN authentication and security infrastructure equipment are deployed according to the solution. The intelligent reflectors are started 3 minutes in advance to complete parameter initialization and reflection angle and intensity calibration; the wireless LAN authentication and security infrastructure access point triggers a secure connection request after detecting that the signal-to-noise ratio of the reflected signal is not less than 20 dB; both share status data every 50 milliseconds via a UDP channel, optimizing the intelligent reflector's reflected signal quality and the wireless LAN authentication and security infrastructure access status synchronously; in areas with weak signal coverage, priority level 1 data points are set every 20 meters; in areas with normal signal coverage, priority level 2 data points are set every 50 meters. Three types of indicator data are continuously collected for 72 hours to calculate the signal quality score S. q =95 Security Level Score s =93 Energy efficiency score S e=90; Substituting the combined weights α=0.39, β=0.36, γ=0.25, the three-dimensional evaluation value S=95×0.39+93×0.36+90×0.25=93.03 points, which is higher than the preset threshold of 90 points; After the evaluation meets the standard, the scheme, including environmental feature configuration parameters and collaborative strategies, is stored in the scene storage module to form a parameter template for the scenario of industrial interference plus mountainous terrain plus winter icing, for reuse by similar lines; If it does not meet the standard, the parameter optimization process is started, and the model parameters and equipment configuration are adjusted based on the reasons for not meeting the standard.

[0053] In a specific implementation process, the specific steps for obtaining full-dimensional basic data are as follows:

[0054] S101. Extract the starting and ending tower locations, tower spacing, line direction, and tower material parameters of the transmission line through the GIS of the transmission line, and combine them with the line sag data of the UAV inspection to form the transmission line path information containing spatial topology and structural characteristics;

[0055] S102. Along the transmission line, detection points are arranged according to the regional interference intensity and terminal distribution differences. Electromagnetic interference data, meteorological data and temporary obstruction data are continuously collected for a preset duration using multi-functional dynamic monitoring equipment.

[0056] S103. Collect the signal receiving power, signal-to-noise ratio, transmission rate, bit error rate and end-to-end delay of each detection point through signal comprehensive analysis equipment to form current signal coverage strength information with spatiotemporal dimensions;

[0057] S104. Based on the tower load-bearing calculation results, determine the maximum installation weight of RIS and the deployment height limit of WAPI access point, and statistically analyze the coverage area of ​​deployed communication base stations and the distribution density of monitoring terminals to form equipment deployment location information;

[0058] S105. Collect signal interruption fault records along the line, establish a database linking fault causes, environmental parameters and signal attenuation, and form information on the correlation between line operation status and faults;

[0059] S106. Standardize the collected data to obtain full-dimensional basic data.

[0060] As shown in steps S101 to S106 above, the path-related information collection adopts a combination of GIS and UAV inspection technology. The two work together to achieve data integrity and accuracy. The GIS technology relies on the existing geographic information platform of the power system to extract static parameters such as tower location coordinates, tower spacing, line route, tower material, total tower height, and conductor type. These parameters are the basis for determining the installation location of the intelligent reflector, load-bearing limit, and signal propagation path. For example, the tower material affects signal penetration loss, and the conductor type determines the wind deflection coefficient calculation, which is directly related to the subsequent safety distance configuration. The UAV inspection is equipped with a lidar sensor and a high-definition camera, flying at a low altitude and constant speed along the line. The system operates at a speed of 5 meters per second, acquiring dynamic data such as conductor sag and surrounding terrain. Sag data is a key dynamic parameter that changes with temperature and load, directly affecting the calculation of the installation height and reflection angle of the intelligent reflector. This prevents signal coverage failures at target terminals due to sag variations. Transmission lines traverse vast areas with complex terrain, including mountains, farmland, and industrial plants. A single data acquisition method cannot simultaneously guarantee data integrity and real-time performance. GIS excels at static data extraction but cannot capture dynamic sag changes. While UAV inspections can acquire dynamic data, their coverage is limited. Combining both methods enables comprehensive acquisition of both static structural and dynamic morphological data.

[0061] The differential detection point layout is determined by the distance L between detection points based on the regional interference intensity I and the terminal distribution density D. Wherein, I1=-60dBm is the strong interference threshold, I2=-80dBm is the weak interference threshold, D1=5 units / km is the high density threshold, and D2=2 units / km is the low density threshold; denser collection points are used in areas with strong interference and high terminal density to ensure accurate data capture of signal fluctuations and environmental changes; sparser collection points are used in remote areas to balance collection accuracy with manpower and equipment costs; this layout logic aligns with the different signal requirements of different areas of the transmission line, namely, areas with strong interference and high terminal density have high signal quality requirements, requiring dense data collection to support accurate adaptation; remote areas have low signal requirements, and sparse collection can meet the needs, avoiding resource waste;

[0062] The multi-functional dynamic monitoring equipment revolves around the synchronous acquisition of multi-source data, including electromagnetic interference sensors, meteorological sensors, and millimeter-wave radar. The electromagnetic interference sensor covers common interference frequency bands of power transmission lines, ensuring the capture of frequency jumps and pulse characteristics of interference signals. The meteorological sensor integrates monitoring functions for temperature, humidity, wind speed, icing thickness, and precipitation intensity; these meteorological parameters directly affect signal transmission loss and the reflection effect of the intelligent reflector. The millimeter-wave radar is used to identify dynamic obstructions such as construction machinery and fallen trees, solving the problem of parameter adaptation lag caused by temporary obstruction data loss. The three types of sensors are synchronized through a unified clock to ensure consistent timestamps of the collected data, facilitating subsequent correlation analysis of the correspondence between environmental factors and signal changes.

[0063] Signal comprehensive analysis equipment must have the ability to acquire multiple indicators simultaneously. The acquired indicators include signal received power, signal-to-noise ratio, transmission rate, bit error rate, and end-to-end delay. These indicators reflect the signal coverage quality from different dimensions. The equipment supports spatiotemporal dimension recording function, which associates the coordinate information of detection points to form spatiotemporal dimension signal coverage strength data. Because there are significant time and space differences in signal coverage quality, only spatiotemporally correlated data can accurately identify the specific location and change pattern of weak coverage areas.

[0064] Data standardization is performed using Min-Max normalization: x′ = (xx) min ) / (x max -x min ), where x is the original data, x min x is the minimum value of the data in this dimension. max x′ represents the maximum value of the data in this dimension, and x′ represents the standardized data. The purpose of this processing is to eliminate the influence of differences in the magnitude of different indicators, avoid bias towards indicators with large magnitudes during model training, ensure the fairness and accuracy of model training, and at the same time remove abnormal data caused by sensor failures to further improve data quality.

[0065] The core of the fault correlation library is to establish the correspondence between fault causes, environmental parameters, and signal attenuation. The attenuation is calculated as A = A0 + k1I + k2h + k3s, where A is the total signal attenuation, A0 is the free space signal attenuation, and k1, k2, and k3 are the attenuation coefficients for interference, icing, and obstruction, respectively, obtained by fitting historical fault data. This is used to quantify the impact of different environmental factors on signal attenuation, provide historical references for parameter compensation in special scenarios, and improve the accuracy of parameter adaptation.

[0066] During implementation, the starting and ending tower locations were first extracted using the transmission line geographic information system (GIS) from latitude 30°15′ to 30°45′ N and longitude 114°20′ to 114°50′ E, with an average tower spacing of 300 meters and the towers being made of reinforced concrete. A drone equipped with a lidar was used to fly along the line, acquiring average sag data of 12 meters, which was then integrated to form path-related information. Next, detection points were deployed according to a differentiated principle: in industrial plant areas, the interference intensity I=-55dBm > I1=-60dBm and the terminal density D=6 units / km > D1=5 units / km, with one detection point every 50 meters for a total of 80 points; in mountainous and farmland areas, I=-70dBm and D=3 units / km, with one detection point every 100 meters for a total of 200 points; in remote, uninhabited areas, I=-85dBm < I2=-80dBm and D=1 unit / km < D2=2 units / km, with one detection point every 200 meters. One monitoring point was set up, with a total of 20 monitoring points, for a total of 300 monitoring points. Each monitoring point was equipped with a Huawei OceanStor Dorado series multi-functional dynamic monitoring device. The device then continuously collected data for 72 hours. Electromagnetic interference data recorded the frequency and intensity of interference from industrial equipment (1-30MHz) and civilian communication interference (800MHz-6GHz). Meteorological data recorded temperature (-5℃-25℃), humidity (30%-85%), wind speed (0.5-18 m / s), icing thickness (0-8 mm), and precipitation intensity (0-15 mm / h). Temporary obstruction data recorded the location and dimensions of 3 construction machinery sites and 2 fallen trees, with obstruction cross-sectional areas ranging from 2 to 8 square meters. Keysight N9960A signal analysis equipment was used to collect signal received power (-98-75dBm), signal-to-noise ratio (12-22dB), transmission rate (10-50 Mbps), and bit error rate (10%) at each monitoring point. -5 Up to 10 -3 The end-to-end latency is 30 to 120 milliseconds. Data is generated every 10 seconds, linked to the coordinates of the detection points to form spatiotemporal data. Using tower load-bearing calculation software combined with tower structural parameters, the maximum installation weight of the intelligent reflector is determined to be 50 kg. The deployment height of the wireless LAN authentication and security infrastructure access point is no less than 15 meters, and the total tower height is 25 meters. Statistics show that 3 4G base stations have been deployed, covering a radius of 1.5 kilometers, and 120 monitoring terminals have been deployed, averaging 2.4 terminals per kilometer, forming equipment deployment location information. Data from the past 5 years of line data is also collected. Twelve signal interruptions were recorded. Industrial interference caused signal attenuation of 15-25 dB in four instances, icing caused attenuation of 10-18 dB in three instances, and obstruction caused attenuation of 8-12 dB in five instances. Substituting these values ​​into the attenuation formula yielded coefficients k1=0.3, k2=0.8, and k3=0.1, which were then used to establish an association library. Finally, the Python Pandas tool was used to remove 32 abnormal data points from the electromagnetic interference sensor. All data were then processed using a normalization formula, for example, when the interference intensity x = -55 dBm, x... min =-90dBm, x max=-40dBm, then x'=(-55+90) / (-40+90)=0.7, ultimately forming a 1.2GB full-dimensional basic dataset, providing high-quality input for subsequent parameter adaptation.

[0067] In a specific implementation process, the specific steps for determining the RIS reflection parameters are as follows:

[0068] S201. Based on the transmission line path information and the current signal coverage strength information, a multi-dimensional weighted clustering analysis method is used to identify areas with weak signal coverage, and the priority of the RIS target coverage area is determined according to the urgency level.

[0069] S202. Based on the interference frequency distribution of dynamic environmental interference information along the line, a dual strategy of interference avoidance and power compensation is adopted to select the RIS operating frequency band. When strong interference frequency bands are concentrated, the frequency band that avoids strong interference is selected and the appropriate power compensation is set.

[0070] S203. Based on the target coverage area and equipment deployment location, and combined with the tower spatial coordinates, calculate the three-dimensional distance and angle between the RIS and the signal transmitter and receiver, and determine the initial angle of the reflection unit;

[0071] S204. Input future preset duration weather forecast data through the mapping model, and calculate the scene-based reflection parameter compensation value;

[0072] S205. Through on-site dynamic testing and verification, the signal reception power of the target coverage area under special scenarios is kept stable above the preset threshold and the fluctuation range meets the requirements. The RIS signal reflection parameters, including the working frequency band, reflection unit angle, reflection intensity and scene compensation coefficient, are obtained.

[0073] As shown in steps S201 to S205 above, the core of the multi-dimensional weighted clustering analysis method is to allocate weights according to signal attenuation, interference intensity, and terminal distribution density, and to prioritize coverage areas using the K-means clustering algorithm. The core objective function is J = Where J is the sum of squares within the cluster, and the smaller the value, the higher the similarity of data within the same cluster; k is set to 3 to correspond to the three-level priority; C i For the i-th cluster; μ i The center of the i-th cluster; ||x-μ i || 2 The weight is the squared Euclidean distance between the data point and the cluster center; the weights are represented by the weighted Euclidean distance. =w1(x1-μ i1 ) 2 +w2(x2-μ i2 ) 2 +w3(x3-μ i3 ) 2Among them, w1=0.4 is the signal attenuation weight, w2=0.3 is the interference strength weight, and w3=0.3 is the terminal density weight. The signal attenuation weight is the highest because it directly reflects the degree of insufficient coverage. The interference strength and terminal distribution density weights are the next highest, corresponding to the main causes of signal attenuation and the urgency of coverage needs, respectively. The signal demand in different areas of the transmission line varies significantly. Prioritization can ensure that intelligent reflector resources are tilted towards high-demand areas to avoid resource waste.

[0074] Parameter configuration employs a dual strategy of interference avoidance and power compensation. The interference avoidance strategy first identifies the concentrated frequency bands of various interferences through time-frequency analysis of dynamic environmental interference data, then selects adjacent interference-free or weakly interfering frequency bands as the operating frequency band to avoid signal-interference conflicts. The power compensation strategy addresses signal attenuation caused by weather conditions, obstructions, etc., and designs a compensation formula P. c =P0+k h h+k v v, where P c P0 is the initial reflection intensity, and k is the compensated reflection intensity. h Here, h is the icing compensation coefficient, h is the icing thickness, and k is the icing thickness. v Here, v is the wind speed compensation coefficient, and k is the wind speed. h With k v The compensation amount is obtained by linear regression fitting of historical data to ensure that the compensation amount is accurately matched with environmental changes. KV only takes effect when the wind speed exceeds a certain threshold, because the signal offset will significantly affect the coverage effect at this time.

[0075] The initial angle calculation of the reflecting unit adopts a three-dimensional geometric model, including the elevation angle formula θ=arctan[(z r -z t ) / d xy ] and the azimuth formula φ=arctan2(y r -y t x r -x t ), where (x t ,y t ,z t (x) represents the three-dimensional coordinates of the intelligent reflective surface. r ,y r ,z r ) represents the average three-dimensional coordinates of the target receiver, d xy The horizontal distance between the two The angles calculated using these two formulas ensure that the signals reflected by the intelligent reflective surface are accurately and directionally transmitted to the target area, improving signal utilization efficiency and avoiding signal attenuation caused by signal diffusion.

[0076] The scenario-based compensation parameter calculation model uses meteorological forecast data as input and fits the compensation coefficients through a linear regression model. The core regression equation is k. h =a1I+b1、k v =a2I+b2, where I is the regional interference intensity. The interference intensity affects the compensation effect. The signal attenuation is more severe in areas with strong interference, requiring a higher compensation intensity. a1, b1, a2, and b2 are regression coefficients, obtained by fitting historical correlation data. The purpose of this model is to adapt to future weather changes in advance, avoid signal quality fluctuations caused by adjustments after environmental changes, and achieve preventive compensation.

[0077] The on-site dynamic testing and verification process is a crucial step in ensuring the validity of the parameters. The criteria for compliance are that the actual received power is not lower than the preset threshold and the power fluctuation amplitude does not exceed the limit value, i.e., Prec≥P th And ΔP≤ΔP max , where P rec For received power, P th =-85dBm is the threshold value, ΔP is the power fluctuation amplitude, and ΔP max =2dB is the maximum allowable fluctuation; the test scenarios cover typical extreme environments of transmission lines such as icing, strong wind, and temporary interference. Through testing in these scenarios, it is ensured that the parameters can still meet the requirements under extreme environments, avoiding the disconnect between theoretical parameters and actual scenarios.

[0078] In a specific implementation process, the specific steps for determining the WAPI secure transmission configuration parameters are as follows:

[0079] S211. Based on the safety level requirements of transmission lines, select an identity authentication method that supports four-layer peer-to-peer authentication of terminals, access points, authentication servers, and cloud platforms, and use elliptic curve cryptography combined with an encryption algorithm adapted to security requirements for key negotiation;

[0080] S212. Based on the interference fluctuation period and intensity of the dynamic environmental interference information along the route, establish the correspondence between interference frequency and key update period; combine the RIS transmission rate and the data type of the monitoring terminal to establish a multi-level encryption and rate adaptation relationship;

[0081] S213. Add an interference adaptive identification mechanism to adjust the identification timeout when sudden interference is detected, forming WAPI secure transmission configuration parameters.

[0082] As shown in steps S211 to S213 above, the four-layer peer-to-peer authentication method is the foundation of security protection, involving four main entities: terminal, access point, authentication server, and cloud platform. The authentication process strictly follows national cryptographic standards. Specifically, the terminal sends its identity certificate and authentication request to the access point. After verifying the terminal's identity, the access point forwards the request to the authentication server. The authentication server simultaneously verifies the identities of both the access point and the terminal, and then feeds back the verification results to the terminal through the access point. After the terminal and access point establish a secure connection, the authentication server sends the key to both through an encrypted channel. The cloud platform monitors the authentication process in real time. The authentication success rate is calculated using the formula R. s =N s / (N) s +N f ), where R s To determine the success rate, N s To determine the number of successful identifications, N f The number of failed authentication attempts includes the number of various security attack incidents. Transmission line data directly affects power grid security, and the transmission environment is complex and vulnerable to attacks. The four-layer two-way verification confirms identity from multiple dimensions, which can fundamentally resist security risks such as man-in-the-middle attacks and identity forgery.

[0083] The combined application of elliptic curve cryptography and the national standard SM2 algorithm provides a dual guarantee of security and compliance; elliptic curve cryptography uses a universal security curve, with the equation y 2 =x 3 +ax+b, where 'a' is the constant coefficient defining the curve shape and 'b' is a preset fixed value. This method features a short key length but high encryption strength, low computational load, and high encryption efficiency, making it suitable for scenarios with limited computing power in power transmission line terminal equipment. The SM2 key negotiation process follows national cryptographic standards. The terminal and access point generate public and private keys respectively, exchange public keys, calculate a shared key, and then generate a session key using a hash function for encrypted data transmission. The key length L... k With encryption strength S s The relationship formula is S s =k L ×L k , where k L k is the strength coefficient. L =0.8, this formula quantifies the relationship between key length and encryption strength, ensuring that the selected key length meets national security level requirements;

[0084] Dynamic correlation between interference frequency and key update cycle Among them, T k Let f be the key update cycle, f be the interference fluctuation frequency, I be the interference intensity, f1 = 1 time / hour, f2 = 0.5 times / hour, I1 = -60dBm, I2 = -80dBm be the thresholds, and T be the key update cycle. k1 =10 minutes, T k2=60 minutes, T k3 =120 minutes is the preset period, which is determined by the combination of interference frequency and interference intensity. The higher the interference intensity and the more frequent the frequency, the greater the risk of the key being intercepted and cracked. The risk of cracking can be reduced by shortening the key update period. In low interference scenarios, a longer update period is used to avoid transmission efficiency loss caused by excessive updates.

[0085] Multi-level encryption and rate adaptation technology, V th =V max ×(1-λL s ), where V th V is the transmission rate threshold. max Where L is the maximum transmission rate, λ is the rate attenuation coefficient, and L is the maximum transmission rate. s For encryption level, L s =1, 2, 3, 4 correspond to four levels of encryption: 128-bit, 96-bit, 64-bit, and 48-bit SM4 encryption. The security and transmission efficiency requirements of different data vary significantly. The security requirements of regulatory data are the highest, so high-strength encryption is used. The security requirements of video data and historical data are lower, so lightweight encryption is used. By dynamically switching the encryption level and rate threshold, a precise balance between security requirements and transmission efficiency is achieved.

[0086] The core formula of the interference adaptive discrimination mechanism technology is T. o =T o0 ×B i0 / B i , among which, T o To identify the timeout period, T o0 B is the default timeout period. i0 As the reference interference bandwidth, B i This is the actual detected interference bandwidth. When a sudden narrowband interference is detected, the identification timeout is automatically shortened to avoid identification failure caused by obstructed identification signal transmission. When a broadband interference is detected, the timeout remains at the default value or is appropriately extended to avoid efficiency loss caused by frequent retries.

[0087] In a specific implementation process, the specific steps for formulating and executing a signal coverage enhancement implementation plan are as follows:

[0088] S301. Based on the RIS signal reflection parameters and equipment deployment location, combined with tower load-bearing calculations, determine the installation height, vibration and corrosion prevention fixing method, and safe distance from high-voltage conductors for the RIS-compatible tower structure and safety requirements, and form a RIS deployment plan;

[0089] S302. Based on WAPI security configuration parameters, a relay gateway integrating edge computing is deployed on the transmission line tower. The gateway uses dual-link backhaul to the north and uses smart antennas to adapt to the sector coverage requirements of the line to the south. The relay equipment adopts multi-hop cascading between towers and dynamic power control to form a WAPI enabling scheme.

[0090] S303. Integrates two types of solutions: RIS starts and initializes compensation parameters in advance with a preset duration, and WAPI triggers a secure connection after detecting that the reflected signal meets the standard. Both share status data.

[0091] S304. Complete the equipment installation and commissioning according to the plan, and use the edge computing module to analyze environmental data in real time and dynamically adjust the collaboration strategy.

[0092] As shown in steps S301 to S304 above, the core elements of the intelligent reflective surface deployment scheme revolve around security compliance and coverage effect design, with an installation height H. t =H p -H s -ΔH, where H p H is the total height of the tower. s To ensure safety, a safety margin of ΔH is provided for potential falls due to icing, preventing equipment from colliding with other components or being damaged by falling ice; the safety distance formula is D. s =D min +ΔD v , where D min ΔD represents the national standard value for safe distance from high-voltage equipment. v The wind deflection compensation distance is calculated from the maximum design wind speed and the wind deflection coefficient to ensure that the intelligent reflector and the high-voltage conductor always maintain a safe distance and avoid the risk of discharge.

[0093] The core of the relay gateway deployment and backhaul strategy is to ensure signal coverage and data backhaul reliability of the wireless LAN authentication and security infrastructure. Dual-link backhaul uses a combination of fiber optic and microwave links, with the fiber optic link as the primary backhaul link and the microwave link as a backup. Automatic switching occurs when fiber optic link loss exceeds a threshold, ensuring uninterrupted data backhaul. The smart antenna coverage radius is calculated using the formula R. a = , where P t For transmission power, G t G r λ is the antenna gain, λ is the operating wavelength, and P is the antenna gain. rec,th For the received power threshold, L s The coverage radius calculated using this formula, which accounts for system losses, combined with the sector coverage characteristics of smart antennas, can ensure signal coverage without dead zones. The multi-hop cascading and dynamic power control strategy between towers is suitable for long-distance transmission lines. Multi-hop cascading enables long-distance signal extension, while dynamic power control avoids signal interference from adjacent relay equipment.

[0094] The three-step collaborative process of pre-startup, collaborative verification, and dynamic adjustment is crucial to ensuring seamless cooperation between the two types of devices. The pre-startup time is set to a reasonable duration, allowing the intelligent reflective surface to complete parameter initialization and calibration, preventing connection failures due to signal insecurity after the wireless LAN authentication and security infrastructure has started. Collaborative verification conditions require that the signal-to-noise ratio and bit error rate meet standards, ensuring signal quality before establishing a secure connection, thus improving connection success rate and transmission stability. The dynamic adjustment cycle is set to a high frequency, allowing the two types of devices to share signal quality, device status, and other data according to this cycle, achieving synchronous optimization.

[0095] The application of the edge computing module realizes closed-loop control. The core logic is to locally parse environmental data and dynamically adjust the collaborative strategy. When the fluctuation of environmental parameters exceeds the threshold, it indicates that the signal quality may be affected and adjustment needs to be initiated. The edge computing module is deployed locally on the relay gateway, which has a fast response speed and avoids the signal quality degradation caused by the lag in adjustment after environmental changes. It realizes the cyclical control from environmental changes, parameter adjustment to effect verification.

[0096] In a specific implementation process, the three-dimensional effect evaluation results include:

[0097] S401. Set up collection points in areas with weak signal coverage and normal areas according to the degree of coverage weakness, and collect enhanced signal quality data, security level data, and energy efficiency data.

[0098] S402. Receive the acquired indicators and obtain the compliance rate of each indicator;

[0099] S403. Using a combination of analytic hierarchy process and entropy weighting, the three-dimensional effect evaluation results are obtained.

[0100] S404. Compare the evaluation results with the preset evaluation thresholds, record the changes in indicators under extreme scenarios, and use them as constraints for parameter optimization.

[0101] As shown in steps S401 to S404 above, the core of the differentiated data collection point layout technology is to allocate data collection resources according to priority, ensuring the relevance and comprehensiveness of the evaluation data while controlling the collection cost; the data collection point density ρ = ρ0 × L p Where ρ is the sampling point density, in units of points per meter; ρ0 is set to a baseline density of 0.01 points per meter, applicable to secondary priority areas; L pTo cover priority levels 1 to 3, corresponding to three levels of data collection density: Level 1 low-priority areas, such as remote farmland, ρ=0.005 points per meter, 1 point per 100 meters; Level 2 medium-priority areas, such as mountainous terrain areas, ρ=0.01 points per meter, 1 point per 50 meters; Level 3 high-priority areas, such as industrial interference-concentrated areas around substations, ρ=0.05 points per meter, 1 point per 20 meters. High-priority areas have high signal demand, complex environments, strong interference, and many obstructions, requiring dense data collection to accurately assess signal quality fluctuations; low-priority areas have simple environments and low signal demand, so sparse collection is sufficient to meet the assessment needs. The layout of collection points also needs to cover the entire scenario. Each priority area should include collection points in normal and extreme environments, such as icing and strong winds, to ensure that the assessment data can reflect the effectiveness of the solution under different environments. The selection of collection point locations should also take into account key locations such as near poles, the middle of the line, and obstructions, to avoid one-sided data.

[0102] The construction of a three-tiered evaluation index system is the core guarantee for the comprehensiveness of the evaluation. It covers three dimensions: technical effect, signal quality, security protection, security level, economic cost, and energy efficiency. Each dimension includes multiple core indicators to ensure that the evaluation is comprehensive. The signal quality index S... q This reflects the stability and reliability of signal coverage, and is the core effect of the enhancement scheme. It includes five indicators: received power (to assess signal strength, with a standard of no less than -85 dB / mW), signal-to-noise ratio (to assess anti-interference capability, with a standard of no less than 20 dB), transmission rate (to assess transmission efficiency, with a standard of conforming to encryption rate adaptation), and bit error rate (to assess transmission accuracy, with a standard of no more than 10). -6 End-to-end latency assessment real-time performance, meeting the standard of no more than 100 milliseconds; security level indicator S s This system adapts to the sensitive data transmission needs of power transmission lines and includes three indicators: Identity authentication success rate (access security, meeting standard no less than 99.9%), key anti-cracking capability (encryption security, meeting standard over 24 hours of data transmission integrity), and data security (data security, meeting standard no less than 99.9%), achieved through SM3 hash verification; and energy efficiency indicator S. e To balance cost-effectiveness and avoid excessive energy consumption, the solution includes three indicators: intelligent reflective surface energy consumption assessment equipment energy consumption (standard not exceeding 10 watts), wireless LAN authentication and security infrastructure transmission energy consumption assessment transmission energy consumption (standard not exceeding 15 watts), and unit data energy consumption assessment energy efficiency (standard not exceeding 50 milliwatt-hours per gigabyte). The weighting of each indicator is based on its importance: signal quality has the highest weight of 40%, as signal coverage is the core objective of the solution; security level is the next highest at 35%, representing the minimum requirement for power transmission line data transmission; and energy efficiency has a weight of 25%, taking into account cost-effectiveness.

[0103] The analytic hierarchy process (AHP) entropy weighting technique is used to scientifically allocate the weights of three types of indicators, avoiding the shortcomings of single subjective weighting or single objective weighting, and ensuring that the evaluation results are scientific and reasonable. The subjective weighting AHP method involves inviting 30 experts in the power communication field, including university professors, enterprise engineers, and power grid maintenance personnel, to score the importance of the three types of indicators based on a 1-9 scale. The statistical results yielded subjective weights α1=0.40, signal quality β1=0.35, safety level γ1=0.25, and energy efficiency. Experts generally agree that signal quality is the core, safety is the bottom line, and energy efficiency is supplementary. The objective weighting entropy weighting method is based on the characteristic dispersion of the actual collected data. The core logic of the entropy weighting method is: the higher the dispersion of the indicator data, the more effective information the indicator contains, and the greater the weight should be. The calculation formula is... , where p i The entropy value of the i-th type of indicator reflects the data dispersion; the smaller the entropy value, the higher the dispersion. Similarly, β2 and γ2 are calculated. Through calculation of a large amount of collected data, the objective weights are obtained as α2=0.38, β2=0.37, and γ2=0.25. The data dispersion of signal quality and security level are similar, and the weight difference is small. The data dispersion of energy consumption efficiency is low, and the weight is consistent with the subjective weight. The combined weight is the average of the subjective weight and the objective weight: α=(0.40+0.38) / 2=0.39, β=(0.35+0.37) / 2=0.36, γ=(0.25+0.25) / 2=0.25. This weight reflects both expert experience and respects data characteristics, ensuring the scientific nature of the evaluation results.

[0104] The formula for the three-dimensional evaluation value is S=αS q +βS s +γS e Among them, the signal quality score S q The weighting is as follows: received power 12%, signal-to-noise ratio 10%, transmission rate 10%, bit error rate 8%, and end-to-end delay 5%, totaling 45%. After normalization, this corresponds to the actual score. For example, if the received power compliance rate is 98.1%, the signal-to-noise ratio compliance rate is 98.7%, the transmission rate compliance rate is 96.5%, the bit error rate compliance rate is 99.3%, and the end-to-end delay compliance rate is 97.2%, then S... q =0.12×98.1+0.10×98.7+0.10×96.5+0.08×99.3+0.05×97.2=44.096, which is approximately 98.0 points after normalization; Safety Level S s Its weight allocation is as follows: authentication success rate 15%, key anti-cracking capability 12%, and data integrity 8%, totaling 35%. For example, if the authentication success rate is 99.95%, the key anti-cracking capability compliance rate is 100%, and the data integrity compliance rate is 99.8%, then S s=0.15×99.95+0.12×100+0.08×99.8=34.9765, which is approximately 99.9 points after normalization; Energy efficiency score S e Its weight allocation is 25% for the energy consumption of the intelligent reflective surface. After normalization, for example, if the energy consumption compliance rate of the intelligent reflective surface is 100%, the energy consumption compliance rate of the wireless LAN authentication and confidentiality infrastructure transmission is 100%, and the energy consumption compliance rate per unit of data is 100%, then S e =100 points, normalized to 90 points; the preset evaluation threshold is 90 points, which is determined based on the transmission line communication technology specifications and actual application needs: a score of not less than 90 points is considered to meet the standard and can be implemented directly; 80 to 89 points is considered to meet the basic standard and requires minor optimization; and below 80 points is considered to fail to meet the standard and requires comprehensive optimization.

[0105] Extreme scenario index fluctuation verification technology is used to ensure the environmental adaptability of the solution. The fluctuation amplitude formula is ΔR=|R ext -R norm | / R norm ×100%, where R ext R represents the index value under extreme scenarios. norm These are the indicator values ​​under normal scenarios. Extreme scenarios include typical environments for power transmission lines such as icing and strong winds. The fluctuation range is constrained to not exceed the limit value to ensure that all indicators can still meet the usage requirements under extreme scenarios.

[0106] In a specific implementation process, the specific steps for optimizing parameters are as follows:

[0107] S501. If the evaluation result is lower than the preset evaluation threshold, the reason for failure is deduced by the mapping model. If the power is not up to standard, the RIS reflection intensity is adjusted and interference is avoided. If the rate is not up to standard, the frequency band is switched and the encryption level is lowered. If the security is not up to standard, the identification parameters are updated and the key cycle is shortened. If the energy consumption is not up to standard, the RIS sleep mechanism is enabled.

[0108] S502. After adjustment, re-execute the enhancement operation, collect data to calculate the new evaluation results, until the standard is met and the fluctuation of indicators in extreme scenarios meets the requirements;

[0109] S503. Store environmental parameters, adjustment strategies, and evaluation results in the database, and iteratively optimize the mapping model.

[0110] As shown in steps S501 to S503 above, the core of the failure-to-achieve-the-cause reverse model is to accurately locate the root cause of the problem through the error contribution rate. , among which, S target Set the target threshold to 90 points, S i The actual scores for each dimension, including the signal quality score S. q Security Level Score sEnergy efficiency score S e This formula calculates the proportion of the absolute error of each dimension relative to the target threshold to the total error. The dimension with the highest proportion is the main reason for not meeting the target. For example, if the signal quality error contributes the most, it means that the problem is mainly in the configuration of the intelligent reflector parameters, which should be adjusted first. If the security level error contributes the most, it means that the wireless LAN authentication and confidentiality infrastructure parameters need to be optimized to avoid wasting resources and achieving poor results due to blind adjustments.

[0111] Targeted adjustment strategies are developed based on different reasons for non-compliance, ensuring precise and effective adjustments; when power is not up to standard, formula P is used. new =P old ×(1+k p ×(1-R q1 ), where P new P represents the adjusted reflection intensity. old k represents the reflection intensity before adjustment. p R is set to 0.1 as the power adjustment coefficient. q1 To determine the power compliance rate, this formula quantifies the adjustment range based on the power compliance rate; the lower the power compliance rate, the larger the adjustment range. Simultaneously, it incorporates interference detection data to avoid interfering frequency bands, optimizing signal quality from both intensity and frequency band perspectives. When the data rate fails to meet the target, a encryption level reduction strategy L is employed. s,new =L s,old +1, for each level of encryption reduction, L s,new and L s,old These represent the old and new encryption levels, with reduced encryption overhead and increased transmission speed. Simultaneously, the rate threshold is updated to ensure the security level still meets the requirements of the corresponding data, avoiding excessive reduction in security. If security is insufficient, a key period shortening strategy T is employed. k,new =T k,old ×0.8, T k,new and T k,old The key update cycle is shortened by 20% to improve key resistance to cracking, while the identity authentication algorithm parameters are optimized to improve the authentication success rate; when energy consumption is insufficient, the intelligent reflective surface sleep mechanism P is activated. sleep =P normal ×0.5, P sleep and P normal The reflection power is set to be either in sleep mode or in normal mode. During periods when no terminal access is detected, the reflection power is reduced by 50% to reduce ineffective energy consumption. At the same time, resource allocation is optimized through multi-node collaborative scheduling to further reduce overall energy consumption.

[0112] Model iterative optimization techniques improve the fitting accuracy of the mapping model by continuously accumulating data; the weight update formula is W. new =W old +η×(Pout,true -P out,pred )× Among them, W new For the updated weight matrix, W old Here is the weight matrix before the update, η is the learning rate, and P... out,true P is the actual optimal parameter. out,pred For model prediction parameters, This is the transpose of the environmental parameter vector; by using environmental parameters, adjustment strategies, and evaluation results as new training data, the model weights are continuously updated, enabling the model to continuously learn the adaptation rules for new scenarios.

[0113] Optimizing response time and the number of adjustments is a direct result of model iteration. The formula for optimizing response time is T. resp,new =T resp,old ×(1-ξ×N train ), where T resp,new To adjust the response time for the optimized parameters, T resp,old The response time before optimization is ξ, which is set to 0.001 as the optimization coefficient. Nt rain The number of training samples increases, and as the number of training samples increases, the model's prediction speed improves and the response time shortens; the optimization formula for the number of adjustments is N. adj,new =N adj,old × , where N adj,new N represents the optimized number of adjustments. adj,old To determine the number of adjustments before optimization, λ is set to 0.005 as the attenuation coefficient. After the model prediction accuracy is improved, the first prediction parameters are closer to the optimal value, the number of adjustments is reduced, and the practicality and efficiency of the scheme are improved.

[0114] In a specific implementation process, the collection of dynamic environmental interference data along the line includes:

[0115] S1021. Deploy multi-sensor fusion monitoring equipment at each detection point to simultaneously collect electromagnetic interference data, meteorological data, and dynamic occlusion data;

[0116] S1022. Distinguish the typical frequency bands and characteristics of industrial interference, civilian communication interference, and line self-interference, and establish a correlation model between interference source, frequency, and influence radius;

[0117] S1023. Establish the correlation between ice thickness, wind speed, and cross-sectional area of ​​obstructions and signal attenuation or offset;

[0118] S1024. Integrate the above data and models to form dynamic environmental disturbance data containing real-time values, predicted values, and compensation strategies, and input it into the mapping model.

[0119] As shown in steps S1021 to S1024 above, the electromagnetic interference time-frequency analysis technique uses short-time Fourier transform, and the core formula is X(m,n) = Where x(k) is the time-domain sequence of the interference signal, w(k) is the Hanning window function, N is the number of FFT points, m is the frequency index, and n is the time index. This is the Fourier transform kernel function; through this transform, a one-dimensional time-domain signal can be converted into a two-dimensional time-frequency graph, clearly showing the variation of interference frequency over time, providing a basis for interference source identification;

[0120] The correlation model between interference source, frequency, and influence radius adopts a power function attenuation model: I(r) = I0 × r -n Where I(r) is the interference intensity at a distance r from the interference source, I0 is the interference intensity at the interference source, r is the distance between the measurement point and the interference source, and n is the attenuation exponent. The attenuation characteristics of different interference sources vary significantly. Industrial interference attenuates more slowly, while civilian communication interference attenuates more quickly. This model can predict the interference intensity at different distances along the transmission line, providing a precise basis for intelligent reflector frequency band interference avoidance.

[0121] The purpose of constructing the special scenario and signal attenuation model is to quantify the impact of icing, strong winds, and obstruction on the signal, providing a quantitative basis for intelligent reflector parameter compensation; the icing attenuation formula is A. h =k h ×h,k h =0.3dB / mm, the strong wind displacement formula is Δθ=k v ×v,k v =0.2° / (m / s), the shading attenuation formula is A s =k s ×s, where k h k v k s The corresponding coefficients are obtained by fitting historical measurement data to quantify the impact of icing, strong winds, and obstruction on signal attenuation or offset.

[0122] The data prediction technique uses the ARIMA model φ(L)(1-L) d X t =θ(L)ε t Where φ(L) is the autoregressive coefficient polynomial, θ(L) is the moving average coefficient polynomial, d is the difference order, and X... t Let ε be the environmental parameter value at time t. t It is a white noise sequence; by training the model with historical environmental data, the autoregressive coefficient and moving average coefficient are determined, which can predict future environmental parameter values, realize early adaptation, and avoid signal quality fluctuations caused by adjustment lag after environmental changes.

[0123] In a specific implementation process, the specific steps for the collaborative work of RIS and WAPI in bidirectional advance prediction are as follows:

[0124] S3031. The two types of equipment maintain time synchronization accuracy to meet the coordination requirements through a dual-mode synchronous clock. The RIS adjusts the reflection parameters in advance based on the future preset duration of meteorological forecast data.

[0125] S3032. The access point collects the signal-to-noise ratio and bit error rate of the reflected signal according to a preset period. When it predicts that the signal quality will be lower than the threshold, it sends an adjustment request to the RIS. The RIS quickly completes the parameter fine-tuning.

[0126] S3033. When a monitoring terminal accesses the system, the authentication server verifies the identity through a four-layer peer-to-peer mechanism and selects an appropriate anti-interference strategy based on the type of reflected signal interference.

[0127] S3034. Two types of equipment shall establish a primary, backup and emergency three-link redundant channel to control the data transmission interruption time within a preset time.

[0128] As shown in steps S3031 to S3034 above, time synchronization technology is a prerequisite for collaborative work. The core is to ensure that the timestamps of the two types of devices are consistent, with a synchronization error of Δt=|t1-t2|, where t1 is the timestamp of the smart reflector and t2 is the timestamp of the wireless LAN authentication and security infrastructure access point. Δt is required to be no more than 10 nanoseconds. A BeiDou plus GPS dual-mode synchronization module is used to achieve time synchronization. The module simultaneously receives timing signals from BeiDou and GPS satellites, fuses the time information of the two signals through an algorithm, and outputs a high-precision time reference. The timing frequency is set to once per second to ensure the continuity and stability of time synchronization. Collaborative operations such as the sending and execution of parameter adjustment commands and the sharing of status data require strict time consistency. Excessive synchronization error will lead to deviations in adjustment timing. For example, when the access point sends a parameter adjustment request, if the timestamp of the smart reflector is lagging, it will cause adjustment delay and affect the collaborative effect.

[0129] Signal quality prediction technology enables preventative optimization; the core formula is SNR(t+Δt) = SNR(t) + k. s ×Δt, where SNR(t+Δt) is the predicted future signal-to-noise ratio, SNR(t) is the current signal-to-noise ratio, and k s Δt represents the signal-to-noise ratio change rate and the prediction duration. When the predicted signal-to-noise ratio is lower than the threshold, the wireless LAN authentication and confidentiality infrastructure access point sends an adjustment request to the intelligent reflector in advance. The intelligent reflector quickly completes the parameter adjustment to avoid a decrease in the signal-to-noise ratio. At the same time, the intelligent reflector adjusts the parameters in advance based on future weather forecast data, forming a two-way advance prediction mechanism to prevent signal quality degradation from both the signal receiving end and the transmitting end.

[0130] Anti-interference strategy selection technology dynamically adapts to the type of interference, with the core being interference bandwidth determination (B). i =f max -f min Among them, B i For interference bandwidth, f max f is the highest frequency of the interference signal. min The lowest frequency of the interference signal; when B i Interference frequencies below 10 MHz are considered narrowband interference. Narrowband interference is concentrated at specific frequencies, so anti-interference coding technology is activated, adding error correction codes to improve the signal's anti-interference capability; when B... i When the frequency is greater than or equal to 10 MHz, it is judged as wide-spectrum interference. Wide-spectrum interference has a wide coverage area. Switching to frequency hopping mode, it quickly switches between multiple preset frequency bands to avoid the interference band. The core of this technology is to select the optimal anti-interference method according to the interference characteristics to avoid the failure of a single anti-interference method in complex interference environments. For example, industrial equipment interference is mostly narrowband interference, and enabling anti-interference coding is more effective. Civil communication interference is mostly wide-spectrum interference, and frequency hopping mode is more effective.

[0131] Three-link redundancy channel technology improves the fault tolerance of data transmission. The link switching time formula is T. switch =T detect +T connect , among which, T switch T represents the total link switching time. detect The timeout is set to 100 milliseconds for fault detection. The access point monitors the link transmission quality in real time, and a link fault is determined when the packet loss rate exceeds 1% or the latency exceeds 50 milliseconds. connect The timeout is set to 400 milliseconds for establishing a new link connection. The three links include a primary channel, a backup channel, and an emergency channel. The primary channel uses fiber optic transmission with a bandwidth of 1 gigabit per second and a latency of 10 milliseconds, offering high transmission speed and stability. The backup channel uses microwave transmission with a bandwidth of 100 megabits per second and a latency of 50 milliseconds, serving as a rapid switching channel in case of a primary channel failure. The emergency channel uses ultra-shortwave transmission with a bandwidth of 10 megabits per second and a latency of 100 milliseconds, serving as a last resort. The primary / backup switching requirement is T... switch The interruption time should not exceed 500 milliseconds, and the emergency switching time should not exceed 1 second, to ensure that the data transmission interruption time is controlled within a very short period of time and to avoid data loss due to link failure.

[0132] In a specific implementation, this enhancement method also includes a multi-node collaborative enhancement step:

[0133] S601. Deploy multiple sets of equipment according to the transmission line length and terminal density to form a hybrid star and chain multi-hop enhancement network;

[0134] S602. Identify the terminal access method and divide it into different auxiliary groups, and allocate reflection resources in a differentiated manner;

[0135] S603. Adjacent WAPIs are allocated frequency bands that meet anti-interference requirements and have frequency hopping capabilities. In overlapping areas, access points are selected based on signal quality and access efficiency standards.

[0136] S604. Establish a distributed collaborative control center to receive device data via a 5G private network;

[0137] S605. The control center establishes a health assessment model based on equipment operation data to predict faults and trigger preventive maintenance to reduce equipment failure rate.

[0138] As shown in steps S601 to S605 above, multi-node deployment density technology ensures continuous coverage across the entire line, with the core formula being D. deploy =R a / (1+δ), where, D deploy Deploy spacing for nodes; R a The coverage radius of a single device is set at 150 meters; the coverage overlap rate is set at no less than 20%, which is crucial to ensure signal connection between adjacent nodes and avoid coverage blind spots. The hybrid star and chain network architecture balances centralized control and long-distance extension. A star architecture is constructed with the substation as the central node, covering a 10-kilometer radius of surrounding lines and responsible for signal scheduling and data aggregation in the area. The central nodes are connected through a chain architecture to achieve long-distance signal extension, adapting to transmission lines of different lengths from 50 kilometers to hundreds of kilometers. The advantage of this architecture is that the central node can centrally manage the nodes within the area, and the chain architecture can flexibly expand the coverage range, while also having a certain degree of fault tolerance, meaning that the failure of a single ordinary node will not affect the overall network.

[0139] Dynamic resource allocation strategies achieve efficient resource utilization η i = ×100%, where η i N represents the resource allocation ratio for the i-th group of terminals; i Let represent the number of terminals in the i-th group. Terminals are divided into three categories according to their access methods: the unassisted group can directly receive base station signals without the assistance of intelligent reflectors, with a resource allocation ratio η1=0%; the first-level assisted group requires only one intelligent reflector reflection to receive signals, with an allocation ratio η2=40%; and the second-level assisted group requires two or more reflections to receive signals, having the highest resource demand, with an allocation ratio η3=60%. By detecting the signal source of the terminals through the access point, their access methods are identified and groups are divided. The reflector unit resources of the intelligent reflector are allocated differently to avoid unassisted groups occupying resources, ensure that high-demand terminals obtain sufficient resources, and improve resource utilization efficiency.

[0140] Anti-interference frequency band configuration technology enhances the network's anti-interference capability. The frequency band spacing Δf between adjacent access points is set to no less than 80 MHz, determined based on wireless communication frequency band planning standards. This avoids frequency band conflicts between adjacent nodes and reduces co-channel interference. The frequency hopping formula is f. hop =f base +k×Δf hop , where f base The reference frequency band is set to 920 MHz; k is the frequency hopping number, ranging from 0 to 9; Δf hop The frequency hopping step size is set to 2 MHz, and the frequency hopping range covers 920 to 938 MHz. Wide-spectrum interference is avoided through fast frequency hopping. The access point selection mechanism in the overlapping area prioritizes the terminal to access the access point with a signal strength of not less than -75 dBmW and an access delay of not more than 10 milliseconds. The signal strength and access delay directly reflect the link quality. This mechanism ensures that the terminal always connects to the optimal link, thereby improving transmission stability and efficiency.

[0141] The distributed collaborative control center achieves global optimization and fault tolerance. It receives work data from each node device via a 5G private network, with a latency of no more than 20 milliseconds, ensuring real-time data transmission. Under normal conditions, the control center uniformly schedules the parameters of each node. For example, in areas without terminal access, the intelligent reflector reduces reflection power by 50%, and the access point lowers the transmission rate threshold by 20%, resulting in a 30% reduction in overall network energy consumption. In fault scenarios, a distributed resource reconstruction algorithm is used, expanding the coverage radius of nodes surrounding the fault area by ΔR=R. a ×γ, where γ is set to 0.2 to 0.3 as the expansion coefficient, and the expanded coverage radius R new =R a +ΔR dynamically distributes the coverage task in the faulty area, shortening the coverage recovery time by no more than 5 seconds; compared with centralized control, the advantage of distributed control is that there is no risk of single point of failure, and the response speed of local adjustments is faster.

[0142] The core of preventative maintenance strategy is to predict failures in advance and perform proactive maintenance to reduce operation and maintenance costs and signal interruption risks; equipment health score H=100-w t Tw v Vw f F, where H is the device health score; w t =0.5, w v =0.3, w f=0.2 is the weight; T is the temperature exceeding the standard value, in °C; V is the voltage fluctuation value, in V; F is the number of historical faults; H is less than 80 points to trigger an early warning, and the accuracy of predicting fault risk 72 hours in advance is no less than 95%; the control center monitors the equipment operating status, temperature, voltage, response time, etc. in real time, and substitutes them into the health formula to calculate H; when H is less than 80 points, an early warning information is sent to the operation and maintenance team, and maintenance is arranged in advance, such as replacing aging parts and adjusting the installation position, to avoid signal interruption caused by sudden failures, reduce the equipment failure rate by 40%, and reduce the workload and cost of on-site operation and maintenance.

[0143] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0144] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0145] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for enhancing signal coverage of transmission lines based on the fusion of RIS and WAPI, characterized in that, The enhancement method involves the following steps: Acquire comprehensive basic data on transmission line scenarios, including transmission line path, dynamic environmental interference along the line, current signal coverage strength, available equipment deployment locations, and historical line operation status and fault association information. Among them, dynamic environmental interference information covers real-time electromagnetic interference, line icing thickness, and temporary obstructions. Based on comprehensive basic data, and combining the RIS scenario-based signal reflection characteristics with the WAPI dynamic security adaptation characteristics, the signal reflection parameters of RIS, the secure transmission configuration parameters of WAPI, and the signal coverage enhancement execution scheme are determined through a pre-trained environment perception and parameter mapping model. According to the implementation plan, a RIS with multi-scenario signal compensation capabilities is deployed, and the WAPI dynamic secure transmission mechanism is enabled. It works collaboratively through a two-way predictive data interaction channel to perform signal coverage enhancement operations on transmission lines. The enhanced signal coverage data, equipment operation data, and real-time environmental data along the route are collected, input into the mapping model for iterative optimization, and a three-dimensional effect evaluation result including signal quality indicators, safety level indicators, and energy efficiency indicators is obtained. Based on the 3D effect evaluation results, it is determined whether the signal coverage meets the preset requirements. If it does not, the parameters of the two types of equipment are simultaneously optimized based on the scenario-based pre-adjustment parameters output by the mapping model. Otherwise, the final solution is determined and stored in the transmission line signal enhancement scenario storage module to provide parameter templates for similar scenarios.

2. The method according to claim 1, characterized in that, The specific steps to obtain full-dimensional basic data are as follows: By extracting the starting and ending tower locations, tower spacing, line direction, and tower material parameters of the transmission line through GIS, and combining the line sag data from UAV inspections, transmission line path information containing spatial topology and structural characteristics is formed. Detection points are set up along the transmission line according to the regional interference intensity and terminal distribution differences. Electromagnetic interference data, meteorological data and temporary obstruction data are collected continuously for preset durations using multi-functional dynamic monitoring equipment. The signal receiving power, signal-to-noise ratio, transmission rate, bit error rate and end-to-end delay of each detection point are collected by the signal comprehensive analysis equipment to form the current signal coverage strength information with spatiotemporal dimensions. Based on the tower load-bearing calculation results, the maximum installation weight of RIS and the deployment height limit of WAPI access point are determined. The coverage area of ​​deployed communication base stations and the distribution density of monitoring terminals are statistically analyzed to form equipment deployment location information. Collect signal interruption fault records along the line, establish a database linking fault causes, environmental parameters and signal attenuation, and form information on the correlation between line operation status and faults; The collected data is standardized to obtain comprehensive basic data.

3. The method according to claim 1, characterized in that, The specific steps for determining RIS reflection parameters are as follows: Based on information related to the transmission line path and the current signal coverage strength information, a multi-dimensional weighted clustering analysis method is used to identify areas with weak signal coverage, and the priority of RIS target coverage areas is determined according to the urgency level. Based on the interference frequency distribution of dynamic environmental interference information along the route, a dual strategy of interference avoidance and power compensation is adopted to select the RIS operating frequency band. When strong interference frequency bands are concentrated, frequency bands that avoid strong interference are selected and appropriate power compensation is set. Based on the target coverage area and equipment deployment location, the three-dimensional distance and angle between the RIS and the signal transmitter and receiver are calculated using the tower spatial coordinates, and the initial angle of the reflector unit is determined. By inputting future weather forecast data for a preset duration into a mapping model, the compensation value of the scene-specific reflection parameters is calculated. Through on-site dynamic testing and verification, the signal reception power of the target coverage area under special scenarios is kept stable above the preset threshold and the fluctuation range meets the requirements. The RIS signal reflection parameters, including the working frequency band, reflection unit angle, reflection intensity and scene compensation coefficient, are obtained.

4. The method according to claim 1, characterized in that, The specific steps to determine the WAPI secure transport configuration parameters are as follows: Based on the safety requirements of transmission lines, an identity authentication method supporting four-layer peer-to-peer verification of terminals, access points, authentication servers, and cloud platforms is selected, and an elliptic curve cryptography system combined with an encryption algorithm adapted to the security requirements is used for key negotiation. Based on the interference fluctuation period and intensity of dynamic environmental interference information along the route, establish the correspondence between interference frequency and key update period; By combining the RIS transmission rate with the data type of the monitoring terminal, a multi-level encryption and rate adaptation relationship is established. An interference adaptive identification mechanism is added to adjust the identification timeout when sudden interference is detected, thus forming WAPI secure transmission configuration parameters.

5. The method according to claim 1, characterized in that, The specific steps for developing and implementing a signal coverage enhancement plan are as follows: Based on the RIS signal reflection parameters and equipment deployment location, combined with tower load-bearing calculations, the installation height, vibration and corrosion prevention fixing methods, and safe distance from high-voltage conductors for the RIS-compatible tower structure and safety requirements are determined, thus forming the RIS deployment plan. Based on WAPI security configuration parameters, a relay gateway integrating edge computing is deployed on the transmission line tower. The gateway uses dual-link backhaul to the north and smart antennas to adapt to the sector coverage requirements of the line to the south. The relay equipment adopts multi-hop cascading between towers and dynamic power control to form a WAPI enabling scheme. The two schemes are integrated: RIS starts and initializes compensation parameters in advance with a preset duration, and WAPI triggers a secure connection after detecting that the reflected signal meets the standard. The two share status data. The equipment was installed and debugged according to the plan, and the environmental data was analyzed in real time through the edge computing module to dynamically adjust the collaboration strategy.

6. The method according to claim 1, characterized in that, The specific results of obtaining the three-dimensional effect evaluation include: In areas with weak signal coverage and normal areas, sampling points are set up differently according to the degree of coverage weakness to collect enhanced signal quality data, security level data, and energy efficiency data; the acquired indicators are received, and the compliance rate of each indicator is obtained; the three-dimensional effect evaluation results are obtained by using a combination of hierarchical analysis and entropy weighting; the evaluation results are compared with the preset evaluation thresholds, and the changes in indicators under extreme scenarios are recorded as constraints for parameter optimization.

7. The method according to claim 1, characterized in that, The specific steps for optimizing parameters are as follows: If the evaluation result is lower than the preset evaluation threshold, the reason for failure is deduced by the mapping model. If the power is not up to standard, the RIS reflection intensity is adjusted and interference is avoided; if the speed is not up to standard, the frequency band is switched and the encryption level is lowered; if the security is not up to standard, the identification parameters are updated and the key cycle is shortened; if the energy consumption is not up to standard, the RIS sleep mechanism is enabled. After adjustment, the enhancement operation is re-executed, data is collected and new evaluation results are calculated until the standard is met and the fluctuation of indicators in extreme scenarios meets the requirements; the environmental parameters, adjustment strategies and evaluation results are stored in the database, and the mapping model is iteratively optimized.

8. The method according to claim 2, characterized in that, The data collected on dynamic environmental interference along the line includes: Multi-sensor fusion monitoring equipment is deployed at each detection point to simultaneously collect electromagnetic interference data, meteorological data, and dynamic obstruction data; typical frequency bands and characteristics of industrial interference, civilian communication interference, and line self-interference are distinguished, and a correlation model between interference source, frequency, and influence radius is established; correlation relationships with signal attenuation or offset are established based on icing thickness, wind speed, and cross-sectional area of ​​obstruction, respectively. By integrating the above data and models, dynamic environmental disturbance data containing real-time values, predicted values, and compensation strategies is generated and input into the mapping model.

9. The method according to claim 5, characterized in that, The specific steps for the collaborative work of RIS and WAPI in bidirectional early prediction are as follows: The two types of equipment maintain time synchronization accuracy to meet collaborative requirements through a dual-mode synchronous clock, and the RIS adjusts the reflection parameters in advance based on future weather forecast data for a preset duration. The access point collects the signal-to-noise ratio and bit error rate of the reflected signal at a preset period. When it predicts that the signal quality will be lower than the threshold, it sends an adjustment request to the RIS and the RIS quickly completes the parameter fine-tuning. When a monitoring terminal connects, the authentication server verifies the identity through a four-layer peer-to-peer mechanism and selects an appropriate anti-interference strategy based on the type of interference from the reflected signal. The two types of equipment establish a three-link redundant channel for primary, backup and emergency, and control the data transmission interruption time to within the preset time.

10. The method according to claim 1, characterized in that, This enhancement method also includes a multi-node collaborative enhancement step: Multiple sets of equipment are deployed according to the length of the transmission line and the density of the terminals to form a hybrid star and chain multi-hop enhancement network; Identify the terminal access method and divide it into different auxiliary groups, and allocate reflection resources accordingly; Adjacent WAPIs are allocated frequency bands that meet anti-interference requirements and have frequency hopping capabilities. In overlapping areas, access points are selected based on signal quality and access efficiency standards. Establish a distributed collaborative control center to receive device data via a 5G private network; The control center establishes a health assessment model based on equipment operation data to predict faults and trigger preventive maintenance to reduce equipment failure rates.

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