An example of a data acquisition system, the composition of a data acquisition system

3.Configure collection rules. You can use the intelligent recognition function to let Octopus automatically identify the data structure of the e-commerce ** page, or manually set the collection rules.

4.If you manually set the collection rules, you can select the product elements on the page, such as the product name, **, comments, etc., with the mouse, and set the corresponding collection rules to ensure that the required data is obtained correctly. 5.

Run the collection task. After confirming that the settings are correct, you can start the collection task and let the octopus start collecting the product information on the e-commerce**. 7.

Wait for the collection to complete. Octopus will automatically scrape the product data on the page according to the set rules and save it locally or export to a specified database, etc. 8.

Use the collected product data for analysis and processing, such as ** comparison, sales trend analysis, etc. Octopus has prepared a series of concise and easy-to-understand tutorials for users to help you quickly master the collection skills and easily deal with all kinds of ** data collection, please go to the official website tutorial and help for more details.

The composition of the data acquisition system is as follows:

Sensor part, data collector part and computer part.

1. The sensor part includes the various electrical sensors mentioned above, and their function is to feel various physical variables, such as force, linear displacement, angular displacement, strain and temperature, etc., and convert these physical quantities into electrical signals.

2. The function of the data acquisition instrument is to scan all the sensors through the liquid bright channel, convert the electrical signal obtained from the scanning into digital quantity, and then convert the sensor coefficient of the data according to the characteristics of the sensor, and then transmit the data to the computer, and can also print out the data and store it on the disk.

3. The computer part includes the main unit, the display width display, the memory, the buried locust printer, the plotter and the keyboard.

Meaning of Data Acquisition System

Visualized report definition, audit relationship definition, report approval and release, data filling, data preprocessing, data review, comprehensive query statistics and other functional modules. Through the networking and digitization of information collection, the coverage of data collection will be expanded, and the comprehensiveness, timeliness and accuracy of audit work will be improved. Finally, the relevant business work management is modernized, the procedures are standardized, the decision-making is scientific, and the service is networked.

A data acquisition system is usually composed of the following components:1Data source:

The data collection system needs to specify the data source to be collected, which can be a web page, API interface, database, etc. 2.Data Collector:

The data collector is the core component and is responsible for scraping data from data sources. It can mimic human actions in a browser, automatically visit web pages, and extract data. 3.

Data processing: The collected data may need to be cleaned, transformed, filtered, etc., to meet the needs of subsequent analysis and application. 4.

Storage: The collected data needs to be stored in a database or file for subsequent query simplification and analysis. 5.

Scheduling and monitoring: The data acquisition system needs to have a scheduling and monitoring mechanism to ensure the stable operation of the collection task and detect abnormalities in time. 6.

Visualization and reporting: The data acquisition system can provide a visual interface and reporting functions to facilitate users to view and analyze the collected data. Octopus Collector is a comprehensive, simple and widely applicable Internet data collector.

If you need to collect data, Octopus Collector can provide you with intelligent identification and flexible custom collection rule settings to help you quickly obtain the data you need. To learn more about the functions and cooperation cases of octopus collectors, please go to the official website for more details.

There are several methods of data collection, and here are five of the common ones:1Manual acquisition:

A method of copying and pasting the required data by manually browsing the web. This approach is useful for situations where the amount of data is small or manual screening is required, but it is less efficient and error-prone. 2.

Web crawler: Write a crawler program using a programming language that simulates browser behavior, automatically accesses web pages, and extracts the required data. This method is suitable for large-scale data acquisition, but requires some programming skills.

3.Database export: Yuque extracts the required data from the database through a database query language (such as SQL).

This method is suitable for data that has already been stored in the database, and a large amount of data can be retrieved quickly. 4.API API call:

Get the data you need by calling ** or the API interface provided by the application. This method is suitable for ** or where the application provides an API interface that enables real-time data collection. 5.

Data subscription: Get the data you need by subscribing to the data service provided by the data provider. This approach is useful when the data provider offers a subscription service that can get data that is updated in real time.

Octopus Collector is a full-featured, easy-to-operate Internet data collector that can help users quickly collect all kinds of data. To learn more about the methods and techniques of data collection, you can refer to the tutorial of Octopus Collector, please go to the official website Tutorial and Help for more details.

Data collection is fundamental to the business conduct of MES MES, and it is also the basis for statistical analysis of MES MES. In the application of MES manufacturing execution system software, different data collection methods and different data collection equipment need to be selected according to different factors such as data, application posture scenarios, personnel capabilities, equipment investment, etc. According to the classification of various types of data, different data collection methods are adopted.

Below, a brief introduction to several common types of data collection methods.

First: the data that must be entered;

The second is: the data automatically generated by the system;

The third is: through the way of barcode collection;

Fourth: sensor collection data;

Fifth: RFID data collection.

In general, data analysis and data visualization are heard more often, and data collection is heard relatively less. Data collection generally refers to data being stored in various business systems or manually entered into databases. Here we have to mention a function called data filling.

The data filling function is a new product launched by Yixin Huachen, a one-stop data analysis platform - a feature of data collection in ABI. The data filling function can backfill the report, fill in the missing data, and make a new filling form for data entry, which truly realizes the integration of data analysis and filling. The backfill form supports the import of Excel data, so that the large amount of data is no longer a problem, and at the same time, it supports data review to ensure the correctness of the data.

Yixin Huachen's one-stop data analysis platform - ABI is an all-round product of Qiaomu, which integrates core functions such as data source adaptation, ETL data processing, data modeling, data analysis, data filling, workflow, portal, and mobile application. Among them, data analysis and data visualization are the strengths of Exin ABI and its core functions. In addition to Chinese-style complex reports, dashboards, and large-screen reports, ABI also supports self-service Xiaozaosen analysis, including drag-and-drop multi-dimensional analysis, Kanban and Kanban sets, and business users can carry out exploratory self-service analysis as they want by simply dragging and dropping.

At the same time, the word impromptu report and slide report make the report more brilliant. The data visualization of Exin ABI is also rich and colorful, and its reports have hundreds of built-in visualization elements and graphics. It not only supports more than 80 kinds of statistical charts, but also includes maps and GIS maps of the world and various provinces and cities in China, and thousands of visualizations can be derived through design and collocation.

At the same time, ABI also supports dynamic and cool cool screen analysis, unique 3D panoramic perspective, and freely and quickly produces various interactive conventional screen and large screen reports, turning creativity into reality.

Traditional data collection methods can be applied to big data collection, but they need to be upgraded and optimized in combination with new technical means. Here's an example:

Traditional data collection methods are carried out through questionnaires, interviews, observations, and literature, which can focus on in-depth understanding of user needs and behaviors, but the collection efficiency is low and the coverage is narrow.

In the era of big data, data collection can be carried out by combining new technologies such as Internet technology and machine learning algorithms. For example, through the network crawling worm technology, the user's data on social networking, e-commerce, etc., can be obtained, so as to realize the rapid collection and analysis of massive data.

Traditional data collection methods also include traditional questionnaires, interviews and other methods, but these methods often have problems such as sample bias and strong subjectivity.

In the era of big data, Internet technology and big data analysis platforms can be used for data collection and analysis. For example, user data on social platforms can be used for analysis to obtain more objective and comprehensive data conclusions.

Traditional data collection methods also include laboratory experiments, survey research and other methods, but these methods require a lot of time and energy, and are not suitable for large-scale data collection and analysis.

In the era of big data, devices such as IoT technology and sensors can be used for data collection. For example, sensors can be used to collect information such as weather data and traffic data, enabling large-scale data collection and analysis.

In summary, the traditional data collection methods can be upgraded and optimized by combining new technical means, which is suitable for data collection and analysis in the era of big data.

The four common methods of data collection include questionnaire surveys, data review, field investigations, and experiments, and each of these methods has its own disadvantages and disadvantages, which are analyzed as follows.

The first is a questionnaire. Surveys are the most common method of data collection because they are less expensive and more comprehensive. However, the answers obtained from the questionnaire are often untargeted, which means that the data collected by the questionnaire needs to be further analyzed.

In addition, in the past, the promotion of questionnaires would be slower because it was very labor-intensive. But now there are a lot of questionnaires on the Internet**, and if you collect data through questionnaires**, it will be more convenient and faster. Therefore, the questionnaire survey is easy to operate, but the disadvantage is that the data is not targeted and cannot get in-depth data.

The second is to consult information. Consulting is the oldest way of data collection, by consulting books, records, etc., to get the data you want. In this data collection process, there is an inherently filtering and analytical nature, which means that the data obtained from the data may be relatively close to the results you want.

Nowadays, whether it is a library or an Internet query, it is very convenient, and it provides a good environment for consulting materials. The disadvantage of consulting the data is that the requirements for the operator are very high, and the current data is cumbersome, and the truth is mixed, which requires a high degree of judgment.

The third is on-site inspection. A field trip is to go to a designated place to do research. It refers to the fact that in order to understand the truth of a thing and the development process of the situation, we go to the field to conduct an intuitive and partial detailed investigation.

In the course of the investigation, it is necessary to analyze the phenomena observed by oneself at any time and strive to grasp the characteristics of the object of investigation. This method of collecting data is time-consuming, labor-intensive, and requires everyone's cooperation. The advantage of this method of collection is that you can get first-hand information in the first place, and the disadvantage is that you may not be able to achieve the goal you want, because there are also a lot of variables in the process of investigation.

Fourth, experiments. Experimental design data is the most time-consuming of the four methodsBecause it is through a variety of experiments to get a unified direction, that is, in the process, there can be countless failures. But the data from the experiment is the most accurate, and it may drive progress in a certain industry.

So,The advantage of experimental data collection is that the accuracy of the data is high, while the disadvantage is that it is highly unknown, and both the period of the experiment and the results of the experiment are uncertain.

With the development of technology and the advent of the era of big data, it is becoming easier and easier to collect data, and everyone should pay more attention to protecting and using data.

I have been using the simple data collection platform recently, no need to install any software, you can use it when you open it, and it also supports visual operation, intelligent selection, which greatly improves the efficiency of collection, and does not always need to look at the configuration for half a day, which also means that novices who do not understand ** can also be proficient in operation, can export excel format, and big data analysis should also be used;

Is it similar for each of your products? All a little slightly elliptical? I don't know your product requirements, but generally speaking, if you want to control the circle, you need to control the roundness in addition to the diameter.

The difference between your three values is actually a problem with roundness, which is at least mm. When it comes to CPK calculation, in fact, the definition of the diameter of the general engineering drawing is the average value, so you can take the average of these three values as a value to collect data. However, in this case, the requirements for roundness must be clarified.

That is, the diameter and roundness should be calculated separately for CPK. If you don't have roundness requirements on your drawings, check with the product design department. In addition, according to the national standard, there is also an unmarked tolerance for roundness, which should be equal to the tolerance value of the diameter.