Intelligent lampblack purification method
By building a machine learning algorithm model to extract and generate feature and strategy for oil fume data, the problem of existing oil fume purification equipment lacking intelligent analysis and flexibility is solved, and the intelligence and automation of the oil fume purification process is realized, and the stability and reliability of the purification effect are improved.
Patent Information
- Application Number
- CN202510193824.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-21
- Publication Date
- 2025-06-10
AI Technical Summary
The existing oil fume purification equipment has not introduced machine learning algorithms, cannot make full use of data for intelligent analysis, lacks flexibility and adaptability, and the maintenance of traditional methods is unscientific and cannot be adjusted in real time according to the actual oil fume situation.
By using sensors to collect oil smoke data and preprocess it, a machine learning algorithm model is built to extract features, generate the optimal purification strategy, and automatically adjust the working parameters and operating status of the purification module.
The fume purification process is intelligent and automated, the stability and reliability of the purification effect are improved, labor costs are saved, and the equipment is always in the best operating state.
Smart Images
Figure CN120120618A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the technical field of oil fume purification, and in particular to an intelligent oil fume purification method. Background Art
[0002] With the improvement of people's living standards and the enhancement of environmental awareness, the issue of oil fume purification has received more and more attention in the catering, food processing and other industries. In these fields, traditional oil fume purification methods face many challenges. Early oil fume purification technologies were relatively simple. For example, relying solely on filter filtration, only larger oil fume particles can be intercepted, and tiny oil mist, harmful gases and other pollutants are powerless. In some large restaurants, the oil fume produced during the cooking process has complex components, not only grease particles, but also harmful gases such as volatile organic compounds. A single filter filtration cannot meet the needs of comprehensive purification. Although electrostatic adsorption technology can remove some tiny particles, it is prone to discharge abnormalities in high humidity environments, resulting in a significant decrease in purification efficiency. Traditional electrostatic purification equipment has fixed working parameters and cannot be adjusted in real time according to actual oil fume conditions. When the oil fume concentration is low, the equipment still runs at high power, resulting in energy waste. ; However, when the oil fume concentration suddenly increases, the purification intensity cannot be increased in time, affecting the purification effect. In terms of equipment maintenance, traditional methods lack scientific basis and are often maintained at a fixed time cycle, such as regularly replacing filters and cleaning electrodes. The actual amount and composition of oil fume generated will vary depending on factors such as cooking methods and types of ingredients. Fixed-cycle maintenance may lead to untimely or excessive maintenance. The filter may be blocked prematurely due to excessive oil fume concentration in actual use, but it cannot be replaced in time before the fixed maintenance cycle, affecting the purification effect; or when the oil fume concentration is low, the filter is frequently replaced at a fixed cycle, which increases operating costs. With the development of machine learning technology, some methods have emerged that attempt to apply it to the field of oil fume purification, but most of them simply use machine learning to monitor oil fume concentration, fail to make full use of multi-source data for in-depth analysis, and cannot comprehensively and intelligently generate the optimal purification strategy.
[0003] However, the current common solutions have many shortcomings, including: the existing oil fume purification equipment has not introduced machine learning algorithms at all, cannot make full use of data for intelligent analysis, can only rely on preset fixed rules to operate, lack of flexibility and adaptability, and some simple oil fume purifiers only perform purification operations at fixed time intervals without considering changes in the actual oil fume situation. Summary of the invention
[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.
[0005] In view of the above problems existing in the existing intelligent oil fume purification method, the present invention is proposed.
[0006] Therefore, the purpose of the present invention is to provide an intelligent oil fume purification method, which is suitable for solving the problem that the existing oil fume purification equipment has not introduced machine learning algorithms at all, cannot make full use of data for intelligent analysis, can only rely on preset fixed rules to operate, lacks flexibility and adaptability, and the existing oil fume purifiers only perform purification operations at fixed time intervals without considering changes in actual oil fume conditions.
[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:
[0008] In a first aspect, an embodiment of the present invention provides an intelligent oil fume purification method, which includes using sensors to collect oil fume data and perform preprocessing; constructing a machine learning algorithm model through a machine learning algorithm and performing feature extraction on the oil fume data; quickly generating an optimal purification strategy under the current circumstances based on the extracted data; and automatically adjusting the working parameters and operating status of the purification module according to the optimal purification strategy.
[0009] As a preferred solution of the intelligent oil fume purification method described in the present invention, wherein: the sensors include oil fume concentration sensors, temperature sensors, humidity sensors, gas composition sensors and flow sensors; the oil fume data include oil fume concentration data, temperature data, humidity data, gas composition data and flow data; the optimal purification strategy includes purification technology combination strategy, purification equipment operation parameter adjustment strategy, purification equipment maintenance strategy and intelligent control strategy.
[0010] As a preferred solution of the intelligent oil fume purification method described in the present invention, the specific steps of constructing the machine learning algorithm model are as follows: dividing the oil fume data and generating oil fume feature labels; constructing a machine learning algorithm model based on a recurrent neural network and extracting oil fume features; using an unsupervised learning algorithm to further analyze the extracted oil fume features; evaluating the model and adjusting the model structure or parameters according to the evaluation results.
[0011] As a preferred solution of the intelligent oil fume purification method of the present invention, the specific formula for extracting oil fume data features is as follows:
[0012]
[0013] Among them, F(x(t)) is the extracted oil smoke data feature; x 1 (t) is the instantaneous concentration of oil smoke collected at that moment; x 2 (t) is the average particle size of the particles in the oil smoke at that moment.
[0014] As a preferred solution of the intelligent oil fume purification method of the present invention, the specific formula for generating the optimal purification strategy is as follows:
[0015]
[0016] Among them, S is the optimal purification strategy; F(x(t)) is the extracted oil fume data feature; C is the oil fume concentration; H is the humidity value in the oil fume; G is the gas composition; A is the flow rate of the oil fume.
[0017] As a preferred scheme of the intelligent oil fume purification method described in the present invention, the specific steps of generating the optimal purification strategy are as follows: inputting the data after feature extraction and processing into the machine learning model; the model generates the optimal purification strategy under the current situation; interpreting and converting the generation results output by the model to determine the specific optimal purification strategy; converting the determined optimal purification strategy into working parameters and operating status executable by the purification module.
[0018] As a preferred solution of the intelligent oil fume purification method of the present invention, the purification module includes a physical filtration unit, an electrostatic purification module, a chemical purification module, a ventilation and power module, and an intelligent control and monitoring module.
[0019] In the second aspect, in order to further solve the above-mentioned technical problems, the embodiments of the present invention provide an intelligent oil fume purification system, which includes: a data collection module, used to collect and preprocess oil fume data; a model building module, used to build a machine learning model and extract features of the oil fume data; a strategy generation module, used to generate the optimal purification strategy under the current circumstances; an automatic adjustment module, used to automatically adjust the working parameters and operating status of the purification module.
[0020] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of an intelligent oil fume purification method as described in the first aspect of the present invention is implemented.
[0021] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of an intelligent oil fume purification method as described in the first aspect of the present invention is implemented.
[0022] The beneficial effects of the present invention are as follows: the present invention extracts features from oil fume data by constructing a machine learning algorithm model, and can mine the complex relationships and patterns hidden in the data. By analyzing long-term oil fume data, the model can discover the changing patterns of oil fume components and flow rates in different seasons and time periods. The unsupervised learning algorithm is used to further analyze the extracted features, which is helpful to discover the potential structure and abnormal conditions in the data, making the model more intelligent and adaptive. This means that even in the face of new and unexpected oil fume conditions, the model can make reasonable analysis. According to real-time data, the most suitable purification technology combination for the current oil fume condition can be selected. For conditions containing a large amount of large-particle oil fume accompanied by harmful gases, physical filtration and chemical purification technologies can be used simultaneously. Compared with traditional single purification technology, the purification effect is more comprehensive. The working parameters and operating status of the purification module are automatically adjusted according to the generated optimal purification strategy, realizing the intelligence and automation of the purification process. This not only saves labor costs, but also ensures that the purification equipment is always in the best operating state, and improves the stability and reliability of the purification effect. During the peak cooking period, the equipment can automatically adjust to the high-load operation mode to effectively deal with the generation of a large amount of oil fume. BRIEF DESCRIPTION OF THE DRAWINGS
[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:
[0024] Figure 1 This is a flow chart for implementing the present invention in Example 1.
[0025] Figure 2 This is a flow chart for generating the optimal purification strategy of the present invention in Example 1. DETAILED DESCRIPTION
[0026] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.
[0027] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.
[0028] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.
[0029] Example 1
[0030] Reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides an intelligent oil fume purification method, comprising the following steps:
[0031] S1: Use sensors to collect oil smoke data and perform preprocessing.
[0032] Preferably, Figure 1 The figure shows the implementation process of the present invention. First, the oil fume data is collected and preprocessed by using sensors. Then, a machine learning algorithm model is constructed through a machine learning algorithm and features of the oil fume data are extracted. Then, the optimal purification strategy under the current situation is quickly generated based on the extracted data. Finally, the working parameters and operating status of the purification module are automatically adjusted according to the optimal purification strategy.
[0033] Furthermore, the sensor includes an oil fume concentration sensor, a temperature sensor, a humidity sensor, a gas composition sensor and a flow sensor.
[0034] Furthermore, the oil fume data includes oil fume concentration data, temperature data, humidity data, gas composition data and flow data.
[0035] Specifically, the preprocessing ensures the quality and consistency of the data by preprocessing the oil smoke data in real time on the data node, including data cleaning, noise reduction and standardization.
[0036] Preferably, multiple sensors work together to capture the physical and chemical properties of oil smoke in all directions, making the description of the oil smoke condition more accurate and complete.
[0037] S2: Build a machine learning algorithm model through machine learning algorithm and extract features of oil fume data.
[0038] Preferably, the specific steps of constructing a machine learning algorithm model are as follows: dividing the oil fume data and generating oil fume feature labels.
[0039] A machine learning algorithm model is constructed based on recurrent neural network to extract oil smoke features.
[0040] The extracted oil smoke features are further analyzed using unsupervised learning algorithms.
[0041] Evaluate the model and adjust the model structure or parameters based on the evaluation results.
[0042] Furthermore, the specific formula for extracting the characteristics of oil smoke data is as follows:
[0043]
[0044] Among them, F(x(t)) is the extracted oil smoke data feature; x 1 (t) is the instantaneous concentration of oil smoke collected at that moment; x 2 (t) is the average particle size of the particles in the oil smoke at that moment.
[0045] Preferably, the features are further analyzed through an unsupervised learning algorithm, so that the model can adapt to different oil fume conditions and environmental changes, and improve the ability to respond to various complex situations.
[0046] S3: Quickly generate the optimal purification strategy for the current situation based on the extracted data.
[0047] Furthermore, the optimal purification strategy includes a purification technology combination strategy, a purification equipment operation parameter adjustment strategy, a purification equipment maintenance strategy, and an intelligent control strategy.
[0048] Preferably, the specific formula for generating the optimal purification strategy is as follows:
[0049]
[0050] Among them, S is the optimal purification strategy; F(x(t)) is the extracted oil fume data feature; C is the oil fume concentration; H is the humidity value in the oil fume; G is the gas composition; A is the flow rate of the oil fume.
[0051] Furthermore, the specific steps for generating the optimal purification strategy are as follows: the feature-extracted and processed data are input into the machine learning model.
[0052] The model generates the optimal purification strategy for the current situation.
[0053] Interpret and transform the generated results of the model output to determine the specific optimal purification strategy.
[0054] The determined optimal purification strategy is converted into working parameters and operating status executable by the purification module.
[0055] Preferably, the optimal combination of purification technologies is selected based on real-time data, which can significantly improve the purification effect, adjust the equipment operating parameters in real time, achieve energy saving and consumption reduction while ensuring the purification effect, and formulate maintenance strategies based on equipment operation data and oil fume data characteristics, which can effectively extend the service life of the equipment, reduce maintenance costs, realize automation and intelligent control of the purification process, reduce manual intervention, and improve management efficiency.
[0056] S4: Automatically adjust the working parameters and operating status of the purification module according to the optimal purification strategy.
[0057] Preferably, the purification module includes a physical filtration unit, an electrostatic purification module, a chemical purification module, a ventilation and power module, and an intelligent control and monitoring module.
[0058] Preferably, the purification module can be automatically adjusted in real time according to the generated optimal purification strategy to ensure that the purification equipment is always in the best operating state and to respond to changes in the oil fume conditions in a timely manner.
[0059] This embodiment also provides an intelligent oil fume purification system, including: a data collection module, used to collect oil fume data and perform preprocessing; a model construction module, used to build a machine learning model and extract features of the oil fume data; a strategy generation module, used to generate the optimal purification strategy under the current circumstances; an automatic adjustment module, used to automatically adjust the working parameters and operating status of the purification module.
[0060] This embodiment also provides a computer device, which is applicable to a smart oil fume purification method, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement a smart oil fume purification method as proposed in the above embodiment.
[0061] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.
[0062] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, an intelligent oil fume purification method as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, disk or optical disk.
[0063] In summary, the present invention extracts features from oil fume data by constructing a machine learning algorithm model, which can mine the complex relationships and patterns hidden in the data. By analyzing long-term oil fume data, the model can discover the changing patterns of oil fume components and flow rates in different seasons and time periods. The unsupervised learning algorithm is used to further analyze the extracted features, which helps to discover potential structures and abnormalities in the data, making the model more intelligent and adaptive. This means that even in the face of new and unexpected oil fume conditions, the model can make reasonable analysis. According to real-time data, the most suitable purification technology combination for the current oil fume condition can be selected. For conditions containing a large amount of large-particle oil fume accompanied by harmful gases, physical filtration and chemical purification technologies can be used simultaneously. Compared with traditional single purification technologies, the purification effect is more comprehensive. The working parameters and operating status of the purification module are automatically adjusted according to the generated optimal purification strategy, realizing the intelligence and automation of the purification process. This not only saves labor costs, but also ensures that the purification equipment is always in the best operating state, improving the stability and reliability of the purification effect. During the peak cooking period, the equipment can automatically adjust to the high-load operation mode to effectively deal with the generation of a large amount of oil fume.
[0064] Example 2
[0065] Referring to Tables 1 to 3, the second embodiment of the present invention is shown. This embodiment is different from the first embodiment in that, in order to verify its beneficial effects, operating data and related instructions of the present invention in an actual environment are provided.
[0066] Tables 1 and 2 show the oil fume data and purification effects collected in this example, including oil fume concentration, temperature, humidity, formaldehyde content, benzene content and flow data, which provide a data basis for subsequent oil fume purification.
[0067] Table 1 Oil smoke data record table
[0068]
[0069] Table 2 Comparison of purification effects
[0070]
[0071] Table 3 shows a comparison table of equipment operation and maintenance costs in this example, including total energy consumption per week, number of filter changes, amount of chemical purifiers used, number of equipment failures, maintenance man-hours, and weekly maintenance costs.
[0072] Table 3 Equipment operation and maintenance cost table
[0073] Compare Projects Experimental Group Control group Total energy consumption per week (kW·h) 750 900 Frequency of filter replacement (weekly) 1 3 Chemical purification agent usage (L / week) 15 20 Equipment failure times (per week) 0 2 Maintenance man-hours (hours / week) 2 8 Weekly maintenance cost (yuan) 500 1200
[0074] It can be seen from the above table that compared with the traditional single purification technology, the present invention has a more comprehensive purification effect. The working parameters and operating status of the purification module are automatically adjusted according to the generated optimal purification strategy, realizing the intelligence and automation of the purification process. This not only saves labor costs, but also ensures that the purification equipment is always in the best operating state, thereby improving the stability and reliability of the purification effect.
[0075] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.
Claims
1. An intelligent oil fume purification method, characterized in that: include: Use sensors to collect oil smoke data and perform pre-processing; Build a machine learning algorithm model through machine learning algorithm and extract features of oil smoke data; Rapidly generate an optimal purification strategy under the current circumstances based on the extracted data; The working parameters and operating status of the purification module are automatically adjusted according to the optimal purification strategy.
2. The intelligent oil fume purification method according to claim 1, characterized in that: The sensors include a fume concentration sensor, a temperature sensor, a humidity sensor, a gas composition sensor and a flow sensor; The oil fume data includes oil fume concentration data, temperature data, humidity data, gas composition data and flow data; The optimal purification strategy includes a purification technology combination strategy, a purification equipment operation parameter adjustment strategy, a purification equipment maintenance strategy and an intelligent control strategy.
3. The intelligent oil fume purification method according to claim 2, characterized in that: The specific steps of constructing the machine learning algorithm model are as follows: Dividing the oil fume data and generating oil fume feature labels; Build a machine learning algorithm model based on recurrent neural network and extract oil smoke features; The extracted oil smoke features are further analyzed using unsupervised learning algorithms; Evaluate the model and adjust the model structure or parameters based on the evaluation results.
4. The intelligent oil fume purification method according to claim 3, characterized in that: The specific formula for extracting the characteristics of oil smoke data is as follows: Among them, F(x(t)) is the extracted oil fume data feature; x1(t) is the instantaneous concentration of oil fume collected at the moment; x2(t) is the average particle size of particles in the oil fume at the moment.
5. The intelligent oil fume purification method according to claim 4, characterized in that: The specific formula for generating the optimal purification strategy is as follows: Among them, S is the optimal purification strategy; F(x(t)) is the extracted oil fume data feature; C is the oil fume concentration; H is the humidity value in the oil fume; G is the gas composition; A is the flow rate of the oil fume.
6. The intelligent oil fume purification method according to claim 5, characterized in that: The specific steps of generating the optimal purification strategy are as follows: Input the feature-extracted and processed data into the machine learning model; The model generates the optimal purification strategy for the current situation; Interpret and convert the generated results of the model output to determine the specific optimal purification strategy; The determined optimal purification strategy is converted into working parameters and operating status executable by the purification module.
7. The intelligent oil fume purification method according to claim 6, characterized in that: The purification module includes a physical filtration unit, an electrostatic purification module, a chemical purification module, a ventilation and power module, and an intelligent control and monitoring module.
8. An intelligent oil fume purification system, based on an intelligent oil fume purification method according to any one of claims 1 to 7, characterized in that: include, Data collection module, used to collect oil smoke data and perform pre-processing; Model building module, used to build machine learning models and extract features from oil smoke data; A strategy generation module is used to generate the optimal purification strategy under the current situation; The automatic adjustment module is used to automatically adjust the working parameters and operating status of the purification module.
9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a sponge city smart monitoring method described in any one of claims 1 to 7 are implemented.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a sponge city smart monitoring method described in any one of claims 1 to 7 are implemented.