IoT | A estação meteorológica TensorFlow prevê a intensidade da chuva

Código

• IoT_weather_station_neural_network.py
• ESP12E_remote_weather_station.ino
• SIM808_remote_weather_station.ino
• index.php (web application)
• account_verification_token.php (web application)

IoT_weather_station_neural_network.pyPython

# IoT Weather Station Predicting Rainfall Intensity w/ TensorFlow# Windows, Linux, or Ubuntu# By Kutluhan Aktar# Collates weather data on Google Sheets and interprets it with a neural network built in TensorFlow to make predictions on the rainfall intensity. # For more information:# https://www.theamplituhedron.com/projects/IoT_Weather_Station_Predicting_Rainfall_Intensity_with_TensorFlowimport tensorflow as tffrom tensorflow import kerasimport matplotlib.pyplot as pltimport numpy as npimport pandas as pd# Create a class to build a neural network after getting, visualizing, and scaling (normalizing) weather data.class Weather_Station:def __init__(self, data):self.df =data self.input =[] self.label =[] # Define class names for different rainfall intensity predictions and values. self.class_names =['None', 'Light Rain', 'Moderate Rain', 'Heavy Rain', 'Violent Rain'] # Create graphics for requested columns. def graphics(self, column_1, column_2, xlabel, ylabel):# Show requested columns from the data set:plt.style.use("dark_background") plt.gcf().canvas.set_window_title('IoT Weather Station') plt.hist2d(self.df[column_1], self.df[column_2]) plt.colorbar() plt.xlabel(xlabel) plt.ylabel(ylabel) plt.title(xlabel) plt.show() # Visualize data before creating and feeding the neural network model. def data_visualization(self):# Inspect requested columns to build a model with appropriately formatted data:self.graphics('WD', '1h_RF', 'Wind Direction (deg)', 'One-Hour Rainfall (mm)') self.graphics('Av_WS', '1h_RF', 'Average Wind Speed (m/s)', 'One-Hour Rainfall (mm)') self.graphics('Mx_WS', '1h_RF', 'Maximum Wind Speed (m/s)', 'One-Hour Rainfall (mm)') self.graphics('24h_RF', '1h_RF', '24-Hour Rainfall (mm)', 'One-Hour Rainfall (mm)') self.graphics('Tem', '1h_RF', 'Temperature (C)', 'One-Hour Rainfall (mm)') self.graphics('Hum', '1h_RF', 'Humidity (%)', 'One-Hour Rainfall (mm)') self.graphics('b_PR', '1h_RF', 'Barometric Pressure (hPA)', 'One-Hour Rainfall (mm)') # Scale (normalize) data depending on the neural network model. def scale_data(self):# Wind Direction and Speed:wv =self.df.pop('Av_WS') max_wv =self.df.pop('Mx_WS') # Convert to radians. wd_rad =self.df.pop('WD')*np.pi / 180 # Calculate the wind x and y components. self.df['scaled_WX'] =wv*np.cos(wd_rad) self.df['scaled_WY'] =wv*np.sin(wd_rad) # Calculate the max wind x and y components. self.df['scaled_max_WX'] =max_wv*np.cos(wd_rad) self.df['scaled_max_WY'] =max_wv*np.sin(wd_rad) # Temperature:tem =self.df.pop('Tem') self.df['scaled_Tem'] =tem / 25 # Humidity:hum =self.df.pop('Hum') self.df['scaled_Hum'] =hum / 70 # Barometric Pressure:bPR =self.df.pop('b_PR') self.df["scaled_bPR"] =bPR / 1013 # 24 Hour Rainfall (Approx.) rain_24 =self.df.pop('24h_RF') self.df['scaled_24h_RF'] =rain_24 / 24 # Define the input and label arrays. def create_input_and_label(self):n =len(self.df) # Create the input array using the scaled variables:for i in range(n):self.input.append(np.array([self.df['scaled_WX'][i], self.df['scaled_WY'][i], self.df['scaled_max_WX'][i], self.df['scaled_max_WY'][i], self.df['scaled_Tem'][i], self.df['scaled_Hum'][i], self.df['scaled_bPR'][i]])) self.input =np.asarray(self.input) # Create the label array using the one-hour and 24-hour rainfall variables:for i in range(n):_class =0 # Evaluate the approximate rainfall rate:approx_RF_rate =(self.df['1h_RF'][i] + self.df['scaled_24h_RF'][i]) * 100 # As labels, assign classes of rainfall intensity according to the approximate rainfall rate (mm):if approx_RF_rate ==0:_class =0 elif approx_RF_rate <2.5:_class =1 elif 2.5  ", self.class_names[np.argmax(prediction[i])]) # Save the model for further usage without training steps:def save_model(self):self.model.save("E:\PYTHON\weather_station.h5") # Run Artificial Neural Network (ANN):def Neural_Network(self, save):self.scale_data() self.create_input_and_label() self.split_data() self.build_and_train_model() if save ==True:self.save_model() # Example Input and Layer:print("\nScaled Input [EXP]:\n") print(self.train_input[0]) print("\nScaled Label [EXP]:\n") print(self.train_label[0]) # Read data (Remote Weather Station.csv):csv_path ="E:\PYTHON\Remote Weather Station.csv"df =pd.read_csv(csv_path)# Define a new class object named 'station':station =Weather_Station(df)# Visualize data:#station.data_visualization()# Artificial Neural Network (ANN):station.Neural_Network(False)# Enter inputs for making predictions:prediction_array =np.array([ [0, 0, 0.31819805, 0.31819805, 0.6988, 0.81498571, 0.99349753], [0, -0, 0, -0, 0.8444, 1, 0.96835143], [0, 0, 0.45, 0, 0.87577, 0.95857143, 1.00128332], [-0, -0, -0, -0, 0.8224, 1.05714286, 0.99279368]])# Prediction Results:station.make_prediction(prediction_array)

ESP12E_remote_weather_station.inoArduino

 ///////////////////////////////////////////// // IoT Weather Station Predicting // // Rainfall Intensity w/ TensorFlow // // ------------------------ // // NodeMCU (ESP-12E) // // by Kutluhan Aktar // // // ///////////////////////////////////////////////// Collates weather data on Google Sheets and interprets it with a neural network built in TensorFlow to make predictions on the rainfall intensity. //// For more information:// https://www.theamplituhedron.com/projects/IoT_Weather_Station_Predicting_Rainfall_Intensity_with_TensorFlow//// Connections// NodeMCU (ESP-12E) :// Weather Station// VV --------------------------- 5V// D5 --------------------------- RX// D6 --------------------------- TX// G --------------------------- GND// Include required libraries:#include #include  #include #include #include // Define your WiFi settings.const char *ssid ="";const char *password ="";// Define weather station settings:char databuffer[35];double temp;int transferring =0;// Define the serial connection pins - RX and TX.SoftwareSerial Serial_1(D6, D5); // (Rx, Tx)void setup() { // Wait until connected. atraso (1000); // Initiate serial ports:Serial.begin(115200); Serial_1.begin(9600); // It is just for assuring if connection is alive. WiFi.mode(WIFI_OFF); atraso (1000); // This mode allows NodeMCU to connect any WiFi directly. WiFi.mode(WIFI_STA); // Connect NodeMCU to your WiFi. WiFi.begin(ssid, password); Serial.print("\n\n"); Serial.print("Try to connect to WiFi. Please wait! "); Serial.print("\n\n"); // Halt the code until connected to WiFi. enquanto (WiFi.status ()! =WL_CONNECTED) {atraso (500); Serial.print("*"); } // If connection is successful:Serial.print("\n\n"); Serial.print("-------------------------------------"); Serial.print("\n\n"); Serial.print("Connection is successful!"); Serial.print("\n\n"); Serial.print("Connected WiFi SSID :"); Serial.print(ssid); Serial.print("\n\n"); Serial.println("Connected IPAddress :"); Serial.println(WiFi.localIP()); Serial.print("\n\n");}void loop() { // Get data from the remote weather station:getBuffer(); // Debug the information and create the link:String weather_data ="wd=" + String(WindDirection()) + "&a_ws=" + String(WindSpeedAverage()) + "&m_ws=" + String(WindSpeedMax()) + "&1_rf=" + String(RainfallOneHour()) + "&24_rf=" + String(RainfallOneDay()) + "&tem=" + String(Temperature()) + "&hum=" + String(Humidity()) + "&b_pr=" + String(BarPressure()); String server ="http://192.168.1.24/remote_weather_station/?"; Serial.println("Weather Data => " + weather_data); Serial.println("Buffer => " + String(databuffer)); // Send data packets every 5 minutes to Raspberry Pi (or any server). transferring++; Serial.println("Time => " + String(transferring) + "s / " + String(int(5*60)) + "s\n\n"); if(transferring ==5*60){ // Create the HTTP object to make a request to the server. HTTPClient http; http.begin(server + weather_data); int httpCode =http.GET(); String payload =http.getString(); Serial.println("Data Send...\nHTTP Code => " + String(httpCode) + "\nServer Response => " + payload + "\n\n"); http.end(); transferring =0; } // Wait 1 second... delay(1000);}// WEATHER STATIONvoid getBuffer(){ int index; for (index =0;index <35;index ++){ if(Serial_1.available()){ databuffer[index] =Serial_1.read(); if (databuffer[0] !='c'){ index =-1; } } else{ index --; } }}int transCharToInt(char *_buffer, int _start, int _stop){ int _index; int result =0; int num =_stop - _start + 1; int _temp[num]; for (_index =_start;_index <=_stop;_index ++){ _temp[_index - _start] =_buffer[_index] - '0'; result =10*result + _temp[_index - _start]; } return result;}int WindDirection(){ return transCharToInt(databuffer,1,3); } // Wind Direction (deg)float WindSpeedAverage(){ temp =0.44704 * transCharToInt(databuffer,5,7); return temp; } // Average Air Speed (1 minute)float WindSpeedMax(){ temp =0.44704 * transCharToInt(databuffer,9,11); return temp; } //Max Air Speed (5 minutes)float Temperature(){ temp =(transCharToInt(databuffer,13,15) - 32.00) * 5.00 / 9.00; return temp; } // Temperature ("C")float RainfallOneHour(){ temp =transCharToInt(databuffer,17,19) * 25.40 * 0.01; return temp; } // Rainfall (1 hour)float RainfallOneDay(){ temp =transCharToInt(databuffer,21,23) * 25.40 * 0.01; return temp; } // Rainfall (24 hours)int Humidity(){ return transCharToInt(databuffer,25,26); } // Humidity (%)float BarPressure(){ temp =transCharToInt(databuffer,28,32); return temp / 10.00; } // Barometric Pressure (hPA)

SIM808_remote_weather_station.inoArduino

 ///////////////////////////////////////////// // IoT Weather Station Predicting // // Rainfall Intensity w/ TensorFlow // // ------------------------ // // Arduino Uno (SIM808) // // by Kutluhan Aktar // // // ///////////////////////////////////////////////// Collates weather data on Google Sheets and interprets it with a neural network built in TensorFlow to make predictions on the rainfall intensity. //// For more information:// https://www.theamplituhedron.com/projects/IoT_Weather_Station_Predicting_Rainfall_Intensity_with_TensorFlow//// Connections// Arduino Uno:// SIM808 GPS/GPRS/GSM Shield For Arduino// D0 --------------------------- RX// D1 --------------------------- TX// D12 --------------------------- POWER // Weather Station// 5V --------------------------- 5V// D5 --------------------------- RX// D6 --------------------------- TX// GND --------------------------- GND// Include required libraries:#include #include // Define the sim808.DFRobot_SIM808 sim808(&Serial);// Define weather station settings:char databuffer[35];double temp;int transferring =0;// Define the serial connection pins - RX and TX.SoftwareSerial Serial_1(6, 5); // (Rx, Tx)void setup() { // Wait until connected. atraso (1000); // Initiate serial ports:Serial.begin(9600); Serial_1.begin(9600); //******** Initialize sim808 module ************* while(!sim808.init()) { delay(1000); Serial.print("Sim808 init error\r\n"); } delay(2000); // Continue if the SIM808 Module is working accurately. Serial.println("Sim808 init success"); delay(5000);}void loop() { // Get data from the remote weather station:getBuffer(); // Debug the information and create the link:String weather_data_1 ="wd=" + String(WindDirection()) + "&a_ws=" + String(WindSpeedAverage()) + "&m_ws=" + String(WindSpeedMax()); String weather_data_2 ="&1_rf=" + String(RainfallOneHour()) + "&24_rf=" + String(RainfallOneDay()) + "&tem=" + String(Temperature()); String weather_data_3 ="&hum=" + String(Humidity()) + "&b_pr=" + String(BarPressure()); Serial.print("Weather Data => " + weather_data_1); Serial.print(weather_data_2); Serial.println(weather_data_3); Serial.println("Buffer => " + String(databuffer)); // Send data packets every 5 minutes to the server. transferring++; Serial.println("Time => " + String(transferring) + "s / " + String(int(5*60)) + "s\n\n"); if(transferring ==5*60){ //*********** Attempt DHCP ******************* while(!sim808.join(F("cmnet"))) { Serial.println("Sim808 join network error!"); atraso (2000); } //************ Successful DHCP **************** delay(5000); Serial.println("Successful DHCP"); //*********** Establish a TCP connection ************ if(!sim808.connect(TCP,"192.168.1.24", 80)) { // Change it with your server. Serial.println("Connection Error"); }else{ Serial.println("Connection OK"); } delay(2000); //*********** Send a GET request ***************** String line ="GET /remote_weather_station/?" + weather_data_1 + weather_data_2 + weather_data_3 + " HTTP/1.0\r\n\r\n"; Serial.println(line); char buffer[512]; // Convert the line from string to char array to make an HTTP Get Request with the SIM808. char web_hook[110]; String_to_Char(line, 110, web_hook); sim808.send(web_hook, sizeof(web_hook)-1); while (true) { int ret =sim808.recv(buffer, sizeof(buffer)-1); if (ret <=0){ Serial.println("Fetch Over..."); pausa; } // Uncomment to view the response in the serial monitor. /* buffer[ret] ='\0'; Serial.print("Recv:"); Serial.print(ret); Serial.print(" bytes:"); Serial.println(buffer); */ Serial.println("\nData Send"); pausa; } //************* Close TCP or UDP connections ********** sim808.close(); //*** Disconnect wireless connection, Close Moving Scene ******* sim808.disconnect(); // Exit. transferring =0; } // Wait 1 second... delay(1000);}// WEATHER STATIONvoid getBuffer(){ int index; for (index =0;index <35;index ++){ if(Serial_1.available()){ databuffer[index] =Serial_1.read(); if (databuffer[0] !='c'){ index =-1; } } else{ index --; } }}int transCharToInt(char *_buffer, int _start, int _stop){ int _index; int result =0; int num =_stop - _start + 1; int _temp[num]; for (_index =_start;_index <=_stop;_index ++){ _temp[_index - _start] =_buffer[_index] - '0'; result =10*result + _temp[_index - _start]; } return result;}int WindDirection(){ return transCharToInt(databuffer,1,3); } // Wind Direction (deg)float WindSpeedAverage(){ temp =0.44704 * transCharToInt(databuffer,5,7); return temp; } // Average Air Speed (1 minute)float WindSpeedMax(){ temp =0.44704 * transCharToInt(databuffer,9,11); return temp; } //Max Air Speed (5 minutes)float Temperature(){ temp =(transCharToInt(databuffer,13,15) - 32.00) * 5.00 / 9.00; return temp; } // Temperature ("C")float RainfallOneHour(){ temp =transCharToInt(databuffer,17,19) * 25.40 * 0.01; return temp; } // Rainfall (1 hour)float RainfallOneDay(){ temp =transCharToInt(databuffer,21,23) * 25.40 * 0.01; return temp; } // Rainfall (24 hours)int Humidity(){ return transCharToInt(databuffer,25,26); } // Humidity (%)float BarPressure(){ temp =transCharToInt(databuffer,28,32); return temp / 10.00; } // Barometric Pressure (hPA)void String_to_Char(String _String, int _size, char _convert[]){ for(int i=0;i<_size;i++){ _convert[i] =_String[i]; }} 

index.php (web application)PHP

 (int)$_GET['wd'], "a_ws" => (float)$_GET['a_ws'], "m_ws" => (float)$_GET['m_ws'], "1_rf" => (float)$_GET['1_rf'], "24_rf" => (float)$_GET['24_rf'], "tem" => (float)$_GET['tem'], "hum" => (int)$_GET['hum'], "b_pr" => (float)$_GET['b_pr'] ];}else{ $variables_from_module =[ "wd" => "err", "a_ws" => "err", "m_ws" => "err", "1_rf" => "err", "24_rf" => "err", "tem" => "err", "hum" => "err", "b_pr" => "err" ];}/** * Returns an authorized API client. * @return Google_Client the authorized client object */function getClient(){ $client =new Google_Client(); $client->setApplicationName('Remote Weather Station'); // Enter your application name. $client->setScopes('https://www.googleapis.com/auth/spreadsheets'); $client->setAuthConfig('credentials.json'); $client->setAccessType('offline'); $client->setPrompt('select_account consent'); // Load previously authorized token from a file, if it exists. // The file token.json stores the user's access and refresh tokens, and is // created automatically when the authorization flow completes for the first // time. $tokenPath ='token.json'; if (file_exists($tokenPath)) { $accessToken =json_decode(file_get_contents($tokenPath), true); $client->setAccessToken($accessToken); print("Token Found!"); } // If there is no previous token or it's expired. if ($client->isAccessTokenExpired()) { // Refresh the token if possible, else fetch a new one. if ($client->getRefreshToken()) { $client->fetchAccessTokenWithRefreshToken($client->getRefreshToken()); } else { // Request authorization from the user. $authUrl =$client->createAuthUrl(); // Do not forget to refresh the page after getting the verification code and entering it to the account_verification_token.php. printf("Open the following link in your browser:

%s

", $authUrl); // <=Comment print 'Do not forget to refresh the page after getting the verification code and entering it to the account_verification_token.php.

Set the verification code in the account_verification_token.php file.'; // <=Comment // Set the verification code to create the token.json. $authCode =trim($GLOBALS['account_verification_token']); // Exchange authorization code for an access token. $accessToken =$client->fetchAccessTokenWithAuthCode($authCode); $client->setAccessToken($accessToken); // Check to see if there was an error and the account_verification_token is entered. if (array_key_exists('error', $accessToken)) { throw new Exception(join(', ', $accessToken)); }else{ print("Successful! Refresh the page."); } } // Save the token to a file. if (!file_exists(dirname($tokenPath))) { mkdir(dirname($tokenPath), 0700, true); } file_put_contents($tokenPath, json_encode($client->getAccessToken())); } return $client;}// Get the API client and construct the service object.$client =getClient();$service =new Google_Service_Sheets($client);// Enter your spreadsheetId:$spreadsheetId ='';// Enter the range (the first row) under which new values will be appended (8 rows):$range ='A1:H1';// Append recent findings from the weather station to the spreadsheet.$values =[ [$variables_from_module["wd"], $variables_from_module["a_ws"], $variables_from_module["m_ws"], $variables_from_module["1_rf"], $variables_from_module["24_rf"], $variables_from_module["tem"], $variables_from_module["hum"], $variables_from_module["b_pr"]]];$body =new Google_Service_Sheets_ValueRange([ 'values' => $values]);$params =[ 'valueInputOption' => "RAW"];// Append if only requested!if(isset($_GET['wd']) &&isset($_GET['a_ws']) &&isset($_GET['m_ws']) &&isset($_GET['1_rf']) &&isset($_GET['24_rf']) &&isset($_GET['tem']) &&isset($_GET['hum']) &&isset($_GET['b_pr'])){ $result =$service->spreadsheets_values->append($spreadsheetId, $range, $body, $params); printf("

%d cells appended.", $result->getUpdates()->getUpdatedCells());}else{ print ("
Missing Data!");}

account_verification_token.php (web application)PHP

"; // Enter the verification code after authorization. ?>

Peças personalizadas e gabinetes

Esquemas