Air purification method, device, equipment and medium based on Internet of Things device data

Abstract

The present application relates to an air purification method, apparatus, equipment and medium based on IoT device data. The working data of each IoT device in the IoT of the area to be purified is obtained; based on the working data of each IoT device, the current cleanliness of the area to be purified is determined; based on the scene attributes of the area to be purified and the working data of each IoT device, the purification target is determined; the purification target includes air quality target parameters and purification task execution time; based on the purification target, purification range, current cleanliness, and equipment parameters of the air purification equipment, a specific purification task is determined to execute the specific purification task within the purification task execution time to achieve the air quality target parameters. The entire process is based on data processing and does not require user operation, and can achieve dynamic air purification intelligently and efficiently.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to an air purification method, apparatus, device and medium based on Internet of Things device data. Background Art

[0002] Air purification equipment is primarily used to filter out or eliminate pollutants in the air, thereby improving air cleanliness. It can generally be used in a variety of scenarios, such as homes, hospitals, factories, and vehicles.

[0003] In the related art, multiple working modes are generally pre-set in air purification equipment, which can be selected by users according to different needs. Alternatively, a mode setting option is further provided for users to set parameters to set different working modes.

[0004] However, the above methods still have some problems in design and use. For manufacturers, it is necessary to design different working modes for equipment used in different scenarios, which is costly. Moreover, the modes set by the manufacturer may not fully adapt to the actual needs of users, which can easily lead to energy waste or incomplete purification. For users, it is difficult to understand the actual purification effects corresponding to various parameters, making it cumbersome and difficult to accurately switch or set modes during use. Summary of the Invention

[0005] This application provides an air purification method, apparatus, device, and medium based on IoT device data. Based on the data from various devices in the same IoT, the method processes and analyzes the data, dynamically adjusting the parameters of the air purification device, achieving intelligent air purification and improving the efficiency of the air purification device.

[0006] In a first aspect, the present application provides an air purification method based on IoT device data, comprising:

[0007] Obtain the working data of each IoT device in the IoT area to be purified;

[0008] Determine the current cleanliness level of the area to be purified based on the working data of each of the IoT devices;

[0009] Determine a purification target based on the scene attributes of the area to be purified and the working data of each of the IoT devices; the purification target includes air quality target parameters and purification task execution time;

[0010] In combination with the purification target, purification range, current cleanliness, and equipment parameters of the air purification equipment, a specific purification task is determined to perform the specific purification task within the purification task execution time to achieve the air quality target parameters.

[0011] Optionally, the working data includes working data of historical periods and working data of current periods;

[0012] Determining the current cleanliness of the area to be purified based on the working data of each of the IoT devices includes:

[0013] Determine the current predicted cleanliness level of the area to be purified based on the previous specific purification task of the air purification equipment and the corresponding task execution progress;

[0014] Determine the cleanliness influencing factors of the current period based on the historical period working data and current period working data of the relevant equipment;

[0015] Determine the near-end air cleanliness of the current air purification equipment based on the current detection data of the air purification equipment;

[0016] Based on the cleanliness influencing factor of the current time period and the proximal air cleanliness of the current air purification equipment, the current predicted cleanliness is adjusted to determine the current cleanliness of the area to be purified.

[0017] Optionally, determining a purification target according to scene attributes of the area to be purified and working data of each of the IoT devices includes:

[0018] Determine the target air quality parameter range based on the scene attributes of the area to be purified;

[0019] Predict the cleanliness influencing factors of future periods based on the historical and current period working data of relevant equipment;

[0020] Determining the execution time of the purification task according to the cleanliness influencing factor of the future period;

[0021] The air quality target parameter is determined from the air quality target parameter range in combination with the scene attributes of the area to be purified and the purification task execution time.

[0022] Optionally, the prediction of cleanliness influencing factors in future time periods based on historical time period working data and current time period working data of relevant equipment includes:

[0023] Analyze the operating characteristics of the relevant equipment in each period based on the historical and current period working data of the relevant equipment;

[0024] For each relevant device, based on the operating characteristics of the relevant device in each time period, predict the operating status of the relevant device in the future time period;

[0025] Comprehensively consider the operating status of all relevant equipment in the future time period and predict the factors affecting cleanliness in the future time period.

[0026] Optionally, adjusting the current predicted cleanliness based on the cleanliness influencing factor of the current time period and the proximal air cleanliness of the current air purification device to determine the current cleanliness of the area to be purified includes:

[0027] Based on the current predicted cleanliness, simulate and predict the near-end air cleanliness;

[0028] Analyzing the difference in air quality characteristics between the proximal air cleanliness of the current air purification device and the predicted proximal air cleanliness;

[0029] The current predicted cleanliness is adjusted according to the cleanliness influencing factor of the current time period and the difference in the air quality characteristics to determine the current cleanliness of the area to be purified.

[0030] Optionally, the method further includes:

[0031] After detecting that the air purification device is connected to the Internet of Things, basic data of existing Internet of Things devices in the Internet of Things is obtained;

[0032] Based on the basic data of the existing IoT devices, the scene attributes of the area to be purified are determined.

[0033] Optionally, the basic data includes device type and device model; the scene attributes include scene type and site range;

[0034] Determining the scene attributes of the area to be purified based on the basic data of the existing IoT devices includes:

[0035] Determining whether there is a dedicated device among the IoT devices based on the device type;

[0036] If there is a dedicated device, determine the scene type of the area to be purified based on the applicable scene of the dedicated device;

[0037] If no dedicated device exists, the scenario type is determined based on the combination of device types;

[0038] Determine the site scope of the area to be purified based on the number of IoT devices of each device type and the device models.

[0039] In a second aspect, the present application provides an air purification device based on IoT device data, comprising:

[0040] The acquisition module is used to obtain the working data of each IoT device in the IoT area to be purified;

[0041] A current cleanliness determination module, configured to determine the current cleanliness of the area to be purified based on the working data of each of the IoT devices;

[0042] A purification target determination module is used to determine the purification target based on the scene attributes of the area to be purified and the working data of each of the IoT devices; the purification target includes air quality target parameters and purification task execution time;

[0043] The specific purification task determination module is used to determine the specific purification task in combination with the purification target, purification range, current cleanliness, and equipment parameters of the air purification equipment, so as to perform the specific purification task within the purification task execution time and achieve the air quality target parameters.

[0044] Optionally, the working data includes working data of historical periods and working data of current periods;

[0045] The current cleanliness determination module is specifically used to:

[0046] Determine the current predicted cleanliness level of the area to be purified based on the previous specific purification task of the air purification equipment and the corresponding task execution progress;

[0047] Determine the cleanliness influencing factors of the current period based on the historical period working data and current period working data of the relevant equipment;

[0048] Determine the near-end air cleanliness of the current air purification equipment based on the current detection data of the air purification equipment;

[0049] Based on the cleanliness influencing factor of the current time period and the proximal air cleanliness of the current air purification equipment, the current predicted cleanliness is adjusted to determine the current cleanliness of the area to be purified.

[0050] Optionally, the purification target determination module is specifically configured to:

[0051] Determine the target air quality parameter range based on the scene attributes of the area to be purified;

[0052] Predict the cleanliness influencing factors of future periods based on the historical and current period working data of relevant equipment;

[0053] Determining the execution time of the purification task according to the cleanliness influencing factor of the future period;

[0054] The air quality target parameter is determined from the air quality target parameter range in combination with the scene attributes of the area to be purified and the purification task execution time.

[0055] Optionally, when predicting the cleanliness influencing factor of a future time period based on the historical time period working data and the current time period working data of the relevant equipment, the purification target determination module is specifically used to:

[0056] Analyze the operating characteristics of the relevant equipment in each period based on the historical and current period working data of the relevant equipment;

[0057] For each relevant device, based on the operating characteristics of the relevant device in each time period, predict the operating status of the relevant device in the future time period;

[0058] Comprehensively consider the operating status of all relevant equipment in the future time period and predict the factors affecting cleanliness in the future time period.

[0059] Optionally, when the current cleanliness determination module adjusts the current predicted cleanliness based on the cleanliness influencing factor of the current time period and the proximal air cleanliness of the current air purification equipment to determine the current cleanliness of the area to be purified, it is specifically configured to:

[0060] Based on the current predicted cleanliness, simulate and predict the near-end air cleanliness;

[0061] Analyzing the difference in air quality characteristics between the proximal air cleanliness of the current air purification device and the predicted proximal air cleanliness;

[0062] The current predicted cleanliness is adjusted according to the cleanliness influencing factor of the current time period and the difference in the air quality characteristics to determine the current cleanliness of the area to be purified.

[0063] Optionally, the device further includes a scene attribute determination module, configured to:

[0064] After detecting that the air purification device is connected to the Internet of Things, basic data of existing Internet of Things devices in the Internet of Things is obtained;

[0065] Based on the basic data of the existing IoT devices, the scene attributes of the area to be purified are determined.

[0066] Optionally, the basic data includes device type and device model; the scene attributes include scene type and site range;

[0067] When determining the scene attributes of the area to be purified based on the basic data of the existing IoT devices, the scene attribute determination module is specifically configured to:

[0068] Determining whether there is a dedicated device among the IoT devices based on the device type;

[0069] If there is a dedicated device, determine the scene type of the area to be purified based on the applicable scene of the dedicated device;

[0070] If no dedicated device exists, the scenario type is determined based on the combination of device types;

[0071] Determine the site scope of the area to be purified based on the number of IoT devices of each device type and the device models.

[0072] In a third aspect, the present application provides an electronic device comprising: a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute the method of the first aspect.

[0073] In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and execute the method of the first aspect.

[0074] In a fifth aspect, the present application provides a computer program product, comprising: a computer program; when the computer program is executed by a processor, it implements the method as described in any one of the first aspects.

[0075] This application provides an air purification method, apparatus, device, and medium based on IoT device data. The current cleanliness level is analyzed based on the operating data of IoT devices in the area to be purified. Reasonable purification targets and specific purification tasks are then determined based on scene attributes and user preferences. The air purification device achieves the purification target by performing the specific purification task. The entire process is based on data processing, requiring no user interaction, and enables intelligent and efficient dynamic air purification. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0077] Figure 1 A flowchart of an air purification method based on IoT device data provided in one embodiment of the present application;

[0078] Figure 2 A schematic diagram of the structure of an air purification device based on IoT device data provided by one embodiment of the present application;

[0079] Figure 3 A schematic structural diagram of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0080] To make the purpose, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0081] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document, unless otherwise specified, generally indicates an "or" relationship between the related objects.

[0082] The embodiments of the present application are described in further detail below with reference to the accompanying drawings.

[0083] Air purifiers are primarily used to filter out or eliminate airborne pollutants such as dust, pollen, smoke (including secondhand smoke), pet hair, particulate matter like PM2.5 and PM10, pathogenic microorganisms like bacteria and viruses, cooking fumes, pet odors, and musty odors, thereby improving air cleanliness. They are commonly used in a variety of settings, including homes, hospitals, factories, and vehicles.

[0084] Traditional air purifiers rely solely on their own sensors and are unable to predict pollution events. Their efficiency is low when they start operating after the air quality deteriorates.

[0085] In the related art, multiple working modes are generally pre-set in air purification equipment, which can be selected by users according to different needs. Alternatively, a mode setting option is further provided for users to set parameters to set different working modes.

[0086] However, the above methods still have some problems in design and use. For manufacturers, it is necessary to design different working modes for equipment used in different scenarios, which is costly. Moreover, the modes set by the manufacturer may not fully adapt to the actual needs of users, which can easily lead to energy waste or incomplete purification. For users, it is difficult to understand the actual purification effects corresponding to various parameters, making it cumbersome and difficult to accurately switch or set modes during use.

[0087] Based on this, the present application provides an air purification method, device, equipment and medium based on IoT device data. This method can be adapted to air purification equipment used in different scenarios. For manufacturers, only this set of methods is needed, and there is no need to design different working modes for different devices, saving R&D costs and labor costs. In addition, this method is based on IoT device data and dynamically adjusts the parameters of air purification equipment, which can be adapted to the actual usage needs of users, greatly reducing the difficulty of user operation and improving the convenience of use.

[0088] The "air purification equipment" mentioned in this application is a general term for equipment with air purification functions. In one of the implementation scenarios of this application, all networkable devices in the area to be purified are connected to the Internet to form an Internet of Things system. When new devices are added, they are also connected to the Internet of Things system through the Internet. A control device can be set up in the Internet of Things system to be responsible for data interaction control of the entire Internet of Things system. This control device can be a server or a computer. The air purification method based on Internet of Things device data of this application can be integrated into a software or functional module, loaded in a control device or an air purification device, and dynamic air purification can be achieved by performing data processing.

[0089] The specific implementation of the present application solution can be referred to the following embodiments.

[0090] Figure 1 This is a flow chart of an air purification method based on IoT device data provided in one embodiment of the present application. Figure 1 As shown, the method includes:

[0091] S101. Obtain working data of each IoT device in the IoT area to be purified.

[0092] In this application, the area to be purified refers to the area where the air purification equipment needs to be purified when it is working. In practice, it is generally consistent with the enclosed space area. For example, in a home scenario, the area to be purified is generally the indoor area of the home; in a factory scenario, the area to be purified is generally the factory workshop area or factory office area; in an office scenario, the area to be purified is generally the area inside the office. These areas generally have separate local area networks, and the devices in the area are connected to the network to form the Internet of Things.

[0093] In this application, IoT devices refer to devices with internet connectivity. Once connected to the IoT, these devices can record and transmit their operating data in real time. This includes air purification devices.

[0094] In a specific embodiment, the IoT device can transmit operating data to the control device in real time. When the control device performs this step, the control device selects the required operating data from the received data. When the air purification device performs this step, the air purification device sends a data request to the control device to obtain the required operating data.

[0095] In another specific embodiment, the IoT device can store the operating data locally in real time. When the control device performs this step, the control device sends a data request to the IoT device to obtain the required operating data. When the air purification device performs this step, the air purification device sends a data request to the IoT device to obtain the required operating data.

[0096] S102. Determine the current cleanliness level of the area to be purified based on the working data of each IoT device.

[0097] The working data of the Internet of Things devices can directly reflect the air conditions in the area to be purified, or indirectly reflect the air conditions in the area to be purified by reflecting the changes in the air in the area to be purified.

[0098] For example, air purification equipment is generally equipped with an air quality detection module, which can detect the air conditions within a certain range and reflect the changes in the air within a certain range in the time dimension; the working data of the purification module can also reflect the changes in the air.

[0099] For example, the sweeping frequency and dust box data of a vacuum cleaner in a home scenario can, to a certain extent, reflect the air dust conditions in the area to be purified.

[0100] For example, in an office environment, analyzing the range of people's activities through video acquisition equipment can reflect the air dust conditions in the area to be purified to a certain extent.

[0101] In this step, by comprehensively analyzing the working data of each IoT device, the changes in the air quality of the area to be purified can be determined, thereby determining the current cleanliness. In this application, the current cleanliness is used to characterize the overall air quality of the area to be purified, and can be expressed in the form of an air quality score. Compared with the air quality measured only by the sensors in the air purification equipment in the related art, it will be more accurate and comprehensive, and will not be limited to the detection range of the sensor. Based on this, subsequent purification will be more precise. Avoid insufficient purification when the measured air quality is better than the actual air quality, and avoid excessive purification when the measured air quality is worse than the actual air quality, resulting in waste of energy.

[0102] S103: Determine the purification target based on the scene attributes of the area to be purified and the working data of each IoT device.

[0103] Among them, the purification target includes air quality target parameters and purification task execution time.

[0104] In this application, scene attributes refer to the scene-specific attributes of the area to be purified. There are many scenarios in which air purification equipment can be applied, such as homes, companies, hospitals, factories, vehicles, and many other scenarios. These scenarios each correspond to some specific attributes, such as the number of people, the time of people's activities, the scope of people's activities, the characteristics of people, the scope of the site, the type of equipment, the type of air pollutants, the source of pollution, etc.

[0105] In different scenarios, the pollutants in the air are different and the requirements for air quality are different, so different purification targets can be corresponded.

[0106] By combining scene attributes, we can analyze the general air quality requirements of the area to be purified. By combining the working data of IoT devices, we can analyze user habits. Then, by combining these two aspects of information, we can determine the user's requirements for air quality and preferences for the working status of the air purification equipment. In this way, the purification target is determined, that is, the purification task is executed at the purification task execution time to achieve the air quality target parameters.

[0107] S104. Determine a specific purification task based on the purification target, purification range, current cleanliness, and equipment parameters of the air purification equipment, so as to perform the specific purification task within the purification task execution time and achieve the air quality target parameters.

[0108] The gap between the current cleanliness level and the target air quality parameters represents the amount of air that needs to be purified within the execution time of the purification task. Combined with the equipment parameters of the air purification equipment, the specific purification task can be analyzed and determined. In this application, a specific purification task can be represented by the air purification equipment operating within a certain execution time period and with a certain set of parameters.

[0109] In this application, the purification range refers to the spatial extent of the area to be purified. In some implementations, an interface can be provided, allowing the user to enter a numerical value for the purification range, or to enter relevant parameters of the area to be purified, and then calculate the purification range. In other implementations, the purification range can also be calculated based on the relevant operating data of each IoT device.

[0110] Using the solution of this embodiment, the current cleanliness level can be analyzed based on the operating data of IoT devices in the area to be purified. This analysis then combines scene attributes and user preferences to determine appropriate purification targets and specific purification tasks. The air purification device then performs the specific purification task to achieve the purification target. This entire process is based on data processing, requiring no user interaction, and enables intelligent and efficient dynamic air purification.

[0111] In a specific embodiment, the above-mentioned determination of the current cleanliness of the area to be purified based on the working data of each IoT device may specifically include: determining the current predicted cleanliness of the area to be purified based on the previous specific purification task of the air purification equipment and the corresponding task execution progress; determining the cleanliness influencing factor of the current period based on the historical period working data and the current period working data of the relevant equipment; determining the proximal air cleanliness of the current air purification equipment based on the current detection data of the air purification equipment; adjusting the current predicted cleanliness based on the cleanliness influencing factor and proximal air cleanliness of the current period to determine the current cleanliness of the area to be purified.

[0112] In actual scenarios, S101-S104 in the above embodiment can be executed cyclically at a certain frequency. For example, an execution cycle is set, and the detection step is executed at the beginning of each cycle. When the air purification equipment detects that the air quality parameter is worse than the set value, it starts to execute the above steps to formulate and execute the purification task. For another example, it can be set to execute the above steps at a set time after each specific purification task is completed to formulate and execute a new purification task. For another example, a specific execution time can be planned in combination with the scene attributes of the area to be purified, and the above steps can be executed at the specific execution time to formulate and execute the purification task.

[0113] Each time the above air purification method is executed, the specific purification task for this round is redefined, regardless of whether the previous specific purification task (the previous specific purification task) has been completed. Based on the previous specific purification task and its execution progress, the current predicted cleanliness level can be determined—that is, the cleanliness level expected to be achieved if only the air purification equipment is operating to perform the purification task. However, in reality, the air purification equipment may not be the only one operating. Therefore, further analysis can be conducted on the operating status of other IoT devices to determine their impact on the air, i.e., the cleanliness influencing factor.

[0114] Based on the current detection data of the air purification device, the proximal air cleanliness of the current air purification device can be determined. The detection range of the air purification device is limited to the part of the air near the air purification device. Therefore, its detection result can only represent the local air quality. Therefore, in this application, the detection result is referred to as the proximal air cleanliness.

[0115] The cleanliness influencing factor of the current time period can be used to understand the impact of other equipment on air quality. The proximal air cleanliness of the current air purification equipment and the current predicted cleanliness can be used to analyze the overall air quality characteristics of the current area to be purified. Both parameters indicate the deviation from the current predicted cleanliness. After correction, the current cleanliness of the area to be purified can be determined.

[0116] Specifically, if the air conditioner is in "heating mode" and the door and window sensors display "off", it can be determined that the air circulation is poor, and the impact factor is +20%. Alternatively, if the kitchen smoke sensor detects "oil fume release", it can be determined that the pollution source is active, and the impact factor is +30%. The specific numerical adjustment of the impact factor can be determined by technical personnel based on actual experimental data. For example, a learning model is created, and the equipment operating status under different working conditions and the corresponding air purification process data are used as training data. The trained model can match the equipment operating status under the same working condition combination and the corresponding air purification process data. From these corresponding relationships, the size of the impact factor corresponding to different equipment and different working conditions can be determined.

[0117] In some specific embodiments, the above-mentioned determination of the purification target based on the scene attributes of the area to be purified and the working data of each IoT device includes: determining the air quality target parameter range based on the scene attributes of the area to be purified; predicting the cleanliness influencing factor of the future time period based on the historical time period working data and the current time period working data of the relevant equipment; determining the purification task execution time based on the cleanliness influencing factor of the future time period; and determining the air quality target parameters from the air quality target parameter range in combination with the scene attributes of the area to be purified and the purification task execution time.

[0118] Combining scene attributes allows analysis of general air quality requirements for the area to be purified, thereby defining a broad range for each air quality target parameter (the air quality target parameter range). Combining operational data from IoT devices allows analysis of user habits, such as how devices are used, which implicitly impact air quality. However, existing operational data only directly represents past device performance, requiring further analysis of future operational periods to determine factors influencing cleanliness in future periods.

[0119] The cleanliness influencing factor of the time period determines the time when the air quality changes, which can correspond to the execution time of the purification task, which mainly refers to the length of time in this application. This correspondence is not necessarily equal or synchronized. In order to improve the efficiency of air purification, preferably, in a home scenario, the air purification equipment can be turned on before the air quality deteriorates, so as to ensure operation at a lower power, while saving energy and reducing noise. Specifically, the specific purification task execution time can be determined according to the type of cleanliness influencing factor. Ultimately, the air quality target parameters can be determined by combining the two aspects of information.

[0120] For example, in a home setting, users may use gas appliances multiple times a day but rarely use range hoods. The resulting steam and fumes may exacerbate air quality issues during these times, necessitating appropriate cleaning. In this home setting, the set air quality target is "PM2.5 ≤ 20 μg / m³, CO2 ≤ 800 ppm." Future impact factors are predicted based on device operating characteristics: the air conditioner will switch to "ventilation mode" within the next hour (increasing air circulation, impact factor -15%). Kitchen fumes are expected to cease after 30 minutes (the pollution source disappears, impact factor -30%). After comprehensive calculations, the purification task execution time is set to 40 minutes.

[0121] In some specific embodiments, based on the historical period working data and the current period working data of the relevant equipment, the cleanliness influencing factors of the future period are predicted, including: analyzing the operating characteristics of the relevant equipment in each period based on the historical period working data and the current period working data of the relevant equipment; for each relevant equipment, based on the operating characteristics of the relevant equipment in each period, predicting the operating status of the relevant equipment in the future period; and comprehensively analyzing the operating status of each relevant equipment in the future period to predict the cleanliness influencing factors of the future period.

[0122] Specifically, the convolutional neural network model can be used to predict the operating status of related equipment in future time periods.

[0123] C(t)=Σ(α_i×D_i(t)) + β×H(t-1)

[0124] Among them, α_i is the equipment weight coefficient, D_i(t) is the operating parameter of equipment i at time t, and H(t-1) is the working data of the previous period.

[0125] In this application, time is divided into "periods" as units.

[0126] In some specific embodiments, an operation interface can be provided, allowing the user to input scene attributes of the purification range. In other specific embodiments, scene attributes can be determined by performing an analysis step. Specifically, after detecting that the air purification device is connected to the Internet of Things, basic data of existing IoT devices in the Internet of Things is obtained; based on the basic data of the existing IoT devices, the scene attributes of the area to be purified are determined.

[0127] Among them, basic data includes device type and device model; scene attributes include scene type and site range; based on the basic data of existing IoT devices, the scene attributes of the area to be purified are determined, which may include: based on the device type, determining whether there is a dedicated device in the IoT device; if there is a dedicated device, determining the scene type of the area to be purified based on the applicable scenario of the dedicated device; if there is no dedicated device, determining the scene type based on the combination of device types; determining the site range of the area to be purified based on the number and device models of IoT devices of each device type.

[0128] When an air purification device is detected as connected to the IoT, the system scans the list of connected devices on the same network and extracts basic data from each device to form a device matrix. A dedicated device feature library is pre-established. For example, industrial devices include PM2.5 detectors and workshop temperature controllers; commercial devices include fresh air units and people counters; and residential devices include baby monitors, smart curtains, water heaters, gas stoves, and range hoods.

[0129] If specialized equipment such as a "smart kitchen smoke sensor" is found, the scene is determined to be a home environment; if the equipment is mainly a "conference room projector + multi-person presence sensor," it is determined to be an office environment.

[0130] A multi-level decision tree can also be used to determine the scene type. For example, if an SA-5G alarm is detected, the kitchen safety scene is triggered; if the associated devices include an AC-9000 air conditioner, the scene is modified to a residential kitchen scene; and by analyzing the frequency of device usage (the air conditioner runs 14 hours a day), the residential attribute is confirmed.

[0131] Estimate the area based on the number and model of devices: For example, based on the coverage area of 5 temperature and humidity sensors, the site is inferred to be a "50 square meter living room." Another example is the AC-9000 air conditioner model, which has a suitable area of 40-60 square meters. Combined with the deployment location of the ES-200 environmental monitoring device (living room + bedroom), the total purification area S is calculated to be 35 square meters for the living room + 18 square meters for the bedroom = 53 square meters. Adding a safety redundancy factor of 1.2, the final purification range is 63.6 square meters.

[0132] Figure 2 This is a schematic diagram of the structure of an air purification device based on IoT device data provided by an embodiment of the present application, such as Figure 2 As shown, the air purification device 200 of this embodiment includes: an acquisition module 201 , a current cleanliness determination module 202 , a purification target determination module 203 , and a specific purification task determination module 204 .

[0133] The acquisition module 201 is used to obtain the working data of each IoT device in the IoT area to be purified;

[0134] The current cleanliness determination module 202 is used to determine the current cleanliness of the area to be purified based on the working data of each IoT device;

[0135] The purification target determination module 203 is used to determine the purification target based on the scene attributes of the area to be purified and the working data of each IoT device; the purification target includes the air quality target parameters and the purification task execution time;

[0136] The specific purification task determination module 204 is used to determine the specific purification task based on the purification target, purification range, current cleanliness, and equipment parameters of the air purification equipment, so as to perform the specific purification task within the purification task execution time and achieve the air quality target parameters.

[0137] Optionally, the work data includes work data for historical periods and work data for the current period;

[0138] The current cleanliness determination module is specifically used for:

[0139] Determine the current predicted cleanliness level of the area to be purified based on the previous specific purification task of the air purification equipment and the corresponding task execution progress;

[0140] Determine the cleanliness influencing factors of the current period based on the historical period working data and current period working data of the relevant equipment;

[0141] Determine the near-end air cleanliness of the current air purification equipment based on the current detection data of the air purification equipment;

[0142] Based on the cleanliness influencing factors of the current period and the proximal air cleanliness of the current air purification equipment, the current predicted cleanliness is adjusted to determine the current cleanliness of the area to be purified.

[0143] Optionally, a purification target determination module is used to:

[0144] Determine the target air quality parameter range based on the scene attributes of the area to be purified;

[0145] Predict the cleanliness influencing factors of future periods based on the historical and current period working data of relevant equipment;

[0146] Determine the execution time of the purification task based on the cleanliness influencing factors in the future period;

[0147] The air quality target parameters are determined from the air quality target parameter range in combination with the scene attributes of the area to be purified and the execution time of the purification task.

[0148] Optionally, when the purification target determination module predicts the cleanliness influencing factors of future time periods based on the historical time period working data and the current time period working data of the relevant equipment, it is specifically used to:

[0149] Analyze the operating characteristics of the relevant equipment in each period based on the historical and current period working data of the relevant equipment;

[0150] For each relevant device, based on the operating characteristics of the relevant device in each time period, predict the operating status of the relevant device in the future time period;

[0151] Comprehensively consider the operating status of all relevant equipment in the future time period and predict the factors affecting cleanliness in the future time period.

[0152] Optionally, the current cleanliness determination module adjusts the current predicted cleanliness based on the cleanliness influencing factor of the current time period and the proximal air cleanliness of the current air purification equipment to determine the current cleanliness of the area to be purified, specifically for:

[0153] Based on the current predicted cleanliness, simulate and predict the near-end air cleanliness;

[0154] Analyze the differences in air quality characteristics between the proximal air cleanliness of current air purification equipment and the predicted proximal air cleanliness;

[0155] The current predicted cleanliness is adjusted based on the cleanliness influencing factors and air quality characteristics of the current period to determine the current cleanliness of the area to be purified.

[0156] Optionally, the device further includes a scene attribute determination module 205, configured to:

[0157] After detecting that the air purification device is connected to the Internet of Things, obtain the basic data of the existing IoT devices in the Internet of Things;

[0158] Determine the scene attributes of the area to be purified based on the basic data of existing IoT devices.

[0159] Optionally, basic data includes device type and device model; scene attributes include scene type and site range;

[0160] The scene attribute determination module determines the scene attributes of the area to be purified based on the basic data of existing IoT devices. Specifically, it is used to:

[0161] Based on the device type, determine whether there are dedicated devices among the IoT devices;

[0162] If there is dedicated equipment, determine the scene type of the area to be purified based on the applicable scene of the dedicated equipment;

[0163] If no dedicated device exists, the scenario type is determined based on the combination of device types;

[0164] Determine the site scope of the area to be purified based on the number and device models of each type of IoT devices.

[0165] The device of this embodiment can be used to execute the method of any of the above embodiments. Its implementation principles and technical effects are similar and will not be described in detail here.

[0166] Figure 3 A schematic diagram of the structure of an electronic device provided in one embodiment of the present application is shown in FIG. Figure 3 As shown, the electronic device 300 of this embodiment may include: a memory 301 and a processor 302.

[0167] The memory 301 stores a computer program that can be loaded by the processor 302 and execute the method in the above embodiment.

[0168] The processor 302 and the memory 301 are connected, for example, via a bus.

[0169] Optionally, the electronic device 300 may further include a transceiver. It should be noted that in actual applications, the number of transceivers is not limited to one, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present application.

[0170] Processor 302 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the present disclosure. Processor 302 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, or a combination of a DSP and a microprocessor.

[0171] A bus includes a path that transmits information between the components mentioned above. Examples include a PCI (Peripheral Component Interconnect) bus and an EISA (Extended Industry Standard Architecture) bus. Buses can be categorized as address buses, data buses, and control buses. For ease of illustration, the diagram uses a single thick line, but this does not imply a single bus or type of bus.

[0172] The memory 301 may be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0173] The memory 301 is used to store application code for executing the solution of the present application, and the execution is controlled by the processor 302. The processor 302 is used to execute the application code stored in the memory 301 to implement the content shown in the above method embodiment.

[0174] Electronic devices include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. They may also include servers, etc. Figure 3 The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0175] The electronic device of this embodiment can be used to execute the method of any of the above embodiments. Its implementation principles and technical effects are similar and will not be described in detail here.

[0176] The present application also provides a computer-readable storage medium storing a computer program that can be loaded by a processor and execute the method in the above embodiment.

[0177] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented using hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

Claims

1. An air purification method based on IoT device data, characterized in that: include: Obtain the working data of each IoT device in the IoT area to be purified; Determine the current cleanliness level of the area to be purified based on the working data of each of the IoT devices; Determine a purification target based on the scene attributes of the area to be purified and the working data of each of the IoT devices; the purification target includes air quality target parameters and purification task execution time; Determine a specific purification task based on the purification target, purification range, current cleanliness, and equipment parameters of the air purification equipment, so as to perform the specific purification task within the purification task execution time and achieve the air quality target parameters; The working data includes working data of historical periods and working data of current periods; Determining the current cleanliness of the area to be purified based on the working data of each of the IoT devices includes: Determine the current predicted cleanliness level of the area to be purified based on the previous specific purification task of the air purification equipment and the corresponding task execution progress; Determine the cleanliness influencing factors of the current period based on the historical period working data and current period working data of the relevant equipment; Determine the near-end air cleanliness of the current air purification equipment based on the current detection data of the air purification equipment; Based on the cleanliness influencing factor of the current time period and the proximal air cleanliness of the current air purification equipment, the current predicted cleanliness is adjusted to determine the current cleanliness of the area to be purified; The step of determining a purification target based on scene attributes of the area to be purified and the working data of each of the IoT devices includes: Determine the target air quality parameter range based on the scene attributes of the area to be purified; Predict the cleanliness influencing factors of future periods based on the historical and current period working data of relevant equipment; Determining the execution time of the purification task according to the cleanliness influencing factor of the future period; The air quality target parameter is determined from the air quality target parameter range in combination with the scene attributes of the area to be purified and the purification task execution time.

2. The air purification method based on IoT device data according to claim 1, characterized in that: The cleanliness influencing factors of future periods are predicted based on the historical period working data and the current period working data of the relevant equipment, including: Analyze the operating characteristics of the relevant equipment in each period based on the historical and current period working data of the relevant equipment; For each relevant device, based on the operating characteristics of the relevant device in each time period, predict the operating status of the relevant device in the future time period; Comprehensively consider the operating status of all relevant equipment in the future time period and predict the factors affecting cleanliness in the future time period.

3. The air purification method based on IoT device data according to claim 1, characterized in that: The adjusting the current predicted cleanliness based on the cleanliness influencing factor of the current time period and the proximal air cleanliness of the current air purification equipment to determine the current cleanliness of the area to be purified includes: Based on the current predicted cleanliness, simulate and predict the near-end air cleanliness; Analyzing the difference in air quality characteristics between the proximal air cleanliness of the current air purification device and the predicted proximal air cleanliness; The current predicted cleanliness is adjusted according to the cleanliness influencing factor of the current time period and the difference in the air quality characteristics to determine the current cleanliness of the area to be purified.

4. The air purification method based on IoT device data according to claim 1, characterized in that: Also includes: After detecting that the air purification device is connected to the Internet of Things, basic data of existing Internet of Things devices in the Internet of Things is obtained; Based on the basic data of the existing IoT devices, the scene attributes of the area to be purified are determined.

5. The air purification method based on IoT device data according to claim 4, characterized in that: The basic data includes device type and device model; the scene attributes include scene type and site range; Determining the scene attributes of the area to be purified based on the basic data of the existing IoT devices includes: Determining whether there is a dedicated device among the IoT devices based on the device type; If there is a dedicated device, determine the scene type of the area to be purified based on the applicable scene of the dedicated device; If no dedicated device exists, the scenario type is determined based on the combination of device types; Determine the site scope of the area to be purified based on the number of IoT devices of each device type and the device models.

6. An air purification device based on IoT device data, characterized in that: include: The acquisition module is used to obtain the working data of each IoT device in the IoT area to be purified; A current cleanliness determination module, configured to determine the current cleanliness of the area to be purified based on the working data of each of the IoT devices; A purification target determination module is used to determine the purification target based on the scene attributes of the area to be purified and the working data of each of the IoT devices; the purification target includes air quality target parameters and purification task execution time; A specific purification task determination module is used to determine a specific purification task based on the purification target, purification range, current cleanliness, and equipment parameters of the air purification equipment, so as to perform the specific purification task within the purification task execution time and achieve the air quality target parameters; The working data includes historical working data and current working data; the current cleanliness determination module is specifically used to determine the current predicted cleanliness of the area to be purified based on the previous specific purification task of the air purification equipment and the corresponding task execution progress; determine the cleanliness influencing factor of the current period based on the historical working data of the relevant equipment and the current working data of the relevant equipment; determine the near-end air cleanliness of the current air purification equipment based on the current detection data of the air purification equipment; Based on the cleanliness influencing factor of the current time period and the proximal air cleanliness of the current air purification equipment, the current predicted cleanliness is adjusted to determine the current cleanliness of the area to be purified; The purification target determination module is used to determine the air quality target parameter range based on the scene attributes of the area to be purified; based on the historical and current working data of the relevant equipment, it predicts the cleanliness influencing factors of the future period; Determining the execution time of the purification task according to the cleanliness influencing factor of the future period; The air quality target parameter is determined from the air quality target parameter range in combination with the scene attributes of the area to be purified and the purification task execution time.

7. An electronic device, characterized in that: include: memory and processor; The memory is used to store program instructions; The processor is used to call and execute program instructions in the memory to perform the air purification method based on Internet of Things device data as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program; when the computer program is executed by the processor, the air purification method based on IoT device data as described in any one of claims 1 to 5 is implemented.

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