Steam discharge control system of integrated cooker

Abstract

The invention provides an integrated cooker steam discharge control system, and relates to the technical field of intelligent control, the integrated cooker steam discharge control system comprises eight basic subsystems and eight intelligent subsystems, and by arranging the eight basic subsystems and the eight intelligent subsystems, the comprehensive and powerful steam discharge control system is constructed. Accurate detection, adjustment and intelligent management of steam emission are achieved, the steam detection subsystem transmits data to the intelligent control subsystem in real time, and it is ensured that the steam state is mastered in time. The quantum calculation aided decision-making subsystem utilizes a quantum algorithm to efficiently process complex steam emission data, the decision-making speed and accuracy are greatly improved, and therefore the steam emission control strategy is optimized, and the artificial intelligence learning subsystem adopts a deep learning algorithm for training to improve the accuracy of the steam emission control strategy. Personalized and accurate steam discharge control can be provided according to historical data and user habits. And the environment self-adaptive subsystem dynamically adjusts an emission strategy through a self-adaptive algorithm according to a sensor signal.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent control, and more specifically, particularly relates to an integrated stove steam emission control system. Background Art

[0002] With the continuous advancement of science and technology and the improvement of people's living standards, integrated stoves have been increasingly widely used in modern kitchens. However, in the use of integrated stoves, steam emission control has always been an important issue.

[0003] Traditional integrated stove steam emission systems have many limitations. For example, the detection of steam emissions is not accurate enough, and the emission strategy cannot be adjusted timely and effectively according to actual conditions. It lacks intelligent control methods and cannot adapt well to different usage scenarios and user needs. It also performs poorly in terms of energy saving, resulting in unnecessary waste of energy.

[0004] At the same time, users have higher and higher requirements for the user experience of integrated stoves, and expect them to have smarter, more efficient, more energy-saving and more adaptable steam emission control functions. In addition, under the current technological development trend, electronic information technology, intelligent control technology, Internet of Things technology and other fields have made continuous breakthroughs, providing strong technical support for the innovation and improvement of integrated stove steam emission control systems.

[0005] In this context, there is an urgent need for a new integrated stove steam emission control system that can comprehensively use advanced technologies and algorithms to achieve accurate detection and intelligent management of steam emissions, improve the adaptability, energy saving and user-friendliness of the system, and meet the higher requirements of modern kitchens for steam emission control. The integrated stove steam emission control system of the present invention is developed to solve these problems in the prior art. Summary of the invention

[0006] In order to solve the above technical problems, the present invention provides an integrated stove steam emission control system to solve the above problems.

[0007] An integrated stove steam emission control system comprises eight basic subsystems and eight intelligent subsystems, wherein the eight basic subsystems comprise a steam detection subsystem, an emission regulation subsystem, a ventilation subsystem, an intelligent control subsystem, a safety protection subsystem, a sensor subsystem, a drive subsystem, and a display and interaction subsystem; the eight intelligent subsystems comprise an Internet of Things communication subsystem, an artificial intelligence learning subsystem, a voice recognition control subsystem, an environmental adaptation subsystem, an intelligent energy-saving subsystem, an image recognition subsystem, a wireless charging subsystem, and a quantum computing auxiliary decision-making subsystem; the steam detection subsystem is connected to the intelligent control subsystem via a data transmission line, and the detected steam data is transmitted to the intelligent control subsystem in real time; the quantum computing auxiliary decision-making subsystem is connected to the intelligent control subsystem via a high-speed data interface; the environmental adaptation subsystem is connected to the intelligent control subsystem via a sensor signal line and a data transmission line; the Internet of Things communication subsystem is connected to the intelligent control subsystem via a network communication line, and data upload and download are realized.

[0008] Preferably, the artificial intelligence learning subsystem adopts a deep learning algorithm and is trained based on historical steam data and user usage habits. The training formula is: E = ∑ i (y i -y′ i ) 2 , where E represents the error, y i Indicates actual steam data or user habit value, y′ i Represents the predicted value.

[0009] Preferably, the environmental adaptive subsystem dynamically adjusts the steam emission strategy according to the sensor signal through an adaptive algorithm S=k1T+k2H+k3P, where S represents the adjustment strategy, T represents the temperature sensor value, H represents the humidity sensor value, P represents the pressure sensor value, and k1, k2, and k3 are weight coefficients.

[0010] Preferably, the speech recognition control subsystem is based on the hidden Markov model algorithm, and the recognition accuracy of the speech command reaches

[0011] Preferably, the intelligent energy-saving subsystem is based on the energy consumption model formula C=P s t+∑ i k i F i Calculate the best energy-saving strategy, where C represents energy consumption, P s represents steam discharge power, t represents time, F i represents other relevant factors, k i is the corresponding coefficient.

[0012] Preferably, the image recognition subsystem uses a convolutional neural network algorithm to identify the cooking state, and its recognition accuracy is

[0013] Preferably, the charging efficiency calculation formula of the wireless charging subsystem is η=P out / P in , where η: represents the charging efficiency, P out Indicates output power, P in Indicates input power.

[0014] Preferably, the quantum computing assisted decision-making subsystem utilizes quantum algorithm T q =T c log2(N) handles complex steam emission data, where T q represents the quantum computing processing time, T c represents the traditional computing processing time, and N represents the data size.

[0015] Preferably, the data upload and download rates of the IoT communication subsystem satisfy the formula DataVolume: indicates the data volume, and Time indicates the transmission time.

[0016] Preferably, the intelligent subsystems interact with each other through an algorithm I=∑ i a i x i +∑ j b j Y j Work together, where I represents the synergistic effect and X represents i , Y j Represents the relevant parameters of each intelligent subsystem, a i 、b j is the weight coefficient.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] In the present invention, by setting up eight basic subsystems and eight intelligent subsystems, a comprehensive and powerful steam emission control system is constructed, which realizes the accurate detection, regulation and intelligent management of steam emission.

[0019] In the present invention, the steam detection subsystem transmits data to the intelligent control subsystem in real time, ensuring timely grasp of the steam status. The quantum computing-assisted decision-making subsystem uses quantum algorithms to efficiently process complex steam emission data, greatly improving the speed and accuracy of decision-making, thereby optimizing the steam emission control strategy.

[0020] In the present invention, the artificial intelligence learning subsystem adopts deep learning algorithm for training, and can provide personalized and accurate steam emission control according to historical data and user habits. The environmental adaptive subsystem dynamically adjusts the emission strategy through adaptive algorithm according to sensor signals, effectively adapts to different environmental conditions, and improves the environmental adaptability of the system.

[0021] In the present invention, the voice recognition control subsystem realizes high-accuracy voice command recognition based on the hidden Markov model algorithm, providing users with a convenient operation method. The intelligent energy-saving subsystem calculates the optimal energy-saving strategy based on the energy consumption model formula, significantly reducing energy consumption. The image recognition subsystem uses the convolutional neural network algorithm to accurately identify the cooking state, further improving the intelligence level of the system.

[0022] In the present invention, the charging efficiency calculation formula of the wireless charging subsystem ensures efficient charging performance. The data upload and download rates of the IoT communication subsystem meet specific formulas, ensuring the timeliness and stability of data transmission. The intelligent subsystems work together through interactive algorithms to improve the synergy and comprehensive performance of the entire system. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a system schematic diagram of the present invention. DETAILED DESCRIPTION

[0024] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0025] See also Figure 1 , the present invention provides an integrated stove steam emission control system, including eight basic subsystems and eight intelligent subsystems;

[0026] Among them, the eight basic subsystems include steam detection subsystem, emission regulation subsystem, ventilation subsystem, intelligent control subsystem, safety protection subsystem, sensor subsystem, drive subsystem, display and interaction subsystem;

[0027] The eight intelligent subsystems include the Internet of Things communication subsystem, artificial intelligence learning subsystem, speech recognition control subsystem, environmental adaptation subsystem, intelligent energy-saving subsystem, image recognition subsystem, wireless charging subsystem, and quantum computing-assisted decision-making subsystem;

[0028] The steam detection subsystem monitors the relevant parameters of the steam in the integrated stove in real time, such as concentration and pressure, to provide a data basis for subsequent control. The emission regulation subsystem accurately adjusts the steam emission and emission speed according to the detection information or other instructions. The ventilation subsystem ensures the circulation of air around the integrated stove, discharges steam and odors in time, and keeps the environment fresh. The intelligent control subsystem, as the control core of the entire system, coordinates and manages the operation of each subsystem to achieve intelligent control. The safety protection subsystem ensures the safety of system operation, such as alarming and shutting down in abnormal situations to prevent accidents. The sensor subsystem provides perception data of various environments and equipment states for other subsystems. The drive subsystem provides power support for related equipment and components to ensure the normal operation of the system. The display and interaction subsystem displays system status, parameters and other information to users, and receives user operation instructions. The network communication subsystem realizes connection and data interaction with the external network, which is convenient for remote monitoring and control. The artificial intelligence learning subsystem continuously optimizes the system control strategy through learning and analysis of data to adapt to different usage scenarios and user habits. The voice recognition control subsystem allows users to conveniently operate the integrated stove through voice commands. The environmental adaptation subsystem automatically adjusts system parameters according to changes in the surrounding environment to achieve better operating results. The intelligent energy-saving subsystem reduces energy consumption through optimized control to achieve energy-saving purposes. The image recognition subsystem may be used to identify cooking status, ingredients, etc., to provide a more accurate control basis for the system. The wireless charging subsystem provides convenient wireless charging functions for related equipment or user equipment. The quantum computing auxiliary decision-making subsystem uses the powerful processing power of quantum computing to provide efficient support for complex decisions, further improving system performance and intelligence level;

[0029] The steam detection subsystem is connected to the intelligent control subsystem through a data transmission line, and transmits the detected steam data to the intelligent control subsystem in real time. The emission adjustment subsystem is connected to the intelligent control subsystem through a control signal line, and receives instructions from the intelligent control subsystem to adjust the emission. The ventilation subsystem can be connected to the intelligent control subsystem through a control line, and adjust the ventilation state according to the instructions. The intelligent control subsystem, as the core, is connected to other subsystems through various lines, receives and sends data and control instructions, and is connected to the safety protection subsystem, sensor subsystem, drive subsystem, display and interaction subsystem, etc. through signal lines or data buses. The safety protection subsystem is connected to the intelligent control subsystem through a signal line, and promptly provides feedback to the intelligent control subsystem when an abnormality occurs. The sensor subsystem is connected to the intelligent control subsystem through a data line to transmit various detected parameters. The drive subsystem is connected to the intelligent control subsystem through a control line to execute the drive instructions of the intelligent control subsystem. The display and interaction subsystem is connected to the intelligent control subsystem through a data communication line The system is connected to display the system status and receive user interaction information. The Internet of Things communication subsystem is connected to the intelligent control subsystem through the network communication line to realize data upload and download. The artificial intelligence learning subsystem is connected to the intelligent control subsystem through the data interface to exchange data and update the algorithm. The voice recognition control subsystem is connected to the intelligent control subsystem through the audio signal line to transmit the recognized voice commands to the intelligent control subsystem. The environmental adaptation subsystem is connected to the intelligent control subsystem through the sensor signal line and the data transmission line to provide environmental information and receive control commands. The intelligent energy-saving subsystem is connected to the intelligent control subsystem through the data signal line to share energy consumption and other data and receive energy-saving strategies. The image recognition subsystem is connected to the intelligent control subsystem through the image data transmission line to transmit image recognition results. The wireless charging subsystem is connected to other devices that need to be charged through the power line to provide them with power. The quantum computing assisted decision-making subsystem is connected to the intelligent control subsystem through the high-speed data interface to perform complex data processing and decision support.

[0030] The artificial intelligence learning subsystem uses a deep learning algorithm and is trained based on historical steam data and user usage habits. The training formula is:

[0031] E=∑ i (y i -y′ i ) 2 ;

[0032] Where E represents the error, y i Indicates actual steam data or user habit value, y′ iIt represents the predicted value, more accurately predicts future steam generation and user needs, so as to perform emission adjustment and other operations in advance, and performs personalized adaptation according to the unique usage habits of different users, providing a steam emission control mode that better meets the needs of specific users, and continuously learning and improving the control strategy to make the operation of the steam emission control system more intelligent, efficient and energy-saving, and better adapt to various usage scenarios and changes in conditions, enhance the robustness and reliability of the system, and because it can better meet user needs, it can significantly improve user satisfaction and comfort during the use of integrated stoves.

[0033] The environmental adaptive subsystem dynamically adjusts the steam emission strategy according to the sensor signal through the adaptive algorithm S=k1T+k2H+k3P, where S represents the adjustment strategy, T represents the temperature sensor value, H represents the humidity sensor value, P represents the pressure sensor value, and k1, k2, and k3 are weight coefficients. It can automatically adjust the emission strategy according to the changes in environmental conditions (such as temperature, humidity, pressure, etc.) in real time, so that the system can better adapt to different cooking scenarios and external environmental changes, ensure that the steam emission is more reasonable and efficient, avoid steam accumulation due to insufficient emission or energy waste due to excessive emission, make timely adjustments according to actual conditions, reduce safety risks that may be caused by improper steam emission, such as dangers caused by excessive pressure, etc., so that the steam emission control system can operate stably, reduce fluctuations and anomalies caused by environmental changes, and make precise adjustments according to actual needs. While ensuring the effect, the rational use of energy is achieved to achieve the purpose of energy saving.

[0034] The speech recognition control subsystem is based on the hidden Markov model algorithm, and the recognition accuracy of speech commands reaches Significantly improve the probability of correct recognition of various complex voice commands, reduce the possibility of misrecognition, ensure that the integrated stove can accurately understand the user's voice commands and respond correctly, improve the reliability of system operation, allow users to conveniently control the integrated stove through natural voice, improve the convenience and smoothness of operation, and maintain a high recognition accuracy even in an environment with certain noise or other interference, thereby expanding the scope of application of the system.

[0035] Intelligent energy-saving subsystem is based on the energy consumption model formula C=P s t+∑ i k i F i Calculate the best energy-saving strategy, where C represents energy consumption, P s represents steam discharge power, t represents time, F i represents other relevant factors, k iFor the corresponding coefficient, the most energy-saving operating mode is obtained through precise calculation, which effectively reduces the energy waste in the operation of the integrated stove steam emission control system, ensures that energy is more reasonably allocated and utilized, and improves the overall energy utilization efficiency. In the long run, it can significantly reduce the user's energy usage costs, reduce unnecessary energy consumption, be more environmentally friendly, meet the development requirements of green environmental protection, and ensure the stability and efficient operation of the system while saving energy, without affecting the normal steam emission control function.

[0036] The image recognition subsystem uses a convolutional neural network algorithm to identify the cooking state, and its recognition accuracy is It can more accurately identify various states in the cooking process, such as the type of ingredients, degree of cooking, etc., and provide a more accurate basis for controls such as steam emissions based on the identification results, achieve more intelligent regulation, better adapt to different cooking methods and user needs, provide a personalized cooking environment, and promptly detect abnormal cooking states, such as dry burning of pots, etc., improve safety protection capabilities, help users better grasp the cooking progress and status, improve the convenience and satisfaction of cooking, and provide valuable data for subsequent cooking mode optimization and improvement.

[0037] The charging efficiency calculation formula of the wireless charging subsystem is η=P out / P in , where η: represents the charging efficiency, P out Indicates output power, P in It represents input power, can accurately quantify and evaluate the efficiency of wireless charging, let users and developers clearly understand the charging performance, provide data basis for system optimization and improvement, help improve the relevant components and parameters of wireless charging to improve charging efficiency, and by clarifying the efficiency status, it is convenient to reasonably arrange charging time and resources, avoid energy waste, and facilitate efficiency comparison and analysis with other similar wireless charging systems. When the charging efficiency is abnormal, it can assist in judging whether there is a system failure or abnormal situation. Efficient charging can reduce user waiting time, improve user satisfaction with the wireless charging function, and help better energy planning and management to ensure the rationality of energy utilization during the charging process.

[0038] The quantum computing-assisted decision-making subsystem uses quantum algorithms T q =T c log2(N) handles complex steam emission data, where T q represents the quantum computing processing time, T cRepresents the traditional computing processing time, N represents the data scale. It can process a large amount of complex steam emission data at an extremely fast speed, quickly draw decision results, make more accurate and optimized decisions based on more comprehensive and in-depth data analysis, and improve the accuracy of steam emission control. It can better handle various complex and changeable situations and uncertainties, enhance the system's adaptability to complex environments, help to more reasonably allocate resources such as energy, air volume, etc., achieve more efficient steam emission management, significantly enhance the performance and intelligence level of the entire steam emission control system, ensure rapid response and processing of real-time data, timely adjust steam emission strategies, and conduct more forward-looking planning and forecasting based on powerful data processing capabilities.

[0039] The data upload and download rates of the IoT communication subsystem satisfy the formula Among them, DataVolume: represents the data volume, and Time represents the transmission time, which ensures that data can be uploaded and downloaded quickly and stably, enables various parts of the system to exchange information in time, allows remote monitoring and control to be carried out in real time, and promptly reflects the operating status and steam emission of the integrated stove, promotes data interaction and collaborative work between various subsystems, improves the operating efficiency of the entire system, ensures that remote commands can be issued and executed quickly and accurately, and achieves a good remote control experience, which is conducive to quickly uploading the collected data to the cloud or other analysis platforms, facilitating timely data analysis and processing, and both local and remote operations and requests can get a quick response, enhancing the agility of the system, and based on efficient data transmission, better scheduling related resources to adapt to different needs and scenarios.

[0040] Intelligent subsystems interact with each other through the algorithm I = ∑ i a i x i +∑ j b j Y j Work collaboratively, where l represents the synergistic effect, X i , Y j Represents the relevant parameters of each intelligent subsystem, a i 、b jAs the weight coefficient, each subsystem cooperates and complements each other, thereby significantly improving the overall performance of the entire steam emission control system, reasonably allocating and scheduling the resources of each subsystem, avoiding resource waste and conflict, achieving efficient use of resources, and better adapting to different usage scenarios and user needs. The system has a stronger ability to adapt to the environment. Through data fusion and collaborative analysis of multiple subsystems, more accurate and reliable decision-making results can be obtained, giving full play to the advantages of each subsystem, making up for each other's shortcomings, providing more comprehensive and complete functions, and establishing an effective collaboration mechanism between subsystems. It helps to maintain the stable operation of the system, provides the possibility for the development of new application scenarios and functions, and promotes continuous innovation and development of the system. When a subsystem fails, other subsystems can compensate to a certain extent, improve the reliability of the system, quickly coordinate the actions of the subsystems, shorten the response time, and improve the real-time performance of the system.

[0041] First, by setting up eight basic subsystems and eight intelligent subsystems, a comprehensive and powerful steam emission control system was constructed, realizing the precise detection, adjustment and intelligent management of steam emissions.

[0042] The steam detection subsystem transmits data to the intelligent control subsystem in real time, ensuring timely grasp of the steam status. The quantum computing-assisted decision-making subsystem uses quantum algorithms to efficiently process complex steam emission data, greatly improving the speed and accuracy of decision-making, thereby optimizing steam emission control strategies.

[0043] The artificial intelligence learning subsystem uses deep learning algorithms for training, and can provide personalized and accurate steam emission control based on historical data and user habits. The environmental adaptive subsystem dynamically adjusts the emission strategy through adaptive algorithms based on sensor signals, effectively adapting to different environmental conditions and improving the environmental adaptability of the system.

[0044] The voice recognition control subsystem realizes high-accuracy voice command recognition based on the hidden Markov model algorithm, providing users with a convenient operation method. The intelligent energy-saving subsystem calculates the optimal energy-saving strategy based on the energy consumption model formula, significantly reducing energy consumption. The image recognition subsystem uses the convolutional neural network algorithm to accurately identify the cooking status, further improving the intelligence level of the system.

[0045] The charging efficiency calculation formula of the wireless charging subsystem ensures efficient charging performance. The data upload and download rates of the IoT communication subsystem meet specific formulas, ensuring the timeliness and stability of data transmission. The intelligent subsystems work together through interactive algorithms, improving the synergy and overall performance of the entire system.

[0046] To sum up, the integrated stove steam emission control system of the present invention performs well in many aspects such as accuracy of steam emission control, degree of intelligence, energy-saving effect, user experience, and adaptability, and has great application value and market prospects.

[0047] The embodiments of the present invention are given for the purpose of illustration and description, and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

Claims

1. An integrated stove steam emission control system, characterized in that: Includes eight basic subsystems and eight intelligent subsystems; Among them, the eight basic subsystems include steam detection subsystem, emission regulation subsystem, ventilation subsystem, intelligent control subsystem, safety protection subsystem, sensor subsystem, drive subsystem, display and interaction subsystem; The eight intelligent subsystems include the Internet of Things communication subsystem, artificial intelligence learning subsystem, speech recognition control subsystem, environmental adaptation subsystem, intelligent energy-saving subsystem, image recognition subsystem, wireless charging subsystem, and quantum computing-assisted decision-making subsystem; The steam detection subsystem is connected to the intelligent control subsystem through a data transmission line, and the detected steam data is transmitted to the intelligent control subsystem in real time. The quantum computing assisted decision-making subsystem is connected to the intelligent control subsystem through a high-speed data interface. The environmental adaptation subsystem is connected to the intelligent control subsystem through a sensor signal line and a data transmission line. The Internet of Things communication subsystem is connected to the intelligent control subsystem through a network communication line to realize data upload and download.

2. The integrated stove steam emission control system according to claim 1, characterized in that: The artificial intelligence learning subsystem uses a deep learning algorithm and is trained based on historical steam data and user usage habits. The training formula is: E=∑ i (and i -and' i ) 2 ; Where E represents the error, y i Indicates actual steam data or user habit value, y′ i Represents the predicted value.

3. The integrated stove steam emission control system according to claim 1, characterized in that: The environmental adaptive subsystem dynamically adjusts the steam emission strategy according to the sensor signal through the adaptive algorithm S=k1T+k2H+k3P, where S represents the adjustment strategy, T represents the temperature sensor value, H represents the humidity sensor value, P represents the pressure sensor value, and k1, k2, and k3 are weight coefficients.

4. The integrated stove steam emission control system according to claim 1, characterized in that: The speech recognition control subsystem is based on the hidden Markov model algorithm, and the recognition accuracy of speech commands reaches 5. The integrated stove steam emission control system according to claim 1, characterized in that: Intelligent energy-saving subsystem is based on the energy consumption model formula C=P s t+∑ i k i F i Calculate the best energy-saving strategy, where C represents energy consumption, P s represents steam discharge power, t represents time, F i represents other relevant factors, k i is the corresponding coefficient.

6. The integrated stove steam emission control system according to claim 1, characterized in that: The image recognition subsystem uses a convolutional neural network algorithm to identify the cooking state, and its recognition accuracy is 7. The integrated stove steam emission control system according to claim 1, characterized in that: The charging efficiency calculation formula of the wireless charging subsystem is η=P out / P in , where η represents the charging efficiency, P out Indicates output power, P in Indicates input power.

8. The integrated stove steam emission control system according to claim 1, characterized in that: The quantum computing-assisted decision-making subsystem uses quantum algorithms T q =T c clog2(N) handles complex steam emission data, where T q represents the quantum computing processing time, T c represents the traditional computing processing time, and N represents the data size.

9. The integrated stove steam emission control system according to claim 1, characterized in that: The data upload and download rates of the IoT communication subsystem satisfy the formula DataVolume indicates the data volume, and Time indicates the transmission time.

10. The integrated stove steam emission control system according to claim 1, characterized in that: Intelligent subsystems interact with each other through the algorithm I = ∑ i a i x i +∑ j b j Y j Work together, where I represents the synergistic effect and X represents i , Y j Represents the relevant parameters of each intelligent subsystem, a i , b j is the weight coefficient.

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