Power transmission line ad hoc network internet-of-things monitoring method based on multi-dimensional association

Abstract

The invention relates to the technical field of power transmission lines, in particular to a power transmission line ad hoc network internet-of-things monitoring method based on multi-dimensional association, and the monitoring method comprises the steps: collecting the state data of a monitored object through each sensor for each edge computing network; each aggregation node aggregates monitored object state data collected by each sensor in the sensor network managed by the aggregation node and transmits the monitored object state data to the edge computing node; the edge computing node judges whether the monitored object in the corresponding edge computing network has a fault or not according to the monitored object state data gathered by each gathering node, and sends the fault information of the monitored object to the management subsystem through the electric power wide area network under the condition of judging that the monitored object has the fault; and the management subsystem receives the fault information of the monitored object of the edge computing node, determines a fault area on the power transmission line according to the fault information and carries out early warning. The power transmission line monitoring range is effectively expanded through the heterogeneous network, and the window period of system inspection is effectively filled up.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission lines, and in particular to a self-organizing network IoT monitoring method for power transmission lines based on multi-dimensional association. Background Art

[0002] The transmission lines of the power grid connect every corner of the city, and the safe operation of the three-span area at the intersection of the power grid is particularly important. The three-span area refers to the area where the power transmission lines cross high-speed railways, highways and other important facilities. The safety of the three-span area is directly related to the stable operation of high-speed railways and highways.

[0003] There are complex power grid inspection business scenarios such as large-scale mobile signal blind spots and mobile signal penetration failures in the three-span areas of the transmission network. Manual inspections or drone inspections have window periods, which cannot effectively guarantee real-time status monitoring of the transmission network.

[0004] In response to the needs of complex power grid inspection business scenarios such as large-scale mobile signal blind spots and mobile signal penetration failures in the transmission network, especially the three-span area, this application constructs a new wide-area integrated self-organizing network function and heterogeneous Internet gateway adaptation function model to improve the monitoring scope of the transmission network, especially the three-span lines, effectively fill the window period of system inspection (manual or unmanned), and ensure the real-time status of the entire power system. The Internet of Things perception lays a solid practical theoretical foundation, assists in significantly shortening the perception, warning and response time of faults, so as to further reduce the system's interruption time and power loss, thereby extending the inspection cycle and saving a lot of human resources or inspection equipment attendance rate. Summary of the invention

[0005] The embodiment of the present invention provides a transmission line self-organizing network Internet of Things monitoring method based on multi-dimensional association, which is used to realize the monitoring of the transmission line.

[0006] The present invention provides a transmission line self-organizing network Internet of Things monitoring method based on multi-dimensional association, the monitoring method is used for a transmission line monitoring system, the transmission line monitoring system includes a management subsystem and a monitoring subsystem; the monitoring subsystem includes multiple edge computing networks, each edge computing network is composed of an edge computing node, multiple aggregation nodes and multiple sensor networks; wherein the edge computing node is connected to multiple aggregation nodes, each aggregation node manages a sensor network; each sensor network includes multiple sensors; each sensor is arranged on a monitored object of the transmission line;

[0007] The monitoring method comprises:

[0008] For each edge computing network, each sensor collects the status data of the monitored object;

[0009] Each aggregation node aggregates the state data of the monitored objects collected by each sensor in the sensor network it manages and transmits it to the edge computing node;

[0010] The edge computing node determines whether there is a fault in the monitored object in the corresponding edge computing network based on the state data of the monitored object aggregated by each aggregation node. If it is determined that there is a fault, the fault information of the monitored object is sent to the management subsystem through the power wide area network;

[0011] The management subsystem receives the fault information of the monitored object of the edge computing node, and determines the fault area on the transmission line and issues an early warning based on the fault information.

[0012] Optionally, each sensor in the sensor network is connected to a corresponding aggregation node via a wireless transmission protocol.

[0013] Optionally, the aggregation node is divided into a dedicated node and a dynamic node; the monitoring method further includes:

[0014] The dedicated node sets a plurality of wireless transmission protocols according to the types of sensors in the sensor network managed by the dedicated node.

[0015] Optionally, the monitoring method further includes:

[0016] In the case that the dedicated node fails, the sensors in the sensor network managed by the dedicated node form a self-organizing network, and a dynamic node is selected from each sensor; the sensor network is managed by the dynamic node.

[0017] Optionally, the monitoring method further includes:

[0018] The edge computing node evaluates the data transmission status of each dedicated node in the edge computing network, and when the data transmission status does not reach a preset status threshold, triggers each sensor in the sensor network managed by the dedicated node to self-organize the network.

[0019] Optionally, the monitoring method further includes:

[0020] The edge computing node fails to receive the detected object status data gathered by the dedicated node during multiple data collection cycles, and determines that the dedicated node is physically damaged; or, in the current data collection cycle, determines that the data reception rate and / or data transmission rate of the dedicated node does not reach a preset threshold; and triggers each sensor in the sensor network managed by the dedicated node to self-organize.

[0021] Optionally, the monitoring method further includes:

[0022] At the end of each data collection cycle, the aggregation node records the amount of monitored object status data collected by the sensors in this cycle, determines the data reception rate based on the ratio of the amount of monitored object status data collected by the sensors in this cycle to the amount of monitored object status data collected by the sensors in the previous cycle, and sends the data reception rate to the edge computing node.

[0023] Optionally, the monitoring method further includes:

[0024] At the end of each data collection cycle, the edge computing node records the state quantity of the monitored object aggregated by the aggregation node received in this cycle, and determines the data sending rate based on the ratio of the state quantity of the monitored object aggregated by the aggregation node received in this cycle to the state data quantity of the monitored object aggregated by the aggregation node received in the previous cycle.

[0025] Optionally, the monitoring method further includes:

[0026] The edge computing node predicts the abnormal health status of the transmission line based on the monitored object status data aggregated by each aggregation node.

[0027] Optionally, the abnormal health status categories of the transmission lines include corrosion and aging, geological disasters, natural damages, and hidden dangers of animal and plant activities.

[0028] The transmission line self-organizing network Internet of Things monitoring method based on multi-dimensional association provided by the present invention is used for a transmission line monitoring system. The transmission line monitoring system includes a management subsystem and a monitoring subsystem. The monitoring subsystem includes multiple edge computing networks, each edge computing network is composed of an edge computing node, multiple aggregation nodes and multiple sensor networks. The monitoring method constructs a multi-dimensionally associated transmission line self-organizing network Internet of Things heterogeneous network by realizing the fusion of a power wide area network and a sensor network. In areas that cannot be reached by the power wide area network, such as three-span areas, the area is monitored through a sensor network. The monitoring range of the transmission line is effectively expanded through a heterogeneous network, and the window period of system inspection (manual or unmanned) is effectively filled, so as to ensure the real-time status perception of the entire power system, lay a solid practical theoretical foundation for Internet of Things perception, assist in significantly shortening the perception, warning and response time of faults, further reduce the interruption time of the transmission line and the power loss, thereby lengthening the manual inspection cycle and saving a large amount of human resources or the attendance rate of inspection equipment.

[0029] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0031] Figure 1 A flow chart of a method for monitoring a transmission line ad hoc network based on multi-dimensional association provided by an embodiment of the present invention;

[0032] Figure 2 A block diagram of a power transmission line monitoring system provided by an embodiment of the present invention;

[0033] Figure 3 A schematic diagram of an edge computing network provided for an embodiment of the present invention;

[0034] Figure 4 This is a diagram of the interaction between sensors and aggregation nodes provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0036] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are used only to facilitate the description of the present invention and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner. Expressions such as "first" and "second" are only used to distinguish technical terms.

[0037] The embodiment of the present invention provides a method for monitoring the Internet of Things of self-organizing networks of power transmission lines based on multi-dimensional association, such as Figure 1 As shown, the monitoring method comprises:

[0038] S101. For each edge computing network, each sensor collects state data of the monitored object;

[0039] S102. Each aggregation node aggregates the state data of the monitored object collected by each sensor in the sensor network it manages, and transmits it to the edge computing node;

[0040] S103. The edge computing node determines whether there is a fault in the monitored object in the corresponding edge computing network based on the state data of the monitored object aggregated by each aggregation node. If it is determined that there is a fault, the fault information of the monitored object is sent to the management subsystem through the power wide area network;

[0041] S104. The management subsystem receives the fault information of the monitored object of the edge computing node, and determines the fault area on the transmission line based on the fault information and issues an early warning.

[0042] The monitoring method is used in a transmission line monitoring system, such as Figure 2 , Figure 3 As shown, the transmission line monitoring system includes a management subsystem and a monitoring subsystem; the monitoring subsystem includes multiple edge computing networks, each edge computing network is composed of an edge computing node, multiple aggregation nodes and multiple sensor networks;

[0043] The edge computing node is connected to multiple aggregation nodes, each aggregation node manages a sensor network; each sensor network includes multiple sensors, each sensor is set on the monitored object of the transmission line, and is used to collect status data of the monitored object;

[0044] Each aggregation node is used to aggregate the state data of the monitored object collected by each sensor in the sensor network it manages, and transmit it to the edge computing node;

[0045] The edge computing node is used to determine whether there is a fault in the monitored object in the corresponding edge computing network according to the state data of the monitored object aggregated by each aggregation node, and if it is determined that there is a fault, send the fault information of the monitored object to the management subsystem through the power wide area network;

[0046] The management subsystem is used to receive fault information of the monitored object of the edge computing node, and determine the fault area on the transmission line and issue an early warning based on the fault information.

[0047] The embodiment of the present invention provides a transmission line self-organizing network Internet of Things monitoring method based on multi-dimensional association, the monitoring method is used for a transmission line monitoring system, the system includes a management subsystem and a monitoring subsystem; the monitoring subsystem includes multiple edge computing networks, each edge computing network is composed of an edge computing node, multiple aggregation nodes and multiple sensor networks, the monitoring method realizes the fusion of a power wide area network with a sensor network, builds a heterogeneous network, and monitors an area that cannot be reached by the power wide area network, such as a three-span area, through a sensor network, effectively expands the monitoring range of the transmission line through the heterogeneous network, effectively fills the window period of system inspection (manual or unmanned), ensures the real-time status perception of the entire power system, lays a solid practical theoretical foundation for Internet of Things perception, and assists in significantly shortening the perception, warning and response time of faults, further reduces the interruption time of the transmission line and the power loss, thereby lengthening the manual inspection cycle, saving a lot of human resources or the attendance rate of inspection equipment.

[0048] The power wide area network serves as the main communication access network, mainly using Ethernet (fiber optic communication) and 4G / 5G communication backbone networks for remote communications.

[0049] Each sensor network is mainly composed of multiple sensors, and the sensors in the sensor network mainly communicate locally. The sensors mainly monitor the transmission lines in the area that the wide area network cannot cover, collect the status data of the monitored objects, and transmit the status data of the monitored objects to their corresponding aggregation nodes. The aggregation node is mainly used to aggregate the status data of the monitored objects collected by the sensors within its range. The edge computing node is mainly used to analyze the status data of the monitored objects aggregated by the aggregation node, determine the status information of the transmission line where the sensor network is located, and store the status information of the transmission line, periodically transmit the status information to the power wide area network, and when the status information is a fault state, transmit the fault status data of the transmission line to the management subsystem through the power wide area network.

[0050] The objects monitored in power transmission lines can be roughly divided into two categories: the main equipment, such as lines, poles and towers, etc.; the other is auxiliary equipment, such as connecting wires, shock-absorbing hammers, ground wires and other ancillary facilities. Sensors can include: GNSS displacement monitoring sensors, which monitor the plane displacement and vertical displacement (settlement) of transmission towers in real time. Steel bar stress gauges, which monitor the stress state of steel bars in the tower foundation. Surface inclinometers, which monitor the tilt deformation of the tower body. Deep displacement sensors of the tower foundation rock and soil, which monitor the deep displacement of the tower foundation rock and soil. Temperature and humidity sensors, which monitor the temperature and humidity of key equipment such as transformers, cables, and generators. Pressure sensors, which monitor pressure changes in equipment such as pipelines, containers, and oil tanks, ensure that the equipment operates within the normal pressure range, and prevent equipment damage caused by abnormal pressure. Flow sensors: Monitor the flow of media such as water, gas, and oil in hydropower and thermal power plants to ensure accurate control of the production process. Liquid level sensors, which are used to monitor the liquid level in containers such as oil pumps, oil tanks, batteries, and pools, ensure that the liquid level is within a safe range to avoid overflow or drying up. Force sensors monitor the working status and load conditions of the generator set, as well as the tension of the transmission line and the stress of the tower to ensure the stable operation and safety of the equipment.

[0051] The transmission line monitoring system is equipped with multiple edge computing networks. Each edge computing network can set up multiple aggregation nodes according to the size of the local communication area. Through the communication links formed between the multiple aggregation nodes, the status data of the monitored objects collected by the sensors aggregated by each aggregation node is transmitted to the edge computing node, thereby further expanding the monitoring range of the transmission line.

[0052] In some embodiments, the sensor network has different types of sensors, and the sensors support different types of communication protocols, such as micropower wireless, Bluetooth, RS485, ZigBee, serial port, industrial Ethernet, WiFi, NB-IoT, etc. The aggregation node is used to solve the networking difficulties caused by different types of communication protocols, improve the efficiency of data transmission, and expand the capacity of the network.

[0053] The hardware part of the aggregation node can be composed of a low-power MCU (Microcontroller Unit), a wireless transceiver circuit, an ultra-low-power remote control circuit, a voltage regulator circuit, a clock module, and a display circuit. Each sensor in the sensor network is connected to the corresponding aggregation node through a wireless transmission protocol, and the sensors do not communicate with each other.

[0054] The data exchange process between sensors and sink nodes usually adopts the verification method. For example, in a sensor network, the sink node returns ACK (Acknowledge character) after receiving the state data of the monitored object collected by the sensor. Figure 3As shown in the figure, it is ensured that the data sent by the sensor as the sender can effectively and accurately reach the sink node as the receiver. When sending important data, the receiver needs to respond with an ACK to confirm the correct reception, indicating that the receiver has successfully received the information. If the data verification fails, it means that an error occurred during the data transmission process, and an ACK with data error needs to be replied. After the sensor resends the data and the receiver receives the data for the second time, the verification passes, and the correct ACK is replied to the sender. The interaction between the sink node and the edge computing node can also be confirmed by ACK.

[0055] In order to avoid the failure of the aggregation node to transmit sensor data to the edge computing node, ensure the sustainability of communication work, and enhance the fault tolerance of the network, in some embodiments, the aggregation node is divided into a dedicated node and a dynamic node. The dedicated node sets multiple wireless transmission protocols according to the sensor type in the sensor network it manages.

[0056] In some embodiments, the monitoring method further comprises:

[0057] In the event of a failure of the dedicated node, each sensor in the sensor network managed by the dedicated node conducts self-networking, and a dynamic node is selected from each sensor; the sensor network is managed by the dynamic node. Thus, the state data of the monitored object collected by each sensor in the sensor network can be aggregated through the dynamic node. The dynamic node can be selected based on the sensor with the strongest signal transmission energy intensity in the sensor network.

[0058] In some embodiments, the monitoring method further comprises:

[0059] For each edge computing network, the edge computing node evaluates the data transmission status of each aggregation node in the edge computing network. When the data transmission status does not reach the preset status threshold, it triggers each sensor in the sensor network managed by the dedicated node to self-organize and select dynamic nodes. The details are as follows:

[0060] In multiple data collection cycles, the edge computing node does not receive the state data of the detected object gathered by the dedicated node, and determines that the dedicated node is physically damaged; or, in the current data collection cycle, the data reception rate and / or data transmission rate of the dedicated node does not reach a preset threshold; the sensors in the sensor network managed by the dedicated node are triggered to self-organize and select dynamic nodes.

[0061] In detail, in each sensor network, the state data of the monitored object collected by each sensor is relatively fixed, and the positions of the sensor and the aggregation node are also relatively fixed. Therefore, in each data transmission cycle, the amount of data received by the aggregation node from each sensor is basically the same; similarly, the amount of data sent by the aggregation node to the edge computing node is also basically the same.

[0062] Determination of data reception rate: At the end of each data collection cycle, the aggregation node records the amount of monitored object status data collected by the sensors in this cycle, determines the data reception rate based on the ratio of the amount of monitored object status data collected by the sensors in this cycle to the amount of monitored object status data collected by the sensors in the previous cycle, and sends the data reception rate to the edge computing node.

[0063] Determination of data transmission rate: At the end of each data collection cycle, the edge computing node records the state quantity of the monitored object aggregated by the aggregation node received in this cycle, and determines the data transmission rate based on the ratio of the state quantity of the monitored object aggregated by the aggregation node received in this cycle to the state quantity of the monitored object aggregated by the aggregation node received in the previous cycle.

[0064] When the data transmission rate and / or the data reception rate does not reach 90%, the sensors of the aggregation node are triggered to form a self-organizing network.

[0065] In some embodiments, the aggregation node includes a networking aggregation node and a link aggregation node. The networking aggregation node is an aggregation node used to establish a communication link. The aggregation node other than the networking aggregation node in the edge computing network is called a link aggregation node.

[0066] After the edge computing network is running, due to the use status of each aggregation node, the amount of aggregated sensor data, etc., the reliability and availability of the aggregation node will often decrease, resulting in a decrease in the speed of aggregated data, affecting the performance of the entire network. In order to solve this problem, in some embodiments, the monitoring method also includes:

[0067] During the initial networking process of the edge computing network, the edge computing node uses the aggregation node with the strongest RSSI as the networking aggregation node. After the edge computing network is running, the network aggregation node is determined according to the RSSI, bandwidth, trouble-free operation time, and storage space size of each aggregation node according to the preset cycle. For example, the edge computing node evaluates the performance indicators of each aggregation node based on the bandwidth, trouble-free operation time, and storage space size of each aggregation node, and uses the aggregation node with the largest performance indicator as the networking aggregation node:

[0068] k=ε1W1+ε2W2+ε3W3+ε4W4

[0069] Wherein, ε1, ε2, ε4 and ε3 represent weights, the sum of the four weight values ​​is 1, and W1, W2, W3 and W4 represent the evaluation scores of RSSI, bandwidth, trouble-free operation time and storage space, respectively. The evaluation scores are mainly obtained by evaluation, data analysis and other methods during operation, and are not specifically limited here. W1, W2, W3 and W4 are preferably 0.3, 0.2, 0.2 and 0.3, respectively.

[0070] In some embodiments, the monitoring method further includes: the networking aggregation node acts as a backup node of the edge computing node to back up the sensor data sent to the edge computing node.

[0071] The network aggregation nodes are sorted according to the RSSI (Received Signal Strength Indication) strength value of the link aggregation nodes, and other link aggregation nodes are added one by one to establish the communication link of the sensor network. After the final communication link is determined, the adjacent two-level aggregation nodes establish a communication link through the pre-established routing table to transmit data.

[0072] For example, when multiple aggregation nodes are set in an edge computing network, the edge computing node uses the aggregation node with the strongest RSSI as the networking aggregation node to establish a communication link based on the RSSI of each aggregation node.

[0073] The networking aggregation node first performs channel scanning, uses an idle channel that is not used by other edge computing networks, and determines node configuration parameters, such as the maximum number of aggregation nodes, routing table lifetime, etc. The networking aggregation node can be connected to the network using a simple flooding method. The entire communication link and the link aggregation node position are located by receiving the RSSI of the adjacent link aggregation node, thereby forming the aggregation node location of the entire link. Each link aggregation node receives the RSSI of the surrounding aggregation nodes and records the RSSI of each aggregation node to the link aggregation node. By comparing the RSSI of each aggregation node, the approximate distance between each aggregation node and the link aggregation node is estimated. According to the characteristics of a single link, the number of the next link aggregation node is determined one by one starting from the networking aggregation node. Each link aggregation node first removes the link aggregation node that has been determined in the previous stage from the link aggregation node information it receives, and then selects the link aggregation node with the strongest energy from the remaining link aggregation nodes as the next-stage link aggregation node until there are no remaining link aggregation nodes, and the communication link is completed. Each aggregation node aggregates the state data of the monitored object of the corresponding sensor, and uploads the state data of the monitored object to the edge computing node through the communication link.

[0074] After the communication link is completed, all the aggregation nodes of the entire communication link share the path information, and save the RSSI and power status of the adjacent aggregation node at the same time. When data is transmitted, the optimal front and back aggregation nodes are selected for transmission based on the above conditions. The routing status of the entire network is not fixed. After each data transmission, each aggregation node needs to update the RSSI of the adjacent aggregation node saved in the aggregation node according to the information in the received data packet and the signal quality.

[0075] In some implementations, the monitoring method further includes: the networking aggregation node assigning routing address information to each aggregation node of the communication link, and each aggregation node completing the communication link between two adjacent levels of aggregation nodes according to the assigned routing address information.

[0076] In the IoT ad hoc network, the source node address, target node address, next node address, route hop count and other parameters need to be set in the routing table of the device (sensor, etc.). Among them, the target node address reflects which node this route is to. The next hop node address indicates the address between the source node and the target node, which is used for forwarding. Route hop count, the routing of the wireless ad hoc network is multi-hop, and the transmission between two nodes may require an intermediate node as a router to forward the message. A route with high transmission efficiency and low latency should generally have fewer hops. In order to reflect the path transmission efficiency and latency, a route hop count field is added. When two nodes communicate, if no intermediate node forwarding is required, it is one hop, and if one node forwarding is required, it is two hops, and so on. It can be seen that the routing of the ad hoc network is complex. In the embodiment of the present invention, the networking aggregation node numbers each aggregation node of the communication link and allocates routing address information. Each aggregation node completes the communication link between the adjacent two-level aggregation nodes according to the allocated routing address information, thereby effectively simplifying the setting of the routing table and realizing the rapid establishment of the communication link.

[0077] In the following embodiments, when the networking aggregation node is networking, it adopts the requirements of the self-networking itself and the basic routing function. When a certain networking aggregation node in the networking is looking for a route, there is a corresponding feedback regardless of whether it is successful. The networking aggregation node can correctly network, and each networking aggregation node plays the role of a terminal host and a relay router. For the message that needs to be forwarded, it can process and forward. After the networking aggregation node sends a routing request to a certain networking aggregation node in the network, it ends correctly after the preset networking time, and it cannot wait indefinitely. It has routing convergence, and by controlling the parameters in the message, it avoids the formation of loop routing, so that the message is forwarded in an infinite loop in the network. The networking aggregation node maintains its own routing table, and can forward the message in a targeted manner according to the routing table, make full use of the routing table to improve communication efficiency, and report the correct route errors for invalid routing table items to avoid using invalid routes. A data transceiver module is set in each networking aggregation node, and the judgment and analysis of the communication protocol on the data is reduced by the transceiver module, thereby diluting the restriction of the protocol itself on the maximum speed of the communication of the transceiver module itself. Data is received through the data transceiver module without processing the data. The processing part is performed by the communication protocol software of the edge computing node.

[0078] Since the networking aggregation node is responsible for the establishment and maintenance of the entire sensor network, it is also responsible for the upload function of the aggregated data; the remaining link aggregation nodes are low-power nodes. When exchanging data, network congestion may occur. In some embodiments, QoS (Quality of Service) control must be performed to solve this problem. For example, the following control method can be used:

[0079] Before sending the aggregated sensor information, each aggregation node first listens to the signal transmission energy intensity in the communication link according to the preset duration. When the signal transmission energy intensity exceeds the threshold, it gives an avoidance signal and suspends sending. The aggregation node enters a sleep state and enters the listening mode again when the preset random time is reached. If the communication link is idle, the aggregated monitored object data is sent.

[0080] Since the communication link is a unidirectional communication link, if a convergence node in the link fails, the entire communication link will be disconnected. Therefore, in some embodiments, the networking convergence node is also used to determine the link convergence node that cannot communicate according to the RSSI of each link convergence node when the communication link is disconnected, and set the target address of the previous link convergence node of the link convergence node that cannot communicate as the next link convergence node of the link convergence node that cannot communicate, thereby establishing a new communication link. And send the information of the link convergence node that cannot communicate to the edge computing node.

[0081] The edge computing node can determine whether the monitored object has a fault based on the data of the monitored object. In addition, due to the large number of sensor types, a large amount of power heterogeneous sensor data is generated. The physical meaning and representation form of various sensor data are very different. Therefore, the edge computing node can also use the state data of the monitored object aggregated by each aggregation node to predict the abnormal health state of the transmission line.

[0082] Currently, categories of abnormal health conditions of transmission lines include rust and aging, geological disasters, natural damage, hidden dangers of animal and plant activities, etc.

[0083] The factors affecting the health status of the transmission line corresponding to the rust and aging category include the rust of metal components, such as the degree of rust of metal parts such as towers, conductors, ground wires, and hardware. The aging of insulators, such as whether there are cracks, damage, aging and discoloration on the surface of the insulator. The wear of the conductor, the degree of wear caused by long-term operation of the conductor, especially the wear of the contact part with the insulator.

[0084] The factors affecting the health status of transmission lines corresponding to geological disasters include landslides, the impact of terrain changes caused by landslides on the stability of tower foundations, collapses, direct impacts or potential threats of collapse bodies on towers and conductors, and debris flows.

[0085] The factors affecting the health status of transmission lines corresponding to the natural damage category include the number of lightning strikes, the number of transmission line failures caused by lightning, wind deflection flashover, the discharge of wire wind deflection caused by strong winds on the tower head components, ice thickness, the thickness of ice on the transmission line in cold seasons, etc.

[0086] The factors affecting the health status of the transmission lines corresponding to the animal and plant activity hazards include the construction of bird nests, the growth of trees, etc.

[0087] In some embodiments, for an abnormal health state of a transmission line, the calculation method of the influence degree of the corresponding health state influencing factor is expressed as:

[0088]

[0089] In the formula, H iis the degree of influence of the health status influencing factors corresponding to the abnormal health status of category i in the transmission line; ni is the number of health status influencing factors corresponding to the abnormal health status of category i; x0 is the threshold data of the health status influencing factors corresponding to the abnormal health status of category i; x is the actual data of the health status influencing factors corresponding to the abnormal health status of category i; k is the factor correction coefficient. Among them, the threshold data represents the maximum data of the health status influencing factors on the abnormal health status, which can be set according to the actual situation and is not specifically limited here. For example, the accumulation of dirt on the surface of the insulator does not exceed 0.2mm, and 0.2mm is the threshold data. The factor correction coefficient can also be set according to the actual situation and is not specifically limited here.

[0090] On this basis, the prediction result of the overall health status of the transmission line is expressed as:

[0091]

[0092] Where f(x) is the prediction result of the health status index of the transmission line; t is the number of years the current transmission line has been put into use; t0 is the designed service life of the transmission line; and m is the aging correction coefficient of the transmission line.

[0093] Combining the above formula, it can be seen that in the initial design state, the overall health status index of the transmission line is 1. The overall health status index of the transmission line gradually decreases as the health status influencing factors corresponding to each category of abnormal health status increase the degree of the specific status, and the longer the service life, the lower the corresponding health status prediction result. By introducing the transmission line aging correction factor, the interference of accidental situations on the prediction results can be avoided. Among them,

[0094]

[0095] Where max(n) represents the maximum number of failures when the service life is in constant relationship with the design service life; min(n) represents the minimum number of failures when the service life is in constant relationship with the design service life; avg(n) represents the average number of failures when the service life is in constant relationship with the design service life.

[0096] By combining the service life and actual status information in the above way, an accurate prediction of the health status of the transmission line can be achieved.

[0097] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0098] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0099] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A transmission line self-organizing network Internet of Things monitoring method based on multi-dimensional association, characterized in that: The monitoring method is used in a transmission line monitoring system, the transmission line monitoring system includes a management subsystem and a monitoring subsystem; the monitoring subsystem includes multiple edge computing networks, each edge computing network is composed of an edge computing node, multiple aggregation nodes and multiple sensor networks; The edge computing node is connected to a plurality of aggregation nodes, each aggregation node manages a sensor network; each sensor network includes a plurality of sensors; each sensor is arranged on a monitored object of a power transmission line; the monitoring method includes: for each edge computing network: Each sensor collects status data of the monitored object; Each aggregation node aggregates the state data of the monitored objects collected by each sensor in the sensor network it manages and transmits it to the edge computing node; The edge computing node determines whether there is a fault in the monitored object in the corresponding edge computing network based on the state data of the monitored object aggregated by each aggregation node. If it is determined that there is a fault, the fault information of the monitored object is sent to the management subsystem through the power wide area network; The management subsystem receives the fault information of the monitored object of the edge computing node, and determines the fault area on the transmission line and issues an early warning based on the fault information.

2. The monitoring method according to claim 1, characterized in that: Each sensor in the sensor network is connected to a corresponding aggregation node via a wireless transmission protocol.

3. The monitoring method according to claim 1, characterized in that: The aggregation node is divided into a dedicated node and a dynamic node; the monitoring method also includes: The dedicated node sets a plurality of wireless transmission protocols according to the types of sensors in the sensor network managed by the dedicated node.

4. The monitoring method according to claim 3, characterized in that: The monitoring method further comprises: In the case that the dedicated node fails, the sensors in the sensor network managed by the dedicated node form a self-organizing network, and a dynamic node is selected from each sensor; the sensor network is managed by the dynamic node.

5. The monitoring method according to claim 4, characterized in that: The monitoring method further comprises: The edge computing node evaluates the data transmission status of each dedicated node in the edge computing network, and when the data transmission status does not reach a preset status threshold, triggers each sensor in the sensor network managed by the dedicated node to self-organize the network.

6. The monitoring method according to claim 5, characterized in that: The monitoring method further comprises: The edge computing node fails to receive the detected object status data gathered by the dedicated node during multiple data collection cycles, and determines that the dedicated node is physically damaged; or, in the current data collection cycle, determines that the data reception rate and / or data transmission rate of the dedicated node does not reach a preset threshold; and triggers each sensor in the sensor network managed by the dedicated node to self-organize.

7. The monitoring method according to claim 6, characterized in that: The monitoring method further comprises: At the end of each data collection cycle, the aggregation node records the amount of monitored object status data collected by the sensors in this cycle, determines the data reception rate based on the ratio of the amount of monitored object status data collected by the sensors in this cycle to the amount of monitored object status data collected by the sensors in the previous cycle, and sends the data reception rate to the edge computing node.

8. The monitoring method according to claim 6, characterized in that: The monitoring method further comprises: At the end of each data collection cycle, the edge computing node records the state quantity of the monitored object aggregated by the aggregation node received in this cycle, and determines the data sending rate based on the ratio of the state quantity of the monitored object aggregated by the aggregation node received in this cycle to the state data quantity of the monitored object aggregated by the aggregation node received in the previous cycle.

9. The monitoring method according to any one of claims 1 to 8, characterized in that: The monitoring method further comprises: The edge computing node predicts the abnormal health status of the transmission line based on the monitored object status data aggregated by each aggregation node.

10. The monitoring method according to claim 9, characterized in that: The categories of abnormal health status of the transmission lines include rust and aging, geological disasters, natural damages, and hidden dangers of animal and plant activities.

Images