Control method and device of microminiature negative oxygen ion generator

Abstract

The invention discloses a control method and device for a micro-miniature negative oxygen ion generator, and relates to the technical field of air purification. The control method comprises the steps that air quality information is collected and preprocessed; the preprocessed air quality information is input into a machine learning model to be analyzed, and the current working efficiency of the negative oxygen ion generator is obtained; according to the current working efficiency, the electrode material is enhanced through the quantum dot material to obtain the optimal ionization efficiency; based on the optimal ionization efficiency, scanning surrounding negative oxygen ion generators through an application program to establish communication connection so as to form a distributed purification network; according to the distributed purification network, the negative oxygen ion generator shares the position, the air quality data and the working efficiency of the negative oxygen ion generator and performs task allocation; and based on task allocation, the user checks the working efficiency of the negative oxygen ion generator to implement safety protection measures. According to the invention, the electrode is enhanced by using the quantum dot material, and the ionization efficiency is optimized, so that the negative oxygen ion generator can efficiently operate in different environments.

Description

Technical Field

[0001] The present invention relates to the technical field of air purification, and in particular to a control method and device for a micro-sized negative oxygen ion generator. Background Art

[0002] As an air purification device, oxygen ion generator has the functions of refreshing the air and eliminating odors. It is considered to be an effective means of air purification. As the problem of air pollution becomes increasingly serious, the application scope of negative oxygen ion generators has gradually expanded. It is not only limited to homes and offices, but also covers public places and cars. Traditional negative oxygen ion generators mainly rely on electrostatic fields or high-voltage discharge principles to generate negative ions, and ionize water molecules, oxygen molecules, etc. in the air to achieve the effect of purifying the air. However, there are still some technical bottlenecks in performance improvement, intelligent control and energy management.

[0003] At present, the main limitation of negative oxygen ion generators is their optimal ionization efficiency, especially their unstable performance in complex environments. The intelligence and networking capabilities of negative oxygen ion generators are relatively weak, and they are unable to adjust their working efficiency according to real-time air quality data, resulting in some equipment not being able to be adjusted in a timely manner under inefficient operation or high load conditions. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a control method for a micro-negative oxygen ion generator to solve the problems of low optimal ionization efficiency, insufficient equipment intelligence, and inability to adjust working efficiency in real time.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a control method for a micro-sized negative oxygen ion generator, which comprises:

[0008] Collect air quality information and perform pre-processing;

[0009] The pre-processed air quality information is input into the machine learning model for analysis to obtain the current working efficiency of the negative oxygen ion generator;

[0010] Based on the current working efficiency, the optimal ionization efficiency is obtained by enhancing the electrode material with quantum dot materials;

[0011] Based on the optimal ionization efficiency, the application scans the surrounding negative oxygen ion generators to establish communication connections and form a distributed purification network;

[0012] According to the distributed purification network, negative oxygen ion generators share their locations, air quality data and work efficiency and perform task allocation;

[0013] Based on task allocation, users check the working efficiency of negative oxygen ion generators and implement safety protection measures.

[0014] As a preferred solution of the control method of the micro-negative oxygen ion generator of the present invention, the air quality information is collected and pre-processed, including the following steps:

[0015] The collected air quality information includes particulate matter concentration, volatile organic compounds, temperature and humidity, and the collected air quality information is cleaned and denoised.

[0016] As a preferred solution of the control method of the micro-negative oxygen ion generator of the present invention, the pre-processed air quality information is input into the machine learning model for analysis, and the current working efficiency of the negative oxygen ion generator is obtained, which includes the following steps:

[0017] The pre-processed particle concentration, volatile organic compounds, temperature and humidity data are input into the deep neural network model to obtain the current working efficiency of the negative oxygen ion generator, which is expressed as:

[0018]

[0019] Among them, S is the current working efficiency, P is the particle concentration, V is the particle concentration, H is the humidity, T is the temperature, α is the weight coefficient of particulate matter and volatile organic compounds, β is the weight coefficient of temperature and humidity, and γ is the sensitivity coefficient of comprehensive environmental factors.

[0020] As a preferred solution of the control method of the micro-miniature negative oxygen ion generator of the present invention, wherein: according to the current working efficiency, the optimal ionization efficiency is obtained by enhancing the electrode material by quantum dot material, including the following steps:

[0021] Select quantum dot materials that interact strongly with particulate matter concentration, volatile organic compounds, temperature and humidity data;

[0022] Dispersing quantum dot materials on a conductive substrate to form an enhanced electrode material;

[0023] According to the current working efficiency, the optimal ionization efficiency is calculated by combining quantum dot material to enhance the electrode material. The expression is:

[0024]

[0025] Where E is the optimal ionization efficiency, λ is the exponential decay coefficient, and X i is the ith variable of air quality data, Yi is the i-th weight coefficient of air quality data, η is the adjustment coefficient of optimal ionization efficiency, γ is the coefficient of adjusting environmental factors, n is the dimension of air quality data for adjusting optimal ionization efficiency, and i is the index of air quality data.

[0026] As a preferred solution of the control method of the micro-negative oxygen ion generator of the present invention, based on the optimal ionization efficiency, the application scans the surrounding negative oxygen ion generators to establish a communication connection to form a distributed purification network, including the following steps:

[0027] Scan the surrounding environment through the app to find the negative oxygen ion generator nearby;

[0028] According to the scanned negative oxygen ion generators, the application communicates with each negative oxygen ion generator through a communication protocol to establish a connection;

[0029] The application sends adjustment instructions to the negative oxygen ion generator according to the optimal ionization efficiency, and sends the instructions to the negative oxygen ion generator through a distributed network protocol to form a distributed purification network.

[0030] As a preferred solution of the control method of the micro-negative oxygen ion generator of the present invention, wherein: according to the distributed purification network, the negative oxygen ion generator shares its location, air quality data and work efficiency and performs task allocation, including the following steps:

[0031] Each negative oxygen ion generator uploads its geographical location, air quality data and working efficiency to the central controller when establishing a communication protocol;

[0032] The geographical location of the negative oxygen ion generator is obtained by using GPS positioning through local positioning technology;

[0033] The negative oxygen ion generators are assigned tasks based on their working efficiency according to the air quality data of each device, and the workload of the negative oxygen ion generators is increased first in areas with poor air quality;

[0034] After receiving the task, each negative oxygen ion generator adjusts its working efficiency according to the optimal ionization efficiency.

[0035] As a preferred solution of the control method of the micro-negative oxygen ion generator of the present invention, wherein: based on task allocation, the user checks the working efficiency of the negative oxygen ion generator and implements safety protection measures, including the following steps:

[0036] Users can check the working efficiency of each negative oxygen ion generator through the mobile application;

[0037] The power and temperature thresholds are set by the power of the negative oxygen ion generator. When the power of the negative oxygen ion generator is greater than the power threshold and the temperature of the negative oxygen ion generator is less than the temperature threshold, it works normally.

[0038] When the power of the negative oxygen ion generator is equal to the power threshold, and the temperature of the negative oxygen ion generator is equal to the temperature threshold, an alarm is issued;

[0039] When the power of the negative oxygen ion generator is less than the power threshold and the temperature of the negative oxygen ion generator is greater than the temperature threshold, the negative oxygen ion generator stops working.

[0040] In a second aspect, the present invention provides a control device for a micro-sized negative oxygen ion generator, comprising:

[0041] Data acquisition module, collects air quality information and performs preprocessing;

[0042] The machine learning analysis module inputs the pre-processed air quality information into the machine learning model for analysis to obtain the current working efficiency of the negative oxygen ion generator;

[0043] Ionization efficiency calculation module, based on the current working efficiency, the optimal ionization efficiency is obtained by enhancing the electrode material through quantum dot materials;

[0044] The distributed purification network establishment module, based on the optimal ionization efficiency, scans the surrounding negative oxygen ion generators through the application to establish communication connections to form a distributed purification network;

[0045] Task allocation module, based on the distributed purification network, negative oxygen ion generators share their locations, air quality data and work efficiency and perform task allocation;

[0046] Safety protection module, based on task allocation, users can view the working efficiency of the negative oxygen ion generator and implement safety protection measures.

[0047] In a third aspect, 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 the control method of the micro-negative oxygen ion generator as described in the first aspect of the present invention is implemented.

[0048] In a fourth aspect, 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 the control method of the micro-negative oxygen ion generator as described in the first aspect of the present invention is implemented.

[0049] The beneficial effects of the present invention are as follows: by collecting and preprocessing air quality information, the accuracy of data input into the machine learning model is ensured, thereby improving the analysis accuracy of work efficiency; the electrodes are enhanced by using quantum dot materials to optimize the ionization efficiency, so that the negative oxygen ion generator can operate efficiently in different environments; by scanning and connecting the surrounding negative oxygen ion generators through the application, a distributed purification network is established, and the collaborative work of multiple devices is achieved, thereby expanding the coverage and effect of air purification; the devices in the network share locations and work efficiency according to air quality data, dynamically allocate tasks, and ensure that areas with poor air quality are given priority; through task allocation and safety protection measures, the device status is monitored in real time to ensure that the device automatically shuts down for protection when the power is low or the temperature is too high, thereby extending the device life and improving stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0051] Figure 1 This is a flow chart of the control method of the micro-negative oxygen ion generator in Example 1.

[0052] Figure 2 This is a schematic diagram of the control device of the micro-negative oxygen ion generator in Example 1. DETAILED DESCRIPTION

[0053] 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.

[0054] 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.

[0055] 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.

[0056] Example 1, reference Figure 1 and Figure 2, which is the first embodiment of the present invention, provides a control method for a micro-sized negative oxygen ion generator, comprising the following steps:

[0057] S1. Collect air quality information and perform preprocessing.

[0058] S1.1. Collect air quality information including particulate matter concentration, volatile organic compounds, temperature and humidity, and clean and denoise the collected air quality information.

[0059] Furthermore, air quality information includes particulate matter concentration (such as PM2.5 and PM10), volatile organic compounds (such as TVOC), temperature and humidity;

[0060] Check whether there are missing values ​​or invalid values, and fill in the missing values ​​by interpolation based on the data of adjacent time periods;

[0061] Use the box plot method to identify and mark outliers, choose to delete or replace outliers with reasonable estimates, check for duplicate data points, and remove duplicate records to retain unique values;

[0062] For high-frequency noise, a low-pass filter is used to smooth the data curve and retain the low-frequency components. The wavelet transform is used to decompose the signal, separate the high-frequency noise part and remove it while retaining the effective information.

[0063] S2. Input the pre-processed air quality information into the machine learning model for analysis to obtain the current working efficiency of the negative oxygen ion generator.

[0064] S2.1. Input the pre-processed particle concentration, volatile organic compounds, temperature and humidity data into the deep neural network model to obtain the current working efficiency of the negative oxygen ion generator, which is expressed as:

[0065]

[0066] Among them, S is the current working efficiency, P is the particle concentration, V is the particle concentration, H is the humidity, T is the temperature, α is the weight coefficient of particulate matter and volatile organic compounds, β is the weight coefficient of temperature and humidity, and γ is the sensitivity coefficient of comprehensive environmental factors.

[0067] Furthermore, the processed particle concentration P, volatile organic compounds V, temperature T and humidity H are input into the deep neural network model for calculation to obtain the current working efficiency of the negative oxygen ion generator;

[0068] By incorporating temperature and humidity environmental factors into the deep neural network model, the negative oxygen ion generator can adapt to various environmental changes, especially in environments with large changes in temperature and humidity, and can still ensure its stable operation and ensure continuous improvement of air quality.

[0069] S3. Based on the current working efficiency, the electrode material is enhanced by quantum dot materials to obtain the optimal ionization efficiency.

[0070] S3.1. Select quantum dot materials that interact strongly with particulate matter concentration, volatile organic compounds, temperature, and humidity data.

[0071] Furthermore, quantum dot materials have strong surface activity and can produce strong physical or chemical interactions with particulate matter, volatile organic compounds, temperature and humidity. Quantum dot materials have strong charge transfer capabilities, helping to capture particulate matter in the air and reducing the impact of particulate matter on electrode surface ionization.

[0072] The chemical reactivity of the QD surface should be able to adsorb and transform VOCs, thereby improving the ionization efficiency;

[0073] The optical and electrical properties of quantum dots are related to the ambient temperature and humidity, so quantum dot materials that are stable under changing temperature and humidity conditions should be selected.

[0074] S3.2. Disperse the quantum dot material on a conductive substrate to form an enhanced electrode material.

[0075] Furthermore, the selected quantum dot material is evenly dispersed on a conductive substrate (such as a metal or carbon-based conductor) to form a composite electrode. The role of the conductive substrate is to provide a stable carrier for the quantum dots and ensure good conduction of current. The quantum dot material effectively improves the efficiency of the ionization process by enhancing the electron density and charge transfer ability of the electrode surface. The size, morphology and surface state of the quantum dots determine the degree of electronic interaction with the electrode surface, which can increase the ionization rate and improve the working efficiency of the negative oxygen ion generator.

[0076] S3.3. According to the current working efficiency, the optimal ionization efficiency is calculated by combining the quantum dot material to enhance the electrode material. The expression is:

[0077]

[0078] Where E is the optimal ionization efficiency, λ is the exponential decay coefficient, and X i is the ith variable of air quality data, Y i is the i-th weight coefficient of air quality data, η is the adjustment coefficient of optimal ionization efficiency, γ is the coefficient of adjusting environmental factors, n is the dimension of air quality data for adjusting optimal ionization efficiency, and i is the index of air quality data.

[0079] Furthermore, by combining air quality data (such as particulate matter concentration, volatile organic compounds, temperature and humidity) with calculations of ionization efficiency, the negative oxygen ion generator can adjust according to real-time environmental conditions to achieve optimal performance;

[0080] By introducing the exponential decay coefficient λ and the weight coefficient Y i As well as the adjustment coefficient η and the environmental factor coefficient γ, this formula can accurately reflect the impact of each environmental parameter on the ionization efficiency, so that the ionization efficiency can not only be optimized according to the air quality, but also fine-tuned according to the specific work efficiency, further improving the work efficiency of the equipment.

[0081] S4. Based on the optimal ionization efficiency, the application scans the surrounding negative oxygen ion generators to establish communication connections and form a distributed purification network.

[0082] S4.1. Scan the surrounding environment through the application to find the negative oxygen ion generator nearby.

[0083] Furthermore, the application scans the devices in the surrounding environment by using wireless communication technology. The application sends a request signal to find a negative oxygen ion generator with a specific identifier (such as device ID, MAC address).

[0084] S4.2. Based on the scanned negative oxygen ion generators, the application communicates with each negative oxygen ion generator through a communication protocol to establish a connection.

[0085] Furthermore, after scanning the negative oxygen ion generators, the application selects the appropriate communication protocol to connect to each device. When connected, each negative oxygen ion generator will exchange device status information such as power, current working efficiency and temperature parameters through the communication protocol to ensure that the latest information of the device can be obtained in real time.

[0086] S4.3. The application sends adjustment instructions to the negative oxygen ion generator according to the optimal ionization efficiency, and sends the instructions to the negative oxygen ion generator through a distributed network protocol to form a distributed purification network.

[0087] Furthermore, after the device connection is completed, the application will send adjustment instructions to each negative oxygen ion generator based on the optimal ionization efficiency calculated previously. These adjustment instructions may include adjusting the ionization efficiency, changing the working mode, setting the operating time and adjusting the load to optimize the purification effect of the negative oxygen ion generator under a specific environment. The application sends the adjustment instructions to each negative oxygen ion generator through a distributed network protocol, which can ensure that the instructions are transmitted synchronously between multiple devices.

[0088] S5. According to the distributed purification network, negative oxygen ion generators share their locations, air quality data and work efficiency and perform task allocation.

[0089] S5.1. Each negative oxygen ion generator uploads its geographical location, air quality data and working efficiency to the central controller when establishing a communication protocol.

[0090] Furthermore, after establishing a communication connection with the central controller, each negative oxygen ion generator will upload its current geographic location, real-time air quality data and work efficiency to the central controller through the wireless communication protocol. The uploaded data includes the geographical coordinates of the negative oxygen ion generator, air quality data (such as particulate matter concentration, volatile organic compound concentration, humidity and temperature) and the working efficiency of the equipment.

[0091] S5.2. Use GPS positioning through local positioning technology to obtain the geographical location of the negative oxygen ion generator.

[0092] Furthermore, each negative oxygen ion generator is equipped with GPS. When the device is turned on, the GPS will obtain the current geographic coordinates of the device.

[0093] S5.3. Assign tasks to the negative oxygen ion generator based on the air quality data of each device, and increase the workload of the negative oxygen ion generator in areas with poor air quality.

[0094] Furthermore, the central controller analyzes the air quality data collected from each negative oxygen ion generator (such as particulate matter concentration, volatile organic compound concentration, temperature and humidity, etc.), and areas with poor air quality will be given priority to be assigned more workloads. In areas with poor air quality, the workload of the negative oxygen ion generators will increase work intensity, extend working hours, or increase the number of negative oxygen ion generators, aiming to better improve the air quality in the area.

[0095] S5.4. After receiving the task, each negative oxygen ion generator adjusts the working efficiency of the negative oxygen ion generator according to the optimal ionization efficiency.

[0096] Furthermore, the negative oxygen ion generator automatically adjusts its operating mode, output ionization amount, and increases or decreases work intensity based on the received task instructions and the calculation results of the optimal ionization efficiency. Each negative oxygen ion generator adjusts its working efficiency according to the environment and the requirements of the optimal ionization efficiency to ensure that each device operates at the most suitable working efficiency.

[0097] S6. Based on task allocation, the user checks the working efficiency of the negative oxygen ion generator and implements safety protection measures.

[0098] S6.1. Users can check the working efficiency of each negative oxygen ion generator through the mobile application.

[0099] Furthermore, users can monitor the status of each negative oxygen ion generator in real time, including power, temperature, air quality and operating mode, through a mobile application connected to the distributed network. The application displays real-time data of each device through an intuitive user interface, allowing users to easily understand the current status of the device.

[0100] S6.2. Set the power and temperature thresholds according to the power of the negative oxygen ion generator. When the power of the negative oxygen ion generator is greater than the power threshold and the temperature of the negative oxygen ion generator is less than the temperature threshold, it works normally.

[0101] Furthermore, when the power of the negative oxygen ion generator is greater than the power threshold and the temperature is less than the temperature threshold, the negative oxygen ion generator enters normal operating efficiency, and the application displays that the generator is in normal operating efficiency, indicating that the device is operating efficiently.

[0102] S6.3. When the power level of the negative oxygen ion generator is equal to the power threshold and the temperature of the negative oxygen ion generator is equal to the temperature threshold, an alarm is issued.

[0103] Furthermore, when the charge level of the negative oxygen ion generator is equal to the charge threshold or the temperature is equal to the temperature threshold, an alarm is triggered and the user receives a warning in the application indicating that there is a potential problem with the generator and that it needs to be addressed (such as charging or cooling).

[0104] S6.4. When the power of the negative oxygen ion generator is less than the power threshold and the temperature of the negative oxygen ion generator is greater than the temperature threshold, it stops working.

[0105] Furthermore, when the power of the negative oxygen ion generator is less than the power threshold and the temperature is higher than the temperature threshold, the negative oxygen ion generator will automatically stop working to protect the safety of the device. The application will prompt the user that the device has stopped working and recommend taking measures (such as charging or cooling down).

[0106] This embodiment also provides a control device for a micro-sized negative oxygen ion generator, comprising: a data acquisition module for collecting air quality information and performing pre-processing;

[0107] The machine learning analysis module inputs the pre-processed air quality information into the machine learning model for analysis to obtain the current working efficiency of the negative oxygen ion generator;

[0108] Ionization efficiency calculation module, based on the current working efficiency, the optimal ionization efficiency is obtained by enhancing the electrode material through quantum dot materials;

[0109] The distributed purification network establishment module, based on the optimal ionization efficiency, scans the surrounding negative oxygen ion generators through the application to establish communication connections to form a distributed purification network;

[0110] Task allocation module, based on the distributed purification network, negative oxygen ion generators share their locations, air quality data and work efficiency and perform task allocation;

[0111] Safety protection module, based on task allocation, users can view the working efficiency of the negative oxygen ion generator and implement safety protection measures.

[0112] This embodiment also provides a computer device, which is suitable for the control method of a micro-negative oxygen ion generator, 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 the control method of the micro-negative oxygen ion generator proposed in the above embodiment.

[0113] 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.

[0114] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by the processor, the control method for realizing the micro-negative oxygen ion generator 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.

[0115] In summary, the present invention collects and preprocesses air quality information, ensures the accuracy of data input into the machine learning model, thereby improving the analysis accuracy of work efficiency, utilizes quantum dot materials to enhance the electrode, optimizes the ionization efficiency, and enables the negative oxygen ion generator to operate efficiently in different environments. By scanning and connecting the surrounding negative oxygen ion generators through the application, a distributed purification network is established, and the collaborative work of multiple devices is realized, thereby expanding the coverage and effect of air purification. The devices in the network share locations and work efficiency according to air quality data, dynamically allocate tasks, and ensure that areas with poor air quality are given priority. Through task allocation and safety protection measures, the device status is monitored in real time to ensure that the device automatically shuts down for protection when the power is low or the temperature is too high, thereby extending the device life and improving stability.

[0116] 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. A control method for a micro-sized negative oxygen ion generator, characterized in that: include, Collect air quality information and perform pre-processing; The pre-processed air quality information is input into the machine learning model for analysis to obtain the current working efficiency of the negative oxygen ion generator; Based on the current working efficiency, the optimal ionization efficiency is obtained by enhancing the electrode material with quantum dot materials; Based on the optimal ionization efficiency, the application scans the surrounding negative oxygen ion generators to establish communication connections and form a distributed purification network; According to the distributed purification network, negative oxygen ion generators share their locations, air quality data and work efficiency and perform task allocation; Based on task allocation, users check the working efficiency of negative oxygen ion generators and implement safety protection measures.

2. The control method of the micro-miniature negative oxygen ion generator according to claim 1, characterized in that: Collecting air quality information and preprocessing it includes the following steps: The collected air quality information includes particulate matter concentration, volatile organic compounds, temperature and humidity, and the collected air quality information is cleaned and denoised.

3. The control method of the micro-miniature negative oxygen ion generator according to claim 2, characterized in that: The pre-processed air quality information is input into the machine learning model for analysis, and the current working efficiency of the negative oxygen ion generator is obtained, which includes the following steps: The pre-processed particle concentration, volatile organic compounds, temperature and humidity data are input into the deep neural network model to obtain the current working efficiency of the negative oxygen ion generator, which is expressed as: Among them, S is the current working efficiency, P is the particle concentration, V is the particle concentration, H is the humidity, T is the temperature, α is the weight coefficient of particulate matter and volatile organic compounds, β is the weight coefficient of temperature and humidity, and γ is the sensitivity coefficient of comprehensive environmental factors.

4. The control method of the micro-miniature negative oxygen ion generator according to claim 3, characterized in that: According to the current working efficiency, the optimal ionization efficiency can be obtained by enhancing the electrode material with quantum dot materials, including the following steps: Select quantum dot materials that interact strongly with particulate matter concentration, volatile organic compounds, temperature and humidity data; Dispersing quantum dot materials on a conductive substrate to form an enhanced electrode material According to the current working efficiency, the optimal ionization efficiency is calculated by combining quantum dot material to enhance the electrode material. The expression is: Where E is the optimal ionization efficiency, λ is the exponential decay coefficient, and X i is the ith variable of air quality data, Y i is the i-th weight coefficient of air quality data, η is the adjustment coefficient of optimal ionization efficiency, γ is the coefficient of adjusting environmental factors, n is the dimension of air quality data for adjusting optimal ionization efficiency, and i is the index of air quality data.

5. The control method of the micro-miniature negative oxygen ion generator according to claim 4, characterized in that: Based on the optimal ionization efficiency, the application scans the surrounding negative oxygen ion generators to establish communication connections to form a distributed purification network, including the following steps: Scan the surrounding environment through the app to find the negative oxygen ion generators nearby; According to the scanned negative oxygen ion generators, the application communicates with each negative oxygen ion generator through a communication protocol to establish a connection; The application sends adjustment instructions to the negative oxygen ion generator according to the optimal ionization efficiency, and sends the instructions to the negative oxygen ion generator through a distributed network protocol to form a distributed purification network.

6. The control method of the micro-miniature negative oxygen ion generator according to claim 5, characterized in that: According to the distributed purification network, negative oxygen ion generators share their locations, air quality data and work efficiency and perform task allocation including the following steps: Each negative oxygen ion generator uploads its geographical location, air quality data and working efficiency to the central controller when establishing a communication protocol; The geographical location of the negative oxygen ion generator is obtained by using GPS positioning through local positioning technology; The negative oxygen ion generators are assigned tasks based on their working efficiency according to the air quality data of each device, and the workload of negative oxygen ion generators is increased first in areas with poor air quality; After receiving the task, each negative oxygen ion generator adjusts its working efficiency according to the optimal ionization efficiency.

7. The control method of the micro-miniature negative oxygen ion generator according to claim 6, characterized in that: Based on the task allocation, the user checks the working efficiency of the negative oxygen ion generator and implements safety protection measures including the following steps: Users can check the working efficiency of each negative oxygen ion generator through the mobile application; The power and temperature thresholds are set by the power of the negative oxygen ion generator. When the power of the negative oxygen ion generator is greater than the power threshold and the temperature of the negative oxygen ion generator is less than the temperature threshold, it works normally. When the power of the negative oxygen ion generator is equal to the power threshold, and the temperature of the negative oxygen ion generator is equal to the temperature threshold, an alarm is issued; When the power of the negative oxygen ion generator is less than the power threshold and the temperature of the negative oxygen ion generator is greater than the temperature threshold, the negative oxygen ion generator stops working.

8. A control device for a micro-sized negative oxygen ion generator, based on the control device for a micro-sized negative oxygen ion generator according to any one of claims 1 to 7, characterized in that: include, Data acquisition module, collects air quality information and performs preprocessing; The machine learning analysis module inputs the pre-processed air quality information into the machine learning model for analysis to obtain the current working efficiency of the negative oxygen ion generator; Ionization efficiency calculation module, based on the current working efficiency, the optimal ionization efficiency is obtained by enhancing the electrode material through quantum dot materials; The distributed purification network establishment module, based on the optimal ionization efficiency, scans the surrounding negative oxygen ion generators through the application to establish communication connections to form a distributed purification network; Task allocation module, based on the distributed purification network, negative oxygen ion generators share their locations, air quality data and work efficiency and perform task allocation; Safety protection module, based on task allocation, users can view the working efficiency of the negative oxygen ion generator and implement safety protection measures.

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 the control method of the micro-negative oxygen ion generator 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 the control method of the micro-negative oxygen ion generator described in any one of claims 1 to 7 are implemented.

Images