Which data collector is better, the operation is relatively simple

It is the earliest in China that specializes in the study of data collection technology, which is accurate and applicable.

1. Object feature point selection.

The main object feature points are the turning points of the contour of the object, the corner of the house, the turning point of the fence and power line, the turning point of the road bank line, the intersection point, the electric pole, the independent tree point, etc. Connect some feature points to resemble the shape of the real object.

2. Appearance feature point selection.

The morphological feature points should be selected to reflect the morphological characteristics of the ridge line, valley line and other lines, the summit, saddle, ridge valley shape transformation, and slope slope transformation of the foot of the mountain.

3.Field data collection should not only record the distance, horizontal angle and vertical angle, but also record the four types of information such as point number, code, and connection point connection line. The connection point records the connection point number with the observation point, and the connection line records the straight line and curve arc connection between the measurement point and the connection point.

4.Pay attention to the safety of personnel and equipment.

Not even mentioning RPA software? Advertise it.

1. Software interface mode.

Each software vendor provides data interfaces to achieve data collection and aggregation.

2. Open database approach.

To achieve data collection and aggregation, open database is the most direct way.

The two systems have their own databases, and it is more convenient to have the same type of database:

1.If the two databases are on the same server, as long as the username is not set, they can directly access each other, and you need to bring the database name and the schema owner of the table after from.

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2.If the databases of the two systems are not on the same server, it is recommended to use the form of a linked server, or use openset and opendatasource, which need to configure the peripheral server to access the database.

3. Direct data collection method based on underlying data exchange.

101 Heterogeneous data acquisition technology collects all the data generated by the target software based on the underlying I/O request and network analysis technologies by obtaining the underlying data exchange of the software system, the network traffic packet between the software client and the database, and converts and restructures the data to a new database for the software system to call.

The technical characteristics are as follows:

1.No need for the original software vendor to cooperate;

2.Real-time data collection, end-to-end response speed of data up to the second level;

3.Strong compatibility, can collect and aggregate various software system data of Windows platform;

4.Output structured data as the basis for data mining and big data analysis applications;

5.Automatically establish data associations, with short implementation cycle, simple and efficient;

6.Support automatic import of historical data, and automatically write data to the target software through io artificial intelligence;

7.Simple configuration and short implementation cycle.

Based on the direct data collection method of the underlying data exchange, get rid of the dependence on the software vendor, do not need the cooperation of the software vendor, not only need to invest a lot of time, energy and money, but also do not have to worry about the disintegration of the system development team, the loss of the source and other reasons that lead to the system data collection into a dead end.

Mining data directly from a variety of software systems, continuously obtaining accurate and real-time data, automatically establishing data associations, and outputting structured data with high utilization rate, so that the data sources of different systems can be circulated in an orderly, safe and controllable manner, providing decision support, improving operational efficiency and generating economic value.

We collect data wherever we go, the data is accurate, and it can be used for life.