Control method, cloud-edge control system, storage medium, and program product

Abstract

The application provides a control method, a cloud-edge control system, a storage medium and a program product. The cloud-edge collaborative control method is applied to the cloud-edge control system. The cloud-edge control system is in communication connection with a controlled device. The cloud-edge control system comprises a cloud end and an edge end. The cloud end and the edge end are deployed with the same and independent device control systems. The method comprises the following steps: collecting real-time running data of the controlled device; performing operation according to the real-time running data through the device control systems respectively deployed on the cloud end and the edge end, and obtaining corresponding cloud end control instructions and edge end control instructions; and transmitting one of the cloud end control instructions and the edge end control instructions to the controlled device based on a preset cloud-edge collaborative strategy.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a control method, a cloud-edge control system, a storage medium, and a program product. Background Technology

[0002] With the rapid development of new-generation information and communication technologies and new-generation artificial intelligence technologies, competition among nations in manufacturing and even in terms of overall national strength is becoming increasingly fierce, leading to growing attention on intelligent manufacturing. Due to the high demands for real-time analysis and control in manufacturing environments, as well as the high security and privacy requirements of some manufacturing enterprises, edge computing is commonly used for equipment control. However, to meet the demands for low cost, high flexibility, and convenient data management, the introduction of cloud computing into intelligent manufacturing systems is becoming a trend.

[0003] In view of this, the technical problem that the existing technology urgently needs to solve is to provide a new cloud-edge control system. Summary of the Invention

[0004] In view of this, embodiments of this application provide a control method to at least partially solve the above-mentioned problems.

[0005] According to a first aspect of the embodiments of this application, a cloud-edge collaborative control method is provided, applied to a cloud-edge control system. The cloud-edge control system is communicatively connected to a controlled device. The cloud-edge control system includes a cloud and an edge, and the cloud and the edge are deployed with identical and independent device control systems. The method includes: collecting real-time operating data of the controlled device; performing calculations based on the real-time operating data through the device control systems deployed on the cloud and the edge respectively, and obtaining corresponding cloud control instructions and edge control instructions; and transmitting one of the cloud control instructions and the edge control instructions to the controlled device based on a preset cloud-edge collaborative strategy.

[0006] According to a second aspect of the embodiments of this application, a cloud-edge collaborative control method for a combustion device is provided. The method employs a cloud-edge control system, which is communicatively connected to the controlled device. The cloud-edge control system includes a cloud terminal and an edge terminal. The cloud terminal and the edge terminal are deployed with identical and independent combustion control systems. The method includes: collecting real-time operating data from various measuring points of the combustion device during combustion operation; performing calculations based on the real-time operating data using the combustion control systems deployed on the cloud terminal and the edge terminal respectively, and obtaining corresponding cloud control commands and edge terminal control commands; and transmitting one of the cloud control commands and the edge terminal control commands to the combustion device based on a preset cloud-edge collaborative strategy.

[0007] According to a third aspect of the embodiments of this application, a cloud-edge control system is provided, comprising: a cloud and an edge, wherein the cloud and the edge are deployed with the same and independent device control system, which is used to perform calculations according to the real-time operating data and obtain corresponding cloud control instructions and edge control instructions, so as to transmit one of the cloud control instructions and the edge control instructions to the controlled device based on a preset cloud-edge collaboration strategy.

[0008] According to a fourth aspect of the embodiments of this application, a computer storage medium is provided that stores a computer program thereon, which, when executed by a processor, implements the method described above.

[0009] According to a fifth aspect of the embodiments of this application, a computer program product is provided, including computer instructions that instruct a computing device to perform an operation corresponding to the method described above.

[0010] According to the solution provided in the embodiments of this application, by deploying the same and independent device control systems in the cloud and at the edge, dual redundancy backup of the cloud and the edge can be achieved. When using the cloud-edge control system, the two independent device control systems perform calculations based on the real-time operating data to obtain corresponding cloud control commands and edge control commands. Then, based on a preset cloud-edge collaboration strategy, one of the cloud control commands and the edge control commands can be transmitted to the controlled device. Thus, the advantages of low cost and high flexibility of the cloud and low latency and data security of the edge are taken into account, and the high reliability of the entire cloud-edge control system is ensured by the two device control systems. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings.

[0012] Figure 1 This application provides a schematic diagram of a common cloud-edge-device system control structure.

[0013] Figure 2A A structural block diagram of a cloud-edge control system provided in an embodiment of this application;

[0014] Figure 2B for Figure 2A The flowchart of the control method of the cloud-edge control system is shown.

[0015] Figure 3A structural block diagram of another cloud-edge control system provided in an embodiment of this application;

[0016] Figure 4A A structural block diagram of a cloud-edge control system provided in an embodiment of this application;

[0017] Figure 4B A structural block diagram of a combustion control system provided in an embodiment of this application;

[0018] Figure 4C A flowchart illustrating a control method for a cloud-edge control system provided in an embodiment of this application;

[0019] Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0020] To enable those skilled in the art to better understand the technical solutions in the embodiments of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art should fall within the protection scope of the embodiments of this application.

[0021] To illustrate the solution provided in this embodiment in more detail, the usage scenarios in this application will be described below as examples.

[0022] Generally, when the edge computing device 102 is introduced, see [reference needed]. Figure 1 This will form a cloud-edge-device system among the cloud platform 101, edge computing device 102, and industrial field device 103.

[0023] The industrial field device 103 is mainly used to collect and send data to the edge computing device 102, as well as to receive and execute related instructions.

[0024] Edge computing device 102 (edge) is distributed between cloud platform 101 and industrial field device 103. It is mainly used for preprocessing data of industrial field device 103 and real-time control of industrial field device 103. At the same time, it is used to communicate with cloud platform 101, send preprocessed data to cloud platform 101, or receive relevant instructions issued by cloud platform 101.

[0025] Cloud Platform 101 (Cloud) is primarily responsible for data processing that requires the use of big data or artificial intelligence.

[0026] However, the aforementioned platform is affected by numerous factors, especially the edge computing device 102. Insufficient resources or operational errors may cause the edge computing device 102 to stop operating, leading to system malfunctions in the cloud-edge-device architecture. Similarly, network errors between the cloud platform 101 and the edge computing device 102 can also cause system malfunctions in the cloud-edge-device architecture. Therefore, existing cloud-edge-device systems cannot meet the requirements for secure, stable, and long-term operation.

[0027] Therefore, this application provides an equipment control system that can meet the manufacturing industry's requirements for the safe, stable, and long-term operation of the control system.

[0028] Figure 2A This is a schematic diagram of the structure of a cloud-edge control system provided in an embodiment of this application, as shown below. Figure 2A As shown, the cloud-edge control system 20 includes: a cloud 21 and an edge 22. The cloud and the edge are equipped with the same and independent device control system 23, which is used to perform calculations based on the real-time operating data and obtain corresponding cloud control commands 24 and edge control commands 25. Based on a preset cloud-edge collaboration strategy, one of the cloud control commands 24 and the edge control commands 25 is transmitted to the controlled device 26.

[0029] Specifically, see Figure 2B When using the equipment control system, the following steps can be performed:

[0030] S201. Collect the real-time operating data of the controlled device.

[0031] The controlled equipment can be any equipment, such as circulating fluidized bed and grate furnace in waste incineration power plants, production lines in smart manufacturing enterprises, large-scale oil refining and steelmaking equipment, etc. This embodiment does not limit it.

[0032] The specific methods for collecting real-time operational data and the specific content of the collected real-time operational data can be determined by those skilled in the art, and will not be elaborated here.

[0033] S202. The device control systems deployed in the cloud and the edge respectively perform calculations based on the real-time operating data and obtain corresponding cloud control commands and edge control commands.

[0034] In this embodiment, the cloud and the edge are each deployed with the same device control system, thereby achieving dual redundancy backup for the cloud and the edge. This combines the advantages of low cost and high flexibility of the cloud with the low latency and data security of the edge, and ensures high reliability of the entire cloud-edge control system during operation through two sets of device control systems.

[0035] Specifically, the equipment control system can be determined by those skilled in the art based on the controlled equipment, and will not be elaborated here.

[0036] S203. Based on a preset cloud-edge collaboration strategy, transmit one of the cloud control command and the edge control command to the controlled device.

[0037] In this embodiment, the preset cloud-edge collaboration strategy is used to select a preferred control command from cloud control commands and edge control commands and transmit it to the controlled device. The specific content of the cloud-edge collaboration strategy can be determined by those skilled in the art, and this embodiment does not limit it.

[0038] For example, in order to prioritize meeting the real-time requirements of the controlled device, the preset cloud-edge collaboration strategy may include: prioritizing the transmission of edge control commands to the controlled device.

[0039] The solution provided in this embodiment achieves dual redundancy backup at the cloud and edge by deploying identical and independent device control systems at both the cloud and edge. When using the cloud-edge control system, the two independent device control systems perform calculations based on the real-time operating data to obtain corresponding cloud control commands and edge control commands. Then, based on a preset cloud-edge collaboration strategy, one of the cloud control commands and the edge control commands can be transmitted to the controlled device. Thus, it combines the advantages of low cost and high flexibility of the cloud with the low latency and data security of the edge, and ensures high reliability throughout the operation of the entire cloud-edge control system through the two device control systems.

[0040] Figure 3 The figure shows a schematic diagram of a cloud-edge control system provided in an embodiment of this application. It includes a cloud 31 and an edge 32. The cloud and the edge are equipped with the same and independent device control systems, which are used to perform calculations based on the real-time operating data and obtain corresponding cloud control commands and edge control commands. Based on a preset cloud-edge collaboration strategy, one of the cloud control commands and the edge control commands is transmitted to the controlled device.

[0041] See Figure 3 The device control systems at both the cloud and edge include: a data acquisition and storage subsystem 33, an environmental perception subsystem 34, and a control command generation subsystem 35. The cloud-edge control system may also include a cloud-edge collaboration subsystem 36 for deploying cloud-edge collaboration strategies.

[0042] The device control system performs calculations based on the real-time operating data through the following steps, and outputs the cloud control command or the edge control command: The environmental perception subsystem 34 reads the real-time operating data of the controlled device from the data acquisition and storage subsystem 33, and predicts the operating environment based on the real-time operating data to obtain the environmental perception data of the controlled device and stores it in the data acquisition and storage subsystem 33; the control command generation subsystem 35 reads the real-time operating data of the controlled device and the environmental perception data from the data acquisition and storage subsystem 33, and generates the cloud control command or the edge control command based on the real-time operating data and the environmental perception data; the cloud control command or the edge control command is stored in the data acquisition and storage subsystem 33, specifically storing the control command data corresponding to the cloud control command or the edge control command.

[0043] In general, large-scale equipment, especially combustion equipment, the internal operating environment is quite extreme, which can lead to situations where sensors used for data acquisition cannot be installed, thus increasing the difficulty of controlling the equipment. In this embodiment, the environmental perception subsystem 34 can predict the environment based on the real-time operating data of the controlled equipment, obtain certain environmental perception data that cannot be measured, and enable the control command generation subsystem 35 to take the environmental perception data into account when generating control commands, thereby improving the accuracy of the generated control commands.

[0044] Optionally, in this embodiment, since the cloud can leverage big data and has sufficient computing resources, the environment perception subsystem 34 includes multiple environment prediction models. After the environment prediction models are trained in the cloud, they are simultaneously deployed in the environment perception subsystem 34 in both the cloud and the edge.

[0045] For example, when the controlled device is a combustion device, the historical equipment operation data and target setpoint data of the combustion device can be cleaned, processed, feature-selected, and the prediction model trained in the cloud to obtain environmental prediction models such as the feed level identification model, combustion temperature prediction model, flue gas prediction model, and feeder current model.

[0046] Optionally, in this embodiment, the cloud and the edge are deployed with the same preset cloud-edge collaboration strategy, that is, the cloud and the edge together form a cloud-edge collaboration subsystem 36 for deploying the cloud-edge collaboration strategy. The step of transmitting one of the cloud control command and the edge control command to the controlled device based on the preset cloud-edge collaboration strategy includes: instructing the cloud and the edge to transmit the corresponding cloud control command and the edge control command to the other end; and determining, respectively, to transmit one of the cloud control command and the edge control command to the controlled device through the preset cloud-edge collaboration strategy deployed on the cloud and the edge. By deploying the same cloud-edge collaboration strategy on the cloud and the edge, more timely interaction between the cloud and the edge can be ensured.

[0047] Similarly, in another implementation of this application, an electronic device can be set between the controlled device and the edge computing device, and cloud control commands and edge control commands can be sent to the electronic device. That is, the electronic device runs a cloud-edge collaboration subsystem 36 for deploying cloud-edge collaboration strategies. Through the cloud-edge collaboration strategies deployed on it, one of the cloud control commands and edge control commands is transmitted to the controlled device, which is also within the scope of protection of this application.

[0048] Optionally, in this embodiment, the preset cloud-edge collaboration strategy includes at least one of the following: transmitting a command that can be normally generated from the cloud control command or the edge control command to the controlled device; if the parameters of the controlled device are abnormal, switching the command transmitted to the controlled device from one of the two commands, the cloud control command or the edge control command, to the other; if both the obtained cloud control command and the edge control command are abnormal, stopping the transmission of control commands to the controlled device.

[0049] For example, it can promptly determine whether the control systems of devices at the cloud and edge can generate control commands normally. If the cloud cannot generate control commands normally, the edge control commands are transmitted to the controlled device; otherwise, the cloud control commands are transmitted to the controlled device. Reasons that the cloud cannot generate control commands normally may include: cloud data center malfunctions, network communication failures between the cloud and the edge; reasons that the edge cannot generate control commands normally may include: power outages of the edge computing device, depletion of computing resources on the edge computing device, and operational errors of the edge computing device.

[0050] See Figure 4A The diagram illustrates a cloud-edge control system for a combustion device. The cloud-edge control system 40 includes a cloud end 41 and an edge end 42, wherein the cloud end 41 and the edge end 42 are equipped with the same and independent combustion control system 43.

[0051] The steps of the cloud-edge control system may include: collecting real-time operating data of each measuring point of the combustion device during combustion; performing calculations based on the real-time operating data through the combustion control systems 43 deployed on the cloud 41 and the edge 42 respectively, and obtaining corresponding cloud control commands and edge control commands 44 and 45; and transmitting one of the cloud control commands and the edge control commands to the combustion device 46 based on a preset cloud-edge collaboration strategy.

[0052] See Figure 4B The diagram illustrates a structural block diagram of a combustion control system 43, which includes a data acquisition and storage subsystem 431, an environmental perception subsystem 432, and a combustion control command generation subsystem 433. This combustion control system 43 can be deployed in the cloud or at the edge.

[0053] like Figure 4B As shown, the data acquisition and storage subsystem 431 includes a data storage module 4311, a data acquisition module 4312, and a data analysis module 4313. The data acquisition module 4312 can be used to acquire real-time operating data of the combustion equipment, acquire environmental perception data, and acquire control commands (cloud control commands or edge control commands). The data storage module 4311 can be used to store the acquired data. The data analysis module 4313 can be used to perform preliminary analysis on the acquired real-time operating data of the combustion equipment. The data acquisition and storage subsystem 431 can also be used to send the acquired control commands (cloud control commands or edge control commands) to devices deployed with cloud-edge collaborative strategies.

[0054] The environmental perception subsystem 432 includes an operating data reading module 4321, a data processing module 4322, a feed image recognition module 4323, a combustion parameter AI prediction module 4324, a feed current modeling module 4325, and a perception data writing module 4326. The operating data reading module 4321 reads real-time operating data of the combustion equipment from the data acquisition and storage subsystem. The data processing module 4322 processes the read real-time operating data into data that the environmental prediction model can process. The perception data writing module 4326 writes the environmental perception data from the environmental prediction model back to the data acquisition and storage subsystem.

[0055] Specifically, the environmental prediction model in the environmental perception subsystem 432 includes at least one of the following: a feed level identification model, a combustion temperature prediction model, a flue gas emission prediction model, and a feeding equipment current prediction model. The feed level identification model is specifically the feed image recognition module 4323 shown in the figure, used to identify and predict based on the features of the waste feed image using deep learning methods; the combustion temperature prediction model and the flue gas emission prediction model are located in the combustion parameter AI prediction module 4324 shown in the figure, used to predict the temperature and flue gas change trends during the combustion process using machine learning methods; the feeding equipment current prediction model is specifically the feed current modeling module 4325 shown in the figure, used to predict the change trend of the feeding equipment current residual using machine learning methods.

[0056] The combustion control command generation subsystem 433 may include a setpoint reading module 4331, a status data reading module 4332, a main system control module 4333, an auxiliary system control module 4334, and a command writing module 4335.

[0057] The setpoint reading module 4331 can be used to read various setpoints of the combustion equipment. The control commands generated by the combustion control command generation subsystem 433 are used to control the combustion equipment so that its parameters are close to the setpoints. The status data reading module 4332 is used to read the real-time operating data of the combustion equipment and environmental perception data from the data acquisition and storage subsystem, and send them to the main system control module 4333 and the auxiliary system control module 4334.

[0058] The main system control module 4333 is used to control the feed rate according to the temperature of the combustion equipment, the main steam flow rate and the flue gas variables. The auxiliary system control module 4334 is used to control the auxiliary combustion variables according to the oxygen content, negative pressure and main steam temperature of the combustion process of the combustion equipment. The auxiliary combustion variables include at least one of the following: air intake, water supply, slag discharge, coal supply and oil supply.

[0059] The main system control module 4333 can control the feed rate during the combustion process, and the amount of feed is a decisive factor affecting the combustion process. The auxiliary system control module 4334 can control secondary factors affecting the combustion process, such as air intake, water supply, ash discharge, coal feed, or oil feed, etc.

[0060] The main system control module 4333 includes at least one of the following: a zero-reamer control submodule, an upper-level reamer control submodule, and a drum feeder control submodule. These submodules comprehensively control the feed rate to stabilize variables such as temperature, main steam flow, and flue gas during the combustion process. The auxiliary system control module 4334 includes at least one of the following: a primary air fan control submodule, a secondary air fan control submodule, an induced draft fan control submodule, a desuperheating water control submodule, a slag cooler control submodule, a coal feeding control submodule, and an auxiliary combustion control submodule. These submodules comprehensively control the air intake, water supply, slag discharge, coal feed, and oil supply to stabilize variables such as oxygen content, negative pressure, and main steam temperature during the combustion process.

[0061] The aforementioned control submodule can specifically be a control loop file. After outputting control commands, the control loop file can obtain relevant information about the combustion equipment after executing the control commands, and output new control commands based on the obtained relevant information.

[0062] In this embodiment, multiple control sub-modules of the main system control module 4333 and the auxiliary system control module 4334 can realize multi-variable coordinated control of the combustion equipment, thereby improving the control efficiency of the combustion equipment.

[0063] The instruction writing module 4335 is used to write the control instructions of the above control submodules to the data acquisition and storage subsystem.

[0064] See Figure 4C Taking circulating fluidized bed boilers as an example, including Figure 4B The execution method of the cloud-edge control system of the combustion control system shown in the figure is illustrated by way of example, and includes:

[0065] S401. Based on the actual circulating fluidized bed boiler object, the data of each measuring point of boiler combustion operation is obtained through the cloud data acquisition and storage subsystem, including equipment operation data and target set value data, and the data of each measuring point is synchronized to the edge data acquisition and storage subsystem.

[0066] S402. Based on the real-time acquired equipment operation data, target setpoint data, and the trained feed level recognition model, temperature prediction model, flue gas prediction model, and feeder current model, run the model prediction algorithm files for each variable in the cloud to obtain the predicted values ​​of feed level, combustion temperature, flue gas emission, and feeder current residual in real time.

[0067] It should be noted that in this embodiment, if there is no trained feed level identification model, temperature prediction model, flue gas prediction model and feeding equipment current model, or if these models need to be adjusted, the historical equipment operation data and target set value data obtained in step S401 can be used to perform data cleaning, feature selection and prediction model training in the cloud to obtain the feed level identification model, combustion temperature prediction model, flue gas prediction model and feeding equipment current model.

[0068] S403. The predicted values ​​of feed level, combustion temperature, flue gas emission, and feeder current residual are output to the cloud data acquisition and storage subsystem in real time.

[0069] S404. Run multiple control loops in the combustion control system in the cloud and generate control commands corresponding to each control loop.

[0070] In the combustion control system, the fluidized bed boiler combustion system can be decomposed into a main system and an auxiliary system for separate control. The main system control includes the zero-level reamer control, the upper-level reamer control, and the drum feeder control loop. By comprehensively controlling the feed rate of the main system, the temperature, main steam flow, flue gas, and other variables in the combustion process can be stabilized. The auxiliary system control includes the primary air fan control, secondary air fan control, induced draft fan control, desuperheating water control, slag cooler control, coal feed control, and auxiliary combustion control loop. By comprehensively controlling the air intake, water feed, slag discharge, coal feed, and oil feed of the auxiliary system, the oxygen content, negative pressure, main steam temperature, and other variables in the combustion process can be stabilized.

[0071] Each control loop can achieve multi-variable coordinated control, which can effectively overcome the multi-variable coupling, large time delay and time-varying parameters in the circulating fluidized bed waste combustion process, and effectively improve the stability, economy and safety of the combustion process.

[0072] Specifically, the algorithm files for each control loop of the combustion control system can be designed, built, and run using a modular programming system in the cloud. These include the main control system file and the auxiliary control system file, corresponding to the main system control module and the auxiliary system control module, respectively. The main control system algorithm files include the zero-reamer control loop algorithm file, the upper-level reamer control loop algorithm file, and the drum feeder control loop algorithm file, corresponding to multiple control sub-modules included in the main system control module. The auxiliary control system files include the primary air fan control loop algorithm file, the secondary air fan control loop algorithm file, the induced draft fan control loop algorithm file, the desuperheating water control loop algorithm file, the slag cooler control loop algorithm file, the coal feeder control loop algorithm file, and the auxiliary combustion control loop algorithm file, corresponding to multiple control sub-modules included in the auxiliary system control module. Based on the equipment operation data and target setpoint data collected in real time in step S401 and the environmental perception data output in step S402, the control algorithms of each control loop are tested in an open-loop manner to ensure that the control command output of each control loop meets the design requirements.

[0073] Then, based on the actual real-time operating status of the circulating fluidized bed boiler, the instruction outputs of each control loop can be written to each loop device of the circulating fluidized bed boiler through the cloud data acquisition and storage subsystem. Based on the closed-loop control effect, the control strategy and control parameters of each control loop can be adjusted and optimized to obtain the final implemented control loop. The algorithm files of each control loop can be copied to the edge for execution.

[0074] S405. Execute the trained feed level recognition model, temperature prediction model, flue gas prediction model and feeder current model at the edge end to obtain the predicted values ​​of feed level, combustion temperature, flue gas emission, and feeder current residual.

[0075] S406. The predicted values ​​of feed level, combustion temperature, flue gas emission, and feeder current residual are output to the edge data acquisition and storage subsystem in real time.

[0076] S407. Run multiple control loops included in the combustion control system at the edge end and generate control commands corresponding to each control loop.

[0077] Steps S402-S404 and steps S405-S407 are executed in parallel.

[0078] S408. The control command data of each control loop at the cloud and edge are output to the cloud-edge collaboration subsystem through the corresponding data acquisition and storage subsystem.

[0079] S409. Run the heartbeat signal detection file in the cloud and at the edge respectively, and run and output the heartbeat square wave signal in real time. Then, output the signal to the cloud-edge collaboration subsystem through the corresponding data acquisition and storage subsystem.

[0080] S410. In the cloud-edge collaboration subsystem, monitor the control command output status and heartbeat square wave signal status of each algorithm file in the cloud and edge in real time.

[0081] The state of the heartbeat square wave signal can be used to characterize whether the equipment is operating normally.

[0082] S411. Based on the control command output status and heartbeat square wave signal status, determine whether the edge file operation and control command output are normal through cloud-edge collaboration strategy.

[0083] If the instruction output is normal, proceed to step S412a; otherwise, proceed to step S412b.

[0084] S412a: Write the edge control commands to each control loop device of the circulating fluidized bed boiler.

[0085] S412b: Write cloud control commands to each control loop device of the circulating fluidized bed boiler.

[0086] Reference Figure 5 The diagram shows a structural schematic of an electronic device provided in an embodiment of this application. The specific embodiments of this application do not limit the specific implementation of the electronic device.

[0087] like Figure 5 As shown, the electronic device may include: a processor 502, a communications interface 504, a memory 506, and a communications bus 508.

[0088] in:

[0089] The processor 502, communication interface 504, and memory 506 communicate with each other via communication bus 508.

[0090] Communication interface 504 is used to communicate with other electronic devices or servers.

[0091] The processor 502 is used to execute program 510, specifically to perform the relevant steps in the above control method embodiments.

[0092] Specifically, program 510 may include program code that includes computer operation instructions.

[0093] Processor 502 may be a CPU, an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. The smart device may include one or more processors of the same type, such as one or more CPUs; or it may include processors of different types, such as one or more CPUs and one or more ASICs.

[0094] Memory 506 is used to store program 510. Memory 506 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0095] The specific implementation of each step in program 510 can be found in the corresponding steps and units described in the above control method embodiments, and will not be repeated here. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the devices and modules described above can be referred to the corresponding process descriptions in the foregoing method embodiments, and will not be repeated here.

[0096] This application also provides a computer program product, including computer instructions that instruct a computing device to perform an operation corresponding to any of the control methods in the above-described multiple method embodiments.

[0097] It should be noted that, depending on the implementation needs, the various components / steps described in the embodiments of this application can be broken down into more components / steps, or two or more components / steps or parts of the operation of components / steps can be combined into new components / steps to achieve the purpose of the embodiments of this application.

[0098] The methods described in the embodiments of this application can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as a CD-ROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code originally stored on a remote recording medium or a non-transitory machine-readable medium and subsequently stored on a local recording medium, downloaded over a network. Thus, the methods described herein can be processed by software stored on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that the computer, processor, microprocessor controller, or programmable hardware includes storage components (e.g., RAM, ROM, flash memory, etc.) capable of storing or receiving software or computer code that, when accessed and executed by the computer, processor, or hardware, implements the control methods described herein. Furthermore, when a general-purpose computer accesses code used to implement the control methods shown herein, the execution of the code transforms the general-purpose computer into a dedicated computer for executing the control methods shown herein.

[0099] Those skilled in the art will recognize that the units and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this application.

[0100] The above embodiments are only used to illustrate the embodiments of this application, and are not intended to limit the embodiments of this application. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the embodiments of this application. Therefore, all equivalent technical solutions also fall within the scope of the embodiments of this application, and the patent protection scope of the embodiments of this application should be defined by the claims.

Claims

1. A cloud-edge collaborative control method, applied to a cloud-edge control system, wherein the cloud-edge control system is communicatively connected to the controlled device, the cloud-edge control system includes a cloud and an edge, and the cloud and the edge are deployed with identical and independent device control systems, the method comprising: Collect real-time operating data of the controlled equipment; The device control systems deployed in the cloud and at the edge respectively perform calculations based on the real-time operating data and obtain corresponding cloud control commands and edge control commands. The cloud and the edge terminal are instructed to transmit the corresponding cloud control commands and edge terminal control commands to the other end; By deploying the same preset cloud-edge collaboration strategy in the cloud and at the edge, it is determined whether to transmit one of the cloud control command and the edge control command to the controlled device. The preset cloud-edge collaboration strategy includes: Transmit the commands that can be generated normally from the cloud control commands or the edge control commands to the controlled device; If the parameters of the controlled device are abnormal, the instruction transmitted to the controlled device will be switched from one of the cloud control instruction or the edge control instruction to the other. If both the cloud control command and the edge control command are abnormal, then the transmission of control commands to the controlled device will be stopped.

2. The method according to claim 1, wherein, The device control system includes: a data acquisition and storage subsystem, an environmental sensing subsystem, and a control command generation subsystem. The device control system performs calculations based on the real-time operating data through the following steps to output the cloud-based control command or the edge-based control command: The environmental perception subsystem reads the real-time operating data of the controlled device from the data acquisition and storage subsystem, and performs operating environment prediction based on the real-time operating data to obtain the environmental perception data of the controlled device and store it in the data acquisition and storage subsystem. The control command generation subsystem is used to read the real-time operating data of the controlled device and the environmental perception data from the data acquisition and storage subsystem, and generate the cloud control command or the edge control command based on the real-time operating data and the environmental perception data. The cloud control commands or edge control commands are stored in the data acquisition and storage subsystem.

3. The method according to claim 2, wherein, The environmental perception subsystem includes multiple environmental prediction models, which are trained in the cloud and then deployed simultaneously in the environmental perception subsystem at both the cloud and the edge.

4. A cloud-edge collaborative control method for a combustion device, employing a cloud-edge control system, wherein the cloud-edge control system is communicatively connected to the controlled device, the cloud-edge control system comprising a cloud terminal and an edge terminal, wherein the cloud terminal and the edge terminal are deployed with identical and independent combustion control systems, the method comprising: Collect real-time operating data from various measuring points during the combustion operation of the combustion device; The combustion control systems deployed in the cloud and at the edge respectively perform calculations based on the real-time operating data and obtain corresponding cloud control commands and edge control commands. The cloud and the edge terminal are instructed to transmit the corresponding cloud control commands and edge terminal control commands to the other end; By deploying the same preset cloud-edge collaboration strategy in the cloud and at the edge, it is determined whether to transmit one of the cloud control command and the edge control command to the combustion device. The preset cloud-edge collaboration strategy includes: Transmit the commands that can be generated normally from the cloud control commands or the edge control commands to the controlled device; If the parameters of the controlled device are abnormal, the instruction transmitted to the controlled device will be switched from one of the cloud control instruction or the edge control instruction to the other. If both the cloud control command and the edge control command are abnormal, then the transmission of control commands to the controlled device will be stopped.

5. The method according to claim 4, wherein, The combustion control system includes a data acquisition and storage subsystem, an environmental perception subsystem, and a combustion control command generation subsystem. The environmental prediction model in the environmental perception subsystem includes at least one of the following: a feed level identification model, a combustion temperature prediction model, a flue gas emission prediction model, and a feeding equipment current prediction model.

6. The method according to claim 5, wherein, The combustion control command generation subsystem includes at least one of the following: a main system control module for controlling the feed rate based on the temperature of the combustion equipment, the main steam flow rate, and the flue gas variables; and an auxiliary system control module for controlling auxiliary combustion variables based on the oxygen content, negative pressure, and main steam temperature during the combustion process of the combustion equipment. The auxiliary combustion variables include at least one of the following: air intake, water supply, slag discharge, coal supply, and oil supply.

7. The method according to claim 6, wherein, The main system control module includes at least one of the following: zero reamer control submodule, upper-level reamer control submodule, and roller feeder control submodule; Alternatively, the auxiliary system control module may include at least one of the following: a primary air fan control submodule, a secondary air fan control submodule, an induced draft fan control submodule, a desuperheating water control submodule, a slag cooler control submodule, a coal feeding control submodule, and an auxiliary combustion control submodule.

8. A cloud-edge control system, comprising: It includes a cloud and an edge terminal. The cloud and the edge terminal are deployed with the same and independent device control system, which is used to perform calculations based on the real-time operating data of the controlled device and obtain corresponding cloud control commands and edge terminal control commands, so that the cloud and the edge terminal can transmit the corresponding cloud control commands and edge terminal control commands to the other end. By deploying the same preset cloud-edge collaboration strategy in the cloud and at the edge, it is determined whether to transmit one of the cloud control command and the edge control command to the controlled device. The preset cloud-edge collaboration strategy includes: Transmit the commands that can be generated normally from the cloud control commands or the edge control commands to the controlled device; If the parameters of the controlled device are abnormal, the instruction transmitted to the controlled device will be switched from one of the cloud control instruction or the edge control instruction to the other. If both the cloud control command and the edge control command are abnormal, then the transmission of control commands to the controlled device will be stopped.

9. A computer storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described in any one of claims 1-7.

10. A computer program product comprising computer instructions that instruct a computing device to perform the method as described in any one of claims 1-7.

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