Operating parameter regulation and control method and platform of air purifier

Abstract

The invention discloses an operation parameter regulation and control method and platform of an air purifier, and relates to the technical field of air purification. The method comprises the steps that real-time air detection data are obtained; performing purification demand evaluation, and generating an air purification instruction; activating the air purification regulation and control model to obtain an air purification regulation and control scheme; regulation and control prediction evaluation is conducted on the air purifier in the target space, and a purification regulation and control prediction evaluation result is obtained; judging whether the purification regulation and control prediction evaluation result meets the purification regulation and control prediction evaluation expectation or not; if not, an air purification regulation and control strategy is generated; and the air purifier is controlled according to the air purification regulation and control strategy. The technical problems that in the prior art, an air purifier is not flexible enough in regulation and control and cannot respond to the air quality change in real time, so that the air purification efficiency is low, and the energy consumption is high are solved, the operation parameters of the air purifier are dynamically adjusted according to the real-time air quality data, and the air purification efficiency is improved. The technical effects of improving the air purification efficiency and reducing the energy consumption are achieved.

Description

Technical Field

[0001] The present application relates to the technical field related to air purification, and in particular to a method and platform for controlling operating parameters of an air purifier. Background Art

[0002] With the acceleration of the industrialization process in modern society and the continuous improvement of people's living standards, the problem of air pollution is becoming increasingly serious. As an important device for improving indoor air quality, air purifiers have been widely used in homes, offices, shopping malls, hospitals and other places. Air purifiers remove harmful substances in the air, such as PM2.5, formaldehyde, bacteria, viruses, etc., through filters, activated carbon and other methods. However, in the actual use of air purifiers, they are affected by many factors, such as real-time changes in air quality, different spatial layouts, and limitations of purifier performance. The traditional static operation mode is difficult to reasonably adjust its wind speed, filter efficiency, working mode, etc. according to the characteristics of the space, so as to improve the air purification efficiency and achieve the best air purification effect, and it is impossible to achieve a balance between purification efficiency and energy consumption. In actual applications, excessive operation of the purifier not only wastes energy, but also may cause unnecessary noise and fluctuations in air quality.

[0003] Therefore, in the current relevant technologies, there are technical problems that the air purifier is not flexible enough in control and cannot respond to changes in air quality in real time, which in turn leads to low air purification efficiency and high energy consumption. Summary of the invention

[0004] The present application provides an air purifier operating parameter control method and platform, thereby solving the technical problems in the prior art that the air purifier control is not flexible enough and cannot respond to air quality changes in real time, which leads to low air purification efficiency and high energy consumption. The application realizes dynamic adjustment of the air purifier operating parameters according to real-time air quality data, achieving the technical effect of improving air purification efficiency and reducing energy consumption.

[0005] The present application provides a method for controlling the operating parameters of an air purifier, the method comprising: obtaining real-time air detection data of a target space; evaluating purification needs based on the real-time air detection data and generating an air purification instruction; activating an air purification control model based on the air purification instruction, and inputting the real-time air detection data into the air purification control model to obtain an air purification control scheme; based on the real-time air detection data, performing a control prediction and evaluation on the air purifier of the target space according to the air purification control scheme to obtain a purification control prediction and evaluation result; judging whether the purification control prediction and evaluation result meets the purification control prediction and evaluation expectation; if the purification control prediction and evaluation result does not meet the purification control prediction and evaluation expectation, optimizing the air purification control scheme according to the purification control prediction and evaluation expectation to generate an air purification control strategy; and controlling the air purifier according to the air purification control strategy.

[0006] In a possible implementation, the air purifier of the target space is controlled, predicted and evaluated according to the air purification control scheme to obtain a purification control prediction and evaluation result, and the following processing is also performed: the air purifier is controlled, evaluated and recorded, and a purification control prediction and evaluation channel is established, wherein the purification control prediction and evaluation channel includes an air purification quality prediction branch, a purification control energy consumption prediction branch and a purification control noise prediction branch; the real-time air detection data and the air purification control scheme are input into the air purification quality prediction branch to obtain a predicted air purification quality coefficient; based on the real-time air detection data and the air purification control scheme, according to the purification control energy consumption prediction branch, a purification control energy consumption prediction coefficient is obtained; the real-time air detection data and the air purification control scheme are input into the purification control noise prediction branch to obtain a purification control noise prediction coefficient; the predicted air purification quality coefficient, the purification control energy consumption prediction coefficient and the purification control noise prediction coefficient are output as the purification control prediction and evaluation result.

[0007] In a possible implementation, the control evaluation record of the air purifier is studied, a purification control prediction evaluation channel is established, and the following processing is performed: the control evaluation record of the air purifier is retrieved to obtain an air detection sample set, a purification control scheme sample set, a purification quality evaluation sample set, a purification control energy consumption evaluation sample set, and a purification control noise evaluation sample set; integrated fusion learning is performed based on the air detection sample set, the purification control scheme sample set, and the purification quality evaluation sample set to establish the air purification quality prediction branch; integrated fusion learning is performed based on the air detection sample set, the purification control scheme sample set, and the purification control energy consumption evaluation sample set to establish the purification control energy consumption prediction branch; integrated fusion learning is performed based on the air detection sample set, the purification control scheme sample set, and the purification control noise evaluation sample set to generate the purification control noise prediction branch; the air purification quality prediction branch, the purification control energy consumption prediction branch, and the purification control noise prediction branch are connected to generate the purification control prediction evaluation channel.

[0008] In a possible implementation, integrated fusion learning is performed according to the air detection sample set, the purification control scheme sample set and the purification quality evaluation sample set to establish the air purification quality prediction branch, and the following processing is also performed: supervised training is performed on P meta-learners according to the air detection sample set, the purification control scheme sample set and the purification quality evaluation sample set to obtain P air purification quality predictors, where P is a positive integer greater than 1; the output data sets of the P air purification quality predictors are used as input information and the purification quality evaluation sample set is used as output information to train a purification quality prediction fusion model; the P air purification quality predictors are merged as parallel independent nodes to generate an air purification quality prediction model; the air purification quality prediction model is merged with the input layer of the purification quality prediction fusion model to generate the air purification quality prediction branch.

[0009] In a possible implementation, if the purification control prediction and evaluation result does not meet the purification control prediction and evaluation expectation, the air purification control scheme is optimized and regulated according to the purification control prediction and evaluation expectation to generate an air purification control strategy, and the following processing is also performed: the operating parameter constraint information of the air purifier is collected to establish a purification control constraint domain; the air purification control scheme is adjusted according to the purification control constraint domain to generate a purification adjustment scheme space; according to the purification adjustment scheme space, a first purification adjustment scheme is extracted; the real-time air detection data and the first purification adjustment scheme are input into the purification control prediction and evaluation channel to obtain a purification prediction result of the first adjustment scheme; it is determined whether the purification prediction result of the first adjustment scheme meets the purification control prediction and evaluation expectation; if the purification prediction result of the first adjustment scheme meets the purification control prediction and evaluation expectation, the first purification adjustment scheme is added to the air purification control strategy.

[0010] In a possible implementation, it is determined whether the purification prediction result of the first adjustment scheme meets the purification control prediction and evaluation expectations, and the following processing is also performed: if the purification prediction result of the first adjustment scheme does not meet the purification control prediction and evaluation expectations, the first purification adjustment scheme is eliminated; according to the purification adjustment scheme space, the second purification adjustment scheme is extracted; the real-time air detection data and the second purification adjustment scheme are input into the purification control prediction and evaluation channel to obtain the purification prediction result of the second adjustment scheme; it is determined whether the purification prediction result of the second adjustment scheme meets the purification control prediction and evaluation expectations; if the purification prediction result of the second adjustment scheme meets the purification control prediction and evaluation expectations, the second purification adjustment scheme is added to the air purification control strategy; if the purification prediction result of the second adjustment scheme does not meet the purification control prediction and evaluation expectations, the second purification adjustment scheme is eliminated, and the purification adjustment scheme space is continuously optimized and iterated according to the purification control prediction and evaluation expectations until the air purification control strategy is generated.

[0011] In a possible implementation, a purification demand evaluation is performed based on the real-time air detection data, an air purification instruction is generated, and the following processing is performed: the air quality of the target space is evaluated based on the real-time air detection data to obtain a real-time air quality coefficient; based on the air quality constraints of the target space, a purification demand evaluation is performed on the real-time air quality coefficient to generate an air purification demand coefficient; it is determined whether the air purification demand coefficient is greater than or equal to an air purification demand threshold; if the air purification demand coefficient is greater than or equal to the air purification demand threshold, the air purification instruction is obtained.

[0012] The present application also provides an operating parameter control platform for an air purifier, including: a real-time air detection data acquisition module, used to obtain real-time air detection data of a target space; a purification demand evaluation module, used to perform purification demand evaluation based on the real-time air detection data and generate an air purification instruction; an air purification control scheme acquisition module, used to activate an air purification control model based on the air purification instruction, and input the real-time air detection data into the air purification control model to obtain an air purification control scheme; a control prediction evaluation module, used to perform control prediction evaluation on the air purifier of the target space based on the real-time air detection data and according to the air purification control scheme, and obtain a purification control prediction evaluation result; a prediction evaluation result judgment module, used to judge whether the purification control prediction evaluation result meets the purification control prediction evaluation expectation; an air purification control strategy generation module, used to optimize the air purification control scheme according to the purification control prediction evaluation expectation if the purification control prediction evaluation result does not meet the purification control prediction evaluation expectation, and generate an air purification control strategy; an air purifier control module, used to control the air purifier according to the air purification control strategy.

[0013] Through the air purifier operating parameter control method and platform proposed in this application, real-time air detection data is obtained; purification demand evaluation is performed to generate air purification instructions; the air purification control model is activated to obtain an air purification control plan; the air purifier in the target space is controlled and predicted to obtain a purification control prediction evaluation result; it is determined whether the purification control prediction evaluation result meets the purification control prediction evaluation expectation; if not, an air purification control strategy is generated; and the air purifier is controlled according to the air purification control strategy. The technical problem that the air purifier control in the prior art is not flexible enough and cannot respond to air quality changes in real time, which leads to low air purification efficiency and high energy consumption is solved, and the air purifier operating parameters are dynamically adjusted according to real-time air quality data, achieving the technical effect of improving air purification efficiency and reducing energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure are briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the platform according to the embodiment of the present application. It should be understood that the previous or following operations are not necessarily performed precisely in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0015] Figure 1 A schematic diagram of a method for controlling operating parameters of an air purifier provided in an embodiment of the present application;

[0016] Figure 2 A schematic diagram of the structure of an operating parameter control platform for an air purifier provided in an embodiment of the present application.

[0017] Explanation of the accompanying drawings: real-time air detection data acquisition module 10, purification demand evaluation module 20, air purification control plan acquisition module 30, control prediction evaluation module 40, prediction evaluation result judgment module 50, air purification control strategy generation module 60, air purifier control module 70. DETAILED DESCRIPTION

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0019] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0020] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, platform, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0021] The present application embodiment provides a method for controlling operating parameters of an air purifier, such as Figure 1 As shown, the method includes:

[0022] Step S100, obtaining real-time air detection data of the target space.

[0023] Preferably, the air quality in the target space (e.g., indoor environment such as room, office, shopping mall, etc.) is monitored in real time by air quality monitoring equipment (such as sensors, detection instruments, etc.), and relevant air parameter data are obtained, usually including but not limited to PM2.5 concentration, the concentration of particles with a diameter less than or equal to 2.5 microns in the air, which is used to measure the level of fine particulate pollution in the air; CO2 concentration, the concentration of carbon dioxide in the air, which is used to evaluate the ventilation and freshness of the air; TVOCs (total volatile organic compounds) concentration, the concentration of harmful gases in the air, such as formaldehyde, benzene, xylene, etc.; temperature and humidity, which directly affect air quality and comfort; oxygen concentration, changes in oxygen concentration have a greater impact on health. Through the data obtained by these sensors, the air purifier can evaluate the air quality of the target space in real time, so as to make intelligent adjustments and regulate the operating status of the purifier to optimize the air purification effect.

[0024] Step S200, performing purification demand evaluation based on the real-time air detection data and generating an air purification instruction.

[0025] Preferably, based on the real-time air quality data obtained from the target space, by analyzing and evaluating the current air pollution situation, it is determined whether it is necessary to start the air purifier and how to adjust its operating parameters. Specifically, the degree of air pollution is evaluated based on the real-time detected air pollutant concentration (such as PM2.5, CO2, formaldehyde, etc.). For example, when the PM2.5 concentration is higher than a certain threshold, it indicates that the air pollution is serious and an air purification instruction needs to be generated. In addition to the pollutant concentration, environmental factors such as temperature, humidity and oxygen concentration will also be evaluated. For example, if the indoor temperature is too high or the humidity is too low, the air purifier may need to adjust the wind speed or enable the humidification function. The purification demand evaluation refers to the evaluation of the air quality based on the air quality. The air quality assessment results determine whether there is a need for air purification. For example, when certain harmful substances (such as formaldehyde or PM2.5) exceed the standard, it means that the current air quality is poor and the air purification function needs to be activated. If multiple pollutants are detected, it is analyzed which pollutant has the most serious impact on the air quality, so as to set the purification priority and generate air purification instructions, including purifier start-up instructions (set to the appropriate operating mode) and operating parameter adjustment instructions, that is, according to the type and concentration of pollutants, instruct the purifier to adjust the wind speed, filtration method or other functions (such as enabling ozone disinfection, humidification, etc.) to ensure that it performs efficient purification according to the actual air quality needs to achieve the best purification effect.

[0026] Furthermore, step S200 also includes step S210, evaluating the air quality of the target space according to the real-time air detection data to obtain a real-time air quality coefficient; step S220, evaluating the purification demand of the real-time air quality coefficient according to the air quality constraints of the target space to generate an air purification demand coefficient; step S230, determining whether the air purification demand coefficient is greater than or equal to an air purification demand threshold; step S240, if the air purification demand coefficient is greater than or equal to the air purification demand threshold, obtaining the air purification instruction.

[0027] Preferably, the air quality of the target space is evaluated based on the acquired real-time air detection data of the target space (such as PM2.5, CO2, temperature and humidity, formaldehyde and other pollutant concentrations), including comparing multiple air quality indicators (such as PM2.5 concentration, CO2 concentration, VOC content, etc.) with preset standards, and calculating a comprehensive air quality coefficient to reflect the overall status of the current air quality. The larger the coefficient, the more serious the air pollution, and vice versa. According to the air quality constraints of the target space, the real-time air quality coefficient is evaluated for purification demand, that is, the air quality coefficient is processed according to the predetermined air quality constraints to obtain an air purification demand coefficient, which indicates the urgency or degree of air purification. When the air quality coefficient is higher, the purification demand coefficient will increase accordingly, indicating that the air The stronger the air purification demand, the air quality constraint is the air quality standard or constraint in the purification demand evaluation, such as the maximum allowable PM2.5 concentration, the minimum comfortable temperature and humidity range, etc.; the air purification demand coefficient and the air purification demand threshold are compared to determine whether the purification conditions are met. If the purification demand coefficient reaches or exceeds the air purification demand threshold, it means that the air quality has reached the level that needs to be purified, and a specific air purification instruction is generated. This instruction instructs the air purifier to start and adjust it appropriately according to the demand. It can include multiple control instructions, such as starting the air purifier; adjusting the purifier wind speed and mode (such as strong purification, quiet mode, etc.); starting specific filters or functions (such as HEPA filters, activated carbon filters, etc.); enabling other additional functions (such as humidification, negative ion functions, etc.).

[0028] Step S300: based on the air purification instruction, activate the air purification control model, and input the real-time air detection data into the air purification control model to obtain an air purification control plan.

[0029] Preferably, the air purification instruction generated in the purification demand evaluation stage instructs the air purifier to start and adjust, and activates the air purification control model. The air purification control model is used to intelligently adjust the working state and parameters of the air purifier according to the air quality data and environmental conditions. Activating the control model means starting to make decisions and optimize parameters based on real-time data. It is no longer a simple timer switch control, but intelligently adjusts the operating state of the air purifier. The acquired real-time air detection data is then input into the air purification control model. The air purification control model analyzes these data to determine the current air pollution status, which pollutants have high concentrations, and how the air circulation is. For example, if the PM2.5 concentration is high, the model will analyze whether it is necessary to adjust the wind speed or enable a more efficient filtering mode; if the temperature and humidity are too low, the model may enable the humidification function. Based on the input real-time air quality data, the control model generates a specific control plan, which includes how to adjust the operating parameters of the air purifier, such as adjusting the wind speed to a strong mode or a low-speed mode according to the air quality; if the concentration of harmful gases in the air is high, choose to enable the activated carbon filter to absorb harmful gases; if there are more particulate matter, enable the HEPA filter; according to the degree of air pollution, extend or shorten the purification time; if the air humidity is low, the control plan may require the humidification function to be enabled. In general, the air purifier is no longer simply running, but is accurately and efficiently adjusted according to the changes in the ambient air quality to ensure that the air purifier is always in the best working state to achieve the best air purification effect.

[0030] Step S400, based on the real-time air detection data, and according to the air purification control scheme, a control prediction evaluation is performed on the air purifier of the target space to obtain a purification control prediction evaluation result.

[0031] Preferably, the air purifier in the target space is controlled and predicted and evaluated based on the real-time air detection data and the air purification control plan, that is, the air purification control plan is used to predict the purification effect, energy consumption and noise of the air purifier in the current environment. Specifically, in combination with the real-time air detection data, the air purifier control plan is simulated and analyzed to estimate the improvement effect of the air purifier on the air quality under specific operation. This is a predictive calculation based on existing data and control strategies, that is, the air quality, energy consumption and noise generated are predicted after the purifier is simulated and operated according to the current control plan (such as adjusting the wind speed, filter, humidification, etc.). For example, if the current PM2.5 concentration is high, the control plan may It is required to increase the wind speed of the purifier and use a high-efficiency HEPA filter. On this basis, it is estimated how much the PM2.5 concentration will be reduced within a certain period of time after this adjustment; if there is a high concentration of formaldehyde in the target space, the control plan may require the use of an activated carbon filter, and the trend of formaldehyde concentration changes under the control plan is evaluated through simulation; and then the purification control prediction evaluation results are obtained, which describe the purification effect that the air purifier is expected to achieve according to the current control plan, such as the PM2.5 concentration is expected to drop to a certain level, the CO2 concentration is expected to reach the health standard range, the indoor temperature and humidity are restored to a comfortable range, or whether the concentration of other harmful gases (such as formaldehyde, TVOCs) is reduced, whether the energy consumption is reduced, whether the noise is reduced, etc. Through the control prediction evaluation, it is evaluated whether the control plan is effective and whether it needs further optimization to ensure that the air purifier can truly achieve the expected purification effect and control the corresponding energy consumption and noise.

[0032] Furthermore, step S400 also includes step S410, performing control evaluation record learning on the air purifier, and establishing a purification control prediction evaluation channel, wherein the purification control prediction evaluation channel includes an air purification quality prediction branch, a purification control energy consumption prediction branch, and a purification control noise prediction branch; step S420, inputting the real-time air detection data and the air purification control scheme into the air purification quality prediction branch to obtain a predicted air purification quality coefficient; step S430, based on the real-time air detection data and the air purification control scheme, according to the purification control energy consumption prediction branch, obtaining a purification control energy consumption prediction coefficient; step S440, inputting the real-time air detection data and the air purification control scheme into the purification control noise prediction branch to obtain a purification control noise prediction coefficient; step S450, outputting the predicted air purification quality coefficient, the purification control energy consumption prediction coefficient, and the purification control noise prediction coefficient as the purification control prediction evaluation result.

[0033] Preferably, the control evaluation record learning of the air purifier refers to recording the control data (including air quality, energy consumption, noise, etc.) of each operation of the air purifier and the corresponding operation results (such as purification effect, energy consumption, noise level, etc.), and learning these data, including constantly monitoring and recording the performance of the air purifier when executing the control strategy, and gradually optimizing the control strategy, that is, improving the control strategy according to the learned historical data, so that the air purifier can always maintain a high efficiency and low energy consumption operating state under different environmental conditions, thereby establishing a purification control prediction evaluation channel, which is an integrated prediction model for evaluating the performance of the air purifier when executing the control strategy from multiple dimensions, including air purification quality prediction branch, purification control energy consumption prediction branch and purification control noise prediction branch. The air purification quality prediction branch is used to predict the purification effect of the air purifier under a specific control scheme. By inputting real-time air detection data (such as PM2.5, CO2, formaldehyde concentration, etc.) and the control scheme, the expected air purification quality can be calculated; the purification control energy consumption prediction branch is used to predict the energy consumption of the air purifier when executing the control scheme. Based on the parameters set in the control scheme (such as wind speed, mode, working time, etc.), this branch can calculate the power consumption of the purifier; the purification control noise prediction branch is used to predict the noise level of the air purifier under different control modes. By evaluating different operating modes (such as high wind speed mode, low wind speed mode) and air quality data, the change in noise level is predicted, and a low-noise working mode can be selected to improve user comfort.

[0034] Preferably, the real-time air detection data and the air purification control scheme are input into the air purification quality prediction branch to predict the purification effect that the air purifier can achieve, and then a predicted air purification quality coefficient is obtained, which indicates the purification capacity and effect of the air purifier on air pollution under the current control scheme, for example, how much the PM2.5 concentration is expected to decrease, or how much the concentration of formaldehyde and TVOCs will decrease; the real-time air detection data and the air purification control scheme are input into the purification control energy consumption prediction branch to predict the energy consumption performance of the purifier under the scheme, reflecting the energy consumption of the purifier under different working modes, and then a purification control energy consumption prediction coefficient is obtained, indicating the amount of electricity or energy that the air purifier is expected to consume under a given control scheme, for example, the strong mode may consume more electricity, while the low-speed mode is relatively energy-efficient; the real-time air data and the control scheme are input into the purification control noise prediction branch to predict the noise level that the air purifier may generate under different control schemes, and then a purification control noise prediction coefficient is obtained, which describes the noise level of the purifier under a given control scheme, for example, in high wind speed mode, the air purifier may generate louder noise, while in low wind speed mode, the noise is smaller. Finally, the predicted air purification quality coefficient, purification control energy consumption prediction coefficient and purification control noise prediction coefficient are output as purification control prediction evaluation results, ensuring that while optimizing the air purification effect, energy consumption and noise are minimized to provide users with a better user experience.

[0035] Furthermore, step S410 also includes step S411, retrieving the control evaluation records of the air purifier to obtain an air detection sample set, a purification control scheme sample set, a purification quality evaluation sample set, a purification control energy consumption evaluation sample set and a purification control noise evaluation sample set; step S412, performing integrated fusion learning based on the air detection sample set, the purification control scheme sample set and the purification quality evaluation sample set to establish the air purification quality prediction branch; step S413, performing integrated fusion learning based on the air detection sample set, the purification control scheme sample set and the purification control energy consumption evaluation sample set to establish the purification control energy consumption prediction branch; step S414, performing integrated fusion learning based on the air detection sample set, the purification control scheme sample set and the purification control noise evaluation sample set to generate the purification control noise prediction branch; step S415, connecting the air purification quality prediction branch, the purification control energy consumption prediction branch and the purification control noise prediction branch to generate the purification control prediction evaluation channel.

[0036] Preferably, the air detection sample set, purification control scheme sample set, purification quality evaluation sample set, purification control energy consumption evaluation sample set and purification control noise evaluation sample set are retrieved from the historical control evaluation record of the air purifier. The air detection sample set contains the air detection data at different times and environmental conditions in history, such as the concentration of pollutants such as PM2.5, CO2, formaldehyde, temperature and humidity; the purification control scheme sample set records the various control schemes adopted by the air purifier under specific air quality conditions, such as wind speed adjustment, filter selection, operation mode setting, etc.; the purification quality evaluation sample set records the purification effect evaluation results of the air purifier after actual execution under a specific control scheme, for example, PM2.5 concentration, VOC concentration, formaldehyde concentration before and after purification, etc.; the purification control energy consumption evaluation sample set records the energy consumption data of the air purifier under different purification control schemes. For example, power consumption under wind speed mode, power consumption during operation time, etc.; the purification control noise evaluation sample set records the noise level data under different purification control schemes.

[0037] Preferably, integrated fusion learning is a strategy for combining multiple models or multiple data sets for learning. By combining different types of data and evaluation criteria, the model can obtain more comprehensive information in multiple dimensions, thereby improving the accuracy and generalization ability of the prediction. Specifically, by integrating and fusion learning the air detection sample set, the purification control scheme sample set and the purification quality evaluation sample set, a branch model for predicting air purification quality, namely, the air purification quality prediction branch, is established, and the goal is to predict the purification effect that the air purifier can achieve under a specific control scheme (for example, the degree of reduction in PM2.5 concentration); by integrating and fusion learning the air detection sample set, the purification control scheme sample set and the purification control energy consumption evaluation sample set, a branch model for predicting energy consumption, namely, the purification control energy consumption prediction branch, is established, and the goal is to evaluate Estimate the energy consumption performance of the purifier under different purification and control schemes; by integrating and fusion learning the air detection sample set, the purification control scheme sample set and the purification control noise evaluation sample set, a branch model for predicting noise is established, namely the purification control noise prediction branch. The goal is to predict the noise level of the air purifier under different control schemes; finally, the three prediction branches (air purification quality prediction branch, purification control energy consumption prediction branch, purification control noise prediction branch) are connected together to form a purification control prediction and evaluation channel, which can simultaneously consider the air purification effect, energy efficiency and noise level, conduct a comprehensive evaluation according to different input data (such as air quality, control scheme) during operation, and optimize the scheme to achieve the best purification effect, the lowest energy consumption and the smallest noise, improve user experience and reduce resource waste.

[0038] Furthermore, step S412 also includes step S412a, performing supervised training on P meta-learners according to the air detection sample set, the purification control scheme sample set and the purification quality evaluation sample set to obtain P air purification quality predictors, wherein P is a positive integer greater than 1; step S412b, training a purification quality prediction fusion model using the output data sets of the P air purification quality predictors as input information and the purification quality evaluation sample set as output information; step S412c, merging the P air purification quality predictors as parallel independent nodes to generate an air purification quality prediction model; step S412d, merging the air purification quality prediction model with the input layer of the purification quality prediction fusion model to generate the air purification quality prediction branch.

[0039] Preferably, the purification effect prediction accuracy of the air purifier is improved by meta-learning and ensemble learning methods, including using multiple independent machine learning models (such as decision trees, neural networks, etc.) for prediction training, and finally merging their outputs to create a more comprehensive and higher prediction performance air purification quality prediction branch. Specifically, a training prediction model is constructed based on P different meta-learners (such as decision trees, neural networks, support vector machines, etc.), and the P prediction models are supervised and trained using air detection sample sets, purification control scheme sample sets, and purification quality evaluation sample sets to learn how to map inputs (such as real-time air detection data and control schemes) to outputs (such as purification effect evaluation results), where P represents the number of trained models, and P is a positive integer greater than 1, indicating that at least two models are working simultaneously; the output data sets of P predictors are used as input, and the actual purification effect (purification quality evaluation sample set) is used as output, and the purification quality prediction fusion model is trained by the fusion learning method, aiming to use the results of multiple predictors to optimize the prediction accuracy. For example, the purification quality prediction fusion model can reduce erroneous predictions based on the strengths of each predictor, and ultimately provide a more accurate purification effect prediction.

[0040] Preferably, the trained P air purification quality predictors are combined together as independent parallel nodes, each model independently gives a prediction result, and then these results are integrated in some way, for example, using voting method, weighted average method, etc. to merge the outputs of these predictors, and then generate an air purification quality prediction model, which can comprehensively consider the results of multiple predictors and provide a final air purification quality prediction; finally, by merging the input layer of the air purification quality prediction model and the purification quality prediction fusion model, an air purification quality prediction branch is generated, and the air purification quality prediction branch will integrate all the trained models and data, and can provide accurate air purification quality predictions, providing data support for optimizing the operating parameters of the air purifier.

[0041] Step S500, determining whether the purification control prediction evaluation result meets the purification control prediction evaluation expectation.

[0042] Preferably, during the optimization of the air purifier control strategy, the predicted purification effect is compared with the preset purification target (purification control prediction evaluation expectation) to determine whether the current control scheme is effective, wherein the purification control prediction evaluation expectation is a target value of air quality, energy efficiency and noise preset based on actual needs and environmental conditions. For example, the air quality target is that the PM2.5 concentration is reduced to a certain value (for example, below 50 μg / m 3 ) or TVOCs concentration is reduced to a safe standard range; energy efficiency target, while maintaining a good purification effect, reduce energy consumption as much as possible and optimize electricity use (for example, reduce power consumption to meet a certain energy-saving standard); noise target, ensure that the operating noise of the purifier does not exceed a certain maximum value (for example, less than 40dB) to ensure the user's comfortable experience.

[0043] Step S600: If the purification control prediction evaluation result does not meet the purification control prediction evaluation expectation, the air purification control scheme is optimized according to the purification control prediction evaluation expectation to generate an air purification control strategy.

[0044] Preferably, if the purification control prediction evaluation result does not meet the purification control prediction evaluation expectations, for example, the purification effect is not ideal, the energy efficiency is too low or the noise is too loud, indicating that the current control scheme needs to be optimized, then the air purification control scheme is optimized according to the purification control prediction evaluation expectations, that is, when the prediction result does not meet the expectations, the current air purification control scheme is adjusted and improved by a certain optimization method to make it as close to or reach the predetermined goal as possible, such as adjusting the operating parameters (adjusting the wind speed, filter type, working mode, etc.), optimizing energy efficiency (optimizing the scheme by reducing unnecessary energy consumption), reducing noise (reducing the wind speed or selecting a quieter operating mode), changing the purification mode (switching to a more powerful purification mode, or changing the filtering scheme), and then generating an air purification control strategy, which integrates multiple effective parameters and operating steps, can meet the air purification requirements while ensuring that other factors (such as energy efficiency, noise, etc.) are also balanced, and can include a combination of multiple control schemes, dynamically adjusting the working mode of the purifier according to real-time air quality and other parameters, to ensure that the air purifier can always maintain the best operating state in a constantly changing environment.

[0045] Furthermore, step S600 also includes step S610, collecting the operating parameter constraint information of the air purifier and establishing a purification control constraint domain; step S620, adjusting the air purification control scheme according to the purification control constraint domain to generate a purification adjustment scheme space; step S630, extracting a first purification adjustment scheme according to the purification adjustment scheme space; step S640, inputting the real-time air detection data and the first purification adjustment scheme into the purification control prediction and evaluation channel to obtain a purification prediction result of the first adjustment scheme; step S650, judging whether the purification prediction result of the first adjustment scheme meets the purification control prediction and evaluation expectations; step S660, if the purification prediction result of the first adjustment scheme meets the purification control prediction and evaluation expectations, adding the first purification adjustment scheme to the air purification control strategy.

[0046] Preferably, the operating parameter constraint information of the air purifier is collected, such as wind speed, filter type, working time, etc., and a purification control constraint domain is established based on these constraint information to define which control schemes are acceptable. The purification control constraint domain is actually a parameter space that specifies the allowable range of various operating parameters of the air purifier, such as the wind speed must be between 0.5m / s and 3m / s; the filter selection can be HEPA or activated carbon, but not both; the energy consumption cannot exceed a preset standard, etc.; based on the purification control constraint domain, the air purification control scheme of the air purifier is preliminarily adjusted, that is, within the scope of the control constraint domain, all possible control schemes are considered, and then a purification adjustment scheme space is generated, including all control scheme combinations that meet the constraint domain, and then one is randomly selected from the purification adjustment scheme space as the first purification adjustment scheme.

[0047] Preferably, the first purification adjustment scheme and real-time air detection data are input into the purification control prediction and evaluation channel to predict the purification effect that can be achieved by the scheme under the current environment, that is, to obtain the purification prediction result of the first adjustment scheme (whether the scheme can achieve the expected air quality improvement effect), so as to judge whether the first adjustment scheme is effective, and then compare the purification prediction result of the first adjustment scheme with the purification control prediction and evaluation expectations. If the prediction result of the first adjustment scheme can meet expectations (for example, the PM2.5 concentration drops below the expected value), it means that the scheme is effective, and the scheme is added to the final air purification control strategy as one of the operating modes of the air purifier, wherein the air purification control strategy includes multiple effective purification adjustment schemes, which are used to guide the working mode of the air purifier under different circumstances, so as to achieve the best air purification effect.

[0048] Furthermore, step S650 also includes step S651, if the purification prediction result of the first adjustment scheme does not meet the purification control prediction evaluation expectation, the first purification adjustment scheme is eliminated; step S652, according to the purification adjustment scheme space, the second purification adjustment scheme is extracted; step S653, the real-time air detection data and the second purification adjustment scheme are input into the purification control prediction evaluation channel to obtain the purification prediction result of the second adjustment scheme; step S654, judging whether the purification prediction result of the second adjustment scheme meets the purification control prediction evaluation expectation; step S655, if the purification prediction result of the second adjustment scheme meets the purification control prediction evaluation expectation, the second purification adjustment scheme is added to the air purification control strategy; step S656, if the purification prediction result of the second adjustment scheme does not meet the purification control prediction evaluation expectation, the second purification adjustment scheme is eliminated, and the purification adjustment scheme space is continuously optimized and iterated according to the purification control prediction evaluation expectation until the air purification control strategy is generated.

[0049] Preferably, if the purification prediction result of the first adjustment scheme does not meet the purification control prediction evaluation expectations, for example, the purification effect is not up to standard, the energy efficiency is too low or the noise is too loud, the adjustment scheme is considered inappropriate, and this scheme is eliminated, and then a purification adjustment scheme is randomly selected in the purification adjustment scheme space as the second purification adjustment scheme. Similarly, the real-time air detection data and the second purification adjustment scheme are input into the purification control prediction evaluation channel to predict how much air quality improvement the second adjustment scheme can bring, and then the purification prediction result of the second adjustment scheme is generated, and then it is judged whether the purification prediction result of the second adjustment scheme meets the purification control prediction evaluation expectations, that is, the purification prediction result of the second adjustment scheme is compared with the purification control prediction evaluation channel. If the purification prediction result of the second adjustment scheme meets the purification control prediction evaluation expectation, that is, it meets the predetermined air quality, energy efficiency, noise and other goals, it means that the scheme is effective, and the scheme is added to the air purification control strategy. If the purification prediction result of the second scheme does not meet the expectations, the scheme is eliminated, and other schemes are selected from the purification adjustment scheme space for further evaluation. According to the results of each evaluation (whether it meets the expectations), it is iterated to optimize until an air purification control strategy that meets the expected goals is generated, which will include a variety of effective adjustment schemes, comprehensively consider factors such as air quality, energy efficiency and noise, and ensure the optimal operation of the air purifier.

[0050] Step S700: controlling the air purifier according to the air purification control strategy.

[0051] Preferably, the operating state of the air purifier is actually controlled and adjusted according to the air purification control strategy to achieve an optimized air purification effect. The core is to convert the theoretical control strategy into actual operating instructions to ensure that the air purifier operates efficiently in the actual environment. For example, the wind speed of the air purifier is adjusted according to the air quality and pollutant concentration; according to the control strategy, the purifier is instructed to switch to the appropriate filter type; the operating mode of the air purifier (such as strong mode, energy-saving mode, automatic mode, etc.) is controlled; according to the control strategy, additional functions of the air purifier, such as humidification, dehumidification, negative ions, etc., are enabled or disabled to optimize the air quality and meet user comfort requirements.

[0052] In the above, refer to Figure 1 A method for controlling operating parameters of an air purifier according to an embodiment of the present invention is described in detail. Figure 2 An operating parameter control platform for an air purifier according to an embodiment of the present invention is described.

[0053] An operating parameter control platform for an air purifier according to an embodiment of the present invention is used to solve the technical problems in the prior art that the air purifier control is not flexible enough and cannot respond to air quality changes in real time, which leads to low air purification efficiency and high energy consumption. It realizes dynamic adjustment of air purifier operating parameters according to real-time air quality data, and achieves the technical effect of improving air purification efficiency and reducing energy consumption. An operating parameter control platform for an air purifier includes: a real-time air detection data acquisition module 10, a purification demand evaluation module 20, an air purification control scheme acquisition module 30, a control prediction evaluation module 40, a prediction evaluation result judgment module 50, an air purification control strategy generation module 60, and an air purifier control module 70.

[0054] A real-time air detection data acquisition module 10 is used to obtain real-time air detection data of the target space; a purification demand evaluation module 20 is used to evaluate the purification demand according to the real-time air detection data and generate an air purification instruction; an air purification control scheme acquisition module 30 is used to activate the air purification control model based on the air purification instruction, and input the real-time air detection data into the air purification control model to obtain an air purification control scheme; a control prediction evaluation module 40 is used to perform control prediction evaluation on the air purifier of the target space according to the air purification control scheme based on the real-time air detection data to obtain a purification control prediction evaluation result; a prediction evaluation result judgment module 50 is used to judge whether the purification control prediction evaluation result meets the purification control prediction evaluation expectation; an air purification control strategy generation module 60 is used to optimize the air purification control scheme according to the purification control prediction evaluation expectation if the purification control prediction evaluation result does not meet the purification control prediction evaluation expectation, and generate an air purification control strategy; an air purifier control module 70 is used to control the air purifier according to the air purification control strategy.

[0055] The specific configuration of the control prediction and evaluation module 40 will be described in detail below. The control prediction and evaluation module 40 may further include: learning the control evaluation record of the air purifier, establishing a purification control prediction and evaluation channel, wherein the purification control prediction and evaluation channel includes an air purification quality prediction branch, a purification control energy consumption prediction branch, and a purification control noise prediction branch; inputting the real-time air detection data and the air purification control scheme into the air purification quality prediction branch to obtain a predicted air purification quality coefficient; based on the real-time air detection data and the air purification control scheme, according to the purification control energy consumption prediction branch, obtaining a purification control energy consumption prediction coefficient; inputting the real-time air detection data and the air purification control scheme into the purification control noise prediction branch to obtain a purification control noise prediction coefficient; outputting the predicted air purification quality coefficient, the purification control energy consumption prediction coefficient, and the purification control noise prediction coefficient as the purification control prediction evaluation result.

[0056] The specific configuration of the control prediction and evaluation module 40 will be described in detail below. The control prediction and evaluation module 40 may further include: retrieving the control evaluation record of the air purifier to obtain an air detection sample set, a purification control scheme sample set, a purification quality evaluation sample set, a purification control energy consumption evaluation sample set, and a purification control noise evaluation sample set; performing integrated fusion learning based on the air detection sample set, the purification control scheme sample set, and the purification quality evaluation sample set to establish the air purification quality prediction branch; performing integrated fusion learning based on the air detection sample set, the purification control scheme sample set, and the purification control energy consumption evaluation sample set to establish the purification control energy consumption prediction branch; performing integrated fusion learning based on the air detection sample set, the purification control scheme sample set, and the purification control noise evaluation sample set to generate the purification control noise prediction branch; connecting the air purification quality prediction branch, the purification control energy consumption prediction branch, and the purification control noise prediction branch to generate the purification control prediction evaluation channel.

[0057] The specific configuration of the control prediction and evaluation module 40 will be described in detail below. The control prediction and evaluation module 40 may further include: supervised training of P meta-learners according to the air detection sample set, the purification control scheme sample set and the purification quality evaluation sample set to obtain P air purification quality predictors, where P is a positive integer greater than 1; using the output data sets of the P air purification quality predictors as input information and the purification quality evaluation sample set as output information to train the purification quality prediction fusion model; merging the P air purification quality predictors as parallel independent nodes to generate an air purification quality prediction model; merging the air purification quality prediction model with the input layer of the purification quality prediction fusion model to generate the air purification quality prediction branch.

[0058] The specific configuration of the air purification control strategy generation module 60 will be described in detail below. The air purification control strategy generation module 60 may further include: collecting the operating parameter constraint information of the air purifier and establishing a purification control constraint domain; adjusting the air purification control scheme according to the purification control constraint domain to generate a purification adjustment scheme space; extracting a first purification adjustment scheme according to the purification adjustment scheme space; inputting the real-time air detection data and the first purification adjustment scheme into the purification control prediction evaluation channel to obtain the purification prediction result of the first adjustment scheme; judging whether the purification prediction result of the first adjustment scheme meets the purification control prediction evaluation expectation; if the purification prediction result of the first adjustment scheme meets the purification control prediction evaluation expectation, adding the first purification adjustment scheme to the air purification control strategy.

[0059] The specific configuration of the air purification control strategy generation module 60 will be described in detail below. The air purification control strategy generation module 60 may further include: if the purification prediction result of the first adjustment scheme does not meet the purification control prediction evaluation expectation, the first purification adjustment scheme is eliminated; according to the purification adjustment scheme space, the second purification adjustment scheme is extracted; the real-time air detection data and the second purification adjustment scheme are input into the purification control prediction evaluation channel to obtain the purification prediction result of the second adjustment scheme; it is determined whether the purification prediction result of the second adjustment scheme meets the purification control prediction evaluation expectation; if the purification prediction result of the second adjustment scheme meets the purification control prediction evaluation expectation, the second purification adjustment scheme is added to the air purification control strategy; if the purification prediction result of the second adjustment scheme does not meet the purification control prediction evaluation expectation, the second purification adjustment scheme is eliminated, and the purification adjustment scheme space is continuously optimized and iterated according to the purification control prediction evaluation expectation until the air purification control strategy is generated.

[0060] The specific configuration of the purification demand evaluation module 20 will be described in detail below. The purification demand evaluation module 20 may further include: performing air quality evaluation on the target space according to the real-time air detection data to obtain a real-time air quality coefficient; performing purification demand evaluation on the real-time air quality coefficient according to the air quality constraint of the target space to generate an air purification demand coefficient; determining whether the air purification demand coefficient is greater than or equal to an air purification demand threshold; and obtaining the air purification instruction if the air purification demand coefficient is greater than or equal to the air purification demand threshold.

[0061] An operating parameter control platform for an air purifier provided in an embodiment of the present invention can execute an operating parameter control method for an air purifier provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0062] Although the present application makes various references to certain modules in the platform according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be implemented; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0063] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A method for controlling operating parameters of an air purifier, characterized in that: The method comprises: Obtain real-time air detection data of the target space; Perform purification demand evaluation based on the real-time air detection data and generate air purification instructions; Based on the air purification instruction, an air purification control model is activated, and the real-time air detection data is input into the air purification control model to obtain an air purification control plan; Based on the real-time air detection data, the air purifier in the target space is controlled, predicted and evaluated according to the air purification control scheme to obtain a purification control prediction evaluation result; Determining whether the purification control prediction evaluation result meets the purification control prediction evaluation expectation; If the purification control prediction evaluation result does not meet the purification control prediction evaluation expectation, optimizing the air purification control scheme according to the purification control prediction evaluation expectation to generate an air purification control strategy; The air purifier is controlled according to the air purification regulation strategy.

2. The method for controlling the operating parameters of an air purifier according to claim 1, characterized in that: Based on the real-time air detection data, the air purifier of the target space is controlled, predicted and evaluated according to the air purification control scheme to obtain a purification control prediction evaluation result, including: Performing control evaluation record learning on the air purifier, and establishing a purification control prediction evaluation channel, wherein the purification control prediction evaluation channel includes an air purification quality prediction branch, a purification control energy consumption prediction branch, and a purification control noise prediction branch; Inputting the real-time air detection data and the air purification control scheme into the air purification quality prediction branch to obtain a predicted air purification quality coefficient; Based on the real-time air detection data and the air purification control scheme, and according to the purification control energy consumption prediction branch, a purification control energy consumption prediction coefficient is obtained; Inputting the real-time air detection data and the air purification control scheme into the purification control noise prediction branch to obtain a purification control noise prediction coefficient; The predicted air purification quality coefficient, the purification control energy consumption prediction coefficient and the purification control noise prediction coefficient are output as the purification control prediction evaluation result.

3. The method for controlling the operating parameters of an air purifier according to claim 2, characterized in that: The control evaluation record of the air purifier is studied to establish a purification control prediction evaluation channel, including: Retrieving the control evaluation records of the air purifier to obtain an air detection sample set, a purification control scheme sample set, a purification quality evaluation sample set, a purification control energy consumption evaluation sample set, and a purification control noise evaluation sample set; Perform integrated fusion learning based on the air detection sample set, the purification control scheme sample set and the purification quality evaluation sample set to establish the air purification quality prediction branch; Perform integrated fusion learning based on the air detection sample set, the purification control scheme sample set and the purification control energy consumption evaluation sample set to establish the purification control energy consumption prediction branch; Performing integrated fusion learning based on the air detection sample set, the purification control scheme sample set, and the purification control noise evaluation sample set to generate the purification control noise prediction branch; The air purification quality prediction branch, the purification control energy consumption prediction branch and the purification control noise prediction branch are connected to generate the purification control prediction evaluation channel.

4. The method for controlling the operating parameters of an air purifier according to claim 3, characterized in that: The air purification quality prediction branch is established by performing integrated fusion learning according to the air detection sample set, the purification control scheme sample set and the purification quality evaluation sample set, including: Performing supervised training on P meta-learners according to the air detection sample set, the purification control scheme sample set, and the purification quality evaluation sample set to obtain P air purification quality predictors, where P is a positive integer greater than 1; Using the output data sets of the P air purification quality predictors as input information and the purification quality evaluation sample set as output information, training a purification quality prediction fusion model; Merging the P air purification quality predictors as parallel independent nodes to generate an air purification quality prediction model; The air purification quality prediction model is merged with the input layer of the purification quality prediction fusion model to generate the air purification quality prediction branch.

5. The method for controlling the operating parameters of an air purifier according to claim 2, characterized in that: If the purification control prediction evaluation result does not meet the purification control prediction evaluation expectation, the air purification control scheme is optimized and regulated according to the purification control prediction evaluation expectation to generate an air purification control strategy, including: Collecting the operating parameter constraint information of the air purifier and establishing a purification control constraint domain; Adjust the air purification control scheme according to the purification control constraint domain to generate a purification adjustment scheme space; Extracting a first purification adjustment scheme according to the purification adjustment scheme space; Inputting the real-time air detection data and the first purification adjustment scheme into the purification control prediction evaluation channel to obtain a purification prediction result of the first adjustment scheme; Determining whether the purification prediction result of the first adjustment scheme meets the purification control prediction evaluation expectation; If the purification prediction result of the first adjustment scheme meets the purification control prediction evaluation expectation, the first purification adjustment scheme is added to the air purification control strategy.

6. The method for controlling the operating parameters of an air purifier according to claim 5, characterized in that: Determining whether the purification prediction result of the first adjustment scheme meets the purification control prediction evaluation expectation includes: If the purification prediction result of the first adjustment scheme does not meet the purification control prediction evaluation expectation, the first purification adjustment scheme is eliminated; Extracting a second purification adjustment scheme according to the purification adjustment scheme space; Inputting the real-time air detection data and the second purification adjustment plan into the purification control prediction evaluation channel to obtain a purification prediction result of the second adjustment plan; Determining whether the purification prediction result of the second adjustment scheme meets the purification control prediction evaluation expectation; If the purification prediction result of the second adjustment scheme meets the purification control prediction evaluation expectation, adding the second purification adjustment scheme to the air purification control strategy; If the purification prediction result of the second adjustment scheme does not meet the purification control prediction evaluation expectation, the second purification adjustment scheme is eliminated, and the purification adjustment scheme space is continuously optimized and iterated according to the purification control prediction evaluation expectation until the air purification control strategy is generated.

7. The method for controlling the operating parameters of an air purifier according to claim 1, characterized in that: Performing purification demand evaluation according to the real-time air detection data and generating air purification instructions includes: Performing air quality evaluation on the target space according to the real-time air detection data to obtain a real-time air quality coefficient; According to the air quality constraint of the target space, the real-time air quality coefficient is evaluated for purification demand, and an air purification demand coefficient is generated; Determining whether the air purification demand coefficient is greater than or equal to an air purification demand threshold; If the air purification demand coefficient is greater than or equal to the air purification demand threshold, the air purification instruction is obtained.

8. An air purifier operating parameter control platform, characterized in that: The platform is used to implement the method for controlling the operating parameters of an air purifier according to any one of claims 1 to 7, and the platform comprises: A real-time air detection data acquisition module is used to obtain real-time air detection data of the target space; A purification demand evaluation module, used to evaluate the purification demand according to the real-time air detection data and generate an air purification instruction; An air purification control scheme acquisition module is used to activate an air purification control model based on the air purification instruction, and input the real-time air detection data into the air purification control model to obtain an air purification control scheme; A control prediction and evaluation module is used to perform control prediction and evaluation on the air purifier in the target space based on the real-time air detection data and the air purification control scheme to obtain a purification control prediction and evaluation result; A prediction and evaluation result judgment module is used to judge whether the purification control prediction and evaluation result meets the purification control prediction and evaluation expectations; An air purification control strategy generation module is used to optimize the air purification control scheme according to the purification control prediction evaluation expectation to generate an air purification control strategy if the purification control prediction evaluation result does not meet the purification control prediction evaluation expectation; An air purifier control module is used to control the air purifier according to the air purification regulation strategy.

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