Intelligent sensing control method and system based on Internet of Things technology

Abstract

The present invention relates to the technical field of Internet of Things environmental control, and in particular to an intelligent connection sensing control method and system based on Internet of Things technology, which improves purification effect and user experience, realizes efficient utilization of resources and reduces maintenance costs; the method is applied to automatically adjust air purification equipment, monitoring sensors are deployed on the input side and output side of the air purification equipment, and a temperature and humidity sensor is also installed on the air purification equipment. The method includes: according to a set collection time node, controlling the monitoring sensor to collect the air quality of the input side and the output side, and respectively obtaining first air quality data information and second air quality data information; and controlling the temperature and humidity sensor to collect the ambient temperature and ambient humidity of the space where the air purification equipment is located; quantifying the first air quality data information and the second air quality data information, and calculating the air quality optimization amount based on the quantification result.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things environment control, and in particular to an intelligent connection sensing control method and system based on Internet of Things technology. Background Art

[0002] As people's requirements for quality of life increase, air quality issues are receiving increasing attention. As an important means to improve indoor air quality, air purification equipment has been widely used in homes, offices, hospitals and other places.

[0003] Although existing air purification equipment is equipped with simple sensing devices and can perform basic automatic power on and off operations based on air quality indicators such as indoor PM2.5 concentration, as the equipment is used for a longer time, the performance of key components such as filters and motors will gradually decline, resulting in a decrease in the purification efficiency of the equipment. Existing air purification equipment often lacks monitoring and response mechanisms for such attenuation, making the purification effect of the equipment unsatisfactory. At the same time, it also fails to fully consider the impact of ambient temperature and humidity on the purification effect, and lacks accurate quantification and intelligent management of purification efficiency. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides an intelligent sensing control method and system based on Internet of Things technology, which improves purification effect and user experience, realizes efficient utilization of resources and reduces maintenance costs.

[0005] In a first aspect, the present invention provides an intelligent sensing control method based on the Internet of Things technology, the method is applied to automatically adjust an air purification device, the input side and the output side of the air purification device are both equipped with monitoring sensors, and the air purification device is also equipped with a temperature and humidity sensor, the method comprises:

[0006] According to the set collection time node, the monitoring sensor is controlled to collect the air quality of the input side and the output side to obtain the first air quality data information and the second air quality data information respectively; and the temperature and humidity sensor is controlled to collect the ambient temperature and ambient humidity of the space where the air purification equipment is located;

[0007] Quantifying the first air quality data information and the second air quality data information, and calculating the air quality optimization amount based on the quantification result; each collection time node corresponds to an air quality optimization amount, wherein the time distance between two adjacent collection time nodes is the same;

[0008] Arrange the preset number of air quality optimization quantities in chronological order to obtain an air quality optimization feature set for the air purification equipment;

[0009] Extract features from the air quality optimization feature set to obtain a purification efficiency evaluation index for the air purification equipment;

[0010] Input the ambient humidity and ambient temperature into the pre-built equipment purification impact analysis model to obtain the purification environment impact coefficient;

[0011] Based on the purification environment impact coefficient, the purification efficiency evaluation index is corrected to obtain the purification efficiency correction index of the air purification equipment;

[0012] When the purification efficiency correction index is lower than the preset efficiency threshold, the air purification equipment operating parameters are automatically adjusted until the purification efficiency correction index is no lower than the preset efficiency threshold.

[0013] Furthermore, the method for quantifying the first air quality data information and the second air quality data information includes:

[0014] Performing data cleaning on the first air quality data information and the second air quality data information to remove outliers and smooth noise;

[0015] For each acquisition time node, the difference in air quality parameters between the input side and the output side is calculated respectively;

[0016] Set up a quantitative scoring system for purification effects, and set scores corresponding to different purification effect levels;

[0017] Based on the quantitative scoring of purification effect, the purification effect at each collection time node is scored to form the air quality optimization amount.

[0018] Furthermore, the method for acquiring the air quality optimization feature set of the air purification equipment includes:

[0019] Arrange the air quality optimization quantities in the time sequence of the collection time nodes; match each optimization quantity with its corresponding timestamp to form a time series data set;

[0020] Define a set of characteristics used to describe and quantify the change of air quality optimization quantity over time

[0021] Calculate and extract actual values ​​from time series datasets based on defined features;

[0022] All the extracted feature values ​​are integrated into a structured data set, namely the air quality optimization feature set of the air purification equipment.

[0023] Furthermore, the method for extracting features from the air quality optimization feature set includes:

[0024] Preprocess the air quality optimization feature set, including checking data integrity, removing outliers, and filling missing values;

[0025] According to the needs of purification effect evaluation, select the features that affect the purification efficiency;

[0026] Based on the air quality optimization feature set, a new feature variable is constructed;

[0027] The importance of each feature is determined through machine learning methods, and weights are assigned to the features.

[0028] Furthermore, the method for constructing the equipment purification impact analysis model includes:

[0029] Collect data about the operation of air purification equipment and clean the collected data, including removing outliers, filling missing values ​​and normalizing data;

[0030] Selecting a machine learning model as the infrastructure of the equipment purification impact analysis model, wherein the machine learning model includes linear regression, decision tree, random forest, support vector machine, and neural network;

[0031] Divide the processed data set into a training set and a validation set;

[0032] Use the training set data to train the selected model and adjust the model parameters;

[0033] Use cross-validation to evaluate the performance of the model on unseen data;

[0034] Adjust model parameters based on the model’s performance on the validation set;

[0035] Deploy the optimized model to actual applications.

[0036] Furthermore, the method of automatically adjusting the operating parameters of the air purification equipment includes adjusting the wind speed, changing the working mode, reminding the filter to clean and replace, and optimizing the motor power.

[0037] Furthermore, the collection time node is set based on equipment working cycle, frequency of environmental changes, filter motor performance attenuation rate, energy consumption efficiency balance, and abnormal situation response.

[0038] On the other hand, the present application also provides an intelligent sensor control system based on Internet of Things technology, the system comprising:

[0039] The environmental perception module controls the monitoring sensor to collect the air quality of the input side and the output side according to the set collection time node, and obtains the first air quality data information and the second air quality data information respectively; and controls the temperature and humidity sensor to collect the ambient temperature and ambient humidity of the space where the air purification equipment is located;

[0040] A quantification processing module performs quantification processing on the first air quality data information and the second air quality data information, and calculates an air quality optimization amount based on the quantification result; each acquisition time node corresponds to an air quality optimization amount, wherein the time distance between two adjacent acquisition time nodes is the same;

[0041] The optimization feature set generation module arranges the preset number of air quality optimization quantities in chronological order to obtain the air quality optimization feature set of the air purification equipment;

[0042] The purification efficiency evaluation module extracts the air quality optimization feature set and obtains the purification efficiency evaluation index of the air purification equipment;

[0043] The purification impact analysis module inputs the ambient humidity and ambient temperature into the pre-built equipment purification impact analysis model to obtain the purification environment impact coefficient;

[0044] The purification efficiency correction module performs impact correction on the purification efficiency evaluation index based on the purification environment impact coefficient to obtain the purification efficiency correction index of the air purification equipment;

[0045] The adjustment control module automatically adjusts the operating parameters of the air purification equipment when the purification efficiency correction index is lower than the preset efficiency threshold until the purification efficiency correction index is not lower than the preset efficiency threshold.

[0046] In a third aspect, the present application provides an electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and the computer program, when executed by the processor, implements the steps of any one of the above methods.

[0047] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps in any one of the above-mentioned methods when executed by a processor.

[0048] Compared with the prior art, the present invention has the following beneficial effects: by regularly collecting air quality data and temperature and humidity information, it is possible to monitor indoor environmental changes in real time. Compared with basic automatic control relying only on simple sensing devices, this method provides a more sophisticated environmental monitoring and instant feedback mechanism;

[0049] By calculating the air quality optimization quantity and establishing the optimization feature set, it is possible to quantitatively evaluate the attenuation of the purification effect of the air purification equipment over time, filling the gap of the lack of attenuation monitoring and response mechanism of existing equipment, and facilitating timely maintenance and replacement of components to maintain purification efficiency;

[0050] Incorporating ambient temperature and humidity into the analysis model and correcting the purification efficiency evaluation can more comprehensively consider the factors that affect the purification effect, no longer ignore the actual impact of temperature and humidity on air purification efficiency, and improve the intelligence and adaptability of the purification process;

[0051] Through quantitative processing and feature extraction, purification efficiency evaluation index and correction index are formed, making the evaluation of purification effect more scientific and accurate, which is conducive to users' intuitive understanding of equipment performance status and also provides a quantitative basis for the automatic management of equipment;

[0052] Automatically adjust the equipment operating parameters according to the correction index to ensure that the purification efficiency is always maintained above the preset threshold, improve the equipment utilization efficiency and user satisfaction, and reduce energy waste caused by reduced efficiency;

[0053] Through continuous monitoring and data analysis, this method can provide early warning of equipment performance degradation and provide users with recommendations for filter replacement or maintenance services, extending equipment life and reducing unexpected downtime and repair costs;

[0054] In summary, this intelligent sensing control method realizes the intelligent management of air purification equipment through the Internet of Things technology, which not only improves the purification effect and user experience, but also realizes the efficient use of resources and reduces maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a flow chart of the present invention;

[0056] Figure 2 This is the structural diagram of the intelligent sensing control system based on Internet of Things technology. DETAILED DESCRIPTION

[0057] In the description of the present application, those skilled in the art should know that the present application can be implemented as a method, an apparatus, an electronic device, and a computer-readable storage medium. Therefore, the present application can be specifically implemented in the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), a combination of hardware and software. In addition, in some embodiments, the present application can also be implemented in the form of a computer program product in one or more computer-readable storage media, and the computer-readable storage medium contains computer program code.

[0058] The above-mentioned computer-readable storage medium may adopt any combination of one or more computer-readable storage media. Computer-readable storage media include: electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or devices, or any combination of the above. More specific examples of computer-readable storage media include: portable computer disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, optical disc read-only memories, optical storage devices, magnetic storage devices, or any combination of the above. In the present application, computer-readable storage media can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or device.

[0059] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws.

[0060] The present application describes the provided methods, devices, and electronic devices through flowcharts and / or block diagrams.

[0061] It should be understood that each box in the flowchart and / or block diagram and the combination of boxes in the flowchart and / or block diagram can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine, and these computer-readable program instructions are executed by a computer or other programmable data processing device to produce a device that implements the functions / operations specified by the boxes in the flowchart and / or block diagram.

[0062] These computer-readable program instructions may also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to work in a specific manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction device product including functions / operations specified in the blocks in the flowchart and / or block diagram.

[0063] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby enabling the instructions executed on the computer or other programmable data processing apparatus to provide a process for implementing the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0064] The present application is described below in conjunction with the drawings in the present application.

[0065] Embodiment 1: Figure 1 As shown, the intelligent connection sensing control method based on the Internet of Things technology of the present invention specifically includes the following steps:

[0066] S1. According to the set collection time node, control the monitoring sensor to collect the air quality of the input side and the output side, and obtain the first air quality data information and the second air quality data information respectively; and control the temperature and humidity sensor to collect the ambient temperature and humidity of the space where the air purification equipment is located;

[0067] Install high-precision air quality sensors on the input and output sides of the air purification equipment. The sensors can monitor the concentration of key pollutants such as PM2.5, formaldehyde, and TVOCs in real time. At the same time, deploy temperature and humidity sensors near the equipment to monitor the temperature and humidity of the surrounding environment.

[0068] Set a fixed collection time node to automatically trigger the sensor to sample data; the time node ensures the regularity and continuity of data collection, which is convenient for subsequent analysis;

[0069] At a predetermined time point, the sensor transmits the collected first air quality data information and second air quality data information to a cloud server via wireless communication technology;

[0070] The basis for setting the acquisition time node includes:

[0071] Equipment working cycle: Considering the working cycle of the air purification equipment and the filter clogging rate, the collection time node should match the purification cycle of the equipment; if the equipment is designed to complete a full air cycle every hour, the collection time can be set to once an hour to capture the changes in purification effect in a timely manner;

[0072] Frequency of environmental changes: Indoor environmental factors can affect air quality, so the frequency of these changes should be taken into account when collecting data. During crowded hours or when activities are frequent, the frequency of data collection can be appropriately increased to more accurately reflect purification needs and effects.

[0073] Filter motor performance decay rate: As the use time increases, the speed of filter blockage and motor efficiency decline is uneven, but usually there is a certain pattern; in the early stage, data may be collected at a relatively long interval, and as the equipment ages, the collection interval should be shortened to detect signs of efficiency decline in a timely manner;

[0074] Balance between energy consumption and efficiency: Although high-frequency data collection can provide more detailed monitoring, it also means higher energy consumption and data processing burden. Therefore, the setting of the collection time node needs to find a balance between monitoring accuracy and energy efficiency.

[0075] Response to abnormal situations: Set special rules to immediately start collection when a sharp change in air quality is detected, so as to quickly respond and adjust the purification strategy.

[0076] In this step, the regular and flexible monitoring mechanism can timely capture changes in air quality, including the concentration of pollutants on the input side, the purification effect on the output side, and temperature and humidity conditions, providing users with real-time environmental health feedback and preventing potential health risks; by comparing the air quality data on the input and output sides, the actual purification efficiency of the air purification equipment can be accurately quantified, providing a scientific basis for equipment performance evaluation and efficiency calibration, ensuring that the air purification effect always meets the expected standards; dynamically adjusting the collection frequency according to the performance attenuation laws of components such as filters and motors helps to discover and predict equipment maintenance needs in advance, reduce the loss of purification efficiency due to equipment performance degradation, extend the service life of the equipment, and optimize energy consumption. Utilization can achieve green and efficient operation; considering the frequency of environmental changes and special collection rules under specific conditions, the system can better adapt to different usage scenarios and environmental conditions, whether in office areas with high personnel mobility or in quiet home environments, it can ensure the flexibility and effectiveness of purification strategies; by balancing monitoring accuracy and energy consumption, a sustainable operation and maintenance strategy is achieved, reducing unnecessary energy consumption, which is in line with environmental protection concepts and reduces users' long-term operating costs; the implementation of step S1 not only improves the intelligent management level of air purification equipment, but also provides users with a healthier and more comfortable living and working environment through precise data collection and analysis, while optimizing resource utilization.

[0077] S2. quantify the first air quality data information and the second air quality data information, and calculate the air quality optimization amount based on the quantization result; each collection time node corresponds to an air quality optimization amount, wherein the time distance between two adjacent collection time nodes is the same;

[0078] Step S2 is responsible for converting the original collected air quality data into measurable optimization indicators for further analysis and decision-making;

[0079] The method for quantifying the first air quality data information and the second air quality data information includes:

[0080] Performing data cleaning on the first air quality data information and the second air quality data information to remove outliers and smooth noise to ensure the accuracy and reliability of the data;

[0081] For each collection time node, the difference in air quality parameters between the input side and the output side is calculated respectively. The absolute value of the difference reflects the purification effect of the air purification equipment during that period.

[0082] Setting a quantitative scoring system for purification effect can be to directly convert the purification amount into a percentage improvement rate, or to set scores corresponding to different purification effect levels according to industry standards or experimental data, such as "excellent", "good", "average", "poor";

[0083] Based on the quantitative scoring of purification effect, the purification effect at each collection time node is scored to form the air quality optimization amount.

[0084] In this step, data cleaning and outlier processing are used to ensure the accuracy and reliability of the analysis basis, remove misjudgments caused by sensor errors or instantaneous environmental interference, and improve the credibility of data analysis. The difference between the input and output air quality parameters is calculated to directly reflect the immediate purification efficiency of the air purification equipment, so that the abstract purification effect is transformed into a specific quantitative value, which is easy to understand and compare. The established purification effect quantitative scoring system provides a unified measurement standard for the purification performance of different devices and different time points, which is conducive to cross-device and cross-scenario performance comparison and optimization. An air quality optimization quantity is generated for each acquisition time node, forming a performance tracking map on a time series, which helps to observe the trend of purification efficiency over time and promptly discover signs of efficiency decline. Step S2 lays a solid foundation for the efficient management, precise regulation and long-term performance optimization of air purification equipment through in-depth processing and quantification of the original data.

[0085] S3, arranging the preset number of air quality optimization quantities in chronological order to obtain an air quality optimization feature set for the air purification equipment;

[0086] The goal of this step is to capture the trends and patterns of performance changes of air purification equipment through time series data obtained through continuous monitoring; the air quality optimization feature set is a form of data aggregation that summarizes the ability of the equipment to improve air quality at different time points, providing a basic data set for subsequent performance evaluation and efficiency analysis;

[0087] The method for obtaining the air quality optimization feature set of the air purification equipment includes:

[0088] Arrange the air quality optimization quantities in the time sequence of the collection time nodes; match each optimization quantity with its corresponding timestamp to form a time series data set;

[0089] A set of characteristics is defined to describe and quantify the change of air quality optimization quantity over time, including:

[0090] Average optimization amount, the average value of all optimization amounts during the entire observation period, reflects the overall purification effect;

[0091] The fluctuation of the optimized amount, the standard deviation or variance of the optimized amount, indicates the stability of the purification effect;

[0092] Trend characteristics, through linear regression, moving average or other time series analysis methods, to identify the upward or downward trend of purification efficiency over time;

[0093] Peaks and valleys, identifying the maximum and minimum values ​​in the optimization quantity sequence, corresponding to specific events or extreme conditions of equipment performance;

[0094] Periodic patterns: analyzing whether the optimization amount shows some periodic changes, patterns related to usage time, seasonal changes, or indoor activities;

[0095] Calculate and extract actual values ​​from time series datasets based on defined features;

[0096] All the extracted feature values ​​are integrated into a structured data set, namely the air quality optimization feature set of the air purification equipment; this set contains multi-dimensional information reflecting the changes in the purification efficiency of the equipment over time, providing a basis for subsequent performance evaluation, prediction and control strategy formulation.

[0097] In this step, by integrating the optimization quantity in the time series, a comprehensive method is provided to evaluate the performance of air purification equipment, and to grasp the purification efficiency and stability of the equipment as a whole; by extracting trend features, the trend of purification efficiency over time can be identified, which is helpful to timely adjust the maintenance plan or optimize the use strategy; the analysis of optimization quantity fluctuations and peak and valley values ​​helps to identify the stability of equipment performance and potential abnormal conditions, so that maintenance personnel can respond quickly to avoid air quality affected by sudden equipment failures; identifying the periodic change pattern of the optimization quantity, associated with specific usage habits, seasonal changes and other factors, helps to pre-adjust equipment operating parameters under specific conditions to improve purification efficiency and energy saving effects; the formed optimization feature set provides a detailed and structured data basis for subsequent performance evaluation, prediction model construction and control strategy formulation; this not only improves the scientificity and accuracy of decision-making, but also makes equipment management more intelligent and efficient; S3 step realizes in-depth analysis and management of air purification equipment performance by constructing an air quality optimization feature set, promotes the intelligence and efficiency of equipment operation, and provides strong technical support for improving indoor air quality.

[0098] S4, extracting features from the air quality optimization feature set to obtain a purification efficiency evaluation index of the air purification equipment;

[0099] The method for extracting features from the air quality optimization feature set includes:

[0100] Preprocess the air quality optimization feature set, including checking data integrity, removing outliers and filling missing values ​​to ensure data quality;

[0101] According to the needs of purification effect evaluation, select the features that have a significant impact on purification efficiency, including trend features, periodic features and volatility features of time series;

[0102] Based on the original optimization data, construct new feature variables; calculate the average value, maximum value, minimum value, fluctuation range and other statistics of the optimization data in each collection cycle; use time series analysis methods to extract trend characteristics;

[0103] Determine the importance of each feature through statistical analysis or machine learning methods, and assign weights to the features accordingly, so that important features can play a greater role in the subsequent evaluation index calculation;

[0104] The selected features are multiplied by their weights and then added together to form a comprehensive purification efficiency evaluation index.

[0105] In this step, the accuracy and reliability of the analysis basis are ensured through the preprocessing link, the interference of erroneous information on the purification efficiency evaluation is avoided, and the credibility of the evaluation results is improved; according to the specific needs of the purification effect evaluation, the features that have a significant impact on the purification efficiency are carefully selected, which helps to focus on the key influencing factors and make the evaluation more accurate and efficient; by constructing new feature variables, not only the evaluation index system is enriched, but also the performance of the air purification equipment is comprehensively described from multiple dimensions, providing a multi-perspective for in-depth understanding of the working status of the equipment; with the help of statistical analysis or machine learning technology, the features are ranked by importance and given reasonable weights, ensuring that in the calculation of the comprehensive evaluation index, the key features can play their due influence, making the evaluation results more scientific and reasonable, and improving the discrimination and accuracy of the evaluation; the purification efficiency evaluation index finally formed is a quantitative indicator that comprehensively considers multiple factors, which can intuitively reflect the overall purification efficiency of the air purification equipment, and provides a strong basis for equipment performance monitoring, maintenance scheduling, and user understanding and trust; S4 step constructs a purification efficiency evaluation system with high practicality and accuracy through scientific and rigorous data processing and feature extraction methods, which provides strong support for the intelligent management and efficiency optimization of air purification equipment.

[0106] S5. Input the ambient humidity and ambient temperature into a pre-built equipment purification impact analysis model to obtain a purification environment impact coefficient;

[0107] Step S5 is to use environmental factors to analyze the impact of air purification equipment on purification effects, so as to achieve a more accurate efficiency evaluation;

[0108] The ambient temperature and humidity data of the space where the air purification equipment is located are collected in real time through the Internet of Things technology; combined with the air quality optimization feature set, a data set containing multi-dimensional information is formed;

[0109] Construct an equipment purification impact analysis model to analyze how ambient temperature and humidity affect the purification efficiency of air purification equipment;

[0110] Using the constructed model, input the current real-time monitored ambient temperature and humidity values, and the model output is the purification environment impact coefficient; the purification environment impact coefficient reflects the gain or loss ratio of air purification efficiency under the current environmental conditions relative to the standard or ideal conditions;

[0111] The method for constructing the equipment purification impact analysis model includes:

[0112] Collect data about the operation of air purification equipment, including ambient temperature, humidity, air quality data before and after air purification, equipment operating parameters, and equipment purification efficiency evaluation index;

[0113] Clean the collected data, including removing outliers, filling missing values, and normalizing data, to ensure data quality and prepare for subsequent analysis;

[0114] According to the nature of the problem and the characteristics of the data, a machine learning model is selected as the infrastructure of the equipment purification impact analysis model, and the machine learning model includes linear regression, decision tree, random forest, support vector machine and neural network;

[0115] The processed data set is divided into a training set and a validation set, with 70% or 80% of the data used for training and the rest used to verify the model performance;

[0116] Use the training set data to train the selected model and find the optimal parameter combination by continuously adjusting the model parameters;

[0117] Use cross-validation methods, such as k-fold cross-validation, to evaluate the performance of the model on unseen data and improve the generalization ability of the model;

[0118] Based on the performance of the model on the validation set, use grid search, random search and other methods to adjust the model parameters to further improve the model performance;

[0119] Deploy the optimized model to actual applications.

[0120] In this step, the model can provide an accurate purification environment impact coefficient, which directly reflects the relative change of air purification efficiency under the current temperature and humidity conditions, so that the equipment can make adaptive adjustments based on the actual environmental conditions, and improve the accuracy and reliability of the purification effect; by real-time monitoring of the ambient temperature and humidity and inputting the model, the impact coefficient is automatically calculated, so that the equipment can dynamically respond to environmental changes, automatically optimize the operating parameters, ensure that the purification efficiency is always maintained at a high level, and improve the user experience; the establishment and application of the model is based on a large amount of operating data, which helps to identify the changing laws of equipment performance under different environmental conditions, and provides a scientific basis for the formulation of forward-looking maintenance plans and filter replacement, etc., and reduces the efficiency reduction caused by improper maintenance; by accurately quantifying the environmental impact, the equipment can use energy more effectively, avoid excessive operation under environmental conditions where the purification efficiency is less affected, save electricity, and comply with the environmental protection concept of energy conservation and emission reduction; the integration of Internet of Things technology and machine learning models not only improves the efficiency and accuracy of data processing, but also enhances the autonomous learning and optimization capabilities of the air purification system, and promotes the development of smart home and smart office environments; step S5, by constructing and applying the purification environment impact analysis model, not only enhances the adaptability and response speed of air purification equipment to environmental changes, but also promotes the intelligence and refinement of equipment management.

[0121] S6. Based on the purification environment impact coefficient, the purification efficiency evaluation index is corrected to obtain a purification efficiency correction index of the air purification equipment;

[0122] The purification environment impact coefficient is a value that reflects the ratio of environmental factors to the gain or loss of purification efficiency. If the coefficient is greater than 1, it means that the current environmental conditions are conducive to the purification process, otherwise it means that the environmental conditions are not conducive to purification.

[0123] The method for obtaining the purification efficiency correction index of the air purification equipment includes:

[0124] According to the purification environment impact coefficient, the purification efficiency evaluation index is mathematically adjusted; the purification efficiency correction index of the air purification equipment is obtained by multiplying the purification efficiency evaluation index and the purification environment impact coefficient;

[0125] After the calculation is performed, the purification efficiency correction index is obtained which takes into account the influence of current environmental factors. This index can accurately reflect the actual purification efficiency of the equipment under the current temperature and humidity environment.

[0126] When environmental conditions change, the correction index should be updated in real time; the system needs to have dynamic monitoring and rapid response capabilities to ensure that the correction index always reflects the latest status. At the same time, the system should have a verification mechanism to ensure the accuracy of the correction process;

[0127] The calculated purification efficiency correction index is stored in the cloud or local database together with the corresponding timestamp each time, which is convenient for long-term performance tracking, analysis and prediction.

[0128] In this step, by considering the impact of ambient temperature and humidity on purification efficiency in real time, the correction index can dynamically reflect the actual purification efficiency of the equipment under different environmental conditions, ensuring that the air purification equipment can maintain efficient operation in a variety of indoor environments; based on the corrected purification efficiency index, the real-time status of the equipment performance can be more accurately identified, and the decline in efficiency can be discovered in time, providing an accurate basis for automatically adjusting the operating parameters, avoiding the blind reduction of purification efficiency due to environmental changes; the real-time correction mechanism ensures that the equipment will not consume energy excessively under adverse environmental conditions, and only adjusts parameters when necessary, effectively saving energy, improving operating efficiency, and reducing long-term operating costs; by maintaining a stable air purification effect, even under unfavorable external environmental conditions, it can also ensure indoor air quality, improve the quality of the user's living or working environment, and increase the user's trust and satisfaction with air purification equipment; the implementation of step S6 not only optimizes the performance of air purification equipment, but also effectively improves user experience through intelligent environmental adaptability adjustment, and realizes refined management of the air purification process, which is an important technological advancement in improving the quality of life and promoting healthy living.

[0129] S7. When the purification efficiency correction index is lower than the preset efficiency threshold, automatically adjust the air purification equipment operating parameters until the purification efficiency correction index is not lower than the preset efficiency threshold;

[0130] A preset efficiency threshold is set, which represents the minimum purification efficiency standard that the air purification equipment should achieve; the system compares the current purification efficiency correction index with the threshold in real time;

[0131] When the purification efficiency correction index is detected to be lower than the preset efficiency threshold, the automatic adjustment mechanism is triggered;

[0132] Methods for automatically adjusting the operating parameters of air purification equipment include:

[0133] Adjust wind speed: Increase the wind speed of the air purification equipment to improve air circulation and filtration efficiency;

[0134] Change the working mode: switch to a more efficient working mode, such as strong purification mode, sacrificing a certain amount of energy consumption to increase the purification rate;

[0135] Filter cleaning and replacement reminder: If the purification efficiency drops and the filter is clogged, the system will send a maintenance reminder to advise the user to clean or replace the filter;

[0136] Optimize motor power: Dynamically adjust motor power according to equipment load and current purification efficiency requirements to maintain purification effect and save energy;

[0137] After adjustment, the system continues to monitor the purification efficiency correction index until the index returns to above the preset efficiency threshold;

[0138] Each automatic adjustment parameter change, time point, and changes in purification efficiency before and after are recorded in the cloud or local storage, which is convenient for later analysis of equipment performance, optimization of control strategies, and provision of maintenance history records.

[0139] In this step, by real-time monitoring and automatic adjustment of the operating parameters of the air purification equipment, it is ensured that the equipment can maintain above the preset minimum purification efficiency standard under different environmental conditions and use cycles, thereby continuously providing high-quality air purification effects; the automatic adjustment mechanism can dynamically adjust the wind speed, working mode and motor power according to actual conditions. This on-demand adjustment method not only ensures purification efficiency, but also avoids unnecessary energy consumption and achieves the optimization of energy use; through the filter cleaning and replacement reminder function, key components are maintained in time, which effectively extends the service life of the air purification equipment, reduces performance degradation caused by improper maintenance, and also improves the user experience; Automatically records detailed information of each adjustment, provides valuable data support for equipment performance analysis, continuous optimization of control strategies and maintenance services, and promotes intelligent and refined equipment management; users can enjoy continuous and efficient air purification services without manual intervention, reducing the user's operating burden, and the maintenance reminder function also improves the user's convenience and equipment maintenance awareness; ensures that the air purification equipment is always in an efficient working state, effectively filters indoor pollutants, and provides users with a healthier and safer living or working environment; the automatic adjustment mechanism of the S7 step not only improves the efficiency and response speed of the air purification equipment, but also takes into account energy efficiency and equipment maintenance.

[0140] Embodiment 2: Figure 2 As shown, the intelligent connection sensing control system based on Internet of Things technology of the present invention specifically includes the following modules:

[0141] The environmental perception module controls the monitoring sensor to collect the air quality of the input side and the output side according to the set collection time node, and obtains the first air quality data information and the second air quality data information respectively; and controls the temperature and humidity sensor to collect the ambient temperature and ambient humidity of the space where the air purification equipment is located;

[0142] A quantification processing module performs quantification processing on the first air quality data information and the second air quality data information, and calculates an air quality optimization amount based on the quantification result; each acquisition time node corresponds to an air quality optimization amount, wherein the time distance between two adjacent acquisition time nodes is the same;

[0143] The optimization feature set generation module arranges the preset number of air quality optimization quantities in chronological order to obtain the air quality optimization feature set of the air purification equipment;

[0144] The purification efficiency evaluation module extracts the air quality optimization feature set and obtains the purification efficiency evaluation index of the air purification equipment;

[0145] The purification impact analysis module inputs the ambient humidity and ambient temperature into the pre-built equipment purification impact analysis model to obtain the purification environment impact coefficient;

[0146] The purification efficiency correction module performs impact correction on the purification efficiency evaluation index based on the purification environment impact coefficient to obtain the purification efficiency correction index of the air purification equipment;

[0147] The adjustment control module automatically adjusts the operating parameters of the air purification equipment when the purification efficiency correction index is lower than the preset efficiency threshold until the purification efficiency correction index is not lower than the preset efficiency threshold.

[0148] The system uses the environmental sensing module to monitor the air quality and ambient temperature and humidity on the input and output sides in real time, and promptly capture the degradation signs of the performance of components such as filters and motors, thus overcoming the shortcomings of traditional equipment that lack degradation monitoring.

[0149] The quantitative processing module and the optimized feature set generation module not only quantify the differences before and after air purification, but also form an optimized feature set through time series analysis, so that the purification effect can be accurately quantified, improving the scientificity and accuracy of management;

[0150] The purification efficiency evaluation module and the correction module dynamically evaluate and correct the purification efficiency in combination with environmental factors, ensuring the comprehensiveness and timeliness of the evaluation; the adjustment control module can automatically respond to changes in the correction index, automatically adjust the equipment operating parameters, and maintain the purification efficiency above the preset level, greatly improving the intelligent management level of the equipment;

[0151] The purification impact analysis module takes into account the actual impact of ambient temperature and humidity on the purification effect, and makes corrections through a preset model to ensure that the best purification effect can be achieved under different environmental conditions;

[0152] The system can predict and respond to the decline in purification efficiency, proactively prompt or perform necessary maintenance operations, avoid the problem of purification effect gradually declining over time, extend the effective service life of the equipment, and protect the health and quality of life of users;

[0153] Through refined management and adaptive adjustment, excessive or ineffective operation can be avoided, thereby saving energy consumption, reducing long-term operating costs, and reducing the burden on the environment, which is in line with the concept of green living;

[0154] In summary, by integrating IoT technology, the system has achieved the transformation of air purification equipment from passive response to active management, improved the purification effect, enhanced the user experience, and is an effective solution to existing problems.

[0155] The various variations and specific embodiments of the intelligent sensing control method based on the Internet of Things technology in the aforementioned embodiment 1 are also applicable to the intelligent sensing control system based on the Internet of Things technology in this embodiment. Through the aforementioned detailed description of the intelligent sensing control method based on the Internet of Things technology, those skilled in the art can clearly know the implementation method of the intelligent sensing control system based on the Internet of Things technology in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here.

[0156] In addition, the present application also provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor. The transceiver, the memory, and the processor are respectively connected via a bus. When the computer program is executed by the processor, each process of the above-mentioned method for controlling output data is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0157] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An intelligent sensing control method based on Internet of Things technology, characterized in that: The method is applied to automatically adjust an air purification device, wherein monitoring sensors are deployed on both the input side and the output side of the air purification device, and a temperature and humidity sensor is also installed on the air purification device, and the method comprises: According to the set collection time node, the monitoring sensor is controlled to collect the air quality of the input side and the output side to obtain the first air quality data information and the second air quality data information respectively; and the temperature and humidity sensor is controlled to collect the ambient temperature and ambient humidity of the space where the air purification equipment is located; Quantifying the first air quality data information and the second air quality data information, and calculating the air quality optimization amount based on the quantification result; each collection time node corresponds to an air quality optimization amount, wherein the time distance between two adjacent collection time nodes is the same; Arrange a preset number of air quality optimization quantities in chronological order to obtain an air quality optimization feature set for the air purification equipment; Extract features from the air quality optimization feature set to obtain a purification efficiency evaluation index for the air purification equipment; Input the ambient humidity and ambient temperature into the pre-built equipment purification impact analysis model to obtain the purification environment impact coefficient; Based on the purification environment impact coefficient, the purification efficiency evaluation index is corrected to obtain the purification efficiency correction index of the air purification equipment; When the purification efficiency correction index is lower than the preset efficiency threshold, the air purification equipment operating parameters are automatically adjusted until the purification efficiency correction index is not lower than the preset efficiency threshold; The method for obtaining the air quality optimization feature set of the air purification equipment includes: Arrange the air quality optimization quantities in the time sequence of the collection time nodes; match each optimization quantity with its corresponding timestamp to form a time series data set; Define a set of characteristics for describing and quantifying the change of air quality optimization quantity over time; Calculate and extract actual values ​​from time series datasets based on defined features; Integrate all the extracted feature values ​​into a structured data set, i.e., the air quality optimization feature set of the air purification equipment; The method for extracting features from the air quality optimization feature set includes: Preprocess the air quality optimization feature set, including checking data integrity, removing outliers, and filling missing values; According to the needs of purification effect evaluation, select the features that affect the purification efficiency; Based on the air quality optimization feature set, a new feature variable is constructed; The importance of each feature is determined through machine learning methods, and weights are assigned to the features.

2. The intelligent sensing control method based on Internet of Things technology as claimed in claim 1, characterized in that: The method for quantifying the first air quality data information and the second air quality data information includes: Performing data cleaning on the first air quality data information and the second air quality data information to remove outliers and smooth noise; For each acquisition time node, the difference in air quality parameters between the input side and the output side is calculated respectively; Set up a quantitative scoring system for purification effects, and set scores corresponding to different purification effect levels; Based on the quantitative scoring of purification effect, the purification effect at each collection time node is scored to form the air quality optimization amount.

3. The intelligent sensing control method based on Internet of Things technology as claimed in claim 1, characterized in that: The method for constructing the equipment purification impact analysis model includes: Collect data about the operation of air purification equipment and clean the collected data, including removing outliers, filling missing values ​​and normalizing data; Selecting a machine learning model as the infrastructure of the equipment purification impact analysis model, wherein the machine learning model includes linear regression, decision tree, random forest, support vector machine, and neural network; Divide the processed data set into a training set and a validation set; Use the training set data to train the selected model and adjust the model parameters; Use cross-validation to evaluate the performance of the model on unseen data; Adjust model parameters based on the model’s performance on the validation set; Deploy the optimized model to actual applications.

4. The intelligent sensing control method based on Internet of Things technology as claimed in claim 1, characterized in that: Methods for automatically adjusting the operating parameters of air purification equipment include adjusting wind speed, changing working mode, reminding filter cleaning and replacement, and optimizing motor power.

5. The intelligent sensing control method based on Internet of Things technology as claimed in claim 1, characterized in that: The basis for setting the collection time node includes equipment working cycle, environmental change frequency, filter motor performance attenuation rate, energy consumption efficiency balance and abnormal situation response.

6. An intelligent sensor control system based on Internet of Things technology, characterized in that: The system is applied to the intelligent sensing control method based on Internet of Things technology as claimed in claim 1, and the system includes: The environmental perception module controls the monitoring sensor to collect the air quality of the input side and the output side according to the set collection time node, and obtains the first air quality data information and the second air quality data information respectively; and controls the temperature and humidity sensor to collect the ambient temperature and humidity of the space where the air purification equipment is located; A quantification processing module performs quantification processing on the first air quality data information and the second air quality data information, and calculates an air quality optimization amount based on the quantification result; each acquisition time node corresponds to an air quality optimization amount, wherein the time distance between two adjacent acquisition time nodes is the same; The optimization feature set generation module arranges a preset number of air quality optimization quantities in chronological order to obtain an air quality optimization feature set for the air purification device; The purification efficiency evaluation module extracts the air quality optimization feature set and obtains the purification efficiency evaluation index of the air purification equipment; The purification impact analysis module inputs the ambient humidity and ambient temperature into the pre-built equipment purification impact analysis model to obtain the purification environment impact coefficient; The purification efficiency correction module performs impact correction on the purification efficiency evaluation index based on the purification environment impact coefficient to obtain the purification efficiency correction index of the air purification equipment; The adjustment control module automatically adjusts the operating parameters of the air purification equipment when the purification efficiency correction index is lower than the preset efficiency threshold until the purification efficiency correction index is not lower than the preset efficiency threshold.

7. An intelligent sensor-controlled electronic device based on Internet of Things technology, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, characterized in that: When the computer program is executed by the processor, the steps in the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 5 are implemented.

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