Power grid emergency wireless command system based on Beidou

Abstract

The invention relates to the technical field of power system management, in particular to a Beidou-based power grid emergency wireless command system, which comprises a Beidou positioning communication terminal, a multi-source data fusion terminal, an intelligent decision-making terminal, a heterogeneous communication terminal and a visual command terminal. The Beidou positioning communication module accurately obtains the position of the power equipment and transmits an encryption instruction; the multi-source data fusion module integrates power, weather and geographic information, and provides support for decision making through data cleaning and space-time correlation analysis; the intelligent decision-making module generates a multi-target optimization decision by applying an improved multi-target genetic algorithm based on a digital twinborn dynamic plan library and a deep reinforcement learning algorithm; the heterogeneous communication module integrates various communication units, realizes mode dynamic switching and bandwidth aggregation, and ensures stable communication. And the visual command terminal displays the power grid situation and efficiently issues an instruction by means of augmented reality and multi-level authority management. The power grid emergency response and command efficiency is greatly improved, and power supply stability is powerfully guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of power system management, and particularly to a Beidou-based wireless emergency command system for power grids. Background Technique

[0002] With the continuous expansion of the power grid scale and the increase in the complexity of the power system, power grid emergency management faces many challenges; traditional emergency command systems have deficiencies in aspects such as positioning accuracy, communication reliability, data fusion capabilities, and visualization operations, and it is difficult to meet the requirements of high efficiency, accuracy, and safety for modern power grid emergency command; for example, in emergency situations such as natural disasters, conventional communication networks may be damaged, resulting in the inability to transmit emergency command information in a timely manner; at the same time, multi-source data in the power system is scattered and difficult to quickly integrate and analyze, affecting the accurate judgment of power grid faults and decision-making; in addition, power grid emergency command needs to achieve efficient visualization operations and safety management in complex on-site environments, and traditional systems also have certain limitations in these aspects. Therefore, a Beidou-based wireless emergency command system for power grids is proposed to address the above problems. Summary of the Invention

[0003] The purpose of the present invention is to provide a Beidou-based wireless emergency command system for power grids to solve the problems raised in the above background technique.

[0004] To achieve the above purpose, the present invention provides the following technical solutions:

[0005] A Beidou-based wireless emergency command system for power grids includes:

[0006] A Beidou positioning and communication module that establishes two-way communication with the Beidou satellite navigation system, and is used to obtain the geographical location information of power equipment in real time and transmit encrypted emergency instructions;

[0007] A multi-source data fusion module that is connected to the power SCADA system, meteorological monitoring terminal, and GIS platform, and has multi-protocol adaptation interfaces, and is used to integrate power grid operation parameters, meteorological warning data, and three-dimensional geographical information;

[0008] An intelligent decision-making module that includes a dynamic pre-plan library based on digital twins and a deep reinforcement learning algorithm engine, and is used to generate multi-objective optimization decisions including material dispatching paths and personnel deployment plans;

[0009] A heterogeneous communication module that integrates a Beidou short message communication unit, a Mesh wireless ad-hoc network unit, and a Ka-band satellite communication unit, and supports dynamic switching and bandwidth aggregation of three communication modes;

[0010] A visualization command terminal that is equipped with an augmented reality display device and a multi-level permission management unit, and is used to display a three-dimensional heat map of the power grid situation and execute the operation of issuing emergency instructions.

[0011] As a preferred solution, the intelligent switching mechanism of the heterogeneous communication module includes:

[0012] A communication quality evaluation unit that monitors the signal strength, delay, and bit error rate parameters of each link in real time;

[0013] A fault self-healing unit that switches to the Beidou short message to establish a basic communication link within 200 milliseconds when a ground communication interruption is detected;

[0014] A bandwidth aggregation unit that dynamically superimposes the bandwidths of available communication links using software-defined network technology;

[0015] The switching mechanism adopts an improved reinforcement learning algorithm, and its decision-making process updates the policy value through the following steps:

[0016] Update the value function of the current state to 0.8 times the original value's learning rate retention part, plus a new part that includes the immediate reward and the future expected reward. The immediate reward is composed of the weighted sum of 0.6 times the normalized signal-to-noise ratio value and 0.4 times the reciprocal of the delay. The future reward calculates the optimal subsequent state value according to a discount factor of 0.9.

[0017] As a preferred solution, the working method of the Mesh wireless ad-hoc network unit includes:

[0018] In the neighbor discovery stage, a dynamic message interval adjustment strategy is adopted. The benchmark interval is set to 500 milliseconds, and linear compensation is performed according to the ratio of the remaining battery power of the node to the full battery power. The interval is increased by 30 milliseconds for every 10% reduction in power;

[0019] In the routing selection stage, a comprehensive evaluation index is constructed, which consists of a weighted sum of 50% hop count weight, 30% reciprocal of remaining battery power weight, and 20% delay weight, and the transmission path with the smallest comprehensive index is selected.

[0020] As a preferred solution, the multi-source data fusion module includes:

[0021] A data cleaning sub-module that uses a window sliding mechanism to detect abnormal data. When the difference between the current data point and the window mean exceeds three standard deviations, it is determined as abnormal. The mean and variance are dynamically updated according to the weight of 20% of the new data, and the window capacity remains 10 sampling points;

[0022] A spatio-temporal correlation analysis sub-module that iteratively updates the feature representation through the graph neural network layer. When calculating each layer, first normalize the node features and the adjacency matrix, then multiply by the weight matrix and output through the ReLU activation function.

[0023] As a preferred solution, the optimization method of the intelligent decision-making module includes:

[0024] The scheme evaluation function is composed of a linear combination of a 50% exponentially decaying term of the emergency repair time, a 30% resource consumption ratio term, and a 20% risk coefficient term. The emergency repair time is normalized with 72 hours as the benchmark;

[0025] The genetic crossover probability is dynamically adjusted according to the population evolution state. The basic probability is set to 70%. When the difference between the maximum fitness and the average fitness of the population expands, the crossover probability is increased according to the difference ratio.

[0026] As a preferred scheme, the visual command terminal includes:

[0027] An augmented reality positioning unit that minimizes the sum of the squares of the errors between the three-dimensional map point projection and the image features by optimizing the device pose parameters;

[0028] A path planning unit that updates the path weights using a pheromone evaporation mechanism. The pheromone increment is inversely proportional to the path length. The basic evaporation rate is set to 10%, and the pheromone intensity constant is 100.

[0029] As a preferred scheme, the system also includes a security encryption module that adopts a composite encryption strategy:

[0030] When encrypting data, perform an exclusive OR masking operation on the plaintext and then encrypt it using the national cryptographic algorithm. The key is derived from the hash value of the Beidou card hardware fingerprint and the dynamic password;

[0031] The key update period introduces a fluctuation adjustment based on a 24-hour reference value. The fluctuation amplitude is 10% of the network threat index. The value is increased during odd-numbered updates and decreased during even-numbered updates.

[0032] As a preferred scheme, the data interaction method between the system and the power Internet of Things platform includes:

[0033] The device health assessment model processes time series data through a long short-term memory network. The final output layer maps the 128-dimensional hidden state to a health index between 0 and 1, and uses the Sigmoid function to achieve normalization.

[0034] As a preferred scheme, the clock synchronization algorithm of the Beidou positioning and communication module includes:

[0035] The state vector consists of two components: clock deviation and frequency deviation. The state prediction value is updated linearly every second;

[0036] The observation process only captures the clock deviation component, and eliminates the influence of process noise and observation noise through the Kalman filtering algorithm.

[0037] As a preferred scheme, the bandwidth aggregation unit adopts a dynamic window adjustment strategy:

[0038] The sending window size takes the smaller value between the maximum value of 1024 and the product of bandwidth and delay. Among them, the bandwidth term is converted according to (1 - packet loss rate), and the delay term is compensated by taking the ratio of the current value to 0.5 times the minimum value.

[0039] As can be seen from the technical solution provided by the present invention above, a Beidou-based power grid emergency wireless command system provided by the present invention has the following beneficial effects:

[0040] 1. Precise positioning and reliable communication:

[0041] Beidou positioning and communication module: Establish two-way communication with the Beidou satellite navigation system to accurately and real-time obtain the geographical location information of power equipment; in power grid emergency scenarios, such as equipment fault troubleshooting and repair team dispatching, it can quickly locate the location of the faulty equipment, shorten the fault location time, and improve the repair efficiency; at the same time, its encrypted emergency command transmission function ensures the security and integrity of information during transmission, avoids commands being stolen or tampered with, and ensures the accuracy of emergency command;

[0042] Heterogeneous communication module: Integrate multiple communication units and achieve dynamic switching and bandwidth aggregation; in complex emergency environments, when ground communication is interrupted, quickly switch to Beidou short message within 200 ms to establish a basic communication link to ensure uninterrupted communication; through bandwidth aggregation technology, such as in scenarios where large amounts of data such as high-definition video surveillance data need to be transmitted, integrate the bandwidth of multiple links to improve the data transmission rate, meet the demand for high-speed transmission of a large amount of data in emergency command, and ensure the stability and efficiency of emergency communication;

[0043] 2. Comprehensive data fusion and intelligent decision-making:

[0044] Multi-source data fusion module: Integrate data from the power SCADA system, meteorological monitoring terminals, and GIS platforms; use an improved sliding window outlier detection algorithm to clean the data to ensure data accuracy; mine spatio-temporal correlation characteristics through a graph convolutional neural network model to provide comprehensive and accurate data support for intelligent decision-making; for example, combine meteorological warning data with power grid operation parameters to predict in advance the impact of bad weather on the power grid and provide a basis for formulating preventive measures;

[0045] Intelligent decision-making module: Adopt an improved multi-objective genetic algorithm, comprehensively consider multiple objectives such as repair time, resource cost, and risk level; dynamically adjust the probability of the crossover operator to generate better decision-making schemes; in power grid emergencies, it can quickly formulate material dispatching paths and personnel deployment plans, optimize resource allocation, improve the emergency response speed, reduce the losses caused by power grid failures, and ensure the stability and reliability of power supply;

[0046] 3. Efficient visual command and operation:

[0047] Visual Command Terminal: Equipped with an augmented reality display device and a multi-level permission management unit; realizes spatial positioning through an improved SLAM algorithm, integrates and displays virtual power grid situation information with the real physical environment, enabling commanders to intuitively understand the power grid situation; for example, in on-site command, they can directly see the real-time operating parameters and fault warning information superimposed on actual power equipment and make quick decisions; multi-level permission management ensures the safety of system operations, and personnel with different permissions perform corresponding operations to prevent misoperations and information leakage; the path planning sub-module adopts a dynamic pheromone-updated ant colony algorithm to provide the optimal path for emergency material transportation and personnel allocation, improving the efficiency of emergency resource allocation;

[0048] 4. Strong Security Assurance and Stable Operation:

[0049] Security Encryption Module: Adopts an improved national cryptographic algorithm, with strict mechanisms from data encryption to key update; the data encryption process generates keys by combining Beidou card ID, dynamic passwords, etc., and the auxiliary encrypted information combines salt values and timestamps to improve encryption security; the dual-period rotation key update mechanism dynamically adjusts the update period according to the network threat index to prevent key cracking, ensuring the security of data during transmission and storage and ensuring the stable operation of the power grid emergency command system in a complex network environment;

[0050] Data Interaction with the Power Internet of Things Platform: Evaluates the health status of equipment through an improved LSTM prediction algorithm to achieve data sharing and collaborative decision-making; the system obtains the device operation data of the power Internet of Things platform, discovers potential device failures in a timely manner, and takes preventive measures in advance; at the same time, it feeds back the emergency handling data to the platform to achieve closed-loop management, improving the overall operation stability and emergency response ability of the power grid. Description of the Drawings

[0051] Figure 1 It is a schematic diagram of the overall structure of a Beidou-based power grid emergency wireless command system of the present invention. Detailed Embodiments

[0052] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0053] In order to better understand the above technical solutions, the above technical solutions will be described in detail below in conjunction with the specification drawings and specific embodiments.

[0054] As Figure 1 shown, an embodiment of the present invention provides a Beidou-based power grid emergency wireless command system, including a Beidou positioning and communication module, a multi-source data fusion module, an intelligent decision-making module, a heterogeneous communication module, and a visual command terminal.

[0055] In the present invention, a Beidou positioning and communication module establishes two-way communication with the Beidou satellite navigation system, which is used to obtain the geographical location information of power equipment in real time and transmit encrypted emergency instructions.

[0056] Among them, the clock synchronization algorithm of the Beidou positioning and communication module includes:

[0057] The state vector consists of two components: clock deviation and frequency deviation, and the state prediction value is updated linearly every second.

[0058] The observation process only captures the clock deviation component, and the influence of process noise and observation noise is eliminated through the Kalman filtering algorithm.

[0059] Furthermore, specifically, the Beidou positioning and communication module:

[0060] Overall function overview:

[0061] The Beidou positioning and communication module is the core component of the Beidou-based power grid emergency wireless command system. Its main function is to establish stable two-way communication with the Beidou satellite navigation system. On the one hand, it obtains the accurate geographical location information of power equipment in real time. On the other hand, it reliably transmits the encrypted emergency instructions to relevant equipment and personnel, providing accurate positioning and efficient communication support for power grid emergency command and ensuring the stable operation of the power grid in case of emergency.

[0062] Data source:

[0063] The data source of this module mainly has two aspects:

[0064] Beidou satellite navigation system: Beidou satellites continuously broadcast positioning and timing signals. The module calculates the accurate geographical location information of power equipment, including longitude, latitude, altitude, etc., by receiving these signals and using the satellite positioning principle.

[0065] Power grid emergency command center: According to the operation status and emergency requirements of the power grid, the emergency command center generates emergency instructions and sends them to the Beidou positioning and communication module; these instructions are encrypted to ensure security and confidentiality during transmission.

[0066] Positioning and communication principle:

[0067] Positioning principle:

[0068] The Beidou positioning and communication module adopts the positioning technology of the Beidou satellite navigation system, and calculates its own position by receiving the signals transmitted by multiple Beidou satellites and using the triangulation principle; the specific formula is as follows:

[0069] (x - x1) 2 +(y - y1) 2 +(z - z1)2 =(c·Δt1) 2 ;

[0070] (x - x2) 2 +(y - y2) 2 +(z - z2) 2 =(c·Δt2) 2 ;

[0071] (x - x3) 2 +(y - y3) 2 +(z - z3) 2 =(c·Δt3) 2 ;

[0072] (x - x4) 2 +(y - y4) 2 +(z - z4) 2 =(c·Δt4) 2 ;

[0073] Where (x, y, z) are the three - dimensional position coordinates of the module; (x i , y i , z i )(i = 1, 2, 3, 4) are the position coordinates of the Beidou satellites; Δt i is the time difference for the signal to travel from the satellite to the module; C is the speed of light; By solving the above equations, the position information of the module can be obtained;

[0074] Communication principle:

[0075] A specific communication protocol is used for data transmission between the module and the Beidou satellite navigation system; When sending an emergency instruction, the module encodes and encrypts the instruction and then sends it to the Beidou satellite via a radio frequency signal, and the satellite then forwards the signal to the target receiving end; When receiving data, the module receives the signal from the Beidou satellite and performs decoding and decryption operations to restore the original data;

[0076] Time - synchronization algorithm:

[0077] The time - synchronization algorithm of the Beidou positioning communication module is optimized by Kalman filtering to improve the accuracy and stability of time - synchronization; The specific principle is as follows:

[0078] State equation:

[0079] x k = A·x k-1 + w k , where is the clock deviation and frequency offset; is the state transition matrix; ΔT = 1s is the update period; is the process noise;

[0080] Observation equation:

[0081] z k = H·x k + v k , where H =

[10] is the observation matrix; is the observation noise;

[0082] Through the Kalman filtering algorithm, the clock deviation and frequency offset are estimated and corrected in real time according to the state equation and the observation equation, so as to achieve high-precision timing synchronization;

[0083] Encryption mechanism:

[0084] To ensure the security of emergency instructions during transmission, the Beidou positioning and communication module uses an improved national cryptography algorithm for encryption processing; the specific process is as follows:

[0085] Data encryption process:

[0086] where P is the plaintext data, C is the ciphertext data; K = KeyDerive(HMAC(Beidou card ID || dynamic password)); S = SM3(K salt || timestamp);

[0087] Key update strategy: Adopt a dual-period rotation mechanism, and the update period formula is:

[0088] T update = T base + (-1) n ·ΔT, where T base = 24h is the reference update period; ΔT = 0.1·(network threat index); n is the parity flag of the current update times;

[0089] Application value of the module:

[0090] The Beidou positioning and communication module provides key support for the power grid emergency wireless command system through accurate positioning and reliable communication functions; on the one hand, the real-time obtained geographical location information of power equipment helps to quickly locate the faulty equipment and improve the repair efficiency; on the other hand, the encrypted transmission of emergency instructions ensures the security and timeliness of information, enabling the power grid emergency command to be carried out efficiently and accurately, effectively reducing the impact of power grid faults on power supply, and ensuring the safe and stable operation of the power grid.

[0091] In the present invention, the multi-source data fusion module is connected to the power grid SCADA system, the meteorological monitoring terminal and the GIS platform, and has multi-protocol adaptation interfaces for integrating power grid operation parameters, meteorological warning data and three-dimensional geographical information;

[0092] Among them, the multi-source data fusion module includes:

[0093] A data cleaning sub-module that uses a window sliding mechanism to detect abnormal data. When the difference between the current data point and the window mean exceeds three standard deviations, it is determined as abnormal. Among them, the mean and variance are dynamically updated according to the weight of 20% of the new data, and the window capacity remains 10 sampling points;

[0094] A spatio-temporal correlation analysis sub-module that iteratively updates the feature representation through graph neural network layers. When calculating each layer, first normalize the node features and the adjacency matrix, then multiply by the weight matrix and output through the ReLU activation function;

[0095] Furthermore, the multi-source data fusion module is a key component of the Beidou-based power grid emergency wireless command system. Its core function is to integrate multi-source data from the power SCADA system, meteorological monitoring terminals, and GIS platforms. Through the characteristic of having multi-protocol adaptation interfaces, it organically fuses power grid operation parameters, meteorological warning data, and three-dimensional geographic information, providing comprehensive and accurate data support for the subsequent intelligent decision-making module, thereby helping the power grid emergency command to make scientific and reasonable decisions in complex situations and ensuring the stable operation of the power grid in emergency scenarios. Specifically:

[0096] Data sources:

[0097] Power SCADA system: This system collects the operation parameters of various power equipment in the power grid in real time, such as voltage, current, power, etc.; these data reflect the current real-time operation state of the power grid and play a crucial role in analyzing whether the power grid is working properly, locating faulty equipment, and evaluating the stability of the power grid;

[0098] Meteorological monitoring terminal: The meteorological monitoring terminal collects meteorological information, including but not limited to monitoring data of temperature, pressure, humidity, wind speed, and weather phenomena such as rainfall and snowfall; especially meteorological warning data, such as early warning information of severe weather, can inform in advance the external risks that the power grid may face and provide key basis for power grid emergency preparedness;

[0099] GIS platform: The Geographic Information System (GIS) platform provides three-dimensional geographic information, covering terrain, geographical location distribution of power facilities, and surrounding environment information, etc.; through these geographic information, it is possible to intuitively understand the geographical environment where the power grid equipment is located, which helps to reasonably plan the material dispatching path and personnel deployment plan in emergency command;

[0100] Data fusion process:

[0101] Data cleaning sub-module: Adopts an improved sliding window outlier detection algorithm to ensure the accuracy and reliability of the input data; the principle is as follows:

[0102] x′t = μ t-1 ±3σ t-1 If |x t - μ t-1 | > 3σ t-1 it is marked as an anomaly, where:

[0103] Here, λ = 0.2 is the forgetting factor and the window size N = 10; Through the statistical analysis of historical data, this algorithm dynamically calculates the mean μ and standard deviation σ of the data to determine whether the current data point is an outlier, identifies and processes the abnormal data, and ensures data quality;

[0104] Spatio-temporal correlation analysis sub-module: Uses a graph convolutional neural network model to mine the spatio-temporal correlation characteristics between data from different data sources; The model formula is:

[0105] where is the normalized adjacency matrix, A is the device correlation matrix, which describes the connection relationship between power devices; I is the identity matrix; W (l) is the weight matrix of the l-th layer; σ1 is the ReLU activation function; Through this model, the potential connections between different data in the time and space dimensions can be learned. For example, how meteorological changes affect the grid operation parameters at different geographical locations, thus providing strong support for the deep fusion of multi-source data;

[0106] Multi-protocol adaptation interface:

[0107] The multi-protocol adaptation interface equipped in the multi-source data fusion module can be compatible with the communication protocols adopted by different data sources; Whether it is the IEC61850 protocol commonly used in the power SCADA system, the MODBUS protocol that may be used by meteorological monitoring terminals, or the data transmission protocol of the GIS platform, this module can be effectively adapted; In this way, the communication barriers between different data sources are broken, and the smooth transmission and integration of data are realized;

[0108] Application value of the module:

[0109] The multi-source data fusion module greatly enriches the information on which the grid emergency command and decision-making are based by integrating multi-source data and performing in-depth analysis and processing; Comprehensive and accurate data can help decision-makers more clearly understand the internal and external situations faced by the grid in an emergency, including the grid's own operating status, the potential impact of meteorological conditions on the grid, and geographical environment factors, etc.; This enables various factors to be fully considered when making decisions such as formulating material dispatching routes, personnel deployment plans, and dealing with grid failures, and makes more scientific, reasonable, and efficient decisions, effectively enhancing the grid's response ability and recovery efficiency in emergency scenarios.

[0110] In this embodiment, the intelligent decision-making module includes a digital-twin-based dynamic pre-plan library and a deep reinforcement learning algorithm engine, which are used to generate multi-objective optimization decisions including material scheduling paths and personnel deployment plans;

[0111] Among them, the optimization method of the intelligent decision-making module includes:

[0112] The scheme evaluation function is composed of a linear combination of a 50% exponentially decaying term of the repair time, a 30% resource consumption ratio term, and a 20% risk coefficient term, where the repair time is normalized based on 72 hours;

[0113] The genetic crossover probability is dynamically adjusted according to the population evolution state. The basic probability is set at 70%. When the difference between the maximum fitness and the average fitness of the population expands, the crossover probability is increased according to the difference ratio;

[0114] Furthermore, the intelligent decision-making module is the core intelligent center of the Beidou-based power grid emergency wireless command system. Its main function is to use advanced technologies and algorithms to generate comprehensive and optimized decision-making plans for power grid emergency scenarios. Relying on the digital-twin-based dynamic pre-plan library and the deep reinforcement learning algorithm engine, this module comprehensively considers multi-source data such as power grid operation status, meteorological conditions, and geographical information, and formulates multi-objective optimization decisions covering key contents such as material scheduling paths and personnel deployment plans, providing scientific and efficient decision-making support for power grid emergency command, ensuring that the power grid can quickly and stably return to normal operation in complex emergency situations. Specifically:

[0115] Core technical components:

[0116] Digital-twin-based dynamic pre-plan library: A digital twin model highly mapped to the actual power grid system is constructed, which can reflect the operation status of the power grid in real time. Based on the digital twin model, the dynamic pre-plan library stores various pre-plans for different emergency scenarios. These pre-plans are not static, but are dynamically updated and adjusted through simulation analysis of the digital twin model according to real-time data and changing emergency situations. For example, when it is detected that there is a fault risk in a certain area of the power grid due to bad weather, the pre-plan library will quickly generate or optimize corresponding emergency treatment pre-plans based on the real-time parameters of this area and the surrounding power grid topology in the digital twin model, including possible power outage ranges, repair resource allocation suggestions, etc.;

[0117] Deep Reinforcement Learning Algorithm Engine: The deep reinforcement learning algorithm engine takes the data provided by the multi-source data fusion module as input, and through continuous interaction and learning with the power grid emergency environment, optimizes the decision-making strategy. Its core lies in the action exploration of the agent in the environment, and adjusts its own decision-making behavior according to the reward signal feedback from the environment to achieve the goal of maximizing the long-term cumulative reward. In the power grid emergency scenario, the agent can be understood as a decision-making generation mechanism, and its actions include determining the material scheduling path, arranging personnel deployment, etc., while the reward signal is set based on factors such as the effectiveness, timeliness of the decision-making plan for the power grid to return to normal operation, and resource utilization efficiency.

[0118] Decision Generation Process:

[0119] Multi-objective Optimization Decision-making: An improved multi-objective genetic algorithm is used to generate decision-making plans.

[0120] Fitness Function Design:

[0121] Among them, α + β + γ = 1 is the weight coefficient (by default, α = 0.5, β = 0.3, γ = 0.2); T repair is the actual repair time, T max = 72h, which is the maximum allowable repair time; C recource is the actual resource cost consumed, C max = 10 6 yuan, which is the upper limit of the resource budget; R risk is the risk assessment value brought by the power grid fault; the fitness function comprehensively considers three key objectives: repair time, resource cost, and risk level. By adjusting the weight coefficient, according to different emergency situations and decision-making priorities, the relationship between each objective can be flexibly balanced, so as to generate a decision-making plan that better meets the actual needs.

[0122] The crossover operator adopts an adaptive probability adjustment strategy:

[0123] Among them, P base = 0.7, which is the benchmark crossover probability, F max is the maximum fitness value of the population, F avg is the average fitness value of the population; the adaptive crossover probability adjustment strategy can dynamically adjust the probability of the crossover operation according to the fitness distribution of the individuals in the population. When the fitness differences of the individuals in the population are large, the crossover probability is appropriately increased to promote the recombination of excellent genes and accelerate the convergence speed of the algorithm. When the individual fitness values are relatively close, the crossover probability is reduced to avoid excessive destruction of the structure of excellent individuals and maintain the diversity of the population, thereby improving the search ability of the algorithm in the complex decision-making space and generating a better-quality decision-making plan.

[0124] Application value of the module:

[0125] The intelligent decision-making module plays an irreplaceable and important role in the power grid emergency command. By applying advanced digital twin technology and deep reinforcement learning algorithms, it can quickly and accurately generate scientific and reasonable decision-making plans for complex and changing power grid emergency scenarios. These decision-making plans can not only effectively shorten the power grid fault repair time, reduce resource consumption, but also minimize the risks brought by power grid faults, ensuring the stability and reliability of power supply. At the same time, the dynamic optimization ability of the intelligent decision-making module enables the decision to be adjusted in a timely manner as the emergency situation changes, significantly improving the efficiency and effectiveness of the power grid emergency command, and providing a solid power support for ensuring the normal operation of the social economy.

[0126] In this embodiment, the heterogeneous communication module integrates a Beidou short message communication unit, a Mesh wireless ad hoc network unit, and a Ka-band satellite communication unit, supporting dynamic switching and bandwidth aggregation of three communication modes;

[0127] Among them, the intelligent switching mechanism of the heterogeneous communication module includes:

[0128] A communication quality evaluation unit that real-time monitors parameters such as the signal strength, delay, and bit error rate of each link;

[0129] A fault self-healing unit that switches to the Beidou short message to establish a basic communication link within 200 milliseconds when a ground communication interruption is detected;

[0130] A bandwidth aggregation unit that dynamically superimposes the bandwidths of available communication links using software-defined network technology;

[0131] The switching mechanism uses an improved reinforcement learning algorithm, and its decision-making process updates the policy value through the following steps:

[0132] Update the value function of the current state to 0.8 times the original value, retain a part of the learning rate, and add a new part that includes the immediate reward and the future expected reward. The immediate reward is composed of the weighted sum of 0.6 times the normalized signal-to-noise ratio value and 0.4 times the reciprocal of the delay, and the future reward calculates the optimal subsequent state value according to a discount factor of 0.9;

[0133] Furthermore, the heterogeneous communication module is the key communication hub of the Beidou-based power grid emergency wireless command system. Its main function is to integrate multiple different types of communication units, realize dynamic switching and bandwidth aggregation of diversified communication modes, ensure that a stable and efficient communication connection is always maintained between power equipment and the command center in a complex power grid emergency environment, provide a solid guarantee for the timely transmission of emergency instructions and the real-time feedback of power equipment status information, and strongly support the smooth development of power grid emergency work. Specifically:

[0134] Composition of the core communication unit:

[0135] Beidou short message communication unit: This unit utilizes the short message communication function of the Beidou satellite system and has the unique advantage of being unrestricted by the ground communication network. When the ground communication link is interrupted due to natural disasters, large-scale failures, etc., the Beidou short message communication unit can be quickly activated to provide basic communication guarantee for emergency command. It can send and receive text information of a certain length. Although the communication rate is relatively low, in extreme cases, it can ensure the transmission of key emergency instructions and brief equipment status information to maintain the basic communication needs of emergency command.

[0136] Mesh wireless ad hoc network unit: Mesh wireless ad hoc network technology can quickly build a multi-hop wireless network through the self-organization and self-healing capabilities between nodes without the support of fixed infrastructure. In the power grid emergency scenario, this unit can be flexibly deployed at the power equipment site, and communication links are automatically formed between nodes to adapt to the complex and changeable on-site environment. For example, in mountainous areas and other terrains where traditional communication methods are difficult to cover, the Mesh wireless ad hoc network unit can quickly build a communication network to ensure communication between equipment and with the command center. It adopts an improved OLSR protocol. In the neighbor discovery stage, a dynamic HELLO message interval adjustment algorithm is used:

[0137] Among them, T basc = 500ms, which is the reference interval, k = 300ms, which is the adjustment coefficient, E remin is the remaining battery power of the node, E max is the full battery power value; through this algorithm, the HELLO message sending interval is dynamically adjusted according to the node power, effectively saving power. In the routing selection stage, an energy consumption-aware weighted metric algorithm is adopted:

[0138] Among them, w1 = 0.5, w2 = 0.3, w3 = 0.2, all of which are weight parameters, HopCount is the number of hops, and Delay is the link delay; this algorithm comprehensively considers the number of hops, the remaining battery power of the node, and the link delay, selects a routing path with lower energy consumption and better communication quality, and improves the communication efficiency and stability of the ad hoc network.

[0139] Ka-band satellite communication unit: The Ka-band satellite communication unit uses the high-frequency Ka-band for communication and has the characteristics of large communication capacity and high transmission rate. In scenarios where a large amount of data needs to be transmitted, such as high-definition video surveillance data and detailed power equipment status monitoring data, the Ka-band satellite communication unit can play its advantages to meet the emergency command's demand for large data volume transmission. It can achieve high-speed data upload and download, providing rich and timely information for emergency command.

[0140] Intelligent switching mechanism

[0141] Communication quality assessment unit: Real-time monitor key parameters such as signal strength, delay, and bit error rate of each communication link; Through continuous collection and analysis of these parameters, accurately evaluate the communication quality of each current communication link; For example, when the signal strength is lower than a certain threshold, or the delay exceeds the preset range, or the bit error rate is higher than the acceptable level, it is determined that the communication quality of the link has declined;

[0142] Fault self-healing unit: Once a ground communication interruption is detected, the fault self-healing unit will quickly respond within 200 ms and automatically switch to the Beidou short message communication unit to establish a basic communication link; This fast switching mechanism ensures that in case of emergency, emergency communication will not be interrupted for a long time, guaranteeing the transmission of critical information;

[0143] Bandwidth aggregation unit: Using software-defined network (SDN) technology, dynamically superimpose the bandwidths of available communication links; When multiple communication links are available simultaneously, the bandwidth aggregation unit can intelligently allocate the bandwidth resources of each link according to the actual data transmission requirements, integrate the bandwidths of different links, and achieve a higher total transmission bandwidth; For example, when transmitting a large amount of data files, simultaneously utilize the bandwidths of the Mesh wireless ad hoc network unit and the Ka-band satellite communication unit, and through bandwidth aggregation technology, add the bandwidths of the two links to improve the data transmission speed; The switching mechanism adopts an improved Q-learning algorithm, and its value function update formula is:

[0144] Among them, α = 0.8 is the learning rate, γ = 0.9 is the discount factor, S represents the current communication state (normal / fault / congestion), a ∈ {Beidou link, ad hoc network link, satellite link} is the optional action, r = β1·SNR + β2·(1 / Delay) is the immediate reward function, β1 = 0.6, β2 = 0.4, both are weight coefficients; Through this algorithm, the intelligent switching mechanism can continuously optimize the communication link selection strategy according to different communication states and reward feedback to achieve the best communication effect;

[0145] Application value of the module:

[0146] Heterogeneous communication modules have extremely important application value in power grid emergency scenarios; their diversified communication units and intelligent switching mechanisms enable power grid emergency communications to adapt to various complex and harsh environmental conditions; whether in remote areas, severely affected areas, or facing scenarios with large data transmission needs, heterogeneous communication modules can ensure the stability and efficiency of communications; by ensuring the smooth transmission of emergency commands and timely feedback of equipment status information, it provides accurate and real-time data support for power grid emergency command decisions, greatly improving the power grid's emergency response capabilities and fault handling efficiency, and effectively reducing the impact of power grid failures on social production and life.

[0147] In this embodiment, the visual command terminal is equipped with an augmented reality display device and a multi-level authority management unit for displaying a three-dimensional thermal map of the power grid situation and executing emergency command issuance operations;

[0148] Among them, the visual command terminal includes:

[0149] Augmented reality positioning unit, which minimizes the sum of square errors between the projection of 3D map points and image features by optimizing the device posture parameters;

[0150] The path planning unit uses the pheromone volatilization mechanism to update the path weight. The pheromone increment is inversely proportional to the path length. The basic volatilization rate is set to 10%, and the pheromone intensity constant is 100.

[0151] Furthermore, the visual command terminal is an important human-computer interaction interface of the Beidou-based power grid emergency wireless command system. With the help of advanced technologies and equipment, it provides intuitive and comprehensive power grid situation information to power grid emergency commanders, and supports the efficient issuance of emergency commands. By integrating augmented reality display devices and multi-level authority management units, the terminal can present complex power grid data in a visual form such as a three-dimensional heat map, while ensuring that personnel with different authorities can perform corresponding operations safely and conveniently, thereby improving the efficiency of power grid emergency command and the scientific nature of decision-making. Specifically:

[0152] Core components:

[0153] Augmented reality display unit:

[0154] Spatial positioning function: This unit realizes spatial positioning through the improved SLAM (Simultaneous Localization and Mapping) algorithm; the formula is:

[0155] Among them, θ ∈ SE(3) is the device pose, representing the position and orientation of the device in three-dimensional space; K is the camera intrinsic matrix, containing internal parameters such as the focal length and principal point of the camera; T(θ) is the rigid body transformation matrix, used to describe the rotation and translation transformation of the device in space; X i is a three-dimensional map point, which is the position of power grid equipment or related geographical features in three-dimensional space; x i is the corresponding image feature point, which is the pixel point in the image captured by the camera corresponding to the three-dimensional map point; By minimizing the error in the above formula, the pose of the device can be accurately calculated, thus realizing spatial positioning;

[0156] Augmented reality display: Based on accurate spatial positioning, the augmented reality display unit can fuse and display virtual power grid situation information with the real physical environment; For example, in the augmented reality device worn by on-site commanders, they can directly see real-time operation parameters, fault warning information, and emergency handling procedures superimposed on actual power grid equipment, enabling commanders to more intuitively understand the power grid situation and make accurate decisions;

[0157] Multi-level permission management unit: This unit is used to strictly manage and control the operation permissions of different users; According to the roles and responsibilities of users in the power grid emergency command system, permissions are divided into different levels; For example, senior commanders may have comprehensive permissions, including viewing all power grid information, issuing various emergency instructions, modifying system configurations, etc.; While ordinary operators may only be allowed to view partial equipment status information and perform specific emergency operations; Through this multi-level permission management mechanism, the security of the system and the confidentiality of data are ensured, preventing unauthorized operations from interfering with or damaging the power grid emergency command;

[0158] Realization of main functions:

[0159] Display of three-dimensional heat map of power grid situation: The visualization command terminal generates a three-dimensional heat map of power grid situation based on power grid operation parameters, meteorological warning data, three-dimensional geographical information, etc. provided by the multi-source data fusion module; This heat map intuitively shows the drainage pressure, equipment load, fault risk, etc. of each area in the power grid in different colors, and the darker the color, the more serious the corresponding index; Commanders can view the heat map from different angles and perspectives through the display devices of the terminal, such as large screens, augmented reality glasses, etc., quickly grasp the overall situation of the power grid, and timely discover potential risk areas;

[0160] Emergency instruction issuing operation: After understanding the power grid situation, the command personnel can issue emergency instructions through the visual command terminal according to the actual situation; the terminal supports multiple instruction issuing methods, such as text instructions, voice instructions, etc.; when issuing instructions, the system will verify the authority of the command personnel according to the settings of the multi-level authority management unit to ensure the legality and effectiveness of the instructions; at the same time, the instructions will be accurately transmitted to relevant power equipment and personnel through the heterogeneous communication module to achieve fast and efficient emergency response;

[0161] Path planning sub-module: This sub-module uses the dynamic pheromone update ant colony algorithm to provide the optimal path planning for emergency material transportation and personnel allocation; the algorithm formula is as follows:

[0162] τ ij (t + 1)=(1 - ρ)τ ij (t)+Δτ ij ;

[0163]

[0164] Among them, ρ = 0.1 is the pheromone evaporation coefficient, which is used to simulate the natural evaporation of pheromone over time; Q = 100 is the pheromone intensity, which represents the ability of ants to release pheromone; η = 0.5 is the distance influence factor, which is used to adjust the weight of the distance factor in path selection; D ij is the path length from node i to j; by continuously updating the pheromone concentration, the algorithm can dynamically find the optimal path, considering real-time traffic conditions, geographical environment and other factors to ensure that emergency materials and personnel can reach the destination at the fastest speed;

[0165] Application value of the module:

[0166] The visual command terminal plays a crucial role in power grid emergency command; through visual means such as augmented reality display and 3D heat map, it presents complex power grid information to command personnel in an intuitive and easy-to-understand way, greatly shortening the time for information understanding and analysis, and improving the accuracy and timeliness of decision-making; the multi-level authority management mechanism ensures the security and stability of the system, preventing misoperation and information leakage; the path planning sub-module optimizes the allocation of emergency resources and improves the efficiency of emergency response; overall, the visual command terminal improves the overall efficiency of power grid emergency command, reduces the losses caused by power grid failures, and ensures the safe and stable operation of the power system.

[0167] In this embodiment, the working method of the Mesh wireless ad hoc network unit includes:

[0168] In the neighbor discovery phase, a dynamic message interval adjustment strategy is adopted. The benchmark interval is set to 500 milliseconds, and linear compensation is performed according to the ratio of the remaining battery power of the node to the full battery power. For every 10% reduction in power, the interval is increased by 30 milliseconds.

[0169] In the routing selection phase, a comprehensive evaluation index is constructed, which consists of a weighted sum of 50% hop count weight, 30% reciprocal weight of remaining battery power, and 20% delay weight, and the transmission path with the smallest comprehensive index is selected.

[0170] Furthermore, the Mesh wireless ad-hoc network unit is an important part of the heterogeneous communication module in the Beidou-based power grid emergency wireless command system; it has the ability to quickly build a multi-hop wireless network through self-organization and self-healing capabilities between nodes without the support of fixed infrastructure, can adapt to the complex and changeable power grid emergency environment, and ensure the stability and reliability of communication; its working method mainly covers two key phases: neighbor discovery and routing selection, and a series of improved algorithms are adopted to optimize performance. Specifically:

[0171] Neighbor discovery phase:

[0172] The core task of this phase is to enable each node in the network to discover its adjacent nodes, thereby constructing the network topology; in order to adapt to the power status of different nodes, a dynamic HELLO message interval adjustment algorithm is adopted, and the formula is as follows:

[0173] Among them, T basc = 500ms, which is the benchmark interval and represents the basic time interval for sending HELLO messages when the node's battery power is sufficient; k = 300ms, which is the adjustment coefficient used to control the influence degree of the power factor on the message sending interval; E remin is the remaining battery power of the node, reflecting the current energy reserve of the node; E max is the full battery power value, that is, the maximum capacity of the node's battery power;

[0174] Working principle: When the remaining battery power E remin is close to the full battery power E max , approaches 1, and at this time T hello is close to T base , which means that the node sends HELLO messages at a shorter time interval in order to communicate with neighbor nodes more frequently and discover neighbors quickly and accurately; as the node's battery power is consumed, E remin gradually decreases, becomes smaller, and T hello increases, and the interval for the node to send HELLO messages becomes longer, thereby reducing power consumption and extending the working time of the node;

[0175] Routing selection phase:

[0176] After neighbor discovery is completed, a suitable routing path needs to be selected for data transmission; in this stage, an energy consumption-aware weighted metric algorithm is adopted, and the formula is as follows:

[0177] Among them, w1 = 0.5, w2 = 0.3, w3 = 0.2, all of which are weight parameters, that is, the weight parameters of the number of hops, the reciprocal of the remaining node power, and the link delay respectively, reflecting the importance of each factor in routing selection; HopCount is the number of hops, and the more hops there are, the greater the interference and delay that may be encountered during data transmission; Delay is the link delay, that is, the time required for data to be transmitted on this link. The shorter the delay, the more timely the data transmission; is the reciprocal of the remaining node power. The lower the power, the larger this value, indicating that the power consumption of this node during data transmission may have a greater impact on its subsequent work;

[0178] Working principle: When a node needs to send data to a target node, it will calculate the metric values of all possible paths according to the above formula; the node will preferentially select the path with the smallest metric value for data transmission; for example, if a certain path has fewer hops but lower remaining node power, then the metric value of this path may increase due to the larger value of the term; on the contrary, if a path has more hops but sufficient node power and small link delay, its metric value may be smaller and thus be selected as the optimal route; in this way, the algorithm comprehensively considers factors such as the number of hops, node power, and link delay, selects a routing path with lower energy consumption and better communication quality, and improves the communication efficiency and stability of the ad hoc network;

[0179] Application value:

[0180] The working method of the Mesh wireless ad hoc network unit is of great significance in the power grid emergency scenario; the dynamic HELLO message interval adjustment algorithm in the neighbor discovery stage can dynamically adjust the communication strategy according to the node power, effectively extending the working time of the node and ensuring that the network can still operate stably during a long-term emergency; the energy consumption-aware weighted metric algorithm in the routing selection stage optimizes the data transmission path, reduces network local failures caused by uneven node energy consumption, and improves the reliability and timeliness of data transmission; this enables the Mesh wireless ad hoc network unit to provide a stable and efficient communication connection between power equipment and the command center in a complex power grid emergency environment, strongly supporting the smooth development of power grid emergency command work.

[0181] In this embodiment, the system further includes a security encryption module, and the security encryption module adopts a composite encryption strategy:

[0182] When encrypting data, perform an XOR masking operation on the plaintext and then encrypt it using the national cryptographic algorithm. The key is derived from the hash value of the Beidou card hardware fingerprint and the dynamic password.

[0183] The key update period introduces a fluctuating adjustment based on a 24-hour reference value. The fluctuation range is 10% of the network threat index. The value is increased during odd-numbered updates and decreased during even-numbered updates.

[0184] Furthermore, the security encryption module is a key component of the Beidou-based power grid emergency wireless command system. Its core function is to ensure the security and confidentiality of power grid emergency data during transmission and storage. By adopting an improved national cryptographic algorithm, encrypt important data, and implement an effective key management strategy to prevent data from being illegally obtained, tampered with, or damaged, ensuring that the power grid emergency command system can operate stably and reliably in a complex network environment. Specifically:

[0185] Data encryption process:

[0186] The data encryption process of the security encryption module is mainly based on an improved national cryptographic algorithm. The specific steps are as follows:

[0187] Ciphertext generation:

[0188] Among them, P is the plaintext data, that is, the original information to be encrypted, such as power grid equipment status data, emergency instructions, etc.; C is the ciphertext data, which is the result obtained after encryption processing. Only the correct key can be used to decrypt and restore it. represents the XOR operation, which is used to increase the confusion degree of the data;

[0189] Key generation:

[0190] K = KeyDerive(HMAC(Beidou card ID || dynamic password)); The generation of the key K combines the Beidou card ID and the dynamic password, and is processed through the HMAC (Hash-based Message Authentication Code) algorithm, and then the final encryption key is generated through the key derivation function KeyDerive. This method utilizes the uniqueness of the Beidou card ID and the timeliness of the dynamic password to enhance the security of the key.

[0191] Auxiliary encryption information generation:

[0192] S = SM3(K salt || timestamp); S is the auxiliary encryption information, which is obtained by performing the SM3 hash operation on the salt value K salt and the timestamp; The use of the salt value increases the randomness of the hash result, and the timestamp makes S have timeliness, further improving the security of encryption.

[0193] Key update strategy:

[0194] To cope with the ever-changing network security threats, the security encryption module adopts a dual-cycle rotation mechanism for key update. The formula is as follows:

[0195] T update = T base + (-1) n ·ΔT, where T base = 24h is the reference update period, that is, the normal time interval for key update; ΔT = 0.1·(network threat index), and the network threat index reflects the security risk level of the current network environment. The higher the threat index, the larger the value of ΔT; n is the parity flag of the current update times. When n is odd, (-1) n = -1, and T update will be shortened; when n is even, (-1) n = 1, and T update will be extended; this dual-cycle rotation mechanism enables the key update period to be dynamically adjusted according to the network threat situation, minimizing the impact of key update on the normal operation of the system while ensuring security;

[0196] Application value of the module:

[0197] The security encryption module plays an irreplaceable and important role in the power grid emergency command system; in terms of data transmission, the powerful encryption mechanism ensures that sensitive data such as power grid emergency instructions and equipment status information are not stolen or tampered with during the transmission process, guaranteeing the reliability of communication and the integrity of information; for example, during the emergency rescue process, the accurate transmission of emergency instructions is crucial for quickly restoring the power grid, and the encryption module can prevent the instructions from being maliciously interfered with, ensuring the smooth progress of the rescue work; in terms of data storage, the encryption process makes it impossible for attackers to easily interpret the stored data even if it is illegally obtained, protecting the core data security of the power grid; in addition, the dynamic key update strategy can effectively cope with the increasingly complex network security threats, improving the overall security and anti-attack ability of the system, and providing a solid guarantee for the stable operation of the power grid emergency command system.

[0198] In this embodiment, the data interaction method between the system and the power Internet of Things platform includes:

[0199] The equipment health assessment model processes time series data through a long short-term memory network. The final output layer maps the 128-dimensional hidden state to a health index between 0 and 1, and uses the Sigmoid function to achieve normalization;

[0200] Furthermore, the data interaction method between this system and the power Internet of Things platform aims to achieve efficient, accurate, and secure data sharing and collaborative work between the two; through data interaction, the system can obtain rich device operation data from the power Internet of Things platform, providing more comprehensive and real-time information support for grid emergency command; at the same time, the system can also feedback data related to emergency handling to the power Internet of Things platform, realizing precise management and control of power equipment, improving the emergency response ability and operation stability of the power grid. Specifically:

[0201] Data interaction process:

[0202] Data collection and transmission:

[0203] As the data source, the power Internet of Things platform uses a large number of sensors and intelligent devices distributed in various links of the power grid to collect the operation parameters of power equipment in real time, such as voltage, current, power, temperature, etc., as well as environmental parameters, such as humidity, wind speed, etc.; after preliminary processing and encapsulation, these data are transmitted to the system through wired or wireless communication networks; the communication network can be Ethernet, Wi-Fi, 4G / 5G, etc., ensuring that the data can be transmitted quickly and stably;

[0204] Equipment health assessment model:

[0205] The system uses an improved LSTM (Long Short-Term Memory) prediction algorithm to analyze the collected data to evaluate the health status of power equipment; the specific formula is as follows:

[0206] LSTM unit calculates the hidden state:

[0207] h t = LSTM(x t , h t-1 , c t-1 ), where x t is the input data at time step t, that is, the collected equipment operation parameters; h t-1 is the hidden state of the previous time step; c t-1 is the cell state of the previous time step; h t ∈ is the hidden state at time step t, which contains information from past time steps and is used to capture long-term dependencies in the data;

[0208] Calculate the equipment health index:

[0209] EHI t = σ2(W h ·h t + b), where, is the weight matrix, used to transform the hidden state: h tis a dimension mapped to the Equipment Health Index; b is a bias term; σ2 is the Sigmoid activation function that maps the output value to the interval [0,1], and EHI t ∈[0,1] is the Equipment Health Index. The closer the value is to 1, the better the equipment health condition. The closer it is to, the more likely the equipment may have faults or potential risks;

[0210] Data feedback and collaborative decision-making:

[0211] Based on the equipment health assessment results, the system generates corresponding emergency handling suggestions or decision instructions; this information will be fed back to the Power Internet of Things platform. The Power Internet of Things platform combines its own business rules and other relevant data to comprehensively analyze and process the information fed back by the system. For example, if the system evaluates that the health index of a certain transformer is relatively low and there may be a risk of overheating failure, the system will send a warning message and a maintenance suggestion to the Power Internet of Things platform. After receiving the information, the Power Internet of Things platform will further verify the equipment status, allocate maintenance personnel and materials, and formulate a specific maintenance plan; at the same time, the Power Internet of Things platform will also feed back the processing results to the system to achieve collaborative decision-making and closed-loop management between the two;

[0212] Security guarantee for data interaction:

[0213] In the process of data interaction, security is of utmost importance; the system and the Power Internet of Things platform adopt a variety of security technologies to ensure the confidentiality, integrity, and availability of data. For example, during data transmission, encryption algorithms are used to encrypt the data to prevent the data from being stolen or tampered with during transmission; in terms of data storage, sensitive data is encrypted and stored, and strict access control is set, and only authorized personnel can access and process relevant data; in addition, regular security audits and vulnerability scans are carried out to timely discover and repair potential security hazards;

[0214] Application value of the module:

[0215] The data interaction method between this system and the Power Internet of Things platform has significant application value; through data sharing and collaborative work, it can make full use of the massive data resources of the Power Internet of Things platform, improve the system's monitoring and evaluation capabilities of the operation status of grid equipment, timely discover potential equipment faults, take preventive measures in advance, and reduce the impact of equipment faults on grid operation; at the same time, the collaborative decision-making mechanism makes emergency handling more efficient and accurate, can quickly allocate resources, shorten the fault repair time, and improve the grid's emergency response ability and power supply reliability; in addition, the security guarantee measures in the data interaction process ensure the security and reliability of data, protecting the core information of the grid and the normal operation of the business.

[0216] In this embodiment, the bandwidth aggregation unit adopts a dynamic window adjustment strategy:

[0217] The sending window size takes the smaller value of the maximum value 1024 and the bandwidth-delay product term, where the bandwidth term is converted according to (1-packet loss rate) and the delay term is compensated by the ratio of the current value to 0.5 times the minimum value;

[0218] Furthermore, the bandwidth aggregation unit is a key component of the heterogeneous communication module in the Beidou-based power grid emergency wireless command system. Its core function is to integrate the bandwidth resources of multiple communication links and realize dynamic bandwidth superposition, thereby providing the system with more powerful, stable and efficient data transmission capabilities in a complex power grid emergency communication environment. The unit intelligently dispatches different communication links to meet the demand for high-speed transmission of large amounts of data during emergency command, ensuring that emergency instructions, equipment status data and other key information can be transmitted between the command center and on-site equipment in a timely and accurate manner, effectively supporting the smooth development of power grid emergency work. Specifically:

[0219] Technical principles and implementation mechanisms:

[0220] Application of software-defined network (SDN) technology: The bandwidth aggregation unit adopts software-defined network technology to achieve flexible management and scheduling of network traffic by separating the control plane from the data plane of the network; in a heterogeneous communication environment, different communication links (such as Beidou short message communication links, Mesh wireless ad hoc network links, Ka-band satellite communication links, etc.) have their own bandwidth characteristics and transmission capabilities; the SDN controller can collect the status information of each link in real time, including parameters such as bandwidth utilization, delay, and packet loss rate; based on this information, the controller can dynamically allocate the best link resources to different business flows according to actual data transmission requirements, thereby achieving effective integration of available communication link bandwidth;

[0221] Link status monitoring and evaluation: In order to accurately aggregate bandwidth, the unit continuously monitors the status of each communication link; through a dedicated monitoring module, key performance indicators such as signal strength, delay, bit error rate, etc. of each link are obtained in real time; for example, for Ka-band satellite communication links, the focus is on monitoring the stability and transmission rate of its signals; for Mesh wireless ad hoc network links, attention is paid to the connection quality between nodes and the load of the links; based on these monitoring data, the actual available bandwidth of each link is evaluated to provide a basis for subsequent bandwidth allocation; only on the basis of accurately grasping the status of each link can reasonable bandwidth aggregation be achieved to avoid poor data transmission due to improper link selection;

[0222] Dynamic Bandwidth Allocation Strategy: Based on the monitored link status and the priority of traffic flows, the bandwidth aggregation unit adopts a dynamic bandwidth allocation strategy. For traffic flows with high real-time requirements, such as the transmission of emergency instructions, high-bandwidth and low-latency link resources are preferentially allocated to ensure that the instructions can be conveyed in a timely manner. For some services with relatively low real-time requirements but large amounts of data, such as the transmission of historical data on the operating status of devices, links with larger bandwidth but slightly higher latency can be reasonably utilized. At the same time, when a link fails or its performance severely deteriorates, the unit can quickly switch the traffic flow originally allocated to that link to other available links to ensure the continuity of data transmission.

[0223] Transmission Window Adjustment Strategy:

[0224] To further optimize data transmission performance, the bandwidth aggregation unit adopts an improved TCP acceleration algorithm, and the transmission window adjustment strategy is as follows:

[0225]

[0226] Where:

[0227] W: Represents the current transmission window size. The transmission window determines the amount of data that the sender can send before receiving an acknowledgment from the other party.

[0228] W max = 1024: Is the maximum window size, which limits the upper limit of the transmission window to prevent network congestion caused by an overly large window.

[0229] BDP: That is, the Bandwidth-Delay Product, which reflects the amount of data that the network can accommodate within a round-trip time. The calculation formula is BDP = bandwidth × delay product. Here, the bandwidth refers to the total bandwidth of the current available link, and the delay product takes into account the round-trip delay of data transmission in the network.

[0230] p: Is the current packet loss rate. The packet loss rate reflects the degree of network congestion. When the packet loss rate is high, it indicates that the network may be congested and the transmission window size needs to be adjusted appropriately.

[0231] α1 = 0.5: Is the smoothing coefficient, which is used to balance the influence of the bandwidth-delay product and the round-trip time ratio on the adjustment of the transmission window, making the adjustment process smoother.

[0232] RTT current : Is the current Round-Trip Time, that is, the time required for data to be sent from the sender to the receiver and for an acknowledgment to be received.

[0233] RTT min: is the minimum round trip time, which represents the shortest time required for data to go back and forth under ideal network conditions;

[0234] Adjustment principle: The calculation of the sending window size W takes into account multiple factors. First, the window size is adjusted according to the actual bandwidth and congestion of the network through BDP·(1-p). When the packet loss rate p is low, the network congestion is small, and the value of BDP·(1-p) is large, allowing the sending window to be appropriately increased to fully utilize the network bandwidth. When the packet loss rate is high, it means that the network is seriously congested, the value of BDP·(1-p) decreases, and the sending window is correspondingly reduced to avoid further increasing the network burden. At the same time, This item takes into account the current network delay; if the current round-trip time RTT current Close to the minimum round trip time RTT min , indicating that the network delay is small, the network condition is good, and the sending window can be appropriately increased; on the contrary, if the RTT current If it is larger, it means that the network delay increases and there may be a certain risk of network congestion. The sending window needs to be appropriately reduced. Finally, the sending window size W is the sum of the above calculation result and the maximum window size W. max The minimum value in , to ensure that the window size can adapt to the network conditions and does not exceed the network's carrying capacity;

[0235] Application value of the module:

[0236] Bandwidth aggregation units have extremely important application value in power grid emergency communication scenarios. In emergency situations, a large amount of real-time data needs to be transmitted, such as on-site video surveillance images, detailed data on equipment failures, etc., which places extremely high demands on communication bandwidth. By integrating the bandwidth of multiple communication links, bandwidth aggregation units can significantly improve the rate and stability of data transmission, ensuring that key information can be delivered in a timely manner. For example, in emergency repairs of large-scale power outages, a large amount of on-site equipment status data and repair plans need to be quickly transmitted to the command center, and the emergency instructions of the command center also need to be accurately conveyed to on-site personnel. Bandwidth aggregation units can ensure the high efficiency of data transmission, provide timely and accurate data support for emergency command decisions, greatly improve the speed and efficiency of power grid emergency response, reduce the impact of power outages on social production and life, and effectively ensure the safe and stable operation of the power grid.

[0237] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A Beidou-based power grid emergency wireless command system, characterized by: include: Beidou positioning communication module, which establishes two-way communication with the Beidou satellite navigation system to obtain the geographic location information of power equipment in real time and transmit encrypted emergency instructions; Multi-source data fusion module, connecting the power SCADA system, meteorological monitoring terminal and GIS platform, with multi-protocol adapter interface, used to integrate power grid operation parameters, meteorological warning data and three-dimensional geographic information; Intelligent decision-making module, including a dynamic plan library based on digital twins and a deep reinforcement learning algorithm engine, which is used to generate multi-objective optimization decisions including material dispatch paths and personnel deployment plans; Heterogeneous communication module, integrating Beidou short message communication unit, Mesh wireless ad hoc network unit and Ka-band satellite communication unit, supports dynamic switching and bandwidth aggregation of three communication modes; The visual command terminal is equipped with an augmented reality display device and a multi-level authority management unit, which is used to display the three-dimensional thermal map of the power grid situation and execute emergency command issuance operations.

2. According to claim 1, a Beidou-based power grid emergency wireless command system is characterized in that: The intelligent switching mechanism of the heterogeneous communication module includes: Communication quality assessment unit, real-time monitoring of each link signal strength, delay and bit error rate parameters; The fault self-healing unit switches to Beidou short messages within 200 milliseconds to establish a basic communication link when ground communication is interrupted; The bandwidth aggregation unit uses software-defined networking technology to dynamically add bandwidth to available communication links; The switching mechanism adopts an improved reinforcement learning algorithm, and its decision-making process updates the policy value through the following steps: The value function of the current state is updated to the learning rate retention part of 0.8 times the original value, plus the new part including the immediate reward and the expected reward in the future. The immediate reward is composed of the weighted sum of 0.6 times the normalized value of the signal-to-noise ratio and 0.4 times the inverse of the delay. The future reward is calculated according to the discount factor of 0.9 for the optimal subsequent state value.

3. The Beidou-based power grid emergency wireless command system according to claim 2, characterized in that: The working method of the Mesh wireless ad hoc network unit includes: The neighbor discovery phase uses a dynamic message interval adjustment strategy, with the baseline interval set at 500 milliseconds. Linear compensation is performed based on the ratio of the node's remaining power to its full power, with the interval increased by 30 milliseconds for every 10% reduction in power. In the routing selection stage, a comprehensive evaluation index is constructed, which is a weighted sum of 50% hop count weight, 30% remaining power inverse weight and 20% delay weight, and the transmission path with the smallest comprehensive index is selected.

4. The Beidou-based power grid emergency wireless command system according to claim 1, characterized in that: The multi-source data fusion module comprises: The data cleaning submodule uses a window sliding mechanism to detect abnormal data. When the difference between the current data point and the window mean exceeds three times the standard deviation, it is considered abnormal. The mean and variance are dynamically updated with a new data weight of 20%, and the window capacity is maintained at 10 sampling points. The spatiotemporal correlation analysis submodule iteratively updates the feature representation through the graph neural network layer. When calculating each layer, the node features and the adjacency matrix are first normalized, then multiplied with the weight matrix and output through the ReLU activation function.

5. The Beidou-based power grid emergency wireless command system according to claim 1, characterized in that: The optimization method of the intelligent decision-making module includes: The scheme evaluation function is composed of a linear combination of a 50% exponential decay term for emergency repair time, a 30% resource consumption ratio term, and a 20% risk coefficient term, where the emergency repair time is normalized to 72 hours. The genetic crossover probability is dynamically adjusted according to the evolutionary state of the population, and the basic probability is set to 70%. When the difference between the maximum fitness and the average fitness of the population increases, the crossover probability is increased according to the difference ratio.

6. The Beidou-based power grid emergency wireless command system according to claim 1, characterized in that: The visual command terminal comprises: Augmented reality positioning unit, which minimizes the sum of square errors between the projection of 3D map points and image features by optimizing the device posture parameters; The path planning unit uses the pheromone volatilization mechanism to update the path weight. The pheromone increment is inversely proportional to the path length. The basic volatilization rate is set to 10%, and the pheromone intensity constant is 100.

7. The Beidou-based power grid emergency wireless command system according to claim 1, characterized in that: The system also includes a security encryption module, which adopts a composite encryption strategy: When encrypting data, the plain text is encrypted by performing an XOR mask operation and then using the national secret algorithm. The key is derived from the hash value of the Beidou card hardware fingerprint and the dynamic password. The key update cycle introduces a fluctuation adjustment on the 24-hour base value, with a fluctuation range of 10% of the network threat index. The value is increased for odd-numbered updates and decreased for even-numbered updates.

8. The Beidou-based power grid emergency wireless command system according to claim 1, characterized in that: The data interaction methods between the system and the power Internet of Things platform include: The equipment health assessment model processes time series data through a long short-term memory network. The final output layer maps the 128-dimensional hidden state to a health index between 0 and 1, and uses the Sigmoid function for normalization.

9. The Beidou-based power grid emergency wireless command system according to claim 1, characterized in that: The clock synchronization algorithm of the Beidou positioning communication module includes: The state vector consists of two components: clock deviation and frequency deviation. The state prediction value is updated every second according to a linear relationship. The observation process only captures the clock deviation component, and the Kalman filter algorithm is used to eliminate the influence of process noise and observation noise.

10. The Beidou-based power grid emergency wireless command system according to claim 2, characterized in that: The bandwidth aggregation unit adopts a dynamic window adjustment strategy: The sending window size takes the smaller value of the maximum value 1024 and the bandwidth-delay product term, where the bandwidth term is converted according to (1-packet loss rate) and the delay term is compensated by the ratio of the current value to 0.5 times the minimum value.

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