Adding quadrant data to existing data set and quadrants and rank (202…

Accident_data/accidents19and21_with_quadrant_rank.csv

@@ -1,4 +1,4 @@
-OBJECTID,LAND,BEZ,LOR_ab_2021,UJAHR,UMONAT,USTUNDE,UWOCHENTAG,UKATEGORIE,UART,UTYP1,ULICHTVERH,IstRad,IstPKW,IstFuss,IstKrad,IstGkfz,IstSonstige,USTRZUSTAND,LINREFX,LINREFY,XGCSWGS84,YGCSWGS84,LOR,STRASSE,quadrant,quadrant_rank
+ObjectID,State,District,LOR_ab_2021,AccidentYear,AccidentMonth,AccidentHour,DayOfWeek,AccidentCategory,AccidentType,AccidentTypeDetail,LightingCondition,InvolvingBike,InvolvingCar,InvolvingPedestrian,InvolvingMotorcycle,InvolvingHGV,InvolvingOther,RoadCondition,GraphicCoord1,GraphicCoord2,LongitudeWGS84,LatitudeWGS84,quadrant,quadrant_rank
 219249,11,3,3701658.0,2021,11,18,2,3,0,3,2,0,1,0,1,0,0,1,"800202,0742","5829640,204","13,42689483","52,53393955",,,3773.0,57.0
 219248,11,7,7501134.0,2021,12,19,7,3,6,2,2,0,1,1,0,0,0,1,"798479,5317","5819049,219","13,39209027","52,43995086",,,4865.0,35.0
 219247,11,4,4100101.0,2021,12,17,4,3,5,2,2,0,1,0,0,0,0,0,"793352,6128","5829680,195","13,32624193","52,53802784",,,4069.0,34.0

Hardware/Sensors.json

@@ -1,7 +1,11 @@
 {
-    "velocity": "30",
-    "Temperature": "15",
-    "Humidity": "23",
-    "Speed": "20",
-    "Light": "9"
+    "Velocity": 30,
+    "Temperature": 15,
+    "Humidity": 23,
+    "Light": 9,
+    "Longitude": 52.512937,
+    "Latitude": 13.389481,
+    "Day": 2,
+    "Month": 5,
+    "Hour": 16
 }

Hardware/Server/serverDummy.py

@@ -1,35 +1,99 @@
 from http.server import BaseHTTPRequestHandler, HTTPServer
 import json
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+from keras.models import load_model
+
+model=load_model('Model/NNModel.h5') #parameters
 
 class RequestHandler(BaseHTTPRequestHandler):
     def _set_headers(self):
         self.send_response(200)
         self.send_header('Content-type', 'application/json')
         self.end_headers()
 
-    stored_sensor_data = []
-
     def do_POST(self):
         content_length = int(self.headers['Content-Length'])
         post_data = self.rfile.read(content_length)
         sensor_data = json.loads(post_data.decode('utf-8'))
 
         # Process sensor data
         print("Received sensor data:", sensor_data)
-        self.stored_sensor_data.append(sensor_data)
-
         self._set_headers()
-
+
+        risk_severity = None
+
+        if sensor_data:
+            risk_severity=risk_model.send_data_to_model(sensor_data)
+
+        if risk_severity:
         # Include the sent data in the response
-        response_message = {'message': f'Data received successfully {sensor_data}'}
+            response_message = {'message': {sensor_data}}
+        else:
+            response_message = {'message': f'Error Risk severity was not calculated'}
+
         response = json.dumps(response_message)
-
         self.wfile.write(response.encode('utf-8'))
 
-    def do_GET(self):
-        self._set_headers()
-        response = json.dumps(self.stored_sensor_data)
-        self.wfile.write(response.encode('utf-8'))
+
+class risk_model():
+
+    @staticmethod
+    def send_data_to_model(sensor_data):
+        risk_model.run_risk_model(sensor_data['Temperature'],sensor_data['Humidity'],sensor_data['Light'],sensor_data['Longitude'],sensor_data['Latitude'],sensor_data['Day'], sensor_data['Month'],sensor_data['Hour'])
+    @staticmethod
+    def run_risk_model(temp, hum, light, lon, lat, day, month, hour):
+        quadrant = risk_model.calculate_quadrant(rtv_lat=lat,rtv_lon=lon)
+        rc = risk_model.calculate_road_condition(temp=temp,hum=hum)
+        model_risk = risk_model.calc_model_risk(month=month, hour=hour, day=day, light=light, rc=rc,quadrant=quadrant)
+        return model_risk
+
+    @staticmethod
+    def calculate_quadrant(rtv_lat,rtv_lon): #script from asssigner to convert coordinates to quadrant
+        berlin_latitude_range = (52.336724, 52.675287)
+        berlin_long_range = (13.090365, 13.756247)
+        multiplication_factor = 1000000
+        lat_range = (int(berlin_latitude_range[0] * multiplication_factor), int(berlin_latitude_range[1] * multiplication_factor))
+        lon_range = (int(berlin_long_range[0] * multiplication_factor), int(berlin_long_range[1] * multiplication_factor))
+        step = 100
+        lat_range=np.linspace(lat_range[0],lat_range[1],step)
+        lat_range=lat_range.flatten()
+        lon_range=np.linspace(lon_range[0],lon_range[1],step)
+        lat_delta = lat_range[1]-lat_range[0]
+        lon_delta = lon_range[1]-lon_range[0]
+
+        quadrant_code = 0
+        for lon in lon_range:
+            for lat in lat_range:
+                quadrant_code=quadrant_code+1
+                if lon <= rtv_lon*multiplication_factor <= lon + lon_delta and lat <= rtv_lat*multiplication_factor <= lat + lat_delta:
+                    return quadrant_code
+        return None
+
+    @staticmethod
+    def calculate_road_condition(temp=None, hum=None): #use nicologic to convert temp and humidity to road condition (0:dry,1,wet,2:icy)
+            if temp < 0:
+                return 2
+            elif temp < 10 and hum > 70:
+                return 2
+            elif temp < 10:
+                return 1
+            elif temp >= 30 and hum < 30:
+                return 0
+            elif temp >= 30:
+                return 1
+            else:
+                return 0
+
+    @staticmethod
+    def calc_model_risk(month=None, hour=None, day=None, light=None, rc=None, quadrant=None): #run model with follow parameters: month, hour, day, light condition, road condition, quadrant,
+        print("running model")
+        rtv_values_raw = np.array([[month,hour,day,light,rc,quadrant]]) #month, hour, day, light condition, road condition, quadrant_rank,
+        rtv_vales_scaled = StandardScaler().transform(rtv_values_raw)
+        model_severity=model.predict(rtv_vales_scaled)
+        print(model_severity[0][0])
+        return model_severity[0][0]
+
 
 def run(server_class=HTTPServer, handler_class=RequestHandler, port=8080):
     server_address = ('', port)
@@ -39,3 +103,4 @@ def run(server_class=HTTPServer, handler_class=RequestHandler, port=8080):
 
 if __name__ == "__main__":
     run()
+

Hardware/getpostSampleClient.py

@@ -1,16 +1,3 @@
-# import requests 
-
-# sensor = {"Temperature": "15", 
-#           "Speed": "20"}
-
-# response = requests.get("http://127.0.0.42:8080/Sample.Json")#, params= payload)
-# print(response)
-# print(response.content)
-
-# answer = requests.post("http://127.0.0.42:8080/Post.json", data= sensor)#, params= payload)
-# print(answer)
-# print(answer.content)
-###########################################################
 import requests
 import json
 
@@ -20,22 +7,10 @@
 
 url = "http://127.0.0.1:8080/"  # Change this to your server's address
 
-# Send multiple POST requests with sensor data readings
-#for sensor_data in sensor_data_list:
 post_response = requests.post(url, json=sensor_data_list)
 if post_response.status_code == 200:
     print("POST request successful")
     print("Server response:", post_response.json())
 else:
     print("Error:", post_response.status_code)
 
-# Send a GET request to retrieve all stored sensor data
-get_response = requests.get(url)
-
-if get_response.status_code == 200:
-    print("GET request successful")
-    stored_sensor_data = get_response.json()
-    print(stored_sensor_data)
-else:
-    print("Error:", get_response.status_code)
-