Contact network monitoring method and device

Abstract

The invention provides a contact network monitoring method and device, and the method comprises the steps: obtaining the effective coverage radius of a single Internet of Things node, the length, width and environment data of a contact network region, and determining the number of Internet of Things nodes of the contact network region; according to the environment data and the number of the Internet of Things nodes, the arrangement position of each Internet of Things node is determined, arrangement of each Internet of Things node in the contact network area is completed, and sensor nodes are uniformly distributed at the position of each Internet of Things node; obtaining node residual energy and signal intensity of each Internet of Things node, and determining the Internet of Things node corresponding to each sensor node according to the node residual energy and signal intensity of each Internet of Things node; and receiving the contact network state information sent by each sensor node through the corresponding Internet of Things node, and completing the monitoring of the contact network area according to the contact network state information. According to the invention, the monitoring range can fully cover the whole overhead line system, and the safety and reliability of railway operation are guaranteed.

Description

Technical Field

[0001] This application relates to the technical field of railway electrification, and particularly to a catenary monitoring method and device. Background Art

[0002] Catenary monitoring is an important link to ensure the safe operation of rail transit and railways, and plays an important role in ensuring the safe operation of rail transit and railways. However, there are still many problems and challenges.

[0003] Currently, contact monitoring methods are usually used to monitor the catenary. On the one hand, professional personnel use measuring tools to measure the geometric parameters of the catenary on-site. On the other hand, sensors and cameras on the inspection vehicle are used to dynamically monitor the catenary to obtain the geometric parameters of the catenary and the dynamic parameters of the pantograph-catenary interaction.

[0004] However, this method has many disadvantages, such as affecting the current collection characteristics of locomotives, large safety hazards, limited measurement accuracy, etc.; the contact detection method will cause disturbances due to direct contact with the catenary wire, affecting the measurement accuracy. At the same time, the monitoring ranges of manual on-site measurement and the catenary inspection vehicle are limited, and it is impossible to achieve full coverage of the entire catenary. Some monitoring devices have problems such as aging and damage, and have not been maintained and updated in time, unable to meet the growing monitoring needs. Summary of the Invention

[0005] In view of at least one problem in the prior art, this application proposes a catenary monitoring method and device, which can achieve full coverage of the entire catenary in the monitoring range and ensure the safety and reliability of railway operation.

[0006] To solve the above technical problems, this application provides the following technical solutions:

[0007] In a first aspect, this application provides a catenary monitoring method, including:

[0008] Obtain the effective coverage radius of a single local Internet of Things node, the length, width, and environmental data of the target catenary area, and determine the number of local Internet of Things nodes in the target catenary area according to the effective coverage radius, the length, and the width;

[0009] Determine the layout positions of each local Internet of Things node according to the environmental data and the number of local Internet of Things nodes, complete the layout of each local Internet of Things node in the target catenary area, and sensor nodes are evenly arranged at the positions of each local Internet of Things node;

[0010] Obtain the remaining energy and signal strength of each local Internet of Things node, and determine the corresponding local Internet of Things node for each sensor node according to the remaining energy and signal strength of each local Internet of Things node.

[0011] Receive the catenary status information sent by each sensor node via its corresponding local Internet of Things node, and complete the monitoring of the target catenary area according to the catenary status information.

[0012] In one embodiment, the catenary monitoring method further includes:

[0013] Obtain the actual distance between each sensor node and its corresponding local Internet of Things node;

[0014] Determine the optimal transmission time corresponding to the sensor node according to the preset signal attenuation coefficient and the actual distance between each sensor node and its corresponding local Internet of Things node;

[0015] Judge whether there is a first abnormal sensor node among the sensor nodes whose corresponding optimal transmission time is greater than the preset maximum transmission time. If so, adjust the communication mode of the first abnormal sensor node. The communication modes include short-distance high-frequency and long-distance low-frequency communication modes.

[0016] In one embodiment, the catenary monitoring method further includes:

[0017] Obtain the temperature data and humidity data collected by each sensor node;

[0018] Determine the optimal transmission power of the sensor node according to the temperature data and humidity data collected by each sensor node;

[0019] Judge whether there is a second abnormal sensor node among the sensor nodes whose optimal transmission power is greater than the preset upper limit of safe transmission power. If so, adjust the transmission power of the second abnormal sensor node to the preset upper limit of safe transmission power.

[0020] In one embodiment, the catenary monitoring method further includes:

[0021] Obtain the interference degree information of each local Internet of Things node; determine the optimal communication frequency band of the local Internet of Things node according to the interference degree information of each local Internet of Things node;

[0022] Judge whether there is an abnormal local Internet of Things node among the local Internet of Things nodes whose optimal communication frequency band is greater than the preset safe frequency band range. If so, switch the communication frequency band of the abnormal local Internet of Things node to the preset backup frequency band.

[0023] In one embodiment, the catenary monitoring method further includes:

[0024] Obtain the data volume of each local Internet of Things node, and determine the computing resource requirements of the local Internet of Things node according to the data volume of each local Internet of Things node;

[0025] Determine whether there is an abnormal local Internet of Things node among the local Internet of Things nodes whose computing resource requirements are greater than the computing resource threshold. If so, increase the preset computing resources for the abnormal local Internet of Things node.

[0026] In one embodiment, the catenary monitoring method further includes:

[0027] Obtain the length of the target catenary area and the total length of the local Internet of Things nodes already covered in the target catenary area;

[0028] Determine the coverage rate of the target catenary area according to the length of the target catenary area and the total length of the local Internet of Things nodes already covered in the target catenary area;

[0029] If the coverage rate is greater than or equal to the coverage rate threshold, obtain the number of neighbors of each local Internet of Things node and the distance from the farthest boundary point, and determine the importance score of each local Internet of Things node according to the number of neighbors of each local Internet of Things node and the distance from the farthest boundary point;

[0030] Complete the adjustment of the deployment density of the local Internet of Things nodes according to the importance scores of the local Internet of Things nodes.

[0031] In one embodiment, determining the importance score of each local Internet of Things node according to the number of neighbors of each local Internet of Things node and the distance from the farthest boundary point includes:

[0032] Obtain the average number of successful communications and the average transmission delay of each local Internet of Things node, and determine the stability score of the local Internet of Things node according to the average number of successful communications and the average transmission delay of each local Internet of Things node;

[0033] Determine the importance score of each local Internet of Things node according to the number of neighbors, the distance from the farthest boundary point, and the stability score of each local Internet of Things node.

[0034] In one embodiment, the catenary monitoring method further includes:

[0035] Obtain the effective data processing capacity of each local Internet of Things node per unit time, and determine the cooperation efficiency among the local Internet of Things nodes according to the number of local Internet of Things nodes, the effective data processing capacity of each local Internet of Things node per unit time, and a preset basic cooperation factor.

[0036] Determine whether the cooperation efficiency is greater than the cooperation efficiency threshold. If so, determine that the cooperation status of each local Internet of Things node is normal.

[0037] In a second aspect, the present application provides a catenary monitoring device, including:

[0038] A first acquisition module, configured to acquire the effective coverage radius of a single local Internet of Things node, the length, width, and environmental data of a target catenary area, and determine the number of local Internet of Things nodes in the target catenary area according to the effective coverage radius, the length, and the width;

[0039] An arrangement module, configured to determine the arrangement positions of each local Internet of Things node according to the environmental data and the number of local Internet of Things nodes, complete the arrangement of each local Internet of Things node in the target catenary area, and sensor nodes are evenly arranged at the positions of each local Internet of Things node;

[0040] A second acquisition module, configured to acquire the remaining energy and signal strength of each local Internet of Things node, and determine the local Internet of Things node corresponding to each sensor node according to the remaining energy and signal strength of each local Internet of Things node;

[0041] A monitoring module, configured to receive the catenary status information sent by each sensor node via its corresponding local Internet of Things node, and complete the monitoring of the target catenary area according to the catenary status information.

[0042] In a third aspect, the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the catenary monitoring method described above is implemented.

[0043] In a fourth aspect, the present application provides a computer-readable storage medium, on which computer instructions are stored. When the instructions are executed by a processor, the catenary monitoring method described above is implemented.

[0044] As can be seen from the above technical solutions, the present application provides a catenary monitoring method and device. Among them, the method includes: obtaining the effective coverage radius of a single local Internet of Things node, the length, width and environmental data of the target catenary area, and determining the number of local Internet of Things nodes in the target catenary area according to the effective coverage radius, the length and the width; determining the layout positions of the respective local Internet of Things nodes according to the environmental data and the number of local Internet of Things nodes, completing the layout of the respective local Internet of Things nodes in the target catenary area, and evenly arranging sensor nodes at the positions of the respective local Internet of Things nodes; obtaining the remaining energy and signal strength of each local Internet of Things node, and determining the local Internet of Things node corresponding to each sensor node according to the remaining energy and signal strength of each local Internet of Things node; receiving the catenary status information sent by each sensor node via its corresponding local Internet of Things node, and completing the monitoring of the target catenary area according to the catenary status information, which can achieve full coverage of the entire catenary by the monitoring range, ensuring the safety and reliability of railway operation; realizing a comprehensive and efficient catenary monitoring solution for electrified railways, and greatly improving the reliability and accuracy of the monitoring system. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0046] Figure 1 is the first flowchart of the catenary monitoring method in the embodiment of the present application;

[0047] Figure 2 is the second flowchart of the catenary monitoring method in the embodiment of the present application;

[0048] Figure 3 is the flowchart of the catenary monitoring method in the application example of the present application;

[0049] Figure 4 is the structural schematic diagram of the catenary monitoring system in the application example of the present application;

[0050] Figure 5 is the structural block diagram of the catenary monitoring device in the embodiment of the present application;

[0051] Figure 6 is the schematic block diagram of the system composition of the electronic device in the embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0053] To improve the monitoring effect, it is necessary to continuously improve and optimize the monitoring technology, strengthen the data processing and analysis capabilities, and expand the monitoring scope and frequency. Based on this, the embodiments of this application provide a catenary monitoring method and device, which can achieve a sensor distribution without dead angles along the entire catenary based on the intelligent allocation method of high-density local Internet of Things nodes and wide-area Internet of Things nodes; deploy a dual-frequency communication module on the wide-area Internet of Things nodes to improve the stability of data transmission in remote areas and reduce delays; adopt multi-hop routing and redundant transmission mechanisms to enhance the reliability of data transmission in high electromagnetic interference environments; equip sensors with temperature and humidity self-adaptive calibration functions to ensure the measurement accuracy under variable weather conditions; deploy a distributed data processing and edge computing architecture on the central management platform to improve the ability to process big data under complex network architectures. It can solve the problems of data acquisition blind spots caused by uneven distribution density of sensors in the catenary monitoring of electrified railways, transmission delays caused by unstable wide-area Internet of Things communication signals in remote areas, data transmission errors caused by wireless communication disturbances in high electromagnetic interference environments, sensor accuracy degradation caused by temperature and humidity changes under variable weather conditions, and insufficient system processing capabilities caused by a large increase in data volume under complex network architectures.

[0054] In an embodiment of this application, through an optimization algorithm, local Internet of Things nodes and wide-area Internet of Things nodes with different densities can be reasonably allocated to ensure that sensors are installed in each key area along the catenary, thereby achieving a sensor distribution without dead angles and effectively solving the problem of data acquisition blind spots caused by uneven distribution density of sensors in the catenary monitoring of electrified railways.

[0055] In an embodiment of this application, a dual-frequency communication module can be deployed on the wide-area Internet of Things nodes, enabling the wide-area Internet of Things nodes to communicate simultaneously on two different frequency bands. The selection of dual bands can significantly improve the communication signal strength in remote areas, reduce transmission delay problems caused by signal attenuation or interference, and ensure the real-time transmission and reliability of data.

[0056] In an embodiment of the present application, a multi-hop routing and redundant transmission mechanism can be adopted to enhance the fault tolerance of the system by establishing multiple transmission paths. Even if a certain route has problems, other paths can continue to work, thereby ensuring the smooth transmission of data in a high electromagnetic interference environment, reducing the transmission error rate, and enhancing the reliability of data transmission in a high electromagnetic interference environment.

[0057] In an embodiment of the present application, the sensor can be equipped with a temperature and humidity self-adaptive calibration function. Through the built-in calibration algorithm and the sensor, it can automatically adjust the working parameters of the sensor according to the changes in the environmental temperature and humidity, so as to still maintain a high measurement accuracy under changing weather conditions and ensure the accuracy and stability of the monitoring data.

[0058] In an embodiment of the present application, a distributed data processing and edge computing architecture can be deployed on the central management platform. The edge computing nodes are used to perform preliminary processing and filtering on the data collected by the front-end sensors, reducing the pressure on the central server. At the same time, the distributed data processing architecture can efficiently manage the data streams generated by a large number of sensor nodes, improve the overall processing capacity and response speed of the system, and effectively cope with the problem of the sudden increase in data volume under a complex network architecture.

[0059] Specifically, it will be described through the following various embodiments.

[0060] In order to achieve full coverage of the entire catenary by the monitoring range and ensure the safety and reliability of railway operation, this embodiment provides a catenary monitoring method whose execution entity is a catenary monitoring device. The catenary monitoring device includes but is not limited to a server, such as Figure 1 As shown, the method specifically includes the following content:

[0061] Step 101: Obtain the effective coverage radius of a single local Internet of Things node, the length, width, and environmental data of the target catenary area, and determine the number of local Internet of Things nodes in the target catenary area according to the effective coverage radius, the length, and the width.

[0062] Specifically, the effective coverage radius can represent the maximum distance range within which the local Internet of Things node can effectively transmit data. The environmental data can include: slope, aspect, terrain roughness, terrain curvature, support columns, buildings, etc. The number N of local Internet of Things nodes in the target catenary area can be determined according to the following formula:

[0063]

[0064] Among them, L represents the length of the target catenary area, W represents the width of the target catenary area, and R represents the effective coverage radius of the local Internet of Things node.

[0065] Step 102: Determine the layout positions of each local Internet of Things node according to the environmental data and the number of local Internet of Things nodes, complete the layout of each local Internet of Things node in the target catenary area, and sensor nodes are evenly arranged at the positions of each local Internet of Things node.

[0066] Specifically, since the catenary equipment mainly adopts a fixed static sensor installation method, a clustering-based layout algorithm is mainly applicable in the self-organizing algorithm: some key catenary positions (such as high-wind areas, tunnels, etc.) can be clustered, the area is divided into multiple sub-areas, and each sub-area is responsible for data aggregation by the cluster head node. The cluster head is dynamically elected according to parameters such as remaining energy and signal strength to ensure that the cluster head node is preferentially deployed in the center of the sub-area or the signal coverage overlap area, and ordinary nodes are evenly distributed within the communication range of the cluster head; input environmental data (including topographic maps, obstacle distributions, signal attenuation models, target monitoring area ranges, etc.), the number of local Internet of Things nodes, and the capabilities of local Internet of Things nodes (communication radius, energy consumption model, sensor type, etc.), avoid obstacles through a rasterized map, adjust the position of the cluster head, and then determine the layout positions of each local Internet of Things node. The sensor node can be a sensor equipped with a temperature and humidity adaptive calibration function, etc.

[0067] Step 103: Obtain the remaining energy and signal strength of each local Internet of Things node, and determine the local Internet of Things node corresponding to each sensor node according to the remaining energy and signal strength of each local Internet of Things node.

[0068] Specifically, the local Internet of Things node corresponding to the sensor node can represent the local Internet of Things node with the optimal signal condition under the current conditions. The remaining energy of the local Internet of Things node can represent the remaining power of the local Internet of Things node at the current moment. The node status value P of the local Internet of Things node can be determined according to the following formula:

[0069] P = E / S

[0070] where E represents the remaining energy of the local Internet of Things node, and S represents the signal strength; sort the distances between the local Internet of Things node and the sensor node. If the node status value of the local Internet of Things node A closest to a sensor node O is less than the node status value threshold, then the next local Internet of Things node B closest to the sensor node O can be selected, and it is judged whether the node status value of the local Internet of Things node B is greater than or equal to the node status value threshold. If so, the local Internet of Things node B is used as the local Internet of Things node corresponding to the sensor node O, otherwise, continue to select the next local Internet of Things node C closest to the sensor node O until the node status value of the local Internet of Things node is greater than or equal to the node status value threshold.

[0071] Step 104: Receive the catenary status information sent by each sensor node via its corresponding local Internet of Things node, and complete the monitoring of the target catenary area according to the catenary status information.

[0072] Specifically, the catenary status information may include: displacement of temperature, wind speed, humidity, vibration, current, voltage, poles, cantilevers, suspension conductors, etc.

[0073] Such as Figure 2 shown, in one embodiment, to improve the reliability of the communication mode of the sensor nodes, Step 104 further includes:

[0074] Step 201: Obtain the actual distance between each sensor node and its corresponding local Internet of Things node.

[0075] Step 202: Determine the optimal transmission time corresponding to the sensor node according to the preset signal attenuation coefficient and the actual distance between each sensor node and its corresponding local Internet of Things node.

[0076] Step 203: Determine whether there is a first abnormal sensor node among the sensor nodes whose corresponding optimal transmission time is greater than the preset maximum transmission time. If so, adjust the communication mode of the first abnormal sensor node, and the communication mode includes: short-distance high-frequency and long-distance low-frequency communication modes.

[0077] To improve the reliability of the transmission power of the sensor nodes, in one embodiment, Step 104 further includes:

[0078] Step 301: Obtain the temperature data and humidity data collected by each sensor node.

[0079] Step 302: Determine the optimal transmission power of the sensor node according to the temperature data and humidity data collected by each sensor node.

[0080] Specifically, the optimal transmission power P of the sensor node can be determined according to the following formula:

[0081] P = a×T + b×H + c

[0082] where T represents temperature, H represents humidity, and a, b, and c are constants.

[0083] Step 303: Determine whether there is a second abnormal sensor node among the sensor nodes whose optimal transmission power is greater than the preset upper limit of the safe transmission power. If so, adjust the transmission power of the second abnormal sensor node to the preset upper limit of the safe transmission power.

[0084] Specifically, the preset upper limit of the secure transmission power can be set according to the actual situation, and the present application does not limit this. Further, if there is a second abnormal sensor node, the transmission power of the second abnormal sensor node can be adjusted to the preset upper limit of the secure transmission power, and the current transmission power of the sensor nodes other than the second abnormal sensor node is controlled to remain unchanged. If there is no second abnormal sensor node, the current transmission power of each sensor node can be controlled to remain unchanged.

[0085] In order to improve the reliability of the communication frequency band of the local Internet of Things nodes, in one embodiment, step 104 further includes:

[0086] Step 401: Obtain the interference degree information of each local Internet of Things node; determine the optimal communication frequency band of the local Internet of Things node according to the interference degree information of each local Internet of Things node.

[0087] Specifically, the optimal communication frequency band F can be calculated by using the formula F = d·I + e′, where I represents the interference intensity, and d and e′ are constants.

[0088] Step 402: Determine whether there is an abnormal local Internet of Things node among the local Internet of Things nodes whose optimal communication frequency band is greater than the preset secure frequency band range. If so, switch the communication frequency band of the abnormal local Internet of Things node to the preset backup frequency band.

[0089] Specifically, the preset backup frequency band can be set according to the actual situation, and the present application does not limit this. If there is the abnormal local Internet of Things node, the communication frequency band of the abnormal local Internet of Things node can be switched to the preset backup frequency band, and the current communication frequency band of other local Internet of Things nodes is controlled to remain unchanged. If there is no such abnormal local Internet of Things node, the current communication frequency band of each local Internet of Things node can be controlled to remain unchanged. As a preference, the current communication frequency band of each local Internet of Things node can be defaulted to 2.4 GHz. Further, in order to improve the stability of data transmission in remote areas and reduce latency, a dual-band communication module can be deployed on the wide-area Internet of Things node, that is, communicate in a dual-band communication manner; the wide-area Internet of Things node is communicatively connected to each local Internet of Things node.

[0090] In order to avoid the problem of insufficient system processing capacity caused by a large increase in data volume under a complex network architecture, and thus ensure sufficient computing resources for each local Internet of Things node, in one embodiment, step 104 further includes:

[0091] Step 501: Obtain the data volume of each local Internet of Things node, and determine the computing resource requirements of the local Internet of Things node according to the data volume of each local Internet of Things node.

[0092] Specifically, the computing resource requirement C of the local Internet of Things node can be determined according to the following formula:

[0093] C = f·L + g′

[0094] where L represents the data volume, and f and g′ are constants.

[0095] Step 502: Determine whether there are abnormal local Internet of Things nodes with computing resource requirements greater than the computing resource threshold among the local Internet of Things nodes. If so, increase the preset computing resources for the abnormal local Internet of Things nodes.

[0096] Specifically, the sum of the preset computing resources and the computing resource threshold can be greater than or equal to the computing resource requirement, and the preset computing resources can be specifically set according to the actual situation.

[0097] To improve the reliability of the positions of the local Internet of Things nodes, in one embodiment, after step 102, it further includes:

[0098] Step 601: Obtain the length of the target catenary region and the total length of the local Internet of Things nodes already covered in the target catenary region;

[0099] Step 602: Determine the coverage rate of the target catenary region according to the length of the target catenary region and the total length of the local Internet of Things nodes already covered in the target catenary region.

[0100] Specifically, the coverage rate C of the target catenary region can be determined according to the following formula:

[0101]

[0102] where N S represents the total length of the local Internet of Things nodes already covered, and N t represents the length of the target catenary region.

[0103] Step 603: If the coverage rate is greater than or equal to the coverage rate threshold, obtain the number of neighbors of each local Internet of Things node and the distance from the farthest boundary point, and determine the importance score of each local Internet of Things node according to the number of neighbors of each local Internet of Things node and the distance from the farthest boundary point.

[0104] The importance score W of the local Internet of Things node can be obtained according to the following formula:

[0105] W = a·log(Nn + 1) + b·D

[0106] Wherein, Nn represents the number of neighbors of the local Internet of Things node; D represents the distance between the local Internet of Things node and the farthest boundary point. Further, if the coverage rate is less than the coverage rate threshold, the position or deployment density of the local Internet of Things node can be adjusted until the coverage rate is greater than or equal to the coverage rate threshold.

[0107] Step 604: Complete the adjustment of the deployment density of the local Internet of Things nodes according to the importance scores of each local Internet of Things node.

[0108] Specifically, the local Internet of Things nodes with importance scores greater than the importance score threshold can be determined as important local Internet of Things nodes. A specified number of local Internet of Things nodes can be deployed within the area where the distance to the important local Internet of Things node is less than the distance threshold, so as to increase the deployment density of the local Internet of Things nodes within this area. Sensor nodes can be set at each deployed local Internet of Things node to ensure that there are enough sensor nodes in the entire catenary area to cover all key points.

[0109] In order to further improve the reliability of determining the importance scores of the local Internet of Things nodes, in one embodiment, step 603 of determining the importance score of each local Internet of Things node according to the number of neighbors of each local Internet of Things node and the distance to the farthest boundary point includes:

[0110] Step 701: Obtain the average communication success times and average transmission delays of each local Internet of Things node, and determine the stability score of the local Internet of Things node according to the average communication success times and average transmission delays of each local Internet of Things node.

[0111] Step 702: Determine the importance score of each local Internet of Things node according to the number of neighbors, the distance to the farthest boundary point, and the stability score of each local Internet of Things node.

[0112] Specifically, the average communication success times and average transmission delays can also be considered, and the importance score W of each local Internet of Things node is determined according to the following formula:

[0113]

[0114] W = a·log(Nn + 1) + b·D + c·S

[0115] Wherein, S i is the average communication success times, T i is the average transmission delay, S is the stability score, and c is the stability coefficient.

[0116] In order to improve the reliability of the cooperation of each local Internet of Things node, in one embodiment, step 104 further includes:

[0117] Step 801: Obtain the effective data processing capabilities of each local Internet of Things node within a unit time, and determine the collaboration efficiency among the local Internet of Things nodes according to the number of the local Internet of Things nodes, the effective data processing capabilities of each local Internet of Things node within a unit time, and a preset basic collaboration factor;

[0118] Step 802: Determine whether the collaboration efficiency is greater than a collaboration efficiency threshold. If so, determine that the collaboration status of each local Internet of Things node is normal.

[0119] Specifically, the following formula can be used to determine the overall collaboration efficiency R of the local Internet of Things nodes:

[0120] R = h·N + i

[0121] Wherein, N represents the number of local Internet of Things nodes, h represents the effective data processing capabilities of each local Internet of Things node per unit time, and i represents the basic collaboration factor. Preferably, h = 0.9 and i = 0.3.

[0122] To further illustrate the present solution, the present application provides an application example of a catenary monitoring method. In this application example, a sensing technology based on a hybrid of local Internet of Things and wide area Internet of Things for electrified railway catenary can be used to achieve comprehensive monitoring of the electrified railway catenary. By arranging a large number of sensors along the railway line and combining the local Internet of Things and the wide area Internet of Things, the status information of the catenary is collected and transmitted in real time to ensure the safety and reliability of railway operation. Key issues such as transmission delay caused by unstable communication signals of the wide area Internet of Things in remote areas and interference of wireless communication in a high electromagnetic interference environment are solved. As Figure 3 shown, the specific description is as follows:

[0123] Step 0: Based on the actual topological structure of the railway catenary, establish a node distribution model; use the density clustering algorithm to partition different regions along the catenary.

[0124] For each catenary partition, steps 1 to 8 can be executed to dynamically adjust the number of local Internet of Things nodes within each partition according to the partition result; regularly check the node status (such as, faulty or normal) of the sensor nodes and local Internet of Things nodes within each partition, optimize the node layout, and ensure uniform distribution of the sensor nodes. Through precise partitioning and dynamic adjustment, the problem of data acquisition blind spots caused by uneven distribution density of sensor nodes can be effectively solved. Step 0 includes:

[0125] Step 01: Based on the actual topological structure of the railway catenary, establish a node distribution model:

[0126] Collect the physical topology data of the railway catenary; analyze the physical topology data to extract key nodes and connection relationships; construct a node distribution model using graph theory methods. The key nodes may include: substations, cable joints, support structures, etc. By establishing a detailed node distribution model, the accuracy and practicality of the model can be ensured, and the rationality of sensor distribution can be improved.

[0127] Step 02: Use the density clustering algorithm to partition different regions along the catenary.

[0128] Step 1: Calculate the number of local Internet of Things nodes required according to the pre-acquired length and width of the catenary area (i.e., catenary partition); optimize the node layout through the self-organization algorithm to ensure seamless coverage; monitor the connection status of each local Internet of Things node in real time and dynamically adjust the density of the local Internet of Things nodes; add redundant local Internet of Things nodes to ensure reliable data transmission in local areas.

[0129] Regarding the problem of sensor accuracy degradation caused by changing weather conditions, a temperature and humidity adaptive calibration function can be equipped in the sensor, allowing the sensor to automatically adjust its own calibration parameters when detecting changes in ambient temperature and humidity to maintain the consistency and accuracy of measurement results. For example, in rainy or snowy weather, the ice layer on the catenary will have a significant impact on the on-off of the current, and the sensor will adjust the measurement parameters according to the current environment to provide more accurate feedback information. Step 1 includes:

[0130] Step 11: Assume that the length of the catenary area is L meters, the width is W meters, and the effective coverage radius of a single local Internet of Things node is R meters. Then the number of local Internet of Things nodes N required can be calculated by the following formula:

[0131]

[0132] Among them, L represents the length of the catenary area, W represents the width of the catenary area, and the range can be between several kilometers and dozens of kilometers; R represents the effective coverage radius of the local Internet of Things node, generally in the range of 10 to 50 meters. The total number of local Internet of Things nodes required can be estimated through the proportional relationship between the area of the region and the coverage area of a single local Internet of Things node to ensure the full coverage of the local Internet of Things nodes and ensure that there are enough sensor nodes in the entire catenary area to cover all key points.

[0133] For example, on a 30-kilometer-long and 0.5-kilometer-wide catenary line, if the coverage radius of a single local Internet of Things node is 30 meters, about 336 local Internet of Things nodes need to be arranged to achieve effective monitoring.

[0134] Step 12: Continuously monitor the connection status of each local Internet of Things node and dynamically adjust the density of local Internet of Things nodes. As environmental conditions change or node failures occur, the system must have self-diagnosis and recovery capabilities to maintain normal operation. The specific approach is to regularly check the working conditions of each node, including aspects such as power, communication quality, and data processing performance. When the signal strength in a certain area is found to be lower than the predetermined threshold or there is a disconnection, standby local Internet of Things nodes (i.e., redundant local Internet of Things nodes) in the vicinity will be automatically activated to join the working state. At the same time, some local Internet of Things nodes with lighter workloads will be temporarily transferred to that area for reinforcement, so as to achieve the purpose of balancing resource distribution.

[0135] In a specific application scenario, assume that some local Internet of Things nodes along a certain railway line are detected to have unstable transmissions due to electromagnetic interference. Additional local Internet of Things nodes can be immediately mobilized from adjacent areas to supplement, ensuring that the entire system can still operate normally without being affected.

[0136] Step 13: Add redundant local Internet of Things nodes to ensure reliable data transmission in local areas. Since external interference or internal failures may cause individual local Internet of Things nodes to fail, it is crucial to plan and configure sufficient backup local Internet of Things nodes in advance to ensure the continuous availability of the system. For example, several standby local Internet of Things nodes are added at regular intervals according to actual needs as a reserve force, so that even in case of emergencies, it will not have a major impact on the overall monitoring network. Redundant local Internet of Things nodes can not only enhance the stability and robustness of the system but also provide more flexibility for future expansion.

[0137] By adopting multi-hop routing and redundant transmission mechanisms, the reliability of data transmission in a high electromagnetic interference environment can be enhanced. Multi-hop routing allows multiple sensor nodes to relay data to each other, forming a more robust communication network. Redundant transmission is to add redundant packets during data transmission. When the data on the main path is lost or damaged, the redundant packets can timely supplement the missing information. Specifically, when a high-speed train passes through a high electromagnetic interference area, these mechanisms can ensure the integrity and accuracy of the catenary status data, and maintain the stability of the system even if local communication links fail.

[0138] Step 2: Optimize the node layout through a self-organizing algorithm to ensure seamless coverage. This step is to overcome physical obstacles that may occur during actual deployment, such as support columns or buildings, which may cause uneven signal coverage or form dead zones. The self-organizing algorithm will dynamically adjust the node positions according to the actual terrain and environmental data, so that the optimal layout of each node can maximize the coverage rate while minimizing energy consumption.

[0139] Specifically, the genetic algorithm can also be introduced to optimize the node position arrangement, ensuring seamless signal transmission even in scenarios with complex obstacles, and further enhancing the overall reliability and efficiency of the system.

[0140] Step 3: Install environmental sensors, i.e., sensor nodes, on key nodes to monitor temperature and humidity in real time; adjust the transmission power of the sensor nodes according to the real-time monitoring data. These sensors can provide accurate data under various environmental conditions to ensure that the system is always in the optimal working state; calculate the optimal transmission power P through the following formula:

[0141] P = a × T + b × H + c

[0142] where T represents temperature, H represents humidity, and a, b, and c are constants; when P is greater than the preset upper limit of the safe transmission power, reduce the transmission power. Step 3 includes:

[0143] Step 31: Install environmental sensors. Specifically, install environmental sensors at key nodes of the railway catenary, such as substations, cable joints, and support structures. These sensors can monitor the temperature and humidity changes in the surrounding environment in real time to ensure that the system can work properly in different environments. For example, the sensors are installed on the main support poles of the railway catenary and can monitor the temperature range from -40°C to 70°C and the relative humidity range from 0% to 100%.

[0144] Step 32: Adjust the working frequency and transmission power of the sensor nodes according to the real-time monitoring data. The temperature and humidity data collected by the sensors will be transmitted to the central control unit, and the central control unit will dynamically adjust the working frequency and transmission power of the sensor nodes according to these data. This helps to optimize energy efficiency and reduce unnecessary energy consumption. Specifically, if it is detected that the temperature is too high or the humidity is too high, the system may automatically reduce the working frequency and transmission power to prevent the equipment from overheating or being damaged.

[0145] Step 33: Calculate the optimal transmission power through the formula P = a × T + b × H + c. The constants a and b usually take positive values, indicating the positive impact of temperature and humidity on the transmission power, and c usually takes a positive value as a supplement to the basic transmission power. The values of these parameters are optimized according to the actual application conditions and are usually determined through experiments or simulations. For example, assume that through experiments, a = 0.5, b = 0.3, and c = 50. If the temperature T is 25°C and the humidity H is 60%, then the calculated optimal transmission power P is: P = 0.5 × 25 + 0.3 × 60 + 50 = 80.5 watts.

[0146] When P is greater than the preset upper limit of the safe transmission power, reduce the transmission power. The system sets an upper limit of the safe transmission power, such as 100 watts, to ensure the safety of the device under extreme conditions. If the calculated optimal transmission power exceeds this upper limit, the system will automatically reduce the transmission power to the upper limit value. This is done to avoid device overload or damage and ensure the stability and safety of the system. For example, under extreme high temperature or high humidity conditions, the calculated optimal transmission power may exceed 100 watts. At this time, the system will immediately adjust the transmission power to 100 watts to ensure that no device failure occurs. This can not only improve the energy efficiency of the system but also enhance the reliability and safety of the system, especially suitable for the complex and changeable electrified railway environment. Through real-time monitoring and intelligent adjustment, ensure that each sensor node operates in the most efficient and safe state.

[0147] Step 4: Evaluate the coverage rate of different areas of the catenary, determine the stability score, and adjust the deployment density of the local Internet of Things nodes according to the stability score; the coverage rate C is calculated by the following formula: N S represents the total length of the catenary that has been covered, that is, the total length of the catenary covered by the local Internet of Things nodes; N t represents the total length of the catenary.

[0148] Assign a weight value W to each local Internet of Things node, and the weight value W is calculated by the following formula:

[0149] W = a·log(Nn + 1)+b·D

[0150] where, Nn represents the number of neighbors of the local Internet of Things node; D represents the distance between the local Internet of Things node and the farthest boundary point; the farthest boundary point can represent the end position of the effective power supply range of the catenary; a and b represent adjustment coefficients; calculate the importance score of each local Internet of Things node; sort the local Internet of Things nodes based on the importance score; dynamically adjust the deployment density of the local Internet of Things nodes with higher importance scores.

[0151] The calculation of the weight value comprehensively considers the number of neighboring nodes of the local Internet of Things node and the distance from the boundary point, which helps to more accurately evaluate the importance of each local Internet of Things node.

[0152] Furthermore, assigning a weight value W to each local Internet of Things node can specifically include the following steps:

[0153] Collect the historical communication data of each local Internet of Things node;

[0154] Statistically analyze the average number of successful communications S i and the average transmission delay T i ;

[0155] Calculate the stability score of local Internet of Things nodes based on the average number of successful communications and the average transmission delay:

[0156]

[0157] Add the stability score to the weight value calculation: W = a·log(Nn + 1)+b·D + c·S, where c is the stability coefficient.

[0158] The number of successful communications and the transmission delay can be introduced to improve the comprehensiveness of the weight value calculation and further enhance the rationality of the deployment of local Internet of Things nodes.

[0159] Furthermore, adding the stability score to the weight value calculation specifically includes the following steps:

[0160] Adjust the positions of local Internet of Things nodes according to the stability score S;

[0161] Optimize the network topology structure to reduce the influence of high electromagnetic interference;

[0162] Implement multi-hop routing and redundant transmission strategies to enhance the robustness of the network;

[0163] Regularly monitor and update the stability scores of local Internet of Things nodes, and dynamically adjust the network configuration.

[0164] Dynamically adjusting the network configuration, combined with multi-hop routing and redundant transmission, can significantly improve the data transmission reliability of the network in a high electromagnetic interference environment.

[0165] Regularly monitoring and updating the stability scores of local Internet of Things nodes can specifically include the following steps:

[0166] Timely collect the communication status data of each local Internet of Things node;

[0167] Calculate the average stability score of each local Internet of Things node in the recent period: T represents the length of the time window.

[0168] An intelligent allocation method based on high-density local Internet of Things nodes and wide-area Internet of Things nodes can solve the problem of data acquisition blind spots caused by uneven distribution density of sensors in the monitoring of electrified railway catenaries. According to the specific structure of the catenary, the line direction, and the needs of key monitoring areas, the optimal sensor layout plan is automatically calculated to ensure that the sensors can cover all important nodes and areas. Specific route information and demand parameters can be input through the management system, and the system will automatically output the optimal positions and quantities of the sensors. This can not only improve the comprehensiveness of monitoring but also reduce the risks brought by improper manual layout.

[0169] Step 5: Increase the density of local Internet of Things nodes in important sections and optimize the collaboration between local Internet of Things nodes. That is, increase the density of monitoring nodes in important sections to ensure the comprehensiveness of data collection; use distributed algorithms to optimize the collaboration between nodes and improve the robustness of the overall system. Step 5 includes:

[0170] Step 51: Increase the density of local Internet of Things nodes in important sections to ensure the comprehensiveness of data collection. This means deploying more local Internet of Things nodes in key areas of the railway, such as tunnel entrances, near stations, etc., to collect data more intensively, reduce monitoring blind spots, improve the timeliness and accuracy of fault detection, and ensure higher reliability of the entire system at critical moments.

[0171] Step 52: Use distributed algorithms to optimize the collaboration between nodes and improve the robustness of the overall system. Through efficient communication and data sharing between local Internet of Things nodes, dynamically adjust the working mode of local Internet of Things nodes to ensure that the system can still operate normally when a single local Internet of Things node or multiple local Internet of Things nodes fail. The distributed algorithm can automatically detect and bypass faulty local Internet of Things nodes to ensure the continuity and integrity of data transmission, and improve the stability and anti-interference ability of the system.

[0172] Step 53: Use the following formula to determine the overall collaboration efficiency R of local Internet of Things nodes:

[0173] R = h·N + i

[0174] Where, N represents the number of local Internet of Things nodes, h represents the effective data processing capacity of each local Internet of Things node per unit time, and i represents the basic collaboration factor, indicating the basic collaboration effect between local Internet of Things nodes. Usually, the range of h is from 0.1 to 1.0, indicating the level of data processing capacity of each node; the range of i is from 0 to 0.5, indicating the contribution degree of the basic collaboration between local Internet of Things nodes. The choice of the optimal value depends on the specific application environment and requirements. For example, in a railway system that requires high real-time performance, h = 0.9 and i = 0.3 can be selected, which can not only ensure the effective processing capacity of each local Internet of Things node but also enhance the collaboration effect between local Internet of Things nodes.

[0175] Suppose that on an important railway section, it is necessary to deploy multiple local Internet of Things nodes to monitor the health status of the catenary. First, the number of nodes will be increased on the tracks near the tunnel entrances and stations to form a high-density monitoring area. For example, a node will be deployed every 50 meters. Then, the working states of these local Internet of Things nodes will be dynamically adjusted through a distributed algorithm. When a certain local Internet of Things node fails, other local Internet of Things nodes can fill in the position in time to ensure that data transmission is not interrupted. For example, using R = 0.9·20 + 0.3, it is calculated that R = 18.3, which means that there are 20 local Internet of Things nodes on this section, the data processing capacity of each local Internet of Things node per unit time is 0.9, and the basic cooperation factor between local Internet of Things nodes is 0.3, and the overall cooperation efficiency is calculated to be 18.3. This shows that local Internet of Things nodes can not only process data efficiently, but also cooperate well to ensure the stable operation of the system, and can ensure the high reliability and high efficiency of the railway catenary system, especially for real-time monitoring and fault diagnosis in complex environments.

[0176] Step 6: Select the best path for data transmission according to the energy state and signal strength of the local Internet of Things nodes; balance the workload of each local Internet of Things node through dynamic task allocation; regularly evaluate the layout of the local Internet of Things nodes and adjust it in time to adapt to environmental changes; adopt a self-healing mechanism to automatically find an alternative path when a local Internet of Things node fails. Step 6 includes:

[0177] Step 61: Selecting the best path for data transmission according to the energy state and signal strength of the local Internet of Things nodes means dynamically evaluating the current energy level and signal quality of each local Internet of Things node in the system, and based on this, determining the optimal path for data from the sensor node to the central processing unit. Specifically, by introducing an energy consumption model and a signal attenuation model, a path selection algorithm can be established, such as: P = E / S, where E represents the remaining energy of the local Internet of Things node, usually in the range of 0 to 1 (representing full charge and no charge states), and S represents the signal strength, generally in the range of -90dBm to -30dBm. The larger the calculated P value of the formula, the lower the cost of data transmission of this local Internet of Things node. Therefore, the node with a larger P value should be selected first. For example, in the monitoring system of the electrified railway catenary, if a high-density local Internet of Things node in a certain section is in a low-power and weak-signal state, the system will automatically avoid this high-density local Internet of Things node and instead select a high-power and high-signal-strength high-density local Internet of Things node in another path.

[0178] Step 62: Balance the workloads of each local Internet of Things node through dynamic task allocation, that is, adjust the task volume of each local Internet of Things node according to the real-time working status and load conditions of each local Internet of Things node, so as to avoid the overall efficiency decline caused by overloading of some local Internet of Things nodes. For example, in the catenary fault monitoring system, when a fault occurs in a certain area, the system will preferentially distribute high-frequency monitoring tasks to nearby local Internet of Things nodes with sufficient energy and stable performance, so as to ensure the efficient operation of the entire network.

[0179] Step 63: Regularly evaluate the layout of local Internet of Things nodes and adjust it in time to adapt to environmental changes, which means periodically evaluating the distribution of local Internet of Things nodes, communication effects and overall performance in the network. Once it is found that the layout is unreasonable or some local Internet of Things nodes perform poorly, corresponding measures should be taken immediately for adjustment. For example, in the monitoring of the catenary of electrified railways, due to factors such as terrain and buildings on both sides of the track, the signal transmission effect of some local Internet of Things nodes may be poor. After regular evaluation, the positions of these local Internet of Things nodes can be redeployed or optimized according to the actual situation to ensure smooth communication throughout the network.

[0180] Step 64: Adopt a self-healing mechanism to automatically find an alternative path when a local Internet of Things node fails, which means that when a certain local Internet of Things node cannot work properly due to a failure, the system can automatically detect it and re-plan the routing to ensure that data transmission is not affected by single-point failure. For example, if a group of high-density local Internet of Things nodes for monitoring the damage of catenary insulators suddenly lose contact, the system will quickly identify this problem and use the normally working high-density local Internet of Things nodes around as relay stations to continue sending and receiving data, so as to maintain the stability and continuity of the system. This self-healing function not only improves the robustness of the system, but also effectively extends the service life of the overall network.

[0181] Step 7: In areas with a high density of sensor nodes, adopt a short-distance and high-frequency communication mode; in areas with a low density of sensor nodes, adopt a long-distance and low-frequency communication mode; specifically, the optimal transmission time T can be obtained according to the following formula:

[0182] T = k × D

[0183] where D represents the distance between sensor nodes, and k is a constant reflecting the signal attenuation coefficient; if T is less than or equal to the predetermined maximum transmission time, the current communication mode is selected; otherwise, the communication parameters need to be adjusted or a new communication mode needs to be selected. Step 7 includes:

[0184] Step 71: In areas with a high density of sensor nodes, adopt a short-distance and high-frequency communication mode. A high density of sensor nodes usually means that the average distance between individual sensor nodes is small, and the number of sensor nodes per unit area is large. Adopting a short-distance and high-frequency communication mode is to ensure efficient data transmission in a high-density environment, reduce interference, and improve the communication success rate. Suppose a large number of sensor nodes are installed in a certain station area to monitor parameters such as the voltage, current, and temperature of the catenary. These sensor nodes are close to each other. To ensure real-time performance and reliability, a high-frequency data transmission mode is selected.

[0185] Step 72: In areas with a low density of sensor nodes, adopt a long-distance and low-frequency communication mode. A low density of sensor nodes usually means that the sensor nodes are far apart from each other, and the number of sensor nodes per unit area is small. Adopting a long-distance and low-frequency communication mode is to extend the communication range, reduce power consumption, and at the same time reduce the communication frequency to save resources. For example, in a remote railway section, there may be only a few sensor nodes distributed far apart. A low-frequency communication method can be selected to save energy and ensure the effectiveness of long-distance communication.

[0186] Step 73: Use the formula T = k×D to calculate the optimal transmission time. The value range of T is generally from a few milliseconds to several seconds, and the specific optimal value depends on the requirements of the specific application scenario and the hardware performance. D usually varies between a few meters and several hundred meters. k is usually an empirical constant obtained through experimental calibration, indicating the attenuation degree of the signal during propagation, and the typical value range is between 0.01 and 0.1. Using the formula T = k×D can quantify the relationship between the transmission time and the sensor node spacing, as well as the impact of signal attenuation on the transmission time, so as to provide a basis for selecting a suitable communication mode.

[0187] When T is less than or equal to the predetermined maximum transmission time, select this communication mode. It can ensure that the selected communication mode in different environments can not only meet the transmission requirements but also be completed within a reasonable transmission time. For example, in a specific railway catenary monitoring system, if the maximum transmission time is 5 seconds and the T calculated by the formula is 4 seconds, it means that the current communication mode is feasible and can be selected and applied. If the calculated T is greater than 5 seconds, the parameters need to be adjusted or other more suitable communication modes need to be selected. It can dynamically adjust the communication mode according to different node densities and actual communication requirements, thereby improving the efficiency and reliability of the entire system.

[0188] Deploying a dual - band communication module on wide - area Internet of Things nodes can improve the stability of data transmission in remote areas and reduce latency. The dual - band communication module can switch between different frequency bands to avoid potential communication quality problems in a single frequency band. For example, at a specific remote monitoring station, when the signal of the primary frequency band is weak or interfered with, the module can quickly switch to another frequency band to ensure that the real - time transmission of data is not affected. This technology greatly improves the monitoring effect in remote areas and reduces transmission latency.

[0189] Step 8: Select a suitable communication frequency band according to the degree of environmental interference; avoid transmission errors caused by interference through multi - band switching; specifically, the optimal communication frequency band F can be calculated using the following formula:

[0190] F = d·I + e′

[0191] Where I represents the interference intensity, and d and e′ are constants, usually preset in actual applications; when F exceeds the preset safe frequency band range, switch to the backup frequency band. Step 8 includes:

[0192] Step 81: Select a suitable communication frequency band according to the degree of environmental interference. Specifically, the system will detect the degree of environmental interference on different frequency bands, evaluate which frequency bands have higher signal quality, and thus determine the most suitable communication frequency band to reduce signal transmission errors.

[0193] Step 82: Avoid transmission errors caused by interference through multi - band switching. When the detected interference intensity of the current frequency band increases, the system will automatically switch to another frequency band with a lower interference intensity to ensure the stability and reliability of data transmission. This process is usually real - time and dynamic, and can quickly respond to changing environmental conditions.

[0194] Calculate the optimal communication frequency band using the formula F = d·I + e′. For example, if the interference intensity I on a specific frequency band is high, the coefficient d in the formula may be set to a negative value to reduce the selection probability of that frequency band; while e′ can be used to adjust the offset of the entire formula to make it more adaptable to specific environmental requirements. The meaning of the formula is to comprehensively consider the interference intensity and other environmental factors to select the frequency band with the optimal transmission quality.

[0195] When F exceeds the preset safe frequency band range, switch to the backup frequency band. For example, if the calculated frequency band value F exceeds the safe range of the system, such as being higher than 10 GHz, the system will immediately switch to one or more preset backup frequency bands, which have been strictly tested and verified in advance to ensure their stability and reliability. This can ensure the continuous operation of the system and the accurate transmission of data when environmental conditions deteriorate.

[0196] For example, assume that multiple local Internet of Things nodes within a certain section of the catenary area of an electrified railway are distributed at different geographical locations, and these local Internet of Things nodes need to transmit the collected data to the central control system in real time. During this process, local Internet of Things node A may detect that the 2.4GHz frequency band it is currently using is strongly interfered with by a nearby wireless router, resulting in frequent errors in data transmission. At this time, local Internet of Things node A calculates the optimal frequency band according to the formula F = -0.1·I + 3.5. Assuming that the detected interference intensity I is 50dBm, then F = -0.1·50 + 3.5 = -1.5, which means that the 2.4GHz frequency band is no longer suitable for use. Local Internet of Things node A then switches to the pre-set 5GHz backup frequency band to ensure the smooth transmission of data.

[0197] Step 9: Implement the data preprocessing algorithm to filter out noise and abnormal data; perform real-time data analysis through the edge computing node to reduce the load on the central management platform; determine the computing resource requirement C using the following formula:

[0198] C = f·L + g′

[0199] where L represents the data volume, and f and g′ are constants; when C is greater than or equal to the predetermined computing resource threshold, increase the computing resources. Step 9 includes:

[0200] Step 91: Implement the data preprocessing algorithm to filter out noise and abnormal data. Remove inaccurate or irrelevant information from the collected data to ensure the accuracy and reliability of subsequent analysis. For example, in the monitoring of the catenary of an electrified railway, the edge computing node may be affected by electromagnetic interference or hardware failures, generating incorrect measurement data. By implementing data preprocessing algorithms such as mean filtering or median filtering, these noises can be effectively excluded.

[0201] Step 92: Perform real-time data analysis through the edge computing node to reduce the load on the central management platform. This step uses the computing devices distributed at the edge of the network for preliminary data processing, reducing the burden of data transmission to the central server. Specifically, in the railway catenary monitoring system, each local Internet of Things node can be equipped with a microprocessor with computing capabilities to perform instant analysis on the collected data, identify key events and respond in a timely manner. For example, after detecting abnormal vibrations of the catenary, the alarm mechanism is immediately triggered.

[0202] Step 93: Calculate the required computing resources using the formula C = f·L + g′. In this scenario, f and g′ respectively reflect the computing complexity per unit of data volume and the basic resource consumption. Generally, the value range of f is from 0.01 to 0.1, and the optimal value is 0.05; the value range of g′ is from 10 to 100, and the optimal value is 50. The function of this formula is to dynamically evaluate the amount of computing resources currently required by the system. For example, when the local Internet of Things nodes in a certain railway area detect frequent vibration signals caused by a large number of trains passing by, due to the sharp increase in data volume, the computing resource requirements of the system also increase accordingly.

[0203] When the value of C is greater than or equal to the predetermined computing resource threshold, the system will automatically increase the computing resources. The threshold here is to prevent data processing delays or system overloads caused by insufficient resources. If a certain edge computing node of the above railway catenary monitoring system detects that the computing demand exceeds expectations (such as C > 200), the system will immediately activate the standby computing resources, such as adding more local Internet of Things nodes or enhancing the computing capabilities of existing local Internet of Things nodes, to ensure the normal operation of the system.

[0204] In summary, Step 9 can not only optimize the resource utilization efficiency but also improve the system's response speed to emergencies, and is applicable to application scenarios that require high reliability and real-time performance.

[0205] Step 10: The monitoring data collected by the sensor nodes is transmitted to the central management platform via the corresponding local Internet of Things nodes.

[0206] A distributed data processing and edge computing architecture can be deployed on the central management platform to further improve the ability to process big data under complex network architectures. This architecture distributes data processing tasks to multiple edge computing nodes, reducing the risk of single-point failures. At the same time, it uses edge computing to process part of the data near the data source, reducing the burden of data transmission. In a practical application, when a large amount of monitoring data is transmitted from each local Internet of Things node to the central management platform, the edge computing unit will initially process this data, screen out important information, relieve the processing pressure on the central server, and thus improve the operating efficiency and response speed of the entire system. It not only solves multiple problems in the monitoring of electrified railway catenaries but also can improve the performance and reliability of the overall system.

[0207] Such as Figure 4As shown in the figure, the present application also provides an application example of a catenary monitoring system, which can be divided into: cloud / data center, central control layer, wide-area Internet of Things layer, edge computing layer, local Internet of Things layer, and physical layer; the cloud / data center includes: a central management platform, the central control layer includes: a central control system, the wide-area Internet of Things layer includes: wide-area Internet of Things nodes, the edge computing layer includes: edge computing nodes, and the local Internet of Things layer includes: local Internet of Things nodes; the physical layer includes: B-value sensors, clamp temperature sensors, catenary vibration sensors, image collectors, icing sensors, etc. The functions realized by the combination of the central management platform and the central control system can be equivalent to the functions realized by the above-mentioned catenary monitoring device.

[0208] From a software perspective, in order to achieve comprehensive coverage of the entire catenary in the monitoring scope and ensure the safety and reliability of railway operation, the present application provides an embodiment of a catenary monitoring device for implementing all or part of the content in the above-mentioned catenary monitoring method. Refer to Figure 5 , the catenary monitoring device specifically includes the following contents:

[0209] A first acquisition module 01, configured to acquire the effective coverage radius of a single local Internet of Things node, the length, width, and environmental data of a target catenary area, and determine the number of local Internet of Things nodes in the target catenary area according to the effective coverage radius, the length, and the width.

[0210] An arrangement module 02, configured to determine the arrangement positions of each local Internet of Things node according to the environmental data and the number of local Internet of Things nodes, complete the arrangement of each local Internet of Things node in the target catenary area, and sensor nodes are evenly arranged at the positions of each local Internet of Things node.

[0211] A second acquisition module 03, configured to acquire the remaining energy and signal strength of each local Internet of Things node, and determine the local Internet of Things node corresponding to each sensor node according to the remaining energy and signal strength of each local Internet of Things node.

[0212] A monitoring module 04, configured to receive the catenary status information sent by each sensor node via its corresponding local Internet of Things node, and complete the monitoring of the target catenary area according to the catenary status information.

[0213] In one embodiment, the monitoring module is further configured to:

[0214] Acquire the actual distance between each sensor node and its corresponding local Internet of Things node;

[0215] Determine the optimal transmission time corresponding to the sensor node according to the preset signal attenuation coefficient and the actual distance between each sensor node and its corresponding local Internet of Things node;

[0216] Determine whether there is a first abnormal sensor node among the sensor nodes whose corresponding optimal transmission time is greater than the preset maximum transmission time. If so, adjust the communication mode of the first abnormal sensor node. The communication modes include: short-distance high-frequency and long-distance low-frequency communication modes.

[0217] In one embodiment, the monitoring module is further configured to:

[0218] Obtain the temperature data and humidity data collected by each sensor node;

[0219] Determine the optimal transmission power of the sensor node according to the temperature data and humidity data collected by each sensor node;

[0220] Determine whether there is a second abnormal sensor node among the sensor nodes whose optimal transmission power is greater than the preset upper limit of the safe transmission power. If so, adjust the transmission power of the second abnormal sensor node to the preset upper limit of the safe transmission power.

[0221] In one embodiment, the monitoring module is further configured to:

[0222] Obtain the interference degree information of each local Internet of Things node; determine the optimal communication frequency band of the local Internet of Things node according to the interference degree information of each local Internet of Things node;

[0223] Determine whether there is an abnormal local Internet of Things node among the local Internet of Things nodes whose optimal communication frequency band is greater than the preset safe frequency band range. If so, switch the communication frequency band of the abnormal local Internet of Things node to the preset backup frequency band.

[0224] In one embodiment, the monitoring module is further configured to:

[0225] Obtain the data volume of each local Internet of Things node, and determine the computing resource requirements of the local Internet of Things node according to the data volume of each local Internet of Things node;

[0226] Determine whether there is an abnormal local Internet of Things node among the local Internet of Things nodes whose computing resource requirements are greater than the computing resource threshold. If so, increase the preset computing resources of the abnormal local Internet of Things node.

[0227] In one embodiment, the catenary monitoring device further includes:

[0228] A third acquisition module, configured to acquire the length of the target catenary region and the total length of the local Internet of Things nodes covered in the target catenary region;

[0229] A coverage rate determination module, configured to determine the coverage rate of the target catenary region according to the length of the target catenary region and the total length of the local Internet of Things nodes covered in the target catenary region;

[0230] An importance score determination module, configured to, if the coverage rate is greater than or equal to a coverage rate threshold, acquire the number of neighbors of each local Internet of Things node and the distance from the farthest boundary point, and determine the importance score of each local Internet of Things node according to the number of neighbors of each local Internet of Things node and the distance from the farthest boundary point;

[0231] A deployment density adjustment module, configured to complete the adjustment of the deployment density of the local Internet of Things nodes according to the importance scores of the respective local Internet of Things nodes.

[0232] In one embodiment, the importance score determination module includes:

[0233] A stability score determination unit, configured to acquire the average number of successful communications and the average transmission delay of each local Internet of Things node, and determine the stability score of the local Internet of Things node according to the average number of successful communications and the average transmission delay of each local Internet of Things node;

[0234] An importance score determination unit, configured to determine the importance score of each local Internet of Things node according to the number of neighbors of each local Internet of Things node, the distance from the farthest boundary point, and the stability score.

[0235] In one embodiment, the monitoring module is further configured to:

[0236] Acquire the effective data processing capacity of each local Internet of Things node per unit time, and determine the cooperation efficiency among the local Internet of Things nodes according to the number of local Internet of Things nodes, the effective data processing capacity of each local Internet of Things node per unit time, and a preset basic cooperation factor;

[0237] Determine whether the cooperation efficiency is greater than a cooperation efficiency threshold, and if so, determine that the cooperation status of the respective local Internet of Things nodes is normal.

[0238] The embodiments of the catenary monitoring device provided in this specification can specifically be used to execute the processing procedures of the embodiments of the above catenary monitoring method, and its functions will not be elaborated herein. For details, reference may be made to the detailed description of the embodiments of the above catenary monitoring method.

[0239] Figure 6 The following is a schematic physical structure diagram of an electronic device provided by an embodiment of the present invention, as Figure 6As shown in the figure, the electronic device includes: a memory 601, a processor 602, and a computer program stored on the memory 601 and executable on the processor 602. When the processor 602 executes the computer program, the following method is implemented:

[0240] Obtain the effective coverage radius of a single local Internet of Things node, the length, width, and environmental data of the target catenary area, and determine the number of local Internet of Things nodes in the target catenary area according to the effective coverage radius, the length, and the width.

[0241] Determine the layout positions of the respective local Internet of Things nodes according to the environmental data and the number of local Internet of Things nodes, complete the layout of the respective local Internet of Things nodes in the target catenary area, and sensor nodes are evenly arranged at the positions of the respective local Internet of Things nodes.

[0242] Obtain the remaining node energy and signal strength of each local Internet of Things node, and determine the local Internet of Things node corresponding to each sensor node according to the remaining node energy and signal strength of each local Internet of Things node.

[0243] Receive the catenary status information sent by each sensor node via its corresponding local Internet of Things node, and complete the monitoring of the target catenary area according to the catenary status information.

[0244] This embodiment discloses a computer program product, which includes a computer program. When the computer program is executed by a processor, the following method is implemented:

[0245] Obtain the effective coverage radius of a single local Internet of Things node, the length, width, and environmental data of the target catenary area, and determine the number of local Internet of Things nodes in the target catenary area according to the effective coverage radius, the length, and the width.

[0246] Determine the layout positions of the respective local Internet of Things nodes according to the environmental data and the number of local Internet of Things nodes, complete the layout of the respective local Internet of Things nodes in the target catenary area, and sensor nodes are evenly arranged at the positions of the respective local Internet of Things nodes.

[0247] Obtain the remaining node energy and signal strength of each local Internet of Things node, and determine the local Internet of Things node corresponding to each sensor node according to the remaining node energy and signal strength of each local Internet of Things node.

[0248] Receive the catenary status information sent by each sensor node via its corresponding local Internet of Things node, and complete the monitoring of the target catenary area according to the catenary status information.

[0249] This embodiment provides a computer-readable storage medium storing a computer program, which when executed by a processor implements the following method:

[0250] Obtain the effective coverage radius of a single local Internet of Things (IoT) node, the length, width, and environmental data of a target catenary area, and determine the number of local IoT nodes in the target catenary area according to the effective coverage radius, the length, and the width;

[0251] Determine the layout positions of the respective local IoT nodes according to the environmental data and the number of local IoT nodes, complete the layout of the respective local IoT nodes in the target catenary area, and sensor nodes are evenly arranged at the positions of the respective local IoT nodes;

[0252] Obtain the remaining energy and signal strength of each local IoT node, and determine the local IoT node corresponding to each sensor node according to the remaining energy and signal strength of each local IoT node;

[0253] Receive the catenary status information sent by each sensor node via its corresponding local IoT node, and complete the monitoring of the target catenary area according to the catenary status information.

[0254] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0255] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more flows or multiple flows and / or blocks Figure 1 one or more blocks or multiple blocks.

[0256] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the functions specified in one process Figure 1 or processes and / or blocks Figure 1 specified in one block or multiple blocks.

[0257] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, such that a series of operational steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing steps for implementing the functions specified in one process Figure 1 or processes and / or blocks Figure 1 specified in one block or multiple blocks.

[0258] In the description of this specification, the descriptions with reference to the terms "one embodiment", "a specific embodiment", "some embodiments", "for example", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0259] The above-described specific embodiments further elaborate on the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A catenary monitoring method, characterized in that, Including: Obtain the effective coverage radius of a single local Internet of Things (IoT) node, the length, width, and environmental data of the target catenary area. Determine the number of local IoT nodes in the target catenary area according to the effective coverage radius, the length, and the width. Determine the layout positions of the respective local IoT nodes according to the environmental data and the number of local IoT nodes, and complete the layout of the respective local IoT nodes in the target catenary area. Sensor nodes are evenly arranged at the positions of the respective local IoT nodes. Obtain the remaining energy and signal strength of each local IoT node. Determine the local IoT node corresponding to each sensor node according to the remaining energy and signal strength of each local IoT node. Receive the catenary status information sent by each sensor node via its corresponding local IoT node, and complete the monitoring of the target catenary area according to the catenary status information.

2. The catenary monitoring method according to claim 1, wherein Also including: Obtain the actual distance between each sensor node and its corresponding local IoT node. Determine the optimal transmission time corresponding to the sensor node according to the preset signal attenuation coefficient and the actual distance between each sensor node and its corresponding local IoT node. Judge whether there is a first abnormal sensor node among the respective sensor nodes whose corresponding optimal transmission time is greater than the preset maximum transmission time. If so, adjust the communication mode of the first abnormal sensor node. The communication mode includes short-distance high-frequency and long-distance low-frequency communication modes.

3. The catenary monitoring method according to claim 1, wherein Also including: Obtain the temperature data and humidity data collected by each sensor node. Determine the optimal transmission power of the sensor node according to the temperature data and humidity data collected by each sensor node. Judge whether there is a second abnormal sensor node among the respective sensor nodes whose optimal transmission power is greater than the preset upper limit of the safe transmission power. If so, adjust the transmission power of the second abnormal sensor node to the preset upper limit of the safe transmission power.

4. The catenary monitoring method according to claim 1, characterized in that Also including: Obtain the interference degree information of each local IoT node. Determine the optimal communication frequency band of the local IoT node according to the interference degree information of each local IoT node. Judge whether there is an abnormal local IoT node among the respective local IoT nodes whose optimal communication frequency band is greater than the preset safe frequency band range. If so, switch the communication frequency band of the abnormal local IoT node to the preset backup frequency band.

5. The catenary monitoring method according to claim 1, wherein, Also including: Obtain the data volume of each local IoT node, and determine the computing resource requirements of the local IoT node according to the data volume of each local IoT node. Judge whether there is an abnormal local IoT node among the respective local IoT nodes whose computing resource requirements are greater than the computing resource threshold. If so, increase the preset computing resources for the abnormal local IoT node.

6. The catenary monitoring method according to claim 1, characterized in that Also including: Obtain the length of the target catenary area and the total length of the local IoT nodes already covered in the target catenary area. Determine the coverage rate of the target catenary area according to the length of the target catenary area and the total length of the local IoT nodes already covered in the target catenary area. If the coverage rate is greater than or equal to the coverage rate threshold, obtain the number of neighbors of each local Internet of Things node and the distance from the farthest boundary point, and determine the importance score of each local Internet of Things node according to the number of neighbors of each local Internet of Things node and the distance from the farthest boundary point; Complete the adjustment of the deployment density of local Internet of Things nodes according to the importance scores of each local Internet of Things node.

7. The catenary monitoring method according to claim 6, wherein The determining the importance score of each local Internet of Things node according to the number of neighbors of each local Internet of Things node and the distance from the farthest boundary point includes: Obtain the average number of successful communications and average transmission delay of each local Internet of Things node, and determine the stability score of the local Internet of Things node according to the average number of successful communications and average transmission delay of each local Internet of Things node; Determine the importance score of each local Internet of Things node according to the number of neighbors, the distance from the farthest boundary point, and the stability score of each local Internet of Things node.

8. The catenary monitoring method according to claim 1, wherein, It further includes: Obtain the effective data processing capacity of each local Internet of Things node per unit time, and determine the cooperation efficiency between local Internet of Things nodes according to the number of local Internet of Things nodes, the effective data processing capacity of each local Internet of Things node per unit time, and a preset basic cooperation factor; Judge whether the cooperation efficiency is greater than the cooperation efficiency threshold. If so, determine that the cooperation status of each local Internet of Things node is normal.

9. An overhead contact line monitoring device, characterized in that, It includes: A first acquisition module, configured to acquire the effective coverage radius of a single local Internet of Things node, the length, width, and environmental data of the target catenary area, and determine the number of local Internet of Things nodes in the target catenary area according to the effective coverage radius, the length, and the width; An arrangement module, configured to determine the arrangement positions of each local Internet of Things node according to the environmental data and the number of local Internet of Things nodes, complete the arrangement of each local Internet of Things node in the target catenary area, and sensor nodes are evenly arranged at the positions of each local Internet of Things node; A second acquisition module, configured to acquire the remaining node energy and signal strength of each local Internet of Things node, and determine the local Internet of Things node corresponding to each sensor node according to the remaining node energy and signal strength of each local Internet of Things node; A monitoring module, configured to receive the catenary status information sent by each sensor node via its corresponding local Internet of Things node, and complete the monitoring of the target catenary area according to the catenary status information.

10. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the catenary monitoring method according to any one of claims 1 to 8.

11. A computer-readable storage medium having computer instructions stored thereon, characterized in that, When the instruction is executed by the processor, it implements the catenary monitoring method according to any one of claims 1 to 8.

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