A control method for a negative oxygen ion generator

Abstract

This invention discloses a control method for a negative ion generator, relating to the field of intelligent control technology for negative ion generators. The method includes: performing anti-interference processing on collected environmental data to form anti-interference data; analyzing the anti-interference data to obtain environmental analysis results, while simultaneously recording user-set preference information; generating a working plan based on the environmental analysis results and user preference information, and predicting the optimal working state; optimizing the negative ion generation mode based on the optimal working state, detecting ozone generation indicators, and generating a safety status report. This invention achieves efficient and accurate data acquisition and preprocessing by loading a graphical user interface, initiating a sensor network to collect environmental data, and using a random forest classifier for anti-interference processing. This ensures the reliability and accuracy of subsequent analysis, ultimately improving stability and data quality.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for negative ion generators, and in particular to a control method for a negative ion generator. Background Technology

[0002] Negative ion generators, as devices that can improve indoor air quality and enhance human health, have received widespread attention and rapid development in recent years. With people's increasing demands for living environment quality, negative ion generators are not only widely used in homes but also play an important role in healthcare, air purification, and other fields. Early negative ion generators mainly relied on simple corona discharge or plasma technology. While these methods could generate negative ions to some extent, they had many limitations, such as the generation of ozone byproducts, unstable negative ion concentration, and poor anti-interference capabilities.

[0003] In recent years, with advancements in sensor technology and intelligent control, negative ion generators have gradually evolved towards greater intelligence and precision. Modern negative ion generators are typically equipped with various types of sensors to monitor environmental parameters (such as temperature, humidity, and air quality) in real time, and adjust their operating modes through built-in control programs to optimize negative ion generation efficiency. Furthermore, the application of machine learning and artificial intelligence algorithms enables the devices to automatically adjust their operating status according to environmental changes, further improving device performance and user experience. However, despite significant technological progress, existing technologies still have some shortcomings, particularly in anti-interference processing and adaptation to personalized user needs. Summary of the Invention

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

[0005] Therefore, this invention provides a control method for a negative oxygen ion generator to solve the problems of insufficient environmental data anti-interference processing and inadequate adaptation to users' personalized needs.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a control method for a negative ion generator, comprising: performing anti-interference processing on collected environmental data to form anti-interference data; analyzing the anti-interference data to obtain environmental analysis results, and simultaneously recording user-set preference information; generating a working plan based on the environmental analysis results and user preference information, and predicting the optimal working state; optimizing the negative ion generation mode based on the optimal working state, detecting ozone generation indicators, and generating a safety status report.

[0008] In a preferred embodiment of the control method for the negative ion generator described in this invention, the steps for performing anti-interference processing on the collected environmental data to form anti-interference data are as follows:

[0009] Load the graphical user interface, set parameters through the graphical user interface, and start the sensor network to collect environmental data;

[0010] A random forest classifier is trained using historical data. This trained random forest classifier is then used to process environmental data to resist interference. The expression is as follows:

[0011] ;

[0012] in, For environmental data after anti-interference processing, To apply the trained random forest classifier to the input environmental data The probability score obtained when making a prediction. For the first Weighting factors for each sensor data, For the amount of sensor data, This is the parameter set for a random forest classifier. This is the adjustment coefficient for the sigmoid function. This represents the ideal value for environmental conditions. This represents the average value observed for the current environmental conditions.

[0013] In a preferred embodiment of the control method for the negative ion generator described in this invention, the steps of analyzing the pre-processed environmental data and recording user preference information are as follows:

[0014] Environmental data, after interference suppression processing, is acquired through sensors. A comprehensive evaluation function is then introduced to analyze this data. The expression is:

[0015] ;

[0016] in, To comprehensively evaluate the score, For environmental data after anti-interference processing, For the amount of sensor data, For the first The importance weight of each sensor data point This is the adjustment coefficient for the sigmoid function. This represents the ideal value for environmental conditions. This represents the average value observed for the current environmental conditions.

[0017] Launch the application on your device to access the graphical user interface, then access the preferences page to set and record your preferences.

[0018] In a preferred embodiment of the control method for the negative ion generator described in this invention, the steps for generating the working plan based on environmental analysis results and user preference information are as follows:

[0019] Based on environmental analysis results and user preference information, a grid search method is used to generate parameter combinations for different negative oxygen ion generation modes, forming candidate working schemes. A comprehensive working scheme scoring function is designed to predict the comprehensive working scheme score, and the expression is:

[0020] ;

[0021] in, To evaluate the comprehensive work plan, For the first The importance weight of each sensor data point For the amount of sensor data, To comprehensively evaluate the score, This is the adjustment coefficient for the sigmoid function. This represents the ideal value for environmental conditions. This represents the average value observed for the current environmental conditions. For user preference information, For user identification, These are environmental parameters.

[0022] The comprehensive work plan scoring function is used to calculate the comprehensive work plan score of the candidate work plans, and the candidate work plan with the highest score is selected as the work plan.

[0023] In a preferred embodiment of the control method for the negative ion generator of the present invention, the steps for predicting the optimal working state are as follows:

[0024] Performance data is collected using sensors. A Deep Q-Network (DQN) architecture is defined using a deep learning framework. The performance data is input into the DQN architecture, and the backpropagation algorithm and Adam are used to train the DQN. The working scheme is then input into the trained DQN to predict the optimal working state, expressed as:

[0025] ;

[0026] in, For the predicted optimal working state, the first The device parameters corresponding to each sensor data point To evaluate the comprehensive work plan, For a moment Environmental data, For a moment The equipment parameters, For the first The importance weight of each sensor data point For the amount of sensor data, This is the adjustment coefficient for the sigmoid function. This represents the ideal value for environmental conditions. This represents the average value observed for the current environmental conditions. For at any time The expected return of the equipment parameters adopted under environmental conditions;

[0027] Record the device parameters corresponding to the sensor data under the predicted optimal operating conditions to form the optimal operating conditions.

[0028] As a preferred embodiment of the control method for the negative ion generator of the present invention, the specific steps for optimizing the negative ion generation mode according to the optimal working state are as follows.

[0029] By collecting real-time data from sensors and corresponding actual values ​​of equipment parameters, a comprehensive work plan score is calculated using a comprehensive work plan scoring function. A dynamic adjustment function is defined based on the optimal working state, with the expression:

[0030] ;

[0031] in, For dynamically adjusted values, For the predicted optimal working state, the first The target value of the device parameters corresponding to each sensor data point. For the first The actual values ​​of the device parameters corresponding to each sensor data point. To evaluate the comprehensive work plan, For the first The importance weight of each sensor data point For the amount of sensor data, This is the adjustment coefficient for the sigmoid function. This represents the ideal value for environmental conditions. This represents the average value observed for the current environmental conditions.

[0032] Calculate the dynamic adjustment value of the negative oxygen ion generation mode;

[0033] The dynamic adjustment threshold is defined based on the dynamic adjustment value. ;

[0034] If the absolute value of the dynamically adjusted value Greater than the dynamic adjustment threshold Then the parameters of the negative oxygen ion generation mode are adjusted;

[0035] If the absolute value of the dynamically adjusted value Not greater than the dynamically adjusted threshold If so, the parameters of the negative oxygen ion generation mode will not be adjusted.

[0036] In a preferred embodiment of the control method for the negative ion generator described in this invention, the specific steps for detecting ozone generation indicators are as follows:

[0037] The actual values ​​of the device parameters corresponding to the adjusted sensor data are obtained from the optimized negative ion generation mode. Based on the optimized negative ion generation mode, an ozone generation detection function is introduced to quantify the ozone generation situation. The expression is:

[0038] ;

[0039] in, For a moment Ozone formation indicators For the adjusted number The actual values ​​of the device parameters corresponding to each sensor data point. For the first The importance weight of each sensor data point For the amount of sensor data, This is the adjustment coefficient for the sigmoid function. This represents the ideal value for environmental conditions. This represents the average value observed for the current environmental conditions. This is the adjustment coefficient for ozone formation efficiency. For the expected optimal performance value, For a moment Environmental data;

[0040] The ozone generation index was calculated at each time point using an ozone generation detection function, and the ozone generation index was recorded.

[0041] In a preferred embodiment of the control method for the negative ion generator of the present invention, the specific steps for generating the safety status report are as follows:

[0042] A dataset of ozone formation indicators is compiled, and a comprehensive risk assessment indicator is used to evaluate the risk of ozone formation. The expression is as follows:

[0043] ;

[0044] in, As a comprehensive risk assessment indicator, For a moment Ozone formation indicators For the first The importance weight of each sensor data point For the amount of sensor data, This is the adjustment coefficient for the sigmoid function. This represents the ideal value for environmental conditions. This represents the average value observed for the current environmental conditions. For the expected optimal performance value, For a moment Environmental data, Risk sensitivity coefficient;

[0045] Calculate and record the comprehensive risk assessment indicators in real time;

[0046] Define the comprehensive risk assessment threshold based on comprehensive risk assessment indicators. When the comprehensive risk assessment index exceeds the comprehensive risk assessment threshold Immediately turn off the negative ion generator and remind the user that the equipment has stopped running and to open the windows;

[0047] Simultaneously, view the ozone generation index and calculate the comprehensive evaluation score, and generate a safety status report.

[0048] In a second aspect, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the control method for the negative ion generator as described in the first aspect of the present invention.

[0049] Thirdly, 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, it implements any step of the control method for the negative ion generator as described in the first aspect of the present invention.

[0050] The beneficial effects of this invention are as follows: By loading a graphical user interface, initiating a sensor network to collect environmental data, and using a random forest classifier for anti-interference processing, efficient and accurate data acquisition and preprocessing are achieved, ensuring the reliability and accuracy of subsequent analysis, ultimately improving stability and data quality. By defining a dynamic adjustment value function and a dynamic adjustment threshold τ based on the dynamic adjustment value, the parameters of the negative oxygen ion generation mode exceeding the threshold are adjusted, achieving intelligent optimization of the working mode. This ensures the device is always in its optimal working state, ultimately improving the efficiency of negative oxygen ion generation, reducing resource waste, and lowering the risk of harmful byproduct formation. Attached Figure Description

[0051] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a flowchart of the control method for the negative oxygen ion generator in Example 1.

[0053] Figure 2 This is a schematic diagram of the optimization of the negative oxygen ion generation mode according to the optimal working state in Example 1. Detailed Implementation

[0054] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0055] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0056] 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 phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0057] Example 1, referring to Figure 1 and Figure 2 This is the first embodiment of the present invention, which provides a control method for a negative ion generator, including the following steps:

[0058] S1: Perform anti-interference processing on the collected environmental data to form anti-interference data;

[0059] Load the graphical user interface, set parameters through the graphical user interface, and start the sensor network to collect environmental data;

[0060] A random forest classifier is trained using historical data. This trained random forest classifier is then used to process environmental data to resist interference. The expression is as follows:

[0061] ;

[0062] in, For environmental data after anti-interference processing, To apply the trained random forest classifier to the input environmental data The probability score obtained when making a prediction. For the first Weighting factors for each sensor data, For the amount of sensor data, This is the parameter set for a random forest classifier. This is the adjustment coefficient for the sigmoid function. This represents the ideal value for environmental conditions. This represents the average value observed for the current environmental conditions.

[0063] It should also be noted that using a trained random forest classifier to process environmental data for anti-interference can cope with complex and ever-changing environmental conditions, thereby improving the stability and performance of the entire negative ion generator.

[0064] S2: Analyze the anti-interference data to obtain environmental analysis results, and simultaneously record the user's preference information;

[0065] Environmental data, after interference suppression processing, is acquired through sensors. A comprehensive evaluation function is then introduced to analyze this data. The expression is:

[0066] ;

[0067] in, To comprehensively evaluate the score, For environmental data after anti-interference processing, For the amount of sensor data, For the first The importance weight of each sensor data point This is the adjustment coefficient for the sigmoid function. This represents the ideal value for environmental conditions. This represents the average value observed for the current environmental conditions.

[0068] Launch the application on your device to access the graphical user interface, then access the preferences page to set and record your preferences.

[0069] It should also be noted that: by conducting in-depth analysis of the environmental data after anti-interference processing, a comprehensive evaluation score was obtained. The comprehensive evaluation score quantifies the quality of the environmental data, ensuring that subsequent decisions are based on high-quality data.

[0070] User preference information, including user ID and environmental parameters, is recorded through a graphical user interface.

[0071] S3: Generate a work plan based on the environmental analysis results and user preference information;

[0072] Based on environmental analysis results and user preference information, a grid search method is used to generate parameter combinations for different negative oxygen ion generation modes, forming candidate working schemes. A comprehensive working scheme scoring function is designed to predict the comprehensive working scheme score, and the expression is:

[0073] ;

[0074] in, To evaluate the comprehensive work plan, For the first The importance weight of each sensor data point For the amount of sensor data, To comprehensively evaluate the score, This is the adjustment coefficient for the sigmoid function. This represents the ideal value for environmental conditions. This represents the average value observed for the current environmental conditions. For user preference information, For user identification, These are environmental parameters.

[0075] The comprehensive work plan scoring function is used to calculate the comprehensive work plan score of the candidate work plans, and the candidate work plan with the highest score is selected as the work plan.

[0076] S4: Predict optimal working conditions;

[0077] Performance data is collected using sensors. A Deep Q-Network (DQN) architecture is defined using a deep learning framework. The performance data is input into the DQN architecture, and the backpropagation algorithm and Adam are used to train the DQN. The working scheme is then input into the trained DQN to predict the optimal working state, expressed as:

[0078] ;

[0079] in, For the predicted optimal working state, the first The device parameters corresponding to each sensor data point To evaluate the comprehensive work plan, For a moment Environmental data, For a moment The equipment parameters, For the first The importance weight of each sensor data point For the amount of sensor data, This is the adjustment coefficient for the sigmoid function. This represents the ideal value for environmental conditions. This represents the average value observed for the current environmental conditions. For at any time The expected return of the equipment parameters adopted under environmental conditions;

[0080] Record the device parameters corresponding to the sensor data under the predicted optimal operating conditions to form the optimal operating conditions.

[0081] It should also be noted that a well-trained Deep Q-Network (DQN) can automatically learn and adjust in complex and ever-changing environments to find the optimal working state, ensuring that the negative ion generator is always in a highly efficient and safe operating mode.

[0082] S5: Optimize the negative oxygen ion generation mode according to the optimal working state;

[0083] By collecting real-time data from sensors and corresponding actual values ​​of equipment parameters, a comprehensive work plan score is calculated using a comprehensive work plan scoring function. A dynamic adjustment function is defined based on the optimal working state, with the expression:

[0084] ;

[0085] in, For dynamically adjusted values, For the predicted optimal working state, the first The target value of the device parameters corresponding to each sensor data point. For the first The actual values ​​of the device parameters corresponding to each sensor data point. To evaluate the comprehensive work plan, For the first The importance weight of each sensor data point For the amount of sensor data, This is the adjustment coefficient for the sigmoid function. This represents the ideal value for environmental conditions. This represents the average value observed for the current environmental conditions.

[0086] Calculate the dynamic adjustment value of the negative oxygen ion generation mode;

[0087] The dynamic adjustment threshold is defined based on the dynamic adjustment value. ;

[0088] If the absolute value of the dynamically adjusted value Greater than the dynamic adjustment threshold Then the parameters of the negative oxygen ion generation mode are adjusted;

[0089] If the absolute value of the dynamically adjusted value Not greater than the dynamically adjusted threshold If so, the parameters of the negative oxygen ion generation mode will not be adjusted.

[0090] It should also be noted that the threshold will be dynamically adjusted. The value is set to 0.2, if Greater than and If the value is less than 0, gradually increase the voltage output, fan speed, and negative ion generator's operating intensity. Each voltage output adjustment is 0.1V, each fan speed adjustment is 50RPM, and each negative ion generator operating intensity adjustment is 1000 negative ions per cubic centimeter. Greater than and If the value is greater than 0, gradually reduce the voltage output, fan speed, and negative ion generator's operating intensity. Each voltage output adjustment is 0.1V, each fan speed adjustment is 50RPM, and each negative ion generator operating intensity adjustment is 1000 negative ions per cubic centimeter, until... Less than .

[0091] S6: Detects ozone generation indicators;

[0092] The actual values ​​of the device parameters corresponding to the adjusted sensor data are obtained from the optimized negative ion generation mode. Based on the optimized negative ion generation mode, an ozone generation detection function is introduced to quantify the ozone generation situation. The expression is:

[0093] ;

[0094] in, For a moment Ozone formation indicators For the adjusted number The actual values ​​of the device parameters corresponding to each sensor data point. For the first The importance weight of each sensor data point For the amount of sensor data, This is the adjustment coefficient for the sigmoid function. This represents the ideal value for environmental conditions. This represents the average value observed for the current environmental conditions. This is the adjustment coefficient for ozone formation efficiency. For the expected optimal performance value, For a moment Environmental data;

[0095] The ozone generation index was calculated at each time point using an ozone generation detection function, and the ozone generation index was recorded.

[0096] It should also be noted that the ozone generation detection function can monitor and evaluate the ozone generation of the negative ion generator in different working modes in real time.

[0097] S7: Generate a security status report.

[0098] A dataset of ozone formation indicators is compiled, and a comprehensive risk assessment indicator is used to evaluate the risk of ozone formation. The expression is as follows:

[0099] ;

[0100] in, As a comprehensive risk assessment indicator, For a moment Ozone formation indicators For the first The importance weight of each sensor data point For the amount of sensor data, This is the adjustment coefficient for the sigmoid function. This represents the ideal value for environmental conditions. This represents the average value observed for the current environmental conditions. For the expected optimal performance value, For a moment Environmental data, Risk sensitivity coefficient;

[0101] Calculate and record the comprehensive risk assessment indicators in real time;

[0102] Define the comprehensive risk assessment threshold based on comprehensive risk assessment indicators. When the comprehensive risk assessment index exceeds the comprehensive risk assessment threshold Immediately turn off the negative ion generator and remind the user that the equipment has stopped running and to open the windows;

[0103] Simultaneously, view the ozone generation index and calculate the comprehensive evaluation score, and generate a safety status report.

[0104] It should also be noted that using comprehensive risk assessment indicators to evaluate ozone generation risk ensures the accuracy and reliability of the safety status report;

[0105] Comprehensive risk assessment threshold The value is set to 0.6;

[0106] The safety status report includes current ozone generation indicators, a comprehensive evaluation score of current environmental data, and current environmental data. The safety status report is output to users through a graphical user interface to help them understand the equipment's operating status and potential risks.

[0107] This embodiment also provides a computer device applicable to the control method of a negative ion generator, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the control method of the negative ion generator as proposed in the above embodiment.

[0108] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0109] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the control method for realizing a negative ion generator as proposed in the above embodiments. 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 (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0110] In summary, this invention achieves efficient and accurate data acquisition and preprocessing by loading a graphical user interface, initiating a sensor network to collect environmental data, and using a random forest classifier for anti-interference processing. This ensures the reliability and accuracy of subsequent analysis, ultimately improving stability and data quality. By defining a dynamic adjustment function and a dynamic adjustment threshold τ based on the dynamic adjustment value, the invention adjusts the parameters of the negative oxygen ion generation mode exceeding the threshold, achieving intelligent optimization of the working mode. This ensures the device is always in its optimal working state, ultimately improving the efficiency of negative oxygen ion generation, reducing resource waste, and lowering the risk of harmful byproduct formation.

[0111] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A control method for a negative oxygen ion generator, characterized in that: include, The collected environmental data is processed to remove interference, resulting in anti-interference data. The anti-interference data is analyzed to obtain environmental analysis results, and user-set preference information is recorded simultaneously. Based on the environmental analysis results and user preference information, a work plan is generated, and the optimal working state is predicted. The specific steps are as follows: Performance data is collected using sensors. A Deep Q-Network (DQN) architecture is defined using a deep learning framework. The performance data is input into the DQN architecture, and the backpropagation algorithm and Adam are used to train the DQN. The working scheme is then input into the trained DQN to predict the optimal working state, expressed as: ； in, For the predicted optimal working state, the first The device parameters corresponding to each sensor data point To evaluate the comprehensive work plan, For a moment Environmental data, For a moment The equipment parameters, For the first The importance weight of each sensor data point For the amount of sensor data, This is the adjustment coefficient for the sigmoid function. This represents the ideal value for environmental conditions. This represents the average value observed for the current environmental conditions. For at any time The expected return of the equipment parameters adopted under environmental conditions; Record the device parameters corresponding to the sensor data under the predicted optimal operating conditions to form the optimal operating conditions; Optimize the negative oxygen ion generation mode based on the optimal working conditions, detect ozone generation indicators, and generate a safety status report.

2. The control method for the negative oxygen ion generator as described in claim 1, characterized in that: The process of performing anti-interference processing on the collected environmental data to form anti-interference data involves the following steps: Load the graphical user interface, set parameters through the graphical user interface, and start the sensor network to collect environmental data; A random forest classifier is trained using historical data. This trained random forest classifier is then used to process environmental data to resist interference. The expression is as follows: ； in, For environmental data after anti-interference processing, To apply the trained random forest classifier to the input environmental data The probability score obtained when making a prediction. This is the set of parameters for the random forest classifier.

3. The control method for the negative oxygen ion generator as described in claim 2, characterized in that: The process of analyzing the anti-interference data to obtain environmental analysis results, and simultaneously recording user-set preference information, is detailed below. A comprehensive evaluation function is introduced to analyze the environmental data after anti-interference processing. The expression is: ； in, To evaluate the overall score, Environmental data after anti-interference processing; Access the graphical user interface by launching the application on the device, then access the preferences page through the graphical user interface, set preferences on the preferences page, and record the user's preferences.

4. The control method for the negative oxygen ion generator as described in claim 3, characterized in that: The specific steps for generating a work plan based on environmental analysis results and user preference information are as follows. Based on environmental analysis results and user preference information, a grid search method is used to generate parameter combinations for different negative oxygen ion generation modes, forming candidate working schemes. A comprehensive working scheme scoring function is designed to predict the comprehensive working scheme score, and the expression is: ； in, To evaluate the comprehensive work plan, For user preference information, For user identification, For environmental parameters; The comprehensive work plan scoring function is used to calculate the comprehensive work plan score of the candidate work plans, and the candidate work plan with the highest score is selected as the work plan.

5. The control method for the negative oxygen ion generator as described in claim 1, characterized in that: The specific steps for optimizing the negative oxygen ion generation mode based on the optimal working state are as follows. By collecting real-time data from sensors and corresponding actual values ​​of equipment parameters, a comprehensive work plan score is calculated using a comprehensive work plan scoring function. A dynamic adjustment function is defined based on the optimal working state, with the expression: ； in, For dynamically adjusted values, For the predicted optimal working state, the first The target value of the device parameters corresponding to each sensor data point. For the first The actual values ​​of the device parameters corresponding to each sensor data point; Calculate the dynamic adjustment value of the negative oxygen ion generation mode; The dynamic adjustment threshold is defined based on the dynamic adjustment value. ; If the absolute value of the dynamically adjusted value Greater than the dynamic adjustment threshold Then the parameters of the negative oxygen ion generation mode are adjusted; If the absolute value of the dynamically adjusted value Not greater than the dynamically adjusted threshold If so, the parameters of the negative oxygen ion generation mode will not be adjusted.

6. The control method for the negative oxygen ion generator as described in claim 5, characterized in that: The specific steps for detecting ozone generation indicators are as follows: The actual values ​​of the device parameters corresponding to the adjusted sensor data are obtained from the optimized negative ion generation mode. Based on the optimized negative ion generation mode, an ozone generation detection function is introduced to quantify the ozone generation situation. The expression is: ； in, For a moment Ozone formation indicators For the adjusted number The actual values ​​of the device parameters corresponding to each sensor data point. This is the adjustment coefficient for ozone formation efficiency. This represents the expected optimal performance value. The ozone generation index was calculated at each time point using an ozone generation detection function, and the ozone generation index was recorded.

7. The control method for the negative oxygen ion generator as described in claim 6, characterized in that: The specific steps for generating the security status report are as follows: A dataset of ozone formation indicators is compiled, and a comprehensive risk assessment indicator is used to evaluate the risk of ozone formation. The expression is as follows: ； in, As a comprehensive risk assessment indicator, Risk sensitivity coefficient; Calculate and record the comprehensive risk assessment indicators in real time; Define the comprehensive risk assessment threshold R based on comprehensive risk assessment indicators. th When the comprehensive risk assessment index is greater than the comprehensive risk assessment threshold R th Immediately turn off the negative ion generator and remind the user that the equipment has stopped running and to open the windows; Simultaneously, view the ozone generation index and calculate the comprehensive evaluation score, and generate a safety status report.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the control method for the negative oxygen ion generator according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the control method for the negative oxygen ion generator according to any one of claims 1 to 7.

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