Communication base station electric power safety monitoring alarm system based on Internet of Things technology

Abstract

The invention discloses a communication base station electric power safety monitoring alarm system based on the Internet of Things technology, and relates to the technical field of Internet of Things communication base station electric power safety monitoring. A multi-mode sensing fusion and self-calibration sensor is used for data collection, electric power data are accurately collected, transmission depends on terahertz communication in combination with hybrid encryption and self-adaptive dormancy, and the communication base station electric power safety monitoring alarm system is high in reliability. Safety and low consumption are ensured, edge intelligence and federated learning are analyzed, knowledge graph is used for auxiliary diagnosis, alarm is used for setting a dynamic threshold value, intelligent linkage is achieved, equipment management depends on digital twinning and a block chain, and stable operation of the equipment in the whole life cycle is guaranteed. According to the invention, accurate power data acquisition, high-speed safe transmission, intelligent analysis and processing, multi-level linkage alarm, remote safety control of equipment, linkage environment monitoring and fault prevention can be realized, the power safety of the communication base station can be comprehensively guaranteed, and the communication stability can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power safety monitoring of Internet of Things communication base stations, and in particular to a power safety monitoring and alarm system of a communication base station based on Internet of Things technology. Background Art

[0002] As a key infrastructure of modern communication networks, communication base stations rely on reliable power supply for their stable operation. With the rapid development of communication technology, the number of base stations continues to increase and their distribution becomes more extensive, covering complex geographical environments from bustling urban areas to remote mountainous areas, from plains to plateaus, etc., which puts extremely high demands on the power safety monitoring of base stations.

[0003] Traditional communication base station power monitoring methods have many limitations. In terms of data collection, most of them use a single type of sensor with limited accuracy, making it difficult to fully and accurately obtain the operating data of power equipment and lines. For example, ordinary current sensors are easily affected by environmental interference, have large measurement errors, and cannot detect subtle abnormal changes in current in time, which may lead to potential power failures not being discovered early. In addition, traditional sensor wiring is complex and maintenance costs are high. In some harsh environments, sensor failures occur frequently, seriously affecting the continuity and reliability of data collection.

[0004] In the data transmission and processing link, the traditional system has a low data transmission rate and poor stability. In the face of a large amount of real-time power data, the transmission delay is serious and cannot meet the needs of rapid response. At the same time, the data analysis and processing capabilities are insufficient, relying more on manual experience judgment, and lack of intelligent data analysis models. When a power failure occurs, it is impossible to quickly and accurately locate the source of the fault and assess the scope of the fault, resulting in a long fault handling time, which greatly affects the normal operation of the communication base station, and then affects the quality of communication services, bringing users a very poor communication experience, and causing huge economic losses to communication operators. With the rise of Internet of Things technology, it has brought new opportunities to solve the problem of power safety monitoring of communication base stations. However, the monitoring system based on the Internet of Things on the market is still insufficient in technology integration and innovative applications, and it is difficult to meet the growing demand for power safety protection of communication base stations. Summary of the invention

[0005] The present invention proposes a communication base station power safety monitoring and alarm system based on Internet of Things technology to solve the problems mentioned in the above-mentioned prior art.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: A communication base station power safety monitoring and alarm system based on Internet of Things technology includes the following modules: Data Acquisition Module: Using multi-modal perception fusion technology and self-calibrating sensor networks, it collects power data of communication base stations. It adopts a current sensor based on the optical-electromagnetic composite sensing principle. Through the formula works, where E is the induced electromotive force, K is the composite sensing coefficient, I is the current, B is the magnetic field strength, is the light intensity influence coefficient, P is the light intensity, and d is the size of the sensing element; Data Transmission Module: Based on a data transmission architecture that fuses terahertz communication and quantum key distribution, terahertz communication adjusts the transmission beam direction according to the channel state information where is the channel fading coefficient, is the phase, n is the number of multipaths, represents the imaginary unit; Data Analysis and Processing Module: At the edge node, it uses an anomaly detection model based on deep transfer learning. Through the transfer loss function it detects data anomalies, and are the weight coefficients, , are the source domain and target domain losses respectively. It uses the blockchain distributed ledger to record the analysis results of the edge node. The cloud computing center then integrates the parameters through the federated learning mechanism. Using knowledge graph technology, through the inference formula based on the path ranking algorithm it assists in analyzing faults, where P(y|x) is the inference probability, is the set of paths from x to y, and s(p) is the score of path p; Alarm Module: It sets an adaptive alarm threshold. When the first-level threshold is triggered, it sends a warning message. If a higher-level threshold is exceeded, it activates the on-site alarm device that fuses AR and VR. The operation and maintenance platform determines the optimal scheduling plan through the formula where w i is the weight, S i is the skill score, c j is the resource cost coefficient, D j is the resource usage, m is the number of personnel, and n is the number of resource types; Device Management Module: Using blockchain technology, it records the full life cycle information of the device. According to the current and historical data of the device, it predicts the remaining useful life RUL of the device. The formula is where X t is the state parameter, is the model parameter.

[0007] Further, it also includes a remote control module, which supports remote operation of the base station power equipment. Through a secure encrypted communication channel based on the zero-trust network architecture, multiple authentication methods are carried out. During the communication process, the control instruction transmission technology based on homomorphic encryption is used for verification, and the formula is , , where E is the homomorphic encryption function, a and b are plaintext data, and the operator sends instructions on the operation and maintenance management platform to achieve remote control of the base station.

[0008] Further, it also includes an environmental monitoring linkage module. Environmental monitoring equipment based on the fusion of multispectral imaging and intelligent sensing is deployed to collect environmental data. The multispectral imaging technology is used to obtain images of the base station, and the image recognition algorithm is used to analyze whether the equipment is abnormal. Combining with the intelligent sensing technology, through the formula the temperature and humidity are measured, f is the sensor frequency, is the initial frequency, is the temperature coefficient, is the temperature change, is the humidity coefficient, is the humidity change.

[0009] Further, in the data acquisition module, the sensor power supply and networking technology based on energy harvesting and wireless ad hoc network is introduced. The energy harvesting method is used to supply power to the sensors, and the sensors adopt the wireless ad hoc network technology. Based on the distributed cooperative positioning algorithm, through the formula the distance is calculated, is the distance between sensors i and j, and are the coordinates of sensors i and j respectively.

[0010] Further, in the data transmission module, the chaotic encryption algorithm is adopted, and the formula is , is the chaotic sequence value, is the chaotic control parameter; the low-power adaptive sleep-wakeup mechanism is adopted, and the device working state is adjusted through the formula , is the sleep time, is the data cache volume, is the average data transmission rate, is the sleep coefficient; The intelligent transmission architecture based on the integration of space-air-ground integrated network and software-defined network SDN is adopted, and the network resources are allocated through the formula , is the allocated data transmission rate, is the data priority, is the bandwidth, n is the number of data streams, and the network reputation mechanism based on blockchain is used, through the formula Calculate the reputation value of network nodes, is the weight, is the index score, and m is the number of evaluation indicators.

[0011] Furthermore, in the data analysis and processing module, by constructing a fault diagnosis and prediction environment, the agent is allowed to try different diagnosis and prediction strategies in the environment. The formula is , where Q(s,a) is the value function of taking action a in state s, is the learning rate, r is the reward value, is the discount factor, s' is the next state, and a' is the action in the next state; combined with knowledge graph reasoning, the associated information in the equipment fault knowledge graph is used to assist the agent in making decisions. In addition, transfer learning technology is used to transfer the model trained on one type of power equipment to other similar equipment for fine-tuning.

[0012] Furthermore, in the alarm module, when an alarm occurs, the system analyzes the emotional state of the maintenance personnel through emotional computing technology, and uses the emotional analysis formula to judge the current emotion, is the probability distribution of emotional categories, Text is the text data. At the same time, social network analysis technology is used, and through the formula to determine the core maintenance personnel, is the node centrality, is the shortest path length between nodes i and j, and n is the total number of nodes. In addition, through the social network propagation mechanism, a network for collaborative fault handling is formed. The formula is , is the information propagation range, is the propagation coefficient, is the number of network friends, is the information infection probability.

[0013] Furthermore, in the equipment management module, in the equipment procurement link, blockchain technology is used to record the information of suppliers, and automatic procurement decisions are made through smart contracts. The formula is , is the procurement decision value, is the weight, is the index score, and m is the number of evaluation indicators. During the equipment transportation process, IoT sensors are used to monitor the equipment information in real time and record the data on the blockchain. After arriving at the base station, artificial intelligence image recognition technology is used to accept the equipment.

[0014] Furthermore, in the remote control module, immersive remote operation technology based on virtual reality and haptic feedback is used. The operator enters the virtual base station scenario, and this scenario uses haptic feedback gloves. The formula is , is the tactile feedback force, and k is the elastic coefficient, is the operating displacement. Interaction is carried out through gesture recognition and voice recognition. After the operation instruction is encrypted, it is sent to the base station edge controller through the data transmission module to control the actual power equipment to perform corresponding operations.

[0015] Furthermore, in the environmental monitoring linkage module, using the cooperative control strategy based on the multi-agent system and game theory, each environmental monitoring device and power equipment acts as an agent for information interaction. Based on game theory, each agent selects the optimal strategy through the strategy selection formula to select the optimal strategy, is the utility function of agent i, is the strategy of agent i, is the strategy of other agents, w j is the weight of the jth benefit index, R ij is the benefit of agent i under the jth benefit index, and n is the number of benefit indexes. At the same time, the system adjusts the strategy of the agent in real time according to the environmental change and equipment status to form an adaptive cooperative control mechanism.

[0016] Compared with the existing technologies, the beneficial effects of the present invention are: At the data acquisition level, the application of multi-modal perception fusion technology and self-calibrating sensor network greatly improves the comprehensiveness and accuracy of data acquisition. Through current sensors based on composite sensing principles, voltage sensors with improved nano-level capacitive voltage division, etc., subtle changes in power data can be accurately captured, providing a reliable basis for subsequent analysis.

[0017] In terms of data transmission, the integration of terahertz communication and innovative encryption technology not only realizes high-speed data transmission but also ensures the security and stability of data transmission. The adaptive sleep-wake-up mechanism effectively reduces energy consumption and adapts to the complex deployment environment of the base station.

[0018] The edge intelligence and federated learning collaborative architecture of the data analysis and processing module enables more timely anomaly detection and more accurate fault prediction. The knowledge graph technology provides comprehensive knowledge support for fault diagnosis, can quickly locate the cause of the fault, and reduces the fault troubleshooting time.

[0019] The multi-level intelligent linkage alarm of the alarm module has great advantages. The dynamically adaptive alarm threshold fits the actual operation situation, and the personalized warning information helps the maintenance personnel to respond quickly. The on-site alarm device integrating AR and VR provides intuitive convenience for on-site handling and remote guidance, and intelligent shift scheduling and resource allocation improve the fault handling efficiency.

[0020] The device management module realizes transparent and intelligent management of the entire device life cycle through digital twin and blockchain technologies. The prediction of the remaining life of the device based on advanced algorithms enables early planning of maintenance and updates to ensure the stable operation of the device.

[0021] The zero-trust network architecture and homomorphic encryption technology of the remote control module ensure the security and reliability of remote operations. The fusion of multi-spectral imaging and intelligent sensing in the environmental monitoring linkage module, as well as the collaborative control strategy based on multi-agent systems and game theory, effectively prevent power failures caused by environmental factors, comprehensively ensure the power safety of communication base stations, and improve the stability of communication services. Brief Description of the Drawings

[0022] Figure 1 It is a schematic block diagram of the communication base station power safety monitoring and alarm system proposed by the present invention based on Internet of Things technology; Figure 2 It is a line chart comparing data transmission rates at different distances; Figure 3 It is a radar chart of fault handling efficiency; Figure 4 It is a bar chart comparing the maintenance costs of the entire device life cycle. Detailed Embodiments

[0023] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0024] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be understood as a limitation of the present invention.

[0025] In addition, the terms "first" and "second" are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the said features. In the description of the present invention, "a plurality of" means two or more unless otherwise specifically defined. In addition, the terms "mounted", "connected", and "coupled" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. The present invention will be further described in detail below with reference to the accompanying drawings.

[0026] Refer to Figures 1 to 4 : A communication base station power safety monitoring and alarm system based on Internet of Things technology, comprising the following modules: Data acquisition module: By using multi-modal perception fusion technology and a self-calibrating sensor network, it realizes comprehensive and accurate acquisition of power-related data of communication base stations. An electric current sensor based on the principle of optical-electromagnetic composite sensing is adopted. Through the formula (where E is the induced electromotive force, K is the composite sensing coefficient, I is the current, B is the magnetic field intensity, is the light intensity influence coefficient, P is the light intensity, and d is the size of the sensing element), this sensor can not only sense the current but also perform self-calibration by combining light intensity information to eliminate the influence of ambient light interference on the measurement accuracy. The voltage sensor adopts a nano-level capacitive voltage division technology improved based on the quantum tunneling effect. Through the formula (C is the capacitance, is the permittivity, A is the plate area, d is the plate spacing, is the micro-variable of the plate spacing varying with voltage), it realizes ultra-high-precision voltage measurement. The temperature sensor uses a measurement method based on the cooperation of thermal radiation and thermoelectric effect. Combining the formula (T is the temperature, I is the current, R is the resistance value of the thermistor, is the Stefan-Boltzmann constant, is the emissivity, and A is the radiation area), it improves the accuracy of temperature measurement. The leakage sensor adopts an arc light detection and current mutation joint detection technology based on artificial intelligence image recognition, which can quickly and accurately detect line leakage. At the same time, distributed optical fiber sensors are deployed at the nodes of the base station power line and the key parts of the equipment. Using the optical time domain reflectance principle of the optical fiber, through the formula ( where Δt is the optical round-trip time difference, L is the distance to the fault point, and v is the propagation speed of light in the optical fiber), enabling continuous monitoring of the status along the power line. The collected data covers all power equipment and line nodes within the base station, constructing a comprehensive power data perception network.

[0027] Data transmission module: An ultra-secure and high-speed data transmission architecture based on the integration of terahertz communication and quantum key distribution (improved to a hybrid encryption method combining classical encryption algorithms and chaotic encryption). Terahertz communication utilizes its high-frequency band and large bandwidth characteristics. Through the adaptive beamforming algorithm, according to the channel state information CSI (the formula is , where h is the channel fading coefficient, θ is the phase, and n is the number of multipaths, and i represents the imaginary unit), dynamically adjusts the transmitting beam direction to achieve high-speed data transmission. In terms of encryption, the chaotic encryption algorithm is used to preprocess the data, and the formula is ( where x is the chaotic sequence value, μ is the chaotic control parameter), generates a chaotic key stream, and then combines it with the Advanced Encryption Standard (AES) for secondary encryption to ensure the security of data transmission. The data sender encapsulates the data transmitted by the acquisition module, adds meta-information such as timestamps and device identifiers, and forwards it to the data processing center through the cooperation of terahertz base stations and existing communication networks. Adopts a low-power adaptive sleep-wake mechanism. According to the data transmission volume and base station load, through the formula ( where T is the sleep time, B is the data buffer volume, R is the average data transmission rate, α is the sleep coefficient), dynamically adjusts the device working state to ensure that data can be transmitted stably, with low power consumption and securely in the case of a wide distribution of base stations and complex environments.

[0028] Data analysis and processing module: A distributed data analysis architecture that synergizes edge intelligence and federated learning. At the edge nodes, an anomaly detection model based on deep transfer learning is used to transfer the model pre-trained on a large amount of publicly available power data to the local data of the base station for fine-tuning. By minimizing the transfer loss function ( and where ω and λ_s are the weight coefficients, (which is the target domain loss), quickly and accurately detect abnormal fluctuations in real-time collected data. At the same time, use the blockchain-based distributed ledger to record the analysis results of edge nodes to ensure data credibility. The cloud computing center then integrates the model parameters of each base station edge node through the federated learning mechanism without transmitting the original data, and trains the global power equipment operation status prediction model. For example, for a prediction model based on the fusion of the generative adversarial network (GAN) and the long short-term memory network (LSTM), the generator generates simulated data of the future state of power equipment, and the discriminator judges the difference between the simulated data and the real data. The prediction model is continuously optimized through adversarial training to discover potential fault risks in advance. In addition, use knowledge graph technology to construct a power equipment fault knowledge graph, associate information such as equipment operation data, fault cases, and maintenance records, and through graph reasoning algorithms, such as the reasoning formula based on the path ranking algorithm (PRA) (P(y|x) is the inference probability from node x to node y, is the set of paths from x to y, and s(p) is the score of path p), providing more comprehensive knowledge support for fault diagnosis and analysis.

[0029] Alarm module: Trigger multi-level intelligent linkage alarms based on the data analysis and processing results. Set dynamic adaptive alarm thresholds, and use machine learning algorithms to adjust the alarm thresholds in real-time according to factors such as the historical operation data of the base station, seasonal changes, and load conditions. When the degree of data abnormality exceeds the first-level threshold, send personalized early warning information to the mobile terminals of base station maintenance personnel. The early warning information is generated through natural language generation technology, combined with the historical processing records and preferences of the maintenance personnel, to generate targeted early warning content, and is sent in multiple ways such as text messages, APP push, and voice messages. If the abnormality persists and the data exceeds a higher-level threshold, start the on-site alarm device based on the fusion of augmented reality (AR) and virtual reality (VR). At the base station site, use AR glasses to provide maintenance personnel with intuitive guidance on the location of faulty equipment, fault types, and processing steps; at the same time, use VR technology to construct a virtual base station scene on the operation and maintenance management platform to display the location where the fault occurs and the status of related equipment in real-time, facilitating remote experts to provide guidance. The operation and maintenance management platform uses an intelligent scheduling and resource allocation algorithm based on reinforcement learning. According to factors such as the skill level, location, and workload of maintenance personnel, the formula is (O is the optimal scheduling plan, w i is the skill weight of the i-th maintenance personnel, S i is the skill score of the i-th maintenance personnel for handling this fault, c j is the resource cost coefficient of the j-th resource, D j is the usage amount of the j-th resource, m is the number of maintenance personnel, and n is the number of resource types), to determine the optimal maintenance personnel deployment plan and the resources such as tools and spare parts to be carried.

[0030] Equipment Management Module: It conducts digital and intelligent management of the power equipment in the communication base station throughout its life cycle. It establishes an equipment information management system based on digital twin and blockchain, constructs a high-precision digital twin model for each power equipment, drives the digital twin model through the real-time collected equipment operation data, simulates the actual operation state of the equipment, and realizes real-time monitoring of equipment status and fault prediction. It uses blockchain technology to record the whole life cycle information of the equipment from procurement, installation, operation, maintenance to scrapping, ensuring that the information is tamper-proof and traceable. Through an equipment remaining life prediction model combining Particle Swarm Optimization (PSO) and deep learning, it predicts the remaining useful life (RUL) of the equipment according to the current state parameters and historical data of the equipment. The formula is (X t is the equipment state parameter at time t, are the model parameters, and the parameters of the deep learning model are optimized by PSO), and arranges the equipment update and maintenance plan in advance. At the same time, it introduces an equipment maintenance management mechanism based on blockchain smart contracts. When the equipment needs maintenance, the smart contract is automatically triggered to arrange maintenance tasks, allocate maintenance resources, and record the maintenance process and results, ensuring the stable operation of the equipment.

[0031] In the present invention, it also includes a remote control module. This module supports remote operations on the power equipment of the base station, such as remotely switching on and off the power supply and adjusting the equipment operation parameters. Through a secure encrypted communication channel based on the zero-trust network architecture, it adopts multi-factor identity authentication technology, combines various authentication methods such as biometric recognition (fingerprint, face recognition), dynamic token, and device certificate to ensure the identity security of the operator. During the communication process, it uses the control instruction transmission technology based on homomorphic encryption to process and verify the control instructions in the encrypted state. The formula is , (E is the homomorphic encryption function, and a and b are plaintext data), preventing the instructions from being stolen or tampered with during transmission. The operator sends control instructions on the operation and maintenance management platform. The instructions reach the base station edge controller through the data transmission module. The edge controller parses the instructions and controls the corresponding power equipment to execute the operations, and at the same time feeds back the operation results to the operation and maintenance management platform, realizing real-time remote control of the power equipment of the base station.

[0032] In the present invention, it also includes an environmental monitoring linkage module. It deploys environmental monitoring equipment based on the fusion of multi-spectral imaging and intelligent sensing to collect the environmental data in the base station in real time. It uses multi-spectral imaging technology to obtain the multi-spectral images of the equipment and the surrounding environment in the base station, and analyzes the surface temperature distribution of the equipment, whether there are abnormal heating points, and whether there is smoke through image recognition algorithms. At the same time, it combines intelligent sensing technology, such as a temperature and humidity sensor based on Surface Acoustic Wave (SAW). Through the formula (f is the frequency of the SAW sensor, is the initial frequency, is the temperature coefficient, is the temperature change, is the humidity coefficient, The water immersion sensor uses dual detection technology based on microwave radar and capacitive sensing to improve detection accuracy. When environmental parameters are abnormal, such as high temperature, high humidity, smoke or water immersion, the power equipment is linked to make corresponding adjustments. For example, when the temperature is too high, the air conditioning equipment is automatically started to cool down. Through the temperature adjustment algorithm based on fuzzy control, the formula is ( is the target temperature of the air conditioner, To preset a safe temperature, is the temperature deviation, K p , K i , K d As fuzzy control parameters), the environmental abnormality information is correlated with the power safety data for analysis to prevent power failures caused by environmental problems. The causal relationship model between the environment and power failures is established using big data analysis technology, and the causal inference algorithm, such as the causal inference formula based on the Bayesian network, is used. (P(X|Y) is the probability of X occurring under the condition that Y occurs, P(Y|X) is the probability of Y occurring under the condition that X occurs, P(X) and P(Y) are the prior probabilities of X and Y respectively), explore the deep-seated impact of environmental factors on power equipment failures, formulate targeted preventive measures in advance, and reduce the occurrence rate of power failures.

[0033] In the present invention, the sensor power supply and networking technology based on energy collection and wireless ad hoc networking is introduced into the data acquisition module. The sensor is powered by a variety of energy collection methods such as vibration energy collection and temperature difference energy collection. For example, vibration energy collection is achieved through the piezoelectric effect of piezoelectric materials, and the formula is: (Q is the charge, is the piezoelectric coefficient, F is the force), converting the environmental vibration energy into electrical energy. Design an intelligent energy management system to dynamically adjust the sensor working mode and sampling frequency according to the sensor energy consumption and energy collection. ( is the sampling frequency, To collect energy, is the sensor energy consumption, is the maximum sampling frequency), ensuring stable power supply of the sensor in various environments. The sensor adopts wireless ad hoc network technology and is based on a distributed collaborative positioning algorithm. , is the distance between sensors i and j, and (which are the coordinates of sensors i and j respectively), to achieve self-organizing and self-adaptive network construction, reduce the wiring cost and maintenance difficulty, and improve the reliability and flexibility of the sensor network.

[0034] In the present invention, in the data transmission module, an intelligent transmission architecture based on the integration of space-air-ground integrated network and software-defined network (SDN) is adopted. By coordinating satellite communication, high-altitude platform communication (HAPS) and ground communication networks, a space-air-ground integrated network is constructed to ensure data transmission in remote areas or when the ground network fails. Through SDN technology, intelligent scheduling and management of network traffic are realized. According to factors such as data priority and network congestion, through the formula ( is the allocated data transmission rate, is the data priority, is the bandwidth, and n is the number of data streams), network resources are dynamically allocated to improve data transmission efficiency. At the same time, using a blockchain-based network reputation mechanism, the transmission quality, security, etc. of network nodes are evaluated. Through the formula ( is the network node reputation value, is the weight of the i-th evaluation index, is the score of the i-th evaluation index, and m is the number of evaluation indexes), network nodes are incentivized to provide high-quality services to ensure the security and integrity of data transmission.

[0035] In the present invention, in the data analysis and processing module, an intelligent fault diagnosis and prediction model based on deep reinforcement learning and knowledge graph reasoning is applied. By constructing a fault diagnosis and prediction environment, the intelligent agent continuously tries different diagnosis and prediction strategies in the environment, obtains rewards or punishments according to the diagnosis and prediction results, and continuously adjusts the strategies to achieve the optimal. The formula is (Q(s,a) is the value function of taking action a in state s, is the learning rate, r is the reward value, is the discount factor, s' is the next state, and a' is the action in the next state). At the same time, combined with knowledge graph reasoning, using the association information in the equipment fault knowledge graph, the intelligent agent is assisted to make more accurate decisions. For example, when a certain parameter of the equipment is detected to be abnormal, through knowledge graph reasoning, other possible fault causes and historical handling cases related to it are searched to improve the accuracy and timeliness of fault diagnosis. In addition, transfer learning technology is used to transfer the model trained on one type of power equipment to other similar equipment for fine-tuning, by minimizing the transfer loss function ( and are weight coefficients, is the source domain loss, For the target domain loss), quickly establish a fault diagnosis and prediction model applicable to various devices, and improve the generalization ability of the model.

[0036] In the present invention, in the alarm module, an alarm response optimization mechanism based on emotion computing and social network analysis is introduced. When an alarm occurs, the system analyzes the emotional state of the maintenance personnel through emotion computing technology. According to data such as the historical speech and behavior patterns of the maintenance personnel on the social network platform, using emotion analysis algorithms, such as the emotion analysis formula based on convolutional neural network (CNN) ( is the probability distribution of emotion categories, and Text is the text data), to judge their current emotion. At the same time, using social network analysis technology, analyze the collaboration relationship and information dissemination path among maintenance personnel, and determine the core maintenance personnel through the formula ( is the node centrality, is the shortest path length between nodes i and j, and n is the total number of nodes). For maintenance personnel with a poor emotional state, the system automatically adjusts the expression way of the alarm information, using gentle and encouraging language; preferentially assign the alarm task to the core maintenance personnel or maintenance personnel with a positive emotion and rich experience, to improve the alarm response efficiency and processing quality. In addition, through the social network dissemination mechanism, quickly spread the alarm information to relevant personnel, forming a network for collaborative fault handling. The formula is ( is the information dissemination range, is the dissemination coefficient, is the number of friends in the social network, is the information infection probability).

[0037] In the present invention, in the equipment management module, a supply chain collaborative management mechanism based on blockchain and artificial intelligence is adopted. In the equipment procurement link, use blockchain technology to record the credit information, product quality data, price information, etc. of equipment suppliers, and realize automatic procurement decision-making through smart contracts. The formula is ( is the procurement decision value, is the weight of the i-th evaluation index, is the score of the i-th evaluation index, and m is the number of evaluation indexes), comprehensively considering factors such as quality, price, and delivery date. During the equipment transportation process, use Internet of Things sensors to real-time monitor information such as the location and status of the equipment, and record the data on the blockchain to ensure the safety of equipment transportation. After arriving at the base station, use artificial intelligence image recognition technology to accept the equipment, check whether the appearance of the equipment is damaged and whether the specifications meet the requirements, etc. During the equipment maintenance process, record the maintenance history, replaced parts information, etc. through the blockchain, and use artificial intelligence predictive maintenance algorithms, such as the remaining life prediction formula based on recurrent neural network (RNN) ( is the predicted remaining useful life, is the historical operation data of the equipment), arrange the maintenance plan in advance, and improve the efficiency and transparency of equipment management.

[0038] In the present invention, in the remote control module, an immersive remote operation technology based on virtual reality and haptic feedback is used. The operator enters the virtual base station scene through a virtual reality device on the operation and maintenance management platform, and this scene is synchronized with the actual base station through real-time data. Using the haptic feedback gloves, the operator can sense the resistance, vibration and other feedbacks when operating the virtual device. The formula is ( is the haptic feedback force, k is the elastic coefficient, is the operation displacement), improving the realism and accuracy of the operation. At the same time, through natural interaction methods such as gesture recognition and voice recognition, the operator can operate the virtual device more conveniently. The operation instructions are encrypted and sent to the base station edge controller through the data transmission module to control the actual power equipment to perform corresponding operations. During the operation process, the system displays information such as the device status change and operation result in real time, ensuring the security and efficiency of the remote operation.

[0039] In the present invention, in the environmental monitoring linkage module, a cooperative control strategy based on multi-agent system and game theory is used. Each environmental monitoring device and power equipment is regarded as an agent, and the agents exchange information through a communication network. Based on game theory, each agent selects the optimal strategy according to its own interests and the global goal through the strategy selection formula ( is the utility function of agent i, is the strategy of agent i, is the strategy of other agents, w j is the weight of the jth benefit index, R ij is the benefit of agent i under the jth benefit index, and n is the number of benefit indexes). For example, when the temperature sensor detects that the temperature is too high, the air conditioner agent and the ventilation equipment agent determine the optimal cooperative control strategy through the game process to achieve the best cooling effect and energy-saving goal. At the same time, the system adjusts the strategy of the agent in real time according to the environmental change and the device status, forming an adaptive cooperative control mechanism to improve the efficiency and effect of the environmental monitoring and power equipment linkage.

[0040] The above is only the preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution of the present invention and its inventive concept, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A communication base station power safety monitoring and alarming system based on Internet of Things technology, characterized in that, It includes the following modules: Data acquisition module: Using multi-modal perception fusion technology and self-calibrating sensor network, it collects power data of communication base stations. It adopts a current sensor based on the principle of optical-electromagnetic composite sensing. Through the formula works, where E is the induced electromotive force, K is the composite sensing coefficient, I is the current, B is the magnetic field strength, is the light intensity influence coefficient, P is the light intensity, and d is the size of the sensing element; Data transmission module: A data transmission architecture based on the integration of terahertz communication and quantum key distribution. Terahertz communication adjusts the transmission beam direction according to the channel state information where is the channel fading coefficient, is the phase, n is the number of multipaths, represents the imaginary unit; Data analysis and processing module: Anomaly detection model based on deep transfer learning is applied at the edge node, and the data anomalies are detected through the transfer loss function and and are weight coefficients, and are the source domain and target domain losses respectively. The blockchain distributed ledger is used to record the analysis results of the edge node. The cloud computing center integrates the parameters through the federated learning mechanism, applies the knowledge graph technology, and uses the inference formula based on the path ranking algorithm to assist in fault analysis. P(y|x) is the inference probability, is the set of paths from x to y, and s(p) is the score of path p; Alarm module: Set an adaptive alarm threshold. When the first-level threshold is triggered, send a warning message. If a higher-level threshold is exceeded, start the on-site alarm device that integrates AR and VR; The operation and maintenance platform determines the optimal scheduling plan through the formula where w i is the weight, S i is the skill score, c j is the resource cost coefficient, D j is the resource usage, m is the number of personnel, and n is the number of resource types; Device Management Module: Using blockchain technology to record the full life cycle information of devices, predicting the remaining useful life (RUL) of devices based on the current and historical data of the devices. The formula is , X t is the status parameter, is the model parameter.

2. The communication base station power safety monitoring and alarm system based on the Internet of Things technology according to claim 1, characterized in that, It also includes a remote control module, which supports remote operation of the base station power equipment. Through a secure encrypted communication channel based on the zero-trust network architecture, multiple authentication methods are carried out. During the communication process, a control instruction transmission technology based on homomorphic encryption is used for verification. The formula is , , where E is the homomorphic encryption function, a and b are plaintext data. The operator sends instructions on the operation and maintenance management platform to achieve remote control of the base station.

3. The communication base station power safety monitoring and alarm system based on the Internet of Things technology according to claim 1, characterized in that, It also includes an environmental monitoring linkage module, which deploys environmental monitoring equipment based on the fusion of multispectral imaging and intelligent sensing, collects environmental data, uses multispectral imaging technology to obtain base station images, analyzes whether the equipment is abnormal through image recognition algorithms, and combines intelligent sensing technology. Through the formula measures the temperature and humidity, f is the sensor frequency, is the initial frequency, is the temperature coefficient, is the temperature change, is the humidity coefficient, is the humidity change.

4. The communication base station power safety monitoring and alarming system based on the Internet of Things technology according to claim 1, characterized in that, In the data acquisition module, a sensor power supply and networking technology based on energy harvesting and wireless ad hoc networking is introduced. The energy harvesting method is used to power the sensors, and the sensors adopt wireless ad hoc networking technology. Based on the distributed cooperative positioning algorithm, the distance is calculated through the formula to calculate the distance, is the distance between sensors i and j, and are the coordinates of sensors i and j respectively.

5. The communication base station power safety monitoring and alarming system based on the Internet of Things technology according to claim 1, characterized in that In the data transmission module, a chaotic encryption algorithm is adopted, and the formula is , is the chaotic sequence value, is the chaotic control parameter; a low-power adaptive sleep-wake mechanism is adopted, and the device working state is adjusted through the formula , is the sleep time, is the data cache size, is the average data transmission rate, is the sleep coefficient. Adopt an intelligent transmission architecture based on the integration of space-air-ground integrated network and software-defined network (SDN). Through the formula Allocate network resources, is the allocated data transmission rate, is the data priority, is the bandwidth, n is the number of data streams. Utilize a blockchain-based network reputation mechanism. Through the formula Calculate the network node reputation value, is the weight, is the index score, and m is the number of evaluation indicators.

6. The communication base station power safety monitoring and alarm system based on Internet of Things technology according to claim 1, characterized in that, In the data analysis and processing module, by constructing a fault diagnosis and prediction environment, the agent is allowed to try different diagnosis and prediction strategies in the environment. The formula is , where Q(s,a) is the value function of taking action a in state s, is the learning rate, r is the reward value, is the discount factor, s' is the next state, and a' is the action in the next state; combined with knowledge graph reasoning, the association information in the equipment fault knowledge graph is used to assist the agent in making decisions. In addition, transfer learning technology is used to transfer the model trained on one type of power equipment to other similar equipment for fine-tuning.

7. The communication base station power safety monitoring and alarm system based on the Internet of Things technology according to claim 1, characterized in that, In the alarm module, when an alarm occurs, the system analyzes the emotional state of the maintenance personnel through emotion computing technology and uses the emotion analysis formula to judge the current emotion, is the probability distribution of emotion categories, Text is the text data. At the same time, social network analysis technology is used, and through the formula to determine the core maintenance personnel, is the node centrality, is the shortest path length between nodes i and j, and n is the total number of nodes. In addition, through the social network propagation mechanism, a network for collaborative fault handling is formed, and the formula is , is the information dissemination range, is the propagation coefficient, is the number of network friends, is the information infection probability.

8. The communication base station power safety monitoring and alarm system based on the Internet of Things technology according to claim 1, characterized in that In the device management module, in the device procurement process, blockchain technology is used to record supplier information, and automatic procurement decisions are made through smart contracts. The formula is , is the procurement decision value, is the weight, is the index score, m is the number of evaluation indicators. During the device transportation process, IoT sensors are used to monitor device information in real time and record the data on the blockchain. After arriving at the base station, artificial intelligence image recognition technology is used to inspect the devices.

9. The communication base station power safety monitoring and alarm system based on the Internet of Things technology according to claim 2, characterized in that In the remote control module, the immersive remote operation technology based on virtual reality and haptic feedback is used. The operator enters the virtual base station scenario, and this scenario uses haptic feedback gloves. The formula is , is the haptic feedback force, k is the elastic coefficient, is the operation displacement. Interaction is carried out through gesture recognition and voice recognition. After the operation instructions are encrypted, they are sent to the base station edge controller through the data transmission module to control the actual power equipment to perform corresponding operations.

10. The communication base station power safety monitoring and alarming system based on the Internet of Things technology according to claim 3, characterized in that In the environmental monitoring linkage module, using a cooperative control strategy based on multi-agent systems and game theory, each environmental monitoring device and power device acts as an agent to conduct information interaction. Based on game theory, each agent selects the optimal strategy through the strategy selection formula Select the optimal strategy, is the utility function of agent i, is the strategy of agent i, is the strategy of other agents, w j is the weight of the jth benefit index, R ij is the benefit of agent i under the jth benefit index, and n is the number of benefit indices. At the same time, the system adjusts the strategies of agents in real time according to environmental changes and device states to form an adaptive cooperative control mechanism.

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