Intelligent industrial data sensing and transmission integrated system

Abstract

The invention belongs to the field of industrial big data, particularly relates to an intelligent industrial data sensing and transmission integrated system researched by using a big data technology, and aims to improve the data acquisition efficiency and accuracy in an industrial production process. A central data processing unit; and a user interface. The sensor nodes are deployed on an industrial site and used for monitoring and collecting key parameters in real time, such as boilers, ash removal, dust removal, air compressors, level gauges and flowmeters. And real-time data acquisition is realized through mainstream communication protocols such as RS485, MODBUS TCP (Transmission Control Protocol) and the like. The central data processing unit receives data from the sensor nodes, and stores, analyzes and processes the data. The system comprises system integration, data acquisition, data modeling, data standard and data analysis, and allows efficient data transmission between the sensor nodes and the central data processing unit, so that the reliability and economic benefits of industrial production are remarkably improved.

Description

1. Technical Field

[0001] The present invention relates to an intelligent industrial data perception and transmission integrated system, which can effectively collect and process data in the industrial production process. The system includes multiple sensor nodes for real-time monitoring of the status and performance indicators of industrial equipment. Each sensor node is equipped with a wireless communication module, which can send the collected data to a central server via a wireless network. The central server stores and analyzes the received data and displays it to the operator through a user interface. In addition, the system also has an anomaly detection function, which can automatically trigger an alarm mechanism when the data is abnormal, ensuring the stability and safety of industrial production. 2. Background Technology

[0002] In modern industrial production, data acquisition is essential for monitoring and optimizing the production process. Traditional data acquisition systems often rely on wired connections, which limits the flexibility and scalability of the system. With the development of wireless communication technology, wireless sensor networks have become a more flexible and efficient way to collect data. Wireless sensor networks are composed of multiple sensor nodes, which can be deployed at different locations on the industrial site to monitor various physical parameters in real time, such as medium temperature, pressure, ash volume, cumulative flow of gas mixtures, etc., and transmit the data to the central processing unit through the wireless network for analysis and processing.

[0003] Although wireless sensor networks show great potential in industrial data acquisition, there are still some challenges. For example, the reliability and security of wireless communication need to be guaranteed, especially in harsh industrial environments. In addition, the power consumption management and data processing capabilities of sensor nodes are also key factors to consider when designing an efficient industrial data acquisition system. Therefore, developing an industrial data acquisition system that can adapt to the industrial environment, has high reliability and low power consumption is an important direction of current technological development. III. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent industrial data sensing and transmission integrated system, which can overcome the shortcomings of the prior art and achieve more efficient and reliable data collection.

[0005] In order to achieve the above objectives, the present invention provides an integrated system for intelligent industrial data perception and transmission, comprising:

[0006] 1. Data Modeling

[0007] This module refers to the process of creating data models that are able to represent real-world entities and their relationships. Some of the basic steps and concepts of data modeling are as follows:

[0008] (1) Requirements analysis: Determine the business requirements that the on-site data model needs to meet, define the model theme, and distinguish the subjects, including but not limited to boiler data model, ash removal data model, dust removal data model, air compression data model, passive nuclear material level meter data model, thermal gas flow meter data model, etc.

[0009] (2) Logical model: Convert the above conceptual model into a more detailed logical model to define the attributes and constraints of the entities.

[0010] (3) Physical model: Design the physical structure of the database, including tables, fields, indexes, and stored procedures.

[0011] 2. Data standards

[0012] Data standards are a set of rules and conventions used to ensure data consistency, accuracy, and interoperability. These standards typically cover aspects such as the format, structure, content, and quality of data. Here are some key components of data standards:

[0013] (1) Data definition: clarify the meaning, purpose and context of data items such as boiler, dust removal, ash removal, and air compressor.

[0014] (2) Technical specifications: specify how data is represented, such as date format, numerical precision, etc.

[0015] (3) Data type: defines the types of values ​​that the data can accept, such as integers, floating-point numbers, strings, etc.

[0016] (4) Data length: the maximum length or number of characters of a data item.

[0017] (5) Data integrity: Ensure the integrity and consistency of data, including primary key constraints, foreign key constraints, uniqueness constraints, etc.

[0018] (4) Data exchange standards: define the format and protocol for exchanging data between different systems, such as XML, JSON, EDI, etc.

[0019] 3. Data analysis and calculation

[0020] The data analysis and calculation module is the core part of the system. It performs data analysis and calculation based on the collected real-time data, big data processing and model construction. The main functions include:

[0021] (1) Passive nuclear level meter:

[0022] The gamma-ray intensity data is obtained from the detector of the passive nuclear level meter, and the collected gamma-ray intensity data is converted into the corresponding liquid level height value. A calibration relationship between the current value and the liquid level height is established using a standard sample with a known liquid level height, which involves linear or nonlinear regression analysis. The calibration relationship is used to convert the current value into the corresponding liquid level height value, and the trend of liquid level changes over time is analyzed to monitor the consumption or accumulation of materials.

[0023] (2) Flow meter:

[0024] The medium temperature, voltage value, instantaneous flow rate, standard instantaneous flow rate, accumulated flow rate and other data are obtained from the sensor of the thermal gas flow meter. The collected data is cleaned, including noise removal, missing value processing, data smoothing, etc., to ensure the data quality. Based on historical data and trend analysis, future gas flow changes are predicted to provide support for inventory management and production planning. IV. Description of the drawings

[0025] Figure 1 The system structure diagram can clearly show the various components of the system and their connections

[0026] Figure 2 Real-time data computing solution architecture diagram

[0027] Figure 3 Boiler Data Conceptual Model

[0028] Figure 4 Dust removal data conceptual model

[0029] Figure 5 Dust removal data conceptual model

[0030] Figure 6 Air compressor data conceptual model

[0031] Figure 7 Physical model design drawing

[0032] Figure 8 Data model point importance ranking diagram

[0033] Fig. 9 Analysis of coal ash test report

[0034] Fig.10 Compressed air usage analysis

[0035] Fig.11 Real-time and historical data query

[0036] Fig.12 System abnormal status monitoring

[0037] Please see the attached drawings of the specification for details. V. Specific implementation methods

[0038] The data acquisition system of the present invention includes a sensor array, a data processing unit, a communication interface and a user interaction interface. The system is designed to monitor key parameters in industrial production processes in real time and provide data-driven decision support. The specific implementation is as follows:

[0039] 1. Sensor array: The sensor array consists of multiple high-precision sensors, including boiler (DCS), dust removal, ash removal, air compressor (PLC), material level sensor, flow sensor, etc. These sensors are installed at key locations of the production line to monitor the working status and environmental conditions of the equipment in real time.

[0040] 2. Data processing unit: The data processing unit receives raw data from the sensor array in real time and performs data cleaning, conversion and analysis tasks. The unit uses advanced signal processing algorithms to ensure the accuracy and reliability of the data.

[0041] 3. Communication interface: The communication interface is responsible for transmitting the results of the data processing unit to the central control system or cloud platform. It supports multiple communication protocols to adapt to different network environments.

[0042] 4. User interaction interface: The user interaction interface provides an intuitive visualization platform that enables operators to easily monitor the production process, view historical data and receive alarm notifications to meet the needs of different users.

[0043] 5. Operation process: After the system is started, the sensor array begins to monitor and collect data. The data processing unit analyzes the received data in real time and sends the analysis results to the user interface through the communication interface. Operators can view real-time data and analysis reports through the interface to make timely production adjustments and optimization decisions.

[0044] 6. Anomaly detection and response: The system has anomaly detection capabilities that can automatically identify abnormal patterns in data and trigger corresponding alarm mechanisms. When potential problems are detected, the system will immediately notify operators and provide possible solutions.

[0045] 7. Maintenance and upgrade: The system design takes into account the convenience of maintenance and upgrade, and provides remote diagnosis and firmware update functions to ensure the long-term stable operation of the system.

[0046] 6. Application Module

[0047] The data acquisition system of the present invention includes the following key application modules, each of which has a specific function and together constitutes the overall architecture of the system.

[0048] 1. System integration module:

[0049] (1) Factory test boiler (DCS), dust removal (PLC), ash removal (PLC), air compressor (PLC) control system ( Figure 1 ), through the information security device network gate ( Figure 1 ) to prevent network attacks and data leakage, use solid-state switches and read-write media to connect two independent host systems, and ensure that there is no direct communication path or information transmission protocol between the two systems. Connect the core switch to send data ( Figure 1 )Servers and industrial computers.

[0050] (2) Passive nuclear level meters and thermal gas flow meters convert continuous analog signals into discrete digital signals through AD converters. Sample data transmission ( Figure 1 )server.

[0051] 2. Real-time calculation module:

[0052] (1) Main data boiler (DCS), dust removal (PLC), ash removal (PLC), air compressor (PLC) control system ( Figure 2 ), material level meter, flow meter data real-time acquisition, and transmit it to the processing system ( Figure 2 )Apache Kafka, for real-time data streams ( Figure 2 )Apache Flink processes and analyzes data and provides an interface to users or applications for querying real-time data streams or processing results. The processing results are presented to users in the form of charts, dashboards, etc. for easy understanding and decision-making.

[0053] 3. Data model:

[0054] (1) Data model display Figure 3 , Figure 4 , Figure 5 , Figure 6 )Boiler conceptual model, dust removal conceptual model, ash removal conceptual model, air compressor conceptual model.

[0055] 4. Physical model:

[0056] (1) Physical model display Figure 7 )Boiler, dust removal, ash removal, air compressor, material level meter, flow meter database table structure, field accuracy, index, partition strategy

[0057] 5. Data model point importance ranking diagram ( Figure 8 ) Evaluate the impact of each data point on business decisions. High-quality data points, that is, data with high accuracy, completeness, and timeliness, are generally more important than low-quality data points. Real-time or near real-time data points are often more valuable than historical data, especially in scenarios that require a quick response.

[0058] 6. Analysis of coal ash test report ( Fig. 9 ) Confirm the name, number, sampling location and date of the coal sample to ensure the accuracy and traceability of the report, carefully check the various test items in the report, such as moisture, ash, volatile matter, fixed carbon, sulfur content, calorific value and other indicators, combine all test results, and comprehensively evaluate the overall quality of the coal, collect result data, and provide decision support for the algorithm model.

[0059] 7. Compressed air consumption analysis chart display ( Fig.10 ) Collect data on the operating hours and energy consumption of thermal gas flow meters, as well as the gas consumption and operating hours of all gas-using equipment.

[0060] 8. Real-time and historical ash delivery data strategy display ( Fig.11 ) Real-time display of strategic dynamics in ash transportation, such as ash transportation time, frequency, pressure and other parameters.

[0061] 9. System abnormal status monitoring ( Fig.12 ) Collect data from industrial computers and sensor devices, clean, convert and standardize the collected data, set thresholds for different anomaly types, and when the data exceeds these thresholds, the system will trigger an alarm. Define the response process after receiving the alarm, including preliminary investigation, problem diagnosis and necessary corrective measures. Record all alarm events and response activities for post-analysis and auditing. Continuously adjust and optimize anomaly detection algorithms and threshold settings based on the accuracy of alarms and user feedback.

[0062] Through the above specific implementation methods, the data acquisition system of the present invention can provide comprehensive data support for industrial production, optimize production processes, and improve production efficiency and product quality, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field within the technical scope disclosed by the present invention, according to the technical solution and its improved concept of the present invention, shall be covered within the protection scope of the present invention.

Claims

1. An integrated system for intelligent industrial data perception and transmission; its features include Follow these steps: (1) Determine the master data of the on-site system, including but not limited to the passive nuclear material level meter, thermal gas flow meter, coal ash detection report related parameters, and the data points of the boiler control system, ash removal control system, dust removal control system, and air pressure control system; (2) multiple sensor nodes, each of which is configured with at least one sensor for monitoring the status and performance indicators of industrial equipment; (3) a wireless communication module, connected to each sensor node, for transmitting the data collected by the sensor node to the central server; (4) Central server, used to receive, store and analyze data from sensor nodes and implement analysis based on historical and real-time data; (5) User interface, connected to the central server, used to display analysis results and receive user instructions.

2. The intelligent industrial data perception and transmission integrated system according to claim 1 is characterized in that: Communication protocols include TCP / IP protocol, RS232 / RS485 serial communication protocol, HTTP, MODBUSRTU and MODBUSTCP communication protocol to obtain sensor data in real time.

3. The intelligent industrial data perception and transmission integrated system according to claim 1 is characterized in that: The anomaly detection module is used to respond with an alarm mechanism when data is abnormal.

4. The intelligent industrial data perception and transmission integrated system according to claim 1 is characterized in that: The data backup module is used to regularly back up the data in the central server to the cloud storage.

5. The intelligent industrial data perception and transmission integrated system according to claim 1 is characterized in that: The network security management module is used to manage and optimize the communication network between sensor nodes and the central server to provide security protection.

6. The intelligent industrial data perception and transmission integrated system according to any one of claim 1, characterized in that: The central server also includes a data analysis engine for executing machine learning algorithms to improve the accuracy and efficiency of data processing.

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