CN113494940A - Dangerous house monitoring system based on wireless sensor network - Google Patents
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Abstract
The invention discloses a dangerous house monitoring system based on a wireless sensor network, which comprises a plurality of house state monitoring nodes, house information aggregation nodes, an NB (NB) network, a cloud platform and a user terminal, wherein the house state monitoring nodes are distributed at a plurality of monitoring positions of a house, each house is provided with one house information aggregation node, the house information aggregation nodes are connected with the cloud platform network through the NB network, and the user terminal is connected with the cloud platform network. The method comprises the steps of utilizing an acceleration sensor to collect house state information in real time, improving a sensor calibration algorithm, and improving precision by adopting a temperature sectional type six-position calibration method. Finally, a high-precision low-power consumption remote monitoring scheme is realized, a decision maker can master the house state in real time through a mobile phone, the house inclination angle monitoring precision is improved, and the method has popularization and application values.
Description
Technical Field
The invention relates to the technical field of dangerous house safety monitoring, in particular to a dangerous house monitoring system based on a wireless sensor network.
Background
For a long time, the monitoring and management of the old and old crisis in China mainly depends on manpower, and related informatization systems are gradually proposed in recent years: the dangerous house dynamic monitoring and early warning system provided by the prior art integrates manual patrol and online real-time monitoring information, and regularly issues monitoring reports, but data acquisition still depends on manual work, and is time-consuming, labor-consuming and inaccurate; in addition, a system for monitoring the state of a dangerous house by using aerial photography of an unmanned aerial vehicle is provided, the unmanned aerial vehicle is used for carrying equipment such as remote sensing, satellite positioning and the like, the real-time condition of the house is monitored by identifying a remote sensing image, but the requirement on the image identification rate is high, and the state of the house cannot be mastered in real time; an unattended intelligent monitoring system for the dangerous house based on sensors is also provided, which can remotely observe the structural change of the house and give an alarm in real time. From the world, house monitoring needs to further adopt an informatization means, and health management of buildings is realized through indoor environment monitoring. The above-mentioned several adopt the monitoring system of sensor formula, can real-time sensing building's state, but have the sensor heavy, the circuit is more, difficult problem such as installation on a large scale.
Disclosure of Invention
The invention aims to solve the problems and provide a dangerous house monitoring system based on a wireless sensor network. The method is characterized in that a low-power-consumption micro-electromechanical sensor is deployed in each house, key beam, column and wall information is collected, key beam, column and wall nodes are connected through a wireless radio frequency module to form a wireless sensing network, a transmission path is optimized, information is transmitted to a sink node to be screened and integrated, and finally data is transmitted to a cloud platform through a wireless network, so that a user can remotely master the health condition of each house in real time.
The invention realizes the purpose through the following technical scheme:
the invention relates to a dangerous house monitoring system based on a wireless sensor network, which comprises a plurality of house state monitoring nodes, a plurality of house information aggregation nodes, an NB network, a cloud platform and a user terminal, wherein the house state monitoring nodes are distributed at a plurality of monitoring positions of a house, each house is provided with one house information aggregation node, the house information aggregation nodes are connected with the cloud platform network through the NB network, and the user terminal is connected with the cloud platform network.
Further, house state monitoring node comprises power module, inclination sensor, main control chip and wireless radio frequency module, power module's power output end with main control cabinet chip's power input end is connected, inclination sensor's signal output part with main control chip's signal input part is connected, main control chip's signal transmission end with wireless radio frequency module connects, wireless radio frequency module with house information assembles nodal connection.
Furthermore, the house information aggregation node comprises a power supply module, a main control chip, a wireless radio frequency module, an NB-IOT communication module, a GPS positioning sensor and a temperature sensor, wherein a power supply output end of the power supply module is connected with a power supply input end of the main control chip, the wireless radio frequency module is connected with the house state monitoring node, a signal transmission end of the wireless radio frequency module is connected with a signal transmission end of the main control chip, a network signal transmission end of the main control chip is connected with the NB-IOT communication module, the NB-IOT communication module is connected with an NB network, a signal output end of the GPS positioning sensor is connected with the main control chip, and the temperature sensor is connected with a signal input end of the main control signal.
Preferably, the tilt sensor is a three-axis low-power consumption acceleration sensor of model BMA 253. The wireless radio frequency module is formed by a model nRF24L01 chip.
The invention relates to a control method of a dangerous house monitoring system based on a wireless sensor network, which comprises the following steps:
s1: firstly, initializing each module, setting parameters and ensuring that system data can be normally received and transmitted;
s2: after network registration is successful, the sink node generates an optimal routing table according to network topology, collects state data of each house node according to the routing, and obtains accurate data through filtering and error compensation;
s3: if a data acquisition command from the cloud platform is received, starting a GPS module, acquiring current position and time information, sending time, position and house state information to the cloud platform through an NB (NB) network, and entering a low-power-consumption mode; if the state data exceeds the warning value, the information is reported to the cloud platform; if the alarm value is not exceeded, no action is taken;
s4: and finally, entering a low-power-consumption mode and waiting for the next interrupt wakeup.
Further, the wireless radio frequency module comprises a data sending part and a data receiving part, when data is sent, a data prefix is set, the data is sent, and if a response signal is received within a specified time, the sending is successful; if no response signal is received, the frequency hopping is carried out according to the established rule and the retransmission is carried out until the response signal is received. When receiving data, firstly detecting carrier waves, analyzing a data packet under the condition of frequency channel matching, reading the data when a destination address is equal to a node address, carrying out CRC (cyclic redundancy check), and if the check is unsuccessful, carrying out frequency hopping according to a set rule and re-receiving the data; and when the destination address is not equal to the node address, forwarding is carried out.
The invention has the beneficial effects that:
the invention relates to a dangerous house monitoring system based on a wireless sensor network, which introduces the technologies of the wireless sensor network, a micro-electromechanical sensor and the like into the field of dangerous house monitoring, realizes the monitoring of the whole house by monitoring key beam, column and wall nodes, has the precision of the monitored inclination angle error lower than 0.1 degree, and lays a foundation for making accurate prejudgment by using an artificial intelligence algorithm. The design has the remarkable characteristics of low power consumption and intellectualization. "Low power consumption" is mainly embodied in two aspects: (1) the novel generation of the Bosch BMA253 sensor is adopted, the inclination angle is measured by using the acceleration, and the microcontroller is adopted for real-time control, so that the sensor works in a normal mode, a deep pause mode and other modes for real-time switching, and the energy consumption is effectively reduced; (2) the low-power transmission technology is adopted, the routing algorithm is optimized, and the life cycle of the system is effectively prolonged by using the low-power radio frequency chip. "intelligence" is also embodied in two aspects: (1) the dangerous room data acquisition is intelligentized, the inaccuracy and non-real-time property of the conventional manual acquisition are abandoned, the three-axis acceleration sensor is adopted for remote real-time acquisition, and the calibration algorithm of the sensor is improved, so that the precision of the inclination angle error is lower than 0.1 degree; (2) the collection end finds abnormal data and gives an alarm in time, the upper computer utilizes the cloud platform to store the mass data in a classified mode to generate a house state curve according to the day and the month, and the house state is monitored.
Drawings
FIG. 1 is a general block diagram of a crisis monitoring system of the present invention
FIG. 2 is a block diagram of a hardware circuit of the monitoring system for a dangerous room according to the present invention
In fig. 2: (a) house state monitoring node (b) house information aggregation node
FIG. 3 is a hardware circuit diagram of the present invention
In fig. 3: (a) BMA253 Circuit diagram (b) nRF24L01 Circuit diagram
FIG. 4 is a three-room house network topology diagram of the present invention
FIG. 5 is a flow chart of the house terminal of the monitoring system for dangerous house of the present invention
FIG. 6 is a flow chart of nRF24L01 data transceiving of the present invention
In fig. 6: (a) transmitting data flow diagram (b) receiving data flow diagram
FIG. 7 is a graph of a crisis monitoring system of the present invention
In fig. 7: (a) daily variation curve of inclination angle (b) quartering curve of inclination angle
Detailed Description
The invention will be further described with reference to the accompanying drawings in which:
as shown in fig. 1: the invention relates to a dangerous house monitoring system based on a wireless sensor network, which comprises a plurality of house state monitoring nodes, a plurality of house information aggregation nodes, an NB network, a cloud platform and a user terminal, wherein the house state monitoring nodes are distributed at a plurality of monitoring positions of a house, each house is provided with one house information aggregation node, the house information aggregation nodes are connected with the cloud platform network through the NB network, and the user terminal is connected with the cloud platform network.
The collection end of the dangerous house monitoring system is composed of house state monitoring nodes and house information aggregation nodes. The house state monitoring node is provided with a three-axis acceleration sensor and can acquire the inclination angle information of key beams, columns and walls. Aiming at different house type structures, a routing algorithm of direct transmission and storage forwarding is adopted to ensure that data can be effectively transmitted. The house information aggregation node is a data aggregation and forwarding center, collects information of other monitoring nodes, performs filtering, error compensation and sorting fusion on the data, and finally sends the data to the cloud platform. The user can access the cloud platform through the Internet by utilizing the mobile phone APP, and different house information can be obtained according to different permissions.
As shown in fig. 2 (a): the house state monitoring node comprises power module, tilt sensor, main control chip and wireless radio frequency module, power module's power output end with main control cabinet chip's power input end is connected, tilt sensor's signal output part with main control chip's signal input part is connected, main control chip's signal transmission end with wireless radio frequency module connects, wireless radio frequency module with house information assembles the nodal connection.
As shown in fig. 2 (b): the house information gathering node comprises a power supply module, a main control chip, a wireless radio frequency module, an NB-IOT communication module, a GPS positioning sensor and a temperature sensor, wherein the power supply output end of the power supply module is connected with the power supply input end of the main control chip, the wireless radio frequency module is connected with the house state monitoring node, the signal transmission end of the wireless radio frequency module is connected with the signal transmission end of the main control chip, the network signal transmission end of the main control chip is connected with the NB-IOT communication module, the NB-IOT communication module is connected with the NB network, the signal output end of the GPS positioning sensor is connected with the main control chip, and the temperature sensor is connected with the signal input end of the main control signal.
The dangerous house monitoring system mainly realizes the collection and transmission of house inclination angle data, and according to a modularized design idea, a house state monitoring node mainly comprises an inclination angle sensor module, a power supply module, a wireless radio frequency module and a main control module; the house information aggregation node mainly comprises a GPS positioning module, a temperature sensor module, a wireless radio frequency module, an NB-IoT communication module and a main control module, and the size, the power consumption and the precision of the tilt angle sensor, the type selection and the routing design of the wireless radio frequency module are the key points of system design.
Preferably, the tilt sensor is a three-axis low-power consumption acceleration sensor of model BMA 253. The wireless radio frequency module is formed by a model nRF24L01 chip.
Designing a tilt sensor module:
when a house is settled, distorted or inclined, bearing beams, columns or bearing walls of the building are inclined, and high-precision sensors are required to accurately test the inclination angles of the key beams, columns and walls. Because the inclination angle measuring instrument used in engineering is heavy, the mass is about 500-5000 g, the working voltage is 12-48V, and part of the inclination angle measuring instrument adopts a wired data transmission mode, the inclination angle measuring instrument is not suitable for being directly installed in a house for use, and a sensor with small volume and low power consumption is required to measure the inclination angle.
The three-axis acceleration sensor is small in size and can measure accelerations Ax, Ay and Az on x, y and z axes in real time. Wherein the inclination angle θ on the z-axis satisfies formula (1). If acceleration values of 3 axes can be obtained, the offset angle of the z-axis can be obtained by the formula (1).
The factors such as working voltage, resolution ratio, volume and the like are comprehensively considered, and the inclination angle sensor adopts a Bosch series triaxial low-power consumption acceleration sensor BMA 253. The power supply voltage of the device is 1.62-3.60V, the working mode is switched among a normal mode, a deep pause mode, a low power consumption mode and a pause mode, and the lowest power consumption current is as low as 6.5 muA. The BMA253 adopts a differential capacitance principle to convert the angle change into an acceleration signal for output, the conversion precision is 12-bit digital resolution, and the size is a cuboid of 2mm multiplied by 0.95 mm. In the similar sensors, the resolution is high, the size is small, the power consumption is low, the comprehensive performance is optimal, the design requirements can be met, and a specific hardware circuit is shown in fig. 3 (a).
Designing a communication module:
due to the particularity of the monitoring of the dangerous house, the tilt angle sensor module can be arranged on key beams, columns and walls of the house through rivets or screws to form house state monitoring nodes. The monitoring nodes carry out networking through a pre-designed routing protocol and transmit data to the sink nodes. Each household sets a sink node, and the collected information is integrated and uploaded to the cloud platform.
Currently, the commonly used short-distance wireless communication technologies include ZigBee, WiFi, bluetooth and wireless radio frequency technologies. The communication frequency bands of the wireless radio frequency technology mainly comprise four frequency bands of 230MHz, 315MHz, 433MHz and 2.4GHz, and the first three frequency bands have the defects of weak signal penetration capacity, low transmission speed, difficulty in networking and the like although the communication distance can reach thousands of meters, so that the wireless radio frequency technology with the starting frequency of 2.4Ghz and the bandwidth of 0.125Ghz is adopted as an alternative scheme of short-distance wireless communication. The ZigBee technology, the WiFi technology, the Bluetooth technology and the radio frequency (2.4Ghz) technology are compared, and the radio frequency (2.4Ghz) technology has the characteristics of low power consumption, low development cost, good anti-interference capability and the like, so the radio frequency (2.4Ghz) technology is adopted as a short-distance wireless communication means, and the specific parameter comparison is shown in Table 1.
TABLE 1 comparison of near field communication technologies
The wireless radio frequency module adopts nRF24L01, the working frequency is 2.400-2.525 Ghz, the working frequency range can be divided into 126 optional frequency channels at most, the working frequency of the frequency channel is shown as formula (2), wherein F0 is the working frequency, RFCH is the frequency value configured by the module register, and two communication parties can communicate only by keeping the same working frequency. The nRF24L01 data transmission rate is 1Mbps or 2Mbps, and when operating in 2Mbps mode, the value of RFCH must be greater than or equal to 2 to avoid inter-module interference. Bluetooth, Wi-Fi, etc. also operate at 2.4Ghz, but interfere less with nRF24L01, mainly for two reasons: firstly, the data transmission rate of the radio frequency module is higher, so the transmission time is short, and the collision can be effectively avoided; second, a frequency hopping technique is employed. Dividing a channel with a bandwidth of 0.125Ghz into a plurality of radio frequency channels, carrying out discrete change on carrier waves of transmission signals of a transmitting side and a receiving side according to a preset rule, setting the same time period, and when a transmitting side transmits data, if a response signal is not received in a specified time period, automatically switching to the next channel according to a set rule for retransmitting; when receiving data, if no effective data is received in a specified time period, the receiving end automatically switches to the next frequency channel according to the same rule to continue detecting carrier signals. The time interval of the frequency modulation technology is very short, and data transmission can be effectively completed. The interface circuit design of nRF24L01 module is shown in fig. 3 (b).
Fo＝2 400+RFCH(Mhz) (2)
nRF24L01 transmits data in enhanced ShockBurst mode with a data frame format as shown in table 2. When the measured tilt angle value is between-90 deg. and 90 deg., the entire data frame size is about 9B, i.e., 72B. The sink node collects information in units of users. The sink node integrates the collected data volume of the monitoring node, only the node number and the dip angle information are reserved, and the data volume is more simplified after the sink node is integrated.
TABLE 2 data frame format for enhanced ShockBurst mode
The NB-IoT technology is adopted for long-distance transmission, NB networks in most regions are popularized at present, an NB05-01 module selected by the system is small in size, low in power consumption and cost, the transmission rate is 160-250 Kbps, the trouble of network disconnection of household Wi-Fi can be solved, and the system is very suitable for receiving and transmitting house inclination angle monitoring data with small data volume.
Wireless network routing design:
each household is generally provided with only one sink node, the sink node has no special requirement on the installation position, and the mains supply can be introduced by adopting a power adapter. Other monitoring nodes are different in installation position, the number of the nodes is large, and the power can be supplied only by a battery. Therefore, a routing protocol with low power consumption needs to be designed, so that the monitoring node can work for as long as possible, and inconvenience caused by frequent battery replacement is avoided.
In fact, a house may have only one house or may include more than two houses, and the house area is large. For the case of one house and one house, the nRF24L01 can reach 100m in the communication distance without wall shielding, and the data of all monitoring nodes can be directly transmitted to the sink node. According to the frequency characteristics of nRF24L01, 126 channels can be divided, each monitoring node allocates a different channel as a starting frequency, and when receiving data, the sink node sets the corresponding channel to receive the corresponding data. For the situation of two houses or more than two houses of one house, if the house area is large or a wall is blocked, the transmission signal of the nRF24L01 is greatly attenuated, some monitoring nodes cannot directly communicate with the sink node, and therefore, part of the monitoring nodes are required to be adopted as relay nodes to transmit information to the sink node.
The specific route generation process is as follows: and acquiring the positions of the adjacent nodes by the sink nodes to form an adjacent matrix, and if isolated nodes exist, repeating the steps of the next-level nodes of the sink nodes until all the nodes are traversed. Then, considering the consumption of node sending data, the consumption of the node sending data on adjacent nodes and the consumption of node receiving response, a basic cost matrix from a source node to a destination node is calculated according to the formula (3), element values in the basic cost matrix represent the transmission power consumption of two directly connected nodes, the two matrixes are integrated, and the lowest power consumption from each monitoring node to a sink node is calculated, so that an optimal path table can be obtained.
Eitotal＝Ere-cost×adj(i)+Eitotal×adj(d) (3)
In the formula, EitotalRefers to the total power consumption from node i to the destination node, EiPower consumption of a finger node i for transmitting or receiving data, Etr-costRefers to the power consumption of the effect of node i on each neighboring node when transmitting data, Ere-costAfter receiving data, replying confirmation information to affect power consumption of each adjacent node, wherein adj (i) and adj (d) respectively indicate the neighbor numbers of the node i and the destination node d.
Taking a house in three rooms of one house as an example, a topological graph corresponding to nodes distributed in the rooms is shown in fig. 4, where a node 0 is a sink node and other nodes are monitoring nodes. From fig. 4, an adjacency matrix Asd of the house can be obtained, as shown in equation (4), where "1" in the matrix indicates that the node is an adjacency node and can directly communicate with the node; "0" means not a neighboring node and cannot communicate directly. Further, according to equation (3), considering the energy consumption state and the simplicity of calculation of nRF24L01, E is set to 2mW when communicating directly and 3mW when passing through the walltr-cost＝Ere-costCalculating a basic cost matrix P from the source node to the target node as 0.2mWsdAs shown in formula (5).
By using the formula (4) and the formula (5), the optimal path between the source node and the target node can be determined by solving the lowest energy consumption between the source node and the target node through a minimum node traversal method. Take the example of finding the most complex optimal path from node 2 to node 0 in this example. According to the formula (4), no direct path exists from the node 2 to the node 0, and the data can only arrive in a transit form; by a common traversal method, the following 5 paths can be obtained, which are respectively: (1) node 2 → node 1 → node 0; (2) node 2 → node 3 → node 1 → node 0; (3) node 2 → node 3 → node 4 → node 0; (4) node 2 → node 3 → node 5 → node0; (5) node 2 → node 3 → node 4 → node 5 → node 0. With the formula (5), the power consumptions corresponding to the 5 paths can be obtained as follows: (1) p20-1＝4.2+3.4＝7.6mW；(2)P20-2＝3.2+4.6+3.4＝11.2mW；(3)P20-3＝3.2+4.6+4.4＝12.2mW；(4)P20-4＝3.2+4.4+4.2＝11.8mW；(5)P20-53.2+4.6+ 3.4-14.2 mW. It is clear that the path (1) consumes the least power. In summary, the more intermediate nodes, the more power consumed. Therefore, a path between two nodes is searched by adopting a minimum node traversal method, if the paths have a plurality of paths, the power consumption is compared, and the path with the lowest power consumption is selected as the optimal path.
Calibrating the inclination angle of the acceleration sensor:
in practical engineering application, the inclination angle data collected by the monitoring nodes is a core element for judging the state of a dangerous house, and the requirements on the limit values of the inclination rates of different floor heights are different according to 'dangerous house identification standard' (JGJ125-2016) published by the Ministry of housing and construction of China in 2016, as shown in Table 3.
TABLE 3 Whole House Tilt Rate Limit
Wherein HgThe height of the building from the outdoor ground. As can be seen from table 3, the lower limit of the tilt rate specified in the national standard is 0.5%, and the tilt angle is about 0.3 ° when converted, so the tilt angle error collected by the sensor is less than 0.3 °.
The invention researches error compensation technology based on position, temperature and the like, improves the traditional six-position calibration method, and adopts a temperature sectional six-position calibration method design. According to the error principle of the three-axis acceleration sensor, the following models are established:
in the formula, Ni (i ═ x, y, z) is an output response of the acceleration sensor, Ai (i ═ x, y, z) is an excitation acceleration of a corresponding axis of the acceleration sensor, ki (i ═ x, y, z) is a scale factor of the acceleration sensor, kij (i ═ x, y, z; j ≠ x, y, z; i ≠ j) is a non-orthogonal error of the acceleration sensor, and Di (i ═ x, y, z) is a zero offset of the acceleration sensor. During calibration, the model can obtain 12 parameters by determining only 6 positions, and the position parameters are shown in table 4.
TABLE 4 coordinate axis orientation and gravitational acceleration of each axis of a triaxial acceleration sensor
The acceleration sensor is placed on a rotary table with the inclination precision error lower than 0.01 degrees, and the rotary table is provided with a temperature control device to keep constant temperature. The corresponding value can be obtained by controlling the turntable to make the acceleration sensor reach the corresponding position, recording the output response Nij (i ═ x, y, z;. j ═ 1, 2, 3, 4, 5, 6) at that time, and substituting into equation (6). For example, when the acceleration sensor is at position 1 and position 6, it is obtained
From the formulae (7) and (8)
Similarly, when the acceleration sensors are placed at the position 2 and the position 5, the result is
When the acceleration sensor is placed at the position 3 and the position 4, the result is obtained
The corresponding Dx, Dy and Dz in the formula (10), the formula (12) and the formula (14) are averaged to finally obtain the expression
During calibration, all parameters in the model can be solved by using the equations (9), (11), (13) and (15) through a six-position method. Because the acceleration sensor generates about 0.1 degree of error every 20 ℃, during calibration, the temperature range is divided by taking 20 ℃ as a unit, the highest temperature and the lowest temperature are fixed, a six-position method is used twice to obtain related parameters, and the related parameters are averaged to obtain the final parameter of the temperature section. When a new acceleration value is input, error compensation is carried out according to different temperature section models to obtain accurate values of Nx, Ny and Nz, so that an accurate inclination angle is determined.
System control design
The flow of the main program of the house terminal of the crisis monitoring system is shown in fig. 5. When the system is powered on, the modules are initialized firstly, and parameter setting is carried out to ensure that system data can be normally received and transmitted. And after the network registration is successful, the sink node generates an optimal routing table according to the network topology, acquires the state data of each house node according to the routing, and obtains accurate data through filtering and error compensation. If a data acquisition command from the cloud platform is received, starting a GPS module, acquiring current position and time information, sending time, position and house state information to the cloud platform through an NB (NB) network, and entering a low-power-consumption mode; if the state data exceeds the warning value, the information is reported to the cloud platform; if the alarm value is not exceeded, no action is taken. And finally, entering a low-power-consumption mode and waiting for the next interrupt wakeup.
The part of collecting the house state data information mainly comprises data transceiving by using nRF24L 01. The nRF24L01 processes the data packet in an enhanced ShockBurst mode, which can automatically retransmit data and automatically generate a response signal, and has a short data transmission time, effectively avoids data collision and reduces energy consumption, and the specific data frame format is shown in table 3. The flow of communication using nRF24L01 mainly includes two parts, namely data transmission and data reception, and is determined according to the received "transmission enable" and "reception enable" signals, and the specific flow is shown in fig. 6. When data is transmitted, a data prefix is set, the data is transmitted, and if a response signal is received within a specified time, the successful transmission is indicated; if no response signal is received, the frequency hopping is carried out according to the established rule and the retransmission is carried out until the response signal is received. When receiving data, firstly detecting carrier waves, analyzing a data packet under the condition of frequency channel matching, reading the data when a destination address is equal to a node address, carrying out CRC (cyclic redundancy check), and if the check is unsuccessful, carrying out frequency hopping according to a set rule and re-receiving the data; and when the destination address is not equal to the node address, forwarding is carried out.
The user terminal APP function of the invention mainly comprises a registration login module, a house data check module and a house data analysis module. During design, the IMEI code of the equipment is bound with user information, and a general user can log in and check the state information of the house of the user through identity registration authentication but cannot check the information of other people. And only the administrator account has the authority to check all house information and export the house state information in the area for a period of time so as to realize the integral early warning and decision of the houses in the area.
And (3) testing a system result:
according to the system achieved by the software and hardware design, the precision of the system is tested, different inclination angles are set by using a standard module with the error precision lower than 0.01 degree, the angle output by the system is compared with the standard test angle, and the specific result is shown in table 5.
TABLE 5 Tilt Angle test results comparison
As can be seen from the table 5, in the standard range of-90 degrees to 90 degrees, the error precision of the inclination angle of the sensor in the system is less than 0.1 degree, the requirement that the inclination precision is less than 0.3 degree in the dangerous house standard can be met, the error precision is smaller, and the accurate analysis and the prejudgment of the house state can be realized through the long-term accumulation of data. In order to test the communication reliability of the system, the data acquisition and receiving nodes are placed in spaces with different distances, different interferences and different obstacles for testing, and the data transmission rate of the nRF24L01 is 1 Mbps. The node sends a data acquisition packet once every 1s, interference signals are from uninterrupted use of Wi-Fi and 4G mobile phones, the barrier is the wall of a building, the data packets received within 5min are recorded, the data acquisition rate is calculated according to a formula (16), and a data acquisition rate statistical table of a table 6 can be obtained.
TABLE 6 statistics of data collection rates under different conditions
As can be seen from table 6, under different conditions, the data acquisition rate within 5m is 100%, and no packet loss occurs; the worst data acquisition rate within 10m is 96.33%. 10. The packet loss number of 20 and 50m increases with the increase of the communication distance, and both obstacles and interferences affect the acquisition rate, wherein the influence of the obstacles is larger. The maximum straight-line distance in a house is generally within 10m, so that the practical communication requirement can be met.
In order to test the power consumption of the monitoring nodes of the house state, 1 lithium battery of 3400mAh is used for supplying power, in order to quickly consume the electric quantity and obtain a statistical result, 5 same monitoring nodes are connected in parallel to get power, data is collected and transmitted once every 1s, and the electric quantity is exhausted after the battery is tested for about 75 h. According to the test result, if 1 section of 3400mAh lithium battery is adopted to supply power to 1 node, and the node collects and transmits data every 1h, the operation can be carried out for 22500h, namely 2.57y, so that the power consumption requirement of the system can be met.
The system comprehensively displays the information of each key pillar, beam and wall of the house, real-time inclination angle data and the curve of the change of the day by remotely acquiring the information of the critical house. Through historical data query, the change curve of the inclination angles of all the strut nodes of a certain house can be obtained, as shown in fig. 7. The change in the inclination of the house post can also be obtained from fig. 7, as shown in table 7.
Table 7 inclination angle change table of house pillar
From the above data, it is clear that the data changes for each post in the house over the course of a day and a period of time. Three-level two-dimensional early warning of the house state can be formed by analyzing data into three levels from two dimensions of dip angle values and dip angle changes. Dividing the inclination angle value into three levels, and when the single inclination angle value is less than 0.2 degrees, indicating that the strut is healthy in the current state; when the single dip value is between 0.2 ° and 0.3 °, a mild risk is indicated; and when the single inclination angle value exceeds 0.3 degrees, alarming. Meanwhile, the change degree of the inclination angle of the strut is monitored, the monitoring grade is divided into three levels, and when the change value of the inclination angle is within 0.1 degrees, the current state health of the strut is represented; when the inclination angle change value is between 0.1 degrees and 0.2 degrees, indicating slight risk; and when the inclination angle change value exceeds 0.2 degrees, alarming. In practice, the house state can be effectively predicted by combining the house state data, the building surrounding construction condition and the weather condition.
The foregoing shows and describes the general principles and features of the present invention, together with the advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (7)
1. The utility model provides a dangerous house monitoring system based on wireless sensor network which characterized in that: including house state monitoring node, house information convergent node, NB network, cloud platform and user terminal, house state monitoring node is a plurality of, and is a plurality of house state monitoring node distributes and sets up in a plurality of monitoring positions in house, and every house sets up one house information convergent node, and is a plurality of house information convergent node passes through the NB network with cloud platform internet access, user terminal with cloud platform internet access.
2. The wireless sensor network-based crisis monitoring system according to claim 1, wherein: the house state monitoring node comprises power module, tilt sensor, main control chip and wireless radio frequency module, power module's power output end with main control cabinet chip's power input end is connected, tilt sensor's signal output part with main control chip's signal input part is connected, main control chip's signal transmission end with wireless radio frequency module connects, wireless radio frequency module with house information assembles the nodal connection.
3. The wireless sensor network-based crisis monitoring system according to claim 1, wherein: the house information gathering node comprises a power supply module, a main control chip, a wireless radio frequency module, an NB-IOT communication module, a GPS positioning sensor and a temperature sensor, wherein the power supply output end of the power supply module is connected with the power supply input end of the main control chip, the wireless radio frequency module is connected with the house state monitoring node, the signal transmission end of the wireless radio frequency module is connected with the signal transmission end of the main control chip, the network signal transmission end of the main control chip is connected with the NB-IOT communication module, the NB-IOT communication module is connected with the NB network, the signal output end of the GPS positioning sensor is connected with the main control chip, and the temperature sensor is connected with the signal input end of the main control signal.
4. The wireless sensor network-based crisis monitoring system according to claim 2, wherein: the tilt sensor is a three-axis low-power-consumption acceleration sensor of a model BMA 253.
5. The wireless sensor network-based crisis monitoring system according to claim 2 or 3, characterized in that: the wireless radio frequency module is formed by a model nRF24L01 chip.
6. The control method of the wireless sensor network-based crisis monitoring system according to claim 1, characterized by comprising the following steps:
s1: firstly, initializing each module, setting parameters and ensuring that system data can be normally received and transmitted;
s2: after network registration is successful, the sink node generates an optimal routing table according to network topology, collects state data of each house node according to the routing, and obtains accurate data through filtering and error compensation;
s3: if a data acquisition command from the cloud platform is received, starting a GPS module, acquiring current position and time information, sending time, position and house state information to the cloud platform through an NB (NB) network, and entering a low-power-consumption mode; if the state data exceeds the warning value, the information is reported to the cloud platform; if the alarm value is not exceeded, no action is taken;
s4: and finally, entering a low-power-consumption mode and waiting for the next interrupt wakeup.
7. The wireless sensor network-based crisis monitoring system according to claim 2 or 3, characterized in that: the wireless radio frequency module comprises a data sending part and a data receiving part, when data is sent, a data prefix is set, the data is sent, and if a response signal is received within a specified time, the sending is successful; if no response signal is received, the frequency hopping is carried out according to the established rule and the retransmission is carried out until the response signal is received. When receiving data, firstly detecting carrier waves, analyzing a data packet under the condition of frequency channel matching, reading the data when a destination address is equal to a node address, carrying out CRC (cyclic redundancy check), and if the check is unsuccessful, carrying out frequency hopping according to a set rule and re-receiving the data; and when the destination address is not equal to the node address, forwarding is carried out.
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| CN114234901A (en) * | 2021-11-30 | 2022-03-25 | 中建四局第一建设有限公司 | Information monitoring method and system for dismantling and modifying super high-rise building |
| CN116229672A (en) * | 2022-12-16 | 2023-06-06 | 深圳信可通讯技术有限公司 | Dangerous area emergency early warning and rescue system and method based on big data |
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