TWM566815U - Wireless personalized monitoring weather station system using IoT - Google Patents

Abstract

The present invention provides a wireless personalized monitoring weather station system utilizing the Internet of Things, which mainly includes: a plurality of environmental sensing modules for acquiring environmental parameters of multiple geographical locations; a processing module, and an environment sensing module The ZigBee wireless transmission protocol is used to send and receive the environmental parameters, and correspondingly generate a weather message; a UART interface circuit is electrically connected to the processing module; and a transmission module is electrically connected to the UART interface circuit, and The UART interface circuit enables the transmission module to be integrated by the processing module driver; a cloud host system receives the weather information of the processing module through the transmission module, and establishes a cloud server; and an on-demand software, which is mounted on the cloud The cloud server is used for a user to access and read weather information through an computing device.

Description

Wireless Personalized Monitoring Weather Station System Using Internet of Things

This creation relates to a wireless personalized monitoring weather station system, and particularly to an innovative structure type revealer of a wireless personalized monitoring weather station system using the Internet of Things.

At present, the meteorological observations of various countries in the world do not represent the meteorological state of a certain range around them with fixed-point observation resources. However, for the relevant information to be provided quickly and accurately, the monitoring range and reliability of a single sensor node are limited. Therefore, a large number of sensors must be set in the monitoring area, and sometimes even the monitoring ranges of the sensors need to be overlapped with each other to form a sensor monitoring network to achieve the accuracy of the monitoring information.

However, the conventional sensor monitoring network is usually connected by physical lines. For Taiwan, due to the complex geographical environment and the special terrain, it is difficult to arrange the sensor monitoring network in the manner described above, and only a few can be set up. Decentralized sensors are difficult to ensure the accuracy of the information obtained, so they may not fully represent the current actual meteorological state, which is the biggest disadvantage of the current meteorological observation methods.

The main purpose of this creation is to provide a wireless personalized monitoring weather station system using the Internet of Things.

Based on the foregoing purpose, the new wireless personal monitoring weather station system using the Internet of Things mainly includes: a plurality of environmental sensing modules for correspondingly obtaining environmental parameters of multiple geographical locations, each of which is respectively It has an environmental control module and a ZigBee wireless transmission module. The environmental control module is used to receive and integrate environmental parameters of the environmental sensing module, and the ZigBee wireless transmission module is electrically connected to the environmental control module. And outputting the environmental parameters; a processing module, which is an arithmetic logic processing circuit using an MSP430 chip, and the processing module and the ZigBee wireless transmission module use a ZigBee wireless transmission protocol to send and receive the environmental parameters, A data processing method is used to calculate the average value, thereby improving the accuracy of the relevant values, and correspondingly generating a meteorological message; a UART interface circuit electrically connected to the processing module; a transmission module and the UART interface circuit Electrically connected, and the transmission module is driven and integrated by the processing module through the UART interface circuit; a cloud host receives the transmission through the transmission module Manage the weather information of the module and establish a cloud server; and an on-demand software is mounted on the cloud server for a user to access and read the weather information through a computing device; The on-demand software continuously returns the user's geographic location to the cloud server, and the cloud server then performs calculations to transmit weather information corresponding to the geographic location to the computing device, ensuring the user's income The accuracy of the information.

With this innovative and unique design, compared with the previous technology, the environment parameters obtained by each environmental sensing module are wirelessly transmitted to the processing module, and the two can be not obstructed by terrain and lines. The environment sensing module is set at any position, so as to expand and enhance the area and density of its deployment, thereby improving the accuracy of these environmental parameters; and according to the current geographical location of the user, correspondingly provide the weather of the geographical location. Information to ensure the correctness of the information users receive, and is particularly useful and progressive.

Please refer to Figure 1, which is a preferred embodiment of the wireless personalization monitoring weather station system using the Internet of Things in this creation. However, these embodiments are for illustration only and are not limited by this structure in patent applications. . The wireless personalized monitoring weather station system using the Internet of Things includes: a plurality of environmental sensing modules 10 for correspondingly acquiring environmental parameters of multiple geographical locations, each of which has an environmental control module Group 20 and a ZigBee wireless transmission module 30. The environmental control module 20 is used to receive and aggregate the environmental parameters of the environmental sensing module 10. The ZigBee wireless transmission module 30 is electrically connected to the environmental control module 20. Is used to output the environmental parameter, and the environmental control module 20 uses the relationship between voltage and converted value to further obtain the required weather value. The formula is Converted voltage = Converted value × (Reference voltage (Vref)) / (2 ^ 10-1); A processing module 40 is an arithmetic logic processing circuit using an MSP430 chip. The processing module 40 and the ZigBee wireless transmission module 30 are connected by The ZigBee wireless transmission protocol sends and receives the environmental parameters, and uses a data processing method to calculate the average value, so as to improve the accuracy of the related values, and then generate a meteorological information correspondingly; a UART interface circuit, The processing module 40 is electrically connected. Among them, the UART interface circuit uses a Universal Asynchronous Receiver / Transmitter (UART) to regulate and integrate the corresponding frequencies between multiple wireless transmission devices to achieve flexible dynamics. The effect of ground connection. A transmission module 50 is electrically connected to the UART interface circuit, and the transmission module 50 is driven and integrated by the processing module 40 through the UART interface circuit. A cloud host 60 receives the transmission through the transmission module 50 The weather information of the module 40 is processed, and a cloud server is established; and the sending and exchanging information between the transmission module 50 and the cloud host 60 is based on one or more of the following communication protocols: a universal string Universal Serial Bus (USB), a Wireless Fidelity (WiFi), a Bluetooth and a fourth generation (4G) mobile communication technology; and an on-demand software Is mounted on the cloud server for a user to access and read the weather information through a computing device 70; wherein the on-demand software continuously returns the user's geographic location to the cloud Server, and the cloud server then performs calculations to transmit weather information corresponding to its geographic location to the computing device 70 to ensure the correctness of the information obtained by the user.

Among them, the data fusion means may be selected from a self-weighted average method, a Kalman filter algorithm, Bayesian estimation, D-S (Dempster-Shafter) evidence theory, fuzzy logic, and a neural network algorithm. The following is a further explanation of the means of data fusion:

(1) Weighted average method: The weighted average method is the simplest and intuitive method. The redundant information provided by multiple sensors is weighted and averaged as the fusion value. This method can process dynamic raw data in real time, but the determination of the weight coefficient is subjective. For example, N detections are performed on a detection target, and the average value is for: , Kj is the weight assigned to the i-th detection.

(2) Kalman filter algorithm: The Kalman filter algorithm can fuse dynamic low-level redundant data in real time. The system noise and sensor noise of a linear system can be modeled with Gaussian white noise. Then Kalman Filtering can provide a unique statistically optimal fusion value, and does not require a large amount of storage space in the filtering process, and can be processed immediately.

(3) Bayesian estimation: Bayesian estimation represents various uncertain information as probabilities, and treats independent decisions as a division of a sample space. It is processed using the Bayesian conditional probability formula. Some rules are given. The disadvantage is that the prior probability of the target is required and the calculation is complicated.

(4) D-S (Dempster-Shafter) evidence theory: It is a commonly used method in current data fusion technology. This method is usually used to express the inference of the size, position and existence of the detection target. According to the human reasoning model, the probability interval and uncertainty interval are used to determine the likelihood function hypothesized under multiple evidence for reasoning. The feature parameters extracted from the information detected by various sensors constitute the evidence in this theory. These evidences are used to construct the corresponding basic probability distribution functions and give a degree of trust to all propositions. The basic probability distribution function and its corresponding resolution frame are called a body of evidence. Therefore, each sensor is equivalent to a body of evidence. The fusion of multiple sensor data is essentially a combination of each evidence body into a new evidence body using the Dempster merge rule under the same discrimination frame. The process of generating a new body of evidence is the D-S method data fusion.

(5) Fuzzy logic: For the phenomenon that the target features detected in data fusion have some ambiguity, some people use fuzzy logic methods to identify and classify detection targets. The establishment of fuzzy subsets of standard detection targets and detection targets to be identified is the research basis of this method. However, the establishment of fuzzy subsets requires various standard detection targets, and at the same time, appropriate membership functions must be established. It is more troublesome to determine the membership function, and there is no standard method to follow. And because the establishment of the standard detection target subset is restricted by various conditions, the error is often large. Rules-based reasoning methods also include evidence reasoning, production rules, and so on.

(6) Neural network: The neural network is connected by a large number of widely interconnected processing units. It is based on the research results of modern neurobiology and cognitive science on the Human Information Service. In terms of signal processing mechanism, it is fundamentally different from traditional digital computers. It has the characteristics of large-scale parallel analog processing, continuous-time dynamics, and global network functions. The storage and operation are combined into one. The neural network has a strong adaptive learning theory, which can replace the complex and time-consuming traditional algorithms and make the signal processing process closer to human thinking activities. Utilizing the high-speed parallel computing capabilities of neural networks, real-time optimal signal processing algorithms that are difficult to achieve with computer technology can be realized; using the characteristics of neural network decentralized information storage and parallel processing, modelling and pattern recognition methods can be avoided. Feature extraction process, thereby eliminating the impact caused by model mismatch and improper feature selection, and real-time recognition to improve the performance of the recognition system. The basic principles of neural networks are not repeated here. According to the data, neural networks have been applied in many aspects in data fusion. The applied network models include self-organizing mapping networks, BP networks, probabilistic neural networks, fuzzy neural networks, and DIGNET networks. The effect is better.

Other data fusion methods include quality factor, expert system, template method, cluster analysis, statistical decision theory, and so on. The commonly used method depends on the specific application, and due to the complementarity between the various methods, two or more methods are often combined for data fusion in actual use. In the sensor network according to the present invention, the data fusion step plays a very important role, which is mainly reflected in the energy of the entire network, enhancing the accuracy of the collected information, and improving the efficiency of collecting information.

In this example, each of the environmental sensing modules 10 may be, but is not limited to, one of a temperature and humidity sensing module, a rainwater sensing module, an ultraviolet sensing module, or a wind speed sensing module or Its combination. According to this, the data represented by these environmental parameters can be in the following ways: ° C, relative humidity%, ultraviolet radiation intensity divided into 11 levels, wind speed (Km / H), and divided into no rain, light rain, heavy rain and Heavy rain parameters for four levels of heavy rain.

In addition, the transmission module 50 and the computing device 70 can use one or more of the following communication protocols to send and exchange information: a universal serial bus (USB), and a wireless fidelity (Wireless Fidelity (WiFi), a Bluetooth (Bluetooth) and a fourth generation mobile communication technology (fourth generation (4G).

10‧‧‧environment sensing module 20‧‧‧environment control module 30‧‧‧ZigBee wireless transmission module 40‧‧‧ processing module 50‧‧‧ transmission module 60‧‧‧ cloud host 70‧ Computing device

FIG. 1 is a schematic diagram of a preferred embodiment of the present invention.

Claims (4)

A wireless personalized monitoring weather station system using the Internet of Things includes: a plurality of environmental sensing modules for correspondingly obtaining environmental parameters of multiple geographical locations, each of which has an environmental control module and A ZigBee wireless transmission module, the environmental control module is used to receive and aggregate the environmental parameters of the environmental sensing module, and the ZigBee wireless transmission module is electrically connected to the environmental control module to output the environmental parameters A processing module, which is an arithmetic logic processing circuit using an MSP430 chip, the processing module and the ZigBee wireless transmission module use the ZigBee wireless transmission protocol to send and receive the environmental parameters, and use a data processing method to Calculate the average value to improve the accuracy of the relevant values and generate a meteorological message accordingly; a UART interface circuit electrically connected to the processing module; a transmission module electrically connected to the UART interface circuit and using the UART interface circuit enables the transmission module to be driven and integrated by the processing module; a cloud host receives weather information of the processing module through the transmission module A cloud server is established; and an on-demand software is mounted on the cloud server for a user to access and read the weather information through a computing device; wherein, the on-demand is used The software continuously returns the user's geographic location to the cloud server, and the cloud server then calculates and sends the weather information corresponding to its geographic location to the computing device to ensure the correctness of the information obtained by the user. As described in item 1 of the scope of the patent application, the wireless personalization monitoring weather station system using the Internet of Things, wherein the data processing method is selected from the self-weighted average method, Kalman filter algorithm, Bayesian estimation, DS (Dempster- Shafter) Evidence theory, fuzzy logic, or neural network. As described in item 2 of the scope of the patent application, the wireless personalized monitoring weather station system using the Internet of Things, wherein each of the environmental sensing modules may be, but is not limited to, a temperature and humidity sensing module, a rainwater sensing module, One of an ultraviolet sensing module or a wind speed sensing module or a combination thereof. As described in item 3 of the scope of the patent application, the wireless personalized monitoring weather station system using the Internet of Things, wherein the transmission and exchange information between the transmission module and the cloud host is based on one or more of the following communication protocols: A universal serial bus (USB), a wireless fidelity (WiFi), a Bluetooth (Bluetooth) and a fourth generation mobile communication technology (4G).