DCS control system and method based on machine vision and artificial intelligence

Abstract

The invention discloses a DCS control system and method based on machine vision and artificial intelligence, and the system comprises the steps: obtaining the content of a human-computer interaction interface of the DCS control system through a camera, carrying out the analysis through an artificial intelligence platform, generating a data set, and carrying out the learning and judgment, and generating an instruction set; the software module is used for receiving an instruction set of the artificial intelligence platform; the DCS operator station is used for responding to the instruction set to realize automatic control operation on the DCS system; the method has the advantages that the automatic operation characteristic can be achieved, and the problems that it is difficult to obtain DCS closed system data through an artificial intelligence algorithm, and manual operation of a DCS interaction interface is not accurate enough are solved.

Description

Technical Field

[0001] The present invention relates to the field of industrial control technology, and in particular to a DCS control system and method based on machine vision and artificial intelligence. Background Art

[0002] A DCS control system is a distributed control system (DCS), also known as a distributed control system. It mainly collects and pre-processes process variables, further processes real-time data, and provides display and control for the DCS operator station to achieve open-loop monitoring. Its basic concept is decentralized control and centralized operation.

[0003] In the existing technology, the DCS operator station manually reads the device symbols and text data on the flow chart interface of the DCS operator station, and adjusts the lower-level devices by clicking the control buttons on the flow chart interface of the DCS operator station. However, manual operation is not convenient and accurate enough.

[0004] The current DCS system is a closed system. With the advent of artificial intelligence, it is very difficult for the current DCS system to achieve ATI automated control, obtain lower-level equipment data, control status data, and system status. Rebuilding the entire OS system is too difficult and cannot meet the data requirements of artificial intelligence algorithms. Summary of the Invention

[0005] In response to the technical defects mentioned in the background technology, the purpose of the embodiments of the present invention is to provide a DCS control system and method based on machine vision and artificial intelligence, which parses DCS human-computer interaction interface data to assist artificial intelligence algorithm data collection and solve the problem of inconvenience in artificial intelligence algorithm data acquisition; and controls the DCS operating system mouse and keyboard to solve the problem of manual operation being inconvenient and inaccurate.

[0006] To achieve the above objectives, in a first aspect, an embodiment of the present invention provides a DCS control system based on machine vision and artificial intelligence, the system comprising a camera, an artificial intelligence platform, a software module, and a DCS operator station; wherein the DCS operator station is a human-computer interaction device of the DCS system;

[0007] A camera is used to capture the content of the human-machine interaction interface in the DCS control system; wherein the interface content includes but is not limited to the operator station, engineer station graphical interface content and control interface content;

[0008] Artificial intelligence platform for:

[0009] Parsing the content based on a preset machine vision model, and generating a data set including, but not limited to, coordinate mapping relationships, graphical interface content, relevant process control, and historical data summary based on the parsing results; the data set includes the results parsed by the machine vision model; the machine vision model is trained based on an image recognition algorithm;

[0010] The data set is studied and judged by a preset artificial intelligence algorithm to determine the output result and instruction set; the instruction set includes a viewing instruction set and a control instruction set; the artificial intelligence algorithm is trained based on the data set;

[0011] A software module, installed in a DCS operator station, for receiving an instruction set from the artificial intelligence platform;

[0012] The DCS operator station is used to respond to the instruction set to realize automatic control operation of the DCS system.

[0013] As a specific implementation of the present application, the artificial intelligence platform includes a data acquisition module, a machine vision module, an artificial intelligence algorithm module, a storage module, a storage medium module, and an instruction set output module;

[0014] The artificial intelligence platform is connected to the camera to transmit the content in the DCS operator station;

[0015] The artificial intelligence platform is connected to the DCS operator station and is used to output instruction sets and control the DCS control system;

[0016] The data acquisition module is connected to the camera and is used to receive data transmitted by the camera. The interfaces include HDMI, USB, and BNC.

[0017] The machine vision module analyzes the content transmitted by the data acquisition module to form a data set;

[0018] The artificial intelligence algorithm module learns the content of the data set and forms an instruction set;

[0019] The storage module stores the machine vision analysis data set, artificial intelligence algorithm instruction set and DCS control system return data;

[0020] The storage medium module stores processing execution code and artificial intelligence algorithm code;

[0021] The instruction set output module is used to output the artificial intelligence algorithm instruction set, including but not limited to controlling the DCS control system through the COM port and USB port.

[0022] As a preferred implementation of this application, the content analyzed by the machine vision module in the artificial intelligence platform can be provided to other artificial intelligence algorithms as a data collection part for algorithm training;

[0023] The artificial intelligence algorithm module in the artificial intelligence platform is trained, learned, and judged based on the data set, and will be continuously updated as the data set accumulates;

[0024] The artificial intelligence platform is also used to:

[0025] The instruction set actions implemented by the DCS operator station are identified through machine vision, and the operation results are compared and judged to determine whether the corresponding work content in the instruction set has been completed.

[0026] As a specific implementation of the present application, the machine vision model includes but is not limited to using Yolo, CNN or SDD image recognition algorithms to process the acquired content images and perform high-precision recognition, and the acquisition method is but is not limited to using a camera or screenshot;

[0027] The camera is facing the flow chart interface in the DCS control system and is used to identify the content of the flow chart of the DCS control system. The number of cameras changes with the configuration of the DCS operator station.

[0028] The DCS control system flow chart is a user interface for human-computer interaction of the DCS control system, which includes but is not limited to field device symbols, content text labels, and control configuration buttons; wherein the device symbols include but are not limited to pumps, valves, containers, and various types of sensors; the text labels include but are not limited to device names, parameter values, temperature, liquid level, operation name menu, copy, and paste; the control buttons include but are not limited to switches, opening sizes, regulators, and alarm buttons;

[0029] The high-precision recognition is achieved by extracting the target area through image segmentation, synchronizing the timestamps of continuous frame images and performing data overlay analysis to reduce the data blurring problem caused by the screen refresh rate. The high-precision recognition is achieved based on ResNet-50+Transformer.

[0030] As a specific implementation method of the present application, the artificial intelligence platform is based on a preset machine vision model analysis and follows the DCS control system human-computer interaction interface to parse the data set. The images have clear labels and the data has a time series, so that the format is unified and the labels are clear for algorithm needs.

[0031] As a specific implementation of the present application, the artificial intelligence algorithm includes but is not limited to using logistic regression, support vector machine, neural network to learn, judge and output instruction sets for data sets.

[0032] As a specific implementation of the present application, the DCS operator station is connected to the artificial intelligence platform, receives the instruction set of the artificial intelligence platform, and completes the monitoring and control of the equipment;

[0033] The DCS operator station transmits data with the artificial intelligence platform, including but not limited to trend charts, alarm information, equipment status and historical data.

[0034] As a specific implementation method of the present application, the artificial intelligence platform and the DCS operator station implement the instruction set through the software module. The minimum complete implementation process requires outputting the instruction set twice, once for viewing the instruction set and once for controlling the instruction set; wherein, the complete implementation process refers to the output of the instruction set via the artificial intelligence platform until the machine vision recognizes that the DCS operator station completes the instruction set action.

[0035] As a specific implementation method of the present application, the artificial intelligence platform involves outputting a simulated mouse movement signal, which converts the coordinates captured by the camera into standardized absolute coordinate values; the software module simulates mouse operations by receiving data packets that comply with the HID protocol.

[0036] In a second aspect, an embodiment of the present invention further provides a DCS control method based on machine vision and artificial intelligence, which is applied to the DCS control system based on machine vision and artificial intelligence described in the first aspect, and the method includes:

[0037] a. Acquire images through the camera and obtain data through the data acquisition module of the artificial intelligence platform;

[0038] b. Using an artificial intelligence platform to synchronize timestamps and overlay data on consecutive frames, segment the images, and extract the target area; the target area is the human-computer interaction interface of the DCS operator station;

[0039] c. The AI ​​platform identifies the target area, including but not limited to device recognition, character recognition, and location recognition, and parses the corresponding data set;

[0040] d. The parsed data set is learned and judged by the artificial intelligence platform to determine whether to output the instruction set. If not, steps a to c are repeated. The instruction set is divided into a viewing instruction set and a control instruction set;

[0041] e. If yes, the DCS operator station receives the viewing instruction set from the artificial intelligence platform and performs the corresponding viewing actions, including but not limited to moving the mouse to a specified location, opening device information, and opening text content;

[0042] f. The artificial intelligence platform uses machine vision to identify the instruction set actions implemented by the operator station and determine whether the work content of the viewing instruction set is completed. If not, repeat d~e. If so, execute the next step;

[0043] g. The DCS operator station receives the control instruction set from the artificial intelligence platform and implements the corresponding control actions, including but not limited to moving the mouse to a specified position, clicking a control button on the flow chart interface, and inputting the valve opening;

[0044] h. The artificial intelligence platform uses machine vision to identify the instruction set actions implemented by the operator station and determine whether the work content of the control instruction set has been completed. If not, repeat g; if so, execute a to h.

[0045] The technical solution provided by the embodiment of the present invention uses a machine vision method to obtain content data in a DCS operator station. Based on the obtained data, an artificial intelligence algorithm is used to calculate and judge the obtained data after analysis to determine the output result and generate an instruction set containing a coordinate mapping relationship. The DCS operator station receives the signal output by the artificial intelligence platform and responds to the instruction set to realize automatic control operation of the DCS system. The characteristics of automated operation are thus realized, solving the problem that current manual operation is not convenient and accurate enough. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the specific implementation of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the specific implementation or the description of the prior art.

[0047] Figure 1 This is a principle block diagram of a DCS control system based on machine vision and artificial intelligence provided by an embodiment of the present invention;

[0048] Figure 2 This is a schematic diagram of the structure of an artificial intelligence platform provided by an embodiment of the present invention;

[0049] Figure 3 This is a flow chart of a DCS control method based on machine vision and artificial intelligence provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0051] It will be understood that when used in this specification and the appended claims, the terms “comprises” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0052] Throughout this specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment of the present invention. Thus, the appearances of the phrases "in one embodiment," "in an embodiment," "an example," or "an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in any suitable combinations and / or subcombinations in one or more embodiments or examples.

[0053] It should be noted that, unless otherwise specified, the technical terms in this embodiment have the common meanings understood in the relevant technical field.

[0054] Please refer to Figures 1 to 2 , an embodiment of the present invention provides a DCS control system based on machine vision and artificial intelligence, the system comprising a camera, an artificial intelligence platform, a software module and a DCS operator station; wherein the DCS operator station is a human-computer interaction device of the DCS system;

[0055] A camera is used to capture the content of the human-machine interaction interface in the DCS control system; wherein the interface content includes but is not limited to the operator station, engineer station graphical interface content and control interface content;

[0056] Artificial intelligence platform for:

[0057] Parsing the content based on a preset machine vision model, and generating a data set including, but not limited to, coordinate mapping relationships, graphical interface content, relevant process control, and historical data summary based on the parsing results; the data set includes the results parsed by the machine vision model; the machine vision model is trained based on an image recognition algorithm;

[0058] The data set is studied and judged by a preset artificial intelligence algorithm to determine the output result and instruction set; the instruction set includes a viewing instruction set and a control instruction set; the artificial intelligence algorithm is trained based on the data set;

[0059] a software module, installed at an operator station, for receiving an instruction set from the artificial intelligence platform;

[0060] The DCS operator station is used to respond to the instruction set to realize automatic control operation of the DCS system.

[0061] In this embodiment, the software module is installed in the DCS operator station and is used to receive the instruction set of the artificial intelligence platform;

[0062] The camera is used as an example of machine vision, but it is not limited to it;

[0063] The camera is facing the human-computer interaction interface of the DCS operator station. Usually, the number of cameras changes with the number of human-computer interaction interfaces. The image of the human-computer interaction page in the DCS control system is obtained in real time through the machine vision.

[0064] When applied, the artificial intelligence platform includes a data acquisition module, a machine vision module, an artificial intelligence algorithm module, a storage module, a storage medium module, and an instruction set output module;

[0065] The artificial intelligence platform is connected to the camera to transmit the content in the DCS operator station;

[0066] The artificial intelligence platform is connected to the DCS operator station and is used to output instruction sets and control the DCS control system;

[0067] The data acquisition module is connected to the camera and is used to receive data transmitted by the camera. The interfaces include HDMI, USB, and BNC.

[0068] The machine vision module analyzes the content transmitted by the data acquisition module to form a data set;

[0069] The artificial intelligence algorithm module learns the content of the data set and forms an instruction set;

[0070] The storage module stores the machine vision analysis data set, artificial intelligence algorithm instruction set and DCS control system return data;

[0071] The storage medium module stores processing execution code and artificial intelligence algorithm code;

[0072] The instruction set output module is used to output the artificial intelligence algorithm instruction set, including but not limited to controlling the DCS control system through the COM port and USB port.

[0073] The content analyzed by the machine vision module in the artificial intelligence platform can be provided to other artificial intelligence algorithms as data collection parts for algorithm training;

[0074] The artificial intelligence algorithm module in the artificial intelligence platform is trained, learned, and judged based on the data set, and will be continuously updated as the data set accumulates;

[0075] The artificial intelligence platform is also used to:

[0076] The instruction set actions implemented by the DCS operator station are identified through machine vision, and the operation results are compared and judged to determine whether the corresponding work content in the instruction set has been completed.

[0077] The machine vision model includes but is not limited to using Yolo, CNN or SDD image recognition algorithms to process the acquired content images and perform high-precision recognition, and the acquisition method includes but is not limited to using a camera or screenshots;

[0078] The camera is facing the flow chart interface in the DCS control system and is used to identify the content of the flow chart of the DCS control system. The number of cameras changes with the configuration of the DCS operator station.

[0079] The artificial intelligence platform is based on the preset machine vision model analysis and follows the DCS control system human-computer interaction interface. The parsed data set has clear labels for images and time series for data, with unified format and clear labels for algorithm needs.

[0080] The DCS control system flow chart is a user interface for human-computer interaction of the DCS control system, which includes but is not limited to field device symbols, content text labels, and control configuration buttons; wherein the device symbols include but are not limited to pumps, valves, containers, and various types of sensors; the text labels include but are not limited to device names, parameter values, temperature, liquid level, operation name menu, copy, and paste; the control buttons include but are not limited to switches, opening sizes, regulators, and alarm buttons;

[0081] The high-precision recognition is achieved by extracting the target area through image segmentation, synchronizing the timestamps of continuous frame images and performing data overlay analysis to reduce the data blurring caused by the screen refresh rate. The recognition is achieved based on ResNet-50+Transformer. The high-precision recognition includes but is not limited to characters and instrument screens.

[0082] The artificial intelligence algorithm includes but is not limited to the use of logistic regression, support vector machine, neural network, etc., through learning and judging the data set, outputting the instruction set, and continuously self-updating to store the updated algorithm code in the storage medium.

[0083] Furthermore, the artificial intelligence platform is also used to:

[0084] The instruction set actions implemented by the DCS operator station are identified through machine vision, and the operation results are compared and judged to determine whether the corresponding work content in the instruction set has been completed.

[0085] Specifically, the instruction set actions include operating the DCS control system, including but not limited to viewing various process equipment, viewing parameter values, executing specific control actions, etc.

[0086] The artificial intelligence platform and the DCS operator station implement the instruction set through the software module. The minimum complete implementation process requires outputting the instruction set twice, once to view the instruction set and once to control the instruction set. The complete implementation process refers to the output of the instruction set by the artificial intelligence platform until the machine vision recognizes that the DCS operator station completes the instruction set action.

[0087] That is, the identified data is learned and judged by the artificial intelligence platform to determine whether to output an instruction set, which is divided into a viewing instruction set and a control instruction set;

[0088] The DCS operator station receives the viewing instruction set from the artificial intelligence platform and implements corresponding viewing actions, including but not limited to moving the mouse to a specified location, opening device information, and opening text content;

[0089] The artificial intelligence platform uses machine vision to identify the instruction set actions implemented by the operator station and determine whether the work content of the instruction set is completed. If so, it will execute the next step;

[0090] The DCS operator station receives the control instruction set from the artificial intelligence platform and implements the corresponding control actions, including but not limited to moving the mouse to a specified position, clicking a control button on the flow chart interface, and inputting the valve opening;

[0091] The artificial intelligence platform uses machine vision to identify the instruction set actions implemented by the operator station and determine whether the work content of the control instruction set has been completed.

[0092] Furthermore, the DCS operator station receives the control instruction set of the artificial intelligence platform and generates storage needs, including but not limited to screenshots, operation data, logs, etc., which are placed in the artificial intelligence platform memory;

[0093] The DCS operator station receives the instruction set of the artificial intelligence platform and receives, parses and implements it through the software module. The instruction set includes but is not limited to clicking the mouse and moving the mouse;

[0094] The DCS operator station is a human-machine interaction device for the DCS control system, through which the operator monitors and controls the entire production process;

[0095] The DCS operator station is connected to the artificial intelligence platform, receives the instruction set of the artificial intelligence platform, and completes the monitoring and control of the equipment;

[0096] The DCS operator station transmits data with the artificial intelligence platform, including but not limited to trend charts, alarm information, equipment status and historical data.

[0097] The artificial intelligence platform involves outputting simulated mouse movement signals, which converts the coordinates captured by the camera into standardized absolute coordinate values; the software module simulates mouse operations by receiving data packets that comply with the HID protocol.

[0098] The above solution uses machine vision methods to obtain content data in the DCS operator station. Based on the obtained data, after analysis, the artificial intelligence algorithm calculates and judges the obtained data, determines the output results, and generates an instruction set containing coordinate mapping relationships. The DCS operator station receives the signal output by the artificial intelligence platform and responds to the instruction set to realize automatic control operations of the DCS system. This realizes the characteristics of automated operation and solves the problem that current manual operation is not convenient and accurate.

[0099] The solution provided in this application can replace manual labor and perform comprehensive control of the parts of the industrial system involving the DCS system. The control accuracy is high, and the process is fully automatic, efficient, low-cost, and conducive to improving the level of industrial automation.

[0100] refer to Figure 3 Based on the same inventive concept, an embodiment of the present invention further provides a DCS control method based on machine vision and artificial intelligence, which is applied to the DCS control system based on machine vision and artificial intelligence described in the first aspect. The method includes:

[0101] a. Acquire images through the camera and obtain data through the data acquisition module of the artificial intelligence platform;

[0102] b. Use the artificial intelligence platform to synchronize the timestamps and data of continuous frame images, segment the images, and extract the target area; wherein the target area is the human-computer interaction interface of the DCS operator station; that is, Figure 3 Image data superposition, segmentation, and target area extraction;

[0103] c. The AI ​​platform identifies the target area, including but not limited to device recognition, character recognition, and location recognition, and parses the corresponding data set and stores the relevant information in memory; that is, identifying, parsing, and storing the target area;

[0104] d. The parsed data set is learned and judged by the artificial intelligence platform to determine whether to output the instruction set. If not, steps a to c are repeated. The instruction set is divided into a viewing instruction set and a control instruction set;

[0105] e. If yes, the DCS operator station receives the viewing instruction set from the artificial intelligence platform and performs the corresponding viewing actions, including but not limited to moving the mouse to a specified location, opening device information, and opening text content;

[0106] f. The artificial intelligence platform uses machine vision to identify the instruction set actions implemented by the operator station and determine whether the work content of the viewing instruction set is completed. If not, repeat d~e. If so, execute the next step;

[0107] g. The DCS operator station receives the control instruction set from the artificial intelligence platform and implements the corresponding control actions, including but not limited to moving the mouse to a specified position, clicking a control button on the flow chart interface, and inputting the valve opening;

[0108] h. The artificial intelligence platform uses machine vision to identify the instruction set actions implemented by the operator station and determine whether the work content of the control instruction set has been completed. If not, repeat g; if so, execute a to h.

[0109] The method of acquiring the image in step a includes but is not limited to: facing the DCS operator station interface with a camera to acquire DCS control system flow chart interface data; that is, acquiring a DCS control system flow chart interface image;

[0110] Obtain DCS operator station flow chart interface data through screenshots;

[0111] Obtain DCS flow chart log interface data through the instruction set; the data includes but is not limited to device status, text labels, control buttons, trend charts, alarm information, and historical data;

[0112] View the instruction set, including but not limited to moving the mouse, clicking the mouse, and keyboard signals. The goal is to enable viewing of device symbols and text content;

[0113] The control instruction set includes but is not limited to moving the mouse, clicking the mouse, and keyboard signals, and the goal is to achieve control over the control buttons.

[0114] It should be noted that for a more specific description of the workflow of the method embodiment, please refer to the aforementioned system embodiment section, which will not be repeated here.

[0115] Machine vision models include but are not limited to using image recognition algorithms such as Yolo, CNN, and SDD to obtain the original state of data, including but not limited to obtaining industrial data collected from the flow chart page in the DCS control system in real time through methods such as cameras, screenshots, and DCS data logs;

[0116] Based on the acquired data, the artificial intelligence algorithm calculates and judges the acquired data, determines the output results, and generates an instruction set containing coordinate mapping relationships; the DCS operator station receives the signal output by the artificial intelligence platform to realize the control of the industrial process diagram in the DCS control system;

[0117] The DCS control system controls include but are not limited to switches, regulators, and control buttons. Machine vision compares the operation results to determine whether the instructions have been completed.

[0118] The solution provided in this application can replace manual labor and perform comprehensive control of the parts of the industrial system involving the DCS system with high control accuracy. The process is fully automatic, efficient, low-cost, and conducive to improving the level of industrial automation.

[0119] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and such modifications or substitutions are intended to be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A DCS control system based on machine vision and artificial intelligence, characterized in that: The system includes a camera, an artificial intelligence platform, a software module and a DCS operator station; wherein the DCS operator station is a human-computer interaction device of the DCS system; A camera is used to capture the content of the human-machine interaction interface in the DCS control system; wherein the interface content includes but is not limited to the operator station, engineer station graphical interface content and control interface content; Artificial intelligence platform for: Parsing the content based on a preset machine vision model, and generating a data set including, but not limited to, coordinate mapping relationships, graphical interface content, relevant process control, and historical data summary based on the parsing results; the data set includes the results parsed by the machine vision model; the machine vision model is trained based on an image recognition algorithm; The data set is studied and judged by a preset artificial intelligence algorithm to determine the output result and instruction set; the instruction set includes a viewing instruction set and a control instruction set; the artificial intelligence algorithm is trained based on the data set; A software module, installed in a DCS operator station, for receiving an instruction set from the artificial intelligence platform; The DCS operator station is used to respond to the instruction set to realize automatic control operation of the DCS system.

2. A DCS control system based on machine vision and artificial intelligence as claimed in claim 1, characterized in that: The artificial intelligence platform includes a data acquisition module, a machine vision module, an artificial intelligence algorithm module, a storage module, a storage medium module, and an instruction set output module; The artificial intelligence platform is connected to the camera to transmit the content in the DCS operator station; The artificial intelligence platform is connected to the DCS operator station and is used to output instruction sets and control the DCS control system; The data acquisition module is connected to the camera and is used to receive data transmitted by the camera. The interfaces include HDMI, USB, and BNC. The machine vision module analyzes the content transmitted by the data acquisition module to form a data set; The artificial intelligence algorithm module learns the content of the data set and forms an instruction set; The storage module stores the machine vision analysis data set, artificial intelligence algorithm instruction set and DCS control system return data; The storage medium module stores processing execution code and artificial intelligence algorithm code; The instruction set output module is used to output the artificial intelligence algorithm instruction set, including but not limited to controlling the DCS control system through the COM port and USB port.

3. A DCS control system based on machine vision and artificial intelligence as claimed in claim 1, characterized in that: The content analyzed by the machine vision module in the artificial intelligence platform can be provided to other artificial intelligence algorithms as data collection parts for algorithm training; The artificial intelligence algorithm module in the artificial intelligence platform is trained, learned, and judged based on the data set, and will be continuously updated as the data set accumulates; The artificial intelligence platform is also used to: The instruction set actions implemented by the DCS operator station are identified through machine vision, and the operation results are compared and judged to determine whether the corresponding work content in the instruction set has been completed.

4. A DCS control system based on machine vision and artificial intelligence as claimed in claim 1, characterized in that: The machine vision model includes but is not limited to using Yolo, CNN or SDD image recognition algorithms to process the acquired content images and perform high-precision recognition, and the acquisition method includes but is not limited to using a camera or screenshots; The camera is facing the flow chart interface in the DCS control system and is used to identify the content of the flow chart of the DCS control system. The number of cameras changes with the configuration of the DCS operator station. The DCS control system flow chart is a user interface for human-computer interaction of the DCS control system, which includes but is not limited to field device symbols, content text labels, and control configuration buttons; wherein the device symbols include but are not limited to pumps, valves, containers, and various types of sensors; the text labels include but are not limited to device names, parameter values, temperature, liquid level, operation name menu, copy, and paste; the control buttons include but are not limited to switches, opening sizes, regulators, and alarm buttons; The high-precision recognition is achieved by extracting the target area through image segmentation, synchronizing the timestamps of continuous frame images and performing data overlay analysis to reduce the data blurring problem caused by the screen refresh rate. The high-precision recognition is achieved based on ResNet-50+Transformer.

5. A DCS control system based on machine vision and artificial intelligence as described in claims 1 and 2, characterized in that: The artificial intelligence platform is based on the preset machine vision model analysis and follows the DCS control system human-computer interaction interface. The parsed data set has clear labels for images and time series for data, with unified format and clear labels for algorithm needs.

6. A DCS control system based on machine vision and artificial intelligence as described in claims 1 and 2, characterized in that: The artificial intelligence algorithm includes but is not limited to using logistic regression, support vector machine, neural network to learn, judge and output instruction sets for data sets.

7. A DCS control system based on machine vision and artificial intelligence as claimed in claim 6, characterized in that: The DCS operator station is connected to the artificial intelligence platform, receives the instruction set of the artificial intelligence platform, and completes the monitoring and control of the equipment; The DCS operator station transmits data with the artificial intelligence platform, including but not limited to trend charts, alarm information, equipment status and historical data.

8. A DCS control system based on machine vision and artificial intelligence as claimed in claim 7, characterized in that: The artificial intelligence platform and the DCS operator station implement the instruction set through the software module. The minimum complete implementation process requires outputting the instruction set twice, once to view the instruction set and once to control the instruction set. The complete implementation process refers to the output of the instruction set by the artificial intelligence platform until the machine vision recognizes that the DCS operator station completes the instruction set action.

9. A DCS control system based on machine vision and artificial intelligence as claimed in claim 8, characterized in that: The artificial intelligence platform involves outputting simulated mouse movement signals, which converts the coordinates captured by the camera into standardized absolute coordinate values; the software module simulates mouse operations by receiving data packets that comply with the HID protocol.

10. A DCS control method based on machine vision and artificial intelligence, characterized in that: The method applied to a DCS control system based on machine vision and artificial intelligence as described in claim 1 comprises: a. Acquire images through the camera and obtain data through the data acquisition module of the artificial intelligence platform; b. Using an artificial intelligence platform to synchronize timestamps and overlay data on consecutive frames, segment the images, and extract the target area; the target area is the human-computer interaction interface of the DCS operator station; c. The AI ​​platform identifies the target area, including but not limited to device recognition, character recognition, and location recognition, and parses the corresponding data set; d. The parsed data set is learned and judged by the artificial intelligence platform to determine whether to output the instruction set. If not, steps a to c are repeated. The instruction set is divided into a viewing instruction set and a control instruction set; e. If yes, the DCS operator station receives the viewing instruction set from the artificial intelligence platform and performs the corresponding viewing actions, including but not limited to moving the mouse to a specified location, opening device information, and opening text content; f. The artificial intelligence platform uses machine vision to identify the instruction set actions implemented by the operator station and determine whether the work content of the viewing instruction set is completed. If not, repeat d~e. If so, execute the next step; g. The DCS operator station receives the control instruction set from the artificial intelligence platform and implements the corresponding control actions, including but not limited to moving the mouse to a specified position, clicking a control button on the flow chart interface, and inputting the valve opening; h. The artificial intelligence platform uses machine vision to identify the instruction set actions implemented by the operator station and determine whether the work content of the control instruction set has been completed. If not, repeat g; if so, execute a to h.

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