update ainode in table model

Author: leto-b

src/.vuepress/public/img/AINode-0-en.png

@@ -21,17 +21,25 @@
 
 # AINode
 
-AINode is an IoTDB native node designed to support the registration, management, and invocation of large-scale time series models. It comes with industry-leading proprietary time series models such as Timer and Sundial. These models can be invoked through standard SQL statements, enabling real-time inference of time series data at the millisecond level, and supporting application scenarios such as trend forecasting, missing value imputation, and anomaly detection for time series data.
+AINode is a native IoTDB node that supports the registration, management, and invocation of time-series-related models. It comes with built-in industry-leading self-developed time-series large models, such as the Timer series developed by Tsinghua University. These models can be invoked through standard SQL statements, enabling real-time inference of time series data at the millisecond level, and supporting application scenarios such as trend forecasting, missing value imputation, and anomaly detection for time series data.
 
 The system architecture is shown below:
 ::: center
-<img src="/img/AInode.png" style="zoom:50 percent" />
+<img src="/img/AINode-0-en.png" style="zoom:50 percent" />
 ::: 
+
 The responsibilities of the three nodes are as follows:
 
-- **ConfigNode**: responsible for storing and managing the meta-information of the model; responsible for distributed node management.
-- **DataNode**: responsible for receiving and parsing SQL requests from users; responsible for storing time-series data; responsible for preprocessing computation of data.
-- **AINode**: responsible for model file import creation and model inference.
+- **ConfigNode:**
+  - Manages distributed nodes and handles load balancing across the system.
+- **DataNode:**
+  - Receives and parses user SQL queries.
+  - Stores time-series data.
+  - Performs preprocessing computations on raw data.
+- **AINode:**
+  - Manages and utilizes time-series models (including training/inference).
+  - Supports deep learning and machine learning workflows.
+
 
 ## 1. Advantageous features
 
@@ -55,11 +63,11 @@ Compared with building a machine learning service alone, it has the following ad
 - **Create**: Load externally designed or trained model files/algorithms into AINode for unified management and usage by IoTDB.
 - **Inference**: Use the created model to complete time series analysis tasks applicable to the model on specified time series data.
 - **Built-in Capabilities**: AINode comes with machine learning algorithms or self-developed models for common time series analysis scenarios (e.g., forecasting and anomaly detection).
-![](/img/h3.png)
+![](/img/AINode-en.png)
 
 ## 3. Installation and Deployment
 
-The deployment of AINode can be found in the document [Deployment Guidelines](../Deployment-and-Maintenance/AINode_Deployment_apache.md#ainode-deployment) .
+The deployment of AINode can be found in the document [AINode Deployment](../Deployment-and-Maintenance/AINode_Deployment_apache.md).
 
 
 ## 4. Usage Guide
@@ -80,12 +88,12 @@ Models that meet the following criteria can be registered with AINode:
 The SQL syntax for model registration is defined as follows:
 
 ```SQL
-create model <model_name> using uri <uri>
+create model <model_id> using uri <uri>
 ```
 
 Detailed meanings of SQL parameters:
 
-- **model_name**: The global unique identifier for the model, non-repeating. Model names have the following constraints:
+- **model_id**: The global unique identifier for the model, non-repeating. Model names have the following constraints:
   - Allowed characters: [0-9 a-z A-Z _] (letters, numbers, underscores)
   - Length: 2-64 characters
   - Case-sensitive
@@ -117,7 +125,7 @@ In addition to registering local model files, remote resource paths can be speci
 
 #### Example
 
-The current example folder contains model.pt (trained model) and config.yaml with the following content:
+The [example folder](https://github.com/apache/iotdb/tree/master/integration-test/src/test/resources/ainode-example) contains model.pt (trained model) and config.yaml with the following content:
 
 ```YAML
 configs:                
@@ -157,41 +165,56 @@ Registered models can be queried using the `show models` command. The SQL defini
 ```SQL
 show models
 
-show models <model_name>
+show models <model_id>
 ```
 
 In addition to displaying all models, specifying a `model_id` shows details of a specific model. The display includes:
 
-| **ModelId** | **State**                                                 | **Configs**                                      | **Attributes** |
-| ----------- | --------------------------------------------------------- | ------------------------------------------------ | -------------- |
-| Unique ID   | Registration status (INACTIVE, LOADING, ACTIVE, DROPPING) | InputShape, outputShape, inputTypes, outputTypes | User notes     |
+| **ModelId** | **ModelType** | **Category**   | **State**   |
+|-------------|---------------|----------------|-------------|
+| Model ID    | Model Type    | Model Category | Model State |
 
-**State descriptions:**
+- Model State Transition Diagram
 
-- **INACTIVE**: Model is unavailable.
-- **LOADING**: Model is being loaded.
-- **ACTIVE**: Model is available.
-- **DROPPING**: Model is being deleted.
+![](/img/AINode-State-en.png)
 
-#### Example
+**Instructions:**
+
+1. Initialization: 
+   - When AINode starts, show models only displays BUILT-IN models.
+2. Custom Model Import: 
+   - Users can import custom models (marked as USER-DEFINED). 
+   - The system attempts to parse the ModelTypefrom the config file. 
+   - If parsing fails, the field remains empty.
+3. Foundation Model Weights: 
+   - Time-series foundation model weights are not bundled with AINode. 
+   - AINode automatically downloads them during startup.
+   - Download state: LOADING.
+4. Download Outcomes:
+   - Success → State changes to ACTIVE.
+   - Failure → State changes to INACTIVE.
+5. Fine-Tuning Process:
+   - When fine-tuning starts: State becomes TRAINING.
+   - Successful training → State transitions to ACTIVE.
+   - Training failure → State changes to FAILED.
+
+**Example**
 
 ```SQL
 IoTDB> show models
-
-+---------------------+--------------------+--------+--------+
-|              ModelId|           ModelType|Category|   State|
-+---------------------+--------------------+--------+--------+
-|                arima|               Arima|BUILT-IN|  ACTIVE|
-|          holtwinters|         HoltWinters|BUILT-IN|  ACTIVE|
-|exponential_smoothing|ExponentialSmoothing|BUILT-IN|  ACTIVE|
-|     naive_forecaster|     NaiveForecaster|BUILT-IN|  ACTIVE|
-|       stl_forecaster|       StlForecaster|BUILT-IN|  ACTIVE|
-|         gaussian_hmm|         GaussianHmm|BUILT-IN|  ACTIVE|
-|              gmm_hmm|              GmmHmm|BUILT-IN|  ACTIVE|
-|                stray|               Stray|BUILT-IN|  ACTIVE|
-|             timer_xl|            Timer-XL|BUILT-IN|  ACTIVE|
-|              sundial|       Timer-Sundial|BUILT-IN|  ACTIVE|
-+---------------------+--------------------+--------+--------+
++---------------------+--------------------+--------------+---------+
+|              ModelId|           ModelType|      Category|    State|
++---------------------+--------------------+--------------+---------+
+|                arima|               arima|      BUILT-IN|   ACTIVE|
+|               custom|                    |  USER-DEFINED|   ACTIVE|
+|              timerxl|            timer-xl|      BUILT-IN|  LOADING|
+|              sundial|       timer-sundial|      BUILT-IN|   ACTIVE|
+|           sundialx_1|       timer-sundial|    FINE-TUNED|   ACTIVE|
+|           sundialx_2|       timer-sundial|    FINE-TUNED|   ACTIVE|
+|             sundialx|       timer-sundial|    FINE-TUNED|   ACTIVE|
+|           sundialx_4|       timer-sundial|    FINE-TUNED| TRAINING|
+|           sundialx_5|       timer-sundial|    FINE-TUNED|   FAILED|
++---------------------+--------------------+--------------+---------+
 ```
 
 ### 4.3 Deleting Models
@@ -243,18 +266,24 @@ Parameter descriptions:
 
 1. The `forecast` function predicts all columns in the input table by default (excluding the time column and columns specified in `partition by`).
 2. The `forecast` function does not require the input data to be in any specific order. It sorts the input data in ascending order by the timestamp (specified by the `TIMECOL` parameter) before invoking the model for prediction.
-3. Different models have different requirements for the number of input data rows:
-   - If the input data has fewer rows than the minimum requirement, an error will be thrown.
-   - If the input data exceeds the maximum row limit, the last rows that meet the requirement will be automatically truncated for processing.
-   - Among the currently built-in models in AINode, only `sundial` has a row limit. It supports a maximum of 2880 input rows. If exceeded, the last 2880 rows will be automatically used.
+3. Different models have varying requirements for the number of input data rows. If the input data has fewer rows than the minimum requirement, an error will be reported.
+   - Among the current built-in models in AINode:
+     - Timer-XL requires at least 96 rows of input data.
+     - Timer-Sundial requires at least 16 rows of input data.
 4. The result columns of the `forecast` function include all input columns from the input table, with their original data types preserved. If `preserve_input = true`, an additional `is_input` column will be included to indicate whether a row is from the input data.
    - Currently, only columns of type INT32, INT64, FLOAT, or DOUBLE are supported for prediction. Otherwise, an error will occur: "The type of the column [%s] is [%s], only INT32, INT64, FLOAT, DOUBLE is allowed."
 5. `output_start_time` and `output_interval` only affect the generation of the timestamp column in the output results. Both are optional parameters:
    - `output_start_time` defaults to the last timestamp of the input data plus `output_interval`.
    - `output_interval` defaults to the sampling interval of the input data, calculated as: (last timestamp - first timestamp) / (number of rows - 1).
    - The timestamp of the Nth output row is calculated as: `output_start_time + (N - 1) * output_interval`.
 
-#### Example
+**Example: Database and table must be pre-created**
+
+```sql
+create database etth
+create table eg (hufl FLOAT FIELD, hull FLOAT FIELD, mufl FLOAT FIELD, mull FLOAT FIELD, lufl FLOAT FIELD, lull FLOAT FIELD, ot FLOAT FIELD)
+```
+
 
 Using the ETTh1-tab dataset:[ETTh1-tab](/img/ETTh1-tab.csv)。
 
@@ -314,7 +343,7 @@ It costs 1.615s
 
 ### 4.5 Time Series Large Model Import Steps
 
-AINode supports multiple time series large models. For deployment, refer to [Time Series Large Model](https://timecho.com/docs/zh/UserGuide/latest/AI-capability/TimeSeries-Large-Model.html)
+AINode supports multiple time series large models. For deployment, refer to [Time Series Large Model](../AI-capability/TimeSeries-Large-Model.md)
 
 
 ## 5 Permission Management