This blog trial scenario
Like the core logic of the robot
Code level implementation
Simulated Login
Like machine
summary
In today's , Any community platform , All have the like function , What came into being was the automatic likes machine , Commonly known as brush extension / Praise machine .
This article will introduce you to a like robot , The most simple and easy to understand core logic .
Pseudo code involved in the full text , Use Python To write , Because it's pseudo code , Don't understand, Python, You can understand .
This blog trial scenarioThis time I like robot , It's mainly for computers Web Site , Don't involve APP End .
Like the core logic of the robotSimulation click operation , Trigger likes , Like to wait for the operation .
Before you like it , There's another important code implementation , Simulated Login .
therefore , The basic requirements of the like robot are as follows ：
Simulated Login ;
Like it ;
After extending this requirement , There are two common business scenarios .
Log in to a large number of accounts through simulation , Implement for “ One person / One thing / One article / A video ” A lot of likes , That is to brush others' points ;
By logging into an account , Implement for “ Many people ” I like it in bulk , That is to brush your own points .
Code level implementationAfter sorting out the basic logic , You can enter the actual coding process .
Simulated LoginOn the login implementation , There are two ways of thinking ：
A lot of registration （ You can also buy ） account number , adopt Python The program switches accounts , Every time I log in like , Switch to the next account ;
Advance by technical or manual means , Simulated Login , Record the account generated after login Cookie, Follow up maintenance Cookie The pool implements the operation logic .
The second problem is Cookie The issue of validity , If the website has no such restriction , It is suggested that this method be adopted , More efficient .
Pseudo code implementation
# Train of thought with open("users.txt","r") as f: user_pass = f.readline() # Simulated Login login(user_pass) # Complete the post login operation do_someting()# Train of thought two with open("cookies.txt","r") as f: one_cookie = f.readline() # By carrying cookie Parameter access interface get_detail(one_cookie)with open("users.txt","r") as f: user_pass = f.readline() # Simulated Login login(user_pass) # Complete the post login operation do_someting()# Train of thought two with open("cookies.txt","r") as f: one_cookie = f.readline() # By carrying cookie Parameter access interface get_detail(one_cookie)with open("users.txt","r") as f: user_pass = f.readline() # Simulated Login login(user_pass) # Complete the post login operation do_someting()# Train of thought two with open("cookies.txt","r") as f: one_cookie = f.readline() # By carrying cookie Parameter access interface get_detail(one_cookie)# Train of thought with open("users.txt","r") as f: user_pass = f.readline() # Simulated Login login(user_pass) # Complete the post login operation do_someting()# Train of thought two with open("cookies.txt","r") as f: one_cookie = f.readline() # By carrying cookie Parameter access interface get_detail(one_cookie)The second one is Cookie pool , It can be created manually or by program .
In the simulation login section , You will encounter two learning difficulties
1. Verification code identification problem ;
2.IP Anti climbing limit .
One of the most accessible solutions , Docking coding platform .
The second difficulty is the solution , Buy IP Agent pool , You can also build your own agent pool , Focus on project cost and stability requirements .
Like machineIn many projects , When you have finished the simulated Login operation , Has said that the website is good for you It's completely open .
The next thing you need to do is find the like interface , For example, the following case （ For reference only ）：
CSDN Like interface is as follows ：
# POST Pass user ID and article IDRequest URL: https://blog.csdn.net//phoenix/web/v1/article/likeRequest Method: POST# POST The parameters are as follows articleId=118558076Zhihu like interface is as follows ：
# direct POST Pass on , The user ID is in Cookie in Request URL: https://www.zhihu.com/api/v4/zvideos/1391420717800554497/likersRequest Method: POSTbilibili Like interface is as follows ：
# Passing the user ID at the same time , Pass the corresponding parameters Request URL: https://api.bilibili.com/x/web-interface/archive/likeRequest Method: POST# POST The parameters are as follows aid: 631588341like: 1csrf: b39b26b6b8071e2f908de715c266cb59Through the above cases , You'll find that , The format of the like operation interface is basically similar , It's all through POST Pass on Cookie With specific parameters to the server .
among B The station is special , With one csrf Parameters , This parameter can be accessed from Cookie It's extracted directly from .
Pseudo code implementation
import requestsdef like(params): # Request header Cookie Obtained by simulated Login cookie = get_cookie() # cookie = login() headers = { " Other attributes ":" Property value ", "Cookie":cookie # Focus on the user ID Cookie } res = requests.post(" Address "," Parameters "," Request header ")In the call like interface section , You will come across a learning difficulty
The interface contains positional parameters , For example, the above B The site likes csrf, For solutions to unknown parameters, please refer to the following description .
Keep taking it B For example , Open browser developer tools , Switch to network tab , When you click like , There will be like data requests , As shown in the figure below .
The request appeared at the same time POST Related parameters of , Next , All you have to do is press on the keyboard Ctrl+F, Open the search window （ It's in the current developer tools network Tab ）, In the search box , Enter the value to retrieve , You can find all the request locations where the value appears , Then follow up analysis can be done . The key point is to find the position and principle of the parameter value .
There are various application scenarios of auto like robot , Accurately speaking , This operation can cause some platform imbalances , It will also affect the fairness of platform data , But it's because of the demand , So there are a lot of likes on the market right now , Brush divider , Commenter , There are even a large number of companies running such businesses .
We don't support this kind of business , But you can learn how it works . After all, use Python Implement an automation tool , After understanding the principle , It's going to be very simple .
That's the use Python Details of making simple likes , More about Python Please pay attention to other related articles of the software development network for the information about the likes ！
