MindMap Gallery data structure
A mind map about data structure. The algorithm characteristics include input, output, finiteness, certainty, and feasibility. This map shares the knowledge of linear tables, trees, and graphs.
Edited at 2023-06-21 10:48:44This 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 structure
basic concept
algorithm
Algorithm characteristics
enter
output
Finiteness
certainty
feasibility
Algorithm design requirements
correctness
readability
Robustness
High time efficiency and low storage volume
time complexity
space complexity
recursion
linear table
storage structure
sequential storage
chain storage
static linked list
Described by an array
Single list
Doubly linked list
circular linked list
When using fixed-length array storage, in order to distinguish between empty and full situations, it is generally required that the tail pointer cannot have elements (so the table length must be smaller than the array length)
head pointer
operate
insert
Find
static lookup table
binary search
Hash table (Hash table)
Hash function construction
digital analytics
Square-Medium Method
division leaving remainder method
folding method
random number method
Conflict handling
open address law
Hash function method
chain address method
public overflow method
dynamic lookup table
Performance analysis
index
dense index
block index
Inverted index
sort
Basic sorting
Bubble Sort
insertion sort
selection sort
Improve sorting
Quick sort
The coefficient before time complexity is smaller than that of heap sort
Hill sort
Heap sort
Big/small top pile
merge sort
bucket sort
counting sort
application
polynomial operations
stack
application
Expression evaluation
prefix expression
suffix expression
calculate
Encountered numbers pushed onto the stack
When an operator is encountered, the result of the operation on the top two elements of the stack is pushed onto the stack.
Convert infix to suffix
encounter digital output
operator encountered
If the priority is higher than the top of the stack, it will be pushed onto the stack.
Parentheses have the lowest priority
Otherwise output
When encountering a left bracket, push it onto the stack
When a right parenthesis is encountered, it is popped from the stack in sequence until a left parenthesis is popped from the stack.
queue
string
pattern matching
Naive pattern matching
KMP pattern matching
nextarray
overall match
Tree
storage structure
parent representation
child representation
child brother representation
Classification
full binary tree
All branch nodes have left and right subtrees
complete binary tree
The node numbering in hierarchical order is the same as that of a full binary tree.
Binary tree
Convert ordinary tree to binary tree
Use child sibling notation
child node on the left
The first sibling node is outside
Convert forest to binary tree
Add a common virtual root node to convert to a normal tree and then to a binary tree
Traverse
Pre-order, in-order traversal or mid-order and post-order traversal can determine a tree (but pre-order and post-order traversal cannot)
Binary tree traversal
precedence
Visit the root node first and then traverse the left and right subtrees
mid-order
First traverse the left subtree, then visit the root node, and finally traverse the right subtree.
Afterword
First traverse the left and right subtrees, then visit the root node
sequence
tree traversal
Root traversal first
Preorder traversal can be used in binary tree representation
back root traversal
In-order traversal can be used in binary tree representation
forest traversal
precedence
mid-order
Optimal binary tree (Huffman tree)
All weighted paths and the minimum
path length
The number of nodes passed from the root node to the given node (=number of layers-1)
weighted path length
The weight of the node multiplied by the path length
algorithm
Nodes are sorted by weight from small to large.
Take the smallest two nodes as the child nodes of the new node, and the weight of the new node is the sum of the weights of the two nodes.
Add new nodes and repeat the above process until there is only one node left.
Huffman coding
It is necessary to ensure that any character encoding is not a prefix of another character encoding
Construct Huffman tree using character frequency as weight value
Encoded as a sequence of 0s and 1s (left and right) along the path branches from the root node to the leaves
Binary sorting tree
definition
If the left subtree is not empty, the values of all nodes on the left subtree are less than the value of the root node.
If the right subtree is not empty, the values of all nodes on the right subtree are greater than the value of the root node.
The left and right subtrees are also binary sorted trees.
Balanced binary tree (AVL tree)
definition
A binary sorted tree in which the height difference between the left subtree and the right subtree of each node is less than or equal to 1
balancing factor
The height of the left subtree minus the height of the right subtree
Can only be 1,0,-1
rotate
Select the smallest unbalanced subtree (closest to the inserted node and the absolute value of the balance factor is greater than 1)
Multi-way search tree (B-tree)
red black tree
picture
storage structure
adjacency matrix
adjacency list
cross linked list
adjacency multiple list
edge set array
Traverse
breadth first
depth first
minimum spanning tree
Prim's algorithm
Kruskal algorithm
shortest path
Dijkstra's algorithm
Floyd algorithm
Directed Acyclic Graph (DAG)
topological sort
AOV network
Critical Path
AOE network