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• Getting Started: Storing Data from Third-Party System

Getting Started: Storing Data from Third-Party System

This task helps you get started with Device Integration Service with a scenario that stemmed from real customer needs.

A third-party system uploads device data via HTTP. When the device data is uploaded to EnOS, an integration flow receives the message with HTTP Server and then map the uploaded message to an asset defined in EnOS. Finally, the flow converts the message into the standard EnOS-defined JSON and saves the message data.

About This Task

This task has the following steps.

1. Create Model

2. Create Integration Flow

3. Design Flow

4. Publish and Start Flow

Before You Start

Ensure that you have requested for the Device Integration Resource.

Step 1: Create Model

This step assumes that there is no existing device model that can be used. Create a new model named city_iems_cctv with the features defined as per the below. For more information about models, see Creating a Model.

Feature Type	Name	Identifier	Data Type	Value
Attributes	Device ID No.	device_id_no	string	1024
Measurement Points	Event Name	alarm_data_event_name	string	1024
Measurement Points	People Count	raw_data_count	string	1024
Measurement Points	People Count Percentage	raw_data_count_percentage	string	1024
Measurement Points	Image Path	raw_data_imgpath	string	1024
Measurement Points	Object Type	raw_data_objtype	string	1024
Measurement Points	Raw Image Path wo OBB	raw_data_patch	string	1024
Measurement Points	People Association	raw_data_pplassoc	string	1024
Measurement Points	Raw Image Path wo BB	raw_data_rawimgpath	string	1024
Measurement Points	Bounding Box	raw_data_rect	string	1024
Measurement Points	Event Start Time	raw_data_start_time	string	1024
Measurement Points	Time Stamp	raw_data_timestamp	string	1024
Measurement Points	Time Stamp Unix	raw_data_timestamp_unix	string	1024
Measurement Points	Track ID	raw_data_track_id	string	1024
Measurement Points	Video Path	raw_data_videopath	string	1024
Measurement Points	Camera ID	raw_stream_camera	string	1024
Measurement Points	Region of Interests	raw_stream_roi	string	1024
Measurement Points	Streaming ID	raw_stream_streamingID	string	1024
Measurement Points	Zone	raw_stream_zone	string	1024

Step 2: Create Integration Flow

1. Log in to the EnOS Console Management and click Device Integration > Flow Designer from the left navigation menu.

2. Click New Integration Flow, enter the flow name and description, and click OK.

Step 3: Design Flow

You will enter the Flow Designer page with a blank canvas. A list of nodes will be displayed on the left.

Design the flow with the following nodes in the order shown below.

1. Add the HTTP Server node to the Flow Designer canvas. For more information about the HTTP Server node, see HTTP Server.

2. Add the Script node to the canvas and name it Auth. Go to the Script tab and enter the following code.

   var timestamp = metadata.get("Timestamp");
   print("header timestamp :" + timestamp);
   var auth = metadata.get("Authorization");
   print("header auth :" + auth);
   
   var body = JSON.stringify(msg);
   var appId = "maimnwk3";
   var secretKey = "X56ZXY";
   
   var rawData = appId + body + timestamp;
   print("rawData :" + rawData);
   var result = tools.HMACSHA256Util.encrypt(rawData, secretKey);
   
   print("result :" + result);
   if (result === auth) {
       return tools.resultBuilder.build(true, JSON.stringify(msg));
   } else {
       return tools.resultBuilder.build(false);
   }
   return tools.resultBuilder.build(true, JSON.stringify(msg));
   

   
   

   For more information about the Script node, see Script.

   
   

3. Add another Script node to the canvas and name it Parse Data. Go to the Script tab and enter the following code.

      if (!msg.Data) {
          return tools.resultBuilder.build(false);
      }
      var dataArray = msg.Data.data;
      var stream = msg.Data.stream;
      var device_id = stream.camera;
      var flag = true;
   
      for (var i = 0; i < dataArray.length; i++) {
          var data = dataArray[i];
          if (!data.eventName) {
              flag = false;
              break;
          }
   
          var paramsArray = new Array();
          if (data.eventName === "Abandon") {
              var mps = {};
              mps["raw_data_count"] = JSON.stringify(data.count);
              mps["alarm_data_event_name"] = data.eventName;
              mps["raw_data_imgpath"] = JSON.stringify(data.imgpath);
              mps["raw_data_objtype"] = data.objType;
              mps["raw_data_pplassoc"] = data.peopleassociation;
              mps["raw_data_rawimgpath"] = JSON.stringify(data.rawimgpath);
              mps["raw_data_rect"] = JSON.stringify(data.rect);
              mps["raw_data_start_time"] = data.startTime;
              mps["raw_data_track_id"] = data.trackId;
              mps["raw_data_timestamp"] = data.timestamp;
              mps["raw_data_timestamp_unix"] = data.tsUnix;
              mps["raw_data_videopath"] = JSON.stringify(data.videopath);
              mps["raw_data_patch"] = JSON.stringify(data.patch);
              mps["raw_stream_camera"] = stream.camera;
              mps["raw_stream_roi"] = stream.roi;
              mps["raw_stream_streamingID"] = stream.streamingID;
              mps["raw_stream_zone"] = stream.zone;
   
              var timestamp = data.timestamp;
              var params = {};
              params["measurepoints"] = mps;
              params["time"] = new Date().getTime();
              params["device_id_no"] = device_id;
              paramsArray.push(params);
          } else if (data.eventName == "CrowdAlert") {
              var mps = {};
              mps["raw_data_count_number"] = data.count.number;
              mps["raw_data_count_percentage"] = data.count.percentage;
              mps["alarm_data_event_name"] = data.eventName;
              mps["raw_data_imgpath"] = JSON.stringify(data.imgpath);
              mps["raw_data_objtype"] = data.objType;
              mps["raw_data_pplassoc"] = data.peopleassociation;
              mps["raw_data_rawimgpath"] = JSON.stringify(data.rawimgpath);
              mps["raw_data_rect"] = JSON.stringify(data.rect);
              mps["raw_data_start_time"] = data.startTime;
              mps["raw_data_track_id"] = data.trackId;
              mps["raw_data_timestamp"] = data.timestamp;
              mps["raw_data_timestamp_unix"] = data.tsUnix;
              mps["raw_data_videopath"] = JSON.stringify(data.videopath);
              mps["raw_stream_camera"] = stream.camera;
              mps["raw_stream_roi"] = stream.roi;
              mps["raw_stream_streamingID"] = stream.streamingID;
              mps["raw_stream_zone"] = stream.zone;
   
              var timestamp = data.timestamp;
              var params = {};
              params["measurepoints"] = mps;
              params["time"] = new Date().getTime();
              params["device_id_no"] = device_id;
              paramsArray.push(params);
          } else {
   
          }
      }
   
      if (!flag) {
          return tools.resultBuilder.build(false);
      }
   
      return tools.resultBuilder.build(true, JSON.stringify(paramsArray));
   
   :raw-html-m2r:`<br />`
   

4. Add the Lookup Asset node to the canvas and configure the node as per the below. For more information about the Lookup Asset node, see Lookup Asset.

   

   
   

5. Add the Upload Measurement Point node to the canvas. For more information aboute the Upload Measurement Point node, see Upload Measurement Point.

6. Link all the nodes together as per the image above.

7. Toggle on the Debug switch on the top right to record the logs of the nodes in the flow.

   

   
   

8. Click Save.

Step 4: Publish and Start Flow

1. Go to the Flow Designer page and click Publish for the flow you have created.

2. Allocate the runtime resources needed for the flow and click OK.

   

   
   

3. On your local machine, use POSTMAN to send a request to the HTTP server node, containing the following message as the payload.

   {
       "Data":{
           "data":[
               {
                   "eventName":"Abandon",
                   "count":1,
                   "imgpath":10,
                   "objType":"test",
                   "peopleassociation":"xxx",
                   "rawimgpath":10,
                   "rect":10,
                   "startTime":"2020-09-15 10:00:00",
                   "txUnix":"xx",
                   "vediopath":"xxx",
                   "patch":"xxx",
                   "camera":"xxx",
                   "rox":"xxx",
                   "streamID":"",
                   "zone":"xxx"
               }
           ],
           "stream":{
               "device_id":"xxxxxId"
           }
       }
   }
   

Results

Click Edit to enter the integration flow canvas page. Click the Log tab of each node (if applicable) to see their respective inputs and outputs.