added first pages to nav

2025-12-07 20:16:57 +03:00 · 2023-01-03 08:11:27 +01:00
parent d5d2435214
commit bf5f82131b
7 changed files with 53 additions and 40 deletions
--- a/docs/FAQs.md
+++ b/docs/FAQs.md
@@ -1,6 +1,6 @@
 # Frequently Asked Questions

-#### My device is reboot frequently. What can I do?
+## My device is reboot frequently. What can I do?

 There are several reasons for the reboot:

@@ -11,7 +11,7 @@ There are several reasons for the reboot:
 There is a dedicated Wiki page about this: [[Frequent Reboots]]


-#### How accurate are the detections?
+### How accurate are the detections?

 It is hard to give a specific accuracy number. It depends on many factors, e.g.

@@ -26,12 +26,12 @@ Anecdotally, the authors of this wiki have great success with the meter. While t
 See the FAQs below for more details and configuration hints.


-#### My numbers are not corrected detected. What can I do?
+## My numbers are not corrected detected. What can I do?

 * There is a dedicated Wiki page about the correct setting [[ROI Configuration]]
 * This page also includes the instructions for gathering new images for the training.

-#### How can I ensure invalid numbers are never reported?
+## How can I ensure invalid numbers are never reported?

 As mentioned above, the AI algorithm is not perfect. Sometimes it may read an incorrect value.

@@ -42,7 +42,7 @@ You can change the following settings to reduce incorrect readings (but potentia
 * Change `PostProcessing` configuration option `MaxRateType` to be time based instead of absolute. Set `MaxRateValue` to something realistic (e.g. `5` gal/min). You can often find the max flow rate your meter supports directly on the cover.
 * Reduce `AutoTimer` configuration option `Intervall` to the lowest it can be (e.g. `3` min). The more often you take readings, the less likely for data staleness to occur.

-#### Even after I have setup everything perfect there is a false reading - especially around the zero crossing (roll over to next number)
+## Even after I have setup everything perfect there is a false reading - especially around the zero crossing (roll over to next number)
 * The roll over behavior is different for the different meters. E.g.:
  * Rolling over start with different previous position (e.g. at 7, 8 or 9)
  * The neutral position (no rolling) is not perfectly at zero, but rather at something like 7.9 or 8.1, even if it should be exactly 8
--- a/docs/MQTT-API.md
+++ b/docs/MQTT-API.md
@@ -1,4 +1,4 @@
-# General Information
+# MQTT API
 The device is capable to register to a MQTT broker to publish data and subscribe to specific topics.

 The MQTT service has to be enabled and configured properly in the device configuration via web interface (`Settings` -> `Configuration` -> section `MQTT`)
@@ -11,70 +11,70 @@ The following parameters have to be defined:
 * Password (optional)
 * RetainFlag (optional)

-# Published topics
+## Published topics

-## Status
+### Status
 `MainTopic`/{status topic}, e.g. `watermeter/status`
-* ### Connection
+* #### Connection

-* ### Interval
+* #### Interval

-* ### MAC
+* #### MAC

-* ### IP
+* #### IP

-* ### Hostname
+* #### Hostname

-* ### Uptime
+* #### Uptime

-* ### FreeMem
+* #### FreeMem

-* ### WifiRSSI
+* #### WifiRSSI

-* ### CPUTemp
+* #### CPUTemp

-* ### Status
+* #### Status

-## Result
+### Result
 `MainTopic`/{NumberName}/{result topic}, e.g. `watermeter/main/value`

-* ### Value
+* #### Value

-* ### Raw
+* #### Raw

-* ### Error
+* #### Error

-* ### JSON
+* #### JSON

-* ### Rate
+* #### Rate

-* ### Rate_per_time_unit
+* #### Rate_per_time_unit
  The time Unit gets set with the Homeassistant Discovery, eg. `h` or `m` (minutes)

-* ### Rate_per_digitalization_round
+* #### Rate_per_digitalization_round
  The `interval` defines when the next round gets triggered

-* ### Changeabsolut
+* #### Changeabsolut

-* ### Timestamp
+* #### Timestamp

-* ### JSON
+* #### JSON
  All relevant results in JSON syntax

-## GPIO
+### GPIO
 `MainTopic`/{GPIO topic}, e.g. `watermeter/GPIO/GPIO12`

-* ### GPIO/GPIO{PinNumber}
+* #### GPIO/GPIO{PinNumber}
  Depending on device configuration (`Settings` --> `Configuration` --> Chapter `GPIO`)


-# Subscibed topics
+## Subscibed topics
 `MainTopic`/{subscribed topic}, e.g. `watermeter/ctrl/flow_start`

-## Control
+### Control

-* ### Ctrl/flow_start
+* #### Ctrl/flow_start
  Trigger a flow start by publishing to this topic (any character, length > 0)

-* ### GPIO/GPIO{PinNumber}
+* #### GPIO/GPIO{PinNumber}
  Depending on device configuration (`Settings` --> `Configuration` --> Chapter `GPIO`)
--- a/docs/REST-API.md
+++ b/docs/REST-API.md
@@ -1,9 +1,9 @@
+# REST API
 Various information is directly accessible over specific REST calls.

 For an up-to-date list search the Github repository for [registered handlers](https://github.com/jomjol/AI-on-the-edge-device/search?q=camuri.uri)

-# Often used APIs
-Just append them to the IP, separated with a `/`, eg. `http://192.168.1.1/json`
+To use it, just append them to the IP, separated with a `/`, eg. `http://192.168.1.1/json`

 ## Control
 * ### flow_start
--- a/docs/index.md
+++ b/docs/index.md
@@ -1,4 +1,6 @@
-# Welcome to the AI-on-the-edge-device!
+# Welcome
+Welcome to the AI-on-the-edge-device!
+This is the project documentation. For the source code, please head to [github.com/jomjol/AI-on-the-edge-device](https://github.com/jomjol/AI-on-the-edge-device).

 Artificial intelligence based systems have been established in our every days live. Just think of speech or image recognition. Most of the systems relay on either powerful processors or a direct connection to the cloud for doing the calculations up there. With the increasing power of modern processors the AI systems are coming closer to the end user - which is usually called **edge computing**.
 Here this edge computing is brought into a practical oriented example, where a AI network is implemented on a ESP32 device so: **AI on the edge**.
--- a/docs/nav.yml
+++ b/docs/nav.yml
@@ -3,9 +3,19 @@

 nav:
  # List all files in the expected order
-  - index.md
+  - Getting Started:
+    - index.md
+    - Installation.md
+    - FAQs.md
+
+  - API's:
+      - REST-API.md
+      - MQTT-API.md
+      # - Influx DB
+      # - ...

  - Links:
+    - Releases: https://github.com/jomjol/AI-on-the-edge-device/releases
    - Web Installer/Console: https://jomjol.github.io/AI-on-the-edge-device/index.html
    - Place an Issues: https://github.com/jomjol/AI-on-the-edge-device/issues
    - Join a Discussions: https://github.com/jomjol/AI-on-the-edge-device/discussions
--- a/docs/AI-on-the-edge.md
+++ b/docs/AI-on-the-edge.md
@@ -1,4 +1,4 @@
-# Welcome to the AI-on-the-edge-device wiki!
+# (old) Welcome to the AI-on-the-edge-device wiki!

 Artifical inteligence based systems have been established in our every days live. Just think of speech or image recognition. Most of the systems relay on either powerfull processors or a direct connection to the cloud for doing the calculations up there. With the increasing power of modern processors the AI systems are coming closer to the end user - which is usally called **edge compution**.
 Here this edge computing is brough into a practical example, where a AI network is implemented on a ESP32 device so: **AI on the edge**.