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階段 - 3

在階段 -2 中，我手動(dòng)設(shè)置了溫度傳感器的閾值范圍。但在這個(gè)階段，我將使用Z-score算法來(lái)動(dòng)態(tài)更改發(fā)送警報(bào)的范圍。

這個(gè)階段的要點(diǎn)是——

當(dāng)腔室工作正常時(shí)，溫度變化會(huì)很慢，系統(tǒng)的界限會(huì)隨著這種變化而變化。但是當(dāng)出現(xiàn)任何系統(tǒng)故障或任何問(wèn)題時(shí)，房間內(nèi)的溫度會(huì)突然發(fā)生變化。因此，系統(tǒng)將檢測(cè)到異常并迅速提醒用戶存在問(wèn)題。

3.1 Z分?jǐn)?shù)分析

Z-score 分析是一種用于異常檢測(cè)的算法。在這里，溫度的突然變化超出（閾值）范圍值，是一種異常情況。值的范圍稱為界限（上限和下限）。這些界限是使用輸入值、幀大小和乘法因子計(jì)算的。幀大小是 Z 分?jǐn)?shù)分析所需的最小輸入值數(shù)量，乘法因子確定邊界與輸入值曲線的接近程度。

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上面給出的是計(jì)算邊界的公式。這里輸入表示為“Vi”，“r”表示幀大小，“C”是乘法因子。首先，我們計(jì)算輸入值的平均值（Mn）（對(duì)于每個(gè)新輸入，再次計(jì)算平均值）。每個(gè)輸入值的變化（來(lái)自平均值）為 (Vi - Mn)^2。Z 分?jǐn)?shù) (Zn) 的計(jì)算如上所示（每個(gè)輸入值的變化平均值的平方根乘以乘法因子）。界限表示為“Tn”，上限計(jì)算為 (Vi + Zn)，下限計(jì)算為 (Vi - Zn)。

幀大小和倍增因子是使用試錯(cuò)法確定的。

3.2 修改配置文件

MAILGUN_API_KEY = 'This is the private API key which you can find on your Mailgun Dashboard' 
SANDBOX_URL= 'You can find this on your Mailgun Dashboard' 
SENDER_EMAIL = 'This would be test@your SANDBOX_URL'
RECIPIENT_EMAIL = 'Enter your Email ID Here'
API_KEY = 'This is your Bolt Cloud account API key'
DEVICE_ID = 'This is the ID of your Bolt device'
FRAME_SIZE = 10
MUL_FACTOR = 6

幀大小和倍增因子猜測(cè)為 10 和 6，然后根據(jù)要求進(jìn)行調(diào)整。

3.3異常檢測(cè)的Python代碼

import email_conf   #import the configuration file
from boltiot import Email, Bolt   #import the boltiot module from the Bolt python library
import json, time, math, statistics #import various python libraries

#//---------FUNCTION TO COMPUTE BOUNDS OR Z SCORE ANALYSIS------------//
def compute_bounds(history_data,frame_size,factor):
#//Function to compute bounds
    if len(history_data)        return None

    if len(history_data)>frame_size :
        del history_data[0:len(history_data)-frame_size]
    Mn=statistics.mean(history_data)
    Variance=0
    for data in history_data :
        Variance += math.pow((data-Mn),2)
    Zn = factor * math.sqrt(Variance / frame_size)
    High_bound = history_data[frame_size-1]+Zn
    Low_bound = history_data[frame_size-1]-Zn
    return [High_bound,Low_bound]  # //Returns Low Bound and High Bound

mybolt = Bolt(email_conf.API_KEY, email_conf.DEVICE_ID) # //To identify your bolt device
mailer = Email(email_conf.MAILGUN_API_KEY, email_conf.SANDBOX_URL, email_conf.SENDER_EMAIL, email_conf.RECIPIENT_EMAIL)    # //To identify Mailgun account
history_data=[]    # //Array of input values from LM35
#//---------------------READ INPUT FROM LM35--------------------------//
while True:
    response = mybolt.analogRead('A0')  #//Read input from LM35 at A0 pin
    data = json.loads(response) # //Retrieve the input data in json format
    if data['success'] != 1:
        print("There was an error while retriving the data.")
        print("This is the error:"+data['value'])
        time.sleep(10)
        continue

    print ("This is the value "+data['value'])
    sensor_value=0
    try:
        sensor_value = int(data['value']) # //store current input value in variable
    except e:
        print("There was an error while parsing the response: ",e)
        continue
#//----------------PERFORMING Z SCORE ANALYSIS--------------------------//
    bound = compute_bounds(history_data,email_conf.FRAME_SIZE,email_conf.MUL_FACTOR)
    if not bound:
        required_data_count=email_conf.FRAME_SIZE-len(history_data)
        print("Not enough data to compute Z-score. Need ",required_data_count," more data points")
        history_data.append(int(data['value']))
        time.sleep(10)
        continue
#//-----------DETECTING ANOMALY AND SENDING ALERTS--------------//
    try:
        if sensor_value > bound[0] :  # //If input crosses upper bound
            print ("The temperature has increased suddenly. Sending an E-mail.")
            response = mailer.send_email("Alert", "The temperature has suddenly increased! ")
            print("This is the response ",response)
        elif sensor_value < bound[1]:   # //If input crosses lower bound
            print ("The light level decreased suddenly. Sending an Email.")
            response = mailer.send_email("Alert", "The temperature has suddenly decreased! ")
            print("This is the response ",response)
        history_data.append(sensor_value);   # //Append each new input to array history_data[]
    except Exception as e:
        print ("Error",e)
    time.sleep(10)     # //Wait for 10 seconds

3.4 演示

當(dāng)一杯熱水靠近傳感器時(shí)，溫度突然變化，檢測(cè)到溫度值異常。幀大小和倍增因子已經(jīng)以這種方式進(jìn)行了調(diào)整。

3.5 輸出說(shuō)明

由于我已將幀大小設(shè)置為 10，因此在獲取 10 個(gè)傳感器值之前，它不會(huì)計(jì)算 z 分?jǐn)?shù)。

膩?zhàn)虞敵鼋貓D

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100 秒后（10 幀延遲 10 秒），它會(huì)開(kāi)始計(jì)算 z-score 并檢查是否有異常。帶上一杯熱水后，當(dāng)溫度突然變化時(shí)，異常是檢測(cè)到。

膩?zhàn)虞敵鼋貓D

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收到郵件截圖——

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因此，這是一種異常檢測(cè)中的自力更生系統(tǒng)。

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