Distributed status monitoring system and method for power transmission corridor based on passive Internet of Things

Abstract

The present invention is a distributed state monitoring system and method for a power transmission corridor based on a passive Internet of Things, wherein a passive transmission terminal node is used to obtain radio frequency signals and sensor data, and reflect the modulated signal to a wireless data transmission base station through the backscatter communication principle; the wireless data transmission base station is used to receive and demodulate the signal, and transmit the data to a distributed data processing relay station based on a short message protocol; the distributed data processing relay station is used to receive and process the data, and compress the processed data into a data packet using Huffman coding and transmit it to a data server; the data server is used to receive and decode the data packet, and analyze it using an improved isolation forest algorithm, and determine whether there is an abnormality in the internal state of the power transmission corridor according to the analysis result. The method solves the problems of difficult maintenance, limited data transmission, poor deployment flexibility, high cost, etc., and designs an efficient, reliable, and flexible intelligent power transmission corridor state monitoring solution system and method.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission pipe status monitoring, and mainly to a distributed status monitoring system and method for a power transmission pipe gallery based on a passive Internet of Things. Background Art

[0002] Transmission corridors are important channels for power transmission, and the safety and stability of their status are crucial to the normal operation of the power system. Traditional transmission corridor status monitoring systems usually rely on active sensors, which require regular battery replacement or rely on external power supply, increasing maintenance workload and costs. At present, the monitoring of the internal status of transmission corridors mainly relies on manual work. With the development of the power Internet of Things, the demand for intelligent monitoring of transmission corridors is becoming more and more urgent. Large-scale transmission corridor monitoring using traditional manual patrols is not only time-consuming and labor-intensive, but also unable to achieve real-time monitoring and intelligent early warning, increasing the risk of potential failures and accidents.

[0003] For example, the invention of CN115623438A discloses "a comprehensive pipe gallery monitoring method, comprising: deploying corresponding parameter sensors according to the structural parameters of the pipe gallery, the parameters of the pipelines in the pipe gallery, and the environmental parameters of the pipelines, for obtaining the internal data of the pipe gallery; transmitting the obtained internal data of the pipe gallery to a data processing center by wireless transmission; the data center establishes a parameterized model according to the internal data of the pipe gallery, and predicts the behavior of the pipe gallery based on the parameterized model in a data-driven manner, and outputs early warning information according to the obtained prediction results; the prediction results and the early warning information are sent to the user end and displayed visually", but the method needs to consider the wiring and power supply issues of the sensors, which limits the flexibility of deployment, and the deployment method requires regular maintenance of the batteries of the sensors and related wireless devices, which increases the maintenance cost and work; in addition, the method relies on parameterized models, and has poor adaptability when facing complex and dynamically changing environments. The parameterized model based on preset parameters may cause the monitoring method to affect the accuracy of prediction and early warning when unknown abnormalities or environmental changes occur, causing a burden on the system. Summary of the invention

[0004] In order to solve the above-mentioned problems existing in the prior art, the present application provides a distributed status monitoring system and method for power transmission corridors based on passive Internet of Things.

[0005] The technical solution of this application is as follows:

[0006] On the one hand, the present invention provides a distributed state monitoring system for a power transmission corridor based on a passive Internet of Things, the system comprising a passive transmission terminal node, a wireless data transmission base station, a distributed data processing relay station and a data server, a plurality of wireless data transmission base stations are uniformly and linearly deployed in the power transmission corridor according to a preset distance, the wireless data transmission base station and the distributed data processing relay station are connected via a star networking, wherein:

[0007] The passive transmission terminal node is used to obtain radio frequency signals and sensor data, collect radio frequency energy from the radio frequency signals and convert it into electrical energy to drive its own operation, modulate the sensor data into radio frequency signals to obtain modulated signals, and reflect the modulated signals to the wireless data transmission base station through the backscatter communication principle;

[0008] The wireless data transmission base station is used to receive the modulated signal and demodulate it to obtain the radio frequency signal and sensor data, and transmit the sensor data to the distributed data processing relay station based on the short message protocol;

[0009] The distributed data processing relay station is used to receive sensor data and perform data processing, compress the processed sensor data into data packets using Huffman coding, and transmit the data packets to the data server;

[0010] The data server is used to receive and decode data packets to obtain sensor data, analyze the sensor data through a power transmission corridor abnormal state calculation algorithm, determine whether there is an abnormality in the internal state of the power transmission corridor based on the analysis results, and mark the abnormal data, wherein the power transmission corridor abnormal state calculation algorithm is designed based on an improved isolation forest algorithm, specifically using a hierarchical clustering algorithm to cluster the abnormal scores obtained by the isolation forest algorithm to determine the abnormal data in the sample.

[0011] Preferably, the passive transmission terminal node includes an energy collection unit, a sensor unit, a control unit, an information modulation unit and an antenna, wherein:

[0012] The energy collection unit is used to collect radio frequency energy from the radio frequency signal and convert it into electrical energy, and use the electrical energy to drive the passive transmission terminal node to work;

[0013] The sensor unit includes a temperature sensor, a humidity sensor and a pressure sensor, which are used to collect environmental data in the power transmission corridor;

[0014] The control unit is used to control the operation of the passive transmission terminal node, encode the sensor data collected by the sensor unit, obtain the encoded data, and transmit the encoded data to the information modulation unit;

[0015] The information modulation unit modulates the coded data onto the radio frequency signal by changing the reflection coefficient to obtain a modulated signal;

[0016] The antenna is used to reflect the modulated signal to the wireless data transmission base station.

[0017] Preferably, the wireless data transmission base station includes a backscatter signal demodulation module, a data processing module and a wireless network module based on LoRa, and transmits the sensor data to the distributed data processing relay station based on the short message protocol as follows:

[0018] The received modulated signal is demodulated by the backscatter signal demodulation module to obtain sensor data; the wireless data transmission base station sends a registration request message to the distributed data processing relay station through the wireless network module, and the distributed data processing relay station registers the wireless data transmission base station after receiving the registration request message, and returns a registration confirmation data packet to the wireless data transmission base station to complete the registration; the data processing module packs the sensor data into a sensor data packet, and transmits the sensor data packet to the distributed data processing relay station through the wireless network module, and the distributed data processing relay station receives the sensor data packet and returns a confirmation signal.

[0019] Preferably, the distributed data processing relay station is used to receive sensor data and perform data processing, specifically:

[0020] The distributed data processing relay station receives sensor data packets through the equipped LoRa gateway and performs data processing, specifically parsing the received sensor data, filtering out noise and invalid data, and converting them into a unified format; using Huffman coding to compress the sensor data in the unified format, the compressed data packets are transmitted to the data server via the MQTT protocol.

[0021] Preferably, the calculation algorithm for abnormal state of the transmission pipeline gallery is specifically as follows:

[0022] Constructing a sample data set according to the sensor data, and constructing a data matrix based on the sample data set, wherein the data matrix has samples as rows and eigenvalues ​​as columns;

[0023] Set isolated tree height ,in Indicates the number of samples, and loops to build isolated trees;

[0024] Calculate the average path length of each sample in the isolated tree; normalize the average path length to anomaly score , expressed as:

[0025] ;

[0026] ;

[0027] ;

[0028] In the formula, represents the average path length of samples in the isolated tree; represents the normalization factor; Indicates Harmonic number; represents Euler's constant;

[0029] Use the hierarchical clustering algorithm to divide the abnormal scores of the samples into two clusters of data and ,like and If the data volume difference exceeds the data volume threshold, the samples in the cluster with small data volume are judged as abnormal samples and marked; and If the data volume difference does not exceed the data volume threshold, the data and The samples with anomaly scores greater than the preset anomaly score threshold are compared and marked as abnormal samples.

[0030] Preferably, the isolation tree is constructed cyclically, specifically as follows:

[0031] Draw a random sample from the data matrix;

[0032] Starting from the root node of the isolated tree, a feature is randomly selected from the randomly selected samples, and a split point of the feature is determined to split the corresponding sample into two parts, where the two parts correspond to two child nodes respectively;

[0033] Recursively perform random feature selection and split point determination operations on each child node to split the isolated tree until the height of the isolated tree is reached or the number of samples of the child node reaches the preset minimum value, completing the construction of the isolated tree.

[0034] Preferably, a hierarchical clustering algorithm is used to divide the abnormal scores of the sample data into two clusters of data: and , specifically:

[0035] Treat each sample in the sample data set as a separate cluster; calculate the distance between all pairs of samples in the sample data set to construct a distance matrix ; The distance matrix The two clusters with the smallest distance are merged into a new cluster; the distances between the new cluster and all other clusters are recalculated and the distance matrix is ​​updated ; Repeat the steps of merging new clusters until the number of clusters reaches the preset number of clusters, forming the final cluster and .

[0036] On the other hand, the present invention also provides a distributed state monitoring method for a power transmission corridor based on a passive Internet of Things, the method comprising:

[0037] Several passive transmission terminal nodes are uniformly and linearly deployed in the transmission corridor at a preset distance. According to the required transmission signal coverage, a wireless data transmission base station that communicates with the passive transmission terminal nodes is set in the transmission corridor; a distributed data processing relay station is deployed, and the distributed data processing relay station is connected to the wireless data transmission base station through a star networking method; a data server is set up at a central location;

[0038] The passive transmission terminal node obtains radio frequency signals and sensor data from the environment, collects radio frequency energy from the radio frequency signal and converts it into electrical energy to drive the passive transmission terminal node to work, modulates the sensor data into the radio frequency signal to obtain a modulation signal, and generates a modulation signal;

[0039] The modulated signal is transmitted to the wireless data transmission base station closest to the passive transmission terminal node through the backscatter communication principle; the wireless data transmission base station receives the modulated signal and demodulates it to obtain the sensor data, and transmits the sensor data to the distributed data processing relay station using the short message protocol;

[0040] The distributed data processing relay station receives sensor data from the wireless data transmission base station, processes the received sensor data, compresses the data using Huffman coding, generates data packets, and transmits the compressed data packets to the data server;

[0041] The data server receives and decodes the data packets, recovers the original sensor data, and uses the abnormal state calculation algorithm of the power transmission corridor based on the improved isolation forest algorithm to perform abnormality detection on the decoded sensor data to obtain the abnormality detection results;

[0042] Based on the abnormality detection results, determine whether there is any abnormality in the internal state of the transmission corridor. If there is an abnormality, mark the abnormal data and generate an alarm message to notify maintenance personnel to handle it according to the alarm information.

[0043] On the other hand, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the distributed status monitoring method for power transmission corridors based on passive Internet of Things as described in any embodiment of the present invention is implemented.

[0044] On the other hand, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a distributed status monitoring method for a power transmission corridor based on a passive Internet of Things as described in any embodiment of the present invention.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1) The present invention provides a distributed status monitoring system and method for power transmission corridors based on passive Internet of Things, which uses passive transmission terminal nodes to collect radio frequency energy from radio frequency signals and convert it into electrical energy. No external power supply is required, which reduces maintenance costs and complexity and improves the convenience of deployment at key locations in the power transmission corridor.

[0047] 2) The present invention provides a distributed status monitoring system and method for power transmission corridors based on passive Internet of Things. By using the principle of backscatter communication, the sensor data is modulated into a radio frequency signal and reflected to a wireless data transmission base station, which not only reduces the energy demand of the system, simplifies the system layout, but also realizes efficient wireless data transmission;

[0048] 3) The present invention provides a distributed status monitoring system and method for power transmission corridors based on passive Internet of Things. A power transmission corridor abnormal status calculation algorithm is designed based on the improved isolation forest algorithm, which can intelligently mark abnormal data in the massive data information collected by sensors. This method combines anomaly score evaluation and hierarchical clustering analysis, which not only improves the accuracy and robustness of anomaly detection, can accurately identify abnormal data points, but also improves the system adaptability as well as the accuracy and reliability of monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is a system architecture diagram of this embodiment;

[0050] Figure 2 is a schematic diagram of the passive transmission terminal node and sensor structure of this embodiment;

[0051] Figure 3 It is a schematic diagram of the principle of the wireless data transmission base station of this embodiment performing backscatter communication;

[0052] Figure 4 It is the main communication process of the lightweight short message protocol of this embodiment;

[0053] Figure 5 Schematic diagram of the lightweight short message protocol frame structure of this embodiment;

[0054] Figure 6 is a flow chart of an algorithm for analyzing the internal state of a power transmission corridor based on an improved isolation forest algorithm of this embodiment;

[0055] Figure 7 It is a flow chart of the method of this embodiment. DETAILED DESCRIPTION

[0056] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0057] The present invention provides the following technical solution: a distributed status monitoring system and method for a power transmission corridor based on a passive Internet of Things.

[0058] Example 1

[0059] See also Figure 1 This embodiment provides a distributed state monitoring system for a power transmission corridor based on a passive Internet of Things. The system includes a passive transmission terminal node, a wireless data transmission base station, a distributed data processing relay station and a data server. Several wireless data transmission base stations are uniformly and linearly deployed in the power transmission corridor according to a preset distance. The wireless data transmission base station is connected to the distributed data processing relay station through a star network. The system functions include:

[0060] S1, the passive transmission terminal node is used to obtain radio frequency signals and sensor data, collect radio frequency energy from the radio frequency signal and convert it into electrical energy to drive its own work, modulate the sensor data into the radio frequency signal to obtain a modulated signal, and reflect the modulated signal to the wireless data transmission base station through the backscatter communication principle;

[0061] S11, Passive transmission terminal node

[0062] The passive transmission terminal node drives itself by collecting radio frequency energy in the environment without relying on internal batteries or external power supplies, and uses the backscatter communication principle to modulate and transmit the information collected by the sensor to the signal receiving base station;

[0063] See also Figure 2 , the passive transmission terminal node includes an energy collection unit, a sensor unit, a control unit, an information modulation unit and an antenna, wherein:

[0064] The energy collection unit is used to collect radio frequency energy from the radio frequency signal and convert it into electrical energy, and use the electrical energy to drive the passive transmission terminal node to work;

[0065] The sensor unit includes a temperature sensor, a humidity sensor and a pressure sensor, which are used to collect environmental data in the power transmission corridor;

[0066] The control unit is used to control the operation of the passive transmission terminal node, encode the sensor data collected by the sensor unit, obtain the encoded data, and transmit the encoded data to the information modulation unit through the signal path;

[0067] The information modulation unit modulates the coded data onto a carrier signal by changing a reflection coefficient to obtain a modulated signal;

[0068] The antenna is used to reflect the modulated signal to the wireless data transmission base station in the form of a backscattered signal;

[0069] S12. Working Principle

[0070] The passive transmission terminal node is equipped with a variety of different sensors, which are responsible for collecting environmental data in the transmission corridor; according to the monitoring requirements of the transmission corridor, a suitable location is selected to install the passive transmission terminal node. The sensor can be installed at key nodes inside the corridor, such as locations where temperature, humidity, pressure, etc. change significantly. The sensor is fixed at a predetermined position to ensure that the energy collection unit of the sensor can effectively receive the radio frequency energy emitted by the base station, and the working state of the sensor is debugged to ensure that it can work normally and collect data;

[0071] The energy collection module of the passive transmission terminal node is responsible for collecting energy from a part of the incident carrier signal. When the energy is collected to a certain level, the control unit of the passive transmission terminal node encodes the collected data and transmits it to the information modulation unit. The information modulation unit is responsible for modulating the information to be sent onto the carrier signal.

[0072] S2, the wireless data transmission base station is used to receive the modulated signal and demodulate it to obtain the radio frequency signal and sensor data, and transmit the sensor data to the distributed data processing relay station based on the short message protocol;

[0073] S21, wireless data transmission base station

[0074] See also Figure 3 , using the wireless data transmission base station as a passive transmission terminal node to collect carrier signals and backscattered signal reception base stations through terminal node antennas;

[0075] The wireless data transmission base station includes a backscatter signal demodulation module, a data processing module and a wireless network module based on LoRa, and transmits sensor data to a distributed data processing relay station based on a short message protocol. Specifically:

[0076] The received modulated signal is demodulated by the backscatter signal demodulation module to obtain sensor data; see Figure 4The wireless data transmission base station sends a registration request message to the distributed data processing relay station through the wireless network module. After receiving the registration request message, the distributed data processing relay station registers the wireless data transmission base station and returns a registration confirmation data packet to the wireless data transmission base station to complete the registration. The data processing module packages the sensor data into a sensor data packet, and transmits the sensor data packet to the distributed data processing relay station through the wireless network module. The distributed data processing relay station receives the sensor data packet and returns a confirmation signal.

[0077] S22, short message protocol

[0078] See also Figure 5 In this embodiment, the short message protocol is a lightweight short message protocol, and the data frame structure used by the lightweight short message protocol includes frame information, base station ID, data length, data content and tail check code, wherein the data content length is variable and the length of the rest is fixed;

[0079] S221, the frame information occupies 1 byte, and the first bit information of the data packet is composed of 2 bits of data packet type, 1 bit of retransmission flag, 1 bit of source direction and 4 bits of data packet sequence number, specifically:

[0080] The data packet types are data packets, registration packets and feedback packets, and the first bit of the data packet type is used to indicate the data bit corresponding to the data packet information, wherein the data packet and registration packet are sent by the wireless transmission base station, and are used to transmit data and provide registration information respectively; the feedback packet is sent by the distributed data processing relay station, and is used to transmit confirmation information back;

[0081] The retransmission flag is used to identify whether the frame data is retransmission data to distinguish it from the original data packet. The source direction is used to identify which side of the transmission corridor the data packet should be transmitted to. The flag is used to determine the direction of the distributed data processing relay station relative to the base station to simplify the routing algorithm.

[0082] The data packet sequence number is used to identify the order of the data packets;

[0083] S222, the base station ID refers to the provisions of the lightweight short message protocol content, each wireless transmission base station should have an exclusive ID number as the communication address, wherein the wireless transmission base station ID number is determined when the base station is deployed. Due to the special structure of the power transmission corridor, the wireless transmission base station is deployed linearly, and the wireless transmission base station ID should increase or decrease in a certain direction as the basis for the communication routing direction;

[0084] S223, in this embodiment, the data length is at least 3 bits and at most 255 bits;

[0085] S224, the data content includes data information of each passive transmission terminal node collected by the base station, each sensor information includes five bits, the first bit is a sensor identifier, which is used to identify which sensor the data comes from; the following four bits are data information collected by the sensor;

[0086] S225. The tail check code is a mechanism in the transmission protocol, which is used to ensure that the data remains intact and detect errors during the transmission process; a check value is attached to the end of the data frame or data packet. The sender calculates and attaches the check value before sending the data, and the receiver recalculates and compares the check value after receiving the data to verify whether there is any error in the data; if the check values ​​match, the data is considered to be complete; if they do not match, retransmission may be required.

[0087] S3, the distributed data processing relay station is used to receive sensor data and perform data processing, compress the processed sensor data into a data packet using Huffman coding, and transmit the data packet to the data server;

[0088] S31, Distributed Data Processing Relay Station

[0089] The distributed data processing relay station is a core component of the distributed status monitoring system of the power transmission corridor based on the passive Internet of Things, and is responsible for receiving, analyzing and processing data from various wireless data transmission base stations; installing and configuring the server and storage device of the data processing relay station to ensure that it has sufficient computing and storage capabilities, deploying multiple data processing relay stations in the power transmission corridor, and using wired connections to the data server to ensure that the data processing relay station can communicate with the data server;

[0090] The distributed data processing relay station is equipped with a LoRa gateway for receiving data from the wireless data transmission base station and collecting sensor information from different sources, specifically:

[0091] The distributed data processing relay station receives sensor data packets through the LoRa gateway and processes the data. It analyzes the received sensor data, filters out noise and invalid data, and converts them into a unified format. It compresses the sensor data in the unified format using Huffman coding, and transmits the compressed data packets to the data server through the message queue telemetry transmission MQTT protocol.

[0092] S311, LoRa Gateway

[0093] LoRa (Long Range Gateway) is a key component in the IoT system. It is responsible for receiving and forwarding LoRa wireless signals, communicating with LoRa terminal devices through RF modules, and forwarding received data to network servers or cloud platforms through processing units, supporting two-way communication.

[0094] LoRa gateways have the characteristics of wide coverage, long-distance transmission and low power consumption. They are usually installed in high places or open areas to optimize signal coverage, and can be configured and managed through a web interface or dedicated software to help achieve real-time transmission and remote management of data.

[0095] S4, the data server is used to receive and decode the data packet, obtain sensor data, analyze the sensor data through the transmission corridor abnormal state calculation algorithm, judge whether there is an abnormality in the internal state of the transmission corridor according to the analysis result, and mark the abnormal data, wherein the transmission corridor abnormal state calculation algorithm is designed based on the improved isolation forest algorithm, specifically using the hierarchical clustering algorithm to cluster the abnormal scores obtained by the isolation forest algorithm to determine the abnormal data in the sample;

[0096] S41, Data Server

[0097] The functions of the data server include receiving data, decoding data, storing data and analyzing data, specifically:

[0098] The received data is used to receive data packets transmitted by each distributed data processing relay station; the decoded data is used to decode the received data packets and restore the original valid information from the data packets;

[0099] The stored data is used to manage and store the decoded data using a database for the original valid information. In addition, the data server has data management functions, including data indexing, querying and maintenance, to support efficient access and utilization of data;

[0100] The analysis data is used to analyze the stored valid information, determine whether there is any abnormality in the internal state of the transmission pipeline corridor, and mark abnormal data samples;

[0101] S42, Improved Isolation Forest Algorithm

[0102] The Isolation Forest Algorithm is an unsupervised machine learning algorithm for anomaly detection. It identifies anomalies in data by building a random forest model and measures the degree of anomaly by using the path length of the data point in the randomly cut tree structure. The shorter the path, the more likely it is to be an anomaly.

[0103] See also Figure 6 , the calculation process of the improved isolation forest algorithm is as follows:

[0104] S421, the data server collects data from wireless transmission base stations, and the sensor data of each base station constitutes a sample , Indicates the number of sensor types and constructs a sample data set , represents the number of samples, and constructs a data matrix based on the sample data set, wherein the data matrix has samples as rows and eigenvalues ​​as columns;

[0105] Set isolated tree height , is the number of samples, and isolated trees are constructed cyclically; in this embodiment, the number of isolated trees is 100. Since the path length usually converges before 100 trees, the isolation forest algorithm generally constructs 100 isolated trees by default. The specific construction process is:

[0106] (a) randomly extract a sample from the data matrix; in this embodiment, the number of samples that can be randomly extracted is , the number of features that can be randomly selected is , ,and is a power of 2; When it is 256, it can provide enough information to extract abnormal samples, so The maximum value of is 256, so The value range is ; The value of is determined by the amount of characteristic information that can be sampled by the sensors inside the actual transmission corridor;

[0107] (b) starting from the root node of the isolation tree, randomly selecting a feature from the randomly selected samples, and determining the split point of the feature, splitting the corresponding sample into two parts, wherein the two parts correspond to two child nodes respectively;

[0108] (c) Recursively perform random feature selection and split point determination operations on each child node to split the isolated tree until the height of the isolated tree is reached or the number of samples of the child node reaches the preset minimum value, thus completing the construction of the isolated tree;

[0109] S422, calculating the average path length of each sample in the isolated tree; normalizing the average path length into an anomaly score , expressed as:

[0110] ;

[0111] ;

[0112] ;

[0113] In the formula, represents the average path length of samples in the isolated tree; represents the normalization factor; Indicates Harmonic number; represents Euler's constant;

[0114] S423, using hierarchical clustering algorithm The abnormal scores of sample data are divided into two clusters and , the calculation process is as follows:

[0115] Treat each sample in the sample data set as a separate cluster; calculate the distance between all pairs of samples in the sample data set to construct a distance matrix ; The distance matrix The two clusters with the smallest distance are merged into a new cluster; the distances between the new cluster and all other clusters are recalculated and the distance matrix is ​​updated ; Repeat the steps of merging new clusters until the number of clusters reaches the preset number of clusters, forming the final cluster and ;

[0116] S424, calculation and The difference in the amount of data between and If the data volume difference exceeds the data volume threshold, the samples in the cluster with small data volume are judged as abnormal data in the sample and marked; and If the data volume difference does not exceed the data volume threshold, the data and The sample with anomaly score greater than the preset anomaly score threshold is compared with the preset anomaly score threshold, and the sample with anomaly score greater than the anomaly score threshold is marked as anomaly data in the sample.

[0117] Example 2

[0118] See also Figure 7 This embodiment provides a distributed state monitoring method for a power transmission corridor based on a passive Internet of Things, the method comprising:

[0119] T1. Several passive transmission terminal nodes are uniformly and linearly deployed in the transmission corridor at a preset distance. According to the required transmission signal coverage, a wireless data transmission base station that communicates with the passive transmission terminal nodes is set in the transmission corridor; a distributed data processing relay station is deployed, and the distributed data processing relay station is connected to the wireless data transmission base station through a star networking mode; a data server is set up at a central location;

[0120] T2, the passive transmission terminal node obtains the radio frequency signal and sensor data from the environment, collects radio frequency energy from the radio frequency signal and converts it into electrical energy to drive the passive transmission terminal node to work, modulates the sensor data into the radio frequency signal to obtain a modulation signal, and generates a modulation signal;

[0121] T21. The passive transmission terminal node includes an energy collection unit, a sensor unit, a control unit, an information modulation unit and an antenna, wherein:

[0122] The energy collection unit is used to collect radio frequency energy from radio frequency signals and convert it into electrical energy, and use the electrical energy to drive the passive transmission terminal node to work; the sensor unit includes a temperature sensor, a humidity sensor and a pressure sensor, which are used to collect environmental data in the transmission corridor; the control unit is used to control the operation of the passive transmission terminal node, and encode the sensor data collected by the sensor unit to obtain encoded data, and transmit the encoded data to the information modulation unit through a signal path; the information modulation unit modulates the encoded data onto a carrier signal by changing the reflection coefficient to obtain a modulated signal; the antenna is used to reflect the modulated signal to the wireless data transmission base station in the form of a backscattered signal;

[0123] T3, transmitting the modulated signal to the wireless data transmission base station closest to the passive transmission terminal node through the backscatter communication principle; the wireless data transmission base station receives the modulated signal and demodulates it to obtain sensor data, and uses the short message protocol to transmit the sensor data to the distributed data processing relay station;

[0124] T31, the wireless data transmission base station includes a backscatter signal demodulation module, a data processing module and a LoRa-based wireless network module, and uses a short message protocol to transmit sensor data to a distributed data processing relay station. Specifically:

[0125] The received modulated signal is demodulated by the backscatter signal demodulation module to obtain sensor data; the wireless data transmission base station sends a registration request message to the distributed data processing relay station through the wireless network module, and the distributed data processing relay station registers the wireless data transmission base station after receiving the registration request message, and returns a registration confirmation data packet to the wireless data transmission base station to complete the registration; the data processing module packs the sensor data into a sensor data packet, and transmits the sensor data packet to the distributed data processing relay station through the wireless network module, and the distributed data processing relay station receives the sensor data packet and returns a confirmation signal;

[0126] Furthermore, the short message protocol is a lightweight short message protocol, and the data frame structure used by the lightweight short message protocol includes frame information, base station ID, data length, data content and tail check code, wherein the data content length is variable and the length of the rest is fixed.

[0127] T311, the frame information occupies 1 byte, and the first bit information of the data packet is composed of 2 bits of data packet type, 1 bit of retransmission flag, 1 bit of source direction and 4 bits of data packet sequence number, specifically:

[0128] The data packet types are data packets, registration packets and feedback packets, and the first bit of the data packet type is used to indicate the data bit corresponding to the data packet information, wherein the data packet and registration packet are sent by the wireless transmission base station, and are used to transmit data and provide registration information respectively; the feedback packet is sent by the distributed data processing relay station, and is used to transmit confirmation information back;

[0129] The retransmission flag is used to identify whether the frame data is retransmission data to distinguish it from the original data packet. The source direction is used to identify which side of the transmission corridor the data packet should be transmitted to. The flag is used to determine the direction of the distributed data processing relay station relative to the base station to simplify the routing algorithm.

[0130] The data packet sequence number is used to identify the order of the data packets;

[0131] T312. The base station ID refers to the provisions of the lightweight short message protocol. Each wireless transmission base station shall have an exclusive ID number as the communication address, wherein the wireless transmission base station ID number is determined when the base station is deployed. Due to the special structure of the power transmission corridor, the wireless transmission base station is deployed linearly. The wireless transmission base station ID shall increase or decrease in a certain direction as the basis for the communication routing direction;

[0132] T313, the data length is at least 3 bits and at most 255 bits in this embodiment;

[0133] T314, the data content includes data information of each passive transmission terminal node collected by the base station, each sensor information includes five bits, the first bit is the sensor identifier, which is used to identify which sensor the data comes from; the following four bits are the data information collected by the sensor;

[0134] T315. The tail check code is a mechanism in the transmission protocol to ensure that the data remains intact and detect errors during transmission. A check value is attached to the end of the data frame or data packet. The sender calculates and attaches the check value before sending the data, and the receiver recalculates and compares the check value after receiving the data to verify whether the data has errors. If the check values ​​match, the data is considered complete; if they do not match, retransmission may be required.

[0135] T4, the distributed data processing relay station is equipped with a LoRa gateway, which receives sensor data from the wireless data transmission base station, processes the received sensor data, compresses the data using Huffman coding, generates data packets, and transmits the compressed data packets to the data server;

[0136] T5. The data server receives and decodes the data packet, recovers the original sensor data, and uses the abnormal state calculation algorithm of the power transmission corridor based on the improved isolation forest algorithm to perform abnormality detection on the decoded sensor data to obtain the abnormality detection result;

[0137] T51, improved isolation forest algorithm;

[0138] The Isolation Forest Algorithm is an unsupervised machine learning algorithm for anomaly detection. It identifies anomalies in data by building a random forest model and measures the degree of anomaly by using the path length of the data point in the randomly cut tree structure. The shorter the path, the more likely it is to be an anomaly.

[0139] The calculation process of the improved isolation forest algorithm is as follows:

[0140] T511, the data server collects data from wireless transmission base stations, and the sensor data of each base station constitutes a sample , Indicates the number of sensor types and constructs a sample data set , represents the number of samples, and constructs a data matrix based on the sample data set, wherein the data matrix has samples as rows and eigenvalues ​​as columns;

[0141] Set isolated tree height , is the number of samples, and isolated trees are constructed cyclically; in this embodiment, the number of isolated trees is 100. Since the path length usually converges before 100 trees, the isolation forest algorithm generally constructs 100 isolated trees by default. The specific construction process is:

[0142] (a) randomly extract a sample from the data matrix; in this embodiment, the number of samples that can be randomly extracted is , the number of features that can be randomly selected is , ,and is a power of 2; When it is 256, it can provide enough information to extract abnormal samples, so The maximum value of is 256, so The value range is ; The value of is determined by the amount of characteristic information that can be sampled by the sensors inside the actual transmission corridor;

[0143] (b) starting from the root node of the isolation tree, randomly selecting a feature from the randomly selected samples, and determining the split point of the feature, splitting the corresponding sample into two parts, wherein the two parts correspond to two child nodes respectively;

[0144] (c) Recursively perform random feature selection and split point determination operations on each child node to split the isolated tree until the height of the isolated tree is reached or the number of samples of the child node reaches the preset minimum value, thus completing the construction of the isolated tree;

[0145] T512. Calculate the average path length of each sample in the isolated tree; normalize the average path length into anomaly score , expressed as:

[0146] ;

[0147] ;

[0148] ;

[0149] In the formula, represents the average path length of samples in the isolated tree; represents the normalization factor; Indicates Harmonic number; represents Euler's constant;

[0150] T513, using hierarchical clustering algorithm The abnormal scores of sample data are divided into two clusters and , the calculation process is as follows:

[0151] Treat each sample in the sample data set as a separate cluster; calculate the distance between all pairs of samples in the sample data set to construct a distance matrix ; The distance matrix The two clusters with the smallest distance are merged into a new cluster; the distances between the new cluster and all other clusters are recalculated and the distance matrix is ​​updated ; Repeat the steps of merging new clusters until the number of clusters reaches the preset number of clusters, forming the final cluster and ;

[0152] T514, Calculation and The difference in the amount of data between and If the data volume difference is too large, the samples in the cluster with smaller data volume are judged as abnormal samples and marked;

[0153] The abnormal score of the abnormal sample is the abnormal score threshold, and the abnormal scores of all samples are judged. If the abnormal scores are greater than the abnormal score threshold, the sample is considered to be abnormal data in the sample and marked;

[0154] T6. Based on the abnormality detection results, determine whether there is any abnormality in the internal state of the transmission corridor. If there is an abnormality, mark the abnormal data and generate an alarm message to notify maintenance personnel to handle it according to the alarm information.

[0155] Example 3

[0156] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the distributed status monitoring method for power transmission corridors based on passive Internet of Things as described in Example 2 is implemented.

[0157] Example 4

[0158] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the distributed status monitoring method of a power transmission corridor based on a passive Internet of Things as described in Example 2 is implemented.

[0159] It is worth noting that the system, electronic device and computer-readable storage medium described in the present invention are based on the same principle as the distributed status monitoring method of the power transmission corridor based on passive Internet of Things described in Example 2, and will not be repeated here.

[0160] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A distributed state monitoring system for power transmission corridors based on passive Internet of Things, characterized in that: The system includes a passive transmission terminal node, a wireless data transmission base station, a distributed data processing relay station and a data server. A plurality of wireless data transmission base stations are uniformly and linearly deployed in the transmission corridor according to a preset distance. Each of the wireless data transmission base stations is connected to the distributed data processing relay station through a star network, wherein: The passive transmission terminal node is used to obtain radio frequency signals and sensor data, collect radio frequency energy from the radio frequency signals and convert it into electrical energy to drive its own operation, modulate the sensor data into radio frequency signals to form modulated signals, and reflect the modulated signals to the wireless data transmission base station through the backscatter communication principle; The wireless data transmission base station is used to receive the modulated signal and demodulate it to obtain the radio frequency signal and sensor data, and transmit the sensor data to the distributed data processing relay station based on the short message protocol; The distributed data processing relay station is used to receive sensor data and perform data processing, compress the sensor data into data packets, and transmit the data packets to the data server; The data server is used to receive and decode data packets to obtain sensor data, analyze the sensor data through the transmission corridor abnormal state calculation algorithm, determine whether there is an abnormality in the internal state of the transmission corridor according to the analysis results, and mark the abnormal data, wherein the transmission corridor abnormal state calculation algorithm is designed based on an improved isolation forest algorithm, and uses a hierarchical clustering algorithm to cluster the abnormal scores obtained by the isolation forest algorithm to determine the abnormal data in the sample; Among them, the calculation algorithm of abnormal state of transmission pipeline corridor is as follows: Constructing a sample data set according to the sensor data, with the sensor data of each base station as a sample, and constructing a data matrix based on the sample data set, wherein the data matrix has samples as rows and eigenvalues ​​as columns; Set isolated tree height ,in Indicates the number of samples, and loops to build isolated trees; Calculate the average path length of each sample in the isolated tree; normalize the average path length to anomaly score , expressed as: ； ； ； In the formula, represents the average path length of samples in the isolated tree; represents the normalization factor; Indicates Harmonic number; represents Euler's constant; Use the hierarchical clustering algorithm to divide the abnormal scores of the samples into two clusters of data and ,like and If the data volume difference exceeds the data volume threshold, the samples in the cluster with small data volume are judged as abnormal data in the sample and marked; and If the data volume difference does not exceed the data volume threshold, the data and Compare with the preset anomaly score threshold, and mark samples with anomaly scores greater than the anomaly score threshold as abnormal data in the sample; The isolation tree is constructed cyclically, specifically: Draw a random sample from the data matrix; Starting from the root node of the isolated tree, a feature is randomly selected from the randomly selected samples, and a split point of the feature is determined to split the corresponding sample into two parts, where the two parts correspond to two child nodes respectively; Recursively perform random feature selection and split point determination operations on each child node to split the isolated tree until the height of the isolated tree is reached or the number of samples of the child node reaches the preset minimum value, completing the construction of the isolated tree; Divide the abnormal scores of sample data into two clusters and , specifically: Treat each sample in the sample data set as a separate cluster; calculate the distance between all pairs of samples in the sample data set to construct a distance matrix ; The distance matrix The two clusters with the smallest distance are merged into a new cluster; the distances between the new cluster and all other clusters are recalculated and the distance matrix is ​​updated ; Repeat the steps of merging new clusters until the number of clusters reaches the preset number of clusters, forming the final cluster and .

2. The distributed state monitoring system for power transmission corridors based on passive Internet of Things according to claim 1 is characterized in that: The passive transmission terminal node includes an energy collection unit, a sensor unit, a control unit, an information modulation unit and an antenna, wherein: The energy collection unit is used to collect radio frequency energy from the radio frequency signal and convert it into electrical energy, and use the electrical energy to drive the passive transmission terminal node to work; The sensor unit includes a temperature sensor, a humidity sensor and a pressure sensor, which are used to collect environmental data in the power transmission corridor; The control unit is used to control the operation of the passive transmission terminal node, encode the sensor data collected by the sensor unit, obtain the encoded data, and transmit the encoded data to the information modulation unit; The information modulation unit modulates the coded data onto the radio frequency signal by changing the reflection coefficient to obtain a modulated signal; The antenna is used to reflect the modulated signal to the wireless data transmission base station.

3. The distributed state monitoring system for power transmission corridor based on passive Internet of Things according to claim 1 is characterized in that: The wireless data transmission base station includes a backscatter signal demodulation module, a data processing module and a wireless network module based on LoRa. The sensor data is transmitted to the distributed data processing relay station based on the short message protocol. Specifically: The received modulated signal is demodulated by the backscatter signal demodulation module to obtain sensor data; the wireless data transmission base station sends a registration request message to the distributed data processing relay station through the wireless network module, and the distributed data processing relay station registers the wireless data transmission base station after receiving the registration request message, and returns a registration confirmation data packet to the wireless data transmission base station to complete the registration; The data processing module packages the sensor data into a sensor data packet, and transmits the sensor data packet to a distributed data processing relay station through a wireless network module. The distributed data processing relay station receives the sensor data packet and returns a confirmation signal.

4. The distributed state monitoring system for power transmission corridors based on passive Internet of Things according to claim 1 is characterized in that: The distributed data processing relay station is used to receive sensor data and perform data processing, specifically: The distributed data processing relay station receives sensor data packets through the equipped LoRa gateway and performs data processing, specifically parsing the received sensor data, filtering out noise and invalid data, and converting them into a unified format; using Huffman coding to compress the sensor data in the unified format, the compressed data packets are transmitted to the data server via the MQTT protocol.

5. A distributed state monitoring method for power transmission corridors based on passive Internet of Things, characterized in that: The method comprises: Several passive transmission terminal nodes are uniformly and linearly deployed in the transmission corridor at a preset distance. According to the required transmission signal coverage, a wireless data transmission base station that communicates with the passive transmission terminal nodes is set in the transmission corridor; a distributed data processing relay station is deployed, and the distributed data processing relay station is connected to the wireless data transmission base station through a star networking method; a data server is set up at a central location; The passive transmission terminal node acquires radio frequency signals and sensor data from the environment, collects radio frequency energy from the radio frequency signals and converts it into electrical energy to drive the passive transmission terminal node to work, modulates the sensor data into the radio frequency signals to form a modulation signal, and generates a modulation signal; The modulated signal is transmitted to the wireless data transmission base station closest to the passive transmission terminal node through the backscatter communication principle; the wireless data transmission base station receives the modulated signal and demodulates it to obtain the sensor data, and transmits the sensor data to the distributed data processing relay station using the short message protocol; The distributed data processing relay station receives sensor data from the wireless data transmission base station, processes the received sensor data, compresses the data using Huffman coding, generates data packets, and transmits the compressed data packets to the data server; The data server receives and decodes the data packets, recovers the original sensor data, and uses the abnormal state calculation algorithm of the power transmission corridor based on the improved isolation forest algorithm to perform abnormality detection on the decoded sensor data to obtain the abnormality detection results; Based on the abnormality detection results, determine whether there is any abnormality in the internal state of the transmission corridor. If there is an abnormality, mark the abnormal data and generate an alarm message to notify maintenance personnel to handle it according to the alarm information.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the distributed status monitoring method of the power transmission corridor based on the passive Internet of Things as described in claim 5 is implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the distributed status monitoring method of a power transmission corridor based on passive Internet of Things as described in claim 5 is implemented.

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