An intelligent control system and method based on information system integration

Abstract

The application discloses an intelligent control system and method based on information system integration, which comprises the following steps: a data acquisition module collects data, and converts the original data into standardized data through data processing; a data transmission module integrates multiple network fusion, deploys a protocol conversion gateway, and realizes data standardization; an intelligent decision module makes decisions on control objects through control strategies and algorithms; an execution module adjusts and optimizes the control objects in real time, and feeds back the execution state; a monitoring center realizes process visualization, and gives early warning to abnormal states; a man-machine interaction module provides real-time monitoring, parameter setting and fault diagnosis functions for users; and an information security protection module guarantees system data security and operation safety. The application has the advantages that it is convenient to maintain and expand, can automatically complete control tasks, is suitable for environmental changes, accurately controls the control objects, unifies heterogeneous data formats into a standardized data model, and realizes closed-loop control.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, specifically to an intelligent control system and method based on information system integration. Background Technology

[0002] With the development of information technology, information system integration has become an important support for intelligent control systems. By integrating various information systems, information sharing and collaborative work can be achieved, thereby improving the performance of intelligent control systems.

[0003] However, existing intelligent control systems have the following problems: The decision-making process lacks intelligence, relying on preset rules or simple logic control, and is unable to cope with complex and ever-changing scenarios. Poor scalability and compatibility: When adding new devices or functions, interfaces need to be redeveloped, resulting in high system iteration costs.

[0004] The information disclosed in this background section is intended only to enhance the understanding of the overall background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0005] The technical problem to be solved by this invention is to overcome the above-mentioned technical defects and provide an intelligent control system and method based on information system integration.

[0006] To address the above problems, the technical solution of this invention is an intelligent control system based on information system integration, comprising: The data acquisition module is responsible for collecting data and transforming the raw data into standardized data through data processing. The data acquisition module integrates multiple types of sensors and supports multi-protocol compatibility through a standardized interface. The data transmission module integrates multiple networks, deploys a protocol conversion gateway to achieve data standardization, deploys edge computing nodes to reduce cloud computing pressure, and ensures low-latency control requirements. The intelligent decision-making module processes the information and uses control strategies and algorithms to make decisions about the controlled object; the intelligent decision-making module performs system detection based on machine learning algorithms; it predicts demand through predictive models, constructs decision models, and issues decision instructions. The execution module, based on the instructions of the decision system, enables real-time adjustment and optimization of the controlled object, while simultaneously feeding back the execution status to form a closed-loop feedback. The monitoring center module visualizes processes through terminals, supports multi-terminal adaptation and remote monitoring, and provides early warnings for abnormal states. The human-computer interaction module provides users with functions such as real-time monitoring, parameter setting, and fault diagnosis, improving the availability and maintainability of the system. Information security protection module, which is used to ensure system data security and operational security.

[0007] Preferably, the data acquisition module performs multi-source data fusion on the acquired data. The multi-source data includes, but is not limited to, real-time data, historical database data, and external system data. The calculation method is as follows: , in, It is a unified standard output data after fusion. It is the first Heterogeneous raw data, It is the first System data weighting coefficients .

[0008] Preferably, the data acquisition module preprocessing includes operations such as data cleaning, data conversion, data standardization, data filtering, and calibration.

[0009] Preferably, the monitoring center scores the health status of system equipment to determine whether the system equipment is abnormal. The scoring criteria are as follows: , in, It is a system health indicator. It represents the number of fault alarms per unit time. These are weighting coefficients.

[0010] Preferably, the execution module executes decision instructions through a PLC, actuator, or robot device.

[0011] Preferably, the human-computer interaction module displays the real-time data collected by the sensor in a visual manner on the control panel or APP.

[0012] Preferably, the information security protection module uses blockchain and encrypted communication technology to ensure data security and system reliability.

[0013] This invention also discloses an intelligent control method based on information system integration, which, when applied to the above-mentioned control system, includes the following steps: Step 1: The data acquisition module collects data from the control target in real time, processes the data, and forms standardized data. Step 2: The data transmission module transmits standardized data to the intelligent decision-making module and the monitoring center in real time; Step 3: The intelligent decision-making module uses the processed information to make decisions about the controlled object through control strategies and algorithms; Step 4: The execution module adjusts and optimizes the controlled object in real time according to the instructions of the decision system, and at the same time feeds back the execution status to form a closed-loop feedback. Step 5: The monitoring center visualizes the process through terminals and issues timely warnings when anomalies are detected; Step Six: Operators use the human-machine interface module to set parameters and diagnose faults.

[0014] The advantages of this invention compared to existing technologies are: 1. The intelligent control system of this invention adopts a modular design, which facilitates maintenance and expansion. It can automatically complete control tasks, reduce manual intervention, and improve production efficiency and product quality. Through continuous learning, the intelligent control system can adapt to environmental changes, improve system stability and reliability, and achieve precise control of the controlled object, thereby improving product quality.

[0015] 2. This invention supports multi-protocol compatibility through standardized interfaces; integrates multiple networks, deploys protocol conversion gateways to achieve data standardization, deploys edge computing nodes to reduce cloud computing pressure, and unifies heterogeneous data formats into a standardized data model.

[0016] 3. The control system of the present invention realizes closed-loop control. The intelligent decision-making module makes decisions on the controlled object through control strategies and algorithms after processing the information; the execution module realizes real-time adjustment and optimization of the controlled object according to the instructions of the decision system, and at the same time feeds back the execution status, forming a closed-loop feedback. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating an intelligent control system and method based on information system integration according to the present invention. Detailed Implementation

[0018] To make the content of this invention easier to understand, the technical solutions in the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings.

[0019] like Figure 1 As shown, an intelligent control system based on information system integration includes: The data acquisition module is responsible for collecting data and transforming raw data into standardized data through data processing. The data acquisition module integrates multiple types of sensors and supports multi-protocol compatibility through standardized interfaces. The data acquisition module's preprocessing includes data cleaning, data conversion, data standardization, data filtering, calibration, and other operations.

[0020] The data acquisition module performs multi-source data fusion on the collected data. The multi-source data includes, but is not limited to, real-time data, historical database data, and external system data. The calculation method is as follows: , in, It is a unified standard output data after fusion. It is the first Heterogeneous raw data, It is the first System data weighting coefficients .

[0021] The data transmission module integrates multiple networks, deploys a protocol conversion gateway to achieve data standardization, deploys edge computing nodes to reduce cloud computing pressure, and ensures low-latency control requirements. The intelligent decision-making module processes the information and uses control strategies and algorithms to make decisions about the controlled object; the intelligent decision-making module performs system detection based on machine learning algorithms; it predicts demand through predictive models, constructs decision models, and issues decision instructions. The execution module, based on the instructions of the decision system, realizes real-time adjustment and optimization of the controlled object, and at the same time feeds back the execution status to form a closed-loop feedback; the execution module executes the decision instructions through the PLC, actuators, and robot equipment.

[0022] The monitoring center module visualizes processes through terminals, supports multi-terminal adaptation and remote monitoring, and provides early warnings for abnormal states. The monitoring center scores the health status of system devices to determine whether any devices are malfunctioning; the scoring criteria are as follows: , in, It is a system health indicator. It represents the number of fault alarms per unit time. These are weighting coefficients.

[0023] The human-computer interaction module provides users with functions such as real-time monitoring, parameter setting, and fault diagnosis, improving the system's availability and maintainability. The human-computer interaction module displays the real-time data collected by the sensors in a visual manner on the control panel or APP.

[0024] The information security protection module is used to ensure system data security and operational security. It employs blockchain and encrypted communication technologies to safeguard data security and system reliability.

[0025] An intelligent control method based on information system integration, applying the above-mentioned control system, includes the following steps: Step 1: The data acquisition module collects data from the control target in real time, processes the data, and forms standardized data. Step 2: The data transmission module transmits standardized data to the intelligent decision-making module and the monitoring center in real time; Step 3: The intelligent decision-making module uses the processed information to make decisions about the controlled object through control strategies and algorithms; Step 4: The execution module adjusts and optimizes the controlled object in real time according to the instructions of the decision system, and at the same time feeds back the execution status to form a closed-loop feedback. Step 5: The monitoring center visualizes the process through terminals and issues timely warnings when anomalies are detected; Step Six: Operators use the human-machine interface module to set parameters and diagnose faults. Example

[0026] Intelligent control system architecture for factory assembly lines: Data acquisition module: Equipped with various types of sensors (temperature, pressure, vibration, position, image, etc.), RFID tags, smart terminals, vision cameras, and PLC controllers.

[0027] Data transmission module: integrates industrial Ethernet, 5G / 4G, NB-IoT, LoRa, industrial wireless network, etc., to ensure real-time data transmission (latency <10ms).

[0028] Intelligent decision-making module: performs system detection based on machine learning algorithms; predicts demand through predictive models, constructs decision models, and issues decision instructions.

[0029] Execution module: Based on the instructions of the decision system, it realizes real-time adjustment and optimization of the controlled object, and at the same time feeds back the execution status, forming a closed-loop feedback.

[0030] Monitoring Center: Enables process visualization through terminals, supports multi-terminal adaptation and remote monitoring, and provides early warnings for abnormal states.

[0031] Human-computer interaction module: Provides users with functions such as real-time monitoring, parameter setting, and fault diagnosis.

[0032] Workflow: Step 1: The intelligent assembly line data acquisition module dynamically collects data from the assembly line and, after data processing, converts the collected information into standardized data. Step 2: The data transmission module transmits standardized data to the intelligent decision-making module and the assembly line monitoring platform; Step 3: The intelligent decision-making module uses preset control strategies and intelligent algorithms to analyze the received data and generate decision instructions for the operation of the assembly line. Step 4: The execution module, following the instructions of the intelligent decision-making module, conducts real-time control and efficiency optimization of each link of the intelligent assembly line, synchronously transmits the execution dynamics, and builds a closed-loop management and control mechanism. Step 5: The assembly line monitoring platform uses a visual terminal to present the entire assembly process intuitively. Once an abnormal situation is detected, an early warning mechanism is immediately triggered. Step Six: Operators use the human-machine interface to flexibly configure the assembly line operating parameters and carry out troubleshooting and diagnosis.

[0033] Application benefits: Downtime due to failure was reduced by 33.3%, and production capacity was increased by 18.8%.

[0034] The defect rate was reduced from 0.5% to 0.1%, saving rework costs.

[0035] The present invention and its embodiments have been described above, and such description is not restrictive. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. An intelligent control system based on information system integration, characterized in that, include: The data acquisition module is responsible for collecting data and transforming the raw data into standardized data through data processing. The data acquisition module integrates multiple types of sensors and supports multi-protocol compatibility through standardized interfaces; The data transmission module integrates multiple networks, deploys a protocol conversion gateway to achieve data standardization, deploys edge computing nodes to reduce cloud computing pressure, and ensures low-latency control requirements. The intelligent decision-making module processes the information and uses control strategies and algorithms to make decisions about the controlled object; the intelligent decision-making module performs system detection based on machine learning algorithms; it predicts demand through predictive models, constructs decision models, and issues decision instructions. The execution module, based on the instructions of the decision system, enables real-time adjustment and optimization of the controlled object, while simultaneously feeding back the execution status to form a closed-loop feedback. The monitoring center module visualizes processes through terminals, supports multi-terminal adaptation and remote monitoring, and provides early warnings for abnormal states. The human-computer interaction module provides users with functions such as real-time monitoring, parameter setting, and fault diagnosis, improving the availability and maintainability of the system. Information security protection module, which is used to ensure system data security and operational security.

2. The intelligent control system based on information system integration according to claim 1, characterized in that: The data acquisition module performs multi-source data fusion on the acquired data. The multi-source data includes, but is not limited to, real-time data, historical database data, and external system data. The calculation method is as follows: , in, It is a unified standard output data after fusion. It is the first Heterogeneous raw data, It is the first System data weighting coefficients .

3. The intelligent control system based on information system integration according to claim 1, characterized in that: The data acquisition module preprocessing includes operations such as data cleaning, data conversion, data standardization, data filtering, and calibration.

4. The intelligent control system based on information system integration according to claim 1, characterized in that: The monitoring center scores the health status of system equipment to determine whether the system equipment is abnormal. The scoring criteria are as follows: , in, It is a system health indicator. It represents the number of fault alarms per unit time. These are weighting coefficients.

5. The intelligent control system based on information system integration according to claim 1, characterized in that: The execution module executes decision instructions through PLC, actuators, and robotic devices.

6. The intelligent control system based on information system integration according to claim 1, characterized in that: The human-computer interaction module displays the real-time data collected by the sensors in a visual manner on the control panel or APP.

7. The intelligent control system based on information system integration according to claim 1, characterized in that: The information security protection module uses blockchain and encrypted communication technologies to ensure data security and system reliability.

8. An intelligent control method based on information system integration, applying the control system described in any one of claims 1-7, characterized in that, Includes the following steps: Step 1: The data acquisition module collects data from the control target in real time, processes the data, and forms standardized data. Step 2: The data transmission module transmits standardized data to the intelligent decision-making module and the monitoring center in real time; Step 3: The intelligent decision-making module uses the processed information to make decisions about the controlled object through control strategies and algorithms; Step 4: The execution module adjusts and optimizes the controlled object in real time according to the instructions of the decision system, and at the same time feeds back the execution status to form a closed-loop feedback. Step 5: The monitoring center visualizes the process through terminals and issues timely warnings when anomalies are detected; Step Six: Operators use the human-machine interface module to set parameters and diagnose faults.

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