MindMap Gallery Data Structures and Algorithms
This is a mind map about data structures and algorithms, describing algorithmic ideas, strings, linked lists, arrays, hash tables, queues, algorithms, practical solutions, etc.
Edited at 2021-10-26 22:44:34This is a panoramic infographic—currently sweeping across the web—illustrating the comprehensive applications of OpenClaw, a popular open-source AI agent platform. It systematically introduces this intelligent agent framework—affectionately dubbed "Lobster Farming"—helping readers quickly grasp its core value, technical features, application scenarios, and security protocols. It serves as an excellent introductory guide and practical manual.
這是一張最近風靡全網關於熱門開源AI代理平台OpenClaw的全網應用全景圖解。它系統性地介紹了這款被稱為「養龍蝦」的智慧體框架,幫助讀者快速理解其核心價值、技術特性、應用場景及安全規範,是一份極佳的入門指南與實操手冊。此圖主要針對希望利用AI建構自動化工作流程的技術從業人員、中小企業主及效率追求者,透過9大模組層層遞進,全面剖析了OpenClaw從概念到落地的整個過程。 圖中核心內容首先釐清了「養龍蝦」指涉的是OpenClawd開源智能體,並強調其本質是「AI基建」而非一般聊天機器人。隨後詳細比較其與傳統AI助理的區別,擁有記憶管理、權限控制、會話隔離和異常恢復四大基礎能力,支援跨平台存取和多模型相容(如GPT、Claude、Ollama)。同時,圖解提供了完整的部署方案(雲端/本地/Docker),並列舉了辦公室自動化、內容創作、資料收集等五大應用程式場景。此外,還展示了其火爆程度、政府與大廠佈局、安全部署建議及適合/不適合的人群分類。幫助你快速掌握OpenClaw技術架構與應用價值,指導個人或企業建構AI自動化系統,規避資料外洩與權限失控風險,是學習「執行式AI」轉型的權威參考圖譜。
本圖由萬興腦圖繪製,是針對IT研發崗位的結構化個人履歷模板,完整涵蓋求職核心資訊模組。基本資訊區包含姓名、電話、信箱、求職意願及GitHub連結;專業概要要求以2-3句提煉核心優勢;工作經驗以「公司A高級Java開發工程師」為例,以「透過(行動),達成(量化成果)」格式呈現微服務架構設計、系統效能優化、團隊技術規範制定等職責,公司B經歷則聚焦功能模組開發與Elasticsearch搜尋優化;技能專長分程式語言、後端框架、中介軟體、資料庫、容器雲等維度,清楚展示技術堆疊;專案成果以「電商平台秒殺系統」為例,說明技術棧、架構設計、個人貢獻(Redis Lua庫存原子扣減)及KPI;教育背景包含一流大學電腦專業學歷,以及AWS認證解決方案架構師、軟考中級軟體設計師證書。模板邏輯嚴謹,涵蓋IT研發求職全流程關鍵訊息,幫助求職者清晰、量化展示專業能力。
This is a panoramic infographic—currently sweeping across the web—illustrating the comprehensive applications of OpenClaw, a popular open-source AI agent platform. It systematically introduces this intelligent agent framework—affectionately dubbed "Lobster Farming"—helping readers quickly grasp its core value, technical features, application scenarios, and security protocols. It serves as an excellent introductory guide and practical manual.
這是一張最近風靡全網關於熱門開源AI代理平台OpenClaw的全網應用全景圖解。它系統性地介紹了這款被稱為「養龍蝦」的智慧體框架,幫助讀者快速理解其核心價值、技術特性、應用場景及安全規範,是一份極佳的入門指南與實操手冊。此圖主要針對希望利用AI建構自動化工作流程的技術從業人員、中小企業主及效率追求者,透過9大模組層層遞進,全面剖析了OpenClaw從概念到落地的整個過程。 圖中核心內容首先釐清了「養龍蝦」指涉的是OpenClawd開源智能體,並強調其本質是「AI基建」而非一般聊天機器人。隨後詳細比較其與傳統AI助理的區別,擁有記憶管理、權限控制、會話隔離和異常恢復四大基礎能力,支援跨平台存取和多模型相容(如GPT、Claude、Ollama)。同時,圖解提供了完整的部署方案(雲端/本地/Docker),並列舉了辦公室自動化、內容創作、資料收集等五大應用程式場景。此外,還展示了其火爆程度、政府與大廠佈局、安全部署建議及適合/不適合的人群分類。幫助你快速掌握OpenClaw技術架構與應用價值,指導個人或企業建構AI自動化系統,規避資料外洩與權限失控風險,是學習「執行式AI」轉型的權威參考圖譜。
本圖由萬興腦圖繪製,是針對IT研發崗位的結構化個人履歷模板,完整涵蓋求職核心資訊模組。基本資訊區包含姓名、電話、信箱、求職意願及GitHub連結;專業概要要求以2-3句提煉核心優勢;工作經驗以「公司A高級Java開發工程師」為例,以「透過(行動),達成(量化成果)」格式呈現微服務架構設計、系統效能優化、團隊技術規範制定等職責,公司B經歷則聚焦功能模組開發與Elasticsearch搜尋優化;技能專長分程式語言、後端框架、中介軟體、資料庫、容器雲等維度,清楚展示技術堆疊;專案成果以「電商平台秒殺系統」為例,說明技術棧、架構設計、個人貢獻(Redis Lua庫存原子扣減)及KPI;教育背景包含一流大學電腦專業學歷,以及AWS認證解決方案架構師、軟考中級軟體設計師證書。模板邏輯嚴謹,涵蓋IT研發求職全流程關鍵訊息,幫助求職者清晰、量化展示專業能力。
Data Structures and Algorithms
string
KMP algorithm
linked list
Single list
Doubly linked list
Nginx
Jump table
application
timer
Redis
array
application
Nginx
Redis
Hash table
bloomfilter bloomfilter
avoid confict
zipper method
open addressing method
double hash
heap
stack stack
queuequeue
Two-way queue dqueue
priority queue
Tree
the term
Binary tree
Balanced Binary Tree BST
red black tree
6 major features
Advantages and Disadvantages
operate
Code
application
Nginx
B-tree/B-tree
B-tree
dictionary tree
picture
preheat
Application background (which scenarios require it)
shortest path
study
Classification
Undirected graph
directed graph
Weight graph (directed/undirected)
concept
vertex set
edge set
Spend
out degree
degree
storage
vertex storage
array
Edge storage
Adjacency matrix (two-digit array)
adjacency list
accomplish
Encapsulation
operate
create (construct)
Destruction (destruction)
Add, delete, and obtain information
Add, delete, and obtain information on vertices
Traverse
Get the out/in degree of a vertex
Determine whether it is connected (with a loop)
Traverse
depth first search
breadth first search
Dijkstra's algorithm (Dijkstra)
Practical solution
Massive data deduplication (string)
Background (application scenario)
URL deduplication
Spam identification
Prevent cache penetration
word spell check
solution
red black tree rbtree
set/map
hash table hashtable
unordered_set/unordered_map
Bloom filter
background
Solving string comparison issues
definition
Probabilistic data structure, characterized by efficient insertion query and clear judgment that a string must not exist or may exist
Principle (essence)
When inserting, use k hash functions to map it to k points on the bitmap and set them to 1; when retrieving, use k hash functions to detect whether the k points on the bitmap are all 1. If they are all 1, there may be , if there is one that is 0, it must not exist
Bitmap (bit array) storage
Use k hash functions
Advantages and disadvantages (comparison exists)
Compared with traditional query structures (red-black trees, hash tables), it is more efficient and takes up less space.
The results are probabilistic, but the error is controllable
Delete operation is not supported
Practical application
How many elements are stored?
How to control the false positive rate (error rate)?
How to determine the size (storage space) of a bitmap?
How to determine the number of hash functions?
Apply formulas (mathematical derivation)
Use the determined number of elements and false positive rate to determine the size of the storage space and the number of hash functions.
How to choose k hash functions?
Use the same hash function to recalculate the hash by adding different offsets to the old hash value.
Similar to the double hashing method in the "open addressing method" of hashing to avoid conflicts
operate
Construct
destroy
insert
Inquire
delete
time complexity
actual case
Squid web proxy cache server
Venti document storage system
SPIN model checker
Google Chrome
Key-Value system
algorithm
Sorting Algorithm
Bubble Sort
Quick sort
insertion sort
Hill sort
merge sort
bucket sort
Heap sort
type
Find algorithm
Find directly
binary search
greedy algorithm
Recursive and iterative algorithms
Algorithmic thinking
Greedy thoughts
algorithmic thinking process
Thought steps
Code
time/space complexity
Application scenarios