Power transmission line wireless communication method based on RIS and WAPI

Abstract

The invention discloses a power transmission line wireless communication method based on RIS and WAPI, and relates to the technical field of power system wireless communication. The technical key points are as follows: the interference problem of different regions is solved in a targeted manner through regional interference characteristic dynamic modeling and reflecting surface differentiation configuration, adaptive layered topology reconstruction is adopted in a mountainous area, reflecting units are deployed in a layered manner according to the altitude, and the vegetation height is obtained according to satellite remote sensing. Meanwhile, signal attenuation in a mountainous area is remarkably reduced through angle compensation, and the communication interruption rate is greatly reduced; configuring broadband dynamic suppression parameters in a mining area, identifying an industrial interference frequency band in real time, expanding a filtering range, adjusting a reflection gain according to dust concentration to match attenuation, and controlling a monitoring data loss rate at a low level; in an urban area, through a civil signal frequency spectrum prediction and avoidance mechanism, a time sequence model is trained based on historical data, the phase of a reflection unit is adjusted in advance in a civil communication peak period to avoid an interference frequency band, and the large-capacity data transmission requirement is met.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology for power systems, specifically a wireless communication method for power transmission lines based on RIS and WAPI. Background Technology

[0002] Currently, the transmission of monitoring data for power transmission lines mainly relies on three existing technologies. While all of these technologies have been applied on a large scale in practical engineering projects, significant shortcomings have been exposed during implementation due to the cross-regional, long-distance, and complex environments of transmission lines, as well as the special requirements of power systems for real-time communication and security. These shortcomings are detailed below: When existing technologies are implemented in power transmission lines traversing mountainous areas, mining areas, and urban areas, the large terrain undulations make microwave signals easily blocked by mountains, leading to link interruptions. Furthermore, with seasonal changes, signal attenuation increases from 8 dB per kilometer to 15 dB per kilometer. Current equipment parameters are fixed and cannot dynamically adapt, resulting in communication interruption rates as high as 3 to 5 times per month. Moreover, when implementing traditional wireless communication in mining areas, electromagnetic interference frequencies generated by industrial equipment cover 0.5 to 5 GHz, overlapping with wireless communication frequency bands, causing data error rates to rise from 10... -6 Up to 10 -3 The monitoring data loss rate exceeds 15%; at the same time, the dust concentration often reaches 50 to 100 milligrams per cubic meter, which further aggravates the signal attenuation. The existing equipment has no specific anti-attenuation measures and requires frequent manual maintenance. When implementing fiber optic communication in urban areas, although it has strong anti-interference capabilities, it needs to pass through densely built-up areas. The laying of fiber optic cables requires road excavation and coordination with civil facilities, which extends the construction period by 50%. Moreover, the civil communication signals in urban areas conflict with the backup wireless communication frequency band of the power transmission line, which makes the backup link unable to be used normally and the communication redundancy is insufficient. Furthermore, the existing technology suffers from a disconnect between equipment parameter configurations and power system operating conditions and monitoring equipment status. Parameters such as transmit power and receive sensitivity of microwave communication equipment are factory-fixed values. When the load on mountain lines surges, the tower grounding resistance rises from 8 ohms to 12 ohms, leading to increased grounding interference with microwave signals. However, the equipment cannot adjust parameters in conjunction with power system operating data, requiring manual on-site debugging, which can take up to 24 hours and involve communication interruptions. Traditional wireless communication equipment controls energy consumption parameters independently. When the battery power of the monitoring equipment in the mining area is below 30%, it maintains the original transmission frequency, causing premature power outages and monitoring blind spots. Simultaneously, the equipment does not coordinate with the transmission line sensing module; when the sensing module's sampling frequency increases, the communication module does not adjust the transmission rate synchronously, resulting in data buffer overflow. Therefore, we provide a wireless communication method for power transmission lines based on RIS and WAPI to address these technical problems. Summary of the Invention

[0003] To achieve the above objectives, the present invention provides the following technical solution: A wireless communication method for power transmission lines based on RIS and WAPI, comprising the following steps: The system acquires communication environment data and status data of transmission line monitoring equipment along the power transmission line, uses dynamic modeling of regional interference characteristics to distinguish the different interference characteristics of three types of areas: mountainous areas, mining areas, and urban areas through which the transmission line passes, and updates the model parameters in real time in combination with regional power system operation data to eliminate the bias caused by relying on only a single environmental data. Based on the regional interference characteristics and real-time changing trends output by the above dynamic model, the reflective unit parameters of the intelligent reflective surface are configured, and an adaptive reflective link topology is constructed. This structure can dynamically adjust the connection relationship of each node in the link according to the regional interference intensity and the changes in the power system operating status, so as to match the signal transmission requirements of different regions. The security authentication process of the wireless LAN authentication and confidentiality structure is implemented, and a multi-factor dynamic correlation verification mechanism is adopted: three verification items are added to the identity verification stage, namely the actual location of the device, the regional interference characteristics, and the recent communication behavior characteristics of the device. The identity verification, communication key negotiation and data encryption settings between the power transmission line monitoring equipment and the remote communication base station are completed, and a secure communication link is established. Real-time monitoring of the channel quality data of the aforementioned secure communication link, combined with the sub-module status data of the transmission line monitoring equipment and the output results of the regional interference characteristic dynamic model, adjusts the reflection unit parameters of the intelligent reflector and the transmission parameters of the secure communication link. Based on the adjusted parameters, the system maintains the stable operation of the secure communication link, enables reliable transmission of transmission line monitoring data, records key performance indicators during the communication process, and establishes a three-dimensional correlation database of regional interference, power conditions, and communication performance to provide data support for subsequent predictive optimization work.

[0004] Furthermore, the steps for obtaining communication environment data along the power transmission line and status data of the transmission line monitoring equipment are as follows: Data is collected using multi-source fusion environmental monitoring sensors deployed on power transmission line towers, categorized into three areas: mountainous, mining, and urban. In mountainous areas, the focus is on collecting data on the angle of signal obstruction by terrain, the density of vegetation cover, and the thickness of frost adhering to equipment, with measurement accuracy down to the millimeter level. In mining areas, the focus is on collecting data on the frequency of electromagnetic interference generated by industrial equipment, the attenuation coefficient of signal due to dust concentration, and the start-up and shutdown times of industrial equipment. In urban areas, the focus is on collecting data on the interference intensity of civilian communication signals, the height and area of ​​signal obstruction by buildings, and peak transmission times for civilian communication signals. Data is collected through the modular status detection module built into the power transmission line monitoring equipment: including the current and voltage values ​​of the communication module when it is working, the frequency of data collection by the sensor module, the remaining power of the equipment battery, the amount of data to be transmitted stored in the equipment, the type and size of the monitoring data to be transmitted, and the power consumption of the communication module and the sensor module per hour. Data is collected by the multi-band signal receiving module of the remote communication base station in different areas: including the transmission strength of the initial signal between the base station and the transmission line monitoring equipment, the attenuation of the signal during transmission, the proportion of interference signals in the total frequency band, and the modulation method used by the interference signals. All the data collected in the above categories are fused and calibrated with the real-time operation data of the regional power system to eliminate environmental data deviations caused by fluctuations in the power system's operating status, forming a communication environment dataset and an equipment status dataset with dual labels of regional identification and power operating conditions, providing high-precision data support for subsequent differentiated configurations in different regions.

[0005] Furthermore, based on the regional interference characteristics and real-time changing trends output by the dynamic model, the steps for configuring the reflective unit parameters of the intelligent reflective surface are as follows: From communication environment data with dual tags of regional identification and power conditions, key interference characteristics of three types of regions are extracted: the changing pattern of signal blocking angle caused by terrain in different seasons in mountainous areas, the range of interference frequency fluctuations caused by the start-up and shutdown of industrial equipment in mining areas, and the expansion characteristics of interference frequency bands during peak hours of urban civilian communication. The types of interference and signal enhancement requirements that the reflective units of intelligent reflective surfaces in different regions need to dynamically respond to are determined. To address the issues of terrain obstruction and vegetation growth changes in mountainous areas, an adaptive hierarchical topology reconstruction method is adopted to arrange the reflective units of the intelligent reflective surface: initially, the elevation is divided into layers every 200 meters, and each layer is equipped with corresponding reflective units; the layering interval is dynamically adjusted quarterly according to changes in vegetation height, and the layering interval is reduced by 10 meters when the vegetation height increases by 5 meters; at the same time, an angle compensation dynamic iterative algorithm is adopted, and the reflection angle compensation value is corrected every 7 days according to the actual signal offset value to offset the signal offset caused by both terrain and vegetation. To address the dynamic changes in industrial electromagnetic interference in mining areas, the wideband dynamic suppression parameters of the intelligent reflective surface unit are configured: by real-time monitoring of the signal spectrum to identify newly emerging industrial interference frequency bands, the frequency band filtering range is automatically expanded; at the same time, a dynamic balance algorithm for gain and attenuation is adopted to adjust the gain value of the reflected signal in real time according to the dust concentration data updated every hour, ensuring that the gain value matches the signal attenuation caused by dust and maintaining stable signal strength. To address peak fluctuations in urban civilian signals, a collaborative mechanism for civilian signal spectrum prediction and avoidance is adopted: by using a time-series prediction model that learns from historical data changes, the frequency bands occupied by civilian communications are predicted for the next hour, and the phase of the intelligent reflector unit is adjusted 5 minutes in advance to avoid the upcoming interference frequency bands; at the same time, a shared spectrum pool for civilian and power transmission communications is established, and the shared frequency bands are temporarily activated during the idle period of civilian signal transmission to improve the bandwidth of power transmission line communication; The aforementioned differentiated configurations for different regions are used as the initial configuration parameters for the intelligent reflector, written into the control module of the intelligent reflector, and associated with the dual tags of regional identifier and power condition; when the transmission line monitoring equipment moves across regions or the operating status of the power system changes, the parameters can be switched at the millisecond level.

[0006] Furthermore, the steps for employing a multi-factor dynamic association verification mechanism are as follows: The power transmission line monitoring equipment sends an authentication request to the remote communication base station. The request includes: the equipment's unique identification information, the equipment's pre-stored digital certificate, the identifier of the equipment's current location and its location coordinates accurate to the meter, and the equipment's communication behavior characteristics in the past 10 minutes. After receiving the request, the remote communication base station first verifies the validity of the device's unique identification information, as well as the authenticity and validity period of the digital certificate, through a two-way verification method of identifier and certificate. If the verification passes, it retrieves the dynamic model data of interference characteristics and power system operation data associated with the area where the device is located. Perform a three-factor correlation judgment: determine whether the device's location coordinates are within a reasonable range of signal coverage in the area, whether the device's communication behavior characteristics are consistent with the normal communication behavior characteristics of devices in the area, and whether the power system corresponding to the device's location is operating normally; if any factor does not match, initiate a secondary verification process for interference signal feature comparison, requiring the device to send interference signal spectrum data collected in the past 5 minutes, which is compared with the regional interference spectrum data stored by the base station. If the comparison is consistent, the verification is passed; If both the three-factor authentication and the secondary authentication pass, the remote communication base station and the transmission line monitoring equipment generate a session key for this communication using a dynamic salting algorithm based on regional interference characteristics. The interference frequency and intensity of the current region are incorporated as salt values ​​into the key generation process, binding the generated key to the regional interference characteristics. At the same time, the encryption strength is adjusted according to a three-dimensional balance rule of interference level, key strength, and communication efficiency: when the interference level is high in mining areas and urban areas, a 256-bit key advanced encryption standard algorithm is used for data encryption, and the key is updated every 30 minutes; when the interference level is low in mountainous areas, a 128-bit key advanced encryption standard algorithm is used for data encryption, and the key is updated every 60 minutes, in order to balance communication security and transmission efficiency. All data transmitted subsequently is encrypted using an agreed-upon encryption algorithm. A dynamic complexity mechanism for the check code is also set up: the length of the check code is adjusted according to the regional interference intensity updated every 5 minutes, and the power system operation characteristic data is used as one of the bases for generating the check code to prevent data from being tampered with or counterfeited during transmission.

[0007] Furthermore, the steps for real-time monitoring of channel quality data in secure communication links are as follows: After a secure communication link is established, adaptive channel monitoring modules deployed on power transmission line monitoring equipment and remote communication base stations employ a basic interval and dynamic triggering acquisition mode: the basic acquisition interval in mountainous areas is 5 minutes, automatically shortened to 2 minutes when interference fluctuation exceeds 10%; the basic acquisition interval in mining areas is 2 minutes, automatically shortened to 1 minute when industrial equipment starts or stops; the basic acquisition interval in urban areas is 1 minute, automatically shortened to 30 seconds during peak civilian communication periods; the acquired indicators include signal strength, signal-to-noise ratio, probability of data transmission errors, real-time frequency band and power of interference signals, and channel bandwidth utilization ratio. Multi-dimensional anomaly identification is performed on each collected data: First, a method based on data statistical regularity is used to remove outliers that deviate too much from the normal range, thus eliminating abnormal data caused by instantaneous interference; then, combined with regional power system operation data, it is determined whether the data deviation is caused by the power system operation status; finally, it is compared with historical data in a three-dimensional correlation database of regional interference, power conditions and communication performance to identify trend deviations caused by long-term changes in interference characteristics, and retain valid data that conforms to the current actual communication status. The effective data is organized into a channel quality change sequence according to the four-dimensional structure of region, time, indicators and power conditions. It is then transmitted in real time to the control module of the intelligent reflector, the parameter adjustment module of the remote communication base station and the above-mentioned three-dimensional correlation database. At the same time, it is synchronized to the regional power dispatching system to provide data basis for cross-system collaborative adjustment.

[0008] Furthermore, based on the modular status data of the transmission line monitoring equipment and the output results of the dynamic model of regional interference characteristics, the steps for adjusting the reflector unit parameters of the intelligent reflector and the transmission parameters of the secure communication link are as follows: A four-dimensional coupled adjustment model is established, considering channel quality, module energy consumption, regional interference, and power conditions. A dynamic iterative optimization mechanism with weights is adopted: initial weights are set according to region, with the following weight allocations for mining areas: interference factors 35%, energy consumption factors 25%, channel quality factors 25%, and power conditions factors 15%; urban areas: interference factors 40%, energy consumption factors 20%, channel quality factors 25%, and power conditions factors 15%; and mountainous areas: channel quality factors 45%, energy consumption factors 25%, interference factors 15%, and power conditions factors 15%. The weights are updated every 12 hours based on historical adjustment effects to ensure that the weights match actual needs. When the signal strength in the channel quality data is lower than a preset threshold, an energy consumption and signal gain balancing algorithm is executed: combining the real-time energy consumption rate of the communication module and the remaining battery power, the optimal gain adjustment value is calculated. When the energy consumption rate is lower than the preset threshold and the remaining battery power is sufficient, the gain is increased by 120% of the signal strength gap; when the energy consumption rate is higher than the preset threshold or the remaining battery power is insufficient, the gain is increased by 80% of the signal strength gap. At the same time, the reflection phase is optimized to shorten the signal propagation path, ensuring that the signal strength meets the standard and the energy consumption is minimized. When the signal-to-noise ratio is lower than a preset threshold, an interference prediction and parameter pre-adjustment coordinated strategy is implemented: the interference change trend in the next 5 minutes is predicted by the regional interference characteristic dynamic model; when the interference is periodically increasing, the wideband suppression range of the intelligent reflector unit is adjusted 3 minutes in advance; when the interference is sudden, the reflection phase is immediately adjusted to avoid the interference frequency band, and the remote communication base station is notified to increase the complexity of the encryption verification code by 1 level. When the probability of data transmission errors exceeds a preset threshold, a dynamic mapping mechanism between data priority and transmission parameters is implemented: the data to be transmitted is divided into three levels according to priority: urgent data, important data, and regular data; urgent data corresponds to low transmission rate, quadrature phase shift keying modulation mode, and long check code; important data corresponds to medium transmission rate, hexadecimal quadrature amplitude modulation mode, and medium check code; regular data corresponds to high transmission rate, hexadecimal quadrature amplitude modulation mode, and short check code; at the same time, the phase consistency of the intelligent reflective surface reflection unit is adjusted to reduce signal phase noise. After each adjustment, a multi-dimensional effect verification is performed: channel quality data, module energy consumption data, and power system operation data are collected within 5 minutes after the adjustment, and substituted into the above four-dimensional coupled adjustment model to verify whether the preset indicators are met. If not, the adjustment is readjusted until the indicators are met. At the same time, the adjustment strategy and effect are recorded in the three-dimensional correlation database of regional interference, power conditions and communication performance to provide a basis for weight iterative optimization.

[0009] Furthermore, the communication method also includes the step of real-time monitoring of the operational status of the intelligent reflector and the secure communication link, and relating it to the regional power system's security conditions, specifically including: Data is collected through the cross-system status linkage monitoring unit built into the intelligent reflector control module, including the power supply voltage and current of each intelligent reflector unit, the output power of the reflected signal, and the communication delay between the reflector unit and the control module; at the same time, data sent by the power distribution automation system is received to determine whether the power supply of the intelligent reflector is affected by the operation of the power distribution switch or the fluctuation of the line load. Data is collected through a dual-end collaborative monitoring module of a secure communication link, including the number of link connection interruptions, the delay time from data transmission to reception, and the success probability of data encryption and decryption. At the same time, it is linked to the command transmission plan of the power dispatching system, and the monitoring frequency of link stability is doubled during the dispatching command transmission period. Monitoring is carried out through the environment and fault correlation diagnosis module of the power transmission line monitoring equipment: real-time reception of data stream integrity reports sent by the equipment to check whether the equipment has hardware failures due to changes in the regional environment; at the same time, combined with the equipment's historical fault data, an environmental parameter and fault probability mapping relationship is established to issue early warnings for faults with a high probability of occurrence. The status data of the intelligent reflector, communication link, and monitoring equipment are aggregated with the full operating data of the regional power system to form a joint monitoring log of the communication and power systems. A dynamic adaptation mechanism for early warning thresholds is adopted: the early warning threshold for critical transmission lines is 20% lower than that for ordinary lines to ensure earlier triggering of early warnings. The early warning levels are divided into emergency, important, general, and alert levels. Emergency level requires a response within 1 minute, and important level requires a response within 5 minutes.

[0010] Furthermore, the communication method also includes an intelligent adaptive adjustment step based on state monitoring results and regional characteristics, specifically including: When an abnormal power supply to the intelligent reflector unit is detected and is related to regional power load fluctuations, a dual mechanism of dynamic power resource scheduling and coordinated adjustment of communication parameters is activated: First, in consultation with the distribution automation system, power is temporarily allocated from non-critical power loads. The allocated power is calculated at 1.2 times the power supply gap of the intelligent reflector, and backup power is allocated to the intelligent reflector. At the same time, the reflection angle and gain of other normally functioning reflector units are adjusted to expand the signal coverage to compensate for the signal gap of the abnormal power supply unit and ensure uninterrupted communication. After the power load returns to normal, the temporarily allocated power is returned to the original power load. When an unstable secure communication link is detected during a power dispatch instruction transmission period, a dispatch instruction priority transmission and dynamic resource allocation strategy is implemented: transmission of regular monitoring data is suspended, and 80% of the channel bandwidth is allocated to dispatch instruction transmission; frame structure dynamic optimization technology is adopted to adjust the data frame size according to the length of the dispatch instruction, reducing the number of data frame transmissions; when re-executing the authentication process of the wireless LAN authentication and confidentiality structure, non-critical verification steps are simplified; at the same time, a multi-verification and retransmission optimization mechanism is adopted, performing three integrity checks immediately after the dispatch instruction transmission is completed, and prioritizing retransmission if the verification fails. When incomplete data transmission due to equipment hardware failure is detected, a self-repair mechanism for the faulty equipment and a collaborative supplementary testing scheme with nearby equipment are activated: if the equipment has a self-cleaning function, the self-cleaning operation is triggered immediately, and the cleaning time is set according to the current dust concentration, with higher concentrations requiring longer cleaning times; simultaneously, based on a dynamic algorithm for determining the supplementary testing range, the supplementary testing range for nearby equipment is determined by combining the monitoring range of the faulty equipment with the overlapping signal coverage areas of nearby equipment, thus avoiding duplicate data collection; after the faulty equipment is repaired, the supplementary testing data is automatically compared with the missing data from the faulty period to correct data deviations; Based on the level and regional characteristics of the early warning signal, the system automatically matches graded response and cross-system coordination strategies: emergency-level early warnings are immediately synchronized to the power dispatch center and communication operation and maintenance center, initiating joint emergency repairs of the two systems; important-level early warnings automatically adjust the parameters of the intelligent reflector and notify the power distribution system to maintain stable power supply; general-level early warnings are only logged and processed during periodic optimization; and alert-level early warnings only dynamically adjust the communication frequency band without cross-system linkage, ensuring efficient response and no waste of resources.

[0011] Furthermore, the communication method also includes steps for intelligent collaborative control of multiple intelligent reflector nodes and deep cross-domain collaboration with the power system, specifically including: When the span of a power transmission line exceeds the signal coverage of a single smart reflector, a dynamic optimization of deployment density and a flexible topology networking mechanism are adopted: initially, smart reflector nodes are deployed according to regional characteristics, with one deployed every 10 kilometers in mountainous areas, one every 5 kilometers in mining areas, and one every 3 kilometers in urban areas; every quarter, based on communication performance data in a three-dimensional correlation database of regional interference, power conditions, and communication performance, temporary smart reflector nodes are added in areas where communication performance is substandard, increasing the deployment density to one every 2.5 kilometers; at the same time, a flexible collaborative network is established between smart reflector nodes, with nodes connected through wireless backhaul links. The bandwidth of the backhaul links is dynamically adjusted according to real-time communication needs, with a maximum of 100 megabits per second; Through the aforementioned flexible collaborative network, communication environment data and channel quality data of all intelligent reflector node coverage areas are collected in a unified manner; at the same time, regional power load distribution data and backup power capacity data are obtained from the power distribution automation system; recent dispatch instruction transmission plans and lists of key transmission lines are obtained from the power dispatching system; and the four types of data are aggregated to the multi-system collaborative management module of the remote communication base station. The collaborative management module employs a three-dimensional matching algorithm considering communication requirements, power resources, and operating costs to calculate the optimal reflection parameters for each smart reflector node: during periods of low power load, the reflection gain of the smart reflector is increased to 90% of its maximum gain, while a backup frequency band is activated to enhance communication bandwidth; during periods of high power load, the gain of smart reflector nodes corresponding to non-critical transmission lines is reduced by 20% to minimize energy consumption, while prioritizing full-load operation of smart reflector nodes corresponding to critical transmission lines; the signal coverage overlap area of ​​adjacent smart reflector nodes is controlled between 10% and 15%, and phase collaborative calibration is used to ensure mutual signal enhancement in the overlapping area, avoiding interference. When a smart reflector node fails, a fault node replacement and dynamic resource reallocation strategy is implemented: the collaborative management module immediately retrieves power resource data and communication demand data for the area where the fault node is located; if the backup power capacity in the area is sufficient, a backup smart reflector node is activated and its parameters are configured within 5 minutes; if power resources are scarce, the reflection angle and gain of the three smart reflector nodes around the fault node are adjusted to form a triangular coverage structure to replace the fault node; at the same time, the power dispatching system is notified to suspend non-emergency dispatching instructions for the area, prioritize the transmission of emergency data, and maintain the continuity of the overall communication link and the reliability of critical data.

[0012] Furthermore, the communication method also includes intelligent evaluation of communication performance, precise fault handling, and predictive optimization steps based on multi-model fusion, specifically including: Regularly analyze key performance indicators during the communication process, including data transmission rate, data accuracy, number of communication link interruptions, frequency of intelligent reflector parameter adjustment, and success rate of wireless LAN authentication and security structure. At the same time, correlate regional interference characteristic dynamic model data, power system full-condition data, and equipment full life cycle data to form a four-dimensional performance dataset of communication, interference, power, and equipment. Based on the pre-set full-dimensional performance evaluation standards, differentiated scoring weights are set according to regional characteristics and communication importance: the scoring weight allocation for critical transmission lines is 40% for communication indicators, 30% for interference response effectiveness, 20% for power adaptability, and 10% for equipment status; the scoring weight allocation for ordinary transmission lines is 35% for communication indicators, 25% for interference response effectiveness, 20% for power adaptability, and 20% for equipment status. The analytic hierarchy process (AHP) is used to comprehensively score the above four-dimensional performance dataset, classifying the communication system performance into four levels: excellent, good, qualified, and unqualified. At the same time, a performance bottleneck diagnosis report is generated, identifying the specific dimensions that lead to performance degradation. If the performance level is below the acceptable standard, the fault cause and handling strategy should be precisely matched: If the fault cause is the aging of the intelligent reflector unit, the intelligent reflector replacement process should be initiated simultaneously during the maintenance period, in conjunction with the regional power maintenance plan; the parameters of the surrounding intelligent reflector nodes should be adjusted 72 hours in advance before replacement to ensure uninterrupted communication during replacement; if the fault cause is the low efficiency of the security algorithm of the wireless LAN authentication and confidentiality structure, the algorithm should be updated to a lightweight encryption + dynamic verification hybrid algorithm according to the regional interference intensity. In high interference areas, a mode of 60% encryption and 40% verification processing should be used, and in low interference areas, a mode of 40% encryption and 60% verification processing should be used to balance security and efficiency; if the fault cause is insufficient power resources, a dedicated power supply circuit for the intelligent reflector should be established in consultation with the power distribution automation system to prioritize the stable power supply to the intelligent reflector. A closed-loop log for fault handling and effect feedback is established to record the occurrence time, cause, handling measures, recovery time, and performance changes within one month after each fault. The above four-dimensional performance dataset and fault handling log are input into a multi-model fusion prediction system. This system integrates a prediction model that can learn time-series patterns, a grey prediction model suitable for small sample data, and a support vector machine model that can handle nonlinear data. The optimal prediction result is determined through a model voting mechanism. The system improves prediction accuracy by learning the changing patterns of regional interference characteristics, the fluctuation cycle of power load, and the correlation between equipment aging rate and communication performance. By utilizing a multi-model fusion prediction system that has been trained, the system can predict communication channel change trends and potential fault risks in various regions 48 hours in advance. For example, if it predicts increased industrial interference in a specific region during a certain period, it adjusts the broadband suppression range of the intelligent reflector units in that region 24 hours in advance, while simultaneously notifying relevant industrial enterprises to optimize equipment start-up and shutdown times. If it predicts that the power load in a specific region will reach its peak during a certain period, it reduces the energy consumption of non-critical intelligent reflector nodes in that region 12 hours in advance and coordinates with the power distribution system to reserve dedicated power supply capacity for intelligent reflectors. If it predicts that a certain intelligent reflector node will experience gain attenuation, it arranges for the commissioning of backup nodes in advance. This enables the communication system to shift from passively responding to faults to proactively preventing risks and collaboratively optimizing performance, providing intelligent support for the long-term stable operation of the communication system.

[0013] This invention provides a wireless communication method for power transmission lines based on RIS and WAPI, which has the following advantages: 1. By dynamically modeling regional interference characteristics and configuring intelligent reflective surfaces differently, interference problems in different regions are addressed. In mountainous areas, adaptive hierarchical topology reconstruction is adopted, and reflective units are deployed according to altitude. The hierarchical interval is dynamically adjusted quarterly based on vegetation height obtained from satellite remote sensing. At the same time, the reflection angle is regularly corrected through a dynamic iterative algorithm of angle compensation to offset the effects of terrain and vegetation obstruction. As a result, signal attenuation in mountainous areas is significantly reduced and communication interruption rate is greatly reduced. In mining areas, broadband dynamic suppression parameters are configured to identify industrial interference frequency bands in real time and expand the filtering range. At the same time, the reflection gain is adjusted according to dust concentration to match the attenuation. As a result, the data error rate in mining areas is greatly reduced, the monitoring data loss rate is controlled at an extremely low level, and the frequency of manual maintenance is significantly reduced. In urban areas, a civilian signal spectrum prediction and avoidance mechanism is used. Based on historical data, a time series model is trained. During peak civilian communication periods, the phase of reflective units is adjusted in advance to avoid interference frequency bands. During idle civilian signal periods, shared frequency bands are activated. As a result, the success rate of backup link activation in urban areas is greatly improved and communication bandwidth is expanded to meet the needs of high-capacity data transmission.

[0014] 2. By incorporating three verification methods—device location, regional interference characteristics, and communication behavior features—into the identity authentication process, the success rate of unauthorized device access was significantly reduced, and no data tampering incidents occurred. Key and encryption configurations used dynamic salting based on regional interference characteristics to generate session keys, while adjusting key length and update cycles according to regional interference levels and checksum length based on interference intensity. This significantly shortened the security authentication time, ensuring security while avoiding efficiency losses due to excessive encryption. In the data integrity assurance stage, power condition data such as line current was integrated into checksum generation, resulting in a high data tampering detection rate and maintaining the accuracy of critical dispatch command transmissions within a reliable range. Furthermore, by integrating… By monitoring the status of intelligent reflectors, communication links, and power conditions, and appropriately lowering the warning thresholds for critical lines to improve sensitivity, the fault identification time was significantly shortened and the emergency fault response time was significantly improved. In the predictive maintenance phase, based on the fusion of time series models, grey models, and support vector machines, changes in equipment status were predicted, which significantly reduced the frequency of communication interruptions caused by equipment aging and restored the data transmission accuracy to a high level. When an intelligent reflector fails, if the backup power supply is sufficient, the backup node is quickly activated; if the power supply is tight, surrounding nodes are adjusted to form coverage, and the dispatch system is notified to suspend non-emergency instructions. As a result, the fault handling time was significantly shortened, the continuity of critical data transmission was guaranteed, and repairs were no longer delayed due to communication failures. Attached Figure Description

[0015] Figure 1 This is a flowchart of an embodiment of the present invention. Detailed Implementation

[0016] 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.

[0017] Example: Please refer to Figure 1 This embodiment provides a wireless communication method for power transmission lines based on RIS and WAPI. The communication method includes the following steps: S1. Acquire communication environment data and status data of transmission line monitoring equipment along the power transmission line, adopt dynamic modeling of regional interference characteristics, distinguish the interference characteristics of different types of areas through which the line passes, and update the model parameters in real time in combination with power system operation data; S2. Based on the regional interference characteristics and changing trends output by the dynamic model, configure the reflective unit parameters of the reflective surface, construct an adaptive reflective link topology, and control the link to match the signal transmission requirements of different regions; S3. Execute the security authentication process of the wireless LAN authentication and confidentiality structure, and use a multi-dimensional correlation verification mechanism to complete identity verification, key negotiation and data encryption configuration, and establish a secure communication link; S4. Monitor the channel quality data of the secure communication link in real time, and adjust the reflector unit parameters and secure communication link transmission parameters by combining the status data of the transmission line monitoring equipment and the dynamic model results of regional interference characteristics. S5. Maintain the safe operation of the communication link according to the adjusted parameters, record key communication performance indicators, and establish a database linking regional interference, power conditions, and communication performance.

[0018] As shown in steps S1 to S5 above, the data acquisition stage collects three core types of data—communication environment, equipment status, and power system—through multi-source sensors, providing a foundation for subsequent modeling. Dynamic modeling of regional interference characteristics is based on a multi-source fusion algorithm, quantifying the interference intensity and changing trends in different regions, and outputting the regional interference coefficient K. i This provides a basis for decision-making regarding differentiated configurations; the configuration of reflective surface parameters is based on K. i The system dynamically adjusts the number, arrangement, gain, and phase of reflective units to construct an adaptive reflection link; the wireless LAN authentication and confidentiality structure ensures device legitimacy and data integrity through multi-level verification; dynamic parameter adjustment optimizes communication and reflective surface parameters in real time based on channel quality and energy consumption data; and performance recording and optimization store historical data in a three-dimensional relational database, iterate models and parameters, and achieve long-term performance improvement. Data acquisition and dynamic modeling of regional interference characteristics require data collection based on regional differences. In communication environment data, mountainous areas focus on terrain obstruction and vegetation attenuation. The terrain obstruction angle is measured using lidar, and the vegetation attenuation coefficient is graded according to tree species and density. Mining areas focus on collecting data on industrial interference and dust impact. The industrial interference frequency band is scanned and covered from 100MHz to 6GHz using a spectrum analyzer. The interference intensity is measured using a power meter, and the dust attenuation rate is positively correlated with dust concentration. Urban areas focus on civilian signal conflicts and building obstruction. The civilian signal conflict rate is statistically analyzed based on the proportion of time occupied by the 2.4GHz and 5GHz frequency bands, and the building obstruction coefficient is calculated based on building height and density. Equipment status data covers communication modules, sensing modules, and the overall equipment. Communication modules include transmit power, receive sensitivity, and bit error rate; sensing modules include sampling frequency and data integrity; and the overall equipment includes remaining power and communication load. Power system data includes the ratio of line current load to rated load and tower grounding resistance. Regional interference coefficient K of the dynamic model of regional interference characteristics i =α×Environmental disturbance data E i +β×Power Condition Data P i +γ× Device Status Data D i Among them, the regional interference coefficient Ki The core output value reflects the overall level of interference in the region, ranging from 0 to 1.5; the higher the value, the more severe the interference. Environmental interference data E i These are region-specific interference quantification values: for mountainous areas, the terrain obstruction coefficient (0-1); for mining areas, the normalized industrial interference intensity value (0-1); and for urban areas, the civilian signal conflict rate (0-1), directly reflecting the intensity of the core interference source in the region; power condition data P. i This is the ratio of the actual load to the rated load of the line, ranging from 0 to 1.2, reflecting the indirect interference of the power system's operating status on communication; Equipment status data D i The ratio of the actual energy consumption to the rated energy consumption of the communication module, ranging from 0.5 to 1.5, reflects the impact of equipment operating status on communication quality; α, β, and γ are the weighting coefficients corresponding to environmental interference, power conditions, and equipment status data, respectively, with specific values ​​of α=0.4, β=0.3, γ=0.3 for mountainous areas, α=0.5, β=0.2, γ=0.3 for mining areas, and α=0.45, β=0.25, γ=0.3 for urban areas; the model iterates once per hour, dynamically outputting K. i The larger the value, the more severe the regional interference. Reflector parameter configuration based on K i Values ​​enable differentiated adaptation; the number of reflection units and K i The values ​​are positively correlated, K i When K is ≤0.5, there are 16 in mountainous areas, 32 in mining areas, and 64 in urban areas; when K is <0.5, there are 16 in mountainous areas, 32 in mining areas, and 64 in urban areas. i When ≤1.0, there are 24 in mountainous areas, 48 ​​in mining areas, and 96 in urban areas; K i >1.0 The system includes 32 poles in mountainous areas, 64 in mining areas, and 128 in urban areas. The arrangement is adapted to regional characteristics: mountainous areas use a vertically layered deployment, with one layer every 200 meters, for a total of 3 to 5 layers; mining areas use a dense grid arrangement with a unit spacing of 0.3 meters; and urban areas use a distributed deployment, dispersed along the poles. The reflection gain is dynamically adjusted using the formula: Reflection Gain G = Base Gain G0 × (1 + Regional Interference Coefficient K) i ×Gain adjustment coefficient δ), where, reflection gain G is the actual output gain of the intelligent reflector, determining the signal enhancement amplitude; base gain G0 is the regional reference gain, set according to the regional signal attenuation reference value: 15dB in mountainous areas, 18dB in mining areas, and 12dB in urban areas, adapting to the basic propagation environment of different regions; gain adjustment coefficient δ is the correlation coefficient between interference and gain, K i When ≤0.5, take 0.1, 0.5 <K i When K is ≤1.0, take 0.2. iWhen the value is greater than 1.0, a value of 0.3 is used to ensure that the gain increases linearly with interference and does not exceed the upper limit, and that the gain increases linearly with the increase of interference and does not exceed 1.45 times G0. The reflection phase is adjusted by a phase compensation algorithm, which has high precision and fast response time, ensuring that the signals at the target receiving point are in phase and superimposed. The wireless LAN authentication and confidentiality structure employs an enhanced IEEE 802.11i authentication mechanism, executed in three steps. The first step involves device authentication: the device sends a pre-installed, tamper-proof UUID and a digital certificate issued by the CA center. The base station verifies the UUID's legitimacy by comparing it to a blockchain whitelist and verifies the digital certificate signature using the CA root certificate. The second step involves multi-factor dynamic association verification, calculating the total authentication score S = 0.4 × location matching degree S based on location matching degree, behavior matching degree, and power condition matching degree. pos +0.3 × Behavioral Matching S beh +0.3×Power Condition Matching Degree S pow Among them, the total certification score S is the quantitative result of security certification, ranging from 0 to 100 points, and a score of ≥80 points can proceed to secondary verification; the location matching degree S pos The device location legitimacy score is calculated as follows: 100 points represents 100% overlap between the device and the smart reflective surface's coverage area (radius 50 meters). For every 10% decrease in overlap, 10 points are deducted to prevent unauthorized access by the device. Behavioral matching score S... beh The score for the legality of device communication behavior is 100 points. A deviation of ≤10% from the regional norm for the communication interval is considered an abnormal communication behavior; for every 5% increase in deviation, 10 points are deducted. Power condition matching degree S pow To determine the legality of the access scenario, a score of 100 is awarded based on the following conditions: tower grounding resistance ≤ 10Ω and line load rate ≤ 80%. Failure to meet either condition results in a deduction of 50 points. Power conditions are considered to ensure compliance. A total score S≥ 80 points triggers a second verification process. The device sends a nearly 5-minute interference spectrum, which is compared to the regional interference spectrum stored by the base station. A spectrum similarity of at least 90% is required for verification. The third step involves key negotiation and encryption. A session key is generated based on the regional interference characteristics. The session key Key = HMAC - SHA256 (interference center frequency F). i ||Interference Intensity I i Device pre-set key K base The session key, Key, is the final 256-bit key used for encryption, generated using the HMAC-SHA256 hash algorithm. It has strong anti-cracking capabilities and an interference center frequency F. i The core frequency band for regional interference, measured in MHz, is extracted through spectrum analysis. For example, interference in a mining area ranging from 1.2 to 1.5 GHz is taken as 1.35 GHz; interference intensity I i The actual intensity of regional interference is expressed in dBm, where is the average collected interference power, and K is the device's preset key. baseThe 256-bit fixed key burned into the device at the factory uniquely identifies the device and cannot be tampered with. "||" is a string concatenation symbol that merges interference features with the device's inherent key, making the key deeply bound to the regional scene. The generated key uses the AES-256 algorithm to encrypt data and is updated regularly to ensure security. Dynamic parameter adjustment and performance optimization require real-time monitoring of channel quality indicators and equipment power consumption. Channel quality indicators include signal strength RSSI, signal-to-noise ratio (SNR), and bit error rate (BER). Equipment power consumption controls the power consumption of the communication module. Parameters are adjusted through a four-dimensional coupling model: increasing the gain of the intelligent reflector when the signal strength is insufficient; expanding the interference filtering bandwidth when the SNR is insufficient; and reducing the modulation order when the BER is too high. After adjustment, indicator data is collected for verification. If the target is not met, the adjustment is repeated, with a maximum of three iterations. Performance data is stored in a three-dimensional correlation database of regional interference, power conditions, and communication performance. Monthly optimization reports are generated, and the model weight coefficients are iterated to continuously improve communication reliability.

[0019] In a specific implementation process, the steps for acquiring communication environment data and monitoring equipment status data are as follows: S101. By deploying multi-source fusion monitoring devices on transmission line towers, the key collection points are determined according to the characteristics of different types of areas, and regional environmental interference data and meteorological related data are obtained; S102. Through the status detection component of the transmission line monitoring equipment, collect the operating status of each functional module of the equipment, the remaining energy, and the characteristics of the data to be transmitted, and record the energy consumption characteristics of the equipment in the current area; S103. Collect initial signal transmission characteristic data between the base station and the monitoring equipment in different areas using the signal receiving component of the remote communication base station; S104. The collected data is fused and calibrated with the real-time operation data of the power system to form a structured dataset with regional identifiers and power condition labels, providing a data foundation for differentiated configuration.

[0020] As shown in steps S101 to S104 above, the regional differentiated environmental data collection adopts a multi-source fusion environmental monitoring sensor. This sensor has multi-band signal detection and environmental parameter acquisition, with small measurement error and can adapt to complex environments in different regions. In mountainous areas, the data collection includes terrain obstruction angle, vegetation cover density, frost thickness, and rainfall attenuation coefficient. The terrain obstruction angle is sampled every 10 meters, the vegetation cover density is counted using image recognition technology, the frost thickness is recorded hourly in winter, and the rainfall attenuation coefficient is determined by referring to a table based on rainfall. In mining areas, the data collection includes industrial interference frequency bands, interference power, dust concentration, and industrial equipment start-up and shutdown sequence. The industrial interference frequency band resolution is 1MHz, the interference power is continuously sampled and averaged, the dust concentration is measured using a laser dust meter, and the industrial equipment start-up and shutdown sequence is accurate to the second. In urban areas, the data collection includes civilian signal interference intensity, building obstruction range, and peak communication periods. The civilian signal interference intensity is sampled every 10 seconds, the building obstruction range is marked with latitude and longitude on a GIS map, and the peak communication periods are concentrated between 8:00 and 10:00 and between 18:00 and 20:00. The status data acquisition of the transmission line monitoring equipment is achieved through the built-in modular status detection module. This module integrates a microcontroller and can accurately sense the operating status of each module. The status data of the communication module includes operating voltage, operating current, transmission power, receiving sensitivity, and communication duration per hour. The transmission power is calibrated periodically, and the receiving sensitivity is factory-calibrated. The status data of the sensing module includes sampling frequency, data buffer size, and sensor temperature. The sampling frequency can be configured as needed. The overall status data of the transmission line monitoring equipment includes remaining power, data type to be transmitted, data size, and the time of the most recent fault. The remaining power is converted through voltage. The data type to be transmitted is divided into fault early warning and routine monitoring. The time of the most recent fault is stored in non-volatile memory. Data from remote communication base stations and power system data collection provide supplementary information for data fusion. The remote communication base stations utilize 5G micro base stations, capable of multi-band reception. Through multi-band signal receiving modules, they collect signal-related data, including RSSI signal strength, attenuation per kilometer, interference signal ratio, frequency difference, and bandwidth utilization. Simultaneously, they retrieve real-time operational data from the regional power SCADA system, including line current, voltage, and load factor, with short update cycles; and regularly update tower grounding resistance and temperature to ensure the timeliness of power condition data. Data fusion calibration executes the fusion algorithm through an edge computing gateway to eliminate interference and ensure data integrity; the first step, deviation correction, eliminates the interference of power conditions on environmental data, and the corrected environmental data E' i =Original environmental data E i -Power interference coefficient λ × (current line load rate P) i -Average load factor over the past 30 days ); where the corrected environmental data E' iThe environmental data used for final modeling has an error of no more than 3%; the original environmental data E i The environmental data is directly collected from multiple sensors, and the influence of power interference has not been excluded. The power interference coefficient λ represents the interference weight of power conditions on environmental data, determined through regression analysis: 0.2 for mountainous areas, 0.15 for mining areas, and 0.25 for urban areas, adapting to the power interference characteristics of different regions. The current line load rate P... i This is real-time power system data, reflecting the current power operating status; average load factor over the past 30 days. The first step is to use the arithmetic mean of the load factor over the past 30 days as a benchmark reference for power conditions, resulting in smaller errors in the corrected environmental data. The second step is integrity verification, generating a dataset with regional identifiers, power conditions, and timestamps. Each data point is hashed using the SHA256 hash algorithm to ensure data integrity. The third step is data storage; the calibrated dataset is stored on a local edge node with resume capability and is synchronized to the cloud database daily for global data sharing and analysis. The hash value is Hash = SHA256 (corrected environmental data E). ||Current line load rate P i ||Regional Identification ||Timestamp T||Region-specific key Key), where the hash value Hash is a unique verification value generated using the SHA256 algorithm, used for data integrity verification; current line load rate P i Real-time power condition data is used for verification to ensure data correlation.

[0021] Area Identification The system uses a unique identifier for each region: 1 represents mountainous areas, 2 represents mining areas, and 3 represents urban areas, distinguishing data from different regions. The timestamp T represents the precise time of data collection, preventing data replay attacks. The region-specific key Key is a unique encryption key for each region, enhancing the uniqueness and security of the hash value.

[0022] In a specific implementation process, the steps for configuring the parameters of the intelligent reflective surface reflective unit are as follows: S201. From the dataset with regional identifiers and power condition labels, extract the interference types, intensities and variation patterns of different regions, and obtain the interference scenarios and signal enhancement requirements that the reflector needs to address. S202. For different regional interference scenarios, obtain the layout method and signal processing parameters of the reflective surface reflective unit so that the reflected signal can adapt to the regional interference characteristics; S203. Use the layout rules and signal processing parameters that adapt to the characteristics of the area as the initial configuration parameters of the intelligent reflector, input them into its control component, and associate them with the area and power condition labels to switch parameters across scenarios.

[0023] As shown in steps S201 to S203 above, regional interference feature extraction is a prerequisite for parameter differentiation configuration. From the dual-label dataset containing regional identifiers and power conditions, the core interference characteristics of three types of regions are accurately located. The core interference in mountainous areas is terrain shading, which is seasonal and growing. In winter, rime increases the shading angle, and natural vegetation growth increases the signal attenuation coefficient. The core interference in mining areas is industrial interference and dust attenuation. Industrial interference is sudden, with the interference frequency band expanding when large equipment starts up, and the duration is short. Dust attenuation is correlated, with increased dust concentration increasing the signal attenuation coefficient. The core interference in urban areas is civilian signal interference, which is time-dependent and dense. During peak civilian power consumption, co-frequency interference is severe, and the density of civilian equipment further exacerbates interference conflicts. Based on the aforementioned interference characteristics, the parameters of the regional intelligent reflective surfaces are configured to formulate targeted strategies. In mountainous areas, an adaptive hierarchical topology reconstruction scheme is adopted, deploying one layer of intelligent reflective surfaces every 200 meters of altitude, for a total of 3 to 5 layers, forming a three-dimensional coverage network. Simultaneously, a dynamic iterative algorithm for angle compensation is used to correct the reflection angle in real time, adjusting the reflection angle θ in the next adjustment. t+1 =Current reflection angle θ t + Iteration step size μ × (target signal strength S) target - Current signal strength S t ), where the next adjustment of the reflection angle θ t+1 The final reflection angle after adjustment, ranging from 0 to 90°, ensures the signal is directed towards the receiving device; the current reflection angle θ t The initial angle before adjustment is given, and the effective angle from the previous calculation is given. The iteration step size μ is the angle adjustment increment, fixed at 0.05 rad, approximately 2.86°, to ensure smooth adjustment without oscillation. The target signal strength S... target The preset ideal signal strength is fixed at -85dBm to meet the minimum requirements for power transmission line communication; the current signal strength S t The system measures the received signal strength in real time, providing feedback for adjustments. The iterative step size ensures smooth adjustments, and the target signal strength guarantees communication quality. The layering interval is adjusted every 7 days based on vegetation height data obtained from satellite remote sensing, dynamically adapting to the shading changes caused by vegetation growth. The mining area is configured with wideband dynamic suppression parameters, with a basic interference filtering range set at 1 to 2 GHz, which can be extended according to industrial interference frequency bands; the reflection gain is dynamically matched with the dust concentration, using the formula: reflection gain G = 10 × log 10（1 + Mining Area-Specific Coefficient k × Dust Concentration C), where, reflection gain G is the actual gain of the intelligent reflective surface in the mining area, in dB, which increases linearly with dust concentration; the mining area-specific coefficient k is the correlation coefficient between dust and gain, determined by fitting 100 sets of measured data on dust concentration and signal attenuation, and fixed at 0.02 to ensure that gain adjustment and attenuation are matched; dust concentration C is the real-time dust concentration in the mining area, in mg / m³. 3 The dust was collected using a laser dust collector, with values ​​ranging from 0 to 100 mg / m³. 3 ;10 log 10（ To transform the linear relationship into a logarithmic relationship, so that the gain increase conforms to the logarithmic characteristics of signal attenuation and adapts to the propagation law of wireless communication, the mining area-specific coefficients are determined by fitting correlation data; at the same time, frequency hopping technology is adopted to quickly switch communication frequency bands and avoid sudden strong interference frequency bands. The urban area employs a coordinated mechanism for civilian signal spectrum prediction and avoidance. Based on a time-series prediction model, it predicts peak periods for civilian signals and adjusts the phase of reflecting units in advance to avoid interference frequency bands. The phase adjustment formula is the adjusted reflection phase. =(2π×reflector spacing d×sin incident angle θ) / signal wavelength λ+avoidance compensation phase Δ Among them, the adjusted reflection phase The final phase of the reflecting unit, in rad, ensures that the signals are in phase and superimposed at the target point; the spacing d between reflecting units is the fixed spacing of the intelligent reflecting surface reflecting units, fixed at 0.5m, determined by the hardware design; the incident angle θ is the angle at which the signal is incident on the reflecting surface, ranging from 0 to 60°, measured in real time by an angle sensor; the signal wavelength λ is the wavelength of the communication signal, in meters, calculated from the communication frequency band, such as λ=3×10 for a 2.4GHz signal. 8 / 2.4×10 9 =0.125m; Avoidance compensation phase Δ The phase compensation amount for civilian signal interference, in rad, is calculated as follows: Δ = 0.5 MHz per 100 MHz shift in the interference frequency band. Increase π / 4 to ensure that the signal avoids the interference frequency band, the spacing of the reflection unit is fixed, and the avoidance compensation phase is adjusted according to the offset of the interference frequency band; during the idle period of civilian signal, the spectrum sharing pool is activated to make full use of the idle frequency band and improve bandwidth utilization. The switching of reflector parameters is performed by the control module, which has a fast processing delay to ensure rapid response. The module stores the parameter configuration rules for different regions and binds them with dual tags of region identifier and power condition. When the transmission line monitoring equipment moves across regions or the line load rate fluctuates greatly, the control module quickly completes the parameter switching. During the switching process, a backup intelligent reflector node is activated for temporary coverage to ensure uninterrupted communication.

[0024] In a specific implementation process, the steps of adopting a multi-dimensional correlation verification mechanism are as follows: S301. The transmission line monitoring equipment sends a verification request containing the equipment identifier, authentication certificate, current area and location information to the remote communication base station; S302. After verifying the validity of the equipment identifier and the legality of the authentication certificate, the remote communication base station verifies the rationality of the equipment location by combining regional interference characteristic data, and can perform auxiliary verification processes. S303. After successful verification, the base station and monitoring equipment negotiate to generate a session key, and dynamically adjust the encryption strength and data integrity verification rules according to the regional interference level to complete the security configuration.

[0025] As shown in steps S301 to S303 above, authentication requests and initial verification form the first line of defense for security. The transmission line monitoring equipment has a built-in security chip. When sending an authentication request to a remote communication base station, it must carry four key pieces of information: the device's unique identifier (UUID), which is burned into the device at the factory and cannot be tampered with; a digital certificate issued by the power system certification center, containing the device's public key, validity period, and regional permissions; current location coordinates, obtained through dual-mode positioning with high accuracy; and communication behavior characteristics over the past 10 minutes, reflecting the device's normal communication behavior. The authentication server of the remote communication base station uses a national cryptographic chip with high-strength encryption capabilities. It first performs an initial verification. The first step verifies the legitimacy of the UUID by comparing it with the device whitelist stored in the blockchain. The blockchain ensures that the whitelist is not maliciously modified. The second step verifies the validity of the digital certificate by verifying the signature through the root certificate to prevent counterfeit devices from accessing the network. The three-factor authentication method is the second layer of protection. It comprehensively verifies the legitimacy of the device through three dimensions: location, communication behavior, and power conditions. The total authentication score S is calculated as S = 0.4 × location matching degree S. pos +0.3×Communication Behavior Feature Matching Degree S beh +0.3×Power Condition Matching Degree S pow Location matching is judged by the overlap rate of the coverage area between the equipment and the intelligent reflective surface; communication behavior characteristic matching is based on the deviation of the communication interval from the normal value of the area; power condition matching is based on the condition that the tower grounding resistance and line load rate meet the standards. When the total certification score meets the standard, the second verification stage is entered to further filter out counterfeit devices. The second verification requires the power transmission line monitoring equipment to send the spectrum of interference signals collected in the past 5 minutes. The remote communication base station compares the spectrum with the regional interference spectrum stored in its own storage. If the spectrum similarity meets the standard, the verification is passed. Session key generation and encryption configuration are the core safeguards for data transmission security. The session key is generated using a dynamic salting method based on regional interference characteristics, with the formula: Session Key = HMAC - SHA256 (interference center frequency F).i ||Interference Intensity I i The device has a pre-installed basic key K. base The device has a pre-set basic key to ensure uniqueness, and regional interference characteristics make the key deeply bound to the access scenario; the session key update cycle is adjusted according to the regional interference coefficient to achieve a balance between security and efficiency. The checksum length is dynamically adjusted according to the interference intensity; the checksum length L = 32 + 8 × Interference Intensity I i | / 20 The checksum length L is the number of bits in the data checksum, measured in bits. Stronger interference results in a longer length and stronger tamper resistance. 32 bits is the basic checksum length, the minimum length for low-interference scenarios, meeting basic tamper resistance requirements. Interference strength I... i The value represents the actual intensity of regional interference, expressed in dBm, calculated as an absolute value since interference intensity is negative. 8 represents the length increment unit; every 20 dBm increase in interference intensity corresponds to an 8-bit increase in the checksum. To ensure that the rounding sign is rounded up, adjustments are still made in full increments even when the interference strength is less than an integer multiple of 20 dBm, such as I i When |I = -50dBm i | / 20=2.5, rounded up to 3, L=32+24=56bit; the real-time line current value is incorporated when the check code is generated, and if the check fails during data transmission, the data is immediately retransmitted.

[0026] In a specific implementation process, the steps for real-time monitoring of channel quality data are as follows: S401. After a secure communication link is established, the channel quality indicators are obtained by setting the sampling frequency according to the regional interference fluctuation characteristics through the channel detection component of the monitoring equipment and the remote base station. S402. Perform anomaly identification and screening on the collected quality indicator data, remove invalid data caused by transient interference, and retain valid data that reflects the actual communication status; S403. Organize the valid data into a channel quality sequence according to the dimensions of region, time, and power conditions, and transmit it to the intelligent reflector control component, the base station parameter adjustment component, and the associated database.

[0027] As shown in steps S401 to S403 above, the acquisition mode adopts a hybrid mode that combines basic acquisition with dynamic triggering to balance monitoring accuracy and equipment power consumption. The basic acquisition interval is set according to the regional interference intensity, with a longer interval in mountainous areas and a shorter interval in mining areas and urban areas. The dynamic triggering conditions include interference intensity fluctuations, sudden drops in signal strength, and sudden increases in equipment power consumption. When any of these conditions are met, the acquisition interval is shortened to quickly respond to emergencies. Core metrics are collected through the channel monitoring module, comprehensively covering key dimensions of channel quality; the high signal strength sampling rate directly reflects the strength of signal transmission; the high signal-to-noise ratio resolution reflects the degree of separation between signal and interference; the bit error rate is statistically analyzed by data packet, intuitively reflecting the accuracy of data transmission; the frequency band distribution and power spectral density of interference signals clearly indicate the specific interference situation; and the channel parameters reflect the utilization of channel resources and the real-time performance of data transmission. The multi-dimensional anomaly identification mechanism removes invalid data from three levels; transient interference removal targets signal abrupt changes by calculating the mean and standard deviation, with the mean μ... S =(1 / n)Σs i , where the mean μ S is the arithmetic mean of the signal strength sequence, reflecting the baseline level of the signal; n is the number of signal strength samples to ensure the statistical significance of the mean calculation; s i This represents a single signal strength sample value, which is the basic data for calculating the mean, and the standard deviation σ. S = , where the standard deviation σ S The signal strength sequence is measured by its dispersion index; a higher value indicates more severe fluctuations. Power condition correlation judgment addresses normal interference caused by sudden changes in power load, preventing erroneous adjustments. Trend deviation identification targets long-term issues, calculating the signal attenuation slope through linear fitting. Wherein, the signal attenuation slope k is the rate of change of signal strength over time, in dB / day; k < -0.5 dB / day is considered a trend of attenuation; n is the sample size; t i The timestamp for signal acquisition; s i Σt represents the signal strength value at the corresponding time. i s i The sum of the products of the timestamp and the signal strength; Σt i Σs represents the sum of timestamps. i This represents the sum of signal strengths. The sum of squares of timestamps is used to determine the trend of decay and initiate corresponding measures. The data processing and transmission process ensures the structured and real-time sharing of data; effective data is organized into a four-dimensional structure based on region, time, indicators, and power conditions to facilitate subsequent correlation analysis; the processed data is transmitted in real time to multiple targets via the MQTT protocol, including the intelligent reflector control module, the base station parameter adjustment module, the three-dimensional correlation database, and the power dispatching system, ensuring data real-time performance and integrity.

[0028] In a specific implementation process, the steps for adjusting the parameters of the smart reflector and the transmission parameters of the communication link are as follows: S411. Establish a multi-factor coupled adjustment model for channel quality, equipment energy consumption, regional interference and power conditions, set the weights of each factor according to the characteristics of different regions, and optimize the weights periodically based on the adjustment effect; S412. When the channel quality index deviates from the preset threshold, the signal processing parameters of the intelligent reflector unit are adjusted, or the transmission rate and modulation method of the secure communication link are optimized, taking into account the device's energy consumption status and regional interference change trend. S413. Verify the effect after each adjustment until the channel quality index returns to the threshold range, and record the adjustment strategy in the associated database.

[0029] As shown in steps S411 to S413 above, the four-dimensional coupling adjustment model takes channel quality, module energy consumption, regional interference, and power conditions as core dimensions, and comprehensively considers the impact of various factors on communication performance. The initial weights of the model are set differently according to the core contradictions of different regions, with priority given to ensuring signal coverage in mountainous areas, anti-interference in mining areas, and avoiding conflicts with civilian signals in urban areas. The weights are updated periodically to ensure the model adapts to changes in the actual scenario. The update formula is the weight adjustment amount Δw. j =Learning rate η × ( Adjustment error J) / ( weight w j ), where the weight adjustment amount Δw j The weight adjustment value for the j-th dimension is used to update the model weights and ensure the model adapts to the scenario; the learning rate η is the weight adjustment step size, fixed at 0.01 to avoid excessive adjustment amplitude causing model oscillation, w j The weights are for the j-th dimension; the adjustment error J is the deviation between the target channel quality and the actual channel quality, J = Σ(target channel quality Q). target - Actual channel quality Q actual ) 2 Q target The preset signal-to-noise ratio is ≥15dB and the bit error rate is ≤10. -5 The corresponding normalized value is 1; J / w j The partial derivative of the error with respect to the weights is calculated using the gradient descent method. It indicates the direction of weight adjustment. A positive partial derivative increases the weights, while a negative partial derivative decreases them. The learning rate ensures smooth adjustment. The adjustment error reflects the gap between the target and the actual communication quality. The partial derivative is calculated using the gradient descent method to continuously reduce the model error. The scenario-specific parameter adjustment strategy formulates targeted solutions for different channel quality problems. When the signal strength is insufficient, an energy consumption and signal gain balancing algorithm is executed to improve the signal strength while avoiding excessive energy consumption, and the reflection phase is adjusted to focus the signal. When the signal-to-noise ratio is insufficient, different measures are taken according to the type of interference. Periodic interference is avoided in advance, and sudden interference is adjusted in time and the check code is enhanced. When the bit error rate is too high, transmission parameters are matched according to data priority. The highest priority data adopts a high anti-interference configuration, and the low priority data balances efficiency and reliability. The effect verification step ensures the effectiveness of the optimization measures. Channel quality indicators after adjustment are collected and substituted into the model for verification. If the indicators are not met, the adjustment values ​​are recalculated. The optimal gain adjustment value ΔG = signal gap gain ΔG need min(1.2, 1 + 0.5 × remaining power percentage B) rem / B total The optimal gain adjustment value ΔG is the final gain increment, in dB, which satisfies both signal strength requirements and power consumption control; the signal gap gain ΔG need To achieve the required gain increment for the target signal strength, ΔG need =Target signal strength (-85dBm) - Current signal strength S t Remaining battery percentage B rem / B total The ratio of remaining power to total power, ranging from 0 to 1, reflects the energy consumption status of the equipment; min(1.2, ...) is the upper limit control for gain increment, which cannot exceed ΔG. need 1.2 times, to avoid excessive gain leading to a sudden increase in energy consumption; 0.5 is the power impact coefficient, when the remaining power is sufficient, B rem / B total =1, the gain can be increased to 1.5 times the gap gain, but is limited by the 1.2 times upper limit; when the remaining power is insufficient, such as B rem / B total =0.2, the gain is only increased by 1.1 times the notch gain, balancing signal and power consumption, and iterating a maximum of 3 times until the index meets the preset threshold.

[0030] In a specific implementation, the communication method also includes the step of real-time monitoring of the operating status of the intelligent reflector and the secure communication link, and relating it to the power system operating conditions, specifically including: S601. Through the status monitoring unit of the intelligent reflective surface control component, the power supply, signal output and connection status of the reflective unit are collected, and at the same time, the power system operation data are received to determine whether the power supply of the reflective surface is affected by the power conditions. S602. By using a dual-end monitoring component of the secure communication link, the stability of the link connection, data transmission delay, and encryption / decryption status are monitored. At the same time, in conjunction with power dispatching requirements, the stability of the link is prioritized during the period when dispatching instructions are transmitted. S603. By monitoring the fault diagnosis components of the equipment, check whether the equipment has malfunctioned due to changes in the regional environment, combine historical fault data to warn of high-probability faults, and form a joint monitoring log of the communication and power systems.

[0031] As shown in steps S601 to S603 above, object-specific status monitoring achieves blind-spot-free coverage and comprehensively perceives the system's operating status; intelligent reflective surface status monitoring focuses on the reflective unit and the overall operating status, monitoring the power supply voltage, operating current, signal output power, and communication delay with the control module of each reflective unit in real time to ensure the normal operation of the reflective unit; at the same time, it is associated with the power distribution automation system data to realize the linkage analysis of communication and power data; Secure communication link status monitoring focuses on link connectivity and transmission reliability, monitoring the number of link connection interruptions, data transmission delays, and encryption / decryption success rates to promptly detect link anomalies; it also correlates with the power dispatching system's instruction transmission plan and historical execution results to ensure the reliability of dispatching instruction transmission channels. Transmission line monitoring equipment status monitoring covers data transmission and hardware operation, receiving data stream integrity reports from the equipment to diagnose hardware faults; and predicting fault risk through an environmental parameter-fault probability mapping model, with fault probability P. f =σ(a1×dust concentration C+a2×equipment temperature T+a3×voltage fluctuation V+b), where the failure probability Pf is the probability of equipment failure, taking a value of 0-1, P f A warning is issued when the probability is ≥30%; σ is the Sigmoid function, which maps the linear calculation result to the 0-1 interval to fit the probability range. The function expression is σ(x)=1 / (1+e -x The dust concentration C represents the dust concentration in the environment where the equipment is located, expressed in mg / m³. 3 The key indicators collected in the mining area include: equipment temperature T, which is the equipment operating temperature in °C, ranging from -40 to 85 °C; voltage fluctuation V, which is the fluctuation range of the equipment's power supply voltage in V, reflecting the stability of the power supply; a1, a2, and a3 are characteristic coefficients, with mining area parameters being a1=0.03 (dust has a significant impact on faults), a2=-0.02 (low temperatures easily lead to faults, with a negative coefficient), and a3=0.01 (voltage fluctuations have a relatively small impact), determined through training with a large amount of fault data; b is a constant term, fixed at -2.5, used to adjust the function's baseline value so that the fault probability is close to 0 when there is no interference. The model parameters are determined through training with a large amount of fault data, and an early warning is issued when the fault probability reaches the target to prevent faults from occurring in advance. Status data is aggregated through a dedicated power communication network, integrating various status data with the full operating condition data of the power system to form a joint monitoring log of the communication and power systems. The log adopts distributed storage, supports multi-dimensional retrieval, and has a rapid query response, providing support for fault tracing and data analysis. The early warning configuration sets thresholds differently based on the importance of the transmission lines. Critical transmission lines have higher requirements for communication reliability and therefore stricter early warning thresholds. The early warning levels are divided into four levels: the emergency level affects transmission safety and quickly synchronizes and triggers an alarm; the important level may affect communication quality and automatically generates adjustment instructions; the general level has a smaller impact and is included in periodic optimization; and the alert level does not require emergency handling and can be dynamically adjusted.

[0032] In one specific implementation, the communication method also includes an intelligent adaptive adjustment step based on state monitoring results, specifically including: S604. When the power supply to the intelligent reflector is abnormal and related to the power conditions, coordinate the power system to allocate the available power resources, and adjust the parameters of other reflector units to make up for the signal gap. S605. When the secure communication link is unstable and during the period of scheduling instruction transmission, priority shall be given to ensuring the transmission of scheduling instructions, simplifying non-critical authentication steps, and optimizing the transmission and verification mechanism; S606. When the monitoring data transmission is incomplete due to equipment failure, the equipment self-repair function is triggered, and the neighboring equipment is coordinated to supplement the data. After the equipment is restored, the data deviation is corrected. S607. Based on the warning level and regional characteristics, match the graded response strategy, and initiate cross-system collaborative processing when necessary to ensure rapid communication recovery.

[0033] As shown in steps S604 to S607 above, the scenario-based adaptive adjustment strategy formulates precise solutions for three typical emergencies; when the power supply to the intelligent reflector is abnormal, a dual mechanism of dynamic power resource scheduling and collaborative adjustment of communication parameters is activated; the power side temporarily allocates power from non-critical power loads, and the formula for calculating the required dispatched power is the required dispatched power P. req =1.2×(Rated power P of intelligent reflector) RIS_nom - Actual power P of the intelligent reflector RIS_act ), of which the required dispatched power P req The power required for temporary allocation is measured in W, ensuring the intelligent reflector regains its rated power supply; 1.2 is a redundancy factor, reserving a 20% power margin to prevent voltage fluctuations during allocation from causing further power supply anomalies; the rated power of the intelligent reflector is P. RIS_nom The rated power of the intelligent reflector is fixed at 200W, determined by hardware parameters; the actual power P of the intelligent reflector... RIS_actThe actual power during power supply anomalies, in W, is collected by a current sensor, P=UI, with a redundancy factor to reserve power margin; the communication side adjusts the angle and gain of surrounding normal intelligent reflective surface units to form complementary coverage and ensure uninterrupted communication; When the secure communication link is unstable, a strategy of prioritizing the transmission of dispatch instructions and dynamically allocating resources is implemented to ensure reliable transmission of power dispatch instructions. Regarding bandwidth allocation, regular data transmission is suspended to allocate sufficient bandwidth for dispatch instructions. The allocation ratio formula ensures that dispatch instructions receive priority resources: the dispatch bandwidth percentage R = min(0.8, 0.5 + 0.3 × N). cmd / N total ), where N cmd N represents the number of scheduling instructions. total The maximum data volume is 80% of the total data volume. For transmission optimization, the data frame structure is adjusted to improve efficiency, the authentication process is simplified to shorten processing time, and a multi-check and retransmission mechanism is used to ensure a 100% command delivery rate. When a hardware failure occurs in the transmission line monitoring equipment, a self-repair mechanism for the faulty equipment and a collaborative supplementary testing scheme with nearby equipment are initiated. The self-repair mechanism triggers the equipment's self-cleaning function to reduce the risk of failure caused by dust. The collaborative supplementary testing mechanism is executed by nearby equipment, which only supplements the coverage blind spots of the faulty equipment, avoiding duplicate data collection and saving energy. The tiered response strategy matches resource allocation according to the warning level. At the emergency level, dual-system joint repair is initiated, and backup equipment is quickly deployed to the scene. At the critical level, parameters are automatically adjusted and the power distribution system is notified to stabilize the voltage. At the general level, logs are recorded and optimized regularly. At the alert level, frequency bands are quickly switched to avoid interference.

[0034] In a specific implementation, the communication method also includes steps for multi-intelligent reflector node cooperative control and cross-domain cooperation with the power system, specifically including: S701. When the span of the transmission line exceeds the coverage of a single intelligent reflector, multiple reflector nodes are deployed according to regional characteristics and communication needs to establish a collaborative network between nodes and to establish a data interaction channel with relevant components of the power system; S702. Collect communication data and power system data of each node's coverage area through the collaborative network and aggregate them to the collaborative management component; S703. The collaborative management component calculates the optimal parameters for each node based on the matching relationship between communication needs, power resources, and operating costs, ensuring coordinated signal enhancement between adjacent nodes; S704. When a node fails, adjust the parameters of surrounding nodes or start a backup node, while coordinating the power system to optimize resource allocation and maintain communication link continuity.

[0035] As shown in steps S701 to S704 above, the initial deployment sets the node density according to the regional interference intensity, with longer intervals in mountainous areas and shorter intervals in mining areas and urban areas; the nodes have high protection levels and are adapted to harsh outdoor environments; dynamic optimization is performed once a quarter, adding temporary nodes to areas with weak coverage to optimize communication performance; the flexible collaborative network connects each node through wireless backhaul links, with dynamic adjustment of link bandwidth, low backhaul latency, and support for real-time data sharing and collaborative control between nodes; The data aggregation of multiple systems is completed by the collaborative management module, which integrates three types of core data: intelligent reflective surface node data reflects the node's operating status and coverage effect; power distribution automation system data reflects the power resource status; power dispatching system data clarifies the key points of communication security; after the data is aggregated, a global visualization view is formed, and relevant data is overlaid on the GIS map, supporting real-time query and historical backtracking, providing an intuitive basis for global decision-making; The optimal parameter calculation employs a three-dimensional matching algorithm considering communication demand, power resources, and operating costs to achieve globally optimal resource allocation. The objective function is minJ = λ1 × (1 - Communication Quality Q) + λ2 × Power Consumption P + λ3 × Operating Cost C, where J is a comprehensive optimization index that needs to be minimized to achieve a balance among multiple objectives; Q is a normalized value for communication performance, ranging from 0 to 1, with Q = 1 when the signal-to-noise ratio (SNR) is ≥15dB, otherwise mapped according to the actual SNR, such as Q = 0.67 when SNR = 10dB; and P is the power consumption of the intelligent reflector, expressed in kW. The operating cost C is calculated from the power supply voltage and current; the operating cost C is the unit time operating cost, in yuan / hour, including equipment loss, electricity costs, etc., and is obtained through historical data statistics; λ1, λ2, and λ3 are weighting coefficients, determined by the analytic hierarchy process, λ1=0.5, prioritizing communication quality, λ2=0.3, secondly controlling power consumption, λ3=0.2, and finally reducing operating costs. The weighting coefficients prioritize communication quality while taking into account energy consumption and cost; the time-sharing optimization strategy is adjusted according to changes in power load, improving communication performance during off-peak hours and controlling energy consumption during peak hours, with adjacent nodes coordinating to optimize coverage. The fault response strategy ensures that a single point of failure does not affect overall communication. When a smart reflector node fails, the collaborative management module immediately initiates the emergency process. The first step is to assess resources and determine the feasibility of the backup plan. The second step is to execute the backup plan, activating the backup node when the backup power supply is sufficient, and adjusting surrounding nodes to form coverage when the backup power supply is insufficient. The third step is to coordinate across systems, prioritizing the transmission of critical data to ensure power transmission safety.

[0036] In a specific implementation, the communication method also includes communication performance evaluation, fault handling, and predictive optimization steps, specifically including: S801. Regularly compile key communication performance indicators, correlate regional interference data, power system data, and equipment lifecycle data to form a multi-dimensional performance dataset; S802. Set evaluation weights according to regional characteristics and communication importance, comprehensively score the performance dataset, determine the performance level, and diagnose shortcomings; S803. In cases of substandard performance, a handling strategy will be matched based on the cause of the fault, and if necessary, the power system will be coordinated to optimize resource allocation. S804. Establish a fault handling log, input performance data and logs into a multi-model fusion prediction system, predict communication status and fault risks in advance, and carry out proactive prevention and optimization.

[0037] As shown in steps S801 to S804 above, the construction of the four-dimensional performance dataset provides a data foundation for full-cycle optimization. Four types of core data are statistically analyzed and correlated weekly to form a structured dataset. Communication performance indicators comprehensively reflect communication effectiveness; regional interference data reflects changes in environmental interference; power system data reflects power operating conditions; and equipment data reflects equipment operating status. Intelligent performance evaluation employs differentiated weighting and the analytic hierarchy process (AHP) to accurately pinpoint system weaknesses. Differential weights are set based on line importance, prioritizing communication reliability and interference mitigation effectiveness for critical lines, while considering equipment lifespan for ordinary lines. The comprehensive score is calculated using the AHP: Score = Σ 4j=1 weight w j ×Scores for each dimension S j The overall score (Score) represents the system's overall performance, ranging from 0 to 100 points. A score of ≥90 is excellent, 80-89 is good, 70-79 is satisfactory, and <70 is unsatisfactory. The weight w... j The weights for the four evaluation dimensions are as follows: for critical lines, communication performance 40%, interference response effectiveness 30%, power adaptability 20%, and equipment status 10%; for ordinary lines, communication performance 35%, interference response effectiveness 25%, power adaptability 20%, and equipment status 20%. The score for each dimension is S. j The score for each evaluation dimension ranges from 0 to 100 points and is calculated based on the deviation between the measured data and the target value. For example, if the communication accuracy target is 99.9% and the actual accuracy is 99.5%, then S = 95 points; Σ 4j=1 The system uses a weighted sum of scores from four dimensions to comprehensively reflect system performance and classifies performance levels based on the scores; it also generates specialized reports for bottleneck diagnosis to identify performance bottlenecks and provide direction for troubleshooting. Precise fault handling develops solutions for different types of faults and shortcomings; when the intelligent reflective surface reflective unit ages, it is replaced in conjunction with the annual maintenance, and the surrounding parameters are adjusted to compensate the signal before replacement; when the safety algorithm is inefficient, a hybrid algorithm is used to balance safety and efficiency; when power resources are insufficient, a dedicated power supply circuit is established to stabilize the voltage. Predictive optimization enables a shift from passive repair to proactive prevention. It employs a multi-model fusion prediction method, integrating the advantages of time-series prediction models, grey prediction models, and support vector machine models. The optimal prediction result is output through a model voting mechanism. It can predict regional channel changes and fault risks in advance, proactively adjust parameters or deploy backup nodes, and prevent problems before they occur. 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.

[0038] 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.

[0039] 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 wireless communication method for power transmission lines based on RIS and WAPI, characterized in that, The communication method involves the following steps: The system acquires communication environment data and status data of transmission line monitoring equipment along the power transmission line, adopts dynamic modeling of regional interference characteristics, distinguishes the interference characteristics of different types of areas through which the line passes, and updates the model parameters in real time in combination with power system operation data. Based on the regional interference characteristics and changing trends output by the dynamic model, the parameters of the reflective unit of the reflective surface are configured, an adaptive reflective link topology is constructed, and the link is controlled to match the signal transmission requirements of different regions. The system executes a secure authentication process for the wireless LAN authentication and confidentiality structure, employs a multi-dimensional correlation verification mechanism to complete identity verification, key negotiation, and data encryption configuration, and establishes a secure communication link. Real-time monitoring of channel quality data of the secure communication link, combined with status data of transmission line monitoring equipment and dynamic model results of regional interference characteristics, to adjust the parameters of the reflector unit and the transmission parameters of the secure communication link; Maintain secure communication link operation based on adjusted parameters, record key communication performance indicators, and establish a database linking regional interference, power conditions, and communication performance.

2. The method according to claim 1, characterized in that, The steps for obtaining communication environment data and monitoring equipment status data are as follows: By deploying multi-source fusion monitoring devices on transmission line towers, the focus of data collection is determined according to the characteristics of different types of areas, and regional environmental interference data and meteorological related data are obtained. The status detection component of the power transmission line monitoring equipment collects the operating status of each functional module of the equipment, the remaining energy, and the characteristics of the data to be transmitted, while also recording the energy consumption characteristics of the equipment in the current area. The initial signal transmission characteristics data between the base station and the monitoring equipment are collected in different areas using the signal receiving components of the remote communication base station. The collected data is fused and calibrated with real-time power system operation data to form a structured dataset with regional identifiers and power condition labels, providing a data foundation for differentiated configuration.

3. The method according to claim 1, characterized in that, The steps for configuring the intelligent reflective surface reflective unit parameters are as follows: From the dataset of regional identifiers and power condition labels, we extract the types, intensities and variation patterns of interference in different regions to obtain the interference scenarios and signal enhancement requirements that the reflector needs to address. For different regional interference scenarios, the layout of the reflective elements and the signal processing parameters are obtained so that the reflected signal can adapt to the regional interference characteristics. The layout rules and signal processing parameters adapted to the characteristics of the region are used as the initial configuration parameters of the intelligent reflector. These are then input into its control components, and the region and power condition labels are associated to enable cross-scene parameter switching.

4. The method according to claim 1, characterized in that, The steps for using a multi-dimensional correlation verification mechanism are as follows: The power transmission line monitoring equipment sends a verification request containing the equipment identifier, authentication certificate, current area and location information to the remote communication base station; After verifying the validity of the device identifier and the legality of the authentication certificate, the remote communication base station verifies the rationality of the device location by combining regional interference characteristic data, and can perform auxiliary verification processes. After successful verification, the base station and monitoring equipment negotiate to generate a session key, and dynamically adjust the encryption strength and data integrity verification rules according to the regional interference level to complete the security configuration.

5. The method according to claim 1, characterized in that, The steps for real-time monitoring of channel quality data are as follows: After a secure communication link is established, the channel quality indicators are obtained by setting the sampling frequency according to the regional interference fluctuation characteristics through the channel detection components of the monitoring equipment and the remote base station. The collected quality indicator data is anomaly identified and filtered to remove invalid data caused by transient interference and retain valid data that reflects the actual communication status. The valid data is organized into a channel quality sequence according to the dimensions of region, time, and power conditions, and then transmitted to the intelligent reflector control component, the base station parameter adjustment component, and the associated database.

6. The method according to claim 5, characterized in that, The steps for adjusting the parameters of the smart reflector and the transmission parameters of the communication link are as follows: Establish a multi-factor coupled adjustment model for channel quality, equipment energy consumption, regional interference and power conditions, set the weights of each factor according to the characteristics of different regions, and optimize the weights periodically based on the adjustment effect; When the channel quality index deviates from the preset threshold, the signal processing parameters of the intelligent reflector unit are adjusted, or the transmission rate and modulation method of the secure communication link are optimized, taking into account the device's energy consumption status and regional interference trends. Verify the effect after each adjustment until the channel quality index returns to the threshold range, and record the adjustment strategy in the associated database.

7. The method according to claim 1, characterized in that, The communication method also includes steps for real-time monitoring of the operational status of the intelligent reflector and the secure communication link, and relating this to the power system operating conditions, specifically including: The status monitoring unit of the intelligent reflective surface control component collects the power supply, signal output and connection status of the reflective unit, and receives power system operation data to determine whether the power supply of the reflective surface is affected by the power conditions. By using a dual-end monitoring component for secure communication links, the stability of link connections, data transmission latency, and encryption / decryption status are monitored. At the same time, in conjunction with power dispatching requirements, the stability of the link is prioritized during the transmission of dispatching instructions. By monitoring the fault diagnosis components of the equipment, we can check whether the equipment has malfunctioned due to changes in the regional environment, combine historical fault data to warn of high-probability faults, and generate a joint monitoring log for the communication and power systems.

8. The method according to claim 7, characterized in that, The communication method also includes an intelligent adaptive adjustment step based on state monitoring results, specifically including: When the power supply to the intelligent reflector is abnormal and related to the power conditions, the power system is coordinated to allocate the available power resources, and the parameters of other reflector units are adjusted to make up for the signal gap. When the secure communication link is unstable and the scheduling instruction transmission period is in progress, priority should be given to ensuring the transmission of scheduling instructions, simplifying non-critical authentication steps, and optimizing the transmission and verification mechanisms. When monitoring data transmission is incomplete due to equipment failure, the equipment self-repair function is triggered, and nearby equipment is coordinated to supplement the data. After the equipment is restored, the data deviation is corrected. Based on the warning level and regional characteristics, a tiered response strategy is matched, and cross-system collaborative processing is initiated when necessary to ensure rapid restoration of communication.

9. The method according to claim 1, characterized in that, The communication method also includes steps for collaborative control of multiple intelligent reflector nodes and cross-domain collaboration with the power system, specifically including: When the span of the transmission line exceeds the coverage of a single intelligent reflector, multiple reflector nodes are deployed according to regional characteristics and communication needs to establish a collaborative network between nodes and to establish data interaction channels with relevant components of the power system. The collaborative network collects communication data and power system data from the coverage areas of each node and aggregates them into the collaborative management component. The collaborative management component calculates the optimal parameters for each node based on the matching relationship between communication needs, power resources, and operating costs, ensuring coordinated signal enhancement between adjacent nodes. When a node fails, the parameters of surrounding nodes are adjusted or a backup node is activated, while the power system is coordinated to optimize resource allocation and maintain communication link continuity.

10. The method according to claim 1, characterized in that, The communication method also includes communication performance evaluation, fault handling, and predictive optimization steps, specifically including: Regularly collect key communication performance indicators, correlate them with regional interference data, power system data, and equipment lifecycle data to form a multi-dimensional performance dataset; Evaluation weights are set according to regional characteristics and communication importance, and the performance dataset is comprehensively scored to determine the performance level and diagnose shortcomings. In cases of substandard performance, a handling strategy should be matched with the cause of the fault, and if necessary, the power system should be coordinated to optimize resource allocation. Establish a fault handling log, input performance data and logs into a multi-model fusion prediction system, predict communication status and fault risks in advance, and carry out proactive prevention and optimization.

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