Electric power emergency repair disaster-resistant RIS wave beam dynamic optimization method

Abstract

The invention relates to the technical field of wireless communication services, and discloses an electric power emergency repair disaster-resistant RIS beam dynamic optimization method. The method is used for solving the problem that in a traditional method, RIS auxiliary communication lacks an elastic recovery mechanism in a disaster environment. The method comprises the following steps: firstly, acquiring RIS auxiliary communication parameters to form operation data; identifying an interrupt event based on a comparison of the data to a threshold, determining a location, type, and range, generating an event description; assessing disaster factors by using description classification, extracting influence parameters, associating with a power line repair scene, and establishing an interruption model; generating a beam adjustment scheme according to the model, selecting phase shift configuration, planning a reconstruction path and integrating emergency priorities to form an instruction sequence; executing sequence reconfiguration of a phase shift matrix, adjusting the beam direction and amplitude, and coordinating signal routing; after reconfiguration, parameters are circularly collected and updated, interruption is returned for re-identification, and a closed-loop monitoring mechanism is formed; according to the invention, the power communication toughness under disasters is improved, and the emergency repair service continuity is ensured.

Description

Technical Field

[0001] This invention relates to the field of wireless communication service technology, specifically to a dynamic optimization method for RIS beams in power emergency repair and disaster relief. Background Technology

[0002] With the intelligent development of power systems, RIS, as an emerging wireless communication technology, has been widely used to improve signal coverage and transmission efficiency. In power emergency repair and disaster relief scenarios, RIS can achieve beam optimization by dynamically adjusting the phase shift matrix, helping disaster areas establish reliable communication links and supporting remote monitoring, fault diagnosis, and emergency repair scheduling. However, traditional methods suffer from the problem of lacking a resilient recovery mechanism for RIS-assisted communication in disaster environments, which means that the system cannot quickly heal itself when there is a sudden interruption (such as node failure or channel fading caused by storms or earthquakes), affecting the timeliness and reliability of power repair. For example, CN118449131B discloses a data-driven resilient optimization scheduling method for multiple mobile emergency power sources. This method constructs a multi-stage load shedding agent model and uses multi-agent deep reinforcement learning (MASAC algorithm) to achieve collaborative optimization of emergency power sources and grid topology. This patent emphasizes data privacy protection and real-time decision-making, making progress in power resilience recovery. However, its focus is on power scheduling rather than the dynamic beam adjustment of RIS-assisted communication. In disaster environments, this method lacks a resilient mechanism for RIS phase shift reconfiguration. When the communication link is interrupted, it cannot automatically detect the fault and restore the beam path, resulting in unstable emergency repair command signals and delays in grid operation. Recovery process; CN117176255A discloses a power IoT communication optimization method based on RIS, which maximizes throughput in multi-user scenarios by jointly optimizing RIS phase shift and base station beamforming; the method introduces an alternating optimization algorithm to reduce computational complexity and verifies performance improvement in a simulation environment; however, this patent mainly focuses on static optimization and does not consider system resilience under dynamic interference from disasters; when facing sudden events such as floods or high-voltage line faults, the method lacks a resilient recovery protocol, cannot monitor channel status in real time and adaptively reconstruct RIS beams, resulting in excessively long recovery time after communication interruption, which seriously restricts the disaster resistance capability of power emergency repair; The shortcomings of the existing technologies mentioned above are that, although they optimize the static performance of RIS beams, they ignore the complexity of disaster environments, such as the superposition effects of multipath fading, node failure, and environmental noise. These problems make RIS-assisted communication systems vulnerable in practical disaster relief applications and unable to ensure continuity. Specifically, traditional methods lack elastic recovery mechanisms that integrate fault detection, distributed reconfiguration, and self-healing algorithms. When a disaster interruption occurs, the system needs manual intervention or a restart of the optimization process, resulting in repair delays and resource waste. This not only reduces the overall resilience of the power system but may also trigger secondary disasters, such as delays in high-voltage equipment maintenance leading to wider power outages. Therefore, it is necessary to develop a dynamic optimization method for RIS beams in disaster environments to achieve efficient elastic recovery and improve the disaster relief effectiveness of power emergency repairs. Summary of the Invention

[0003] (a) Technical problems to be solved

[0004] To address the shortcomings of existing technologies, this invention provides a dynamic optimization method for RIS beams in power emergency repair and disaster relief, which solves the problem of the lack of elastic recovery mechanism for RIS-assisted communication in disaster environments in traditional methods.

[0005] (II) Technical Solution

[0006] To achieve the goal mentioned in the background section of the invention of addressing the lack of resilient recovery mechanisms in RIS-assisted communication in disaster environments, the present invention provides the following technical solution: A dynamic optimization method for RIS beamforming in power emergency repair and disaster relief includes: S1: Collect RIS auxiliary communication parameters in disaster environments, and obtain current system operation data by real-time monitoring of base station signal strength, RIS phase shift matrix status and power equipment communication link indicators; S2: Based on the collected parameters, identify interruption events in the RIS-assisted communication link, compare the current data with a preset threshold, determine the location, type, and scope of the interruption, and form an interruption event description; S3: Using the description of interruption events, classify and evaluate disaster factors such as node failures or signal attenuation, extract influencing parameters and associate them with power emergency repair scenarios to establish an interruption model; S4: Based on the interruption model, generate a dynamic beam adjustment scheme, select the RIS phase shift configuration option and plan the reconfiguration path, integrate power emergency priorities, and form an optimized instruction sequence; S5: Execute the optimization instruction sequence, reconfigure the RIS phase shift matrix, adjust the beam direction and amplitude, and coordinate the signal routing between the base station and the power equipment; S6: After reconfiguration, the updated communication parameters are collected cyclically, and the process returns to S2 to re-identify the interruption event, forming a closed-loop monitoring mechanism.

[0007] In a preferred embodiment, RIS-assisted communication parameters in a disaster environment are collected. Current system operating data is obtained by real-time monitoring of base station signal strength, RIS phase shift matrix status, and power equipment communication link indicators, including: Deploy weather-resistant sensors and communication monitoring modules on power equipment, deploy monitoring modules on base stations, and deploy phase shift monitoring and control circuits on RIS panels; The system collects power equipment communication link indicators through sensors, base station signal strength through base station monitoring modules, and RIS phase shift matrix status through phase shift monitoring and control circuits. A parallel acquisition mode with unified time synchronization is adopted to complete data acquisition and reporting within the sampling period. Data is aggregated and preprocessed through edge computing nodes, and the data is parsed and fused into structured records through the central processing unit. Construct the RIS-assisted communication operation data matrix in chronological order.

[0008] In a preferred embodiment, based on the collected parameters, interruption events are identified in the RIS-assisted communication link, and the current data is compared with a preset threshold, including: Extract the multidimensional vector of sampling time from the RIS-assisted communication operation data matrix; Each base station signal strength parameter, RIS phase shift matrix status parameter, and power equipment communication link index is compared with a preset threshold library. By scanning continuous samples through a sliding window, the signal quality on the base station side is judged, the stability of the RIS phase shift matrix state is checked, and the communication link indicators of power equipment are compared and analyzed.

[0009] In a preferred embodiment, the location, type, and scope of the interruption are determined to form an interruption event description, including: Based on device identification and geographic coordinate mapping of interruption event location, interruption event type is classified by matching type judgment table through abnormal parameter combination, and the scope of interruption event is defined by network topology analysis; Integrate interrupt event location, type, and range to generate structured records; Set up timestamp fields, anomaly parameter lists, type fields, range fields, and severity score fields in the structured record; Organize records using a hierarchical data structure based on key-value pairs; Batch processing and parallel computing are used to accelerate identification, data streams are processed according to time windows, and threshold and weight parameters are calibrated through the configuration interface.

[0010] In a preferred embodiment, an interruption model is established by using an interruption event description to classify and assess disaster factors such as node failures or signal attenuation, extracting impact parameters and associating them with power restoration scenarios, including: Read the exception parameters from the interrupt event description and construct the event feature vector; Examine the classification of disaster factors by examining the relationships between parameter combinations; Extract the set of influencing parameters from the interrupt event description; The extracted parameters are matched and associated with the power emergency repair task library and scenario library; An interruption model is constructed based on classification results and correlation parameters. Causal relationships are represented in the form of a directed graph, and the quantitative impact matrix is ​​stored in the form of a two-dimensional table. The interruption model is output to the adjustment scheme generation stage through a standardized interface.

[0011] In a preferred embodiment, based on the interruption model, a beam dynamic adjustment scheme is generated, a RIS phase shift configuration option is selected, and a reconfiguration path is planned, including: Root cause nodes and impact scores are extracted from the causal relationship diagram and quantified impact matrix of the interruption model to form a set of parameter vectors related to beam control; Feasibility verification is performed by matching phase shift matrix patterns in the phase shift template library based on the extracted parameters. By combining network topology and geographic information, alternative routes are searched, and path costs are calculated to plan signal reconstruction paths and beam pointing angles.

[0012] In a preferred embodiment, power emergency priorities are integrated to form an optimized instruction sequence, including: Read the task level from the power emergency repair task database and convert it into a priority score; The phase shift adjustment commands, path switching commands, and priority control commands are organized in the order of execution to form an optimized instruction sequence; The sequence is encapsulated using a structured encoding format and transmitted to the execution module through a dedicated interface.

[0013] In a preferred embodiment, an optimization instruction sequence is executed to reconfigure the RIS phase shift matrix, adjust the beam direction and amplitude, and coordinate signal routing between the base station and power equipment, including: Parse the phase shift command from the sequence and update the phase shift angle of the reflection unit according to the priority queue; The parsing amplitude command adjusts the amplitude value of the reflection unit based on the phase shift update. Analyze the beam pointing command, calculate the synthesis direction, and send out control; Parse the path switching command, update the routing table, and establish an alternative path; A distributed controller is used to execute commands synchronously. Verify the link metrics and report the execution status back to the monitoring module.

[0014] In a preferred embodiment, after reconfiguration, the updated communication parameters are collected cyclically, and the process returns to S2 for interrupt event re-identification, forming a closed-loop monitoring mechanism, including: A new round of parameter acquisition is triggered based on the execution status feedback, and the base station signal strength, RIS phase shift matrix status and power equipment link indicators are refreshed through sensors and monitoring modules; The collected parameters are compared with the threshold, and the interruption event is re-identified through abnormal pattern matching. Generate a new interrupt event description and pass it in sequentially to S3 to update the interrupt model, S4 to generate a new scheme, and S5 to execute the new sequence; It uses a timer to trigger acquisition and comparison within an adjustable iteration cycle, and integrates self-diagnostic and priority management functions.

[0015] Compared with existing technologies, this invention provides a dynamic optimization method for RIS beams in power emergency repair and disaster relief, which has the following beneficial effects: 1. This invention, through multi-source collaborative collection of base station signal strength, RIS phase shift matrix status, and power equipment communication link indicators in disaster environments, constructs an operational data matrix that reflects the current channel quality and equipment status. Based on this, a threshold comparison and pattern recognition mechanism is introduced to finely identify the interruption location, type, and impact range. The identification results are structured into interruption event descriptions and associated with a power emergency repair task library and a scenario library to form an interruption model that characterizes the causal relationship and impact intensity between disaster factors, communication links, and emergency repair tasks. This model then drives the dynamic reconstruction of beam pointing and phase shift matrix, as well as route replanning. Power service priorities are introduced to achieve differentiated protection for key equipment and important lines. Finally, the link performance changes are sent back to the interruption identification stage through execution result feedback and a new round of collection. This achieves closed-loop self-healing control from collection, identification, evaluation, scheme generation, instruction execution to result feedback, thereby significantly improving the resilience and automatic recovery capability of RIS-assisted communication under disaster conditions such as typhoons, earthquakes, and floods. It effectively solves the problem of unstable emergency repair communication caused by the lack of integrated fault detection and distributed reconfiguration elastic recovery mechanisms in traditional methods. 2. This invention establishes a unified interruption model and parameter mapping mechanism between the communication side and the power service side, associating communication indicators such as RSSI, SNR, phase shift deviation, latency, and packet loss rate with specific repair targets such as substations, transmission lines, and emergency generator sets. This not only accurately locates fault areas and affected equipment spatially, but also introduces power-side parameters such as repair level and task sensitivity at the service level, quantifying them as weights and priority constraints in the model. Based on this, when the model drives beam reconfiguration and route replanning, it can automatically prioritize critical services such as high-voltage trunk lines and dispatch control links, accurately allocating limited RIS reflection resources and available communication paths according to their importance. This avoids the problem of communication optimization and repair scheduling being disconnected in traditional solutions, requiring repeated manual coordination. Thus, in complex disaster environments, it achieves coordinated and optimized allocation of communication resources and power repair resources, effectively improving overall emergency response efficiency and critical service support capabilities. Attached Figure Description

[0016] Figure 1 This is a flowchart of a dynamic optimization method for RIS beams in power emergency repair and disaster relief, as described in this invention. Detailed Implementation

[0017] 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.

[0018] Example: Figure 1 A dynamic optimization method for RIS beams in power emergency repair and disaster relief is presented, including: S1: Collect RIS auxiliary communication parameters in disaster environments, and obtain current system operation data by real-time monitoring of base station signal strength, RIS phase shift matrix status and power equipment communication link indicators; S2: Based on the collected parameters, identify interruption events in the RIS-assisted communication link, compare the current data with a preset threshold, determine the location, type, and scope of the interruption, and form an interruption event description; S3: Using the description of interruption events, classify and evaluate disaster factors such as node failures or signal attenuation, extract influencing parameters and associate them with power emergency repair scenarios to establish an interruption model; S4: Based on the interruption model, generate a dynamic beam adjustment scheme, select the RIS phase shift configuration option and plan the reconfiguration path, integrate power emergency priorities, and form an optimized instruction sequence; S5: Execute the optimization instruction sequence, reconfigure the RIS phase shift matrix, adjust the beam direction and amplitude, and coordinate the signal routing between the base station and the power equipment; S6: After reconfiguration, the updated communication parameters are collected cyclically, and the process returns to S2 to re-identify the interruption event, forming a closed-loop monitoring mechanism.

[0019] S1: Collect RIS-assisted communication parameters in the disaster environment. Obtain current system operation data by real-time monitoring of base station signal strength, RIS phase shift matrix status, and power equipment communication link indicators. Specific implementation details are as follows: By real-time monitoring of base station signal strength, RIS phase shift matrix status, and power equipment communication link indicators, a RIS-assisted communication operation data matrix is ​​formed that can be directly used by the subsequent interruption event identification module. To address the potential for communication quality degradation or even link interruption in power systems under extreme conditions such as typhoons, earthquakes, and floods, monitoring units are deployed at key locations such as substations, high-voltage transmission towers, and emergency power generation vehicles. The monitoring units include weather-resistant sensors and communication monitoring modules. Their housings are waterproof, dustproof, and vibration-resistant, and they integrate low-power processors and local communication interfaces to continuously operate and report observation results in harsh environments. Base station signal strength parameters are collected by the base station monitoring module, which is installed on a fixed communication tower or mobile base station vehicle. The module is equipped with a multi-band antenna covering the 2.4GHz to 6GHz RIS operating frequency band and acquires Received Signal Strength Indicator (RSSI) and Signal-to-Noise Ratio (SNR) through an RF front-end and signal processing unit. The RSSI quantifies the signal arrival power in dBm and reflects the impact of obstruction, long-distance transmission, and shadow fading caused by disasters on signal strength. The SNR is calculated by comparing the useful signal power to the noise power, expressed in dB, and characterizes the impact of electromagnetic interference and multipath effects on link quality. The base station monitoring module generates a channel quality vector containing RSSI and SNR in each sampling period, and appends a time stamp and base station identifier. The RIS phase shift matrix status is acquired by a phase shift monitoring and control circuit integrated within the RIS panel. The RIS can be installed on building facades, power transmission corridor supports, or temporary support structures. Each reflector unit is equipped with a phase shift controller and a status detection unit to read the current phase shift angle and reflection amplitude of the reflector unit. The phase shift angle is adjustable within the range of 0° to 360°. The system reads the current angle of each reflector unit according to a preset angle resolution, forming a phase shift angle vector arranged by unit index. The reflection amplitude is obtained by estimating the magnitude of the reflection coefficient and normalizing it to the 0-1 range to characterize the energy reflection efficiency of each reflector unit and form a corresponding amplitude vector. The aforementioned phase shift angle vector and amplitude vector are reported to the local aggregation node through the control bus inside the RIS, thereby reducing the impact of external electromagnetic interference on status reading. When a long-term offset of the RIS panel is detected during a disaster, the central processing unit can issue a remote calibration command to fine-tune the phase shift configuration of some reflector units to reduce the cumulative error caused by mechanical displacement and temperature drift. The communication link indicators of power equipment are collected by the link monitoring agent module deployed on power automation equipment and emergency command terminals. The link monitoring agent module is connected to the control command channel and the measurement data channel respectively. It compares the timestamp of each sent message and the corresponding received message to calculate the link delay, which is reflected in milliseconds. Within a preset statistical period, the link monitoring agent module also counts the total number of sent messages and the number of successfully received messages, thereby calculating the packet loss rate and expressing it as a percentage to reflect the proportion of messages dropped during transmission. At the same time, the module verifies the received data through checksum or error correction coding, counts the number of erroneous bits and compares it with the total number of bits to estimate the bit error rate, which is used to evaluate the reliability of critical control information transmission in disaster scenarios. For scenarios with both a main link and a backup satellite link or emergency microwave link, the link monitoring agent module can record the delay, packet loss rate and bit error rate of each link separately, thereby providing basic data for subsequent link selection strategy formulation and redundancy configuration analysis. To ensure consistency of multi-source data across time, a parallel acquisition mode with unified time synchronization is adopted. The base station monitoring module, RIS status acquisition unit, and link monitoring agent module synchronize with the upper-level time source periodically through their built-in clocks, completing data acquisition and reporting within a specified time window within the same sampling period. Data acquisition is triggered by a periodic task driven by a timer. The default sampling interval can be set to 1 second and can be adjusted within the range of 100 milliseconds to 10 seconds through the configuration interface to adapt to different disaster intensities and resource constraints. When a drastic change in key parameters is detected, abnormal trigger supplementary sampling can be performed outside of periodic sampling. For example, when a base station monitoring module detects that the received signal strength drops by more than a preset dB threshold between two adjacent sampling periods, a supplementary sampling is immediately initiated to capture the sudden degradation process, thereby reducing the probability of missed detection. During the sampling interval, each front-end module enters a low-power operating state, maintaining only time synchronization and interruption detection functions to extend the continuous operating time under power-constrained conditions. Edge computing nodes are set up at the edge of the power system to aggregate and preprocess raw data from different monitoring modules. Each monitoring module transmits the collected parameters to the edge nodes via short-range wireless links or wired buses. The edge nodes perform format verification, remove obvious abnormal noise, and resample when necessary. Parameters such as base station signal strength, signal-to-noise ratio, phase shift angle, reflection amplitude, link latency, packet loss rate, and bit error rate are organized into structured records. To reduce transmission bandwidth consumption during disasters, edge nodes can compress non-critical fields and prioritize uploading critical indicators when the link is congested, according to a pre-set priority. In terms of security, edge nodes encrypt and encapsulate messages using symmetric encryption algorithms before sending data, combined with an authentication mechanism, to reduce the risk of malicious access or data tampering during disasters. The data uplink can select appropriate wireless protocols and public networks based on available network conditions. For example, 5G mobile communication networks can be used in areas with good coverage to achieve low latency and high bandwidth transmission, while long-range low-power wide-area network protocols can be used in areas where conventional base stations are damaged or have weak coverage to achieve reliable data backhaul. The central processing unit (CPU) is deployed in the dispatch center's computer room or cloud platform, possessing redundant power supply and backup storage capabilities. It is used to parse and fuse data reported by edge nodes. Following a predefined data model, the CPU maps received signal strength, signal-to-noise ratio, phase shift angle, reflection amplitude, link delay, packet loss rate, bit error rate, and related device identifiers, timestamps, and geographic coordinates into structured records, which are then written into a time-series data set in chronological order. Subsequently, the CPU aligns records from different sources with the same timestamp, combining various parameters at each sampling moment into a multi-dimensional vector. This vector is used to construct a RIS (Resource Injection System) assisted communication operation data matrix, with time as the row and various parameters as the column. This operation data matrix is ​​continuously updated throughout the disaster process, preserving the numerical characteristics of various physical quantities while clearly distinguishing base station signal characteristics, RIS configuration status, and power communication link quality in terms of dimensions. This allows it to be directly used as input for subsequent interruption event identification and optimization decision-making modules.

[0020] S2: Based on the collected parameters, interruption events are identified in the RIS-assisted communication link. The current data is compared with a preset threshold to determine the location, type, and scope of the interruption, and an interruption event description is generated. The specific implementation is as follows: Based on the RIS-assisted communication operation data matrix formed above, interruption events are identified in the RIS-assisted communication link. Specifically, the multi-dimensional vector corresponding to each sampling time in the operation data matrix is ​​used as input. This multi-dimensional vector includes base station signal strength parameters, RIS phase shift matrix state parameters, and power equipment communication link indicators. By comparing the above parameters with a preset threshold library and type judgment rules, it is determined whether an interruption event exists, as well as the location, type, and scope of impact of the interruption. A structured interruption event description record is generated for subsequent cause analysis and scheduling optimization modules to call. First, the signal quality at the base station is assessed. The central processing unit extracts the received signal strength indicator and signal-to-noise ratio (SNR) component for each sampling moment from the operational data matrix and compares them with a preset normal operating threshold range. The received signal strength threshold can be set comprehensively based on the reliability requirements of power emergency communication and historical disaster data. For example, when the received signal strength is below approximately -90 dBm, the sampling point is marked as a candidate event for signal weakening. The specific threshold can be adjusted according to the region and frequency band. Similarly, a lower limit threshold is set for the SNR. When the SNR is below approximately 15 dB, the impact of noise interference on link quality can be considered significant. When the central processing unit scans each time vector in chronological order, a sliding window method can be used to aggregate samples that are continuously below the threshold into a signal quality anomaly interval, thereby reducing misjudgments caused by instantaneous fluctuations at individual sampling points. Subsequently, a stability check is performed on the RIS phase shift matrix state. The system maintains a standard configuration vector to describe the target phase shift angle and reflection amplitude of each reflection unit under the current operating conditions. During identification, the phase shift angle vector and amplitude vector at each sampling moment are extracted from the running data matrix and differ from the standard configuration vector to obtain the phase shift deviation vector and amplitude deviation vector. The allowable range for the phase shift angle deviation can be set. For example, when the actual angle of a reflection unit deviates from the target angle by more than about 5°, the unit is marked as a configuration drift unit. This threshold can be adjusted according to the mechanical stability and control accuracy of the RIS panel. The reflection amplitude can be set with a lower limit for energy reflection efficiency. When the normalized amplitude is less than 0.8, the reflection capability of the unit is considered to have decreased significantly. The central processing unit counts the number of units with deviations exceeding the threshold and their position distribution in the matrix. This can identify both isolated single-point faults and anomalies appearing in patches at the edge of the panel or in a certain area, providing a basis for subsequent spatial positioning. Further comparative analysis of power equipment communication link indicators was conducted, comparing link delay, packet loss rate, and bit error rate at each sampling moment in the operation data matrix with pre-set normal ranges. The upper limit of link delay can be set in conjunction with the real-time requirements of power dispatch instructions. For example, when the round-trip delay of a certain link continuously exceeds approximately 10ms to 100ms for a period of time, it is marked as a transmission congestion or bottleneck candidate event. The packet loss rate threshold can be set according to the allowable loss ratio of control messages. For example, when the packet loss rate within the statistical window is higher than a certain percentage (e.g., 1% to 3%), it is considered that data loss has a substantial impact on the control closed loop. The upper limit of bit error rate can be set with reference to communication standards and error correction capabilities. For example, it is required that the number of erroneous bits in 1,000,000 bits remain at a very low order of magnitude. The above judgment can adopt a hierarchical threshold structure, first using a wider range to screen abnormal candidate links, and then using a stricter threshold to review key links to improve the robustness of interruption event identification. After comparing base station signals, RIS status, and link indicators item by item, the system determines the location of the interruption event based on the device identifiers and geographic coordinate information carried in the operational data. The central processing unit maps the device identifiers corresponding to various abnormal parameters to the spatial location of the Geographic Information System (GIS). For example, it maps base stations with continuously weakened signals to corresponding coordinate points, maps reflection units with concentrated RIS matrix deviations to corresponding areas on high-voltage towers or building facades, and maps terminals with significantly increased link packet loss rate and bit error rate to specific substations or emergency generator sets. By performing cluster analysis on multiple abnormal locations, it can be determined whether the anomaly is limited to the vicinity of a certain device or is distributed in a strip along the transmission corridor. The location accuracy can reach the meter level depending on the performance of the positioning module and the resolution of the base map, thus providing precise spatial guidance for the repair team. Based on the determined location, the system classifies interruption events according to the abnormal patterns of different parameter combinations. A type judgment table is pre-configured in the central processing unit. This table can be designed and updated based on historical disaster case statistics and simulation analysis results, associating representative abnormal parameter combinations with corresponding interruption types. For example, when the received signal strength and signal-to-noise ratio are both below their respective thresholds, while the RIS phase shift matrix deviation and link bit error rate remain within the normal range, the event can be classified as a signal attenuation-type interruption, usually related to deteriorating external environmental conditions. When a large area of ​​cells in the phase shift deviation vector exceeds the phase shift deviation threshold, and the corresponding link packet loss rate and link delay increase synchronously, the event can be classified as a node failure-type interruption, more likely reflecting RIS panel damage or power supply abnormalities. When the received signal strength and signal-to-noise ratio remain within the normal range, while the packet loss rate and bit error rate significantly increase in a certain network segment, the event can be classified as a routing congestion-type or network congestion-type interruption. For complex situations where multiple abnormal conditions are met simultaneously, the central processing unit matches entries in the type judgment table according to a preset priority order, selecting a unique dominant type for each event, thereby avoiding duplicate classification. The system also needs to determine the scope of the outage event. Based on the power communication network topology information and GIS map, the central processing unit analyzes the positional relationship of abnormal nodes and abnormal links in the topology. When the anomaly is mainly limited to a single base station and a small number of directly connected terminals, the outage scope can be marked as local impact. When multiple adjacent RIS panels and their corresponding lines are abnormal and spatially continuous, the scope can be defined as regional impact, corresponding to a certain transmission section or emergency repair area. When the anomalies in link latency, packet loss rate, and bit error rate extend to the backbone links between multiple substations, the event can be marked as global impact, indicating that the outage may affect the communication security of the entire power grid. The scope definition results can be presented by overlaying a highlight display layer on the network topology map and geographic base map, making it easier for dispatchers to intuitively understand the damaged area and the affected scope. After completing the location, type, and scope identification, the central processing unit generates a structured interruption event description record. This record includes at least an event timestamp field to mark the specific moment the interruption was identified (time can be in UTC format and accurate to milliseconds); a location field to record the affected key equipment and its spatial area; an abnormal parameter list to list the key indicators involved in the judgment and their deviations from the corresponding thresholds; a type field to record the classification result of the interruption type; a scope field to describe the network topology scope affected by the interruption and the number of devices involved; and a severity score field to quantify the potential impact of the event on power repair and power supply security. Configurable weights can be assigned to indicators such as signal-to-noise ratio, link latency, packet loss rate, and bit error rate to calculate a comprehensive score between 0 and 1. This score is then mapped to a multi-level severity level according to a preset grading rule, such as low, medium, and high levels. The interruption event description can be organized using a structured data format compatible with the power dispatch automation system, such as a hierarchical data structure based on key-value pairs, to ensure reliable transmission and storage between the dispatch center system, the emergency command platform, and the subsequent classification and evaluation module.

[0021] S3: Using the description of interruption events, classify and assess disaster factors such as node failures or signal attenuation, extract influencing parameters and associate them with power emergency repair scenarios to establish an interruption model. The specific implementation is as follows: Based on the interruption event description records output above, disaster factors are classified and assessed and an interruption model is established. The interruption event description is a structured record, which includes at least information such as timestamp, location field, abnormal parameter list, type field, range field and severity score. Using these descriptions as input, through parameter analysis, scenario association and model building, the interruption of RIS-assisted communication link is closely integrated with power emergency repair business, providing a model basis for subsequent recovery strategies and adjustment plans. First, the abnormal parameters in the interruption event description are analyzed in a structured manner. The central processing unit reads indicators such as changes in received signal strength, signal-to-noise ratio, RIS phase shift matrix deviation, degree of reflection amplitude decrease, link delay, packet loss rate, and bit error rate from each description, and constructs the corresponding event feature vector by combining the event's timestamp and severity score. By examining the combination relationship between the parameters, the possible sources of the disaster factors can be preliminarily determined. For example, when the received signal strength and signal-to-noise ratio are both below their respective thresholds, while the RIS phase shift matrix deviation and bit error rate are still within the normal range, it can be preliminarily inferred that the signal attenuation is caused by the external environment. When the RIS phase shift angle deviation is significant, and the corresponding link delay and packet loss rate increase simultaneously, it is more likely to be an internal node failure. For events with multiple anomalies occurring simultaneously, the severity score can be used as an auxiliary weight to rank multiple candidate disaster factors, select the dominant factor as the main disaster type of the event, and retain the other factors as auxiliary labels. Based on the above analysis results, the disaster factors corresponding to the interruption events are classified. The central processing unit pre-maintains a disaster type rule base, which is continuously updated through historical disaster cases and simulation analysis results. It defines several typical disaster types and their corresponding parameter patterns. For example, the node failure type can deal with situations such as damage to the power station antenna or RIS panel circuit failure. Its characteristics are that the phase shift angle of some reflective units deviates from the standard configuration for a long time, the reflection amplitude is lower than the set threshold for a long time, and the bit error rate increases significantly near the relevant nodes. The signal attenuation type can deal with the obstruction of the propagation path caused by typhoons, rainstorms, or sandstorms. Its characteristics are that the received signal strength gradually decreases over a period of time, the signal-to-noise ratio fluctuates more, while the overall RIS configuration remains stable. The link congestion type is characterized by a concentrated increase in link delay and packet loss rate in a certain network segment, while the base station signal and RIS status remain basically within the normal range. The classification process matches the event feature vector with the pattern vector in the rule base, calculates the similarity, and selects the most suitable type according to the preset priority. At the same time, the secondary types are recorded for subsequent comprehensive analysis and decision reference. Then, the set of influencing parameters is extracted from the interruption event description. The central processing unit restores the spatial location of the fault node to standard geographic coordinates by parsing the location field and device identification field. For example, it can be parsed as approximately 30.5 degrees north latitude and 120.2 degrees east longitude, which is used to locate the corresponding high-voltage tower, substation, or emergency generator set on the geographic information system map. For signal attenuation-related events, the attenuation coefficient can be calculated by comparing the received signal strength values ​​at several sampling times before and after the interruption. For example, a drop from approximately -80dBm in the normal state to approximately -100dBm corresponds to an attenuation of approximately 20dB, which is used to quantify the intensity of environmental interference. For link quality-related events, indicators such as peak latency, average packet loss rate, and bit error rate increase can be extracted. The above influencing parameters are filtered from the list of abnormal parameters through preset filtering rules, retaining only parameters that are closely related to the disaster type judgment and have direct significance for power repair decisions, so as to improve the data quality of subsequent model construction. After parameter extraction, the system associates the influencing parameters with power emergency repair scenarios. To this end, the system pre-maintains a power emergency repair task library and a scenario library. The task library records typical emergency repair task types, work location ranges, and priorities, while the scenario library records the topological and geographical correspondences of facilities such as transmission lines, substations, and RIS panel deployment points. The central processing unit can locate the corresponding line segment or site node on the geographic information system base map and network topology map based on fault coordinates and equipment identifiers. For example, when the coordinates fall in the middle section of a 500kV transmission line, the interruption event can be associated with the emergency repair task template for that line. When the attenuation coefficient exceeds a preset high attenuation threshold, the area can be marked as a key area for emergency communication protection, prioritizing scenarios such as remote control of emergency generators and allocation of backup frequency bands. When the bit error rate continuously increases in a substation monitoring channel, it can be associated with task scenarios such as monitoring data retransmission and log supplementation. Through the above association process, a mapping relationship is formed from event parameters to specific emergency repair tasks and protection measures, providing business semantic support for subsequent strategy formulation. Based on this, an interruption model is constructed to centrally represent the logical relationships and quantified impacts between disaster factors, influencing parameters, and power emergency repair scenarios. The interruption model can include two parts: a causal relationship graph and a quantified impact matrix. The causal relationship graph can be represented in the form of a directed graph, where nodes represent disaster factors, key indicators, and emergency repair tasks, and edges represent influence relationships with attached weights, characterizing the intensity of a disaster factor's effect on a particular indicator or triggering a particular task. The quantified impact matrix can be stored in a two-dimensional table format, where rows can represent disaster factor types, such as signal attenuation, node failure, and link congestion, and columns can represent... The model displays impact indicators such as the increase in latency, the increase in packet loss rate, the increase in bit error rate, and the number of affected devices. The matrix elements are normalized impact scores, which can be small values ​​between 0 and 1, with larger values ​​indicating more significant impacts. The interruption model can accumulate the evaluation results of multiple interruption events within a certain time window. By statistically analyzing the frequency of occurrence of the same combination of factors and indicators and the corresponding severity scores, the edge weights in the causal relationship graph and the scores in the impact matrix are updated. This allows the model to gradually improve as the number of disaster cases increases, thereby more accurately reflecting the vulnerable links and key impact paths of the power system under disaster conditions. After the interruption model is established, the central processing unit (CPU) outputs the model to the subsequent adjustment plan generation stage through a standardized interface. The CPU can organize the nodes, edges and their weights in the causal relationship graph, as well as the scores in the quantified influence matrix, into structured data and encapsulate it in a format compatible with scheduling software. The encapsulated content includes information such as event type, key parameters, impact path, and recommended task type. The above output data is transmitted to the recovery strategy module and the path planning module through a secure communication channel. The recovery strategy module can determine the priority disaster factors based on the causal relationship graph, and the path planning module can optimize the allocation order and routes of emergency repair resources based on the impact range and degree information in the quantified influence matrix. To ensure the security of sensitive power data, the model data can also be protected by encryption and signature mechanisms before output.

[0022] S4: Based on the interruption model, generate a dynamic beam adjustment scheme, select the RIS phase shift configuration option and plan the reconfiguration path, integrate power emergency priorities, and form an optimized instruction sequence. The specific implementation is as follows: Based on the interruption model constructed above, a dynamic beam adjustment scheme is generated. The interruption model includes a causal relationship diagram and a quantized influence matrix. The causal relationship diagram provides information on root causes such as fault nodes and signal attenuation paths, while the quantized influence matrix provides the influence intensity and priority scores of each link and device. Using this interruption model as input, key parameters related to beam control are extracted, an appropriate RIS phase shift configuration scheme is selected, and a signal reconstruction path is planned in conjunction with the power communication network topology. On this basis, the power emergency repair priority is superimposed, and finally an optimized instruction sequence that can be issued to the execution module is formed. First, parameters closely related to beam adjustment are extracted from the interruption model. The central processing unit (CPU) identifies root cause nodes directly associated with the interruption event based on a causal relationship graph. These root cause nodes can include the spatial location of the faulty node, the identifiers of the affected base stations or RIS panels, and the main signal transmission paths and their directional information. Simultaneously, the CPU reads the impact score and priority score corresponding to the above nodes and their associated links from the quantized impact matrix, such as attenuation coefficient, fault impact weight, and number of affected devices. To this end, the set of nodes in the causal relationship graph can be traversed, and the corresponding row or column of each node in the quantized impact matrix can be found, forming a set of parameter vectors related to the current interruption event. Subsequently, factors unrelated to power communication are eliminated through preset filtering rules, retaining only parameters related to critical communication links and repair targets, thereby ensuring that the subsequent solution generation process always revolves around power emergency needs. After parameter extraction, the RIS phase shift configuration scheme is selected using the parameters. A phase shift template library is pre-maintained, storing various predefined phase shift matrix modes, such as a uniform distribution mode for large-area coverage, a focusing mode for enhancing key equipment, and a partitioned enhancement mode for multi-point coverage. The central processing unit can calculate the matching degree between each template and the current requirements based on the attenuation coefficient, impact range, and key equipment location in the interruption model. The matching degree can be obtained by comparing the weighted differences of multiple parameters, such as the applicable signal strength range, target direction interval, and number of available reflection units, and compared with... A preset matching threshold is used for comparison. For example, the matching threshold can be set at around 80%. Templates exceeding this threshold are included in the candidate set. Then, in conjunction with the RIS hardware capabilities, the feasibility of the candidate templates is verified. This involves checking whether the target phase shift angle of each reflection unit in the template falls within the adjustable range of 0° to 360°, and whether the number of units referenced in the template does not exceed the actual number of units available in the panel. For example, the panel can be configured to contain 256 reflection units, and the template should not exceed this number when in use. Through the above matching and verification process, a set of phase shift configuration schemes that can be practically deployed in the current disaster scenario can be obtained. Simultaneously, signal reconstruction paths need to be planned based on the power communication network topology and geographic information. The central processing unit, combined with the fault node coordinates, affected link set, and alternative relay node set given in the interruption model, searches for one or more available backup routes from the network topology map. During the path planning process, a comprehensive cost function can be constructed, which weights and sums indicators such as path length, estimated path loss, node reputation, and current service load according to preset weights, and selects the path with the lowest comprehensive cost as the preferred result. For beam pointing angle planning, the pointing angle that the RIS panel needs to be adjusted can be determined based on the geometric relationship between the fault node, backup base station, and key emergency repair equipment. For example, the angle between the current beam direction and the target direction can be calculated based on the spatial coordinates of each node, and the original pointing angle can be adjusted to the new target angle so that the main lobe of the beam covers the area where the emergency generator set or mobile repair vehicle is located. The path planning result can be represented as a path chain, with the starting point of the chain being the current RIS panel location or the network access node on the gateway side, the ending point being the power equipment to be protected, and the intermediate nodes being backup base stations, satellite relays, or other reflectors. The path is required to cross the interruption area and minimize the number of relay hops to reduce latency and cumulative error. After obtaining the phase shift configuration scheme and reconfiguration path, the power emergency priority needs to be integrated into the adjustment scheme. The central processing unit, based on the correlation results between the interruption model and the power emergency repair task database mentioned earlier, reads the repair level of each type of task from the task database and converts it into a comparable priority score. For example, high-voltage transmission line repair tasks can be mapped to higher priority, while general power distribution equipment restoration tasks can be mapped to lower priority. On this basis, candidate phase shift templates and backup paths are rearranged to ensure that links associated with high-priority tasks receive better phase shift configurations and lower-cost routing. For example, for links associated with 500kV transmission lines, a focused phase shift mode can be prioritized, and a backup path with lower overall cost can be selected. For medium- and low-priority tasks, the remaining reflection units and routing resources are allocated only under the premise of meeting basic communication reliability requirements. This ensures that RIS resource utilization and path selection simultaneously meet the combined requirements of disaster response needs and power service priorities on a global scale. Based on the above steps, an optimized instruction sequence is generated. This sequence can be organized into a command list according to the execution order, with each command having a type field and a parameter field. Phase shift adjustment commands can include the range of reflection unit numbers, the target phase shift angle, and the target reflection amplitude or its increment, used to instruct the RIS control unit to update the configuration of the specified reflection unit. Path switching commands can include parameters such as source route identifier, target route identifier, and planned hop count, used to guide the communication system to migrate data streams from damaged paths to backup paths. Priority control commands can include task identifiers, priority levels, and applicable time windows, used to control the scheduling order and effective period of different commands during execution. The commands in the instruction sequence are ordered according to logical dependencies. First, phase shift adjustment commands are issued to complete beam reconstruction, then path switching commands are issued to complete route adjustment. Finally, the execution order of multiple tasks is refined based on priority control commands to ensure the coherence and atomicity of the adjustment scheme implementation process. To facilitate rapid parsing and processing in the execution module, the optimized instruction sequence can be organized using a structured encoding format. For example, a fixed-length encoding field can be designed for each command, including a command type code, target device or reflection unit identifier, parameter value field, and verification field. A timestamp and configuration version number are appended to the entire sequence to identify the generation time and version information of this adjustment scheme. The instruction sequence can be transmitted to the execution module through a dedicated interface, which can be a wired industrial bus or a wireless communication link with anti-interference capabilities. Verification and authentication information is appended during transmission to ensure reliable delivery and correct parsing even in disaster environments. In actual power emergency scenarios, such as when a storm causes severe signal attenuation of base stations near high-voltage lines, the attenuation parameters and fault node locations in the area can be extracted using an interruption model. An appropriate focusing phase shift template can be selected, and a backup route bypassing the storm center can be planned. Based on the repair level of the high-voltage line, relevant instructions are prioritized at the beginning of the instruction sequence. Based on the interruption model, the evaluation results are transformed into an executable beam adjustment and route reconstruction scheme and provided to the execution module in the form of an optimized instruction sequence, thereby prioritizing the communication needs of critical power equipment under disaster conditions.

[0023] S5: Execute the optimization instruction sequence to reconfigure the RIS phase shift matrix, adjust the beam direction and amplitude, and coordinate the signal routing between the base station and power equipment. Specifically, this is implemented as follows: The optimized instruction sequence generated above is executed to reconfigure the RIS phase shift matrix and coordinate signal routing between the base station and power equipment. The optimized instruction sequence includes phase shift adjustment commands, path switching commands, and priority control commands. The execution process takes this sequence as input and completes the physical update of the reflection unit configuration and communication link status item by item according to the command type and priority. First, phase shift adjustment commands are executed to update the phase shift angles of the RIS reflector units. The central processing unit parses the range of reflector unit numbers and the target phase shift angle parameters contained in each phase shift command from the instruction sequence, and schedules the commands using a priority queue. Within the same priority, commands can be executed in a first-in-first-out order. Between different priorities, commands related to higher-priority power repair tasks are prioritized. For example, the phase shift angle of one group of reflector units can be adjusted from close to 0° to about 45°, and another group of units can be adjusted from about 30° to about 90°. The specific angles are calculated by combining the target direction given by the interrupt model and the device position. Phase shift updates are achieved by sending digital control words or control voltages to the phase shift devices through the RIS control interface. The angle adjustment step size can be set on the order of 1° to reduce disturbance to the existing link while ensuring the precision of beam redirection. After the phase shift angle adjustment is completed, the amplitude adjustment command is executed to configure the reflection coefficient amplitude of the reflection unit. The central processing unit reads the target amplitude or amplitude increment parameter of each reflection unit from the instruction and updates the amplitude value of the corresponding unit sequentially based on the new phase shift configuration. The amplitude parameter can be represented in a normalized form within the range of 0 to 1, and the target amplitude of each unit is determined by combining the attenuation coefficient and impact score given in the interruption model. For example, the normalized amplitude of a certain unit can be increased from about 0.6 to about 0.9 to increase the link gain in that direction by several decibels. The specific increase is calculated by the algorithm based on the path loss under the disaster scenario. In terms of execution order, the angle update of a batch of reflection units can be completed first, and then the amplitude of the corresponding units can be fine-tuned. A consistency check is performed after each batch of adjustments to avoid conflicts between the angle configuration and the amplitude configuration. Based on local phase shift and amplitude updates, commands containing overall beam pointing information are further executed to jointly optimize the main beam direction and overall gain. The central processing unit calculates the updated synthetic beam direction based on the target azimuth and elevation angle parameters given in the command, combined with the current phase shift and amplitude of each reflector, and generates corresponding control commands to be sent to the RIS panel. The target direction can be derived from the fault node coordinates recorded in the interruption model and the location of key emergency repair equipment. For example, the main beam direction can be deflected by a certain degree relative to the original pointing angle so that the main lobe covers the area where the emergency generator set or mobile repair vehicle is located. The overall gain can be adjusted by coordinating the amplitude configuration and spatial distribution of multiple reflectors so that the main lobe gain reaches the target value to counteract the current path attenuation, thereby ensuring that the power communication link has sufficient signal margin in disaster environments. Simultaneously, path switching commands are executed to coordinate and reconstruct the signal routing between the base station and power equipment. The central processing unit updates the corresponding routing table entries in the routing module based on the source and destination route identifiers in the command, and triggers the establishment of a backup path. In actual execution, a backup route can be established first and connectivity verification can be performed. After confirming that the backup route is stable, the dependence on the faulty path is gradually reduced, and finally the service traffic is switched to the backup link, thereby reducing the risk of communication interruption. The backup path can include a backup ground base station, satellite relay link, or dedicated wireless channel. In multi-path fusion scenarios, the command can also carry weight or power allocation ratio parameters for different paths. The routing module allocates traffic between the reflection path and the direct path based on these parameters to improve the redundancy and resilience of the link. Priority control commands are used to guide the scheduling order of different tasks and links during execution. The central processing unit allocates commands related to critical equipment such as high-voltage transmission lines and important substations to high-priority queues for priority execution based on the task identifier and priority level carried in the command, and allocates commands related to general loads or routine monitoring services to lower-priority queues for sequential execution. Within the same task, commands can be processed sequentially according to their enqueue order, so that the execution process meets both task priority requirements and maintains the consistency of command execution order. Through priority-driven execution strategies, the communication capabilities of critical power equipment can be restored first within a limited time window after a disaster. To support the above execution process, a distributed control architecture can be adopted, with multiple controllers deployed on the RIS panel, base station side, and some power equipment nodes. Each controller has a local processing unit and communication interface, and maintains a unified time base through a time synchronization mechanism. By using network time synchronization protocols or external time signals, the clock deviation between controllers is controlled to the millisecond level, enabling phase shift updates and path switching across nodes to be completed collaboratively within a predetermined time slice. The instruction sequence can be optimized by using encryption and integrity verification mechanisms during transmission, such as message authentication and signature verification of instruction content, and access control lists to restrict the subject of instruction execution, preventing malicious instructions or misoperations from causing secondary impacts on the power communication system during disasters. After execution, the link reconstruction effect needs to be verified, and the results fed back to the monitoring module. The verification process may include detecting indicators such as latency, packet loss rate, and bit error rate, and checking whether each indicator meets the pre-configured quality of service thresholds. For example, test messages can be sent to the target power equipment and round-trip latency can be measured. The measured round-trip latency can be compared with the configured threshold to determine whether the link has reached an available state. At the same time, the central processing unit can generate a feedback report containing the latest phase shift matrix snapshot, routing configuration summary, and link performance indicators. The feedback report can be recorded in structured text or tabular form and transmitted to the monitoring module through an independent feedback channel. After receiving the feedback, the monitoring module can incorporate this information into the next round of data acquisition and interruption identification process, realizing closed-loop control from acquisition, identification, evaluation, scheme generation, instruction execution to result feedback. S6: After reconfiguration, the updated communication parameters are collected cyclically, and the process returns to S2 for interrupt event re-identification, forming a closed-loop monitoring mechanism. The specific implementation is as follows: After the optimization instructions are executed and feedback status is obtained, the updated communication parameters are collected again based on the feedback, and the collection results are fed back to the interruption event identification stage of S2, thus forming a closed-loop monitoring mechanism. Starting from the phase shift matrix snapshot and link verification results reported by the execution module, the system can continuously detect new anomalies and trigger subsequent adjustments during the disaster evolution process through periodic collection, threshold comparison and re-identification. After reconfiguration is completed and execution status feedback is received, the central processing unit triggers a new round of parameter acquisition. This process again calls upon the sensors and monitoring modules deployed on power equipment, base stations, and RIS panels to refresh the data of the reconfigured communication environment. For example, in scenarios where the wind field is still fluctuating during the aftershock period of an earthquake or the later stages of a typhoon, the sensors on the power equipment side stably read operating parameters through vibration-resistant structures and filtering circuits, and the base station monitoring module remeasures the received signal strength indication value and signal-to-noise ratio. The received signal strength can be acquired within a preset monitoring range, such as approximately -110dBm to -60dBm, to cover the weak signal fluctuations that may occur during the recovery phase. The signal-to-noise ratio is recalculated based on the signal-to-noise power ratio after reconfiguration to assess the noise residue and multipath interference introduced by the new path or new beam. Simultaneously, the RIS phase shift matrix status is continuously monitored. Using a feedback sensor built into the RIS panel, the phase shift angle and amplitude values ​​of each reflector are periodically scanned. The currently measured phase shift angle is compared with the target angle previously provided to check whether the angle deviation remains within the predetermined tolerance range. Amplitude monitoring assesses energy reflection efficiency by measuring the magnitude of the reflection coefficient. For example, it can record whether the normalized amplitude of a reflector remains stable within a range close to a preset value after reconfiguration. Slight decreases are noted in the record for subsequent trend analysis. Through this monitoring, the slow drift caused by factors such as flooding, long-term wind load, or temperature changes on the RIS hardware performance can be identified, ensuring that the status data accurately reflects the continuity of the reconfiguration effect over time. In addition, key indicators of power equipment communication links need to be reassessed. Link latency can be obtained by transmitting test messages on the restored link and recording the sending and receiving timestamps, with a focus on the trend of current latency relative to the latency before reconfiguration. Packet loss rate can be calculated by statistically analyzing the difference between the number of sent messages and the number of successfully received messages within a new sampling window, and the stability of each path can be analyzed separately in a multi-path fusion scenario. Bit error rate can be estimated by verifying received data and statistically analyzing the frequency of erroneous bits, with monitoring accuracy reaching a preset order of magnitude, used to verify the transmission integrity of high-priority emergency repair commands on the restored link. The above link indicators can be collected in parallel on the main link and backup link to comprehensively understand the overall health status of power emergency communication. The communication parameters collected in this round are compared with pre-configured thresholds and normal operating baselines. The comparison logic can be executed separately according to parameter type. For example, for the base station received signal strength, an early warning threshold can be set. When the latest measured value is lower than the threshold, it is marked as signal weakening. For the signal-to-noise ratio (SNR), a degradation limit can be configured. When the SNR drops below the limit, it is recorded as link quality degradation. For the phase shift angle and amplitude of RIS, a deviation threshold can be set. When the angle deviation exceeds a preset number of degrees or the amplitude is lower than the preset normalization lower limit, it is determined as RIS configuration drift. For link delay, packet loss rate, and bit error rate, multi-level thresholds are set respectively. When a certain indicator exceeds the corresponding level, the corresponding level of alarm is triggered. The above thresholds can be adjusted according to power communication specifications and historical operating data, and can be dynamically adjusted in combination with disaster type and current stage to make the comparison results more consistent with the actual operating environment. After completing the threshold comparison, the central processing unit performs interruption event re-identification based on the comparison results. During the re-identification process, the abnormal markers obtained from the parameter comparison in this round are comprehensively analyzed to determine whether there are new interruptions or insufficient recovery in the current system. For example, when the base station signal strength and link delay both show significant deviations exceeding the threshold, this situation can be identified as an event of insufficient recovery performance. When the anomalies are mainly concentrated in the RIS phase shift angle and amplitude deviation, while the base station-related indicators and link-related indicators are generally within the normal range, it can be determined as a configuration drift event. When multiple backup links simultaneously experience an increase in packet loss rate and bit error rate, it can be identified as a fusion path instability event. The re-identification process can adopt anomaly pattern matching and rule judgment methods similar to those mentioned in S2, except that its input data comes from the new round of collection results after reconfiguration. If a new interruption or significant anomaly is identified, a new interruption event description is generated according to the structure in S2. This event description integrates the parameters and threshold comparison results collected in this round, including a timestamp accurate to milliseconds, a list of abnormal parameters and the deviation values ​​of each parameter, identification information of affected devices and communication links, and a preliminary severity score calculated based on the deviation magnitude and impact range. The severity score can be obtained by weighted summation of the deviation values ​​of several key indicators. When the score exceeds a preset threshold, the event is marked as a high-risk event. The interruption event description is generated according to a unified structured template and uses the same encoding format as the previous steps, which facilitates direct transfer to the classification evaluation and model update module in the next step. The newly generated interruption event descriptions are sequentially passed to step S3 to update the disaster factor classification, causal relationship diagram, and quantified impact matrix in the interruption model. After the model update is completed, step S4 re-extracts key parameters based on the updated model and generates a new beam adjustment scheme and optimization instruction sequence. Step S5 then executes the new instruction sequence to complete a new round of RIS phase shift reconfiguration and routing coordination. Through the continuous transmission from re-identification to model update, scheme generation, and execution, a processing link that can be round-tripped multiple times is formed between S2 and S5. To form an adaptive closed-loop monitoring mechanism, the cyclic process can be set to have an adjustable iteration cycle. The central processing unit can use a timer or real-time clock module to trigger a new round of acquisition and comparison within a preset time interval. For example, during the peak period of drastic changes in the impact of a disaster, the iteration cycle can be configured to be on the order of several seconds to improve the system's sensitivity to new anomalies. After the disaster enters a stable period, the iteration cycle can be appropriately extended to reduce system resource consumption and energy consumption. The cycle parameter can be adjusted on-site through a configuration interface or automatically corrected in combination with external environmental information to match the cycle frequency with the rhythm of disaster evolution. To ensure the reliability and adaptability of the cyclic process itself, this closed-loop mechanism can integrate self-diagnostic and priority management functions. During data acquisition intervals, the system can perform self-checks on sensors and monitoring modules, such as checking for abnormal readings of received signal strength and ensuring the smooth operation of feedback channels. When an anomaly is detected, the relevant modules are automatically restarted, or the corresponding nodes are marked as requiring manual maintenance. Threshold configuration can be reset on-site, allowing maintenance personnel to select the disaster type and load the corresponding threshold configuration set through the power control terminal, enabling the system to adopt matching judgment rules in different scenarios such as earthquakes, typhoons, and floods. In the re-identification and subsequent processing stages, the system can prioritize processing abnormal entries related to core power business such as substation communication and dispatch center links, ensuring the reliability of critical links is prioritized even with limited processing capacity. Through the cyclic acquisition and re-identification mechanism, abnormal changes in signal-to-noise ratio and latency can be captured within the iteration cycle, generating new interruption event descriptions and triggering model updates and scheme regeneration. This allows the RIS-assisted communication configuration and routing scheme to continuously adjust as the disaster evolves, thereby maintaining the stability and continuity of power repair communication links during long-term emergency operations.

[0024] The scheme in this embodiment first uses sensors deployed on power equipment and base stations to monitor base station signal strength, RIS phase shift matrix status, and power equipment link indicators in real time, collecting communication parameters in the disaster environment to form current system operation data. Based on the above operation data, it is compared with preset thresholds to identify interruption events in the RIS-assisted communication link, determine the location, type, and scope of the interruption, and generate corresponding interruption event descriptions. These descriptions are then used to analyze and classify disaster factors, extracting impact parameters related to the interruption and associating them with power emergency repair scenarios to establish an interruption model reflecting the relationship between disaster factors and link status. Based on the interruption model, key parameters are extracted, and a suitable RIS is selected. The system uses phase shift configuration options to plan signal reconstruction paths and integrates priority information from power emergency services to form an optimized instruction sequence. Executing this sequence reconfigures the RIS phase shift matrix, adjusting beam direction and amplitude, while simultaneously coordinating signal routing between base stations and power equipment to re-establish damaged links. After reconfiguration, updated communication parameters are periodically collected, and the updated data is returned to the interruption event re-identification stage. When a new interruption or insufficient recovery is detected, a new interruption event description is generated and sequentially enters the classification and evaluation, model building, solution generation, and execution stages. This constructs a closed-loop monitoring mechanism throughout the entire process, ensuring the system's continuous adaptation and stable operation in disaster environments.

[0025] It should be noted that this invention can be deployed on the device itself to realize embedded applications, or it can run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0026] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wireless or wired transmission; wired transmission methods include optical fiber, twisted pair, coaxial cable, etc.; wireless transmission includes infrared, microwave, etc. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center containing one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0027] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0028] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0029] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0030] In addition, the functional modules in the embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0031] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0032] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations 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. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0033] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A dynamic optimization method for RIS beamforming in power emergency repair and disaster relief, characterized in that, include: S1: Collect RIS auxiliary communication parameters in disaster environments, and obtain current system operation data by real-time monitoring of base station signal strength, RIS phase shift matrix status and power equipment communication link indicators; S2: Based on the collected parameters, identify interruption events in the RIS-assisted communication link, compare the current data with a preset threshold, determine the location, type, and scope of the interruption, and form an interruption event description; S3: Using the description of interruption events, classify and evaluate disaster factors such as node failures or signal attenuation, extract influencing parameters and associate them with power emergency repair scenarios to establish an interruption model; S4: Based on the interruption model, generate a dynamic beam adjustment scheme, select the RIS phase shift configuration option and plan the reconfiguration path, integrate power emergency priorities, and form an optimized instruction sequence; S5: Execute the optimization instruction sequence, reconfigure the RIS phase shift matrix, adjust the beam direction and amplitude, and coordinate the signal routing between the base station and the power equipment; S6: After reconfiguration, the updated communication parameters are collected cyclically, and the process returns to S2 to re-identify the interruption event, forming a closed-loop monitoring mechanism.

2. The power emergency repair and disaster-resistant RIS beam dynamic optimization method according to claim 1, characterized in that, Collect RIS-assisted communication parameters in disaster environments. By real-time monitoring of base station signal strength, RIS phase shift matrix status, and power equipment communication link indicators, obtain current system operating data, including: Deploy weather-resistant sensors and communication monitoring modules on power equipment, deploy monitoring modules on base stations, and deploy phase shift monitoring and control circuits on RIS panels; The system collects power equipment communication link indicators through sensors, base station signal strength through base station monitoring modules, and RIS phase shift matrix status through phase shift monitoring and control circuits. A parallel acquisition mode with unified time synchronization is adopted to complete data acquisition and reporting within the sampling period. Data is aggregated and preprocessed through edge computing nodes, and the data is parsed and fused into structured records through the central processing unit. Construct the RIS-assisted communication operation data matrix in chronological order.

3. The power emergency repair and disaster-resistant RIS beam dynamic optimization method according to claim 1, characterized in that, Based on the collected parameters, interruption events are identified in the RIS-assisted communication link. The current data is compared with a preset threshold, including: Extract the multidimensional vector of sampling time from the RIS-assisted communication operation data matrix; Each base station signal strength parameter, RIS phase shift matrix status parameter, and power equipment communication link index is compared with a preset threshold library. By scanning continuous samples through a sliding window, the signal quality on the base station side is judged, the stability of the RIS phase shift matrix state is checked, and the communication link indicators of power equipment are compared and analyzed.

4. The power emergency repair and disaster-resistant RIS beam dynamic optimization method according to claim 1, characterized in that, Determine the location, type, and scope of the interrupt to form an interrupt event description, including: Based on device identification and geographic coordinate mapping of interruption event location, interruption event type is classified by matching type judgment table through abnormal parameter combination, and the scope of interruption event is defined by network topology analysis; Integrate interrupt event location, type, and range to generate structured records; Set up timestamp fields, anomaly parameter lists, type fields, range fields, and severity score fields in the structured record; Organize records using a hierarchical data structure based on key-value pairs; Batch processing and parallel computing are used to accelerate identification, data streams are processed according to time windows, and threshold and weight parameters are calibrated through the configuration interface.

5. The power emergency repair and disaster-resistant RIS beam dynamic optimization method according to claim 1, characterized in that, Using outage event descriptions, disaster factors such as node failures or signal attenuation are classified and assessed. Impact parameters are extracted and correlated with power restoration scenarios to establish an outage model, including: Read the exception parameters from the interrupt event description and construct the event feature vector; Examine the classification of disaster factors by examining the relationships between parameter combinations; Extract the set of influencing parameters from the interrupt event description; The extracted parameters are matched and associated with the power emergency repair task library and scenario library; An interruption model is constructed based on classification results and correlation parameters. Causal relationships are represented in the form of a directed graph, and the quantitative impact matrix is ​​stored in the form of a two-dimensional table. The interruption model is output to the adjustment scheme generation stage through a standardized interface.

6. The power emergency repair and disaster-resistant RIS beam dynamic optimization method according to claim 1, characterized in that, Based on the interruption model, a dynamic beam adjustment scheme is generated, the RIS phase shift configuration option is selected, and the reconfiguration path is planned, including: Root cause nodes and impact scores are extracted from the causal relationship diagram and quantified impact matrix of the interruption model to form a set of parameter vectors related to beam control; Feasibility verification is performed by matching phase shift matrix patterns in the phase shift template library based on the extracted parameters. By combining network topology and geographic information, alternative routes are searched, and path costs are calculated to plan signal reconstruction paths and beam pointing angles.

7. The power emergency repair and disaster-resistant RIS beam dynamic optimization method according to claim 1, characterized in that, Integrate power emergency priorities to form an optimized instruction sequence, including: Read the task level from the power emergency repair task database and convert it into a priority score; The phase shift adjustment commands, path switching commands, and priority control commands are organized in the order of execution to form an optimized instruction sequence; The sequence is encapsulated using a structured encoding format and transmitted to the execution module through a dedicated interface.

8. The power emergency repair and disaster-resistant RIS beam dynamic optimization method according to claim 1, characterized in that, Execute the optimization instruction sequence to reconfigure the RIS phase shift matrix, adjust the beam direction and amplitude, and coordinate signal routing between the base station and power equipment, including: Parse the phase shift command from the sequence and update the phase shift angle of the reflection unit according to the priority queue; The parsing amplitude command adjusts the amplitude value of the reflection unit based on the phase shift update. Analyze the beam pointing command, calculate the synthesis direction, and send out control; Parse the path switching command, update the routing table, and establish an alternative path; A distributed controller is used to execute commands synchronously. Verify the link metrics and report the execution status back to the monitoring module.

9. The power emergency repair and disaster-resistant RIS beam dynamic optimization method according to claim 1, characterized in that, After reconfiguration, the updated communication parameters are collected cyclically, and the data is returned to S2 for interrupt event re-identification, forming a closed-loop monitoring mechanism, including: A new round of parameter acquisition is triggered based on the execution status feedback, and the base station signal strength, RIS phase shift matrix status and power equipment link indicators are refreshed through sensors and monitoring modules; The collected parameters are compared with the threshold, and the interruption event is re-identified through abnormal pattern matching. Generate a new interrupt event description and pass it in sequentially to S3 to update the interrupt model, S4 to generate a new scheme, and S5 to execute the new sequence; It uses a timer to trigger acquisition and comparison within an adjustable iteration cycle, and integrates self-diagnostic and priority management functions.

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