Industrial process intelligent control method and system

Abstract

The present application discloses an industrial process intelligent control method and system. The industrial process intelligent control method includes the following steps: obtaining the original measurement data of the control object when the stop condition is not met; analyzing the original measurement data to obtain analyzed measurement data; using a deep learning model to learn the analyzed measurement data and determine a control scheme; and controlling the control object according to the control action obtained by the control scheme. The industrial process intelligent control method proposed in an embodiment of the present invention, through the new generation process control platform, analyzes and judges the error between the given value of the controlled quantity and the measured value of the controlled quantity and various evaluation indicators of the industrial process of the control object to obtain a control effect that meets the requirements and expectations.

Description

Technical Field

[0001] The present application relates to the field of industrial process control, and in particular to an industrial process intelligent control method and system. Background Art

[0002] Process control is widely used in industrial production, including power generation, petroleum, chemical engineering, and metallurgy. By controlling process parameters such as temperature, pressure, flow, level, composition, and concentration, industrial process efficiency can be improved, energy consumption can be reduced, and output can be increased. With the continuous advancement of scientific and technological innovation, industrial production processes are developing towards deeper energy conservation and intelligentization.

[0003] Rapid and accurate measurement of process parameters is a fundamental prerequisite for achieving industrial process control. Non-contact laser measurement technology, as a promising and forward-looking online analytical technique, offers significant advantages over existing detection technologies. High-precision non-contact laser measurement can meet the requirements for precise and rapid online measurement in harsh, high-temperature environments. Its measurement data forms the basis for building integrated, intelligent industrial production systems, laying the foundation for the efficient integration and intelligent control of information and physical systems.

[0004] Therefore, the development of laser measurement technologies such as computed tomography-tunable semiconductor laser absorption spectroscopy (CT-TDLAS) and laser-induced breakdown spectroscopy (LIBS) has enabled in-situ, non-contact, and high-temporal-resolution online measurement of industrial process temperature and material composition. Intelligent industrial process monitoring and control platforms that couple high-precision laser measurement with numerical simulation have become a key development direction in the field of industrial measurement and control, possessing high practical application value.

[0005] However, today's industrial process control faces various problems. For example, measurement often only allows for point measurement, not surface measurement, and is not online or fast. Control often involves control delays and adjustment lags due to defects in the measurement method. CFD simulation requires measurement data for verification and optimization, and the simulation results lack accuracy. Furthermore, there are issues such as insufficient data volume.

[0006] For example, CFD simulation analysis has a slow response speed, a long simulation time, and inaccurate simulation results, which lead to problems such as adjustment lag and inaccurate simulation analysis, indirectly causing the parameters output by the process control platform to be inaccurate and unable to achieve precise control. Summary of the Invention

[0007] In view of the above problems, an embodiment of the present invention provides an industrial process intelligent control method and system to solve the problems existing in the prior art.

[0008] To solve the above problems, an embodiment of the present application discloses an industrial process intelligent control method, comprising the following steps:

[0009] When the stop condition is not reached, obtaining the original measurement data of the controlled object;

[0010] Analyzing the original measurement data to obtain analyzed measurement data;

[0011] Using a deep learning model to learn the analyzed measurement data and determine a control solution;

[0012] The control object is controlled according to the control action amount obtained by the control scheme.

[0013] In order to solve the above problem, an embodiment of the present application further discloses a terminal device, including:

[0014] one or more processors; and

[0015] One or more machine-readable media having instructions stored thereon, when executed by the one or more processors, cause the terminal device to execute the method as described above.

[0016] In order to solve the above problems, an embodiment of the present application discloses an industrial process intelligent control system, which includes: a measuring device, an analysis module, a process control platform and an automatic control device;

[0017] The measuring device is used to obtain original measurement data of the controlled object when the stop condition is not met;

[0018] The analysis module is used to analyze the original measurement data to obtain analyzed measurement data;

[0019] The process control platform is used to use a deep learning model to learn the analyzed measurement data and determine a control solution;

[0020] The automatic control device is used to control the control object according to the control action amount obtained by the control scheme.

[0021] An embodiment of the present application further discloses a terminal device, including:

[0022] one or more processors; and

[0023] One or more machine-readable media having instructions stored thereon, when executed by the one or more processors, cause the terminal device to perform the above method.

[0024] An embodiment of the present application further discloses one or more machine-readable media having instructions stored thereon, which, when executed by one or more processors, enable a terminal device to execute the above method.

[0025] As can be seen from the above, the embodiments of the present application have the following advantages:

[0026] According to the intelligent industrial process control method proposed in an embodiment of the present invention, through the new generation process control platform, analysis and judgment are performed based on the error between the given value of the controlled quantity and the measured value of the controlled quantity and various evaluation indicators of the industrial process of the control object to obtain a control effect that meets the requirements and expectations. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0028] Figure 1 FIG. 1 is a block diagram of an industrial process intelligent control system according to an embodiment of the present invention.

[0029] Figure 2 Shown is a schematic diagram of the architecture of a deep learning model corresponding to the deep learning process 1 of an embodiment of the present invention.

[0030] Figure 3 Shown is a schematic diagram of the architecture of a deep learning model corresponding to the deep learning process 2 of an embodiment of the present invention.

[0031] Figure 4 Shown is a flow chart of an industrial process intelligent control method according to an embodiment of the present invention.

[0032] Figure 5 Shown is a block diagram of an intelligent control system in a thermal power plant according to an embodiment of the present invention.

[0033] Figure 6 FIG. 1 is a block diagram of an intelligent control system in a semiconductor industry process according to an embodiment of the present invention.

[0034] Figure 7 A block diagram of a terminal device for executing the method according to the present invention is schematically shown.

[0035] Figure 8 A memory unit for holding or carrying a program code for implementing the method according to the present invention is schematically shown. DETAILED DESCRIPTION

[0036] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of this application.

[0037] First embodiment

[0038] A first embodiment of the present invention provides an industrial process intelligent control system. Figure 1 FIG2 is a block diagram of an intelligent industrial process control system according to an embodiment of the present invention. The intelligent industrial process control system proposed in this embodiment of the present invention has a wide range of applications, such as for measuring temperature and concentration fields using lasers. The intelligent industrial process control system is used to control a control object and includes a measuring device 10, an analysis module 20, a process control platform 30, and an automatic control device 40.

[0039] The measuring device 10 is used to obtain original measurement data of the control object;

[0040] The analysis module 20 is used to analyze the original measurement data to obtain analyzed measurement data;

[0041] The process control platform 30 is used to determine a control solution based on the analyzed measurement data;

[0042] The automatic control device 40 is used to control the control object according to the control action amount obtained by the control scheme;

[0043] When the stop condition is not met, the measuring device 10 repeatedly performs the operation of measuring the raw measurement data.

[0044] The measuring device 10 may be, for example, a laser measuring device that implements various laser measurement technologies, such as computed tomography-tunable semiconductor laser absorption spectroscopy (CT-TLS) and laser-induced breakdown spectroscopy (LIBS), or a conventional measuring device. Raw measurement data is the data obtained through measurement. However, this device is not limited to these devices; other measuring devices that meet the measurement requirements are also applicable to the intelligent industrial process control system described herein. In this embodiment of the present invention, the raw measurement data obtained through real-time measurement by the laser measuring device is analyzed by an analysis module 20 that selects a corresponding measurement method based on the measuring device and measurement parameter requirements. Analysis module 20 may be an analysis model that analyzes the raw measurement data collected in real time by the measuring device to obtain analyzed measurement data, which is the desired process parameter of the industrial process.

[0045] In an embodiment of the present invention, a measurement device is constructed to control the process, based on the characteristics of the industrial process and the industrial process parameters that require rapid and accurate measurement. For example, in fields such as thermal power plants, the semiconductor industry, engines, gas turbines, and the metallurgical industry, process parameters such as temperature, pressure, flow, liquid level, composition, and concentration need to be obtained. The measurement data analyzed by the analysis module 20 in this embodiment of the present invention may be, for example, process parameters such as temperature, pressure, flow, liquid level, composition, and concentration. This example will be used for illustration below.

[0046] like Figure 1 As shown, the process control platform 30 uses a deep learning model 50 to learn from the analyzed measurement data. The analyzed measurement data is not limited to laser measurement methods but also includes other process parameters obtained by other measurement methods. Based on the learning results, the CFD simulation module 70 of the controlled object is optimized, thereby obtaining optimized CFD data, establishing a CFD database 60, and developing a CFD big data analysis platform.

[0047] The analyzed measurement data is combined with existing CFD data through deep learning of the deep learning model 50 to obtain optimized CFD data, and then a control solution is provided through the process control platform 30 based on the optimized CFD data;

[0048] The automatic control device 40 activates the actuator according to the control solution provided by the process control platform 30 , adjusts the control action, and thereby changes the controlled quantity of the control object 100 .

[0049] When the stopping condition is not met, the measuring device 10 is used to repeatedly measure the controlled object 100. Through the process control platform 30, analysis and judgment are performed based on the error between the given value of the controlled quantity and the measured value of the controlled quantity and various evaluation indicators of the industrial process of the controlled object to obtain process parameters that meet the conditions and achieve the expected control effect.

[0050] The measurement and control of process parameters in industrial processes are in a dynamic adjustment state, and the process parameters can be monitored in real time. Based on the error between the given value of the controlled quantity and the measured value of the controlled quantity and various evaluation indicators of the industrial process of the controlled object, it is analyzed whether it is necessary to control the controlled object 100 through the automatic control device 40.

[0051] The evaluation of laser measurement-based intelligent control systems for industrial processes can be analyzed from five perspectives: 1. Evaluation indicators of the laser measurement method itself; 2. Evaluation indicators of the CFD simulation model itself; 3. Errors between measurement and simulation results; 4. Errors between the measured value of the controlled quantity and the given value of the controlled quantity; and 5. Comprehensive evaluation indicators such as efficiency and energy consumption of the industrial process. When all evaluation indicators, errors, and comprehensive evaluation indicators are globally optimal, the desired control effect is achieved. In some embodiments, the stopping condition can be achieving the desired control condition or reaching the production time. When it is determined that the stopping condition has been met, the cyclic measurement is terminated.

[0052] Figure 1 Two deep learning processes involved in the process control platform 30 are shown, namely, deep learning process 1 and deep learning process 2, indicated by dashed lines. Deep learning process 1 optimizes measurement data and CFD data for the controlled object 100 under different operating conditions, and forms a large data set in the CFD database. This learning process is long-term, accumulating data for months or even years, and the amount of data is very important. Deep learning process 2 optimizes CFD data using real-time measurement data and existing CFD data in the CFD database, and then provides a control solution through the process control platform. This learning process is a transient real-time process, and timeliness is very important.

[0053] The two deep learning processes described above can be executed by different deep learning models or by the same deep learning model. In the present invention, only the deep learning model 50 is used to represent the execution entity of the two different deep learning processes 1 and 2. In other embodiments, for example, deep learning process 2 can be executed by one deep learning model, while deep learning process 1 can be executed by another deep learning model.

[0054] For deep learning process 1, Figure 2 is a schematic diagram of the model architecture for deep learning process 1. Figure 2 As shown in the figure, the deep learning model's input layer 1 consists of measurement data at different times and the resulting errors from deep learning process 2. Input layer 2 consists of various CFD model parameters, and the output layer consists of the optimized CFD model parameters. Using the optimized CFD model parameters, the optimized CFD data is calculated based on the CFD model. Based on the output CFD data, a CFD database is established, generating CFD big data.

[0055] For deep learning process 2, Figure 3 is a schematic diagram of the model architecture for deep learning process 2. Figure 3As shown in the figure, in the deep learning model, input layer 1 is real-time measurement data, input layer 2 is CFD data in the existing CFD database, including the results of simple algebraic operations on the existing CFD data, and the output layer is the optimized CFD data obtained by calculation based on the existing CFD data. Then, a control solution is provided through the process control platform based on the optimized CFD data.

[0056] In an optional embodiment, the process control platform may further include a first database unit and a first deep learning model unit, configured to respectively perform the following operations:

[0057] The first database unit is used to store a plurality of analyzed measurement data;

[0058] The first deep learning model unit is used to use the first deep learning model to learn the stored multiple analyzed measurement data, and output CFD data processed by the first deep learning model.

[0059] In an optional embodiment, the process control platform may further include a second database unit and a second deep learning model unit, configured to respectively perform the following operations:

[0060] The second database unit is used to store a plurality of analyzed measurement data and historical CFD data;

[0061] The first deep learning model unit is used to use the second deep learning model to learn the stored multiple analyzed measurement data and historical CFD data, and optimize the CFD simulation module.

[0062] In an optional embodiment, the analysis module may further include an analysis model determination unit and an analysis unit, each configured to perform the following operations:

[0063] The analysis model determination unit is used to determine the analysis model based on the original measurement data of the measurement device collected in real time; and

[0064] The analysis unit is used to perform analysis using the determined analysis model to obtain analyzed measurement data.

[0065] As can be seen from the above, the industrial process intelligent control system proposed in the first embodiment of the present invention has at least the following technical effects:

[0066] The intelligent industrial process control system proposed in accordance with an embodiment of the present invention, through the new generation process control platform, analyzes and judges the error between the given value of the controlled quantity and the measured value of the controlled quantity and various evaluation indicators of the industrial process of the controlled object to obtain a control effect that meets the requirements and expectations.

[0067] In addition, the industrial process intelligent control system proposed in this embodiment has at least the following advantages:

[0068] According to an embodiment of the present invention, an intelligent industrial process control system uses deep learning and other artificial intelligence algorithms to learn from analyzed measurement data within the process control platform. The analyzed measurement data is not limited to laser measurement methods but also includes other process parameters obtained by other measurement methods. Based on the learning results, the CFD simulation module of the controlled object is optimized to obtain optimized CFD data. A CFD database is established, and a CFD big data analysis platform is developed. The analyzed real-time measurement data is combined with existing CFD data through deep learning within the process control platform to obtain optimized CFD data. Based on the optimized CFD data, the process control platform then provides a control solution. Based on the control solution provided by the process control platform, the automatic control device activates the actuator to adjust the control action, thereby changing the controlled variable of the controlled object. A measuring device is used to repeatedly measure the controlled object. The process control platform analyzes and determines the error between the set value of the controlled variable and the measured value of the controlled variable, as well as various evaluation indicators of the controlled industrial process, to obtain process parameters that meet the requirements and achieve the desired control effect.

[0069] The measurement and control of process parameters in industrial processes are in a dynamic adjustment state. The process parameters can be monitored in real time. According to the error between the given value of the controlled quantity and the measured value of the controlled quantity and various evaluation indicators of the industrial process of the controlled object, it is analyzed whether the controlled object needs to be controlled by an automatic control device.

[0070] The control system and method of the present invention are not only applicable to thermal power plants and semiconductor industrial processes, but can also be extended to other fields such as engines, gas turbines, and metallurgical industries to achieve intelligent control of industrial processes. However, it is not limited to these fields.

[0071] The present invention establishes a CFD simulation model of the control object, calculates the CFD data of the control object under different operating conditions, and provides the calculated CFD data to the process control platform in the form of a database; the process control platform accumulates and learns the analyzed measurement data through artificial intelligence algorithms such as deep learning, optimizes the CFD simulation module of the control object according to the learning results, and obtains the optimized CFD data; a CFD database is established based on the CFD data of the control object under different operating conditions and the CFD data optimized by deep learning, and a CFD big data analysis platform is developed in the process control platform. The real-time process parameters of the control object are in a dynamic measurement state, and the CFD data optimized by deep learning are also in a dynamic adjustment state. The established CFD database is also continuously updated and improved. In the process control platform, data analysis is performed between the analyzed measurement data, CFD data, and optimized CFD data through artificial intelligence algorithms such as deep learning.

[0072] Second embodiment

[0073] A second embodiment of the present invention provides an industrial process intelligent control method. Figure 4 The figure shows a flow chart of the steps of the industrial process intelligent control method according to the second embodiment of the present invention. Figure 4 As shown, the industrial process intelligent control method according to the embodiment of the present invention includes the following steps:

[0074] S101, when the stop condition is not met, obtaining original measurement data of the controlled object;

[0075] In this step, the execution subject is, for example, a measuring device 10 of an industrial process intelligent control system. The measuring device 10 may be, for example, a laser measuring device that implements various laser measurement technologies such as computed tomography-tunable semiconductor laser absorption spectroscopy and laser-induced breakdown spectroscopy. It may also be a traditional measuring device, but is not limited to such devices. Other measuring devices that meet the measurement requirements are applicable to the industrial process intelligent control method described in the present invention. Raw measurement data is, for example, data obtained through measurement.

[0076] The evaluation of laser measurement-based intelligent control systems for industrial processes can be analyzed from five perspectives: 1. Evaluation indicators of the laser measurement method itself; 2. Evaluation indicators of the CFD simulation model itself; 3. Errors between measurement and simulation results; 4. Errors between the measured value of the controlled quantity and the given value of the controlled quantity; and 5. Comprehensive evaluation indicators such as efficiency and energy consumption of the industrial process. When all evaluation indicators, errors, and comprehensive evaluation indicators are globally optimal, the desired control effect is achieved. In some embodiments, the stopping condition can be achieving the desired control condition or reaching the production time. When it is determined that the stopping condition has been met, the cyclic measurement is terminated.

[0077] S102, analyzing the original measurement data to obtain analyzed measurement data;

[0078] In this step, the execution subject is, for example, the analysis module 20 of the industrial process intelligent control system. The analysis module 20 can be an analysis model that analyzes the raw measurement data collected in real time by the measurement device to obtain the analyzed measurement data, that is, the process parameters of the industrial process to be obtained.

[0079] S103, using a deep learning model to learn the analyzed measurement data and determine a control solution;

[0080] In this step, the execution entity is, for example, the process control platform 30 of the industrial process intelligent control system. The process control platform 30 learns the analyzed measurement data through the deep learning model 50, optimizes the analyzed measurement data using the deep learning model, and generates a control solution based on the optimized CFD data.

[0081] S104: Control the controlled object according to the control action amount obtained by the control scheme.

[0082] In this step, the execution subject is, for example, the automatic control device 40 of the industrial process intelligent control system. The automatic control device 40 activates the actuator according to the control scheme provided by the process control platform 30, adjusts the control action, and thus changes the controlled quantity of the control object 100.

[0083] As can be seen from the above, the industrial process intelligent control method proposed in the second embodiment of the present invention has at least the following technical effects:

[0084] According to the intelligent industrial process control method proposed in an embodiment of the present invention, through the new generation process control platform, analysis and judgment are performed based on the error between the given value of the controlled quantity and the measured value of the controlled quantity and various evaluation indicators of the industrial process of the control object to obtain a control effect that meets the requirements and expectations.

[0085] In addition, the industrial process intelligent control method proposed in this embodiment has at least the following advantages:

[0086] According to the intelligent industrial process control method proposed in an embodiment of the present invention, the process control platform uses deep learning and other artificial intelligence algorithms to learn from analyzed measurement data. The source of this analyzed measurement data is not limited to laser measurement methods but also includes other process parameters obtained by other measurement methods. Based on the learning results, the CFD simulation module of the controlled object is optimized to obtain optimized CFD data. A CFD database is established, and a CFD big data analysis platform is developed. The analyzed real-time measurement data is combined with existing CFD data through deep learning on the process control platform to obtain optimized CFD data. Based on the optimized CFD data, the process control platform then provides a control solution. Based on the control solution provided by the process control platform, the automatic control device activates the actuator to adjust the control action, thereby changing the controlled variable of the controlled object. A measuring device is used to repeatedly measure the controlled object. The process control platform analyzes and determines the error between the set value of the controlled variable and the measured value of the controlled variable, as well as various evaluation indicators of the controlled industrial process, to obtain process parameters that meet the requirements and achieve the desired control effect.

[0087] The measurement and control of process parameters in industrial processes are in a dynamic adjustment state. The process parameters can be monitored in real time. According to the error between the given value of the controlled quantity and the measured value of the controlled quantity and various evaluation indicators of the industrial process of the controlled object, it is analyzed whether the controlled object needs to be controlled by an automatic control device.

[0088] The control system and method of the present invention are not only applicable to thermal power plants and semiconductor industrial processes, but can also be extended to other fields such as engines, gas turbines, and metallurgical industries to achieve intelligent control of industrial processes. However, it is not limited to these fields.

[0089] Third embodiment

[0090] Figure 5 FIG. 1 is a block diagram of an intelligent control system in a thermal power plant according to an embodiment of the present invention. Figure 5 As shown in the figure, taking the boiler control of a thermal power plant as an example, the intelligent monitoring and control system and method of the laser measurement and numerical simulation coupling of the temperature field and component concentration field of the industrial process are specifically explained. The computed tomography-tunable semiconductor laser absorption spectroscopy technology is used to measure the temperature distribution and other component concentration distribution of the boiler furnace and the tail flue, realizing the application of high-end technologies such as big data, the Internet of Things, and cloud platforms in thermal power plants, thereby improving the efficiency of thermal power plants, achieving energy conservation and emission reduction in thermal power plants, and realizing intelligent control of thermal power plant processes.

[0091] Figure 5 The system shown mainly consists of two parts: a measurement and control system and an intelligent monitoring and control platform. In the measurement and control system part, a CT-TDLAS measuring device is built according to the boiler furnace structure and tail flue structure to measure the temperature field and gas component concentration field inside the furnace, the temperature field of the tail flue, and the nitrogen oxide and ammonia concentration fields before and after the denitrification device. The original measurement data of each measuring device is collected in real time, and the original measurement data is analyzed by the analysis module to obtain the temperature field and gas concentration field of the furnace and tail flue measurement section. The analyzed measurement data can be displayed on the monitor, and the control solution is provided through the analysis of the new generation process control platform. The boiler's powder feed rate and secondary air volume are adjusted through the automatic control device to make the furnace and tail flue temperature and gas concentration meet the set values ​​and achieve the expected control effect.

[0092] In the intelligent monitoring and control platform, the CT-TDLAS measurement data is represented by ai, and the historical measurement data of CT-TDLAS are used to establish the CT-TDLAS database Ai; according to the boiler structure, a CFD simulation model of the boiler combustion process under different operating conditions is established, and the CFD simulation results, such as the CFD data of the temperature field and concentration field, are obtained by setting the CFD model parameters to establish the CFD database Di; according to the laser optical path structure of the CT-TDLAS measurement device, the average value, fluctuation value and probability density function of the temperature and concentration distribution of the CT-TDLAS data and CFD data on each path are obtained, and are represented by Bi and B respectively. ^ i is used to represent the comparison between the database Bi of the path statistics of the CT-TDLAS measurement results and the database B of the path statistics of the CFD simulation results. ^ i; The correction function is formed by the CFD model parameter database Ci and the CFD database Di, as well as the database Bi of the measurement result path statistics and the database B of the simulation result path statistics ^ The difference between the values ​​of i constitutes a label, and a deep learning method is used to undergo data preprocessing, feature selection, model construction, parameter optimization and evaluation. When the model error after deep learning is not less than the set value error ε, the model parameters in the deep learning process are continued to be optimized; when the model error after deep learning is less than the set value error ε, the CFD model parameters with the smallest error between the simulation results and the measurement results are output, the established model parameter database Ci is updated and improved, the optimized CFD data is obtained, and the established CFD database Di is further updated and improved.

[0093] The intelligent monitoring and control platform includes two types of deep learning processes: long-term accumulation and transient real-time. The long-term accumulation process accumulates measurement data and CFD simulation data to form a CFD database to form CFD big data. The transient real-time process uses real-time measurement data and existing CFD data to obtain optimized CFD data and provide control solutions.

[0094] In this example, the evaluation of the thermal power plant intelligent control system based on CT-TDLAS laser measurement during the control process includes not only the model error analysis measurement results and simulation results during the deep learning process, but also the following aspects of analysis and evaluation:

[0095] Evaluation of the CT-TDLAS measurement method includes the accuracy of TDLAS temperature and concentration measurements and the reconstruction accuracy of the CT algorithm; CFD simulation errors such as grid scale error, time step error, iteration error, and input parameter error; errors between measured and given values ​​for furnace internal temperature and gas component concentrations, tail flue temperature, and nitrogen oxide and ammonia concentrations before and after the denitrification device; and thermal economic indicators of the power plant, such as steam and heat consumption of the steam turbine unit, heat consumption of the power plant, coal consumption of the power plant, and power generation efficiency. When all evaluation indicators, errors, and comprehensive evaluation indicators are globally optimized, the desired control effect is achieved.

[0096] Fourth embodiment

[0097] Figure 6 FIG. 1 is a block diagram of an intelligent control system in a semiconductor industry process according to an embodiment of the present invention. Figure 6 As shown, taking the control of the film-forming device in the semiconductor manufacturing process as an example, the intelligent monitoring and control system and method of the laser measurement and numerical simulation coupling of the temperature field and component concentration field in the industrial process are specifically explained. The computer tomography-tunable semiconductor laser absorption spectroscopy technology is used to measure the temperature field and gas concentration field distribution in the film-forming device to achieve the purpose of controlling the performance of semiconductor materials.

[0098] It is worth noting that the control system and method of the present invention are not only applicable to thermal power plants and semiconductor industrial processes, but can also be extended to other fields such as engines, gas turbines, and the metallurgical industry to achieve intelligent control of industrial processes. However, this is not limited to these fields. In addition, the laser measurement technology used in the present invention includes not only computed tomography-tunable semiconductor laser absorption spectroscopy technology, but also laser-induced breakdown spectroscopy technology.

[0099] Figure 7 This is a schematic diagram of the hardware structure of a terminal device provided in one embodiment of the present application. Figure 7 As shown, the terminal device may include an input device 90, a processor 91, an output device 92, a memory 93, and at least one communication bus 94. Communication bus 94 is used to implement communication connections between components. Memory 93 may include high-speed RAM memory or non-volatile memory (NVM), such as at least one disk storage device. Memory 93 may store various programs for performing various processing functions and implementing the method steps of this embodiment.

[0100] Optionally, the processor 91 may be implemented as a central processing unit (CPU), an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components, and the processor 91 is coupled to the input device 90 and the output device 92 via a wired or wireless connection.

[0101] Optionally, the input device 90 may include multiple input devices, such as a user interface for the user, a device interface for the device, a software programmable interface, a camera, and at least one of a sensor. Optionally, the device interface for the device may be a wired interface for data transmission between devices, or a hardware plug-in interface for data transmission between devices (such as a USB interface, a serial port, etc.); optionally, the user interface for the user may be, for example, a user-oriented control button, a voice input device for receiving voice input, and a touch sensing device for receiving user touch input (such as a touch screen or touchpad with touch sensing function); optionally, the software programmable interface may be, for example, an entry for users to edit or modify programs, such as an input pin interface or input interface of a chip. Audio input devices such as microphones may receive voice data. Output devices 92 may include output devices such as displays and speakers.

[0102] In this embodiment, the processor of the terminal device has the function of executing each module of the data processing device in each device. The specific functions and technical effects can be referred to the above embodiments and will not be repeated here.

[0103] Figure 8 A schematic diagram of the hardware structure of a terminal device provided in another embodiment of the present application. Figure 8 Yes Figure 7 A specific embodiment in the implementation process. Figure 8 As shown, the terminal device of this embodiment includes a processor 101 and a memory 102.

[0104] The processor 101 executes the computer program code stored in the memory 102 to implement the above embodiment. Figure 4 Intelligent control methods for industrial processes.

[0105] Memory 102 is configured to store various types of data to support operations on the terminal device. Examples of such data include instructions for any application or method operating on the terminal device, such as messages, images, and videos. Memory 102 may include random access memory (RAM) and may also include non-volatile memory, such as at least one disk drive.

[0106] Optionally, the processor 101 is provided in the processing component 100. The terminal device may further include: a communication component 103, a power component 104, a multimedia component 105, an audio component 106, an input / output interface 107, and / or a sensor component 108. The specific components included in the terminal device are set according to actual needs and are not limited in this embodiment.

[0107] The processing component 100 generally controls the overall operation of the terminal device. The processing component 100 may include one or more processors 101 to execute instructions to complete the above Figure 4 In addition, the processing component 100 may include one or more modules to facilitate interaction between the processing component 100 and other components. For example, the processing component 100 may include a multimedia module to facilitate interaction between the multimedia component 105 and the processing component 100.

[0108] The power supply component 104 provides power to various components of the terminal device. The power supply component 104 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the terminal device.

[0109] The multimedia component 105 includes a display screen that provides an output interface between the terminal device and the user. In some embodiments, the display screen may include a liquid crystal display (LCD) and a touch panel (TP). If the display screen includes a touch panel, the display screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor can not only sense the boundaries of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation.

[0110] The audio component 106 is configured to output and / or input audio signals. For example, the audio component 106 includes a microphone (MIC), which is configured to receive external audio signals when the terminal device is in an operating mode, such as a voice recognition mode. The received audio signal can be further stored in the memory 102 or transmitted via the communication component 103. In some embodiments, the audio component 106 also includes a speaker for outputting audio signals.

[0111] The input / output interface 107 provides an interface between the processing component 100 and peripheral interface modules, such as click wheels, buttons, etc. These buttons may include but are not limited to: volume buttons, start buttons, and lock buttons.

[0112] The sensor assembly 108 includes one or more sensors for providing various status assessments for the terminal device. For example, the sensor assembly 108 can detect the open / closed state of the terminal device, the relative positioning of components, and the presence or absence of user contact with the terminal device. The sensor assembly 108 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact, including detecting the distance between the user and the terminal device. In some embodiments, the sensor assembly 108 may also include a camera, etc.

[0113] The communication component 103 is configured to facilitate wired or wireless communication between the terminal device and other devices. The terminal device can access a wireless network based on a communication standard, such as WiFi, 2G, or 3G, or a combination thereof. In one embodiment, the terminal device may include a SIM card slot for inserting a SIM card, allowing the terminal device to log into a GPRS network and establish communication with a server via the Internet.

[0114] From the above, we can see that Figure 8 The communication component 103, audio component 106, input / output interface 107, and sensor component 108 involved in the embodiment can all be used as Figure 7 Implementation of the input device in the embodiment.

[0115] An embodiment of the present application provides a terminal device, comprising: one or more processors; and one or more machine-readable media having instructions stored thereon, which, when executed by the one or more processors, enable the terminal device to execute one or more methods described in the embodiments of the present application.

[0116] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0117] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0118] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or terminal device that includes the element.

[0119] The above is a detailed introduction to the industrial process intelligent control method and system provided by the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core ideas. At the same time, for general technical personnel in this field, based on the ideas of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. An industrial process intelligent control method, comprising: When the stop condition is not met, obtaining original measurement data of the controlled object through the measuring device; Analyzing the original measurement data to obtain analyzed measurement data; Use deep learning models to learn from the analyzed measurement data and determine the control plan; controlling the controlled object according to the control action obtained by the control scheme; The step of using a deep learning model to learn the analyzed measurement data and determine a control solution includes: Inputting measurement data collected and analyzed at different times, learning result errors of the second deep learning model, and parameters of the CFD model into the first deep learning model for learning, outputting optimized parameters of the CFD model, obtaining CFD data based on the optimized parameters of the CFD model, and establishing a CFD database based on the CFD data; and The real-time collected and analyzed measurement data and the historical CFD data in the CFD database are input into the second deep learning model for learning, and the optimized CFD data is output. Then, the control scheme is determined based on the optimized CFD data.

2. The industrial process intelligent control method according to claim 1, wherein: The step of analyzing the original measurement data to obtain analyzed measurement data comprises: determining an analysis model based on the original measurement data collected in real time by the measuring device; The determined analysis model is used to perform analysis to obtain analyzed measurement data.

3. The industrial process intelligent control method according to claim 1, wherein: The original measurement data includes at least one of temperature, pressure, flow, liquid level, composition, and concentration.

4. The industrial process intelligent control method according to claim 1, wherein: The measuring device includes a laser measuring device.

5. The industrial process intelligent control method according to claim 1, wherein: The measuring device is a computer tomography-tunable semiconductor laser absorption spectroscopy laser measuring device.

6. The industrial process intelligent control method according to claim 1, wherein: The stop condition includes one of the production time being ended or the control target being achieved.

7. An industrial process intelligent control system for controlling a control object, the industrial process intelligent control system comprising: Measuring devices, analysis modules, process control platforms and automatic control devices; The measuring device is used to obtain original measurement data of the controlled object when the stop condition is not met; The analysis module is used to analyze the original measurement data to obtain analyzed measurement data; The process control platform is used to use a deep learning model to learn the analyzed measurement data and determine a control solution; The automatic control device is used to control the control object according to the control action amount obtained by the control scheme; Wherein, the process control platform is used for: Inputting measurement data collected and analyzed at different times, the learning result error of the second deep learning model, and the parameters of the CFD model into the first deep learning model for learning, outputting optimized CFD model parameters, obtaining CFD data based on the optimized CFD model parameters, and establishing a CFD database based on the CFD data; as well as The real-time collected and analyzed measurement data and the historical CFD data in the CFD database are input into the second deep learning model for learning, and the optimized CFD data is output. Then, the control scheme is determined based on the optimized CFD data.

8. The industrial process intelligent control system according to claim 7, wherein: The analysis module includes: an analysis model determination unit, configured to determine an analysis model based on the original measurement data collected in real time by the measurement device; and The analysis unit is used to perform analysis using the determined analysis model to obtain analyzed measurement data.

9. The industrial process intelligent control system according to claim 7, wherein: The original measurement data includes at least one of temperature, pressure, flow, liquid level, composition, and concentration.

10. The industrial process intelligent control system according to claim 7, wherein: The measuring device includes a laser measuring device.

11. The industrial process intelligent control system according to claim 7, wherein: The measuring device is a laser measuring device based on computer tomography-tunable semiconductor laser absorption spectroscopy technology or a laser measuring device based on laser induced breakdown spectroscopy technology.

12. The industrial process intelligent control system according to claim 7, wherein: The stop condition includes one of the production time being ended or the control target being achieved.

13. A terminal device comprising: one or more processors; and One or more machine-readable media having instructions stored thereon, when executed by the one or more processors, cause the terminal device to perform the method of one or more of claims 1-6.

14. One or more machine-readable media having instructions stored thereon, which, when executed by one or more processors, cause a terminal device to perform the method according to one or more of claims 1-6.

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