Subway line-oriented electric power facility monitoring and early warning platform

Abstract

The invention provides a subway line-oriented electric power facility monitoring and early warning platform, and relates to the technical field of monitoring and early warning, and the platform comprises a monitoring node building module which is used for building a multi-layer monitoring node network; the concurrent connection demand analysis module is used for establishing a concurrent connection test sample; the collaborative configuration module is used for establishing a virtual private network; and the abnormity monitoring and early warning module is used for supporting the multi-layer monitoring node network through the virtual private network to carry out data acquisition and abnormity analysis of the electric power facilities, and carrying out abnormity monitoring and early warning. According to the method and the device, the technical problems that network congestion or data delay is easy to occur under a high-load condition due to the fact that data transmission mainly depends on a single communication technology in the prior art, and particularly, the performance is seriously reduced when multi-node concurrent data is processed at the same time can be solved; real-time monitoring and abnormal early warning of the subway electric power facilities are realized, and the technical effects of improving the monitoring coverage range, improving the communication efficiency and enhancing the early warning response capability are achieved.

Description

Technical Field

[0001] The present application relates to the field of monitoring and early warning technology, and in particular to a power facility monitoring and early warning platform for subway lines. Background Art

[0002] With the rapid development of modern urban rail transit, the subway has become an important part of urban transportation. The safety and reliability of its operation are directly related to the quality of life of urban residents and the stability of social economy. The power facilities in the subway line are important basic equipment to ensure the normal operation of the subway system, including substations, contact lines, cables, switches, etc. The failure of these equipment may cause subway operation interruption, equipment damage, and even endanger passenger safety. Therefore, how to efficiently monitor the operating status of power facilities in subway lines and issue timely warnings has become a technical problem that the industry urgently needs to solve.

[0003] In the monitoring of subway power facilities, the communication system needs to ensure efficient and stable data transmission. However, due to the complex underground environment and changeable communication paths, problems such as communication delays, data loss or instability often occur, affecting the response speed of the monitoring system.

[0004] In the existing technology, the monitoring system of subway power facilities mainly relies on a single communication technology for data transmission, which is prone to network congestion or data delay under high load conditions, especially when processing concurrent data from multiple nodes at the same time, the performance is seriously degraded. Summary of the invention

[0005] The purpose of this application is to provide a power facility monitoring and early warning platform for subway lines, in order to solve the technical problems in the prior art that, due to the main reliance on a single communication technology for data transmission, network congestion or data delays are prone to occur under high load conditions, especially when processing concurrent data from multiple nodes at the same time, and performance is seriously degraded.

[0006] In view of the above problems, the present application provides an electric power facility monitoring and early warning platform for subway lines, including: a monitoring node establishment module, used to determine the sensor network and data center used to monitor the electric power facilities in the target subway line, and establish a multi-layer monitoring node network; a concurrent connection demand analysis module, used to perform concurrent connection demand analysis based on the multi-layer monitoring node network, and establish a concurrent connection test sample; a collaborative configuration module, used to perform local network coverage analysis based on the concurrent connection test sample, and perform collaborative configuration of the LoRa communication module and the NB-IoT communication module, and establish a virtual private network for data transmission and processing of the multi-layer monitoring node network; an abnormal monitoring and early warning module, used to support the multi-layer monitoring node network through the virtual private network to perform data collection and abnormal analysis of electric power facilities, and perform abnormal monitoring and early warning.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: The monitoring node establishment module is used to determine the sensor network and data center used to monitor the power facilities in the target subway line, and establish a multi-layer monitoring node network; the concurrent connection demand analysis module is used to perform concurrent connection demand analysis based on the multi-layer monitoring node network and establish a concurrent connection test sample; the collaborative configuration module is used to perform local network coverage analysis based on the concurrent connection test sample, and perform collaborative configuration of the LoRa communication module and the NB-IoT communication module, and establish a virtual private network for data transmission and processing of the multi-layer monitoring node network; the abnormal monitoring and early warning module is used to support the multi-layer monitoring node network through the virtual private network to perform data collection and abnormal analysis of power facilities, and perform abnormal monitoring and early warning. The multi-layer monitoring node network is constructed through the monitoring node establishment module, and the communication network configuration is optimized in combination with the concurrent connection demand analysis module and the collaborative configuration module, so as to realize the collaborative work of the LoRa communication module and the NB-IoT communication module, establish an efficient virtual private network, realize real-time monitoring and abnormal early warning of subway power facilities, and achieve the technical effect of improving monitoring coverage, improving communication efficiency and enhancing early warning response capabilities.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0010] Figure 1 This is a structural diagram of the power facility monitoring and early warning platform for subway lines in this application.

[0011] Figure 2 This is a flow chart of generating concurrent connection test samples in the power facility monitoring and early warning platform for subway lines in this application.

[0012] Explanation of the accompanying drawings: monitoring node establishment module 11, concurrent connection demand analysis module 12, collaborative configuration module 13, abnormal monitoring and early warning module 14. DETAILED DESCRIPTION

[0013] This application solves the technical problem that the existing technology mainly relies on a single communication technology for data transmission, which is prone to network congestion or data delay under high load conditions, especially when processing concurrent data from multiple nodes at the same time, and the performance is seriously degraded by providing a power facility monitoring and early warning platform for subway lines. A multi-layer monitoring node network is constructed through the monitoring node establishment module, and the communication network configuration is optimized in combination with the concurrent connection demand analysis module and the collaborative configuration module, so as to realize the collaborative work of the LoRa communication module and the NB-IoT communication module, establish an efficient virtual private network, realize real-time monitoring and abnormal early warning of subway power facilities, and achieve the technical effect of improving monitoring coverage, improving communication efficiency and enhancing early warning response capabilities.

[0014] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0015] Please refer to the attached Figure 1 This application provides a power facility monitoring and early warning platform for subway lines, specifically including: The monitoring node establishment module 11 is used to determine the sensor network and data center used to monitor the power facilities in the target subway line and establish a multi-layer monitoring node network.

[0016] Specifically, in the process of monitoring power facilities in subway lines, determining the sensor network and data center is a key initial step. The sensor network is a system composed of multiple distributed sensors, which is used to collect real-time operation data of power facilities, such as voltage, current, temperature, humidity, etc. The data center, as the core of data aggregation and processing, is responsible for receiving the data sent by the sensor and storing, analyzing and processing it. In the specific implementation, first of all, it is necessary to reasonably deploy sensors according to the layout of the subway line and the distribution characteristics of power facilities. For example, sensors with high sensitivity and low power consumption characteristics are arranged at key locations such as substations, contact network equipment, and trackside equipment. Secondly, the location selection of the data center needs to consider the reliability and delay of data transmission. The construction of the multi-layer monitoring node network adopts a hierarchical distributed topology. The first layer is the data acquisition layer, which is directly connected to the sensor and is responsible for collecting raw data. The second layer is the relay layer, which undertakes the initial aggregation and transfer functions of the data. The third layer is the data processing layer, which communicates directly with the data center to complete high-level data analysis and anomaly detection tasks. This multi-layer structure can effectively reduce the amount of data transmitted directly to the data center and relieve network communication pressure. As an example, in a subway line, after 100 environmental monitoring sensors and 20 current monitoring sensors were deployed experimentally, these sensors were divided into 10 data acquisition nodes, each of which corresponds to a group of relay nodes.

[0017] The concurrent connection demand analysis module 12 is used to perform concurrent connection demand analysis based on the multi-layer monitoring node network and establish concurrent connection test samples.

[0018] Specifically, the goal of concurrent connection demand analysis is to evaluate and predict the concurrent connection capacity of the network under high load conditions, so as to ensure that in practical applications, the multi-layer monitoring node network can cope with the real-time monitoring needs of high frequency and large data volume. First, based on the structure of the multi-layer monitoring node network, the node characteristics and communication requirements of each layer are analyzed. For example, in the data acquisition nodes of the first layer, there are usually a large number of sensors sending data, and the communication frequency is high; in the relay nodes and data processing nodes of the upper layer, although the number of nodes is relatively small, the data processing capacity and transmission requirements are large. The connection requirements of each layer are different, so the demand analysis of concurrent connections should also be carefully distinguished in combination with the characteristics of each layer. The process of concurrent connection demand analysis includes modeling the communication requirements of nodes in each layer, and calculating the concurrent connection requirements of each layer of the network based on factors such as the frequency of data transmission, data volume, and network bandwidth. For example, in certain periods of time, if sensor nodes frequently update data or abnormal events occur, a large amount of data needs to be transmitted quickly. At this time, it is necessary to evaluate the connection capabilities of each node to ensure that they can communicate concurrently with other nodes at the same time without causing network congestion or data delay. After the concurrent connection demand analysis is completed, a concurrent connection test sample is established. The concurrent connection test sample is based on the analyzed connection demand and simulates the concurrent connection status between nodes under different network load conditions. These test samples include node connection modes in various scenarios, such as processing data requests from multiple sensors at the same time, or quickly aggregating data streams from multiple monitoring nodes within a specific period of time. Through these test samples, the concurrent connection capacity of the network can be predicted and optimized, thereby ensuring stability and efficiency in practical applications.

[0019] Through the analysis of these concurrent connection requirements and the establishment of test samples, the performance of the network under various loads can be predicted in advance, and appropriate optimization adjustments can be made to ensure that the monitoring and early warning platform can still maintain efficient and stable operation under high load conditions.

[0020] The collaborative configuration module 13 is used to perform local network coverage analysis with the concurrent connection test sample, and to perform collaborative configuration of the LoRa communication module and the NB-IoT communication module to establish a virtual private network for data transmission and processing of the multi-layer monitoring node network.

[0021] Specifically, local network coverage analysis is performed based on concurrent connection test samples, with the aim of accurately evaluating the communication coverage capabilities of each area in a multi-layer monitoring node network and providing a basis for the subsequent collaborative configuration of communication modules and the construction of a virtual private network. In a multi-layer monitoring node network, due to the differences in node distribution and the complexity of the monitoring environment, local network coverage may have blind spots or weak coverage areas, so detailed analysis and optimization are required through test samples. First, the implementation of local network coverage analysis is based on a comprehensive simulation of concurrent connection test samples. By embedding multiple scenarios (such as high load, sudden anomalies, and full monitoring cycle activity) into test samples, the communication requirements, coverage, and actual load of each node can be reproduced in a virtual environment. For example, a test sample may simulate concurrent data transmission requests from 300 data source nodes during peak hours, while another sample focuses on the high-frequency data transmission behavior of a specific node when an abnormal event occurs. Through these samples, coverage differences between communication nodes can be identified and potential bottlenecks and blind spots in the network can be discovered.

[0022] Based on the results of local coverage analysis, the LoRa communication module and NB-IoT communication module in the multi-layer monitoring node network are collaboratively configured. The LoRa communication module is characterized by low power consumption and long-distance communication, and is suitable for low-speed data transmission tasks covering a wide area; the NB-IoT (narrowband Internet of Things) communication module has high reliability and high data throughput, and is suitable for scenarios that require real-time transmission of critical data. The key to collaborative configuration is to coordinate the coverage and workload of the two communication technologies. By assigning different node communication tasks, it is ensured that the LoRa communication module and the NB-IoT communication module can collaborate efficiently to avoid communication overlap and resource waste. For example, for cross-regional data collection tasks, the LoRa communication module is responsible for aggregating data from remote nodes to relay nodes, while the NB-IoT communication module provides a high-speed and reliable transmission channel from relay nodes to data centers.

[0023] Finally, by combining the collaborative configuration of the two communication modules, a virtual private network for data transmission and processing of the multi-layer monitoring node network was constructed, and the communication links between different nodes were virtualized into independent transmission channels, which improved the security and stability of data transmission. Especially in the case of concurrent connection peaks or sudden abnormal scenarios, an efficient and stable multi-layer monitoring node network was realized, providing strong support for the accurate monitoring and timely early warning of subway power facilities.

[0024] The abnormal monitoring and early warning module 14 is used to support the multi-layer monitoring node network to perform data collection and abnormal analysis of power facilities and perform abnormal monitoring and early warning through the virtual private network.

[0025] Specifically, through a virtual private network, data is collected from sensor nodes at different locations and transmitted to the data center in real time. The entire process uses a solution that uses LoRa and NB-IoT communication technologies to work together, which not only ensures the high efficiency of data transmission, but also can dynamically adjust the communication strategy according to the needs of different areas of the subway system. After the data center receives the power facility data from each monitoring node, it processes it in real time based on the set abnormal analysis algorithm. The abnormal analysis uses a machine learning algorithm to combine historical data with real-time data to predict and judge the equipment status. If the equipment operation status of a certain node is abnormal, such as temperature rise, current fluctuation, etc., the abnormal monitoring and early warning module can detect these abnormalities immediately, trigger the early warning mechanism, and send alarm information to relevant maintenance personnel in a timely manner, which optimizes the reliability of data transmission and greatly improves the efficiency of abnormal detection, so that the power facility monitoring and early warning platform can respond to potential fault problems more quickly, thereby improving the stability and security of the system.

[0026] Further, as attached Figure 2 As shown, the concurrent connection demand analysis module 12 in the power facility monitoring and early warning platform for subway lines is also used to: extract the data source nodes and passive activation nodes of each layer of the network based on the multi-layer monitoring node network; obtain the monitoring cycle configuration of the data source nodes of each layer of the network, wherein the monitoring cycle configuration includes at least full-cycle monitoring and fixed-cycle monitoring; predict the concurrent connection range according to the monitoring cycle configuration, the data source nodes and passive activation nodes of each layer of the network, and generate a concurrent connection range prediction result; collect abnormal monitoring processing samples based on the concurrent connection range prediction result to generate the concurrent connection test sample.

[0027] Specifically, first, the data source nodes and passive activation nodes of each layer of the network in the multi-layer monitoring node network are identified and extracted. The data source nodes are usually the sensor nodes directly responsible for collecting data and can actively initiate data sending requests, while the passive activation nodes are relay nodes at a higher level. They do not actively initiate data collection requests, but can activate themselves to forward or process data when receiving requests from data source nodes. Next, the monitoring cycle configuration of the data source nodes in each layer of the network is obtained. The monitoring cycle configuration refers to the data collection cycle, which mainly includes two types: full-cycle monitoring and fixed-cycle monitoring. Full-cycle monitoring is usually applicable to nodes that need to continuously collect data around the clock, while fixed-cycle monitoring is applicable to nodes that only need to collect data during specific time periods (for example, certain time periods every hour or every working day). By obtaining these monitoring cycle configurations, the concurrent connection requirements of each node can be predicted under different load conditions, thereby optimizing the allocation of network resources.

[0028] Based on the monitoring cycle configuration, the concurrent connection range prediction is further performed. The goal of the concurrent connection range prediction is to evaluate the concurrent connection capacity and the connection requirements between nodes under different monitoring cycles and node configurations. For example, if multiple data source nodes start full-cycle monitoring at the same time in a certain period of time, the connection requirements of these nodes may increase at the same time. At this time, it is necessary to evaluate whether the network can withstand these concurrent connections. The generated concurrent connection range prediction results can provide an important basis for abnormal monitoring processing. For example, by analyzing the prediction results, it is possible to identify communication bottlenecks that may exist in certain periods or areas. Based on these prediction results, the abnormal monitoring processing samples are further collected. The abnormal monitoring processing samples are generated by combining simulation and actual monitoring data to generate a set of typical load and abnormal scenario samples. These samples can help to timely identify possible abnormal conditions in future operations, such as data loss or transmission delay caused by too many concurrent connections of some sensor nodes. Finally, based on these processing samples, concurrent connection test samples are generated. These test samples simulate the network behavior under different load conditions, including the peak period of concurrent connections between nodes, the delay in data transmission, and the response time of abnormal conditions. Through concurrent connection test samples, the network connection strategy can be further optimized to ensure that high concurrency and sudden abnormal events can be effectively handled in actual operation.

[0029] Furthermore, the concurrent connection demand analysis module 12 in the power facility monitoring and early warning platform for subway lines is also used to: predict the concurrent range of data source nodes based on the monitoring cycle configuration, and generate a concurrent data source node set; predict the concurrent range of passive activation nodes based on the concurrent data source node set and the passive activation nodes of each layer of the network, and generate a concurrent passive activation node set; generate the concurrent connection range prediction result using the concurrent data source node set and the concurrent passive activation node set.

[0030] Specifically, first, according to the monitoring cycle configuration, the concurrent range of the data source node is predicted. The data source node is the main data collection point of the network. Due to the periodic characteristics of its monitoring task, it may initiate data transmission requests at the same time in certain time periods. For example, a full-cycle monitoring node may generate data at any time point throughout the day, while a fixed-cycle monitoring node is only active during a specific period of time. By counting the active time and data transmission frequency of each node, combined with the historical traffic data of the network, the set of nodes that may be active at the same time is calculated, that is, the concurrent data source node set. This set reflects all data source nodes that may have demand for network resources at a certain moment. Next, based on the concurrent data source node set, its impact on the passively activated node is further analyzed. The role of the passively activated node is to forward or process the received data requests, so its concurrent range is directly affected by the active situation of the data source node. For example, if multiple data source nodes transmit data through the same relay node in a certain period of time, the relay node will be in a high-load state. By simulating the transmission path, data traffic and load of the passively activated nodes in each layer of the network, a concurrent passively activated node set is generated. This set reflects the number and distribution of passively activated nodes that may need to process data under high concurrency.

[0031] After the concurrent data source node set and the concurrent passive activation node set are constructed, the two are combined to generate a complete concurrent connection range prediction result. This result shows the connection status of each node in the network and possible bottlenecks under different load conditions from a global perspective. Taking an experiment as an example, in the monitoring network of a subway line, a total of 400 data source nodes and 50 passive activation nodes are deployed. Through the configuration analysis of the monitoring cycle, it is found that during the morning peak period (6:30-9:00), 300 data source nodes are active at the same time, forming a concurrent data source node set. Further prediction shows that the data of 200 nodes needs to be processed by 20 passive activation nodes, which form a concurrent passive activation node set. The final concurrent connection range prediction result shows that the traffic of 5 passive activation nodes is close to the load limit, which may cause data transmission delay. Through this layer-by-layer concurrent range prediction, it is possible to comprehensively evaluate the load problems that may occur in the network, and improve the adaptability of the network through optimized design, providing strong technical support for the subway line power facility monitoring platform.

[0032] Furthermore, the collaborative configuration module 13 in the power facility monitoring and early warning platform for subway lines is also used to: perform network segmentation of LoRa communication and NB-IoT communication based on the multi-layer monitoring node network, and perform test optimization with the concurrent connection test sample to generate a LoRa communication network and a NB-IoT communication network; configure the LoRa communication module with the LoRa communication network, and configure the NB-IoT communication module with the NB-IoT communication network; establish the virtual private network with the LoRa communication module and the NB-IoT communication module.

[0033] Specifically, first of all, network segmentation is based on the topological structure and node characteristics of the multi-layer monitoring node network. LoRa communication technology is characterized by low power consumption and long-distance coverage, which is very suitable for the transmission needs of large-scale, low-rate data. Relatively speaking, NB-IoT communication technology has high data throughput and low latency characteristics, which is suitable for the transmission of high-priority, real-time data. Therefore, in the segmentation process, it is necessary to comprehensively evaluate the node distribution density, data transmission requirements and environmental characteristics of each area. For example, in areas with wide coverage but small data volume, such as trackside equipment monitoring points, LoRa communication is preferentially allocated; while in areas with high density and high data volume, such as substation monitoring points, NB-IoT communication is preferentially used.

[0034] Through the concurrent connection test samples, the network segmentation results are further tested and optimized. This process simulates the network load and communication requirements in various scenarios, such as the sudden increase of data caused by high concurrent node activity or abnormal events in a certain period of time. The test optimization aims to evaluate the performance of the LoRa communication network and the NB-IoT communication network and adjust the network segmentation strategy. For example, if the LoRa communication in a certain area has delays or data loss in the high-load test, some nodes may be reallocated to the NB-IoT communication network to balance the load. After optimization, the generated LoRa communication network is mainly responsible for low-frequency and periodic data collection tasks, while the NB-IoT communication network focuses on the transmission of high-priority and real-time data. After completing the network segmentation and test optimization, the communication module is configured based on the optimized network. Specifically, the LoRa communication network will be configured with the LoRa communication module, covering the relevant channel selection, frequency band configuration and transmission protocol optimization to ensure low power consumption and long-distance communication stability; the NB-IoT communication network will be configured with the NB-IoT communication module, including data rate adjustment, spectrum allocation and interference avoidance, to meet the needs of efficient data transmission. On the basis of independent configuration, the two modules are interconnected through adapters to ensure that data can be seamlessly transmitted between different communication networks.

[0035] Finally, based on the configuration of these two communication modules, a virtual private network was established, that is, through logical isolation technology, the physical resources of the LoRa and NB-IoT communication networks were abstracted to form a unified data transmission and processing platform. For example, after test optimization, environmental sensors along the track mainly use LoRa communication modules for periodic data collection; while the power facility monitoring data of the substation is transmitted at the millisecond level through the NB-IoT communication module, ensuring real-time response capabilities under abnormal conditions, so that the power facility monitoring and early warning platform can still maintain efficient and stable operation in complex environments. In this way, the communication networks of LoRa and NB-IoT can be fully coordinated to achieve optimal resource allocation, which not only meets the communication needs of the multi-layer monitoring node network, but also provides reliable support for power facility monitoring and early warning in complex environments.

[0036] Furthermore, the collaborative configuration module 13 in the power facility monitoring and early warning platform for subway lines is also used to: identify the distribution density of monitoring nodes based on the multi-layer monitoring node network, and perform consistent distribution density segmentation according to the distribution density to establish multiple first sub-node networks; analyze the data processing volume of monitoring nodes based on the multi-layer monitoring node network, and perform consistent data processing volume segmentation on the multiple first sub-node networks according to the data processing volume to generate multiple sub-node networks, and the multiple sub-node networks have node communication distance identifiers and data processing volume identifiers; initialize and map communication networks for LoRa communication and NB-IoT communication for the multiple sub-node networks based on the node communication distance identifier and the data processing volume identifier, and perform communication testing with the concurrent connection test sample, and optimize the configuration of the initialized mapped communication network according to the test results to generate the LoRa communication network and the NB-IoT communication network.

[0037] Specifically, first, the distribution density of monitoring nodes in the multi-layer monitoring node network is identified. This process draws a node density distribution map of the entire network by counting the number and distribution pattern of nodes in each area. Areas with higher density usually correspond to locations where monitoring tasks are concentrated, such as the area around substations or equipment clusters, while areas with lower density may be in the middle of a tunnel or in areas with fewer trackside equipment. Through consistent segmentation of distribution density, areas with similar density characteristics are divided into multiple first sub-node networks. This segmentation method ensures that the number and spatial distribution of nodes within each sub-network remain relatively uniform, making it easier to achieve load balancing in subsequent communication optimization. After completing the distribution density segmentation, the data processing volume of the monitoring nodes is further analyzed. The data processing volume reflects the communication demand intensity of each node in a specific time period, including data collection frequency, data packet size and transmission bandwidth requirements. By combining the data processing volume distribution with the first sub-node network, it is possible to identify which sub-networks have high-load nodes or require additional resource support. Based on these analysis results, the first sub-node network is segmented according to the data processing volume to generate multiple sub-node networks with clear characteristics. Each sub-node network is assigned a node communication distance identifier and a data processing volume identifier, which represent the average communication distance and data transmission requirements between nodes in the network, respectively. These identifiers provide an important basis for the configuration of subsequent communication networks.

[0038] Based on the node communication distance identifier and the data processing volume identifier, multiple sub-node networks are initialized to map LoRa communication and NB-IoT communication, and a mapping communication network is constructed. At this stage, LoRa communication is preferentially assigned to sub-node networks with larger communication distances to take advantage of its low power consumption and long-distance transmission advantages; while NB-IoT communication is adapted to sub-node networks with larger data processing volume to meet the high-frequency and real-time data transmission requirements. For example, nodes in the tunnel area can use LoRa communication preferentially due to the long distance and complex environment; while monitoring equipment in the substation uses NB-IoT communication due to the need for high-throughput data transmission. After completing the preliminary mapping, a comprehensive communication test is performed on the mapped communication network based on concurrent connection test samples. These tests simulate network loads in different scenarios, such as high concurrent data transmission, node failures or abnormal events, etc. The test results will reveal the performance bottlenecks of the communication network in actual operation, such as excessive transmission delays in certain areas or unbalanced loads on some nodes. According to the test results, the configuration of the initial mapping communication network is optimized, including adjusting the communication parameters of the LoRa and NB-IoT modules, adding relay nodes or optimizing routing protocols, etc., to ensure that the network can still operate stably under high load conditions.

[0039] The optimized mapping communication network eventually generates a formal LoRa communication network and NB-IoT communication network. The two networks are both independent and cooperative: the LoRa communication network is responsible for low-frequency, long-distance periodic data transmission, while the NB-IoT communication network focuses on high-frequency, high-precision real-time data transmission. By analyzing the node distribution density and data processing volume to optimizing the mapping network, the reasonable allocation and efficient utilization of communication resources are achieved. The optimized LoRa and NB-IoT communication networks not only meet the complex communication requirements of the multi-layer monitoring node network, but also provide reliable guarantees for the intelligent monitoring and abnormal warning of subway line power facilities.

[0040] Furthermore, the collaborative configuration module 13 in the power facility monitoring and early warning platform for subway lines is also used to: establish preset LoRa communication constraints and preset NB-IoT communication constraints, wherein any communication constraint includes a communication distance constraint and a data volume constraint that meet the preset communication quality; based on the preset LoRa communication constraints and the preset NB-IoT communication constraints, the multiple sub-node networks are subjected to LoRa communication identification and NB-IoT communication identification, and then the corresponding communication equipment is configured to generate an initialized mapping communication network; the initialized mapping communication network is subjected to a concurrent network smoothness test with the concurrent connection test sample to generate a local node to be optimized whose network smoothness does not meet the preset indicators; the local node to be optimized is configured with network signal enhancement to generate the LoRa communication network and the NB-IoT communication network.

[0041] Furthermore, the network signal enhancement configuration for the local node to be optimized includes signal amplifier configuration and relay device configuration.

[0042] Specifically, in the process of building a multi-layer monitoring node network, setting communication constraints, configuring equipment, testing optimization, and enhancing network signals are the core steps to achieve efficient and stable communication. This process ensures that the performance of the LoRa communication network and the NB-IoT communication network meet the needs of subway line power facility monitoring, especially under high load and complex environments. First, according to the monitoring requirements and communication characteristics, preset LoRa communication constraints and preset NB-IoT communication constraints are established. These constraints define the key performance indicators in each communication mode, including communication distance constraints and data volume constraints. The communication distance constraint refers to the maximum communication range between nodes. For example, the coverage radius of LoRa communication is usually within a few kilometers, while NB-IoT is suitable for scenarios with shorter distances but higher data transmission frequencies; the data volume constraint defines the amount of data transmission that each communication mode can support per unit time. For example, LoRa is suitable for low-frequency data collection (such as environmental data once an hour), while NB-IoT can support high-frequency, real-time transmission (such as abnormal equipment signals). Based on these constraints, multiple sub-node networks are identified with LoRa communication and NB-IoT communication. Specifically, for node networks that meet the LoRa communication distance constraints and have low data volume, a LoRa communication identifier is assigned; for networks with high data volume or requiring low-latency transmission, an NB-IoT communication identifier is assigned. This step ensures that each sub-node network can match the most suitable communication mode while avoiding resource waste or performance bottlenecks.

[0043] After completing the assignment of communication identifiers, the corresponding communication equipment is configured for each sub-node network to generate an initial mapping communication network. This mapping network combines the characteristics of LoRa and NB-IoT, and preliminarily establishes a communication framework covering the entire monitoring node network. In order to verify the actual performance of the initial mapping communication network, a concurrent network smoothness test is performed based on the concurrent connection test sample. The test scenario simulates a variety of load conditions, such as nodes sending data at the same time, communication links being interfered with, etc., to evaluate the smoothness of the network under different conditions. The test results show that some local nodes may have a phenomenon of reduced communication quality due to long distance, weak signal or excessive data load. Such nodes are regarded as local nodes to be optimized. For local nodes to be optimized whose network smoothness does not meet the preset indicators, these nodes are optimized using network signal enhancement configuration. Specifically, the network smoothness can be calculated by statistically analyzing the successful connection rate (the percentage of completed communication requests to the total number of requests), packet loss rate (the percentage of lost data packets to the total number of data packets), throughput (the amount of data successfully transmitted per unit time), and average transmission delay during the test process, and then performing weighted calculation after data standardization to obtain the final network smoothness.

[0044] Signal enhancement configuration includes the following two main means: signal amplifier configuration, by adding amplifiers in the signal path, the signal propagation strength is enhanced to reduce signal attenuation caused by distance or obstacles. For example, for LoRa communication nodes far away from relay nodes, adding low-power signal amplifiers can significantly improve the coverage range. Relay device configuration, adding relay nodes in the communication link, shortening the effective communication distance between nodes, thereby improving the reliability of data transmission. For example, for key nodes in high-load areas of NB-IoT networks, by arranging additional relay devices, the communication tasks of the main node can be shared and latency can be reduced.

[0045] After the signal enhancement configuration, the LoRa communication network and NB-IoT communication network were readjusted and optimized to generate the final efficient communication network. In the experimental verification, for example, the monitoring network of a subway line contains 500 nodes. Preliminary tests found that about 20 nodes had communication quality problems. By configuring signal amplifiers and adding 10 relay nodes. In the end, the optimized LoRa communication network and NB-IoT communication network achieved full coverage and high performance. The LoRa network continued to be responsible for low-frequency, long-distance routine monitoring tasks, and the NB-IoT network focused on high-frequency, real-time transmission of critical data. The two collaborated efficiently in the virtual private network to ensure excellent performance in power facility monitoring and abnormal warning in complex environments.

[0046] Furthermore, the monitoring node establishment module 11 in the power facility monitoring and early warning platform for subway lines is also used to: perform node transmission analysis for data processing based on the sensor network and the data center, and establish multiple node sequences; identify overlapping nodes for the multiple node sequences, and use the overlapping nodes to generate the multi-layer monitoring node network for the multiple node sequences.

[0047] Specifically, in the process of establishing a multi-layer monitoring node network, the node transfer analysis of data processing is a very important step. First, through the collaborative work of the sensor network and the data center, the transmission path between the data acquisition nodes is analyzed in detail. Each data acquisition node will transfer data to the upper layer relay node or data processing node. Therefore, the goal of the node transfer analysis is to clarify the data flow of each node and identify the possible path overlap points in the data transmission process. Through the node transfer analysis, multiple node sequences will be established according to the layout of the sensor network. The node sequence is a chain composed of multiple sensor nodes and their data transmission paths. Each sequence represents a complete data transmission path from the data acquisition node to the data processing layer node. After the node sequence is generated, the next step is to identify the overlapping nodes. The overlapping nodes refer to the nodes with the same role and function in multiple node sequences. Usually, these overlapping nodes may undertake the task of data transfer or aggregation in the network topology. Through the identification of overlapping nodes, these overlapping nodes are merged to generate the final multi-layer monitoring node network. Taking an experimental data as an example, assuming that in a network of 100 sensor nodes, 15 overlapping nodes are identified through node transmission analysis and overlapping node identification, and the 15 overlapping nodes are merged and connected to obtain a multi-layer monitoring node network to provide support for monitoring and early warning of power facilities.

[0048] In summary, the power facility monitoring and early warning platform for subway lines provided by this application has the following technical effects: The monitoring node establishment module is used to determine the sensor network and data center used to monitor the power facilities in the target subway line, and establish a multi-layer monitoring node network; the concurrent connection demand analysis module is used to perform concurrent connection demand analysis based on the multi-layer monitoring node network and establish a concurrent connection test sample; the collaborative configuration module is used to perform local network coverage analysis based on the concurrent connection test sample, and perform collaborative configuration of the LoRa communication module and the NB-IoT communication module, and establish a virtual private network for data transmission and processing of the multi-layer monitoring node network; the abnormal monitoring and early warning module is used to support the multi-layer monitoring node network through the virtual private network to perform data collection and abnormal analysis of power facilities, and perform abnormal monitoring and early warning. The multi-layer monitoring node network is constructed through the monitoring node establishment module, and the communication network configuration is optimized in combination with the concurrent connection demand analysis module and the collaborative configuration module, so as to realize the collaborative work of the LoRa communication module and the NB-IoT communication module, establish an efficient virtual private network, realize real-time monitoring and abnormal early warning of subway power facilities, and achieve the technical effect of improving monitoring coverage, improving communication efficiency and enhancing early warning response capabilities.

[0049] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0050] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. The power facility monitoring and early warning platform for subway lines is characterized by: include: A monitoring node establishment module, used to determine the sensor network and data center used to monitor the power facilities in the target subway line, and establish a multi-layer monitoring node network; A concurrent connection demand analysis module, used to perform concurrent connection demand analysis based on the multi-layer monitoring node network and establish a concurrent connection test sample; A collaborative configuration module, used to perform local network coverage analysis with the concurrent connection test sample, and to perform collaborative configuration of the LoRa communication module and the NB-IoT communication module, and to establish a virtual private network for data transmission and processing of the multi-layer monitoring node network; The abnormal monitoring and early warning module is used to support the multi-layer monitoring node network through the virtual private network to carry out data collection and abnormal analysis of power facilities and to carry out abnormal monitoring and early warning.

2. The power facility monitoring and early warning platform for subway lines according to claim 1, characterized in that: The concurrent connection requirement analysis module is also used for: Extracting data source nodes and passive activation nodes of each layer of network based on the multi-layer monitoring node network; Obtaining a monitoring cycle configuration of a data source node of each layer of the network, wherein the monitoring cycle configuration includes at least full-cycle monitoring and fixed-cycle monitoring; Perform concurrent connection range prediction according to the monitoring period configuration, the data source nodes and the passively activated nodes of each layer of the network, and generate a concurrent connection range prediction result; The concurrent connection range prediction result is used to perform abnormal monitoring and processing sample collection to generate the concurrent connection test sample.

3. The power facility monitoring and early warning platform for subway lines as claimed in claim 2, characterized in that: The concurrent connection requirement analysis module is also used for: Predicting the concurrent range of data source nodes based on the monitoring cycle configuration, and generating a concurrent data source node set; Predicting the concurrent range of passively activated nodes according to the concurrent data source node set and the passively activated nodes of each layer of the network, and generating a concurrent passively activated node set; The concurrent connection range prediction result is generated using the concurrent data source node set and the concurrent passive activation node set.

4. The power facility monitoring and early warning platform for subway lines according to claim 1, characterized in that: The collaborative configuration module is also used for: Based on the multi-layer monitoring node network, network segmentation of LoRa communication and NB-IoT communication is performed, and test optimization is performed with the concurrent connection test sample to generate a LoRa communication network and a NB-IoT communication network; The LoRa communication module is configured with the LoRa communication network, and the NB-IoT communication module is configured with the NB-IoT communication network; The virtual private network is established using the LoRa communication module and the NB-IoT communication module.

5. The power facility monitoring and early warning platform for subway lines as claimed in claim 4, characterized in that: The collaborative configuration module is also used for: Based on the multi-layer monitoring node network, the distribution density of the monitoring nodes is identified, and according to the distribution density, the distribution density is uniformly divided to establish a plurality of first sub-node networks; Based on the multi-layer monitoring node network, the data processing volume of the monitoring node is analyzed, and according to the data processing volume, the multiple first sub-node networks are divided into the same data processing volume to generate multiple sub-node networks, and the multiple sub-node networks have node communication distance identifiers and data processing volume identifiers; Based on the node communication distance identifier and the data processing volume identifier, the multiple sub-node networks are initialized and mapped to the communication network for LoRa communication and NB-IoT communication, and a communication test is performed with the concurrent connection test sample. According to the test results, the initialization mapping communication network is configured and optimized to generate the LoRa communication network and the NB-IoT communication network.

6. The power facility monitoring and early warning platform for subway lines as claimed in claim 5, characterized in that: The collaborative configuration module is also used for: Establishing preset LoRa communication constraints and preset NB-IoT communication constraints, wherein any communication constraint includes a communication distance constraint and a data volume constraint that satisfy a preset communication quality; Based on the preset LoRa communication constraints and the preset NB-IoT communication constraints, the multiple sub-node networks are assigned LoRa communication identifications and NB-IoT communication identifications, and then corresponding communication devices are configured to generate an initialization mapping communication network; Performing a concurrent network connectivity test on the initialization mapping communication network using the concurrent connection test sample to generate local nodes to be optimized whose network connectivity does not meet the preset index; Perform network signal enhancement configuration on the local nodes to be optimized to generate the LoRa communication network and the NB-IoT communication network.

7. The power facility monitoring and early warning platform for subway lines as claimed in claim 6, characterized in that: The network signal enhancement configuration for the local node to be optimized includes signal amplifier configuration and relay device configuration.

8. The power facility monitoring and early warning platform for subway lines as claimed in claim 1, characterized in that: The monitoring node establishment module is also used for: According to the sensor network and the data center, node transmission analysis of data processing is performed to establish multiple node sequences; Overlapping nodes are identified for the multiple node sequences, and the multiple node sequences are matched with the overlapping nodes to generate the multi-layer monitoring node network.

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