๐ŸŽฏ Brief Introduction

Youtu-LLM is a new, small, yet powerful LLM, contains only 1.96B parameters, supports 128k long context, and has native agentic talents. On general evaluations, Youtu-LLM significantly outperforms SOTA LLMs of similar size in terms of Commonsense, STEM, Coding and Long Context capabilities; in agent-related testing, Youtu-LLM surpasses larger-sized leaders and is truly capable of completing multiple end2end agent tasks.

Youtu-LLM has the following features:

  • Type: Autoregressive Causal Language Models with Dense MLA
  • Release versions: Base and Instruct
  • Number of Parameters: 1.96B
  • Number of Layers: 32
  • Number of Attention Heads (MLA): 16 for Q/K/V
  • MLA Rank: 1,536 for Q, 512 for K/V
  • MLA Dim: 128 for QK Nope, 64 for QK Rope, and 128 for V
  • Context Length: 131,072
  • Vocabulary Size: 128,256

๐Ÿค— Model Download

Model Name Description Download
Youtu-LLM-2B-Base Base model of Youtu-LLM-2B ๐Ÿค— Model
Youtu-LLM-2B Instruct model of Youtu-LLM-2B ๐Ÿค— Model
Youtu-LLM-2B-GGUF Instruct model of Youtu-LLM-2B, in GGUF format ๐Ÿค— Model

๐Ÿ“ฐ News

๐Ÿ“Š Performance Comparisons

Instruct Model

Comparison between Youtu-LLM-2B and baselines

General Benchmarks

Benchmark DeepSeek-R1-Distill-Qwen-1.5B Qwen3-1.7B SmolLM3-3B Qwen3-4B DeepSeek-R1-Distill-Llama-8B Youtu-LLM-2B
Commonsense Knowledge Reasoning
MMLU-Redux 53.0% 74.1% 75.6% 83.8% 78.1% 75.8%
MMLU-Pro 36.5% 54.9% 53.0% 69.1% 57.5% 61.6%
Instruction Following & Text Reasoning
IFEval 29.4% 70.4% 60.4% 83.6% 34.6% 81.2%
DROP 41.3% 72.5% 72.0% 82.9% 73.1% 86.7%
MUSR 43.8% 56.6% 54.1% 60.5% 59.7% 57.4%
STEM
MATH-500 84.8% 89.8% 91.8% 95.0% 90.8% 93.7%
AIME 24 30.2% 44.2% 46.7% 73.3% 52.5% 65.4%
AIME 25 23.1% 37.1% 34.2% 64.2% 34.4% 49.8%
GPQA-Diamond 33.6% 36.9% 43.8% 55.2% 45.5% 48.0%
BBH 31.0% 69.1% 76.3% 87.8% 77.8% 77.5%
Coding
HumanEval 64.0% 84.8% 79.9% 95.4% 88.1% 95.9%
HumanEval+ 59.5% 76.2% 74.7% 87.8% 82.5% 89.0%
MBPP 51.5% 80.5% 66.7% 92.3% 73.9% 85.0%
MBPP+ 44.2% 67.7% 56.7% 77.6% 61.0% 71.7%
LiveCodeBench v6 19.8% 30.7% 30.8% 48.5% 36.8% 43.7%

Agentic Benchmarks

Benchmark Qwen3-1.7B SmolLM3-3B Qwen3-4B Youtu-LLM-2B
Deep Research
GAIA 11.4% 11.7% 25.5% 33.9%
xbench 11.7% 13.9% 18.4% 19.5%
Code
SWE-Bench-Verified 0.6% 7.2% 5.7% 17.7%
EnConda-Bench 10.8% 3.5% 16.1% 21.5%
Tool
BFCL V3 55.5% 31.5% 61.7% 58.0%
ฯ„ยฒ-Bench 2.6% 9.7% 10.9% 15.0%

๐Ÿš€ Quick Start

This guide will help you quickly deploy and invoke the Youtu-LLM-2B model. This model supports "Reasoning Mode", enabling it to generate higher-quality responses through Chain of Thought (CoT).

Transformers >= 4.56.0, <= 4.57.1

If you wish to use Youtu-LLM-2B based on earlier versions of transformers, please make sure to download the model repository before this commit.

1. Environment Preparation

Ensure your Python environment has the transformers library installed and that the version meets the requirements.

pip install "transformers>=4.56.0,<=4.57.1" torch accelerate

Note

  • (1) We recommend to limit the version of transformers: pip install "transformers>=4.56.0,<=4.57.1", which is comparable with the current remote codes;
  • (2) Do not use transformers==4.57.2, since there is a bug unfixed;
  • (3) If you would like to maintain a higher version (e.g., 4.57.3), you should slightly modify the "check_model_inputs" in modeling_youtu.py to "check_model_inputs()", following the patch.

2. Core Code Example

The following example demonstrates how to load the model, enable Reasoning Mode, and use the re module to parse the "Thought Process" and the "Final Answer" from the output.

import re
from transformers import AutoTokenizer, AutoModelForCausalLM

# 1. Configure Model
model_id = "tencent/Youtu-LLM-2B"

# 2. Initialize Tokenizer and Model
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    trust_remote_code=True
)

# 3. Construct Dialogue Input
prompt = "Hello"
messages = [{"role": "user", "content": prompt}]

# Use apply_chat_template to construct input; set enable_thinking=True to activate Reasoning Mode
input_text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)

model_inputs = tokenizer([input_text], return_tensors="pt").to(model.device)
print("Input prepared. Starting generation...")

# 4. Generate Response
outputs = model.generate(
    **model_inputs,
    max_new_tokens=512,
    do_sample=True,
    temperature=1.0,
    top_k=20,
    top_p=0.95,
    repetition_penalty=1.05
)
print("Generation complete!")

# 5. Parse Results
full_response = tokenizer.decode(outputs[0], skip_special_tokens=True)

def parse_reasoning(text):
    """Extract thought process within <think> tags and the subsequent answer content"""
    thought_pattern = r"<think>(.*?)</think>"
    match = re.search(thought_pattern, text, re.DOTALL)
    
    if match:
        thought = match.group(1).strip()
        answer = text.split("</think>")[-1].strip()
    else:
        thought = "(No explicit thought process generated)"
        answer = text
    return thought, answer

thought, final_answer = parse_reasoning(full_response)

print(f"\n{'='*20} Thought Process {'='*20}\n{thought}")
print(f"\n{'='*20} Final Answer {'='*20}\n{final_answer}")
Transformers >= 5.1.0

1. Environment Preparation

Ensure your Python environment has the transformers library installed and that the version meets the requirements.

git clone https://github.com/huggingface/transformers.git
cd transformers

# pip
pip install '.[torch]'

# uv
uv pip install '.[torch]'

2. Core Code Example

The following example demonstrates how to load the model, enable Reasoning Mode, and use the re module to parse the "Thought Process" and the "Final Answer" from the output.

import re
from transformers import AutoTokenizer, AutoModelForCausalLM

# 1. Configure Model
model_id = "tencent/Youtu-LLM-2B"

# 2. Initialize Tokenizer and Model
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto"
)

# 3. Construct Dialogue Input
prompt = "Hello"
messages = [{"role": "user", "content": prompt}]

# Use apply_chat_template to construct input; set enable_thinking=True to activate Reasoning Mode
input_text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)

model_inputs = tokenizer([input_text], return_tensors="pt").to(model.device)
print("Input prepared. Starting generation...")

# 4. Generate Response
outputs = model.generate(
    **model_inputs,
    max_new_tokens=512,
    do_sample=True,
    temperature=1.0,
    top_k=20,
    top_p=0.95,
    repetition_penalty=1.05
)
print("Generation complete!")

# 5. Parse Results
full_response = tokenizer.decode(outputs[0], skip_special_tokens=True)

def parse_reasoning(text):
    """Extract thought process within <think> tags and the subsequent answer content"""
    thought_pattern = r"<think>(.*?)</think>"
    match = re.search(thought_pattern, text, re.DOTALL)
    
    if match:
        thought = match.group(1).strip()
        answer = text.split("</think>")[-1].strip()
    else:
        thought = "(No explicit thought process generated)"
        answer = text
    return thought, answer

thought, final_answer = parse_reasoning(full_response)

print(f"\n{'='*20} Thought Process {'='*20}\n{thought}")
print(f"\n{'='*20} Final Answer {'='*20}\n{final_answer}")

3. Key Configuration Details

Reasoning Mode Toggle

Controlled via the enable_thinking parameter in the apply_chat_template method:

  • True (Recommended Default): Activates Chain of Thought; ideal for complex logic and reasoning tasks.
  • False: Outputs results directly; faster response time, suitable for simple conversations.

Recommended Decoding Parameters

Depending on your use case, we suggest adjusting the following hyperparameters for optimal generation:

Parameter Reasoning Mode Normal Mode
do_sample True True
temperature 1.0 (Maintains creativity) 0.7 (More stable results)
top_p 0.95 0.8
top_k 20 20
repetition_penalty 1.05 -

Tip: When using Reasoning Mode, a higher temperature helps the model perform deeper, more divergent thinking.

4. vLLM Deployment

We provide support for deploying the model using vLLM 0.10.2. The recommended Docker image is vllm/vllm-openai:v0.10.2.

Integration Steps

First, execute the following commands to integrate the Youtu-LLM model files into the vLLM framework. Note: Please extract our provided modified vllm zip file first. Then, replace <local_modified_vllm_path> with the path to the extracted vllm directory, and replace <vllm_path> with the installation path of vLLM.

cp <local_modified_vllm_path>/0_10_2_official/youtu_llm.py <vllm_path>/vllm/model_executor/models/youtu_llm.py
cp <local_modified_vllm_path>/0_10_2_official/configuration_youtu.py <vllm_path>/vllm/model_executor/models/configuration_youtu.py
cp <local_modified_vllm_path>/0_10_2_official/__init__.py <vllm_path>/vllm/config/__init__.py
cp <local_modified_vllm_path>/0_10_2_official/registry.py <vllm_path>/vllm/model_executor/models/registry.py

Service Startup

Once integrated, you can deploy the model using the following command:

vllm serve <model_path> --trust-remote-code

Tool Call Support: To enable tool calling capabilities, please append the following arguments to the startup command:

--enable-auto-tool-choice --tool-call-parser hermes

5. llama.cpp Deployment

For macOS, you can install and use Youtu-LLM as follows:

brew install llama.cpp
llama-server -hf tencent/Youtu-LLM-2B-GGUF:Q8_0 --host 0.0.0.0 --port 8081  --log-disable

๐Ÿ’ก Highlights

Showcase of General Tasks

User's query Youtu-LLM-2B's response
่ฎพๆ€ปไฝ“$X$็š„ๅˆ†ๅธƒๅพ‹ไธบ
\begin{tabular}{|l|l|l|l|}
\hline$X$&-1&0&2\
\hline$P$&$\frac{1}{3}\theta$&$1-\frac{2}{3}\theta$&$\frac{1}{3}\theta$\
\hline
\end{tabular}
$\left(X_{1},X_{2},\cdots,X_{n}\right)$ไธบๆฅ่‡ชๆ€ปไฝ“็š„ๆ ทๆœฌ,่ฎพๆœ‰ไปฅไธ‹ๅ››ไธช็ปŸ่ฎก้‡
(i)$\frac{3}{n}\sum_{i=1}^{n}X_{i}$,(ii)$\left.X_{1}+\frac{2}{n-1}\right){i=2}^{n}X{i}$,(iii)$\frac{3}{5n}\sum_{i=1}^{n}X_{i}^{2}$,(iv)$\frac{1}{3n}\sum_{i=1}^{n}X_{i}^{2}$
ๅœจไธŠ่ฟฐๅ››ไธช็ปŸ่ฎก้‡ไธญ,ๆ˜ฏๅ‚ๆ•ฐ$\theta$็š„ไธ€่‡ดไผฐ่ฎก้‡็š„ไธชๆ•ฐๆ˜ฏ____
A:0
B:2
C:1
D:3
### ้ข˜็›ฎๅˆ†ๆž
ๆˆ‘ไปฌ่ขซ็ป™ๅ‡บไธ€ไธชๆ€ปไฝ“ $ X $ ็š„ๅˆ†ๅธƒๅพ‹ๅฆ‚ไธ‹๏ผš
$$
\begin{array}{|l|l|l|}
\hline
X & -1 & 0 & 2 \
\hline
P & \frac{1}{3}\theta & 1 - \frac{2}{3}\theta & \frac{1}{3}\theta \
\hline
\end{array}
$$
ๅ…ถไธญ $\theta \in [0, \frac{3}{2}]$๏ผŒๅ› ไธบๆ‰€ๆœ‰ๆฆ‚็އๅฟ…้กป้ž่ดŸใ€‚
ๆ ทๆœฌ $(X_1, X_2, \ldots, X_n)$ ๆฅ่‡ช่ฏฅๆ€ปไฝ“๏ผŒ้ข˜็›ฎ่ฆๆฑ‚ๅˆคๆ–ญไปฅไธ‹ๅ››ไธช็ปŸ่ฎก้‡ไธญๅ“ชไบ›ๆ˜ฏๅ‚ๆ•ฐ $\theta$ ็š„ไธ€่‡ดไผฐ่ฎก้‡ใ€‚
---
### ไธ€่‡ดไผฐ่ฎก้‡็š„ๅฎšไน‰
ไธ€ไธชไผฐ่ฎก้‡ $\hat{\theta}^{(n)}$ ๆ˜ฏๅ‚ๆ•ฐ $\theta$ ็š„ไธ€่‡ดไผฐ่ฎก้‡๏ผŒๅฝ“ไธ”ไป…ๅฝ“๏ผš
$$
\lim_{n \to \infty} P(|\hat{\theta}^{(n)} - \theta| > \varepsilon) = 0 \quad \text{ๅฏนไปปๆ„ } \varepsilon > 0
$$
ๅณ๏ผš้š็€ๆ ทๆœฌๅฎน้‡ $n$ ๅขžๅคง๏ผŒไผฐ่ฎก้‡ไพๆฆ‚็އๆ”ถๆ•›ไบŽ็œŸๅฎžๅ‚ๆ•ฐ $\theta$ใ€‚
้€šๅธธ๏ผŒ่‹ฅไผฐ่ฎก้‡ๆ˜ฏๆธ่ฟ‘ๆ— ๅ็š„๏ผˆๅณ $E[\hat{\theta}^{(n)}] \to \theta$๏ผ‰ไธ”ๆปก่ถณไธ€่‡ดๆ–นๅทฎๆกไปถ๏ผŒๅˆ™ๅฎƒๆ˜ฏไธ€่‡ดไผฐ่ฎก้‡ใ€‚ไฝ†ๅณไฝฟไธๆปก่ถณ่ฟ™ไบ›ๆกไปถ๏ผŒๅช่ฆไพๆฆ‚็އๆ”ถๆ•›ๅณๅฏใ€‚
---
### ่ฎก็ฎ—ๅ…ณ้”ฎๆœŸๆœ›ๅ€ผ
ๆˆ‘ไปฌๅ…ˆ่ฎก็ฎ—ไธ€ไบ›ๅ…ณ้”ฎ็Ÿฉ๏ผš
- $ E[X] = (-1)\cdot \frac{1}{3}\theta + 0 \cdot \left(1 - \frac{2}{3}\theta\right) + 2 \cdot \frac{1}{3}\theta = \frac{1}{3}\theta $
- $ E[X^2] = 1 \cdot \frac{1}{3}\theta + 0 + 4 \cdot \frac{1}{3}\theta = \frac{5}{3}\theta $
---
### ้€ไธชๅˆ†ๆž็ปŸ่ฎก้‡
#### (i) $\frac{3}{n} \sum_{i=1}^{n} X_i$
- ๆœŸๆœ›๏ผš
$$
E\left[\frac{3}{n} \sum_{i=1}^{n} X_i\right] = \frac{3}{n} \cdot n \cdot E[X] = 3 \cdot \frac{1}{3}\theta = \theta
$$
- ็”ฑๅคงๆ•ฐๅฎšๅพ‹๏ผŒ$\frac{1}{n} \sum_{i=1}^{n} X_i \xrightarrow{P} E[X] = \frac{1}{3}\theta$๏ผŒ
ๆ‰€ไปฅ $\frac{3}{n} \sum_{i=1}^{n} X_i \xrightarrow{P} \theta$
โœ… ็ป“่ฎบ๏ผš(i) ๆ˜ฏไธ€่‡ดไผฐ่ฎก้‡
---
#### (ii) $X_1 + \frac{2}{n-1} \sum_{i=2}^{n} X_i$
- ่กจ่พพๅผๅฏๅ†™ไธบ๏ผš
$$
\hat{\theta}^{(ii)} = X_1 + \frac{2}{n-1} \sum_{i=2}^{n} X_i
$$
- ๆœŸๆœ›๏ผš
$$
E[\hat{\theta}^{(ii)}] = E[X_1] + \frac{2}{n-1} \cdot (n-1) E[X] = \frac{1}{3}\theta + 2 \cdot \frac{1}{3}\theta = \theta
$$
- ไฝ†ๆณจๆ„๏ผš่ฏฅ็ปŸ่ฎก้‡ๅŒ…ๅซ $X_1$๏ผŒ่ฟ™ๆ˜ฏไธ€ไธชๅ›บๅฎš้šๆœบๅ˜้‡๏ผŒ่€Œ $\frac{2}{n-1} \sum_{i=2}^{n} X_i \xrightarrow{P} \frac{2}{3}\theta$๏ผŒ
ๆ‰€ไปฅๆ•ดไฝ“่ถ‹ไบŽ $X_1 + \frac{2}{3}\theta$๏ผŒ่€Œ $X_1$ ไธๆ˜ฏ $\theta$ ็š„ๅ‡ฝๆ•ฐ๏ผŒๅ› ๆญคๆž้™ไธๅ”ฏไธ€ใ€‚
โŒ ็ป“่ฎบ๏ผš(ii) ไธๆ˜ฏไธ€่‡ดไผฐ่ฎก้‡
---
#### (iii) $\frac{3}{5n} \sum_{i=1}^{n} X_i^2$
- ๆœŸๆœ›๏ผš
$$
E\left[\frac{3}{5n} \sum_{i=1}^{n} X_i^2\right] = \frac{3}{5n} \cdot n \cdot E[X^2] = \frac{3}{5} \cdot \frac{5}{3}\theta = \theta
$$
- ็”ฑๅคงๆ•ฐๅฎšๅพ‹๏ผŒ$\frac{1}{n} \sum_{i=1}^{n} X_i^2 \xrightarrow{P} E[X^2] = \frac{5}{3}\theta$๏ผŒ
ๆ‰€ไปฅ $\frac{3}{5n} \sum_{i=1}^{n} X_i^2 \xrightarrow{P} \theta$
โœ… ็ป“่ฎบ๏ผš(iii) ๆ˜ฏไธ€่‡ดไผฐ่ฎก้‡
---
#### (iv) $\frac{1}{3n} \sum_{i=1}^{n} X_i^2$
- ๆœŸๆœ›๏ผš
$$
E\left[\frac{1}{3n} \sum_{i=1}^{n} X_i^2\right] = \frac{1}{3n} \cdot n \cdot E[X^2] = \frac{1}{3} \cdot \frac{5}{3}\theta = \frac{5}{9}\theta \neq \theta
$$
- ๆž้™ไธบ $\frac{5}{9}\theta \neq \theta$๏ผŒๆ•…ไธไพๆฆ‚็އๆ”ถๆ•›ไบŽ $\theta$
โŒ ็ป“่ฎบ๏ผš(iv) ไธๆ˜ฏไธ€่‡ดไผฐ่ฎก้‡
---
### ๆœ€็ปˆ็ป“่ฎบ
- ไธ€่‡ดไผฐ่ฎก้‡ๆœ‰๏ผš(i) ๅ’Œ (iii)
- ๅ…ฑ 2 ไธช
---
### ็ญ”ๆกˆ
$$
\boxed{B}
$$
Which of the following statements does NOT accurately describe voting behavior in the United States?
A: Registered voters between the ages of 35 and 45 are more likely to vote than are those under the age of 21.
B: A registered voter who has attained his or her General Educational Development (GED) is less likely to vote than a high school dropout.
C: Registered voters are more likely to vote in general elections than they are in primary elections.
D: More women than men have voted in every presidential election since 1980.
The question asks which statement does NOT accurately describe voting behavior in the United States. Here's the analysis of each option:
- Option A states that registered voters aged 35โ€“45 are more likely to vote than those under 21. This aligns with the general trend of increasing voter turnout with age, especially in the 30s and 40s. While younger voters (e.g., under 21) may have lower turnout, this statement is accurate.
- Option B claims that a registered voter with a GED is less likely to vote than a high school dropout. This contradicts the typical correlation between higher education and increased voter participation. GED holders generally have higher turnout than those with less education, making this statement inaccurate.
- Option C notes that registered voters are more likely to vote in general elections than primary elections. This is correct, as primaries are party-specific and often serve as a gateway to general elections, meaning registered voters are more likely to participate in the latter.
- Option D asserts that more women than men have voted in every presidential election since 1980. Historical data shows women have consistently had slightly higher turnout rates than men in presidential elections since 1980, making this statement accurate.
The incorrect statement is B, as it misrepresents the relationship between education and voting behavior.
\boxed{B}
Let $T$ be the set of all triples $(a,b,c)$ of positive integers for which there exist triangles with side lengths $a,$ $b,$ $c.$ Compute
[\sum_{(a,b,c) \in T} \frac{2^a}{3^b 5^c}.]
To solve the problem, we need to compute the sum over all triples ((a, b, c)) of positive integers that form a valid triangle of the term (\frac{2^a}{3^b 5^c}). The set (T) consists of all such triples where (a + b > c), (a + c > b), and (b + c > a).
First, we express the sum as a double sum over (a) and (b), with the inner sum over (c) constrained by the triangle inequalities. For each (a) and (b), the valid (c) values are (|a - b| + 1) to (a + b - 1). The inner sum over (c) is a geometric series:
[
\sum_{c=|a - b| + 1}^{a + b - 1} \frac{1}{5^c} = \frac{5}{4} \left( \frac{1}{5^{|a - b| + 1}} - \frac{1}{5^{a + b}} \right)
]
Substituting this back into the original sum, we split it into two parts:
[
\frac{5}{4} \left( \sum_{a=1}^\infty \sum_{b=1}^\infty \frac{2^a}{3^b} \left( \frac{1}{5^{|a - b| + 1}} - \frac{1}{5^{a + b}} \right) \right)
]
We compute these sums separately. Let (S_1) be the first part and (S_2) be the second part.
For (S_2):
[
S_2 = \sum_{a=1}^\infty \sum_{b=1}^\infty \frac{2^a}{3^b 5^{a + b}} = \left( \sum_{a=1}^\infty \left( \frac{2}{5} \right)^a \right) \left( \sum_{b=1}^\infty \left( \frac{1}{15} \right)^b \right) = \frac{2/3}{1 - 2/5} \cdot \frac{1/15}{1 - 1/15} = \frac{2}{3} \cdot \frac{1}{14} = \frac{1}{21}
]
For (S_1), we split it into two cases (a \geq b) and (a < b). For (a \geq b):
[
S_{1a} = \sum_{b=1}^\infty \sum_{k=0}^\infty \frac{2^{b + k}}{3^b 5^{k + 1}} = \frac{2}{3} \sum_{b=1}^\infty \left( \frac{2}{3} \right)^b \cdot \frac{1}{5} \sum_{k=0}^\infty \left( \frac{2}{5} \right)^k = \frac{2}{3} \cdot \frac{1}{3} \cdot 2 = \frac{2}{3}
]
For (a < b):
[
S_{1b} = \sum_{a=1}^\infty \sum_{m=1}^\infty \frac{2^a}{3^{a + m} 5^{m + 1}} = \frac{1}{5 \cdot 14} \sum_{a=1}^\infty \left( \frac{2}{3} \right)^a = \frac{1}{70} \cdot 2 = \frac{1}{35}
]
Thus, (S_1 = S_{1a} + S_{1b} = \frac{2}{3} + \frac{1}{35} = \frac{73}{105}).
Combining these results:
[
\frac{5}{4} \left( \frac{73}{105} - \frac{1}{21} \right) = \frac{5}{4} \left( \frac{73}{105} - \frac{5}{105} \right) = \frac{5}{4} \cdot \frac{68}{105} = \frac{17}{21}
]
The final answer is:
[
\boxed{\dfrac{17}{21}}
]

Showcase of Specialized Tasks

Task User's query Youtu-LLM-2B's response
้—ฎ้ข˜ๆ”นๅ†™โœ๏ธ Q1๏ผšๅ—ไบฌๆฌขไน่ฐท็š„ๅ…ทไฝ“ๅœฐๅ€ๆ˜ฏๅ“ช้‡Œ
A1๏ผšๅ—ไบฌๅธ‚ๆฌขไน่ฐทๆ™ฏๅŒบ๏ผˆNanjing Happy Valley๏ผ‰๏ผŒไฝไบŽๆฑŸ่‹็œๅ—ไบฌๅธ‚ๆ –้œžๅŒบ็ปๆตŽๆŠ€ๆœฏๅผ€ๅ‘ๅŒบๆฌขไน่ฐทๅŒ—่ทฏ8ๅทใ€‚
Q2๏ผš็Žฐๅœจ้€‚ๅˆๅŽปๆ—…ๆธธๅ—
A2๏ผšๆ—…ๆธธ็š„้€‚ๅฎœๆ€งๅ–ๅ†ณไบŽๅคšไธชๅ› ็ด ๏ผŒๅŒ…ๆ‹ฌ็›ฎ็š„ๅœฐ็š„็–ซๆƒ…็Šถๅ†ตใ€ๆ—…่กŒ้™ๅˆถๅ’Œไธชไบบ็š„ๅฅๅบท็Šถๅ†ตใ€‚็”ฑไบŽๆˆ‘ๆ— ๆณ•่Žทๅ–ๅฎžๆ—ถไฟกๆฏ๏ผŒๅปบ่ฎฎไฝ ๅ‚่€ƒๅฝ“ๅœฐๆ”ฟๅบœๅ’Œๅซ็”Ÿ้ƒจ้—จ็š„ๆŒ‡ๅฏผ๏ผŒไปฅไบ†่งฃ็›ฎ็š„ๅœฐ็š„ๆ—…่กŒๅปบ่ฎฎๅ’Œ้™ๅˆถใ€‚
Q3๏ผšๅ—ไบฌๆฌขไน่ฐทๅ’ŒๅŒ—ไบฌๆฌขไน่ฐทๅ“ชไธชๅฅฝ็Žฉ
A3๏ผšๅ—ไบฌๆฌขไน่ฐทๅ’ŒๅŒ—ไบฌๆฌขไน่ฐท้ƒฝๆ˜ฏ็Ÿฅๅ็š„ไธป้ข˜ๅ…ฌๅ›ญ๏ผŒๅ„่‡ช้ƒฝๆœ‰ๅ…ถ็‹ฌ็‰น็š„้ญ…ๅŠ›ๅ’Œ็‰น่‰ฒใ€‚ไปฅไธ‹ๆ˜ฏไธ€ไบ›ๆฏ”่พƒๅฎƒไปฌ็š„ๆ–น้ข:
1. ่ง„ๆจกๅ’Œ่ฎพๆ–ฝ๏ผšๅŒ—ไบฌๆฌขไน่ฐทๆ˜ฏไธญๅ›ฝๆœ€ๅคง็š„ไธป้ข˜ๅ…ฌๅ›ญไน‹ไธ€๏ผŒๆ‹ฅๆœ‰ๅนฟ้˜”็š„้ข็งฏๅ’ŒไธฐๅฏŒๅคšๆ ท็š„ๆธธไน่ฎพๆ–ฝใ€‚ๅ—ไบฌๆฌขไน่ฐทไนŸๆ˜ฏไธ€ๅบงๅคงๅž‹ไธป้ข˜ๅ…ฌๅ›ญ๏ผŒ่™ฝ็„ถ่ง„ๆจก่พƒๅฐ๏ผŒไฝ†ๅŒๆ ทๆไพ›ไบ†ๅคš็งๅจฑไน้กน็›ฎๅ’Œ่ฎพๆ–ฝใ€‚
2. ๅœฐ็†ไฝ็ฝฎ๏ผšๅŒ—ไบฌๆฌขไน่ฐทไฝไบŽไธญๅ›ฝ้ฆ–้ƒฝๅŒ—ไบฌ๏ผŒๅ‘จ่พนๆœ‰่ฎธๅคšๅ…ถไป–ๆ—…ๆธธๆ™ฏ็‚นๅ’Œๆ–‡ๅŒ–้—ๅ€๏ผŒๅฏไปฅๆ›ดๆ–นไพฟๅœฐๆŽข็ดขๅŸŽๅธ‚็š„ๅ…ถไป–ๆ™ฏ็‚นใ€‚ๅ—ไบฌๆฌขไน่ฐทๅˆ™ไฝไบŽๆฑŸ่‹็œๅ—ไบฌๅธ‚๏ผŒๅ‘จ่พนไนŸๆœ‰ไธ€ไบ›ๅކๅฒๅ’Œๆ–‡ๅŒ–ๆ™ฏ็‚นใ€‚
3. ไธป้ข˜ๅ’Œๆฐ›ๅ›ด๏ผšๅŒ—ไบฌๆฌขไน่ฐทไปฅๆฌขไนใ€ๅˆบๆฟ€ๅ’Œๅ†’้™ฉไธบไธป้ข˜๏ผŒๆœ‰่ฎธๅคšๆƒŠ้™ฉๅˆบๆฟ€็š„ๆธธไน่ฎพๆ–ฝใ€‚ๅ—ไบฌๆฌขไน่ฐทๅˆ™ๆ›ดๆณจ้‡ๅฎถๅบญๅจฑไนๅ’ŒไบฒๅญๆดปๅŠจ๏ผŒๆไพ›ไบ†ๆ›ดๅคš้€‚ๅˆๅ…จๅฎถไบบไธ€่ตท็Žฉไน็š„้กน็›ฎใ€‚
ๆ นๆฎไปฅไธŠๅทฒ็Ÿฅๆกไปถๅ’Œ้—ฎ็ญ”ๅ†…ๅฎน๏ผŒ็†่งฃไปฅไธ‹้—ฎ้ข˜ๆ„ๅ›พ๏ผŒๅนถๅฐ†้—ฎ้ข˜ไปฅๅฎŒๆ•ด็š„ๅฝขๅผ่พ“ๅ‡บ
Q4๏ผš่ฏฆ็ป†่งฃ้‡Šไธ‹็ฌฌไธ‰็‚น
่ฏฆ็ป†่งฃ้‡Šไธ‹ๅ—ไบฌๆฌขไน่ฐทๅ’ŒๅŒ—ไบฌๆฌขไน่ฐทๆฏ”่พƒไธญ๏ผŒไธป้ข˜ๅ’Œๆฐ›ๅ›ด็š„้ƒจๅˆ†ใ€‚
ๆ–‡ๆœฌๅˆ‡ๅˆ†๐Ÿ“‹ ่„‰ๅ†ฒ็”ตๅญๅ›ดๆ ๆŠฅ่ญฆ็ณป็ปŸไธญๆ–‡LCDๆŽงๅˆถ้”ฎ็›˜ไฝฟ็”จๆ‰‹ๅ†ŒV1.0
็ฌฌไธ€็ซ :่„‰ๅ†ฒ็”ตๅญๅ›ดๆ ๅ…ฅไพตๆŠฅ่ญฆ็ณป็ปŸ็ฎ€ไป‹
ๅฐŠๆ•ฌ็š„ๅฎขๆˆท:
ๆ‚จๅฅฝ!ๆ„Ÿ่ฐขๆ‚จไฝฟ็”จๆœฌๅ…ฌๅธไบงๅ“๏ผŒไธบไบ†ๆ›ดๅฅฝๆ›ดๅฟซ็š„ๅฎ‰่ฃ…ๅŠไฝฟ็”จๆœฌไบงๅ“๏ผŒ่ฏทๅœจไฝฟ็”จๅ‰่ฎค็œŸไป”็ป†ๅœฐ้˜…่ฏปๆœฌๆ‰‹ๅ†Œใ€‚
่„‰ๅ†ฒ็”ตๅญๅ›ดๆ ็ณป็ปŸๅŒ…ๆ‹ฌ่„‰ๅ†ฒ็”ตๅญๅ›ดๆ ไธปๆœบไปฅๅŠๅฏนๅ…ถๆŽงๅˆถ็š„PCไธปๆœบใ€ไธญๆ–‡LCDๆถฒๆ™ถ้”ฎ็›˜๏ผŒๆœฌๆ‰‹ๅ†Œ้’ˆๅฏน่„‰ๅ†ฒ็”ตๅญๅ›ดๆ ไธญๆ–‡LCDๆŽงๅˆถ้”ฎ็›˜้€‚็”จ๏ผŒๅฆ‚ๆ˜ฏๆ‚จๆœ‰ไปป้—ฎๆŠ€ๆœฏ้—ฎ้ข˜ๆˆ–้œ€่ฆๆŠ€ๆœฏๆ”ฏๆŒ๏ผŒ่ฏท่”็ณปๆˆ‘ๅ…ฌๅธ๏ผŒๆˆ‘ๅ…ฌๅธๅฐ†็ซญๅŠ›ไธบๆ‚จๆœๅŠกใ€‚
ไธ€ใ€่„‰ๅ†ฒ็”ตๅญๅ›ดๆ ไธปๆœบๆฆ‚่ฟฐ
1.1็ปๅฏนๅฎ‰ๅ…จ๏ผŒๆ นๆฎGB/T7946-2008่ฆๆฑ‚็ ”ๅ‘ๅˆถ้€ ๏ผŒๅนถ้€š่ฟ‡ไบ†ๅ…ฌๅฎ‰้ƒจ็š„ๅฝขๅผๆฃ€้ชŒใ€‚
1.2่ฏฏๆŠฅ็އไฝŽๅ’Œ้€‚ๅบ”ๆ€งๅผบ
ๆ™บ่ƒฝๅž‹่„‰ๅ†ฒ็”ตๅญๅ›ดๆ ็ณป็ปŸๅŸบๆœฌไธๅ—็Žฏๅขƒ(ๅฆ‚ๆ ‘ๆœจใ€ๅฐๅŠจ็‰ฉใ€้œ‡ๅŠจ็ญ‰)ๅ’Œๆฐ”ๅ€™(ๅฆ‚้ฃŽใ€้›ชใ€้›จใ€้›พ็ญ‰)็š„
ๅฝฑๅ“๏ผŒไธๅ—ๅœฐๅฝข้ซ˜ไฝŽๅ’Œๆ›ฒๆŠ˜็จ‹ๅบฆ็š„้™ๅˆถ๏ผŒ่ฏฏๆŠฅ็އๆžไฝŽใ€‚
1.3้˜ปๆŒกๅ’ŒๆŠฅ่ญฆๅŒ้‡ๅŠŸ่ƒฝ
ๆ™บ่ƒฝๅž‹่„‰ๅ†ฒ็”ตๅญๅ›ดๆ ็ณป็ปŸ็š„ๆ–ฐๆฆ‚ๅฟตๆ˜ฏๆŠŠไผๅ›พๅ…ฅไพต่€…้˜ปๆŒกๅœจ้˜ฒๅŒบไน‹ๅค–๏ผŒไธไฝœๆกˆไธบ็›ฎ็š„ใ€‚
่ƒฝๅคŸๅฎžๅฎžๅœจๅœจ็ป™ๅ…ฅ
ไพต่€…ไธ€็งๅจๆ…‘ๆ„Ÿๅ’Œ้˜ปๆŒกไฝœ็”จ๏ผŒไฝฟๅ…ถไธๆ•ข่ฝปไธพๅฆ„ๅŠจ๏ผŒ่พพๅˆฐ้˜ฒ่Œƒไธบไธป๏ผŒๅ‡ๅฐ‘ไฝœๆกˆๆฌกๆ•ฐใ€‚
1.4่ฟž็ปญๅทฅไฝœใ€ๅธƒ้˜ฒ/ๆ’ค้˜ฒ๏ผŒๆŒ‰้œ€่ฎพๅฎšใ€‚
1.6ๅฏๆ นๆฎ็”จๆˆท่ฆๆฑ‚ๅ’Œ็Žฐๅœบๅœฐ็†็ŽฏๅขƒไปฅๅŠๅฎ‰ๅ…จ็ญ‰็บง่ฟ›่กŒ่ฎพ่ฎกๅ’Œๅฎ‰่ฃ…ใ€‚
ๅนถๅฏๅ’Œๅคš็ง็Žฐไปฃๅฎ‰้˜ฒไบงๅ“๏ผŒไพ‹ๅฆ‚็”ต่ง†็›‘ๆŽง็ณป็ปŸใ€ๅฎ‰้˜ฒๆŠฅ่ญฆ็ณป็ปŸ้…ๅฅ—ไฝฟ็”จ๏ผŒไปฅๆ้ซ˜็ณป็ปŸ็š„ๅฎ‰ๅ…จ้˜ฒ่Œƒ็ญ‰็บงใ€‚
1.7็ปๅฏนๅฎ‰ๅ…จๅŠๆŠฅ่ญฆๆ„Ÿ็Ÿฅๆ€ง
ไผ ็ปŸ็š„้ซ˜ๅŽ‹่„‰ๅ†ฒ็”ต็ฝ‘่ญฆๆˆ’็ณป็ปŸๆฒกๆœ‰ๆŠฅ่ญฆๆ„Ÿ็ŸฅๅŠŸ่ƒฝ๏ผŒไป…ไป…ไปฅ้ซ˜ๅŽ‹ใ€ๅคง็”ตๆต็š„ๆ–นๅผ้˜ปๆญขๅ…ฅไพต่€…๏ผŒๆžๆ˜“้€ ๆˆๅ…ฅ
ไพต่€…ไผคๆฎ‹๏ผŒ็”š่‡ณๆญปไบก็ญ‰ไธฅ้‡ๅŽๆžœใ€‚
ๆ™บ่ƒฝๅž‹่„‰ๅ†ฒ็”ตๅญๅ›ดๆ ็ณป็ปŸ้‡‡็”จไบ†ไฝŽ่ƒฝ้‡็š„่„‰ๅ†ฒ้ซ˜ๅŽ‹(5~10KV)ใ€‚
็”ฑไบŽ่ƒฝ้‡
ๆžไฝŽไธ”ไฝœ็”จๆ—ถ้—ดๆžไธบ็Ÿญๆš‚๏ผŒๅ› ่€Œๅฏนไบบไฝ“ไธไผš้€ ๆˆไผคๅฎณใ€‚
ไธ€ๆ—ฆ่งฆๅŠ๏ผŒไนŸไผšๅ› ็›ดๆŽฅๆœ‰่งฆ็”ตๆ„Ÿ่€Œ็ฆปๅผ€ใ€‚
ไบŒใ€ไธญๆ–‡LCDๆŽงๅˆถ้”ฎ็›˜็‰น็‚นๅŠๆ€ง่ƒฝๅ‚ๆ•ฐ
ๅŠŸ่ƒฝ็‰น็‚น:
1.ๅฏๆŽฅ128ๅฐ่„‰ๅ†ฒ็”ตๅญๅ›ดๆ ๏ผŒไธญๆ–‡ๆถฒๆ™ถๆ˜พ็คบ๏ผŒไธค่ทฏRS485ๆ€ป็บฟ้€š่ฎฏๆŽฅๅฃ๏ผŒๆ–นไพฟ็Žฐๅœบๅฎ‰่ฃ…ๆ–ฝๅทฅ;
2.้‡‡็”จไธญๆ–‡ๆถฒๆ™ถๆ˜พ็คบ๏ผŒๆ˜พ็คบ็›ด่ง‚๏ผŒๆ“ไฝœๆ–นไพฟ
3.ๅฏๅŒๆ—ถ่ฟœ็จ‹ๆŽงๅˆถ128ๅฐ้ซ˜ๅŽ‹่„‰ๅ†ฒๅ›ดๆ ๆŽงๅˆถๅ™จ
4.ๅฏไปฅๅฎžๆ—ถๆ˜พ็คบๅ’ŒๆŽงๅˆถๅ„ๅ›ดๆ ๆŽงๅˆถๅ™จ็š„็Šถๆ€(่„‰ๅ†ฒ็”ตๅŽ‹ๅน…ๅ€ผใ€ๅธƒ้˜ฒ/ๆ’ค้˜ฒ็Šถๆ€)
5.ๆŠฅ่ญฆๆ—ถๆ˜พ็คบๅฏนๅบ”้˜ฒๅŒบ็š„ๆŠฅ่ญฆ็ฑปๅž‹(้˜ฒๆ‹†ใ€็Ÿญ่ทฏใ€ๆ–ญ็บฟใ€็ŸญๆŽฅ)๏ผŒๅŒๆ—ถๆœ‰ๅฃฐ้Ÿณๆ็คบ
6.ไธ€้”ฎๅธƒ/ๆ’ค้˜ฒๅŠŸ่ƒฝ
7.ๅฏไปฅๆŸฅ่ฏขๆŠฅ่ญฆๅކๅฒ่ฎฐๅฝ•.
8.ๅฏไปฅๅฎžๆ—ถๆ˜พ็คบ็บฟไธŠ็”ตๅŽ‹
9.ๅฏๆŸฅ่ฏขๆ“ไฝœ่ฎฐๅฝ•้˜ฒๆญข็ฎก็†ไบบๅ‘˜ๅฏน็ณป็ปŸไนฑๆ’ค้˜ฒ็ญ‰;
10.ๅฏๆŽงๅˆถๅ‰็ซฏ็ปง็”ตๅ™จๆจกๅ—่พ“ๅ‡บไฟกๅท;
11.ๆ”ฏๆŒ้ฅๆŽงๅ™จ้ซ˜๏ผŒไฝŽๅŽ‹ๅธƒ้˜ฒ๏ผŒๆ’ค้˜ฒ๏ผŒๅคไฝ็ญ‰;
12.ๅ…ญไธชLEDๆŒ‡็คบ็ฏๆ˜พ็คบ็ณป็ปŸๅทฅไฝœ็Šถๆ€;
13.่‡ชๅธฆไธ€่ทฏ12V800MA็”ตๅŽ‹ๆŠฅ่ญฆ่พ“ๅ‡บๅŠไธ€่ทฏๅธธๅผ€ๅธธ้—ญไฟกๅทๅฏ้€‰่พ“ๅ‡บ;
14.ๅฏๅญ˜ๅ‚จ100ๆกๆŠฅ่ญฆ่ฎฐๅฝ•๏ผŒๅŠ100ๆกๆ“ไฝœ่ฎฐๅฝ•;
ๆŠ€ๆœฏๅ‚ๆ•ฐ:
ๅฐบๅฏธ(้•ฟxๅฎฝxๅŽš):19.5x14x2.9cm
้‡้‡:250ๅ…‹
ๅทฅไฝœ็Žฏๅขƒ:-20โ„ƒ +50โ„ƒ;-0-85%ๆนฟๅบฆ
ๅทฅไฝœ็”ตๅŽ‹:็›ดๆต:12ไผ~16ไผ
ๅทฅไฝœ็”ตๆต(็›ดๆต):้™ๆ€50ๆฏซๅฎ‰;ๆŠฅ่ญฆ100ๆฏซๅฎ‰
ไธญๆ–‡ๆถฒๆ™ถๆ˜พ็คบ๏ผŒๆ˜พ็คบๆŽงๅˆถ่ฎพๅค‡็š„ๅ„็ฑปไฟกๆฏ๏ผŒๅŒ…ๆ‹ฌๆŠฅ่ญฆใ€็Šถๆ€็ญ‰ใ€‚
็ฌฌไบŒ็ซ :ไธญๆ–‡LCDๆŽงๅˆถ้”ฎ็›˜่ฏดๆ˜Ž
ไธ€.ไธญๆ–‡LCDๆŽงๅˆถ้”ฎ็›˜้ขๆฟ็คบๆ„ๅ›พ:
(ไธญๆ–‡LCDๆŽงๅˆถ้”ฎ็›˜ๅ›พ)
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
1.ๆญฃๅธธๆœ‰็”ต็Šถๆ€ๆ—ถ๏ผŒ็”ตๆบๆŒ‡็คบ็ฏไบฎ
2.็ณป็ปŸ่ฟ่กŒๆ—ถ๏ผŒ่ฟ่กŒๆŒ‡็คบ็ฏไบฎ
3.้”ฎ็›˜ๅธƒ้˜ฒๆ—ถ๏ผŒๅธƒ้˜ฒๆŒ‡็คบ็ฏไบฎ
4.็ณป็ปŸๆœ‰ๆŠฅ่ญฆๆ—ถ๏ผŒๆŠฅ่ญฆๆŒ‡็คบ็ฏไบฎ
5.ๆœ‰ๆ— ็บฟ้ฅๆŽงๅ™จ็š„ๆŽงๅˆถไฟกๅทๆ—ถ๏ผŒไฟกๅทๆŒ‡็คบ็ฏไบฎ
ไบŒ.ๆŽฅ็บฟ็ซฏๅฃ
1.RS485้€š่ฎฏๅฃ2:้”ฎ็›˜็ฌฌไบŒไบŒ่ทฏ485 ้€š่ฎฏๆŽฅๅฃ;
2.RS485้€š่ฎฏๅฃ1:้”ฎ็›˜็ฌฌไธ€่ทฏ485้€š่ฎฏๆŽฅๅฃ;
3. 12V๏ผŒ GND:ไธบ้”ฎ็›˜12Vไพ›็”ตๆŽฅๅ…ฅๅฃ;
4.ๅผ€ๅ…ณ้‡:้”ฎ็›˜ๆŠฅ่ญฆๅผ€ๅ…ณ้‡่พ“ๅ‡บๆŽฅๅฃ(ๅธธๅผ€๏ผŒๅธธ้—ญๅฏ้€‰๏ผŒๅœจ้”ฎ็›˜ๅ†…้ƒจ็ปง็”ตๅ™จๆ—็š„JP่ทณ้’ˆ);
5.่ญฆๅท่พ“ๅ‡บๆŽฅๅฃ:"+"ๆŽฅ่ญฆๅทๆญฃๆž๏ผŒ"ไธ€"ๆŽฅ่ญฆๅท่ดŸๆž;
(ๆŽฅ็บฟ็ซฏๅฃ็คบๆ„ๅ›พ)
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
็ฌฌไธ‰็ซ :็ผ–็จ‹่ฎพ็ฝฎ
ๆณจ:็ณป็ปŸๅˆๅง‹ๅฏ†็ ไธบ:8888;ๅฎ‰่ฃ…่ฐƒ่ฏ•ๅฎŒๆˆๅŽ๏ผŒ่ฏท็ฌฌไธ€ๆ—ถ้—ดไฟฎๆ”น็ณป็ปŸๅฏ†็ !
ไธ€.็ณป็ปŸ่œๅ•้กนๅŠ็ผ–็จ‹่ฎพ็ฝฎ
1.1็ณป็ปŸไธŠ็”ตๅˆๅง‹ๅŒ–็•Œ้ข๏ผŒๆ˜พ็คบๅ…ฌๅธๅ็งฐๅŠๆฌข่ฟŽ็•Œ้ข:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(ๆฌข่ฟŽไฝฟ็”จ็•Œ้ข)
1.2 ๆŠฅ่ญฆไฟกๆฏๆ˜พ็คบ็•Œ้ขๅŠ้˜ฒๅŒบ็Šถๆ€ๅพช็Žฏ็•Œ้ข:
(้˜ฒๅŒบ็Šถๆ€ๅพช็Žฏ็•Œ้ข)
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(ๆŠฅ่ญฆไฟกๆฏๆ˜พ็คบ็•Œ้ข)
ๅœจๆญคไธค็•Œ้ขไธ‹๏ผŒๆŒ‰"โ†‘""โ†“"้”ฎๅฏๆฅๅ›žๅˆ‡ๆข;ๅœจๆŠฅ่ญฆ็•Œ้ขไธ‹๏ผŒAlarm:ๅŽ้ข็š„ๆ•ฐๅญ—่กจ็คบๅฝ“ๅ‰็š„้˜ฒๅŒบๆŠฅ่ญฆๆ•ฐ้‡;All:ๅŽ้ข็š„ๆ•ฐๅญ—่กจ็คบ็ณป็ปŸๆ€ปๅ…ฑ้˜ฒๅŒบๆ•ฐ้‡ใ€‚
1.3็ณป็ปŸๅŠŸ่ƒฝๅ‚ๆ•ฐ่ฎพ็ฝฎ:
ๆŒ‰ไธ‹้”ฎ็›˜ไธŠ็š„"่œๅ•"้”ฎๅŽ๏ผŒๅฏ่ฐƒๅ‡บ่ฟ›ๅ…ฅ่‡ณ"่ฎพ็ฝฎ็ณป็ปŸๅ‚ๆ•ฐ"ไธป็•Œ้ข;ๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
ๆญคๆ—ถๅ…‰ๆ ‡ๆกไบง็”Ÿ่ƒŒๆ™ฏๅฏนๆฏ”ๅบฆ๏ผŒไปฅ็คบๅฝ“ๆ—ถ้€‰ๅฎš็š„่œๅ•้กน;ๆญคๆ—ถๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅŽ๏ผŒๆ็คบ่พ“ๅ…ฅๅฏ†็ :่พ“ๅ…ฅๅฎŒๅฏ†็ ๅŽๅณๅฏ่ฟ›ๅ…ฅๅˆฐ"่ฎพ็ฝฎ็ณป็ปŸๅ‚ๆ•ฐ"็š„ไบŒ็บง่œๅ•้กต;ๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(่พ“ๅ…ฅๅฏ†็ ็•Œ้ข๏ผŒๅฏ†็ ไธบ4ไฝ๏ผŒ่พ“ๅ…ฅๆญฃ็กฎๅŽ่‡ชๅŠจ่ฟ›ๅ…ฅไบŒ็บง่œๅ•)
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(่ฎพ็ฝฎ็ณป็ปŸๅ‚ๆ•ฐไบŒ็บง่œๅ•็•Œ้ข)
1.4้˜ฒๅŒบ่ฎพ็ฝฎ(ๅขžๅŠ /ๅˆ ้™ค้˜ฒๅŒบ)
ๅœจ"่ฎพ็ฝฎ็ณป็ปŸๅ‚ๆ•ฐ"่œๅ•ไธ‹๏ผŒ้€š่ฟ‡ๆŒ‰"โ†“"้”ฎ๏ผŒ้€‰ไธญ"้˜ฒๅŒบ่ฎพ็ฝฎ"ๅŽ๏ผŒๆŒ‰"็กฎๅฎš"้”ฎ่ฟ›ๅ…ฅ่œๅ•;ๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(้˜ฒๅŒบ่ฎพ็ฝฎ็•Œ้ข)
ๆ€;
1.4.1ๅขžๅŠ ้˜ฒๅŒบ
***,ๆŒ‰"็กฎๅฎš"้”ฎๅŽๅณๅฏๅขžๅŠ ็›ธๅบ”็ผ–ๅท็š„้˜ฒๅŒบ๏ผŒๅๅคๆ“ไฝœๅณๅฏๅขžๅŠ ๆ›ดๅคš็š„้˜ฒๅŒบ;ๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(ๅขžๅŠ ้˜ฒๅŒบ็•Œ้ข)
1.4.2ๅˆ ้™ค้˜ฒๅŒบ
ๅœจ"้˜ฒๅŒบ่ฎพ็ฝฎ็•Œ้ข"้€š่ฟ‡"โ†‘""โ†“"้”ฎ้€‰ไธญ้ข„ๅˆ ้™ค็š„้˜ฒๅŒบๅท๏ผŒๆŒ‰ไธ€ไธ‹้”ฎ็›˜ไธŠ็š„"ๅˆ ้™ค"้”ฎๅŽ๏ผŒๅผนๅ‡บๅˆ ้™คๆ็คบ่œๅ•ๅŽ๏ผŒๆŒ‰้”ฎ็›˜ไธŠ็š„"็กฎๅฎš"้”ฎๅŽๅณๅฏๅˆ ้™คๅฝ“ๅ‰้˜ฒๅŒบ;ๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(ๅˆ ้™ค้˜ฒๅŒบ็•Œ้ข)
1.5ๅฟซ้€Ÿๅธƒๆ’ค้˜ฒๆ“ไฝœ
ๅœจ"่ฎพ็ฝฎ็ณป็ปŸๅ‚ๆ•ฐ"่œๅ•ไธ‹๏ผŒ้€š่ฟ‡ๆŒ‰"โ†“"้”ฎ๏ผŒ้€‰ๆ‹ฉ"ๅฟซ้€Ÿๅธƒๆ’ค้˜ฒๆ“ไฝœ"ๅŽ๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅŽ่ฟ›ๅ…ฅ่‡ณ"ๅฟซ้€Ÿๅธƒๆ’ค้˜ฒๆ“ไฝœ"่œๅ•๏ผŒๆญคๆ—ถๅ†้€š่ฟ‡"โ†‘""โ†“"้”ฎ้€‰ไธญ้ข„ๅฏนๅ›ดๆ ็ณป็ปŸ่ฟ›่กŒ็š„ๆ“ไฝœๅŽๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅณๅฏ;ๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(ๅฟซ้€Ÿๅธƒๆ’ค้˜ฒ็•Œ้ข)
1.6ๅ•้˜ฒๅŒบๆ“ไฝœ
ๅœจ"่ฎพ็ฝฎ็ณป็ปŸๅ‚ๆ•ฐ"่œๅ•ไธ‹๏ผŒ้€š่ฟ‡"โ†‘""โ†“"้”ฎ้€‰ๆ‹ฉ"ๅ•้˜ฒๅŒบๆ“ไฝœ"ๅŽ๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅŽ่ฟ›ๅ…ฅ่‡ณ"ๅ•้˜ฒๅŒบๆ“ไฝœ"่œๅ•๏ผŒๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(ๅ•้˜ฒๅŒบๆ“ไฝœ็•Œ้ข)
ๅœจ"ๅ•้˜ฒๅŒบๆ“ไฝœ"็•Œ้ข๏ผŒ้€š่ฟ‡"โ†‘""โ†“"้”ฎๅฎšไฝๅ…‰ๆ ‡๏ผŒ่พ“ๅ…ฅ้ข„ๅ•้˜ฒๅŒบๆ“ไฝœ็š„้˜ฒๅŒบๅท:10(ๅ‡่ฎพๅ€ผ)๏ผŒ็„ถๅŽๆŒ‰"โ†“"้”ฎๅฐ†ๅ…‰ๆ ‡ไธ‹็งปๅˆฐ็”ตๅŽ‹่ฎพ็ฝฎ่กŒ:(ๆŒ‰"2"ไธบ้ซ˜ๅŽ‹๏ผŒ"1"ไธบไฝŽๅŽ‹);ๅ†ๆŒ‰"โ†“"้”ฎๅฐ†ๅ…‰ๆ ‡ไธ‹็งปๅˆฐ่ฎพ็ฝฎ็Šถๆ€่กŒ:(ๆŒ‰"0"ไธบๆ’ค้˜ฒ๏ผŒ"1"ไธบๅธƒ้˜ฒ)๏ผŒๅ†ๆŒ‰"็กฎๅฎš"้”ฎๅณๅฏๅฏนๅ‰็ซฏๅฏนๅบ”้˜ฒๅŒบๅท็š„ๅ›ดๆ ไธปๆœบ่ฟ›่กŒ็›ธๅบ”็š„ๆ“ไฝœใ€‚
1.7ๅฎšๆ—ถๅธƒๆ’ค้˜ฒ่ฎพ็ฝฎ
ๅœจ"่ฎพ็ฝฎ็ณป็ปŸๅ‚ๆ•ฐ"่œๅ•ไธ‹๏ผŒ้€š่ฟ‡""้”ฎ้€‰ๆ‹ฉ"ๅฎšๆ—ถๅธƒๆ’ค้˜ฒ่ฎพ็ฝฎ"ๅŽ๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅŽ่ฟ›ๅ…ฅ่‡ณ"ๅฎšๆ—ถๅธƒๆ’ค้˜ฒ่ฎพ็ฝฎ"่œๅ•๏ผŒๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(ๅฎšๆ—ถๅธƒๆ’ค้˜ฒ่ฎพ็ฝฎ็•Œ้ข)
้€š่ฟ‡"โ†‘""โ†“"้”ฎๅฏ็งปๅŠจๅ…‰ๆ ‡๏ผŒ่พ“ๅ…ฅ้ข„่ฎก็š„ๆ—ถ้—ดๆฎต;็งป่‡ณๆ—ถ้—ดๆฎตๆœ€ๆœช็ซฏๆ—ถ๏ผŒ้€š่ฟ‡ๆŒ‰ไธ‹"0/1/2/3"้”ฎ่ฎพ็ฝฎ่ฏฅๆ—ถ้—ดๆฎต่ฆ่ฟ›่กŒ็š„ๅฎšๆ—ถๆ“ไฝœ("0"=ไธๆ“ไฝœ๏ผŒ"1"=้ซ˜ๅŽ‹ๅธƒ้˜ฒ๏ผŒ"2"=ไฝŽๅŽ‹ๅธƒ้˜ฒ๏ผŒ"3"=ๆ’ค้˜ฒ);
1.8่งฆๅ‘ๆ—ถ้—ด่ฎพ็ฝฎ
ๅœจ"่ฎพ็ฝฎ็ณป็ปŸๅ‚ๆ•ฐ"่œๅ•ไธ‹๏ผŒ้€š่ฟ‡"โ†‘""โ†“"้”ฎ้€‰ๆ‹ฉ"่งฆๅ‘ๆ—ถ้—ด่ฎพ็ฝฎ"ๅŽ๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅŽ่ฟ›ๅ…ฅ่‡ณ"่งฆๅ‘ๆ—ถ้—ด่ฎพ็ฝฎ"่œๅ•๏ผŒๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(่งฆๅ‘ๆ—ถ้—ด่ฎพ็ฝฎ็•Œ้ข)
่พ“ๅ…ฅ้œ€่ฎพ็ฝฎๅ›ดๆ ไธปๆœบๆŠฅ่ญฆ่งฆๅ‘็š„ๆ—ถ้—ดๅ‚ๆ•ฐ(ไปฅ็ง’ไธบๅ•ไฝ๏ผŒๆœ€ๅคง3ไฝๆ•ฐ)ๅŽๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅณๅฏ๏ผŒๆŒ‰"ๅ–ๆถˆ"้”ฎๅฏ้€€ๆ ผๆ—ถ้—ดๅ‚ๆ•ฐ้‡ๆ–ฐ่พ“ๅ…ฅๆ—ถ้—ดใ€‚
1.9ๆŠฅ่ญฆๆ—ถ้—ด่ฎพ็ฝฎ
ๅœจ"่ฎพ็ฝฎ็ณป็ปŸๅ‚ๆ•ฐ"่œๅ•ไธ‹๏ผŒ้€š่ฟ‡"้”ฎ้€‰ๆ‹ฉ"ๆŠฅ่ญฆๆ—ถ้—ด่ฎพ็ฝฎ"ๅŽ๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅŽ่ฟ›ๅ…ฅ่‡ณ"ๆŠฅ่ญฆๆ—ถ้—ด่ฎพ็ฝฎ"่œๅ•๏ผŒๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(ๆŠฅ่ญฆๆ—ถ้—ด่ฎพ็ฝฎ็•Œ้ข)
่พ“ๅ…ฅ้œ€่ฎพ็ฝฎๅ›ดๆ ไธปๆœบๅŠไธญๆ–‡LCDๆŽงๅˆถ้”ฎ็›˜ๆŠฅ่ญฆ็š„ๆ—ถ้—ดๅ‚ๆ•ฐ(ไปฅ็ง’ไธบๅ•ไฝ๏ผŒๆœ€ๅคง3ไฝๆ•ฐ)ๅŽๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅณๅฏ๏ผŒๆŒ‰"ๅ–ๆถˆ"้”ฎๅฏ้€€ๆ ผๆ—ถ้—ดๅ‚ๆ•ฐ้‡ๆ–ฐ่พ“ๅ…ฅๆ—ถ้—ดใ€‚
ๆŠฅ่ญฆๆ—ถ้—ดๅ‚ๆ•ฐๅณไธบๅ‰็ซฏๆฏๅฐๅ›ดๆ ไธปๆœบ็š„ๆŠฅ่ญฆๅŽ่‡ชๅŠจ
ๆขๅค็š„ๆ—ถ้—ดๅ€ผๅŠไธญๆ–‡ๆŽงๅˆถ้”ฎ็›˜็š„ๆŠฅ่ญฆๆ—ถ้—ดใ€‚
1.10้˜ฒๅŒบๅท่ฎพ็ฝฎ
ๅœจ"่ฎพ็ฝฎ็ณป็ปŸๅ‚ๆ•ฐ"่œๅ•ไธ‹๏ผŒ้€š่ฟ‡"โ†‘""โ†“"้”ฎ้€‰ๆ‹ฉ"้˜ฒๅŒบๅท่ฎพ็ฝฎ"ๅŽ๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅŽ่ฟ›ๅ…ฅ่‡ณ"้˜ฒๅŒบๅท่ฎพ็ฝฎ"่œๅ•๏ผŒๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(้˜ฒๅŒบๅท่ฎพ็ฝฎ็•Œ้ข)
้€š่ฟ‡"โ†‘""โ†“"้”ฎ็งปๅ…‰ๆ ‡๏ผŒ่พ“ๅ…ฅๆ—ง้˜ฒๅŒบๅทๅŠๆ–ฐ้˜ฒๅŒบๅทๅŽ๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"ๅณๅฏๅฐ†ๅ‰็ซฏๅ›ดๆ ไธปๆœบ็š„ๆ—ง้˜ฒๅŒบๅทๆ”นๆˆๆ–ฐ็š„้˜ฒๅŒบๅท(ๆณจ:ๅ‡่ฎพๅ‰็ซฏๅ›ดๆ ไธปๆœบ็š„้˜ฒๅŒบๅทไธบ"1"๏ผŒ่ฆๆ”นๆˆๆ–ฐ็š„้˜ฒๅŒบๅทไธบ"2"๏ผŒๆญคๆ—ถๅœจๆญค็•Œ้ขๆ—ง้˜ฒๅŒบๅท่พ“ๅ…ฅ:1๏ผŒๆ–ฐ้˜ฒๅŒบๅท่พ“ๅ…ฅ:2๏ผŒ็กฎๅฎšๅฎŒๅŽ๏ผŒๅŽŸๅ…ˆ้˜ฒๅŒบๅทไธบ"1"็š„ๅ›ดๆ ไธปๆœบๅฐฑๅ˜ๆˆไบ†้˜ฒๅŒบๅทไธบ"2")ใ€‚
1.11้”ฎ็›˜ๆจกๅผ่ฎพ็ฝฎ
ๅœจ"่ฎพ็ฝฎ็ณป็ปŸๅ‚ๆ•ฐ"่œๅ•ไธ‹๏ผŒ้€š่ฟ‡"โ†‘้”ฎ้€‰ๆ‹ฉ"้”ฎ็›˜ๆจกๅผ่ฎพ็ฝฎ"ๅŽ๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅŽ่ฟ›ๅ…ฅ่‡ณ"้”ฎ็›˜ๆจกๅผ่ฎพ็ฝฎ"่œๅ•๏ผŒๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(้”ฎ็›˜ๆจกๅผ่ฎพ็ฝฎ็•Œ้ข)
้€š่ฟ‡"โ†‘""โ†“"้”ฎ็งปๅ…‰ๆ ‡๏ผŒ่พ“ๅ…ฅ้”ฎ็›˜็š„็ผ–ๅท:ๅŽๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅ…‰ๆ ‡็งปๅŠจ่‡ณ้”ฎ็›˜ๆจกๅผ่กŒ๏ผŒๆŒ‰ไธ‹"0"่กจ็คบๅฐ†ๆญค้”ฎ็›˜่ฎพ็ฝฎไธบไปŽ้”ฎ็›˜ๆจกๅผ๏ผŒๆŒ‰ไธ‹"1"่กจ็คบๅฐ†ๆญค้”ฎ็›˜่ฎพ็ฝฎไธบไธป้”ฎ็›˜ๆจกๅผ(ๆณจ:"0"=ไปŽ้”ฎ็›˜๏ผŒ"1"=ไธป้”ฎ็›˜๏ผŒไปŽ้”ฎ็›˜ๅช่ƒฝๆ˜พ็คบ้˜ฒๅŒบ็Šถๆ€ๅŠ้˜ฒๅŒบๆŠฅ่ญฆไฟกๆฏ๏ผŒไธ่ƒฝๅฏนๆ•ดไธช็ณป็ปŸ่ฟ›่กŒๅธƒ้˜ฒ๏ผŒๆ’ค้˜ฒ็ญ‰ๆ“ไฝœ);
1.12ๆ—ถ้—ด่ฎพ็ฝฎ
ๅœจ"่ฎพ็ฝฎ็ณป็ปŸๅ‚ๆ•ฐ"่œๅ•ไธ‹๏ผŒ้€š่ฟ‡ๆŒ‰"โ†‘""โ†“"้”ฎ้€‰ๆ‹ฉ"ๆ—ถ้—ด่ฎพ็ฝฎ"ๅŽ๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅŽ่ฟ›ๅ…ฅ่‡ณ"ๆ—ถ้—ด่ฎพ็ฝฎ"่œๅ•๏ผŒๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(ๆ—ถ้—ด่ฎพ็ฝฎ็•Œ้ข)
้€š่ฟ‡ๆŒ‰"โ†‘""โ†“"้”ฎ็งปๅŠจๅ…‰ๆ ‡๏ผŒ่พ“ๅ…ฅ็›ธๅบ”็š„ๆ—ถ้—ดๅนด๏ผŒๆœˆ๏ผŒๆ—ฅ๏ผŒๅฐๆ—ถ๏ผŒๅˆ†้’ŸๅŽ๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"้”ฎ๏ผŒ่ฎพ็ฝฎๆ—ถ้—ดๅฎŒๆฏ•ใ€‚
1.13ๅฏ†็ ่ฎพ็ฝฎ
ๅœจ"่ฎพ็ฝฎ็ณป็ปŸๅ‚ๆ•ฐ"่œๅ•ไธ‹๏ผŒ้€š่ฟ‡ๆŒ‰"โ†‘""โ†“"้”ฎ้€‰ๆ‹ฉ"ๅฏ†็ ่ฎพ็ฝฎ"ๅŽ๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅŽ่ฟ›ๅ…ฅ่‡ณ"ๅฏ†็ ่ฎพ็ฝฎ"่œๅ•๏ผŒๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(ๅฏ†็ ่ฎพ็ฝฎ็•Œ้ข)
่พ“ๅ…ฅๆ–ฐ็š„4ไฝๅฏ†็ :****๏ผŒ๏ผŒ่พ“้”™ๅฏ†็ ๅ€ผๆ—ถ๏ผŒๅฏๆŒ‰"ๅ–ๆถˆ"้”ฎ้€€ๆ ผๅˆ ้™คๆ•ฐๅญ—ไปฅ้‡ๆ–ฐ่พ“ๅ…ฅๆ–ฐ็š„ๅฏ†็ ๏ผŒๅ†ๆŒ‰"็กฎๅฎš"้”ฎๅฎŒๆˆๅฏ†็ ็š„ไฟฎๆ”น;
1.14ๆขๅคๅ‡บๅŽ‚ๆ—ถ่ฎพ็ฝฎ
ๅœจ"่ฎพ็ฝฎ็ณป็ปŸๅ‚ๆ•ฐ"่œๅ•ไธ‹๏ผŒ้€š่ฟ‡ๆŒ‰"โ†‘""โ†“"้”ฎ้€‰ๆ‹ฉ"ๆขๅคๅ‡บๅŽ‚ๆ—ถ่ฎพ็ฝฎ"ๅŽ๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅŽ่ฟ›ๅ…ฅ่‡ณ"ๆขๅคๅ‡บๅŽ‚ๆ—ถ่ฎพ็ฝฎ"่œๅ•๏ผŒๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(ๆขๅคๅ‡บๅŽ‚ๆ—ถ่ฎพ็ฝฎ็•Œ้ข)
ๆณจ:ๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅณๅฏๅฐ†้”ฎ็›˜็š„ๆ‰€ๆœ‰ๅ‚ๆ•ฐๆขๅค่‡ณๅ‡บๅŽ‚้ป˜่ฎคๅ‚ๆ•ฐ;่ฏทๅœจๅŽ‚ๅฎถ็š„ๆŒ‡ๅฏผไธ‹่ฐจๆ…Žๆ“ไฝœ!
1.15ๅˆ ้™คๆŠฅ่ญฆ่ฎฐๅฝ•
ๅœจไธป่œๅ•ไธ‹๏ผŒ้€š่ฟ‡ๆŒ‰"โ†‘""โ†“"้”ฎ้€‰ๆ‹ฉ"ๅˆ ้™คๆŠฅ่ญฆ่ฎฐๅฝ•"ๅŽ๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅŽๆ็คบ่พ“ๅ…ฅๅฏ†็ :**** ,่พ“ๅ…ฅๆญฃ็กฎๅฏ†็ ๅŽ๏ผŒ่ฟ›ๅ…ฅ่‡ณ"็กฎๅฎšๅˆ ้™คๆ‰€ๆœ‰่ฎฐๅฝ•?"็•Œ้ข๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅŽ๏ผŒๅˆ ้™คๆˆๅŠŸๅŒๆ—ถๆ็คบ"ๆ— ๆ›ดๅคš่ฎฐๅฝ•";ๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
1.16ๅˆ ้™คๆ“ไฝœ่ฎฐๅฝ•
(ๅˆ ้™ค่ฎฐๅฝ•็กฎๅฎš็•Œ้ข)
ๅœจไธป่œๅ•ไธ‹๏ผŒ้€š่ฟ‡ๆŒ‰"โ†‘""โ†“"้”ฎ้€‰ๆ‹ฉ"ๅˆ ้™คๆ“ไฝœ่ฎฐๅฝ•"ๅŽ๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅŽๆ็คบ่พ“ๅ…ฅๅฏ†็ :****๏ผŒ่พ“ๅ…ฅๆญฃ็กฎๅฏ†็ ๅŽ๏ผŒ่ฟ›ๅ…ฅ่‡ณ"็กฎๅฎšๅˆ ้™คๆ‰€ๆœ‰่ฎฐๅฝ•?"็•Œ้ข๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅŽ๏ผŒๅˆ ้™คๆˆๅŠŸๅŒๆ—ถๆ็คบ"ๆ— ๆ›ดๅคš่ฎฐๅฝ•";ๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(ๅˆ ้™ค่ฎฐๅฝ•็กฎๅฎš็•Œ้ข)
็ฌฌๅ››็ซ :็ณป็ปŸๆ—ฅๅธธๆ“ไฝœ
ๆณจ:็ณป็ปŸๅˆๅง‹ๅฏ†็ ไธบ:8888;ๅฎ‰่ฃ…่ฐƒ่ฏ•ๅฎŒๆˆๅŽ๏ผŒ่ฏท็ฌฌไธ€ๆ—ถ้—ดไฟฎๆ”น็ณป็ปŸๅฏ†็ !
ไธ€.ๅฟซ้€Ÿ้ซ˜ไฝŽๅŽ‹ๅธƒ้˜ฒ
ๅœจ็ณป็ปŸ"้˜ฒๅŒบ็Šถๆ€ๅพช็Žฏ็•Œ้ข"ๆˆ–่€…"ๆŠฅ่ญฆไฟกๆฏๆ˜พ็คบ็•Œ้ข"ไธ‹๏ผŒๅฆ‚ๆžœ็ณป็ปŸๅฝ“ๅ‰่œๅ•้กตไธๅœจๆญคไธค็•Œ้ขไธ‹ๆ—ถ๏ผŒๅฏ้€š่ฟ‡ๆŒ‰"่œๅ•"้”ฎๆฅๅˆ‡ๆข่‡ณ"้˜ฒๅŒบ็Šถๆ€ๅพช็Žฏ็•Œ้ข"ๅ’Œ"ๆŠฅ่ญฆไฟกๆฏๆ˜พ็คบ็•Œ้ข";็„ถๅŽๆŒ‰"ๅธƒ้˜ฒ"้”ฎๅŽ๏ผŒๆ็คบ่พ“ๅ…ฅๅฏ†็ :****ๅณๅฏ่ฟ›ๅ…ฅ"ๅฟซ้€Ÿๅธƒ้˜ฒ"่œๅ•้กต;ๆญคๆ—ถ้€š่ฟ‡ๆŒ‰"โ†‘""โ†“"้”ฎ้€‰ไธญ้œ€่ฆ่ฟ›่กŒ"ๅฟซ้€Ÿ้ซ˜ๅŽ‹ๅธƒ้˜ฒ"ๆˆ–่€…"ๅฟซ้€ŸไฝŽๅŽ‹ๅธƒ้˜ฒ"ๅŽ๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅณๅฏๅฏนๆ•ดไธช็ณป็ปŸ่ฟ›่กŒ็›ธๅบ”็š„้ซ˜ๅŽ‹/ไฝŽๅŽ‹ๅธƒ้˜ฒๆ“ไฝœ;ๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
ไบŒ.ๅฟซ้€Ÿๆ’ค้˜ฒ
(ๅฟซ้€Ÿๅธƒ้˜ฒ็•Œ้ข)
ๅœจ็ณป็ปŸ"้˜ฒๅŒบ็Šถๆ€ๅพช็Žฏ็•Œ้ข"ๆˆ–่€…"ๆŠฅ่ญฆไฟกๆฏๆ˜พ็คบ็•Œ้ข"ไธ‹๏ผŒๅฆ‚ๆžœ็ณป็ปŸๅฝ“ๅ‰่œๅ•้กตไธๅœจๆญคไธค็•Œ้ขไธ‹ๆ—ถ๏ผŒๅฏ้€š่ฟ‡ๆŒ‰"่œๅ•"้”ฎๆฅๅˆ‡ๆข่‡ณ"้˜ฒๅŒบ็Šถๆ€ๅพช็Žฏ"็•Œ้ขๅ’Œ"ๆŠฅ่ญฆไฟกๆฏๆ˜พ็คบ"็•Œ้ข;็„ถๅŽๆŒ‰"ๆ’ค้˜ฒ"้”ฎๅŽ๏ผŒๆ็คบ่พ“ๅ…ฅๅฏ†็ :****ๅณๅฏ่ฟ›ๅ…ฅ่‡ณ"ๅฟซ้€Ÿๆ’ค้˜ฒ"่œๅ•้กต;ๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅณๅฏๅฏนๆ•ดไธช็ณป็ปŸ่ฟ›่กŒ็›ธๅบ”็š„ๆ’ค้˜ฒๆ“ไฝœ;ๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
ไธ‰.ๆŠฅ่ญฆๅคไฝ
(ๅฟซ้€Ÿๆ’ค้˜ฒ็•Œ้ข)
ๅฝ“็ณป็ปŸๅ‰็ซฏๆœ‰ๅ›ดๆ ไธปๆœบๆˆ–่€…ๅœฐๅ€ๆจกๅ—้˜ฒๅŒบ่ขซ่งฆๅ‘ๆ—ถ๏ผŒ้”ฎ็›˜่‡ชๅŠจๅˆ‡ๆขๅˆฐ"ๆŠฅ่ญฆไฟกๆฏๆ˜พ็คบ"็•Œ้ข๏ผŒๅฆ‚ไธ‹ๅ›พ:
"ๆŠฅ่ญฆไฟกๆฏๆ˜พ็คบ"ๆ˜พ็คบไบ†ๅฝ“ๅ‰็š„ๆŠฅ่ญฆ้˜ฒๅŒบๆ•ฐ้‡๏ผŒๅŠๆŠฅ่ญฆ็š„่งฆๅ‘ๆกไปถ(้˜ฒๆ‹†๏ผŒ็Ÿญ่ทฏ๏ผŒๆ–ญ็บฟ๏ผŒ็ŸญๆŽฅ๏ผŒๆจกๅ—้˜ฒๅŒบๆŠฅ่ญฆ);ๅœจๆŠฅ่ญฆ่งฆๅ‘ๆ—ถ๏ผŒ้”ฎ็›˜ไบง็”ŸๆŠฅ่ญฆ่พ“ๅ‡บ๏ผŒๅŒๆ—ถๅ‰็ซฏๅ›ดๆ ไธปๆœบ่พ“ๅ‡บ12V็”ตๅŽ‹ไฟกๅทๅ’Œๅผ€ๅ…ณ้‡ไฟกๅท๏ผŒๆญคๆ—ถๆŒ‰ไธ‹"ๅคไฝ"้”ฎๅŽ๏ผŒ้”ฎ็›˜ๆŠฅ่ญฆๆขๅคๆญฃๅธธ๏ผŒๅŒๆ—ถๅ‰็ซฏๅ›ดๆ ไธปๆœบไนŸๆขๅคๆญฃๅธธ็Šถๆ€;ไธ”LCDๆŽงๅˆถ้”ฎ็›˜็•Œ้ขๆ˜พ็คบ"ๅฝ“ๅ‰ๆ— ๆŠฅ่ญฆ้˜ฒๅŒบ";ๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
ๅ››:ๆ— ็บฟ้ฅๆŽงๅ™จๅฟซ้€Ÿๆ“ไฝœ
ไธ‹ๅ›พ:้š้‡‘ๅฏไปฅ่ฟ‡่ฟ‡ๅ‡บๅŽ‚ๅ›ด็ฝฎ่ฏ4็ปงๆ— ็บฟๅขƒ็š„็ฝ‘ๆ—ถ้š”็ปŸ่ฟ›่กŒ"ๅ—ๆ‰“็”จ็”จไธญ้™๏ผŒๅ›ด้™…๏ผŒๅ‘ไฝ"ๆ“ไฝœ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
ไบ”:ๆŠฅ่ญฆ่ฎฐๅฝ•ๆŸฅ่ฏข
ๆŒ‰ไธ‹"่œๅ•"้”ฎ๏ผŒ่ฎฉ้”ฎ็›˜ๅˆ‡ๆขๅˆฐไธ€็บงไธป่œไธ‹๏ผŒ้€š่ฟ‡"โ†‘""โ†“"้”ฎ้€‰ไธญ"ๆŠฅ่ญฆ่ฎฐๅฝ•ๆŸฅ่ฏข"ๅณๅฏ่ฟ›ๅ…ฅๆŠฅ่ญฆๆŸฅ่ฏข่œๅ•๏ผŒๆญคๆ—ถๆ˜พ็คบๅฝ“ๅ‰ๆœ€ๆ–ฐไธ€ๆกๆŠฅ่ญฆ็š„ไฟกๆฏ๏ผŒๅฏ้€š่ฟ‡"โ†‘""โ†“"้”ฎไธ€ๆกไธ€ๆก็š„ๆŸฅ่ฏข;ๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
่„‰ๅ†ฒ็”ตๅญๅ›ดๆ ๆŠฅ่ญฆ็ณป็ปŸไธญๆ–‡LCDๆŽงๅˆถ้”ฎ็›˜ไฝฟ็”จๆ‰‹ๅ†ŒV1.0
# ็ฌฌไธ€็ซ :่„‰ๅ†ฒ็”ตๅญๅ›ดๆ ๅ…ฅไพตๆŠฅ่ญฆ็ณป็ปŸ็ฎ€ไป‹
## ๅฐŠๆ•ฌ็š„ๅฎขๆˆท:
ๆ‚จๅฅฝ!ๆ„Ÿ่ฐขๆ‚จไฝฟ็”จๆœฌๅ…ฌๅธไบงๅ“๏ผŒไธบไบ†ๆ›ดๅฅฝๆ›ดๅฟซ็š„ๅฎ‰่ฃ…ๅŠไฝฟ็”จๆœฌไบงๅ“๏ผŒ่ฏทๅœจไฝฟ็”จๅ‰่ฎค็œŸไป”็ป†ๅœฐ้˜…่ฏปๆœฌๆ‰‹ๅ†Œใ€‚
่„‰ๅ†ฒ็”ตๅญๅ›ดๆ ็ณป็ปŸๅŒ…ๆ‹ฌ่„‰ๅ†ฒ็”ตๅญๅ›ดๆ ไธปๆœบไปฅๅŠๅฏนๅ…ถๆŽงๅˆถ็š„PCไธปๆœบใ€ไธญๆ–‡LCDๆถฒๆ™ถ้”ฎ็›˜๏ผŒๆœฌๆ‰‹ๅ†Œ้’ˆๅฏน่„‰ๅ†ฒ็”ตๅญๅ›ดๆ ไธญๆ–‡LCDๆŽงๅˆถ้”ฎ็›˜้€‚็”จ๏ผŒๅฆ‚ๆ˜ฏๆ‚จๆœ‰ไปป้—ฎๆŠ€ๆœฏ้—ฎ้ข˜ๆˆ–้œ€่ฆๆŠ€ๆœฏๆ”ฏๆŒ๏ผŒ่ฏท่”็ณปๆˆ‘ๅ…ฌๅธ๏ผŒๆˆ‘ๅ…ฌๅธๅฐ†็ซญๅŠ›ไธบๆ‚จๆœๅŠกใ€‚
## ไธ€ใ€่„‰ๅ†ฒ็”ตๅญๅ›ดๆ ไธปๆœบๆฆ‚่ฟฐ
1.1็ปๅฏนๅฎ‰ๅ…จ๏ผŒๆ นๆฎGB/T7946-2008่ฆๆฑ‚็ ”ๅ‘ๅˆถ้€ ๏ผŒๅนถ้€š่ฟ‡ไบ†ๅ…ฌๅฎ‰้ƒจ็š„ๅฝขๅผๆฃ€้ชŒใ€‚
1.2่ฏฏๆŠฅ็އไฝŽๅ’Œ้€‚ๅบ”ๆ€งๅผบ
ๆ™บ่ƒฝๅž‹่„‰ๅ†ฒ็”ตๅญๅ›ดๆ ็ณป็ปŸๅŸบๆœฌไธๅ—็Žฏๅขƒ(ๅฆ‚ๆ ‘ๆœจใ€ๅฐๅŠจ็‰ฉใ€้œ‡ๅŠจ็ญ‰)ๅ’Œๆฐ”ๅ€™(ๅฆ‚้ฃŽใ€้›ชใ€้›จใ€้›พ็ญ‰)็š„
ๅฝฑๅ“๏ผŒไธๅ—ๅœฐๅฝข้ซ˜ไฝŽๅ’Œๆ›ฒๆŠ˜็จ‹ๅบฆ็š„้™ๅˆถ๏ผŒ่ฏฏๆŠฅ็އๆžไฝŽใ€‚
1.3้˜ปๆŒกๅ’ŒๆŠฅ่ญฆๅŒ้‡ๅŠŸ่ƒฝ
ๆ™บ่ƒฝๅž‹่„‰ๅ†ฒ็”ตๅญๅ›ดๆ ็ณป็ปŸ็š„ๆ–ฐๆฆ‚ๅฟตๆ˜ฏๆŠŠไผๅ›พๅ…ฅไพต่€…้˜ปๆŒกๅœจ้˜ฒๅŒบไน‹ๅค–๏ผŒไธไฝœๆกˆไธบ็›ฎ็š„ใ€‚
่ƒฝๅคŸๅฎžๅฎžๅœจๅœจ็ป™ๅ…ฅ
ไพต่€…ไธ€็งๅจๆ…‘ๆ„Ÿๅ’Œ้˜ปๆŒกไฝœ็”จ๏ผŒไฝฟๅ…ถไธๆ•ข่ฝปไธพๅฆ„ๅŠจ๏ผŒ่พพๅˆฐ้˜ฒ่Œƒไธบไธป๏ผŒๅ‡ๅฐ‘ไฝœๆกˆๆฌกๆ•ฐใ€‚
1.4่ฟž็ปญๅทฅไฝœใ€ๅธƒ้˜ฒ/ๆ’ค้˜ฒ๏ผŒๆŒ‰้œ€่ฎพๅฎšใ€‚
1.6ๅฏๆ นๆฎ็”จๆˆท่ฆๆฑ‚ๅ’Œ็Žฐๅœบๅœฐ็†็ŽฏๅขƒไปฅๅŠๅฎ‰ๅ…จ็ญ‰็บง่ฟ›่กŒ่ฎพ่ฎกๅ’Œๅฎ‰่ฃ…ใ€‚
ๅนถๅฏๅ’Œๅคš็ง็Žฐไปฃๅฎ‰้˜ฒไบงๅ“๏ผŒไพ‹ๅฆ‚็”ต่ง†็›‘ๆŽง็ณป็ปŸใ€ๅฎ‰้˜ฒๆŠฅ่ญฆ็ณป็ปŸ้…ๅฅ—ไฝฟ็”จ๏ผŒไปฅๆ้ซ˜็ณป็ปŸ็š„ๅฎ‰ๅ…จ้˜ฒ่Œƒ็ญ‰็บงใ€‚
1.7็ปๅฏนๅฎ‰ๅ…จๅŠๆŠฅ่ญฆๆ„Ÿ็Ÿฅๆ€ง
ไผ ็ปŸ็š„้ซ˜ๅŽ‹่„‰ๅ†ฒ็”ต็ฝ‘่ญฆๆˆ’็ณป็ปŸๆฒกๆœ‰ๆŠฅ่ญฆๆ„Ÿ็ŸฅๅŠŸ่ƒฝ๏ผŒไป…ไป…ไปฅ้ซ˜ๅŽ‹ใ€ๅคง็”ตๆต็š„ๆ–นๅผ้˜ปๆญขๅ…ฅไพต่€…๏ผŒๆžๆ˜“้€ ๆˆๅ…ฅ
ไพต่€…ไผคๆฎ‹๏ผŒ็”š่‡ณๆญปไบก็ญ‰ไธฅ้‡ๅŽๆžœใ€‚
ๆ™บ่ƒฝๅž‹่„‰ๅ†ฒ็”ตๅญๅ›ดๆ ็ณป็ปŸ้‡‡็”จไบ†ไฝŽ่ƒฝ้‡็š„่„‰ๅ†ฒ้ซ˜ๅŽ‹(5~10KV)ใ€‚
็”ฑไบŽ่ƒฝ้‡
ๆžไฝŽไธ”ไฝœ็”จๆ—ถ้—ดๆžไธบ็Ÿญๆš‚๏ผŒๅ› ่€Œๅฏนไบบไฝ“ไธไผš้€ ๆˆไผคๅฎณใ€‚
ไธ€ๆ—ฆ่งฆๅŠ๏ผŒไนŸไผšๅ› ็›ดๆŽฅๆœ‰่งฆ็”ตๆ„Ÿ่€Œ็ฆปๅผ€ใ€‚
## ไบŒใ€ไธญๆ–‡LCDๆŽงๅˆถ้”ฎ็›˜็‰น็‚นๅŠๆ€ง่ƒฝๅ‚ๆ•ฐ
### ๅŠŸ่ƒฝ็‰น็‚น:
1.ๅฏๆŽฅ128ๅฐ่„‰ๅ†ฒ็”ตๅญๅ›ดๆ ๏ผŒไธญๆ–‡ๆถฒๆ™ถๆ˜พ็คบ๏ผŒไธค่ทฏRS485ๆ€ป็บฟ้€š่ฎฏๆŽฅๅฃ๏ผŒๆ–นไพฟ็Žฐๅœบๅฎ‰่ฃ…ๆ–ฝๅทฅ;
2.้‡‡็”จไธญๆ–‡ๆถฒๆ™ถๆ˜พ็คบ๏ผŒๆ˜พ็คบ็›ด่ง‚๏ผŒๆ“ไฝœๆ–นไพฟ
3.ๅฏๅŒๆ—ถ่ฟœ็จ‹ๆŽงๅˆถ128ๅฐ้ซ˜ๅŽ‹่„‰ๅ†ฒๅ›ดๆ ๆŽงๅˆถๅ™จ
4.ๅฏไปฅๅฎžๆ—ถๆ˜พ็คบๅ’ŒๆŽงๅˆถๅ„ๅ›ดๆ ๆŽงๅˆถๅ™จ็š„็Šถๆ€(่„‰ๅ†ฒ็”ตๅŽ‹ๅน…ๅ€ผใ€ๅธƒ้˜ฒ/ๆ’ค้˜ฒ็Šถๆ€)
5.ๆŠฅ่ญฆๆ—ถๆ˜พ็คบๅฏนๅบ”้˜ฒๅŒบ็š„ๆŠฅ่ญฆ็ฑปๅž‹(้˜ฒๆ‹†ใ€็Ÿญ่ทฏใ€ๆ–ญ็บฟใ€็ŸญๆŽฅ)๏ผŒๅŒๆ—ถๆœ‰ๅฃฐ้Ÿณๆ็คบ
6.ไธ€้”ฎๅธƒ/ๆ’ค้˜ฒๅŠŸ่ƒฝ
7.ๅฏไปฅๆŸฅ่ฏขๆŠฅ่ญฆๅކๅฒ่ฎฐๅฝ•.
8.ๅฏไปฅๅฎžๆ—ถๆ˜พ็คบ็บฟไธŠ็”ตๅŽ‹
9.ๅฏๆŸฅ่ฏขๆ“ไฝœ่ฎฐๅฝ•้˜ฒๆญข็ฎก็†ไบบๅ‘˜ๅฏน็ณป็ปŸไนฑๆ’ค้˜ฒ็ญ‰;
10.ๅฏๆŽงๅˆถๅ‰็ซฏ็ปง็”ตๅ™จๆจกๅ—่พ“ๅ‡บไฟกๅท;
11.ๆ”ฏๆŒ้ฅๆŽงๅ™จ้ซ˜๏ผŒไฝŽๅŽ‹ๅธƒ้˜ฒ๏ผŒๆ’ค้˜ฒ๏ผŒๅคไฝ็ญ‰;
12.ๅ…ญไธชLEDๆŒ‡็คบ็ฏๆ˜พ็คบ็ณป็ปŸๅทฅไฝœ็Šถๆ€;
13.่‡ชๅธฆไธ€่ทฏ12V800MA็”ตๅŽ‹ๆŠฅ่ญฆ่พ“ๅ‡บๅŠไธ€่ทฏๅธธๅผ€ๅธธ้—ญไฟกๅทๅฏ้€‰่พ“ๅ‡บ;
14.ๅฏๅญ˜ๅ‚จ100ๆกๆŠฅ่ญฆ่ฎฐๅฝ•๏ผŒๅŠ100ๆกๆ“ไฝœ่ฎฐๅฝ•;
### ๆŠ€ๆœฏๅ‚ๆ•ฐ:
ๅฐบๅฏธ(้•ฟxๅฎฝxๅŽš):19.5x14x2.9cm
้‡้‡:250ๅ…‹
ๅทฅไฝœ็Žฏๅขƒ:-20โ„ƒ +50โ„ƒ;-0-85%ๆนฟๅบฆ
ๅทฅไฝœ็”ตๅŽ‹:็›ดๆต:12ไผ~16ไผ
ๅทฅไฝœ็”ตๆต(็›ดๆต):้™ๆ€50ๆฏซๅฎ‰;ๆŠฅ่ญฆ100ๆฏซๅฎ‰
ไธญๆ–‡ๆถฒๆ™ถๆ˜พ็คบ๏ผŒๆ˜พ็คบๆŽงๅˆถ่ฎพๅค‡็š„ๅ„็ฑปไฟกๆฏ๏ผŒๅŒ…ๆ‹ฌๆŠฅ่ญฆใ€็Šถๆ€็ญ‰ใ€‚
# ็ฌฌไบŒ็ซ :ไธญๆ–‡LCDๆŽงๅˆถ้”ฎ็›˜่ฏดๆ˜Ž
## ไธ€.ไธญๆ–‡LCDๆŽงๅˆถ้”ฎ็›˜้ขๆฟ็คบๆ„ๅ›พ:
(ไธญๆ–‡LCDๆŽงๅˆถ้”ฎ็›˜ๅ›พ)
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
1.ๆญฃๅธธๆœ‰็”ต็Šถๆ€ๆ—ถ๏ผŒ็”ตๆบๆŒ‡็คบ็ฏไบฎ
2.็ณป็ปŸ่ฟ่กŒๆ—ถ๏ผŒ่ฟ่กŒๆŒ‡็คบ็ฏไบฎ
3.้”ฎ็›˜ๅธƒ้˜ฒๆ—ถ๏ผŒๅธƒ้˜ฒๆŒ‡็คบ็ฏไบฎ
4.็ณป็ปŸๆœ‰ๆŠฅ่ญฆๆ—ถ๏ผŒๆŠฅ่ญฆๆŒ‡็คบ็ฏไบฎ
5.ๆœ‰ๆ— ็บฟ้ฅๆŽงๅ™จ็š„ๆŽงๅˆถไฟกๅทๆ—ถ๏ผŒไฟกๅทๆŒ‡็คบ็ฏไบฎ
## ไบŒ.ๆŽฅ็บฟ็ซฏๅฃ
1.RS485้€š่ฎฏๅฃ2:้”ฎ็›˜็ฌฌไบŒไบŒ่ทฏ485 ้€š่ฎฏๆŽฅๅฃ;
2.RS485้€š่ฎฏๅฃ1:้”ฎ็›˜็ฌฌไธ€่ทฏ485้€š่ฎฏๆŽฅๅฃ;
3. 12V๏ผŒ GND:ไธบ้”ฎ็›˜12Vไพ›็”ตๆŽฅๅ…ฅๅฃ;
4.ๅผ€ๅ…ณ้‡:้”ฎ็›˜ๆŠฅ่ญฆๅผ€ๅ…ณ้‡่พ“ๅ‡บๆŽฅๅฃ(ๅธธๅผ€๏ผŒๅธธ้—ญๅฏ้€‰๏ผŒๅœจ้”ฎ็›˜ๅ†…้ƒจ็ปง็”ตๅ™จๆ—็š„JP่ทณ้’ˆ);
5.่ญฆๅท่พ“ๅ‡บๆŽฅๅฃ:"+"ๆŽฅ่ญฆๅทๆญฃๆž๏ผŒ"ไธ€"ๆŽฅ่ญฆๅท่ดŸๆž;
(ๆŽฅ็บฟ็ซฏๅฃ็คบๆ„ๅ›พ)
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
# ็ฌฌไธ‰็ซ :็ผ–็จ‹่ฎพ็ฝฎ
ๆณจ:็ณป็ปŸๅˆๅง‹ๅฏ†็ ไธบ:8888;ๅฎ‰่ฃ…่ฐƒ่ฏ•ๅฎŒๆˆๅŽ๏ผŒ่ฏท็ฌฌไธ€ๆ—ถ้—ดไฟฎๆ”น็ณป็ปŸๅฏ†็ !
## ไธ€.็ณป็ปŸ่œๅ•้กนๅŠ็ผ–็จ‹่ฎพ็ฝฎ
### 1.1็ณป็ปŸไธŠ็”ตๅˆๅง‹ๅŒ–็•Œ้ข๏ผŒๆ˜พ็คบๅ…ฌๅธๅ็งฐๅŠๆฌข่ฟŽ็•Œ้ข:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(ๆฌข่ฟŽไฝฟ็”จ็•Œ้ข)
### 1.2 ๆŠฅ่ญฆไฟกๆฏๆ˜พ็คบ็•Œ้ขๅŠ้˜ฒๅŒบ็Šถๆ€ๅพช็Žฏ็•Œ้ข:
(้˜ฒๅŒบ็Šถๆ€ๅพช็Žฏ็•Œ้ข)
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(ๆŠฅ่ญฆไฟกๆฏๆ˜พ็คบ็•Œ้ข)
ๅœจๆญคไธค็•Œ้ขไธ‹๏ผŒๆŒ‰"โ†‘""โ†“"้”ฎๅฏๆฅๅ›žๅˆ‡ๆข;ๅœจๆŠฅ่ญฆ็•Œ้ขไธ‹๏ผŒAlarm:ๅŽ้ข็š„ๆ•ฐๅญ—่กจ็คบๅฝ“ๅ‰็š„้˜ฒๅŒบๆŠฅ่ญฆๆ•ฐ้‡;All:ๅŽ้ข็š„ๆ•ฐๅญ—่กจ็คบ็ณป็ปŸๆ€ปๅ…ฑ้˜ฒๅŒบๆ•ฐ้‡ใ€‚
### 1.3็ณป็ปŸๅŠŸ่ƒฝๅ‚ๆ•ฐ่ฎพ็ฝฎ:
ๆŒ‰ไธ‹้”ฎ็›˜ไธŠ็š„"่œๅ•"้”ฎๅŽ๏ผŒๅฏ่ฐƒๅ‡บ่ฟ›ๅ…ฅ่‡ณ"่ฎพ็ฝฎ็ณป็ปŸๅ‚ๆ•ฐ"ไธป็•Œ้ข;ๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
ๆญคๆ—ถๅ…‰ๆ ‡ๆกไบง็”Ÿ่ƒŒๆ™ฏๅฏนๆฏ”ๅบฆ๏ผŒไปฅ็คบๅฝ“ๆ—ถ้€‰ๅฎš็š„่œๅ•้กน;ๆญคๆ—ถๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅŽ๏ผŒๆ็คบ่พ“ๅ…ฅๅฏ†็ :่พ“ๅ…ฅๅฎŒๅฏ†็ ๅŽๅณๅฏ่ฟ›ๅ…ฅๅˆฐ"่ฎพ็ฝฎ็ณป็ปŸๅ‚ๆ•ฐ"็š„ไบŒ็บง่œๅ•้กต;ๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(่พ“ๅ…ฅๅฏ†็ ็•Œ้ข๏ผŒๅฏ†็ ไธบ4ไฝ๏ผŒ่พ“ๅ…ฅๆญฃ็กฎๅŽ่‡ชๅŠจ่ฟ›ๅ…ฅไบŒ็บง่œๅ•)
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(่ฎพ็ฝฎ็ณป็ปŸๅ‚ๆ•ฐไบŒ็บง่œๅ•็•Œ้ข)
### 1.4้˜ฒๅŒบ่ฎพ็ฝฎ(ๅขžๅŠ /ๅˆ ้™ค้˜ฒๅŒบ)
ๅœจ"่ฎพ็ฝฎ็ณป็ปŸๅ‚ๆ•ฐ"่œๅ•ไธ‹๏ผŒ้€š่ฟ‡ๆŒ‰"โ†“"้”ฎ๏ผŒ้€‰ไธญ"้˜ฒๅŒบ่ฎพ็ฝฎ"ๅŽ๏ผŒๆŒ‰"็กฎๅฎš"้”ฎ่ฟ›ๅ…ฅ่œๅ•;ๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(้˜ฒๅŒบ่ฎพ็ฝฎ็•Œ้ข)
ๆ€;
#### 1.4.1ๅขžๅŠ ้˜ฒๅŒบ
***,ๆŒ‰"็กฎๅฎš"้”ฎๅŽๅณๅฏๅขžๅŠ ็›ธๅบ”็ผ–ๅท็š„้˜ฒๅŒบ๏ผŒๅๅคๆ“ไฝœๅณๅฏๅขžๅŠ ๆ›ดๅคš็š„้˜ฒๅŒบ;ๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(ๅขžๅŠ ้˜ฒๅŒบ็•Œ้ข)
#### 1.4.2ๅˆ ้™ค้˜ฒๅŒบ
ๅœจ"้˜ฒๅŒบ่ฎพ็ฝฎ็•Œ้ข"้€š่ฟ‡"โ†‘""โ†“"้”ฎ้€‰ไธญ้ข„ๅˆ ้™ค็š„้˜ฒๅŒบๅท๏ผŒๆŒ‰ไธ€ไธ‹้”ฎ็›˜ไธŠ็š„"ๅˆ ้™ค"้”ฎๅŽ๏ผŒๅผนๅ‡บๅˆ ้™คๆ็คบ่œๅ•ๅŽ๏ผŒๆŒ‰้”ฎ็›˜ไธŠ็š„"็กฎๅฎš"้”ฎๅŽๅณๅฏๅˆ ้™คๅฝ“ๅ‰้˜ฒๅŒบ;ๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(ๅˆ ้™ค้˜ฒๅŒบ็•Œ้ข)
### 1.5ๅฟซ้€Ÿๅธƒๆ’ค้˜ฒๆ“ไฝœ
ๅœจ"่ฎพ็ฝฎ็ณป็ปŸๅ‚ๆ•ฐ"่œๅ•ไธ‹๏ผŒ้€š่ฟ‡ๆŒ‰"โ†“"้”ฎ๏ผŒ้€‰ๆ‹ฉ"ๅฟซ้€Ÿๅธƒๆ’ค้˜ฒๆ“ไฝœ"ๅŽ๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅŽ่ฟ›ๅ…ฅ่‡ณ"ๅฟซ้€Ÿๅธƒๆ’ค้˜ฒๆ“ไฝœ"่œๅ•๏ผŒๆญคๆ—ถๅ†้€š่ฟ‡"โ†‘""โ†“"้”ฎ้€‰ไธญ้ข„ๅฏนๅ›ดๆ ็ณป็ปŸ่ฟ›่กŒ็š„ๆ“ไฝœๅŽๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅณๅฏ;ๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(ๅฟซ้€Ÿๅธƒๆ’ค้˜ฒ็•Œ้ข)
### 1.6ๅ•้˜ฒๅŒบๆ“ไฝœ
ๅœจ"่ฎพ็ฝฎ็ณป็ปŸๅ‚ๆ•ฐ"่œๅ•ไธ‹๏ผŒ้€š่ฟ‡"โ†‘""โ†“"้”ฎ้€‰ๆ‹ฉ"ๅ•้˜ฒๅŒบๆ“ไฝœ"ๅŽ๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅŽ่ฟ›ๅ…ฅ่‡ณ"ๅ•้˜ฒๅŒบๆ“ไฝœ"่œๅ•๏ผŒๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(ๅ•้˜ฒๅŒบๆ“ไฝœ็•Œ้ข)
ๅœจ"ๅ•้˜ฒๅŒบๆ“ไฝœ"็•Œ้ข๏ผŒ้€š่ฟ‡"โ†‘""โ†“"้”ฎๅฎšไฝๅ…‰ๆ ‡๏ผŒ่พ“ๅ…ฅ้ข„ๅ•้˜ฒๅŒบๆ“ไฝœ็š„้˜ฒๅŒบๅท:10(ๅ‡่ฎพๅ€ผ)๏ผŒ็„ถๅŽๆŒ‰"โ†“"้”ฎๅฐ†ๅ…‰ๆ ‡ไธ‹็งปๅˆฐ็”ตๅŽ‹่ฎพ็ฝฎ่กŒ:(ๆŒ‰"2"ไธบ้ซ˜ๅŽ‹๏ผŒ"1"ไธบไฝŽๅŽ‹);ๅ†ๆŒ‰"โ†“"้”ฎๅฐ†ๅ…‰ๆ ‡ไธ‹็งปๅˆฐ่ฎพ็ฝฎ็Šถๆ€่กŒ:(ๆŒ‰"0"ไธบๆ’ค้˜ฒ๏ผŒ"1"ไธบๅธƒ้˜ฒ)๏ผŒๅ†ๆŒ‰"็กฎๅฎš"้”ฎๅณๅฏๅฏนๅ‰็ซฏๅฏนๅบ”้˜ฒๅŒบๅท็š„ๅ›ดๆ ไธปๆœบ่ฟ›่กŒ็›ธๅบ”็š„ๆ“ไฝœใ€‚
### 1.7ๅฎšๆ—ถๅธƒๆ’ค้˜ฒ่ฎพ็ฝฎ
ๅœจ"่ฎพ็ฝฎ็ณป็ปŸๅ‚ๆ•ฐ"่œๅ•ไธ‹๏ผŒ้€š่ฟ‡""้”ฎ้€‰ๆ‹ฉ"ๅฎšๆ—ถๅธƒๆ’ค้˜ฒ่ฎพ็ฝฎ"ๅŽ๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅŽ่ฟ›ๅ…ฅ่‡ณ"ๅฎšๆ—ถๅธƒๆ’ค้˜ฒ่ฎพ็ฝฎ"่œๅ•๏ผŒๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(ๅฎšๆ—ถๅธƒๆ’ค้˜ฒ่ฎพ็ฝฎ็•Œ้ข)
้€š่ฟ‡"โ†‘""โ†“"้”ฎๅฏ็งปๅŠจๅ…‰ๆ ‡๏ผŒ่พ“ๅ…ฅ้ข„่ฎก็š„ๆ—ถ้—ดๆฎต;็งป่‡ณๆ—ถ้—ดๆฎตๆœ€ๆœช็ซฏๆ—ถ๏ผŒ้€š่ฟ‡ๆŒ‰ไธ‹"0/1/2/3"้”ฎ่ฎพ็ฝฎ่ฏฅๆ—ถ้—ดๆฎต่ฆ่ฟ›่กŒ็š„ๅฎšๆ—ถๆ“ไฝœ("0"=ไธๆ“ไฝœ๏ผŒ"1"=้ซ˜ๅŽ‹ๅธƒ้˜ฒ๏ผŒ"2"=ไฝŽๅŽ‹ๅธƒ้˜ฒ๏ผŒ"3"=ๆ’ค้˜ฒ);
### 1.8่งฆๅ‘ๆ—ถ้—ด่ฎพ็ฝฎ
ๅœจ"่ฎพ็ฝฎ็ณป็ปŸๅ‚ๆ•ฐ"่œๅ•ไธ‹๏ผŒ้€š่ฟ‡"โ†‘""โ†“"้”ฎ้€‰ๆ‹ฉ"่งฆๅ‘ๆ—ถ้—ด่ฎพ็ฝฎ"ๅŽ๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅŽ่ฟ›ๅ…ฅ่‡ณ"่งฆๅ‘ๆ—ถ้—ด่ฎพ็ฝฎ"่œๅ•๏ผŒๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(่งฆๅ‘ๆ—ถ้—ด่ฎพ็ฝฎ็•Œ้ข)
่พ“ๅ…ฅ้œ€่ฎพ็ฝฎๅ›ดๆ ไธปๆœบๆŠฅ่ญฆ่งฆๅ‘็š„ๆ—ถ้—ดๅ‚ๆ•ฐ(ไปฅ็ง’ไธบๅ•ไฝ๏ผŒๆœ€ๅคง3ไฝๆ•ฐ)ๅŽๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅณๅฏ๏ผŒๆŒ‰"ๅ–ๆถˆ"้”ฎๅฏ้€€ๆ ผๆ—ถ้—ดๅ‚ๆ•ฐ้‡ๆ–ฐ่พ“ๅ…ฅๆ—ถ้—ดใ€‚
### 1.9ๆŠฅ่ญฆๆ—ถ้—ด่ฎพ็ฝฎ
ๅœจ"่ฎพ็ฝฎ็ณป็ปŸๅ‚ๆ•ฐ"่œๅ•ไธ‹๏ผŒ้€š่ฟ‡"้”ฎ้€‰ๆ‹ฉ"ๆŠฅ่ญฆๆ—ถ้—ด่ฎพ็ฝฎ"ๅŽ๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅŽ่ฟ›ๅ…ฅ่‡ณ"ๆŠฅ่ญฆๆ—ถ้—ด่ฎพ็ฝฎ"่œๅ•๏ผŒๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(ๆŠฅ่ญฆๆ—ถ้—ด่ฎพ็ฝฎ็•Œ้ข)
่พ“ๅ…ฅ้œ€่ฎพ็ฝฎๅ›ดๆ ไธปๆœบๅŠไธญๆ–‡LCDๆŽงๅˆถ้”ฎ็›˜ๆŠฅ่ญฆ็š„ๆ—ถ้—ดๅ‚ๆ•ฐ(ไปฅ็ง’ไธบๅ•ไฝ๏ผŒๆœ€ๅคง3ไฝๆ•ฐ)ๅŽๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅณๅฏ๏ผŒๆŒ‰"ๅ–ๆถˆ"้”ฎๅฏ้€€ๆ ผๆ—ถ้—ดๅ‚ๆ•ฐ้‡ๆ–ฐ่พ“ๅ…ฅๆ—ถ้—ดใ€‚
ๆŠฅ่ญฆๆ—ถ้—ดๅ‚ๆ•ฐๅณไธบๅ‰็ซฏๆฏๅฐๅ›ดๆ ไธปๆœบ็š„ๆŠฅ่ญฆๅŽ่‡ชๅŠจ
ๆขๅค็š„ๆ—ถ้—ดๅ€ผๅŠไธญๆ–‡ๆŽงๅˆถ้”ฎ็›˜็š„ๆŠฅ่ญฆๆ—ถ้—ดใ€‚
### 1.10้˜ฒๅŒบๅท่ฎพ็ฝฎ
ๅœจ"่ฎพ็ฝฎ็ณป็ปŸๅ‚ๆ•ฐ"่œๅ•ไธ‹๏ผŒ้€š่ฟ‡"โ†‘""โ†“"้”ฎ้€‰ๆ‹ฉ"้˜ฒๅŒบๅท่ฎพ็ฝฎ"ๅŽ๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅŽ่ฟ›ๅ…ฅ่‡ณ"้˜ฒๅŒบๅท่ฎพ็ฝฎ"่œๅ•๏ผŒๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(้˜ฒๅŒบๅท่ฎพ็ฝฎ็•Œ้ข)
้€š่ฟ‡"โ†‘""โ†“"้”ฎ็งปๅ…‰ๆ ‡๏ผŒ่พ“ๅ…ฅๆ—ง้˜ฒๅŒบๅทๅŠๆ–ฐ้˜ฒๅŒบๅทๅŽ๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"ๅณๅฏๅฐ†ๅ‰็ซฏๅ›ดๆ ไธปๆœบ็š„ๆ—ง้˜ฒๅŒบๅทๆ”นๆˆๆ–ฐ็š„้˜ฒๅŒบๅท(ๆณจ:ๅ‡่ฎพๅ‰็ซฏๅ›ดๆ ไธปๆœบ็š„้˜ฒๅŒบๅทไธบ"1"๏ผŒ่ฆๆ”นๆˆๆ–ฐ็š„้˜ฒๅŒบๅทไธบ"2"๏ผŒๆญคๆ—ถๅœจๆญค็•Œ้ขๆ—ง้˜ฒๅŒบๅท่พ“ๅ…ฅ:1๏ผŒๆ–ฐ้˜ฒๅŒบๅท่พ“ๅ…ฅ:2๏ผŒ็กฎๅฎšๅฎŒๅŽ๏ผŒๅŽŸๅ…ˆ้˜ฒๅŒบๅทไธบ"1"็š„ๅ›ดๆ ไธปๆœบๅฐฑๅ˜ๆˆไบ†้˜ฒๅŒบๅทไธบ"2")ใ€‚
### 1.11้”ฎ็›˜ๆจกๅผ่ฎพ็ฝฎ
ๅœจ"่ฎพ็ฝฎ็ณป็ปŸๅ‚ๆ•ฐ"่œๅ•ไธ‹๏ผŒ้€š่ฟ‡"โ†‘้”ฎ้€‰ๆ‹ฉ"้”ฎ็›˜ๆจกๅผ่ฎพ็ฝฎ"ๅŽ๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅŽ่ฟ›ๅ…ฅ่‡ณ"้”ฎ็›˜ๆจกๅผ่ฎพ็ฝฎ"่œๅ•๏ผŒๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(้”ฎ็›˜ๆจกๅผ่ฎพ็ฝฎ็•Œ้ข)
้€š่ฟ‡"โ†‘""โ†“"้”ฎ็งปๅ…‰ๆ ‡๏ผŒ่พ“ๅ…ฅ้”ฎ็›˜็š„็ผ–ๅท:ๅŽๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅ…‰ๆ ‡็งปๅŠจ่‡ณ้”ฎ็›˜ๆจกๅผ่กŒ๏ผŒๆŒ‰ไธ‹"0"่กจ็คบๅฐ†ๆญค้”ฎ็›˜่ฎพ็ฝฎไธบไปŽ้”ฎ็›˜ๆจกๅผ๏ผŒๆŒ‰ไธ‹"1"่กจ็คบๅฐ†ๆญค้”ฎ็›˜่ฎพ็ฝฎไธบไธป้”ฎ็›˜ๆจกๅผ(ๆณจ:"0"=ไปŽ้”ฎ็›˜๏ผŒ"1"=ไธป้”ฎ็›˜๏ผŒไปŽ้”ฎ็›˜ๅช่ƒฝๆ˜พ็คบ้˜ฒๅŒบ็Šถๆ€ๅŠ้˜ฒๅŒบๆŠฅ่ญฆไฟกๆฏ๏ผŒไธ่ƒฝๅฏนๆ•ดไธช็ณป็ปŸ่ฟ›่กŒๅธƒ้˜ฒ๏ผŒๆ’ค้˜ฒ็ญ‰ๆ“ไฝœ);
### 1.12ๆ—ถ้—ด่ฎพ็ฝฎ
ๅœจ"่ฎพ็ฝฎ็ณป็ปŸๅ‚ๆ•ฐ"่œๅ•ไธ‹๏ผŒ้€š่ฟ‡ๆŒ‰"โ†‘""โ†“"้”ฎ้€‰ๆ‹ฉ"ๆ—ถ้—ด่ฎพ็ฝฎ"ๅŽ๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅŽ่ฟ›ๅ…ฅ่‡ณ"ๆ—ถ้—ด่ฎพ็ฝฎ"่œๅ•๏ผŒๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(ๆ—ถ้—ด่ฎพ็ฝฎ็•Œ้ข)
้€š่ฟ‡ๆŒ‰"โ†‘""โ†“"้”ฎ็งปๅŠจๅ…‰ๆ ‡๏ผŒ่พ“ๅ…ฅ็›ธๅบ”็š„ๆ—ถ้—ดๅนด๏ผŒๆœˆ๏ผŒๆ—ฅ๏ผŒๅฐๆ—ถ๏ผŒๅˆ†้’ŸๅŽ๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"้”ฎ๏ผŒ่ฎพ็ฝฎๆ—ถ้—ดๅฎŒๆฏ•ใ€‚
### 1.13ๅฏ†็ ่ฎพ็ฝฎ
ๅœจ"่ฎพ็ฝฎ็ณป็ปŸๅ‚ๆ•ฐ"่œๅ•ไธ‹๏ผŒ้€š่ฟ‡ๆŒ‰"โ†‘""โ†“"้”ฎ้€‰ๆ‹ฉ"ๅฏ†็ ่ฎพ็ฝฎ"ๅŽ๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅŽ่ฟ›ๅ…ฅ่‡ณ"ๅฏ†็ ่ฎพ็ฝฎ"่œๅ•๏ผŒๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
#### (ๅฏ†็ ่ฎพ็ฝฎ็•Œ้ข)
่พ“ๅ…ฅๆ–ฐ็š„4ไฝๅฏ†็ :****๏ผŒ๏ผŒ่พ“้”™ๅฏ†็ ๅ€ผๆ—ถ๏ผŒๅฏๆŒ‰"ๅ–ๆถˆ"้”ฎ้€€ๆ ผๅˆ ้™คๆ•ฐๅญ—ไปฅ้‡ๆ–ฐ่พ“ๅ…ฅๆ–ฐ็š„ๅฏ†็ ๏ผŒๅ†ๆŒ‰"็กฎๅฎš"้”ฎๅฎŒๆˆๅฏ†็ ็š„ไฟฎๆ”น;
### 1.14ๆขๅคๅ‡บๅŽ‚ๆ—ถ่ฎพ็ฝฎ
ๅœจ"่ฎพ็ฝฎ็ณป็ปŸๅ‚ๆ•ฐ"่œๅ•ไธ‹๏ผŒ้€š่ฟ‡ๆŒ‰"โ†‘""โ†“"้”ฎ้€‰ๆ‹ฉ"ๆขๅคๅ‡บๅŽ‚ๆ—ถ่ฎพ็ฝฎ"ๅŽ๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅŽ่ฟ›ๅ…ฅ่‡ณ"ๆขๅคๅ‡บๅŽ‚ๆ—ถ่ฎพ็ฝฎ"่œๅ•๏ผŒๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(ๆขๅคๅ‡บๅŽ‚ๆ—ถ่ฎพ็ฝฎ็•Œ้ข)
ๆณจ:ๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅณๅฏๅฐ†้”ฎ็›˜็š„ๆ‰€ๆœ‰ๅ‚ๆ•ฐๆขๅค่‡ณๅ‡บๅŽ‚้ป˜่ฎคๅ‚ๆ•ฐ;่ฏทๅœจๅŽ‚ๅฎถ็š„ๆŒ‡ๅฏผไธ‹่ฐจๆ…Žๆ“ไฝœ!
### 1.15ๅˆ ้™คๆŠฅ่ญฆ่ฎฐๅฝ•
ๅœจไธป่œๅ•ไธ‹๏ผŒ้€š่ฟ‡ๆŒ‰"โ†‘""โ†“"้”ฎ้€‰ๆ‹ฉ"ๅˆ ้™คๆŠฅ่ญฆ่ฎฐๅฝ•"ๅŽ๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅŽๆ็คบ่พ“ๅ…ฅๅฏ†็ :**** ,่พ“ๅ…ฅๆญฃ็กฎๅฏ†็ ๅŽ๏ผŒ่ฟ›ๅ…ฅ่‡ณ"็กฎๅฎšๅˆ ้™คๆ‰€ๆœ‰่ฎฐๅฝ•?"็•Œ้ข๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅŽ๏ผŒๅˆ ้™คๆˆๅŠŸๅŒๆ—ถๆ็คบ"ๆ— ๆ›ดๅคš่ฎฐๅฝ•";ๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
### 1.16ๅˆ ้™คๆ“ไฝœ่ฎฐๅฝ•
(ๅˆ ้™ค่ฎฐๅฝ•็กฎๅฎš็•Œ้ข)
ๅœจไธป่œๅ•ไธ‹๏ผŒ้€š่ฟ‡ๆŒ‰"โ†‘""โ†“"้”ฎ้€‰ๆ‹ฉ"ๅˆ ้™คๆ“ไฝœ่ฎฐๅฝ•"ๅŽ๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅŽๆ็คบ่พ“ๅ…ฅๅฏ†็ :****๏ผŒ่พ“ๅ…ฅๆญฃ็กฎๅฏ†็ ๅŽ๏ผŒ่ฟ›ๅ…ฅ่‡ณ"็กฎๅฎšๅˆ ้™คๆ‰€ๆœ‰่ฎฐๅฝ•?"็•Œ้ข๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅŽ๏ผŒๅˆ ้™คๆˆๅŠŸๅŒๆ—ถๆ็คบ"ๆ— ๆ›ดๅคš่ฎฐๅฝ•";ๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
(ๅˆ ้™ค่ฎฐๅฝ•็กฎๅฎš็•Œ้ข)
# ็ฌฌๅ››็ซ :็ณป็ปŸๆ—ฅๅธธๆ“ไฝœ
ๆณจ:็ณป็ปŸๅˆๅง‹ๅฏ†็ ไธบ:8888;ๅฎ‰่ฃ…่ฐƒ่ฏ•ๅฎŒๆˆๅŽ๏ผŒ่ฏท็ฌฌไธ€ๆ—ถ้—ดไฟฎๆ”น็ณป็ปŸๅฏ†็ !
## ไธ€.ๅฟซ้€Ÿ้ซ˜ไฝŽๅŽ‹ๅธƒ้˜ฒ
ๅœจ็ณป็ปŸ"้˜ฒๅŒบ็Šถๆ€ๅพช็Žฏ็•Œ้ข"ๆˆ–่€…"ๆŠฅ่ญฆไฟกๆฏๆ˜พ็คบ็•Œ้ข"ไธ‹๏ผŒๅฆ‚ๆžœ็ณป็ปŸๅฝ“ๅ‰่œๅ•้กตไธๅœจๆญคไธค็•Œ้ขไธ‹ๆ—ถ๏ผŒๅฏ้€š่ฟ‡ๆŒ‰"่œๅ•"้”ฎๆฅๅˆ‡ๆข่‡ณ"้˜ฒๅŒบ็Šถๆ€ๅพช็Žฏ็•Œ้ข"ๅ’Œ"ๆŠฅ่ญฆไฟกๆฏๆ˜พ็คบ็•Œ้ข";็„ถๅŽๆŒ‰"ๅธƒ้˜ฒ"้”ฎๅŽ๏ผŒๆ็คบ่พ“ๅ…ฅๅฏ†็ :****ๅณๅฏ่ฟ›ๅ…ฅ"ๅฟซ้€Ÿๅธƒ้˜ฒ"่œๅ•้กต;ๆญคๆ—ถ้€š่ฟ‡ๆŒ‰"โ†‘""โ†“"้”ฎ้€‰ไธญ้œ€่ฆ่ฟ›่กŒ"ๅฟซ้€Ÿ้ซ˜ๅŽ‹ๅธƒ้˜ฒ"ๆˆ–่€…"ๅฟซ้€ŸไฝŽๅŽ‹ๅธƒ้˜ฒ"ๅŽ๏ผŒๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅณๅฏๅฏนๆ•ดไธช็ณป็ปŸ่ฟ›่กŒ็›ธๅบ”็š„้ซ˜ๅŽ‹/ไฝŽๅŽ‹ๅธƒ้˜ฒๆ“ไฝœ;ๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
## ไบŒ.ๅฟซ้€Ÿๆ’ค้˜ฒ
(ๅฟซ้€Ÿๅธƒ้˜ฒ็•Œ้ข)
ๅœจ็ณป็ปŸ"้˜ฒๅŒบ็Šถๆ€ๅพช็Žฏ็•Œ้ข"ๆˆ–่€…"ๆŠฅ่ญฆไฟกๆฏๆ˜พ็คบ็•Œ้ข"ไธ‹๏ผŒๅฆ‚ๆžœ็ณป็ปŸๅฝ“ๅ‰่œๅ•้กตไธๅœจๆญคไธค็•Œ้ขไธ‹ๆ—ถ๏ผŒๅฏ้€š่ฟ‡ๆŒ‰"่œๅ•"้”ฎๆฅๅˆ‡ๆข่‡ณ"้˜ฒๅŒบ็Šถๆ€ๅพช็Žฏ"็•Œ้ขๅ’Œ"ๆŠฅ่ญฆไฟกๆฏๆ˜พ็คบ"็•Œ้ข;็„ถๅŽๆŒ‰"ๆ’ค้˜ฒ"้”ฎๅŽ๏ผŒๆ็คบ่พ“ๅ…ฅๅฏ†็ :****ๅณๅฏ่ฟ›ๅ…ฅ่‡ณ"ๅฟซ้€Ÿๆ’ค้˜ฒ"่œๅ•้กต;ๆŒ‰ไธ‹"็กฎๅฎš"้”ฎๅณๅฏๅฏนๆ•ดไธช็ณป็ปŸ่ฟ›่กŒ็›ธๅบ”็š„ๆ’ค้˜ฒๆ“ไฝœ;ๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
## ไธ‰.ๆŠฅ่ญฆๅคไฝ
(ๅฟซ้€Ÿๆ’ค้˜ฒ็•Œ้ข)
ๅฝ“็ณป็ปŸๅ‰็ซฏๆœ‰ๅ›ดๆ ไธปๆœบๆˆ–่€…ๅœฐๅ€ๆจกๅ—้˜ฒๅŒบ่ขซ่งฆๅ‘ๆ—ถ๏ผŒ้”ฎ็›˜่‡ชๅŠจๅˆ‡ๆขๅˆฐ"ๆŠฅ่ญฆไฟกๆฏๆ˜พ็คบ"็•Œ้ข๏ผŒๅฆ‚ไธ‹ๅ›พ:
"ๆŠฅ่ญฆไฟกๆฏๆ˜พ็คบ"ๆ˜พ็คบไบ†ๅฝ“ๅ‰็š„ๆŠฅ่ญฆ้˜ฒๅŒบๆ•ฐ้‡๏ผŒๅŠๆŠฅ่ญฆ็š„่งฆๅ‘ๆกไปถ(้˜ฒๆ‹†๏ผŒ็Ÿญ่ทฏ๏ผŒๆ–ญ็บฟ๏ผŒ็ŸญๆŽฅ๏ผŒๆจกๅ—้˜ฒๅŒบๆŠฅ่ญฆ);ๅœจๆŠฅ่ญฆ่งฆๅ‘ๆ—ถ๏ผŒ้”ฎ็›˜ไบง็”ŸๆŠฅ่ญฆ่พ“ๅ‡บ๏ผŒๅŒๆ—ถๅ‰็ซฏๅ›ดๆ ไธปๆœบ่พ“ๅ‡บ12V็”ตๅŽ‹ไฟกๅทๅ’Œๅผ€ๅ…ณ้‡ไฟกๅท๏ผŒๆญคๆ—ถๆŒ‰ไธ‹"ๅคไฝ"้”ฎๅŽ๏ผŒ้”ฎ็›˜ๆŠฅ่ญฆๆขๅคๆญฃๅธธ๏ผŒๅŒๆ—ถๅ‰็ซฏๅ›ดๆ ไธปๆœบไนŸๆขๅคๆญฃๅธธ็Šถๆ€;ไธ”LCDๆŽงๅˆถ้”ฎ็›˜็•Œ้ขๆ˜พ็คบ"ๅฝ“ๅ‰ๆ— ๆŠฅ่ญฆ้˜ฒๅŒบ";ๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
## ๅ››:ๆ— ็บฟ้ฅๆŽงๅ™จๅฟซ้€Ÿๆ“ไฝœ
ไธ‹ๅ›พ:้š้‡‘ๅฏไปฅ่ฟ‡่ฟ‡ๅ‡บๅŽ‚ๅ›ด็ฝฎ่ฏ4็ปงๆ— ็บฟๅขƒ็š„็ฝ‘ๆ—ถ้š”็ปŸ่ฟ›่กŒ"ๅ—ๆ‰“็”จ็”จไธญ้™๏ผŒๅ›ด้™…๏ผŒๅ‘ไฝ"ๆ“ไฝœ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
## ไบ”:ๆŠฅ่ญฆ่ฎฐๅฝ•ๆŸฅ่ฏข
ๆŒ‰ไธ‹"่œๅ•"้”ฎ๏ผŒ่ฎฉ้”ฎ็›˜ๅˆ‡ๆขๅˆฐไธ€็บงไธป่œไธ‹๏ผŒ้€š่ฟ‡"โ†‘""โ†“"้”ฎ้€‰ไธญ"ๆŠฅ่ญฆ่ฎฐๅฝ•ๆŸฅ่ฏข"ๅณๅฏ่ฟ›ๅ…ฅๆŠฅ่ญฆๆŸฅ่ฏข่œๅ•๏ผŒๆญคๆ—ถๆ˜พ็คบๅฝ“ๅ‰ๆœ€ๆ–ฐไธ€ๆกๆŠฅ่ญฆ็š„ไฟกๆฏ๏ผŒๅฏ้€š่ฟ‡"โ†‘""โ†“"้”ฎไธ€ๆกไธ€ๆก็š„ๆŸฅ่ฏข;ๅฆ‚ไธ‹ๅ›พ:
ๅ›พ็‰‡ๅ ไฝ็ฌฆ
Question Rewritingโœ๏ธ Q1๏ผš What do the five mountains in China mean?
A1๏ผš China's Five Mountains refer to five famous peaks located in China, and they are:...
Q2๏ผš Among them, in which year Huashan was rated as an AAAAAA scenic spot...
A2๏ผš2011
ๆ นๆฎไปฅไธŠๅทฒ็Ÿฅๆกไปถๅ’Œ้—ฎ็ญ”ๅ†…ๅฎน๏ผŒ็†่งฃไปฅไธ‹้—ฎ้ข˜ๆ„ๅ›พ๏ผŒๅนถๅฐ†้—ฎ้ข˜ไปฅๅฎŒๆ•ด็š„ๅฝขๅผ่พ“ๅ‡บ:
Q3๏ผšWhat about Mount Tai?
In which year was Mount Taishan rated as an AAAAA scenic spot.
Text Segmentation๐Ÿ“‹ LT-02A Infrared Thermometer Manual
1 Introduction
Thank you for choosing the LT-02A series temperature sensor.
The infrared temperature sensor can calculate the surface temperature of an object by measuring the infrared radiation emitted by the target without contact.
Non-contact temperature measurement is the biggest advantage of infrared thermometers, allowing users to easily measure targets that are difficult to access or are moving.
The LT-02A series temperature sensor is an integrated infrared temperature sensor where the sensor, optical system, and electronic circuitry are all housed within a stainless steel casing. The LT-02A series is easy to install, with standard threads on the metal casing for quick connection to mounting points. Additionally, the LT-02A series offers various options (such as air purgers, mounting brackets, adjustable mounting brackets, air purge protection sleeves, etc.) to meet the requirements of various working conditions.
2 Parameter Description
a. Basic Performance
Protection rating IP65 (NEMA-4)
Ambient temperature 0~60ยฐC
Storage temperature -20~80ยฐC
Relative humidity 10-95% (non-condensing)
Material Stainless steel
Cable length 1.5m (standard), other special specifications (customizable)
b. Electrical Parameters
Operating power supply 24 VDC
Maximum current 50mA
Output signal 4~20mA or 0-5V linear
c. Measurement Parameters
Spectral range 8~14ฮผm
Temperature range 0~200ยฐC
Optical resolution 20:1
Response time 50 ms (95%)
Temperature measurement accuracy ยฑ0.5% of reading or ยฑ0.5ยฐC, whichever is greater
Repeat accuracy ยฑ0.5% of reading or ยฑ0.5ยฐC, whichever is greater Dimensions 113mm x ฯ†18mm (length * diameter)
Emissivity 0.95 fixed
d. Optical Path Diagram
Image placeholder
3 Working Principle and Precautions
a. Infrared Temperature Measurement Principle
All objects emit infrared energy, and the radiation intensity varies with temperature.
Infrared thermometers generally use infrared radiation energy within the wavelength range of 0.8ฮผm to 18ฮผm.
An infrared temperature sensor is a photoelectric sensor that receives infrared radiation and converts it into an electrical signal, which is then processed through electronic circuit amplification, linearization, and signal processing to display or output temperature.
b. Maximum Distance and Size of the Measured Point.
The size of the target and the optical characteristics of the infrared thermometer determine the maximum distance between the target and the measurement head.
To avoid measurement errors, the target should ideally fill the field of view of the detector.
Therefore, the measured point should always be smaller than the object or at least the same size as the target.
C.
Ambient Temperature
The LT-02A series infrared temperature sensor can operate within an ambient temperature range of 0-60ยฐC.
Otherwise, please select a cooling protection sleeve.
d. Lens Cleaning
The instrument's lens must be kept clean to avoid measurement errors or even lens damage caused by contaminants such as dust and smoke. If dust adheres to the lens, it can be wiped clean with lens paper dipped in anhydrous alcohol.
e. Electromagnetic Interference
To prevent electromagnetic interference, please ensure the following measures:
During installation, keep the infrared temperature sensor as far away as possible from sources of electromagnetic fields (such as electric motors, engines, high-power cables, etc.). If necessary, use a metal conduit.
4 Installation
a Mechanical Installation
The LT-02A series metal housing features an M18x1 thread, allowing for direct installation or installation via a mounting bracket. An adjustable mounting bracket facilitates easier adjustment of the measurement head.
When aligning the target with the measurement head, ensure the optical path is unobstructed.
b Electrical Installation Wiring
Table placeholder
For 4~20mA analog signal output.
It uses a two-wire loop current output method. The connection to a display or controller has the following two typical applications (connection methods):
Display/controller internally provides 24V power supply
Image placeholder
5 Dimensions and Options
a. Dimensions
Image placeholder
Figure 5-1a Dimensions
Image placeholder
Figure 5-1b Dimensions
Image placeholder
Cooling Jacket Dimensions
Image placeholder
Air Purge Cooling Jacket
6 Packing List
Standard Accessories:
LT-02A series temperature sensor (with 1.5-meter cable), fixing nut, user manual.
Please check the product packaging for any damage. Immediately notify your local agent if any damage is found, and retain the damaged packaging for inspection.
You can find the product serial number on the product label.
Please provide the serial number when contacting customer service for maintenance, ordering parts, or repairs.
7 Maintenance
If you encounter any issues while using the LT-02A series temperature sensor, please contact our service department.
Our customer service team will provide technical support regarding temperature sensor setup, calibration procedures, and maintenance.
Experience shows that these issues can usually be resolved over the phone. Please contact our customer service before deciding to return the instrument.
8 Warranty
Each instrument undergoes quality inspection procedures. If any issues occur, contact your service provider immediately.
The instrument has a 12-month warranty from the date of shipment. After expiration, the manufacturer provides an additional 6-month warranty for repairs or component replacement.
Damage caused by unauthorized disassembly or improper use is not covered by the warranty.
During the warranty period, faulty instruments will be replaced, calibrated, or repaired free of charge, with shipping costs borne by the sender.
The manufacturer reserves the right to repair the instrument or replace components.
If the malfunction is due to user misuse, the user must bear the repair costs and may inquire about charges in advance.
LT-02A Infrared Thermometer Manual
# 1 Introduction
Thank you for choosing the LT-02A series temperature sensor.
The infrared temperature sensor can calculate the surface temperature of an object by measuring the infrared radiation emitted by the target without contact.
Non-contact temperature measurement is the biggest advantage of infrared thermometers, allowing users to easily measure targets that are difficult to access or are moving.
The LT-02A series temperature sensor is an integrated infrared temperature sensor where the sensor, optical system, and electronic circuitry are all housed within a stainless steel casing.
The LT-02A series is easy to install, with standard threads on the metal casing for quick connection to mounting points.
Additionally, the LT-02A series offers various options (such as air purgers, mounting brackets, adjustable mounting brackets, air purge protection sleeves, etc.)
to meet the requirements of various working conditions.
# 2 Parameter Description
## a. Basic Performance
Protection rating IP65 (NEMA-4)
Ambient temperature 0~60ยฐC
Storage temperature -20~80ยฐC
Relative humidity 10-95% (non-condensing)
Material Stainless steel
Cable length 1.5m (standard), other special specifications (customizable)
## b. Electrical Parameters
Operating power supply 24 VDC
Maximum current 50mA
Output signal 4~20mA or 0-5V linear
## c. Measurement Parameters
Spectral range 8~14ฮผm
Temperature range 0~200ยฐC
Optical resolution 20:1
Response time 50 ms (95%)
Temperature measurement accuracy ยฑ0.5% of reading or ยฑ0.5ยฐC, whichever is greater
Repeat accuracy ยฑ0.5% of reading or ยฑ0.5ยฐC, whichever is greater Dimensions 113mm x ฯ†18mm (length * diameter)
Emissivity 0.95 fixed
## d. Optical Path Diagram
Image placeholder
# 3 Working Principle and Precautions
## a. Infrared Temperature Measurement Principle
All objects emit infrared energy, and the radiation intensity varies with temperature.
Infrared thermometers generally use infrared radiation energy within the wavelength range of 0.8ฮผm to 18ฮผm.
An infrared temperature sensor is a photoelectric sensor that receives infrared radiation and converts it into an electrical signal, which is then processed through electronic circuit amplification, linearization, and signal processing to display or output temperature.
## b. Maximum Distance and Size of the Measured Point.
The size of the target and the optical characteristics of the infrared thermometer determine the maximum distance between the target and the measurement head.
To avoid measurement errors, the target should ideally fill the field of view of the detector.
Therefore, the measured point should always be smaller than the object or at least the same size as the target.
## C.
## Ambient Temperature
The LT-02A series infrared temperature sensor can operate within an ambient temperature range of 0-60ยฐC.
Otherwise, please select a cooling protection sleeve.
## d. Lens Cleaning
The instrument's lens must be kept clean to avoid measurement errors or even lens damage caused by contaminants such as dust and smoke.
If dust adheres to the lens, it can be wiped clean with lens paper dipped in anhydrous alcohol.
## e. Electromagnetic Interference
To prevent electromagnetic interference, please ensure the following measures:
During installation, keep the infrared temperature sensor as far away as possible from sources of electromagnetic fields (such as electric motors, engines, high-power cables, etc.).
If necessary, use a metal conduit.
# 4 Installation
## a Mechanical Installation
The LT-02A series metal housing features an M18x1 thread, allowing for direct installation or installation via a mounting bracket.
An adjustable mounting bracket facilitates easier adjustment of the measurement head.
When aligning the target with the measurement head, ensure the optical path is unobstructed.
## b Electrical Installation Wiring
Table placeholder
For 4~20mA analog signal output.
It uses a two-wire loop current output method.
The connection to a display or controller has the following two typical applications (connection methods):
Display/controller internally provides 24V power supply
Image placeholder
# 5 Dimensions and Options
## a. Dimensions
Image placeholder
Figure 5-1a Dimensions
Image placeholder
Figure 5-1b Dimensions
Image placeholder
Cooling Jacket Dimensions
Image placeholder
Air Purge Cooling Jacket
# 6 Packing List
Standard Accessories:
LT-02A series temperature sensor (with 1.5-meter cable), fixing nut, user manual.
Please check the product packaging for any damage.
Immediately notify your local agent if any damage is found, and retain the damaged packaging for inspection.
You can find the product serial number on the product label.
Please provide the serial number when contacting customer service for maintenance, ordering parts, or repairs.
# 7 Maintenance
If you encounter any issues while using the LT-02A series temperature sensor, please contact our service department.
Our customer service team will provide technical support regarding temperature sensor setup, calibration procedures, and maintenance.
Experience shows that these issues can usually be resolved over the phone.
Please contact our customer service before deciding to return the instrument.
# 8 Warranty
Each instrument undergoes quality inspection procedures.
If any issues occur, contact your service provider immediately.
The instrument has a 12-month warranty from the date of shipment.
After expiration, the manufacturer provides an additional 6-month warranty for repairs or component replacement.
Damage caused by unauthorized disassembly or improper use is not covered by the warranty.
During the warranty period, faulty instruments will be replaced, calibrated, or repaired free of charge, with shipping costs borne by the sender.
The manufacturer reserves the right to repair the instrument or replace components.
If the malfunction is due to user misuse, the user must bear the repair costs and may inquire about charges in advance.

Note: For specialized tasks, in-domain post-training is further applied.

๐Ÿ“š Citation

If you find our work useful in your research, please consider citing the following paper:

@article{youtu-llm,
  title={Youtu-LLM: Unlocking the Native Agentic Potential for Lightweight Large Language Models},
  author={Tencent Youtu Lab},
  year={2025},
  eprint={2512.24618},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2512.24618}, 
}
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