WO2017117853A1

WO2017117853A1 - Intelligent sensing node and sensing method for underground work surface wireless sensor network

Info

Publication number: WO2017117853A1
Application number: PCT/CN2016/074619
Authority: WO
Inventors: 李威; 杨海; 许少毅; 张金尧; 司卓印; 刘玉飞; 魏华贤; 鞠锦勇; 路恩; 董事; 盛连超; 杨康; 王茗; 须晓锋; 徐晗
Original assignee: 中国矿业大学
Priority date: 2016-01-08
Filing date: 2016-02-26
Publication date: 2017-07-13
Also published as: CN105554837A

Abstract

Provided are an intelligent sensing node and sensing method for an underground work surface wireless sensor network. The sensing node comprises a low power consumption microprocessor, a wireless receiving module, a wireless transmitting module, a sound-light alarm module, a temperature sensor, an infrared sensor, a vibration sensor, a sound sensor, a power source monitoring module, a storage battery and an anti-explosion and anti-shock housing. The sensing node monitors the electric quantity of the storage battery in real time, adaptive forwarding of the node information is determined via electric quantity information to realise an intelligent routing algorithm based on energy balance dissipation, meanwhile, the sensing node senses the environmental temperature, sound and infrared signals of a work surface in real time, and vibration information about the sensing node itself is measured. The sensing node measures positioning signals sent by other nodes, and performs node position interpretation under AOA/ TDOA to realise self-positioning of the sensing node. The sensing node has good environmental adaptation, the initial position of each node does not need to be set, and the sensing node and the sensing method can be applied to unstable environments after mining disasters.

Description

Wireless sensor network intelligent sensing node and sensing method for underground working face

Technical field

The invention relates to a sensor node and a method for wireless sensor network, in particular to an intelligent sensing node and a sensing method for a wireless sensor network in a downhole working surface.

Background technique

Currently,

Due to the harsh environment in the coal mining face, heading face, and slot, there are many mobile production equipments and complicated working conditions. The existing coal mine safety monitoring and control system needs to lay a large number of communication cables, and these cables continue to move and maintain with the progress of production. Very inconvenient, and often interrupted to affect the continuity of monitoring. In the event of a major disaster, the transmission of the transmission cable will cause the communication of the entire mine to be interrupted, which greatly restricts the rescue work of the rescue personnel. To reduce casualties and achieve the goal of unmanned coal mining face downhole, it is urgent to introduce new technologies to overcome the problems of restricting coal mine safety and disaster warning levels.

Wireless Sensor Network (WSN) is a new type of network and computing technology. It consists of a large number of micro sensor nodes deployed in the monitoring area. It can collaboratively monitor, sense and collect in real time through various integrated micro sensors. The information of various environments or monitored objects is processed by the embedded system, and the perceived information is transmitted to the access point in a multi-hop relay manner through a random self-organizing wireless communication network, which is especially suitable for environmental monitoring, disaster relief and disaster relief. , remote areas such as hazardous areas.

However, due to the distribution of wireless sensor network nodes and the dynamic change of coal mining face, sudden situations occur. Therefore, the main factors restricting the development of wireless sensor networks in underground working faces are the control of power consumption and the determination of real-time node position. . The power consumption control of the power supply is mainly due to the fact that the wireless sensor network node generally adopts the battery power supply technology, and the power carried is limited. Then, during the transmission of the underground wireless node, the data transmission capacity of the aggregation node and the relay node is large, so that the power consumption of each node is very different. When the energy consumption of one of the two key nodes is exhausted, the power of other nodes is exhausted. There are still many, resulting in the interruption of the entire wireless network, greatly shortening the service life of the wireless sensor network; because the coal mining face is based on the shearer reciprocating coal cutting on the scraper conveyor and the hydraulic support of the sliding frame The advancement of the working surface makes the working surface to be monitored by the wireless node in real time in the changing position. The traditional method of manually calibrating the position of the node can not meet the dynamic environment with the working surface. Moreover, when a disaster occurs underground, the location of each wireless node will also change greatly, making the real-time positioning technology of the wireless node more and more important.

Summary of the invention

The object of the present invention is to provide an intelligent sensing node and a sensing method for a wireless sensor network in a downhole working surface, which can enable each wireless sensor network to sense data in real time while ensuring energy balance consumption and improve the service life of the sensor network; In the dynamic environment, each sensory node is intelligently self-localized, which improves the environmental adaptability of the wireless sensor network in the catastrophic environment of the underground working face.

The object of the present invention is achieved as follows: a wireless sensor network intelligent sensing node includes: a low power consumption microprocessor, a wireless receiving module, a wireless transmitting module, an audible and visual alarm module, a temperature sensor, an infrared sensor, a vibration sensor, Sound sensor, power monitoring module, battery and explosion-proof, anti-shock housing; the input end of low-power microprocessor is connected with wireless receiving module, temperature sensor, infrared sensor, vibration sensor, sound sensor and power monitoring module, power monitoring module and Battery connection; the output of the low-power microprocessor is connected with the wireless transmitting module and the sound and light alarm module; low-power microprocessor, wireless receiving module, wireless transmitting module, sound and light alarm module, temperature sensor, infrared sensor, vibration The sensor, sound sensor, power monitoring module, and battery are all installed in an explosion-proof, shock-proof housing.

Wireless sensor network intelligent sensing method: The intelligent sensing node uses battery power supply. The power monitoring module performs power detection on the battery in real time. The sensing node monitors the ambient temperature, sound and infrared signals in real time, and measures the vibration information of the sensing node itself to perform external environmental information. Real-time sensing; the sensing node measures the positioning signal sent by the external node and performs AOA/TDOA external node position calculation, and simultaneously sends the location information of the located node and the wireless signal used for positioning of other nodes to realize the sleep and wake-up of the sensing node. Function, adaptive routing based on energy balanced dissipation, real-time sensing and information transmission of external environment, and self-positioning of nodes based on AOA/TDOA solution, realize self-positioning of intelligent sensing nodes;

It is implemented by the following steps:

Step 1) The wireless sensor network intelligent sensing node sends the call signal in real time through the wireless signal sending module, and uses the wireless receiving module to determine whether to receive the call signal sent by the external sensor in real time when in the sleep state, only when receiving the signal sent by the external node. At the same time, the node wakes up and wakes up, and uses the power detection module to detect its own power information, and sends a wireless signal carrying its own power information;

Step 2) The intelligent sensing node low-power microprocessor uses a temperature sensor to measure the temperature of the environment in which the sensor is located in real time, the infrared sensor measures the ambient infrared signal, the sound sensor measures the audio parameters of the environment, and the vibration sensor measures the vibration characteristics of the node itself, and The measured signal is analyzed, intelligently judges whether there is an abnormal situation and an alarm is generated, the sound and light alarm module is activated, and the alarm signal is sent out by the wireless transmitting module, otherwise the sensing node normally transmits the wireless signal carrying the sensor measurement parameter normally;

Step 3) Real-time judgment whether the node receives the wireless signal carrying the alarm information sent by other sensing nodes, and if it is received, immediately starts its own sound and light alarm module, and forwards the wireless signal carrying the alarm information and the initial alarm node number. , to achieve priority emergency transmission of alarm signals;

Step 4) The sensing node receives the wireless signal sent by the other sensing node and carries its own power information through the wireless receiving module, and detects the distance between the nodes by detecting the strength of the signal sent by other nodes, and combines the power information of the node. Perform intelligent route judgment under data transmission between nodes, determine whether the node participates in data information forwarding of other nodes, and if the forwarding conditions are met, forward the external node information received by the node to implement relay transmission of wireless sensor network information. , thereby implementing the establishment of a data routing algorithm;

Step 5: The sensing node measures the wireless signal carrying the positioning request sent by the other sensing node through the wireless receiving module, determines the distance range from the sensing node that sends the wireless signal, and uses its own wireless transmitting module to transmit its own wireless positioning. The signal starts the positioning requirement judgment at the same time. Only when the node itself has been calibrated, the AOA/TDOA signal is measured on the received wireless signal, and the unknown node is solved according to the position information of the known node to realize the unknown node. Position calibration

Step 6) According to step 5), the node coordinate parameter of the position calibration has passed the data routing algorithm between the nodes. The data forwarding is performed, and finally the location information is uploaded to the server; the server can detect the location of each sensing node and the environment sensing parameter under the corresponding node in real time, so as to realize the intelligent positioning and sensing of the wireless sensor network sensing node.

The self-positioning method described in the step 5) is a process of performing AOA/TDOA measurement on the wireless positioning signal sent by the unknown location node by the sensing node that has performed the position calibration, and solving the position thereof. The implementation steps are as follows:

Step 5-1) Firstly, using two sensing nodes close to the server as initial sensing nodes, constructing a local positioning coordinate system according to the required mine environment distribution, and performing manual position calibration in the coordinate system of the two initial sensing nodes;

Step 5-2) The two initial sensing nodes receive the positioning signals sent by other unknown nodes in real time, and perform signal measurement under AOA/TDOA, and combine the position parameters of the two initial nodes to calculate the position information of the unknown node, thereby realizing the node's position information. Position calibration, and forward the location information of the node to the server;

Step 5-3) The sensing node that implements the position calibration in step 5-2) combines the sensing node sent by another sensor in real time with the sensing node of another known position, and performs signal measurement under the AOA, and combines the proximity of the known position. The node performs signal measurement under TDOA;

Step 5-4) The AOA/TDOA parameters measured by the sensing node in step 5-3) are combined with the measured two node position information to perform position resolution of the unknown node, and the position calibration of the unknown node is realized, and the known position is also known. The node forwards the node location information and the node number information, and uploads it to the server through the inter-node routing algorithm;

Step 5-5) The sensing node of the determined location in step 5-4) receives the positioning signal sent by the other sensing node in real time, and combines the positioning of the adjacent node with the AOA/TDOA measurement of the positioning signal, and repeats steps 5-3) - Step 5-5), finally achieve position calibration under all sensing nodes and upload it to the server.

The AOA/TDOA is a combined measurement method based on the difference between the arrival angle and the arrival time.

The beneficial effect is that the wireless sensor network intelligent sensing node and sensing method can be used in the catastrophic environment of the mining face under the above-mentioned scheme, and is capable of self-organizing, self-positioning, node energy consumption balance and external environment state sensing. Intelligent wireless node; by arbitrarily arranging the wireless sensor intelligent sensing node in the well, the sensing node detects the temperature, infrared, sound and node carrier vibration information of the node in real time and passes the intelligent routing algorithm based on energy balance consumption to monitor the information in real time. Transmission to the server, and using the AOA/TDOA positioning signal measurement between the nodes to achieve the position calibration of each sensing node; in the key areas of coal mine safety monitoring such as working face, tunneling face and goaf, using the characteristics of wireless sensor network to overcome the existing Safety monitoring system deployment and real-time monitoring difficulties, to achieve rapid access to the environmental monitoring system after the wireless access detection and disaster recovery of coal mining, heading face, goaf gas and personnel positioning, and improve the coal mine safety monitoring system Under catastrophic conditions Survivability, the establishment of reliable coal mine safety monitoring wireless sensor networks.

It can enable each wireless sensor network to sense nodes to ensure energy balance consumption while real-time data transmission, and improve the service life of the sensor network. It can also realize that each sensory node is intelligent self-positioning under dynamic change environment, and the wireless sensor network is underground. The application in the catastrophic environment of the working face improves the environmental adaptability and achieves the object of the present invention.

Advantages: The invention can not only monitor the underground working face information at the node in an all-round way, but also realize the rapid construction and real-time positioning of the wireless monitoring network in the catastrophic environment, and improve the wireless sensor network to the harsh environment in the underground. Adaptability.

BRIEF DESCRIPTION OF THE DRAWINGS:

FIG. 1 is a schematic diagram of hardware components of an intelligent sensing node according to the present invention.

FIG. 2 is a flowchart of execution of a sensing method in an intelligent sensing node according to the present invention.

FIG. 3 is a flowchart of execution of an adaptive positioning method in the intelligent sensing method of the present invention.

In the figure, m01—server; s01, s02—initial sensing node; s11-s18—intellisense node; t01—data transmission path; t02—location signal path; t03—wireless sensing node; t04—node power indication.

detailed description

The technical solution of the present invention will be further described below by way of embodiments and with reference to the accompanying drawings. These examples are for illustrative purposes only and are not intended to limit the scope of the invention.

Embodiment 1: FIG. 1 is a schematic diagram of a module of an intelligent sensing node of a wireless sensor network in a downhole working surface. The wireless sensor network intelligent sensing node includes: a low power consumption microprocessor, a wireless receiving module, a wireless transmitting module, and sound and light. Alarm module, temperature sensor, infrared sensor, vibration sensor, sound sensor, power monitoring module, battery and explosion-proof, anti-shock shell; the input end of low-power microprocessor is connected with wireless receiving module, temperature sensor, infrared sensor, vibration sensor The sound sensor and the power monitoring module, the power monitoring module is connected to the battery; the output of the low-power microprocessor is connected with the wireless transmitting module and the sound and light alarm module; the low-power microprocessor, the wireless receiving module, the wireless transmitting module, The sound and light alarm module, temperature sensor, infrared sensor, vibration sensor, sound sensor, power monitoring module and battery are all installed in the explosion-proof and shock-proof casing.

The low-power microprocessor, the wireless receiving module, the wireless transmitting module, the sound and light alarm module, the temperature sensor, the infrared sensor, the vibration sensor, the sound sensor, the power monitoring module, and the battery are all fixedly installed in an explosion-proof and shock-proof casing. To ensure the applicability of the intelligent sensing node in the underground environment, and to avoid damage to the intelligent sensing node in the long-term bad environment and the catastrophic environment, and increase the reliability of the wireless sensing node.

As shown in FIG. 2, the wireless sensor network intelligent sensing method in the catastrophic environment, the intelligent sensing node uses the battery to supply power, and uses the power monitoring module to perform real-time power detection, and the sensing node uses its installed temperature sensor, infrared sensor, The sound sensor and the vibration sensor monitor the ambient temperature, sound and infrared signals in real time, and measure the vibration information of the sensing node carrier itself to perform real-time perception of the external environment information. At the same time, the sensing node measures the wireless signal carrying the positioning request sent by other sensing nodes in real time, and uses the low-power microprocessor to perform AOA/TDOA node position calculation, and simultaneously sends the location information of the positioned node and the other node positioning. Wireless signal to realize self-positioning of the intelligent sensing node;

The functions of implementing the sleep and wake-up functions of the sensing nodes, adaptive routing based on energy-balanced dissipation, real-time sensing and information transmission of the external environment, and node self-positioning based on AOA/TDOA solution are implemented by the following steps:

Step 1) The wireless sensor network intelligent sensing node broadcasts a transmission call signal to other neighboring nodes through a wireless signal transmitting module while in a sleep state, and uses the wireless receiving module to determine whether to receive the call signal when the node is in a sleep state. The call signal sent by the external sensor only wakes up the node when it receives the signal sent by the external node, and uses the power detection module to detect its own power information, and sends a wireless signal carrying its own power information;

Step 2) The intelligent sensing node low-power microprocessor uses a temperature sensor to measure the temperature of the environment in which the sensor is located in real time, the infrared sensor measures the ambient infrared signal, the sound sensor measures the audio parameters of the environment, and the vibration sensor measures the vibration characteristics of the node itself. And analyzing the measured signal, intelligently judging whether there is an abnormal situation in the measured information, if an abnormal situation occurs and an alarm occurs, the sound and light alarm module is activated at the same time, and the wireless signal carrying the alarm information is sent out by the wireless transmitting module, Otherwise, the sensing node normally transmits a wireless signal carrying the sensor measurement parameters.

Step 6) According to step 5), the node coordinate parameters of the position calibration are forwarded by the data routing algorithm between the nodes, and finally the location information is uploaded to the server; so that the server can detect the location and corresponding of each sensor node in real time. The environment-aware parameter under the node realizes the intelligent positioning and sensing of the sensor nodes of the wireless sensor network.

The intellisense method mainly implements the function that the sensing node detects the call signal sent by other nodes before receiving the call signal sent by other nodes, and only keeps the interval time to detect the call signal sent by other nodes, which can greatly reduce the perceived node in the non-working state. energy consumption.

And the sensing node shown in FIG. 3 sends its own power information (t04), and receives the wireless signal sent by the neighboring sensing node to carry its power information, and determines the remaining energy of the own node by comparing with the own power signal. The energy level of all the nodes of the entire wireless sensor network, and whether to forward the wireless information sent by other nodes according to the energy level of the wireless sensor network, thereby sharing the energy consumption of other sensing nodes due to data transmission, thereby realizing the energy balance consumption of the entire wireless sensor network node. Scattered. Specifically, the sensing node (s16) and the sensing node (s11) are capable of receiving the signal sent by the sensing node (s17), but since the sensing node (s16) has less self-power information than the sensing node (s11), the sensing node is not The information of (s17) is forwarded and transmitted, and the sensing node (s11) undertakes to forward and transmit s17. The same principle, the sensing node (s15) is relatively more charged than the sensing node (s13) itself, and therefore bears the information forwarding transmission to the sensing node (s18). According to this principle, the node energy balance consumption of the entire wireless sensor network is finally realized.

As shown in FIG. 3, the self-localization method described in step 5) of the wireless sensor network intelligent sensing method in the catastrophic environment mainly relies on the sensory node that has performed the position calibration to perform AOA/ on the wireless positioning signal sent by the unknown location node. TDOA measures and solves the process of its location. The main implementation steps are as follows:

Step 5-1) Firstly, using two sensing nodes (s01, s02) close to the server (m01) as initial sensing nodes, constructing a local positioning coordinate system according to the required perceived mine environment distribution, and constructing two initial sensing nodes ( S01, s02) perform manual position calibration in the coordinate system, and the initial node of the manual position calibration is marked with a five-pointed star;

Step 5-2) The two initial sensing nodes (s01, s02) receive the positioning sent by the unknown nodes (s11, s12, s13, s14) in real time. a signal, wherein an initial sensing node (s01) receives a positioning signal of an unknown node (s11, s12), and an initial sensing node (s02) receives a positioning signal of an unknown node (s12, s13, s14), so that the two initial sensing nodes can simultaneously receive The positioning signal to the unknown node (s12) is used to measure the signal of the unknown node (s12) under the AOA/TDOA by using the initial sensing node, and the position of the unknown node (s12) can be solved by combining the calibration position parameters of the two initial nodes. Information, realizing the location calibration of the node, and forwarding the location information of the node to the server;

Step 5-3) The sensing node (s12) implementing the position calibration in step 5-2) combines the sensing node of one of the known positions to receive the positioning signal transmitted by the other node in real time. Therefore, according to the schematic diagram in this embodiment, the sensing node (s12) of the known location can simultaneously receive the positioning signal of the unknown node (s11) in combination with the initial sensing node (s01), and perform signal measurement under the AOA, and the TDOA. The signal measurement further utilizes the position information of the initial sensing node (s01) and the sensing node (s12) to perform location solution calculation of the unknown node (s11), and passes the solved position information together with the node number of the unknown node (s11) through the data. Forwarding is transmitted to the server; the same principle, the sensing node (s12) of the known location can simultaneously receive the positioning signal sent by the unknown node (s13) in combination with the initial sensing node (s02), and can solve the unknown by the AOA/TDOA positioning algorithm. The location information of the node (s13) is forwarded and uploaded by the corresponding location information;

Step 5-4) The sensing node (s11, s13) of the known location obtained in step 5-3) continues to receive the wireless signal sent by the unknown node in real time in combination with the sensing node of the adjacent known location, and performs AOA/TDOA. Signal measurement, combined with the measured two node position information to solve the position of the unknown node, to achieve the location calibration of the unknown node, while the known location node forwards the node location information and the node number information, and uploads to the server through the inter-node routing algorithm. The position of the unknown node (s16, s14) may be further calibrated by using the known node obtained after step 5-3, and adding the initial sensing node;

Step 5-5) Repeat the node positioning algorithm in step 5-3)-step 5-5), finally realize position calibration under all the sensing nodes, and upload the position information and the node number to the server to implement all the sensing nodes. Real-time dynamic positioning.

The invention realizes real-time and comprehensive monitoring of the environmental information of the coal mine working face, and can realize the rapid construction and real-time position calibration of the wireless monitoring network in the disaster environment, and improves the adaptability of the wireless sensor network to the harsh environment in the underground.

Claims

An intelligent sensing node of a wireless sensor network in a downhole working surface, characterized in that: the wireless sensor network intelligent sensing node comprises: a low power consumption microprocessor, a wireless receiving module, a wireless transmitting module, an audible and visual alarm module, a temperature sensor, an infrared sensor, Vibration sensor, sound sensor, power monitoring module, battery and explosion-proof, shock-proof casing; the input end of the low-power microprocessor is connected with a wireless receiving module, a temperature sensor, an infrared sensor, a vibration sensor, a sound sensor and a power monitoring module, and a power supply The monitoring module is connected to the battery; the output of the low-power microprocessor is connected with the wireless transmitting module and the sound and light alarm module; the low-power microprocessor, the wireless receiving module, the wireless transmitting module, the sound and light alarm module, the temperature sensor, and the infrared Sensors, vibration sensors, sound sensors, power monitoring modules, and batteries are all installed in an explosion-proof, shock-proof enclosure.
The method for sensing an intelligent sensing node of a wireless sensor network in a downhole working face according to claim 1 is characterized in that: the wireless sensor network intelligent sensing method: the intelligent sensing node uses battery power supply, and the power monitoring module performs power detection and sensing on the battery in real time. The node monitors the ambient temperature, sound and infrared signals in real time, and measures the vibration information of the sensing node itself to perform real-time sensing of the external environment information; the sensing node measures the positioning signal sent by the external node and performs the AOA/TDOA external node position calculation, and simultaneously Sending location information of the located node and wireless signals for positioning of other nodes, implementing sleep and wake-up functions of the sensing node, adaptive routing based on energy-balanced dissipation, real-time sensing and information transmission of the external environment, and based on AOA/TDOA Self-positioning of the intelligent sensing node by solving functions such as node self-positioning;

It is implemented by the following steps:

Step 1) The wireless sensor network intelligent sensing node sends the call signal in real time through the wireless signal sending module, and uses the wireless receiving module to determine whether to receive the call signal sent by the external sensor in real time when in the sleep state, only when receiving the signal sent by the external node. At the same time, the node wakes up and wakes up, and uses the power detection module to detect its own power information, and sends a wireless signal carrying its own power information;

Step 2) The intelligent sensing node low-power microprocessor uses a temperature sensor to measure the temperature of the environment in which the sensor is located in real time, the infrared sensor measures the ambient infrared signal, the sound sensor measures the audio parameters of the environment, and the vibration sensor measures the vibration characteristics of the node itself, and The measured signal is analyzed, intelligently judges whether there is an abnormal situation and an alarm is generated, the sound and light alarm module is activated, and the alarm signal is sent out by the wireless transmitting module, otherwise the sensing node normally transmits the wireless signal carrying the sensor measurement parameter normally;

Step 3) Real-time judgment whether the node receives the wireless signal carrying the alarm information sent by other sensing nodes, and if it is received, immediately starts its own sound and light alarm module, and forwards the wireless signal carrying the alarm information and the initial alarm node number. , to achieve priority emergency transmission of alarm signals;

Step 4) The sensing node receives the wireless signal sent by the other sensing node and carries its own power information through the wireless receiving module, and detects the distance between the nodes by detecting the strength of the signal sent by other nodes, and combines the power information of the node. Perform intelligent route judgment under data transmission between nodes, determine whether the node participates in data information forwarding of other nodes, and if the forwarding conditions are met, forward the external node information received by the node to implement relay transmission of wireless sensor network information. , thereby implementing the establishment of a data routing algorithm;

Step 5) The sensing node measures the wireless signal carrying the positioning request sent by the other sensing node through the wireless receiving module, determines the distance range of the sensing node that sends the wireless signal, and uses its own wireless sending module to outward. It transmits its own wireless positioning signal and starts the positioning requirement judgment. Only when the node itself has been positionally calibrated, the AOA/TDOA signal is measured on the received wireless signal, and the unknown node is solved according to the position information of the known node. , to achieve location calibration of unknown nodes;

Step 6) according to step 5), the node coordinate parameters of the position calibration are performed by the data routing algorithm between the nodes; finally, the location information is uploaded to the server; so that the server can detect the location and corresponding of each sensor node in real time. The environment-aware parameter under the node realizes the intelligent positioning and sensing of the sensor nodes of the wireless sensor network.
The wireless sensor network intelligent sensing method for a downhole working face according to claim 2, wherein the self-positioning method described in the step 5) is performed by using a sensory node that has performed position calibration to send to an unknown location node. The wireless positioning signal performs AOA/TDOA measurement and solves the process of its position. The implementation steps are as follows:

Step 5-1) Firstly, using two sensing nodes close to the server as initial sensing nodes, constructing a local positioning coordinate system according to the required mine environment distribution, and performing manual position calibration in the coordinate system of the two initial sensing nodes;

Step 5-2) The two initial sensing nodes receive the positioning signals sent by other unknown nodes in real time, and perform signal measurement under AOA/TDOA, and combine the position parameters of the two initial nodes to calculate the position information of the unknown node, thereby realizing the node's position information. Position calibration, and forward the location information of the node to the server;

Step 5-3) The sensing node that implements the position calibration in step 5-2) combines the sensing node sent by another sensor in real time with the sensing node of another known position, and performs signal measurement under the AOA, and combines the proximity of the known position. The node performs signal measurement under TDOA;

Step 5-4) The AOA/TDOA parameters measured by the sensing node in step 5-3) are combined with the measured two node position information to perform position resolution of the unknown node, and the position calibration of the unknown node is realized, and the known position is also known. The node forwards the node location information and the node number information, and uploads it to the server through the inter-node routing algorithm;

Step 5-5) The sensing node of the determined location in step 5-4) receives the positioning signal sent by the other sensing node in real time, and combines the positioning of the adjacent node with the AOA/TDOA measurement of the positioning signal, and repeats steps 5-3) - Step 5-5), finally achieve position calibration under all sensing nodes and upload it to the server.