Indoor air purification method and device and terminal equipment

Abstract

The invention provides an indoor air purification method and device and terminal equipment, and is suitable for the technical field of wireless communication. The method comprises the following steps: according to indoor space structure information, indoor pollution source position information, indoor personnel position information, indoor air quality characterization information, indoor distribution antenna position parameter information, indoor distribution antenna communication parameter information, wireless signal quality characterization information, air purification equipment position information and air purification equipment operation parameter information, obtaining indoor space structure information; and according to the indoor feature fusion weight and the indoor feature fusion bias, target indoor beam forming parameter information is generated, beam forming regulation and control are carried out on an indoor sub-antenna, an indoor wireless directional beam is generated, an indoor air purification instruction signal is transmitted to air purification equipment, and indoor air purification is carried out through the air purification equipment. According to the application, the accuracy and robustness of transmitting the indoor air purification instruction signal to the air purification equipment are improved, so that the efficiency and effect of indoor air purification are improved.

Description

Technical Field

[0001] This application belongs to the field of wireless communication technology, and in particular relates to indoor air purification methods, devices and terminal equipment. Background Technology

[0002] Currently, as people's pursuit of quality of life continues to improve, indoor air quality is receiving increasing attention. Air pollution is becoming increasingly prominent, and the public's demand for respiratory health has shifted from "passive defense" to "active improvement," which has greatly promoted the development of the indoor air purification industry.

[0003] In current technology, air purifiers are commonly used to purify indoor air. Many air purifiers operate on an internal circulation mode, using physical filters to remove large particulate pollutants from the air; some products also employ ultraviolet sterilization technology or activated carbon adsorption technology to remove bacteria, mold, and chemical pollutants.

[0004] However, equipment using ultraviolet sterilization or electrostatic adsorption technologies generates ozone during operation. Ozone, a harmful gas, can cause secondary pollution when it accumulates indoors, affecting the health of residents. Air purifiers using activated carbon as a filter material have selective adsorption capabilities and may experience desorption under different environments. If the filter material is not replaced in time, the activated carbon can become a new source of pollution. Summary of the Invention

[0005] In view of this, the present application provides an indoor air purification method, apparatus and terminal equipment, which aims to solve the problems of poor purification effect, easy generation of secondary pollution, inability to accurately target pollution sources and poor adaptability to complex indoor environments in the prior art.

[0006] The first aspect of this application provides an indoor air purification method, including: Acquire indoor space structure information, indoor pollution source location information, indoor personnel location information, indoor air quality characterization information, indoor distributed antenna location parameter information, indoor distributed antenna communication parameter information, wireless signal quality characterization information, air purification equipment location information, and air purification equipment operation parameter information; Based on multiple preset indoor environmental feature extraction vectors, indoor air environment feature information, indoor wireless communication environment feature information, and indoor air purification equipment feature information are generated according to the indoor space structure information, indoor pollution source location information, indoor personnel location information, indoor air quality characterization information, indoor distributed antenna location parameter information, indoor distributed antenna communication parameter information, wireless signal quality characterization information, air purification equipment location information, and air purification equipment operation parameter information. Based on the indoor air environment characteristic information, indoor wireless communication environment characteristic information, indoor air purification equipment characteristic information, preset indoor feature fusion weights and preset indoor feature fusion biases, multiple initial indoor beamforming parameter information are obtained through fusion and matching calculations. Based on the indoor pollution source location information, indoor distributed antenna location parameter information, air purification equipment location information, preset indoor beam angle adjustment range, preset indoor beam width adjustment range, preset indoor antenna element weighting coefficient vector, and preset indoor air purification efficiency calculation coefficient, the multiple initial indoor beamforming parameter information are optimized and calculated to obtain the target indoor beamforming parameter information. Based on the target indoor beamforming parameter information, beamforming control processing is performed on the indoor distributed antenna to generate multiple indoor wireless directional beams. Based on the multiple indoor wireless directional beams, indoor air purification command signals are transmitted to the air purification equipment for indoor air purification.

[0007] A second aspect of this application provides an indoor air purification device, comprising: The information acquisition module is used to acquire indoor space structure information, indoor pollution source location information, indoor personnel location information, indoor air quality characterization information, indoor distributed antenna location parameter information, indoor distributed antenna communication parameter information, wireless signal quality characterization information, air purification equipment location information, and air purification equipment operation parameter information. The indoor feature information generation module is used to generate indoor air environment feature information, indoor wireless communication environment feature information, and indoor air purification equipment feature information based on multiple preset indoor environmental feature extraction vectors, according to the indoor space structure information, indoor pollution source location information, indoor personnel location information, indoor air quality characterization information, indoor distributed antenna location parameter information, indoor distributed antenna communication parameter information, wireless signal quality characterization information, air purification equipment location information, and air purification equipment operation parameter information. The initial indoor beamforming parameter information generation module is used to perform fusion and matching calculations based on the indoor air environment feature information, indoor wireless communication environment feature information, indoor air purification equipment feature information, preset indoor feature fusion weights, and preset indoor feature fusion biases to obtain multiple initial indoor beamforming parameter information. The target indoor beamforming parameter information generation module is used to optimize and calculate the multiple initial indoor beamforming parameter information based on the indoor pollution source location information, indoor distributed antenna location parameter information, air purification equipment location information, preset indoor beam angle adjustment range, preset indoor beam width adjustment range, preset indoor antenna array weighting coefficient vector, and preset indoor air purification efficiency calculation coefficient to obtain the target indoor beamforming parameter information. The indoor wireless directional beamforming module is used to perform beamforming control processing on the indoor distributed antenna according to the target indoor beamforming parameter information to generate multiple indoor wireless directional beams. The indoor air purification module is used to transmit indoor air purification command signals to the air purification equipment according to the multiple indoor wireless directional beams, so as to perform indoor air purification treatment through the air purification equipment.

[0008] A third aspect of this application provides a terminal device, the terminal device including a memory and a processor, the memory storing a computer program executable on the processor, the processor executing the computer program to implement the steps of the indoor air purification method described in the first aspect above.

[0009] A fourth aspect of this application provides a computer-readable storage medium, comprising: storing a computer program, which, when executed by a processor, implements the steps of the indoor air purification method as described in the first aspect above.

[0010] Compared with the prior art, the beneficial effects of this application embodiment are as follows: This application calculates the target indoor beamforming parameter information to accurately match the distribution of pollution sources and the location of purification equipment. While ensuring the quality of indoor wireless signal coverage, it maximizes the adaptation to the actual needs of air purification, enabling the indoor distributed antenna to generate a suitable directional beam in a timely manner to accurately transmit the indoor air purification command signal to the air purification equipment. This ensures that the equipment can receive the command in a timely and accurate manner and execute the purification operation, avoiding energy waste and interference problems in non-directional signal transmission. It effectively improves the response speed and coordination efficiency of indoor air purification equipment, thereby improving the indoor air purification effect and efficiency. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1This is a schematic diagram illustrating the implementation process of the indoor air purification method provided in Embodiment 1 of this application; Figure 2 This is a schematic diagram illustrating the implementation process of the indoor air purification method provided in Embodiment 2 of this application; Figure 3 This is a schematic diagram illustrating the implementation process of the indoor air purification method provided in Embodiment 3 of this application; Figure 4 This is a schematic diagram illustrating the implementation process of the indoor air purification method provided in Embodiment 4 of this application; Figure 5 This is a schematic diagram of the implementation process of the indoor air purification method provided in Embodiment 5 of this application; Figure 6 This is a schematic diagram illustrating the implementation process of the indoor air purification method provided in Embodiment Six of this application; Figure 7 This is a schematic diagram of the implementation process of the indoor air purification method provided in Embodiment 7 of this application; Figure 8 This is a schematic diagram of the structure of the indoor air purification device provided in the embodiments of this application; Figure 9 This is a schematic diagram of the terminal device provided in the embodiments of this application. Detailed Implementation

[0013] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limiting purposes, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details.

[0014] To illustrate the technical solution described in this application, specific embodiments are provided below.

[0015] Figure 1 A flowchart illustrating the implementation of the indoor air purification method provided in Embodiment 1 of this application is shown, and is described in detail below: Step S101: Obtain indoor space structure information, indoor pollution source location information, indoor personnel location information, indoor air quality characterization information, indoor distributed antenna location parameter information, indoor distributed antenna communication parameter information, wireless signal quality characterization information, air purification equipment location information, and air purification equipment operation parameter information.

[0016] In this embodiment, the indoor space structure information can refer to key data that can completely describe the form and layout of the indoor physical space, including the planar dimensions of the indoor space, the location and distribution of walls and windows, the location and shape of fixed obstacles in the indoor space, and the functional zoning of the indoor space. It can be obtained by on-site measurement using a laser rangefinder or total station, or it can be obtained through three-dimensional scanning technology. The entire indoor space is scanned from all angles by a three-dimensional laser scanner to generate a three-dimensional point cloud model of the indoor space. Then, the three-dimensional point cloud model is processed by professional software to extract various detailed information of the indoor space structure.

[0017] Indoor pollution source location information refers to data that can accurately pinpoint the spatial location of indoor air pollutant sources. This can include the specific spatial coordinates of various indoor pollution sources, such as establishing a coordinate system with a fixed point indoors as the origin and determining the X, Y, and Z axis coordinates of the pollution source in that coordinate system; it can include the type and attributes of the pollution source, such as whether it is a solid pollution source, such as a dust source, a liquid pollution source, or a gaseous pollution source; it can include the boundary of the pollution source's influence range, such as the coordinates of the maximum spatial range that the pollutants released by the pollution source can spread and cover without external interference. Portable air quality detectors can be used for fixed-point detection and investigation. The concentration of pollutants can be detected point by point according to the grid division of the indoor space. Based on the differences in concentration distribution, the area with the highest concentration can be determined, and the detection points can be further densified within that area to finally pinpoint the specific location of the pollution source. Alternatively, video surveillance and sensor linkage analysis can be used. High-definition video surveillance equipment can be installed in key indoor areas, and miniaturized air quality sensors can be deployed. When the sensor detects an abnormal increase in the concentration of pollutants in a certain area, the video surveillance footage of that area can be retrieved to observe whether there is any pollutant generation behavior. By combining the sensor location and video footage information, the location of the pollution source can be accurately located.

[0018] Indoor personnel location information can refer to relevant data that can record the specific location of personnel in an indoor space in real time or periodically. This can include the real-time spatial coordinates of each person in the room, such as determining the horizontal position coordinates and vertical height of a person in the room based on a fixed point on the indoor floor; it can also include the activity trajectory of personnel in the room, such as the path record of a person moving from one area to another over a period of time, including the position coordinates corresponding to each time point. Indoor personnel location information can be obtained in several ways. First, UWB (Ultra-Wideband) positioning technology can be used. Multiple UWB base stations are deployed indoors, and personnel wear UWB positioning tags (e.g., integrated into smart bracelets or name tags). The base stations receive the wireless signals transmitted by the tags and use algorithms such as time difference of arrival (TDOA) or angle of arrival (AOA) to calculate the real-time coordinates of the tags, thus determining the personnel's location. Second, infrared sensing positioning technology can be used. Infrared sensors are installed in various areas of the room. When a person enters the sensor's range, the sensor triggers and records the current location and trigger time. By analyzing the triggering status of multiple sensors, the activity area and direction of movement of the personnel can be roughly determined. Third, video image recognition positioning technology can be used. Surveillance cameras with human body recognition capabilities are installed indoors. Computer vision algorithms are used to identify and track the location of personnel in the surveillance footage in real time, converting the pixel coordinates in the video image into actual indoor spatial coordinates to obtain the personnel's location information.

[0019] Indoor air quality characterization information refers to various parameters that can quantify indoor air quality. This includes the concentration of particulate matter pollutants, the concentration of gaseous pollutants, microbial indicators, physical state parameters (such as specific values ​​for temperature, relative humidity, and atmospheric pressure; temperature is in degrees Celsius, relative humidity is in percentage, and atmospheric pressure is in kilopascals), and air circulation parameters (such as air velocity and fresh air volume; velocity is in meters per second, and fresh air volume is in cubic meters per hour). This information can be used with fixed air quality monitoring systems of the same specifications. A monitoring sensor network involves installing fixed sensors with multi-parameter detection capabilities in different indoor areas, such as near pollution sources, densely populated areas, and ventilation openings. These sensors monitor parameters like PM2.5, PM10, formaldehyde, temperature, and humidity in real time and transmit the data wirelessly or via wired connection to a data acquisition terminal, enabling real-time continuous monitoring of indoor air quality. Portable air quality detectors can also be used for periodic testing, sampling different areas of the room regularly, with a focus on monitoring air quality parameters near pollution sources and areas where people frequently stay.

[0020] Indoor distributed antenna (DDA) location parameter information refers to key parameter data describing the installation position and attitude of the indoor DDA in the indoor space. This can include the DDA's installation coordinates, such as determining the X, Y, and Z axis coordinates of the DDA in the indoor space using a fixed reference point on the indoor ground as the origin, specifying the antenna's exact horizontal and vertical installation position; it can include the DDA's installation height, i.e., the vertical distance from the bottom or center of the DDA to the indoor ground, in meters; it can also include the DDA's installation angles, including the horizontal azimuth and vertical elevation angles. The horizontal azimuth angle refers to the angle between the antenna beam on the horizontal plane and a preset reference direction (such as due north), ranging from 0° to 360°; the vertical elevation angle refers to the angle between the antenna beam on the vertical plane and the horizontal plane, ranging from -90° to 90°; it can also include the relative positional relationship between the DDA and surrounding objects, such as the horizontal distance from the DDA to walls, the vertical distance from the ceiling, and the shortest distance from obstacles such as indoor lights and air conditioning vents. The location parameters of an indoor distributed antenna can be obtained by using a laser rangefinder to measure the installation coordinates and height of the antenna, and an angle measuring instrument to measure the horizontal azimuth and vertical elevation angles. After the indoor antenna is installed, professional antenna testing equipment can be used to verify the actual installation position of the antenna. By detecting the antenna's radiation pattern, the actual installation angle can be deduced to see if it matches the set parameters. If there is a deviation, the antenna installation attitude should be readjusted and the corrected location parameters recorded. For indoor distributed antennas already in use, if it is necessary to update the location parameter information, the installation position, height, angle, and other parameters of the antenna can be remeasured through on-site surveys using tools such as laser rangefinders and angle measuring instruments to ensure the accuracy and timeliness of the location parameter information of the indoor distributed antenna.

[0021] Indoor distributed antenna communication parameters refer to the core technical parameters that determine the communication performance and operating status of indoor distributed antennas. These parameters can include the operating frequency band of the antenna, i.e., the frequency range within which the antenna can normally receive and transmit wireless signals, such as supporting specific frequency ranges like 2G (890-960MHz), 3G (1920-2170MHz), 4G (1710-2690MHz), and 5G (3300-5000MHz); and the transmit power of the antenna, i.e., the maximum signal power that the antenna can transmit when operating, measured in decibels (dB). The measurement (m) can include the receiving sensitivity of the indoor distributed antenna, which refers to the ability of the indoor distributed antenna to accurately receive weak wireless signals, i.e., the minimum signal power that the antenna can detect, measured in decibels per milliwatt (dBm). The smaller the value, the higher the receiving sensitivity. It can also include the gain of the indoor distributed antenna, which is the ability of the indoor distributed antenna to concentrate signal energy to radiate or receive in a specific direction, measured in decibels (dBi). The higher the gain, the stronger the signal strength of the antenna in that specific direction. It can also include the polarization mode of the indoor distributed antenna, i.e., the direction of vibration of the electric field vector when the indoor distributed antenna radiates or receives signals, such as vertical polarization, horizontal polarization, circular polarization, etc. Communication parameters of indoor distributed antennas can be obtained by consulting the product technical specifications. When purchasing an indoor distributed antenna, the equipment supplier will provide a detailed product technical specification, which clearly indicates the standard values ​​of communication parameters such as the operating frequency band, transmit power, receive sensitivity, gain, and polarization. On-site measurements can also be performed using professional communication testing equipment. After the indoor distributed antenna is installed and debugged, professional testing equipment such as signal generators, spectrum analyzers, and power meters are used to test the actual communication parameters. For example, a power meter is used to measure whether the actual transmit power of the antenna meets the requirements; a spectrum analyzer is used to detect the signal response of the antenna in different frequency bands to determine the actual operating frequency band range; and a signal generator is used to generate a weak signal, which is then used in conjunction with the receiving equipment to measure the antenna's receive sensitivity.

[0022] Wireless signal quality characterization information refers to key parameter data used to evaluate the quality of the wireless communication link between indoor distributed antennas and air purification equipment. This can include Received Signal Strength Indicator (RSSI), which indicates the power of the wireless signal received by the air purification equipment's wireless receiving module from the indoor distributed antenna, measured in decibels per milliwatt (dBm). A value closer to 0 indicates a stronger signal. It can also include Signal-to-Noise Ratio (SNR), the ratio of the received signal power to the background noise power at the receiving end, measured in decibels (dB). A higher SNR indicates less noise interference and better communication quality. Finally, it can include Bit Error Rate (BER), which indicates the percentage of received signals within a given time period. The bit error rate (BER) is the ratio of the number of erroneous bits in the wireless signal received by the air purifier to the total number of bits. A lower BER indicates higher communication accuracy. It can also include signal latency, which is the time required for the wireless signal to travel from the indoor distributed antenna to the air purifier's reception and response, measured in milliseconds (ms). A smaller latency indicates stronger real-time communication. For the transmission of control commands for air purifiers, the latency is typically required to be no more than 100ms. It can also include signal jitter, which is the amplitude of the signal latency variation at different times, measured in milliseconds (ms). A smaller jitter indicates better communication link stability, avoiding instability in control command transmission due to latency fluctuations. Wireless signal quality characterization information can be obtained in real time through the built-in wireless module of the air purifier. Air purifiers with wireless communication capabilities typically have a built-in wireless receiving and signal detection module. This module can detect the wireless signals received from the indoor distributed antenna in real time, calculate and record parameters such as Received Signal Strength Indication (RSSI) and Signal-to-Noise Ratio (SNR), and calculate the bit error rate by counting the number of erroneous symbols in the received data. These parameter data are stored locally on the device or uploaded to the terminal device wirelessly. Alternatively, it can be obtained through the signal monitoring function of the indoor distributed antenna. Some indoor distributed antennas with signal monitoring capabilities can record the attenuation of their transmitted signals during propagation. Combined with the received information fed back by the air purifier, the quality parameters such as SNR and latency of the wireless link are calculated through the communication protocol, and then these parameters are transmitted to the terminal device.

[0023] Location information for air purifiers can refer to data that accurately pinpoints the installation or placement location of indoor air purifiers within an indoor space. This can include the specific spatial coordinates of the air purifier, such as determining the X, Y, and Z axis coordinates of the bottom or top center of the air purifier within the indoor space, using a fixed reference point on the indoor floor as the origin, thus clarifying the specific position of the device in the horizontal and vertical directions. It can also include the relative positional relationship between the air purifier and indoor pollution sources, such as the straight-line distance between the device and the nearest pollution source, and whether there are any obstacles obstructing the view between the device and the pollution source. Furthermore, it can include the relative positional relationship between the air purifier and indoor distributed antennas, such as the straight-line distance between the device and the indoor distributed antenna, and whether the direction in which the device receives wireless signals is towards the indoor distributed antenna. The location information of air purification equipment can be obtained in several ways. One method is to use an indoor positioning system for automatic positioning. If indoor positioning infrastructure such as UWB or Bluetooth systems is already deployed indoors, corresponding positioning tags can be installed on the air purification equipment to obtain its real-time location coordinates. Another method is to determine the equipment's location using video image recognition technology. This involves installing a surveillance camera with target recognition capabilities indoors and using computer vision algorithms to identify the air purification equipment in the surveillance footage. The pixel position of the equipment in the video footage is then converted into actual indoor spatial coordinates, thus obtaining the equipment's location information.

[0024] Air purifier operating parameters refer to key data reflecting the real-time working status and performance of the equipment. These parameters can include the operating mode of the air purifier, such as the currently selected mode (automatic, manual, sleep, or high-power purification); the fan speed, which is the rotational speed of the internal fan in revolutions per minute (rpm); and the air volume, which is the volume of purified air output per unit time in cubic meters per hour (m³ / h). These parameters can be read locally on the equipment, such as the operating mode and fan speed, directly viewed on the display screen or indicator lights on the control panel. Alternatively, they can be acquired wirelessly, such as via WiFi or LoRa, to upload real-time operating parameters to the terminal device.

[0025] Step S102: Based on multiple preset indoor environmental feature extraction vectors, indoor air environment feature information, indoor wireless communication environment feature information, and indoor air purification equipment feature information are generated according to the indoor space structure information, indoor pollution source location information, indoor personnel location information, indoor air quality characterization information, indoor distributed antenna location parameter information, indoor distributed antenna communication parameter information, wireless signal quality characterization information, air purification equipment location information, and air purification equipment operation parameter information.

[0026] In this embodiment, multiple preset indoor environmental feature extraction vectors can be manually set. For example, for preset indoor environmental feature extraction vectors corresponding to indoor air environment feature information, three core feature dimensions can be determined first: functional zone pollution density, pollution source concentration gradient, and personnel-pollution overlap. Then, the functional zone pollution density dimension is mapped to the functional zone boundary range and indoor pollution source location information in the indoor spatial structure information, and the pollution source concentration gradient dimension is mapped to the indoor air quality characterization information, such as PM2.5 concentration of 80 micrograms / cubic meter at 1 meter from pollution source 1 and 50 micrograms / cubic meter at 2 meters from pollution source 2, and formaldehyde concentration at 1 meter from pollution source 2. The concentration at 0.15 mg / m³ at 1 meter and 0.08 mg / m³ at 2 meters is set to ensure that each feature dimension can obtain data support from the original information. Then, the weight of each dimension can be set according to actual needs. Taking the preset indoor environment feature extraction vector corresponding to the indoor wireless communication environment feature information as an example, if the scenario requires priority to ensure the signal stability of the air purification device, the weight of the air purification device communication quality dimension can be set to 35%, corresponding to the received signal strength indication in the wireless signal quality characterization information needing ≥-75dBm and signal-to-noise ratio ≥15dB. The weight of the signal propagation path loss dimension can be set to 30%, corresponding to the 20 cm² signal loss in the indoor space structure information. A 1-meter-thick concrete wall results in approximately 15dB signal attenuation. Therefore, the coverage adaptability dimension of the indoor distributed antenna can be weighted at 20%, corresponding to a horizontal azimuth of 120° and a vertical elevation of 5° in the antenna's position parameters. To cover three air purification devices within a 10-meter range, the communication parameter redundancy dimension can be weighted at 15%, corresponding to a transmit power of 23dBm in the antenna's communication parameters. A 5dBm redundancy is reserved to handle interference. Furthermore, feature integration rules and quantification standards can be formulated. Taking the preset indoor environmental feature extraction vector corresponding to the indoor air purification device's feature information as an example, regarding the device-pollution source matching degree dimension, the straight-line distance between the device and the nearest pollution source... When the distance is ≤3 meters and there is no obstruction, the matching degree is excellent; when the distance is 3-5 meters or there is one layer of lightweight wall obstruction, the matching degree is good; when the distance is >5 meters or there is concrete wall obstruction, the matching degree is poor. For example, if the coordinates of the air purification equipment are 3 meters, 2 meters, and 0.8 meters, and the coordinates of the nearest pollution source are 2 meters, 1 meter, and 0.5 meters, the straight-line distance is 1.414 meters and there is no obstruction, the matching degree is judged to be excellent. At the same time, for the matching degree dimension of operating parameters-pollution demand, it can be set that when the surrounding PM2.5 concentration is >75 micrograms / cubic meter, the fan speed must be ≥1500 rpm and the air volume must be ≥300 cubic meters / hour, otherwise the matching degree will not meet the standard, thus forming an indoor environmental feature extraction vector.

[0027] Based on a pre-defined indoor environmental feature extraction vector for air environment categories, the system filters raw information directly related to the indoor air environment from the input multi-source information. Specifically, it filters indoor space structure information such as length, width, height, and functional zone boundaries; indoor pollution source location information including the three-dimensional spatial coordinates of all indoor pollution sources and the boundary coordinates of pollution diffusion ranges; indoor occupant location information including the real-time three-dimensional spatial coordinates of occupants; and indoor air quality characterization information. It strictly excludes irrelevant information such as indoor distributed antenna location parameters, indoor distributed antenna communication parameters, communication-related details in wireless signal quality characterization information, and mechanical parameters of air purification equipment. Then, according to the weights set in the pre-defined indoor environmental feature extraction vector for air environment categories, the importance of the filtered raw information is quantified. For example, indoor pollution source location information might have a weight of 40%, indoor air quality characterization information 35%, and indoor spatial structure information 15%. If the weight of indoor personnel location information is 10%, then all details of high-weight information should be retained in full. For example, indoor pollution source location information should be accurate to the centimeter level in three-dimensional spatial coordinates, and indoor air quality characterization information should retain all real-time data of pollutant concentrations. At the same time, only key data from low-weight information should be extracted. For example, indoor spatial structure information should only retain the functional zone boundary range and indoor space size that affect air circulation. Then, the quantified multi-source information can be transformed into structured indoor air environment feature information: First, the indoor pollution source location information and indoor spatial structure information are linked and integrated to mark the indoor pollution source distribution density and high-concentration pollution areas in each functional zone. Second, the indoor air quality characterization information is matched with the indoor pollution source distribution density to statistically analyze the specific pollutant concentration values ​​and concentration change trends in each high-concentration pollution area, and mark the overlap between indoor personnel activity areas and high-concentration pollution areas. This generates indoor air environment feature information with complete dimensions of "functional zone - indoor pollution source distribution - pollutant concentration - indoor personnel association".

[0028] It can be based on a pre-defined indoor environment feature extraction vector for wireless communication, filtering raw information directly related to the indoor wireless communication environment from the input multi-source information. This includes filtering wall and fixed obstacle coordinates from indoor spatial structure information, and the three-dimensional installation coordinates, installation height, horizontal azimuth, vertical elevation, and relative position of the antenna to surrounding objects from the indoor distributed antenna position parameter information. It can also include the operating frequency band, transmit power, receive sensitivity, gain, polarization, and input impedance of the indoor distributed antenna from the communication parameter information. The system extracts data from various sources, including Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR), Bit Error Rate (BER), and Signal Delay from the wireless signal quality characterization information, as well as the three-dimensional spatial coordinates of the air purifier and its relative position to the indoor distributed antenna from the air purifier location information. It strictly excludes details of indoor human activities irrelevant to the indoor wireless communication environment, microbial indicators from the indoor air quality characterization information, and operating parameters of the air purifier. Then, it prioritizes the filtered raw information according to the weighted proportions of each dimension in the preset indoor environmental feature extraction vector for wireless communication, for example, assigning a 30% weight to wireless signal quality characterization information. The weighting of indoor distributed antenna location parameters (25%), air purification equipment location parameters (20%), indoor space structure information (15%), and indoor distributed antenna communication parameters (10%) is prioritized. Real-time dynamic data of high-weight information, such as wireless signal quality characteristics (updated every 10 seconds for received signal strength indication, signal-to-noise ratio, bit error rate, and signal delay), is retained. For low-weight information, only key parameters are extracted; for example, indoor distributed antenna communication parameters only retain the current operating frequency band and transmit power. Then, based on the feature integration rules in the preset indoor environment feature extraction vector for wireless communication, the priorities are ranked. The information is then transformed into structured indoor wireless communication environment characteristic information: First, the indoor spatial structure information is combined with the location parameter information of the indoor distributed antenna to clarify the wireless signal propagation path between the indoor distributed antenna and each air purification device, such as whether there are fixed obstacles such as walls or pillars obstructing the path. Second, the communication parameter information of the indoor distributed antenna can be correlated with the wireless signal quality characterization information to determine whether the wireless signal quality meets the standards under the current communication parameters of the indoor distributed antenna, such as whether the received signal strength indication corresponding to the transmit power of the indoor distributed antenna meets the communication requirements of the air purification device, whether the signal-to-noise ratio reaches the communication threshold of ≥10dB, and whether the bit error rate is controlled within ≤10. -5 Within a good range, based on the location information of air purifiers, the system can statistically analyze the wireless signal quality data of each air purifier's location, and mark weak wireless signal areas and possible interference sources, thereby generating indoor wireless communication environment characteristic information that includes complete dimensions such as "wireless signal propagation path - air purifier communication quality - weak signal area marking".

[0029] This can be done by using a pre-defined indoor environmental feature extraction vector for the device type to filter raw information directly related to the indoor air purification device from the input multi-source information. This includes filtering air purification device location information, air purification device operating parameters, the coordinates of the nearest indoor pollution source to the air purification device, and pollutant concentration data within a 5-meter radius of the air purification device from the indoor air quality characterization information. Irrelevant information such as the polarization mode of the indoor distributed antenna is excluded. Then, according to the weight allocation of each dimension set in the pre-defined indoor environmental feature extraction vector for the device type, the importance of the filtered raw information is divided. For example, if the weight of air purification device operating parameters is 45%, air purification device location information is 30%, and the weight of indoor pollution sources and indoor air quality information around the air purification device is 25%, then the high-weight air purification device operating parameters must retain all real-time dynamic data, such as air purification device fan speed, air volume, and purification efficiency updated every 5 seconds. The air purification device location information must be accurate to centimeter-level three-dimensional spatial coordinates and fully record the air purification device's installation / placement method. The importance of the surrounding indoor pollution sources and indoor air quality information is further divided according to the weight allocation of each dimension set in the pre-defined indoor environmental feature extraction vector for the device type. The indoor pollution source and indoor air quality information needs to focus on the location coordinates and pollutant concentration data of indoor pollution sources within a 5-meter radius of the air purification equipment. Then, according to the feature integration rules in the indoor environmental feature extraction vector preset by the equipment type, the information after importance classification is transformed into structured indoor air purification equipment feature information: First, the location information of the air purification equipment is combined with the location information of the indoor pollution source to calculate the straight-line distance between the air purification equipment and the nearest indoor pollution source, and whether there are obstacles such as walls blocking the distance between them. Then, the coverage capability of the air purification equipment at its current location to the indoor pollution source is determined. The system analyzes whether the air purifier is located downwind of the indoor pollution source and whether it can effectively capture polluted airflow. It then correlates the air purifier's operating parameters with the surrounding indoor air quality characteristics to determine the match between the air purifier's current operating status and the surrounding pollution purification needs. For example, when the PM2.5 concentration around the air purifier is high, it checks whether the air purifier's fan speed is at its highest setting and whether the airflow meets the rapid purification requirements. This generates characteristic information for the indoor air purifier, including "air purifier location - indoor pollution source association - air purifier operating status - purification requirement matching degree".

[0030] Step S103: Based on the indoor air environment feature information, indoor wireless communication environment feature information, indoor air purification equipment feature information, preset indoor feature fusion weights, and preset indoor feature fusion biases, perform fusion and matching calculations to obtain multiple initial indoor beamforming parameter information.

[0031] In this embodiment, both the preset indoor feature fusion weights and the preset indoor feature fusion biases can be manually set. The method for constructing the preset indoor feature fusion weights can be as follows: First, clarify the core objective and scenario priority of the weight setting. For example, for the "residential living room" scenario, the core objective is "to quickly cover high-concentration pollution areas while ensuring stable command reception by the indoor air purification equipment." This determines the scenario priority: indoor wireless communication environment feature information > indoor air purification equipment feature information > indoor air environment feature information. Then, the weights can be initially set based on historical scenario data. Multiple sets of historical data for residential living room scenarios are collected. Each set of data includes three types of key feature indicators and corresponding indoor beamforming parameter optimization effects, such as command transmission success rate and pollution area coverage rate. The initial weights are determined through statistical analysis. For example, in 80 sets of valid data with "command transmission success rate ≥ 98%", the key indicator compliance rate for indoor wireless communication environment feature information is 100%, and the key indicator compliance rate for indoor air purification equipment feature information is 95%. The key indicators of indoor air environment feature information achieved a compliance rate of 90%. Based on this, the initial weights for indoor wireless communication environment feature information were set at 45%, indoor air purification equipment feature information at 35%, and indoor air environment feature information at 20%. The weights were then adjusted through iterative verification. Twenty sets of residential living room scenario data were selected for testing. If it was found that a certain set of data had an excessively low weight for indoor air environment feature information, resulting in a beam coverage overlap rate of only 65% ​​with high-concentration pollution areas, which was lower than the target of 80%, its weight was appropriately increased to 25%, while the weight for indoor wireless communication environment feature information was reduced to 40%. After retesting, the overlap rate increased to 82%, and the command transmission success rate remained at 98%. Finally, the preset indoor feature fusion weights for this scenario were determined to be: 40% for indoor wireless communication environment feature information, 35% for indoor air purification equipment feature information, and 25% for indoor air environment feature information, as the indoor feature fusion weights. The preset indoor feature fusion bias can be constructed to meet different needs such as varying levels of pollution and population density. For example, for the "real-time pollution concentration requirement," the command response requirements of indoor air purification equipment are tested under three formaldehyde concentration ranges: when the concentration is 0-0.1 mg / m³, the acceptable signal delay is ≤100ms; when the concentration is 0.1-0.2 mg / m³, the signal delay is ≤90ms; and when the concentration is >0.2 mg / m³, the signal delay is ≤80ms. This constructs the "real-time bias for formaldehyde concentration of 0.1-0.2 mg / m³" and the "real-time bias for formaldehyde concentration >0.2 mg / m³." As another example, for the "radiation safety in densely populated areas" requirement, the relationship between population density and beam radiation power is tested: when the population density is >2 people / 10 square meters, the maximum beam radiation power needs to be reduced by 5dBm compared to the conventional value. This constructs the "radiation power bias for densely populated areas."

[0032] This can be achieved by extracting the three-dimensional spatial coordinates of high-concentration pollution areas and the pollution distribution density of each functional zone from indoor air environment characteristic information; extracting wireless signal quality characterization information of the location of air purification equipment and the signal propagation path between indoor distributed antennas and each air purification device from indoor wireless communication environment characteristic information; and extracting the three-dimensional spatial coordinates of each air purification device and the relative position of the air purification device to the nearest indoor pollution source from indoor air purification equipment characteristic information. Furthermore, feature correlations can be established, such as binding "three-dimensional spatial coordinates of high-concentration pollution areas - three-dimensional spatial coordinates of air purification devices - signal propagation path between indoor distributed antennas and air purification devices." This clarifies that if the indoor distributed antenna is to transmit indoor air purification command signals by covering the air purification devices with its beam, the beam pointing in its indoor beamforming parameters must simultaneously consider the three-dimensional spatial coordinates of the air purification devices and the surrounding area of ​​high-concentration pollution areas, and must avoid obstructions in the signal propagation path to ensure wireless signal quality. To ensure the quality of quantitative information, for example, for air purification equipment in a specific location, the horizontal azimuth and vertical pitch angles in the indoor beamforming parameters need to be adjusted to bypass lightweight walls, ensuring that the wireless signal can stably reach the air purification equipment and cover the surrounding high-concentration pollution areas. Furthermore, based on preset indoor feature fusion weights, the importance of the three types of features can be quantitatively differentiated. For example, if the core requirement in the scenario is "ensuring stable transmission of indoor air purification command signals to quickly respond to indoor pollution sources," the weight of indoor wireless communication environment features can be set to 45%, indoor air purification equipment features to 35%, and indoor air environment features to 20%, and resources can be allocated according to this weight. For key indicators in indoor wireless communication environment features, such as received signal strength indication, signal-to-noise ratio, bit error rate, and signal latency, priority should be given to ensuring that communication thresholds are met, such as received signal strength indication ≥ -80dBm, signal-to-noise ratio ≥ 15dB, and bit error rate ≤ 10%. -5The signal latency is ≤100ms. If the wireless signal quality characterization information of air purifiers in a certain area is substandard, even if the pollution concentration in the indoor air quality characterization information of that area is low, the indoor beamforming parameters should be adjusted first during fusion to improve the wireless signal quality. For the characteristic information of indoor air purifiers, the focus is on the relative position of the three-dimensional spatial coordinates of the air purifiers and the indoor pollution sources to ensure that the beam coverage corresponding to the indoor beamforming parameters enables the air purifiers to efficiently receive indoor air purification command signals to target indoor pollution sources. For the characteristic information of indoor air environment, the focus is on the auxiliary constraint of the three-dimensional spatial coordinates of high-concentration pollution areas on the beam coverage range to avoid the beam coverage corresponding to the indoor beamforming parameters deviating from the core pollution area. At the same time, if there is a conflict between the requirements of two types of features, such as the wireless signal quality characterization information being slightly lower than the threshold after adjusting the beam according to the three-dimensional spatial coordinates of the air purifiers, the priority is judged according to the preset indoor feature fusion weight, and the feature requirements with higher weight are given priority. For example, to ensure that the indoor wireless communication environment characteristic information meets the standard, the horizontal azimuth and vertical pitch angles in the indoor beamforming parameters are finely adjusted to improve the received signal strength indication, and then... Optimizing the beamwidth in indoor beamforming parameters compensates for minor impacts on the coverage of air purifiers. Then, a preset indoor feature fusion bias is introduced to correct potential system biases during the fusion process, ensuring the calculation results more closely match real-world scenarios. For example, when multiple walls are present in the indoor spatial structure, experimental tests show that the actual attenuation of the wireless signal is 5%-8% higher than the theoretical calculation. Therefore, an additional bias is added when calculating the beam gain requirement in the indoor beamforming parameters. Similarly, in environments with high pollution concentrations in indoor air quality characterization, the air purifier's coverage... The real-time requirements for purification command signal transmission are higher. When the formaldehyde concentration in the indoor air quality characterization information exceeds 0.15 mg / m³, the constraint standard for the signal delay parameters corresponding to the indoor beamforming parameters is tightened by an additional 5ms. Therefore, in the fusion calculation, the preliminary indoor beamforming parameter direction, such as the horizontal azimuth range, vertical pitch range, and beamwidth range, can be obtained first based on feature association and preset indoor feature fusion weights. Then, it can be corrected by combining the preset indoor feature fusion bias: if the formaldehyde concentration in the indoor air quality characterization information of a specific air purification device is 0.Given a concentration of 18 mg / m³ and a 20 cm thick concrete wall between the air purifier and the indoor distributed antenna, the beam gain in the initial indoor beamforming parameters must meet a received signal strength indication of ≥-75 dBm, based on preset indoor feature fusion weights. This is then compounded by an 8% beam gain (wall obstruction signal attenuation offset) and a ≤95 ms signal delay (pollution concentration real-time offset). This adjusts the beam gain and signal transmission bandwidth parameters in the indoor beamforming parameters, ensuring the corrected parameters can handle wall attenuation while meeting the real-time transmission requirements of indoor air purification commands in high-pollution scenarios. Then, based on each set of feature relationships and the corrected requirements, the core dimensions of the indoor beamforming parameters are determined. These core dimensions include horizontal azimuth, vertical elevation, beamwidth, and antenna element weighting coefficients. For example, for a specific air purification device, by combining its three-dimensional spatial coordinates, the three-dimensional spatial coordinates of the surrounding high-concentration pollution area, and the unobstructed signal path, the horizontal azimuth angle range is determined to be 110°-130°, the vertical pitch angle range is 3°-7°, and the beamwidth range is 8°-12°. Then, within each dimension, multiple initial indoor beamforming parameter information are generated according to different parameter combinations. For instance, within the horizontal azimuth angle range of 110°-130° for this air purification device, 110° and 110° are taken at 5° intervals. Five azimuth angles (5°, 120°, 125°, and 130°), three vertical elevation angles (3°, 5°, and 7° at 2° intervals), and three beamwidths (8°, 10°, and 12° at 2° intervals) are used in combination with different antenna element weighting coefficients, such as weighting coefficients emphasizing received signal strength indication and weighting coefficients emphasizing signal uniformity. This generates multiple sets of initial indoor beamforming parameters containing horizontal azimuth, vertical elevation, beamwidth, and antenna element weighting coefficients.

[0033] Step S104: Based on the indoor pollution source location information, indoor distributed antenna location parameter information, air purification equipment location information, preset indoor beam angle adjustment range, preset indoor beam width adjustment range, preset indoor antenna element weighting coefficient vector, and preset indoor air purification efficiency calculation coefficient, optimize the multiple initial indoor beamforming parameter information to obtain the target indoor beamforming parameter information.

[0034] In this embodiment, the preset indoor beam angle adjustment range can be manually set. The construction method can be to first deploy indoor distributed antennas for testing based on the size range of common indoor space types, such as residential bedrooms (3-5 meters long, 2-4 meters wide), living rooms (4-8 meters long, 3-6 meters wide), and commercial offices (6-12 meters long, 4-8 meters wide). For example, the indoor distributed antenna installation height is uniformly 2.8 meters, and air purification devices are distributed according to actual usage scenarios: 1 unit in the bedroom, 2-3 units in the living room, and 4-6 units in the office. Then, angle adjustment tests are performed. For instance, in a residential bedroom scenario, the initial horizontal azimuth angle of the indoor distributed antenna points towards the air purification device. When the horizontal azimuth angle deviates by 1 degree, the edge of the main lobe of the beam can still cover the air purification device; when the deviation is 2 degrees, the edge of the main lobe of the beam deviates from the device, and the received signal strength indicator drops by 5 dBm. Therefore, the single adjustment range of the horizontal azimuth angle in the bedroom scenario is determined to be 1 degree. In the vertical direction... Upward, a 0.5-degree deviation in the vertical pitch angle results in no significant change in beam coverage; a 1-degree deviation shifts the coverage position by 0.3 meters, exceeding the receiving range of the air purifier. Therefore, the single adjustment increment of the vertical pitch angle is set to 0.5 degrees. For example, in a living room scenario, due to the larger space and more devices, testing revealed that a single 1-degree adjustment of the horizontal azimuth angle can accommodate the fine-tuning needs of multiple devices, while the vertical pitch angle maintains a 0.5-degree adjustment increment, avoiding excessive adjustment that could lead to coverage deviation. For example, in a commercial office scenario, with a large space and dense devices, multiple tests verified that a single 1-degree adjustment of the horizontal azimuth angle and a single 0.5-degree adjustment of the vertical pitch angle can accurately adjust the beam direction without causing other devices to fail to cover due to excessive adjustment. Thus, the preset indoor beam angle adjustment increments applicable to all scenarios are determined to be: a single 1-degree adjustment of the horizontal azimuth angle and a single 0.5-degree adjustment of the vertical pitch angle.

[0035] The preset indoor beamwidth adjustment range can be manually set. This can be achieved by selecting three typical coverage scenarios: single-device close-range coverage (air purifier and indoor antenna distance 3-5 meters); multi-device medium-range coverage (5-8 meters); and single-device long-range coverage (8-12 meters). The effect of different beamwidth adjustments is tested in each scenario. For example, in the single-device close-range coverage scenario, with an initial beamwidth of 8 degrees, increasing the beamwidth by 0.5 degrees expands the coverage area by 0.2 meters, and the received signal strength decreases by 1 dBm, still meeting the requirement of ≥-80 dBm. Increasing it by 1 degree expands the coverage area by 0.4 meters, but the signal strength decreases by 3 dBm, approaching the threshold. Therefore, the single adjustment range for this scenario is determined to be 0.5 degrees. For example, in the multi-device medium-range coverage scenario… In one scenario, two air purifiers are 3 meters apart. The initial beamwidth is 10 degrees. After adjusting by 0.5 degrees, the beam can accurately cover both devices without a significant drop in signal strength. When adjusted by 1 degree, although the coverage is sufficient, the signal strength drops by 4 dBm, affecting communication quality. Therefore, the single adjustment increment is set to 0.5 degrees. For example, in a single-device long-distance coverage scenario, the initial beamwidth is 12 degrees. After adjusting by 0.5 degrees, the coverage changes by 0.3 meters, and the signal strength remains stable. When adjusted by 1 degree, the signal strength drops by 2 dBm, which is still within an acceptable range. However, multiple adjustments can easily lead to cumulative signal attenuation. Therefore, the preset indoor beamwidth adjustment increment for all scenarios is 0.5 degrees per adjustment to ensure that each adjustment meets the coverage adjustment requirements while avoiding excessive degradation of wireless signal quality.

[0036] The preset weighting coefficient vector for indoor antenna elements can be manually set. It can be determined by first establishing the antenna array as a 4-element uniform linear array, and then setting it based on three requirements: "strong signal coverage for a single device," "balanced coverage for multiple devices," and "anti-interference coverage." For example, for the "strong signal coverage for a single device" requirement, the beam energy at the air purifier's location needs to be enhanced. The amplitude weights are set to 1.0, 0.8, 0.8, and 1.0, with slightly lower amplitudes in the middle elements and higher amplitudes in the edge elements to concentrate energy. The phase weights are set to 0 degrees, 0 degrees, 0 degrees, and 0 degrees, with no phase difference, to enhance the main lobe energy. Testing with this vector shows a 3dBm increase in the received signal strength indication and a 2dB increase in the signal-to-noise ratio for the air purifier. For "balanced coverage for multiple devices,"... The requirement is to ensure uniform beam energy distribution. Amplitude weights of 0.9, 0.9, 0.9, and 0.9 are set, ensuring consistent amplitude across all elements. Phase weights of 0, 1, 1, and 0 degrees are used to minimize phase differences and widen the main lobe. Testing with three air purifiers showed a signal strength difference of ≤2dBm between devices, meeting the equalization requirement. For example, regarding "anti-interference coverage," in areas with electromagnetic interference, amplitude weights of 1.0, 0.6, 0.6, and 1.0 are set to weaken the energy in the interference direction. Phase weights of 0, 2, 2, and 0 degrees are used to create destructive interference in the interference direction. Testing under an interference power of -75dBm showed that this vector can improve the signal-to-noise ratio of air purifiers by 4dB and reduce the bit error rate from 10... -4 Reduced to 10 -5 This generates a parameter library containing indoor antenna array weighted coefficient vectors that meet three types of requirements: "strong signal coverage for single devices," "balanced coverage for multiple devices," and "anti-interference coverage," which can be selected during optimization calculations.

[0037] The preset indoor air purification efficiency calculation coefficient can be set manually or based on actual operating data of the air purification equipment and beam parameter correlation tests. Specifically, it can be achieved by first deploying an indoor distributed antenna and air purification equipment in a fixed indoor space with dimensions of 6 meters long, 4 meters wide, and 2.8 meters high, setting different beam coverage ranges and command transmission delays, and recording the corresponding changes in purification efficiency. This establishes the test environment. Then, for the "pollution removal rate coefficient," the PM2.5 removal rate under different beam coverage overlap rates of high-concentration pollution areas can be tested: at an overlap rate of 70%, the PM2.5 removal rate is 60% within 1 hour; at an overlap rate of 80%, the removal rate is 65%; and at an overlap rate of 90%, the removal rate is 70%. It is calculated that for every 1% increase in overlap rate, the removal rate increases by 0.5%, therefore, the pollution removal rate coefficient is set to 0. 5% / 1%; The device response efficiency coefficient can also be tested under different command transmission delays: with a delay of 100ms, it takes 1 second for the device to start the powerful mode after receiving the command; with a delay of 90ms, the response time is 0.7 seconds; with a delay of 80ms, the response time is 0.4 seconds. It is calculated that for every 1ms reduction in delay, the response speed increases by 0.03 seconds, corresponding to a 0.3% increase in response efficiency. Based on 1 second, the device response efficiency coefficient is set to 0.3% / 1ms. This is verified through multiple sets of test data with different pollution concentrations and different device models to ensure that the coefficient accurately reflects the correlation between beam parameters and purification efficiency, thus determining that the preset indoor air purification efficiency calculation coefficient includes: PM2.5 pollution removal rate coefficient 0.5% / 1%, and device response efficiency coefficient 0.3% / 1ms.

[0038] First, extract the three-dimensional spatial coordinates of the high-concentration pollution area from the indoor pollution source location information, the three-dimensional spatial coordinates of the air purifier from the air purifier location information, and the three-dimensional installation coordinates, horizontal azimuth, and vertical elevation angles of the indoor distributed antenna from the indoor antenna location parameter information. Calculate the optimal connection between the indoor antenna, air purifier, and high-concentration pollution area using spatial geometric relationships. For example, if the three-dimensional installation coordinates of the indoor antenna are 0 meters, 0 meters, 2.8 meters, the three-dimensional spatial coordinates of the air purifier are 6 meters, 4 meters, 0.8 meters, and the three-dimensional spatial coordinates of the high-concentration pollution area are 5 meters, 3 meters, 0.5 meters, the calculation shows that when the indoor antenna is pointed at the midpoint between the air purifier and the high-concentration pollution area, it ensures that the air purifier receives the indoor air purification command signal while also covering the high-concentration pollution area. Based on this, determine the initial indoor beamforming. The adjustment directions of the horizontal azimuth and vertical pitch angles in the parameter information can be referenced to the preset indoor beam angle adjustment range. For example, the horizontal azimuth angle can be adjusted by 1 degree upward or downward in a single adjustment, and the vertical pitch angle can be adjusted by 0.5 degrees upward or downward in a single adjustment. The angle parameters of each initial indoor beamforming parameter information are fine-tuned. If the horizontal azimuth angle of an initial indoor beamforming parameter information is 53 degrees, and it is calculated that it needs to be adjusted by 2 degrees towards the high-concentration pollution area, then it is adjusted twice according to the preset indoor beam angle adjustment range, adjusting by 1 degree each time, and finally correcting the horizontal azimuth angle to 55 degrees. If the initial value of the vertical pitch angle is 2 degrees, and it needs to be adjusted downward by 1 degree to cover the lower high-concentration pollution area, then it is adjusted twice according to the adjustment range of 0.5 degrees, correcting to 1 degree, to ensure that the adjusted beam direction can simultaneously cover the air purification equipment and the high-concentration pollution area.Furthermore, we can analyze the coordinates of walls and fixed obstacles in the indoor spatial structure information, the spacing between air purifiers in the air purifier location information, and the range of high-concentration pollution areas in the indoor pollution source location information to determine the beamwidth optimization target corresponding to each initial indoor beamforming parameter. For example, if two air purifiers in a certain area are 3 meters apart and there are no walls or fixed obstacles obstructing them, the beamwidth needs to cover both air purifiers while avoiding wireless signal energy dispersion, and the optimization target is set at 10 degrees. If there is a lightweight wall around an air purifier, the beamwidth needs to be appropriately increased to penetrate the wall and cover the air purifier, and the optimization target is 12 degrees. Then, we can adjust the beamwidth gradually according to the preset indoor beamwidth adjustment range, such as a single adjustment range of 0.5 degrees upwards or downwards, to optimize the beamwidth in the initial indoor beamforming parameter information. If the beamwidth of the internal beamforming parameters is 8 degrees and the optimization target is 10 degrees, then it is adjusted in four increments of 0.5 degrees each time, until the beamwidth is finally corrected to 10 degrees. If the initial indoor beamwidth of the beamforming parameters is 13 degrees, exceeding the optimization target of 12 degrees, then it is adjusted in two increments of 0.5 degrees each time, until it is corrected to 12 degrees. At the same time, during the adjustment process, the received signal strength indication and signal-to-noise ratio in the indoor wireless communication environment characteristics information need to be considered. If the received signal strength indication drops to -85dBm after the beamwidth is increased, which is lower than the communication threshold of -80dBm, then the adjustment increment is appropriately reduced, such as by 0.3 degrees in a single adjustment. This is combined with subsequent preset indoor antenna element weighting coefficient vector optimization to compensate for the loss of wireless signal strength, ensuring that the beamwidth meets the needs of covering air purification equipment and high-concentration pollution areas without affecting the wireless signal quality.Subsequently, the antenna element weights can be optimized based on a preset indoor antenna element weighting coefficient vector to improve beam energy concentration and anti-interference capability. For example, in a scenario where multiple air purifiers are covered, the preset indoor antenna element weighting coefficient vector has an amplitude weight that allows the beam energy to evenly cover multiple devices, and a phase weight that reduces signal interference between devices. In a scenario with strong interference, the preset indoor antenna element weighting coefficient vector has amplitude and phase weights that can enhance the signal strength of the beam at the location of the air purifier and weaken the signal in the interference area. During optimization, the preset indoor antenna element weighting coefficient vector can be selected based on the interference power in the indoor wireless communication environment characteristic information and the number of air purifiers in the air purifier location information. If there is strong interference in a certain area and two air purifiers need to be covered, then the preset indoor antenna element weighting coefficient vector that is suitable for the strong interference area and the coverage of multiple air purifiers is selected. Next, the antenna element weighting coefficients in the initial indoor beamforming parameter information are compared with the selected preset indoor antenna element weighting coefficient vector. Antenna element amplitude weights and phase weights with large deviations are corrected. If the second antenna element in the initial indoor beamforming parameter information has an amplitude weight of 0.9, and the corresponding antenna element in the preset indoor antenna element weighting coefficient vector has 0.6 (a deviation of 0.3), then it is adjusted to 0.6 according to the preset vector standard value. If the third antenna element in the initial indoor beamforming parameter information has a phase weight of 1 degree, and the preset indoor antenna element weighting coefficient vector has 3 degrees (a deviation of 2 degrees), then it is corrected to 3 degrees. After correction, the signal-to-noise ratio (SNR) change in the indoor wireless communication environment characteristic information needs to be tested. If the SNR increases from 12dB to 16dB, reaching the communication threshold of 15dB, then the weight combination is retained. If the SNR improvement is not significant, another set of preset indoor antenna element weighting coefficient vectors needs to be used for re-optimization until the SNR meets the standard.Preset indoor air purification efficiency calculation coefficients can be introduced to quantify and optimize the effect, and the optimal parameters can be selected as the target indoor beamforming parameters. Specifically, for each initial indoor beamforming parameter after optimization of angle, width, and weight, the initial pollutant concentration in the indoor air quality characterization information (e.g., PM2.5 concentration of 80 micrograms / cubic meter, formaldehyde concentration of 0.15 milligrams / cubic meter), the initial purification efficiency in the air purification equipment operating parameters (e.g., PM2.5 removal rate of 60%, formaldehyde removal rate of 55%), and the signal strength in the optimized indoor wireless communication environment characteristics information can be considered. Latency, such as a command transmission latency of 90ms, is used to calculate the overall purification effect based on a preset indoor air purification efficiency calculation coefficient. For example, the overlap rate of the optimized beam coverage in high-concentration pollution areas increases from 70% to 90%. According to the pollution removal rate coefficient, the pollution removal rate increases by 20% multiplied by the value of the pollution removal rate coefficient. The final PM2.5 removal rate is 60% plus the increase, and the formaldehyde removal rate is 55% plus the increase. The command transmission latency is reduced from 100ms to 90ms. According to the device response efficiency coefficient, the device response speed increases by 10ms multiplied by the value of the device response efficiency coefficient. Then, based on the core needs of indoor air purification, weights are assigned to pollution removal rate and equipment response speed, such as a pollution removal rate weight of 70% and an equipment response speed weight of 30%. The comprehensive purification effect score of each optimized parameter information is calculated, and the comprehensive purification effect scores of all optimized initial indoor beamforming parameter information are sorted. The parameter information with the highest score is selected as the target indoor beamforming parameter information. For example, if the comprehensive purification effect score of a certain optimized initial indoor beamforming parameter information is 58.2%, with PM2.5 removal rate of 75%, formaldehyde removal rate of 70%, and equipment response speed improvement of 5%, which is the highest value among all parameters, then this parameter information is determined as the target indoor beamforming parameter information to ensure that it can maximize the indoor air purification effect.

[0039] Step S105: Based on the target indoor beamforming parameter information, perform beamforming control processing on the indoor distributed antenna to generate multiple indoor wireless directional beams.

[0040] In this embodiment, the horizontal azimuth, vertical elevation, beamwidth, and antenna element weighting coefficients in the target indoor beamforming parameter information can be extracted first and then converted into specific control commands that can be executed by the indoor distributed antenna. First, based on the horizontal azimuth and vertical elevation angles in the target indoor beamforming parameter information, combined with the three-dimensional installation coordinates of the indoor distributed antenna in the antenna position parameter information, the beam pointing angle control value of the indoor distributed antenna is calculated. Through spatial geometric relationships, it is confirmed that this control value can accurately align the radiation direction of the indoor distributed antenna with the three-dimensional spatial coordinates of the air purification device in the air purification device position information. The target simultaneously covers the three-dimensional spatial coordinates of the high-concentration pollution area surrounding the indoor pollution source location information. Then, combining the beamwidth from the target indoor beamforming parameters, and referencing the wall and fixed obstacle coordinates from the indoor spatial structure information, as well as the spacing between air purification devices from the air purification device location information, the beam coverage range adjustment value of the indoor distributed antenna is calculated. This clarifies the beam energy diffusion angles in the horizontal and vertical directions, ensuring that the beam completely covers the corresponding air purification device without causing energy dispersion due to excessive coverage or failing to cover the device due to insufficient coverage. Then, based on the target indoor... The antenna element weighting coefficients in the internal beamforming parameters are used to calculate the amplitude and phase control values ​​for each antenna element in the indoor distributed antenna array. By adjusting the signal output amplitude and phase difference of different antenna elements, the indoor distributed antenna array forms a highly concentrated main lobe in the target pointing direction and a weaker sidelobe in the non-target direction to reduce interference with wireless signals in other areas. The calculated beam pointing angle control values, beam coverage control values, antenna element amplitude control values, and phase control values ​​are then integrated into a complete control command set and transmitted to the control module of the indoor distributed antenna array. After receiving the control command set, the control module of the indoor distributed antenna drives the antenna angle adjustment mechanism to adjust the physical pointing of the indoor distributed antenna, and drives the amplitude adjustment unit and phase adjustment unit to adjust the signal output amplitude and phase of each antenna element, until the signal radiated by the indoor distributed antenna forms multiple indoor wireless directional beams that meet the requirements in the indoor space. Each indoor wireless directional beam can accurately cover the three-dimensional spatial coordinates of the air purification device in the location information of the corresponding air purification device, and meet the wireless signal quality standards required for indoor air purification command signal transmission, thereby completing the generation of multiple indoor wireless directional beams.

[0041] Step S106: Based on the plurality of indoor wireless directional beams, transmit the indoor air purification command signal to the air purification device so as to perform indoor air purification treatment through the air purification device.

[0042] In this embodiment, a one-to-one correspondence between multiple indoor directional wireless beams and air purification devices can be established first. By comparing the coverage area coordinates of each indoor directional wireless beam with the three-dimensional spatial coordinates of the air purification device in the device's location information, the target air purification device for which each indoor directional wireless beam needs to accurately transmit instructions can be determined, ensuring that each air purification device has corresponding indoor directional wireless beam coverage. Then, this is combined with the three-dimensional spatial coordinates of high-concentration pollution areas in the indoor air environment characteristic information, the pollution distribution density of each functional zone, and the PM2.5 concentration, formaldehyde concentration, indoor air temperature, and indoor relative humidity in the indoor air quality characterization information. Based on the air purifier's operating parameters, including its current operating mode, fan speed, airflow, and purification efficiency, the specific indoor air purification command signal required for each target air purifier is calculated. For example, for an air purifier located near a high-concentration pollution area with a PM2.5 concentration of 80 micrograms per cubic meter, and considering its current operating mode is automatic and fan speed is 1200 rpm, the command signal would be: "Switch to high-power purification mode, increase fan speed." The indoor air purification command signal is set to "1800 rpm, air purifier output adjusted to 350 cubic meters per hour". Then, referring to the wireless signal quality characterization information in the indoor wireless communication environment characteristics information and the indoor distributed antenna communication parameter information (including the indoor antenna operating frequency band, indoor antenna transmit power, and indoor antenna gain), the transmission parameters corresponding to each indoor wireless directional beam transmission corresponding to the indoor air purification command signal can be calculated. These parameters include the signal modulation method, signal encoding format, and signal transmission rate. For example, if the signal-to-noise ratio in the wireless signal quality characterization information of a certain indoor wireless directional beam coverage area is 16dB and the received signal strength indication is -72dBm, combined with... The indoor distributed antenna communication parameters indicate that the current operating frequency band of the indoor distributed antenna is the 5G band 3300-5000MHz. Calculations determine the use of a modulation method with better anti-interference performance, a coding format with higher redundancy, and a transmission rate that meets the requirements for real-time command transmission, avoiding loss or delay of indoor air purification command signals during transmission. Subsequently, the indoor air purification command signal can be encoded and modulated according to the calculated transmission parameters, and then the processed command signal is loaded into the corresponding indoor wireless directional beam. The indoor distributed antenna accurately radiates the indoor wireless directional beam carrying the indoor air purification command signal to the location of the target air purification device.

[0043] In this embodiment, during signal transmission, the command reception status data fed back by the air purification device can be collected in real time. Combined with the real-time updated wireless signal quality characterization information in the indoor wireless communication environment characteristic information, the transmission effect of the indoor air purification command signal can be verified. If the feedback shows that the air purification device has not successfully received the command, or the bit error rate in the real-time wireless signal quality characterization information increases to 10... -4 Then, the indoor beamforming parameters are recalculated, and the corresponding indoor air purification command signal is transmitted through the adjusted indoor wireless directional beam.

[0044] The indoor air purification method provided in this application calculates the target indoor beamforming parameter information to accurately match the distribution of pollution sources with the location of purification equipment. While ensuring the quality of indoor wireless signal coverage, it maximizes the adaptation to the actual needs of air purification, enabling the indoor distributed antenna to generate a suitable directional beam in a timely manner to accurately transmit the indoor air purification command signal to the air purification equipment. This ensures that the equipment can receive the command in a timely and accurate manner and execute the purification operation, avoiding energy waste and interference problems caused by non-directional signal transmission. It effectively improves the response speed and coordination efficiency of indoor air purification equipment, thereby improving the indoor air purification effect and efficiency.

[0045] Figure 2 The flowchart illustrating the implementation of the indoor air purification method provided in Embodiment 2 of this application is shown. The difference between this method and Embodiment 1 is that step S102 specifically includes: Step S201: Encode and splice the indoor space structure information, indoor pollution source location information, indoor personnel location information, and indoor air quality characterization information to generate indoor air environment vector information.

[0046] In this embodiment, encoding processing can be performed first on the indoor space structure information. This involves extracting the indoor space plan dimensions, wall and door / window locations, the location and shape of fixed obstacles, and the functional zoning of the indoor space. Following a fixed order of "indoor space plan dimensions - wall and door / window locations - location and shape of fixed obstacles - functional zoning," these data types are converted into standardized numerical codes. For example, the length of the indoor space plan dimensions is encoded as "0600," the width as "0400," and the height as "0280." This information is then used to determine the location of indoor pollution sources. The indoor pollution source location information is encoded by extracting the specific spatial coordinates, type attributes, and influence range boundaries of various indoor pollution sources. The three-dimensional spatial coordinates are encoded in the order of "X-axis coordinate - Y-axis coordinate - Z-axis coordinate," the type attributes of the pollution sources are directly coded using corresponding labels, and the influence range boundaries are encoded in the order of "X-axis range - Y-axis range - Z-axis range." Then, the indoor personnel location information is encoded by extracting the real-time spatial coordinates and activity trajectories of each person indoors. This is done by encoding the real-time spatial coordinates and activity trajectories of each person indoors. The "movement trajectory" is sequentially coded, and the real-time spatial coordinate coding method is consistent with the specific spatial coordinate coding of indoor pollution sources. The movement trajectory is coded sequentially according to the moving nodes. Then, the indoor air quality characterization information is coded, extracting the concentration of particulate matter pollutants, concentration of gaseous pollutants, microbial indicators, physical state parameters, and circulation parameters of indoor air contained in the indoor air quality characterization information. This is coded in the order of "particulate matter pollutant concentration - gaseous pollutant concentration - microbial indicators - physical state parameters - circulation parameters". For example, PM2.5 concentration is coded as "0050", and temperature is coded as "0". The encoding results are as follows: 250", relative humidity is coded as "0500", and fresh air volume is coded as "1500". The encoded results of indoor space structure information, indoor pollution source location information, indoor personnel location information, and indoor air quality characterization information can be concatenated in the order of "indoor space structure information encoding result - indoor pollution source location information encoding result - indoor personnel location information encoding result - indoor air quality characterization information encoding result". The concatenation results are then connected into a continuous vector string using character delimiters, thereby generating indoor air environment vector information that carries all the original information encoding related to the air environment.

[0047] Step S202 involves encoding and splicing the indoor antenna location parameter information, indoor antenna communication parameter information, and wireless signal quality characterization information to generate indoor wireless communication environment vector information.

[0048] In this embodiment, the indoor distributed antenna (DDA) position parameter information can be encoded first. This involves extracting the DDA's installation coordinates, installation height, installation angle, and relative positional relationship between the DDA and surrounding objects. The three-dimensional installation coordinates of the DDA are encoded in the order of "X-axis installation coordinate - Y-axis installation coordinate - Z-axis installation coordinate," with the installation height encoded in the same way as the Z-axis coordinate. The horizontal azimuth angle is encoded as "120°," and the vertical elevation angle is encoded as "005°." The relative positional relationship between the DDA and surrounding objects is encoded in the order of "distance from wall - distance from ceiling - distance from obstacle." Next, the indoor DDA communication parameter information is encoded. This involves extracting the DDA's operating frequency band, transmit power, receive sensitivity, gain, and other parameters. The polarization of the antenna is determined by encoding the operating frequency band of the indoor distributed antenna in the order of "start frequency - end frequency". The transmit power is encoded as "0230", the receive sensitivity as "-0950", and the gain as "0120". The polarization is then coded with corresponding labels. Subsequently, the wireless signal quality characterization information is encoded, extracting the received signal strength indication, signal-to-noise ratio, bit error rate, signal delay, and jitter contained in the wireless signal quality characterization information. For example, the received signal strength indication is encoded as "-0700", the signal-to-noise ratio as "0018", the bit error rate as "00001", and the signal delay as "0080". The encoded results can be spliced ​​in the order of "indoor distributed antenna location parameter information encoding result - indoor distributed antenna communication parameter information encoding result - wireless signal quality characterization information encoding result", and delimiters can be used to connect the encoded results to generate indoor wireless communication environment vector information.

[0049] Step S203: Encode and splice the location information and operating parameter information of the air purification device to generate indoor air purification device vector information.

[0050] In this embodiment, the location information of the air purifier can be encoded first. This involves extracting the specific spatial coordinates of the air purifier, its relative position to the indoor pollution source, and its relative position to the indoor distributed antenna. The three-dimensional spatial coordinates of the air purifier are encoded in the order of "X-axis coordinate - Y-axis coordinate - Z-axis coordinate," the relative position to the indoor pollution source is encoded in the order of "straight-line distance from the nearest pollution source - obstruction status," and the relative position to the indoor distributed antenna is encoded in the order of "straight-line distance from the indoor distributed antenna - signal receiving direction." Then, the operating parameter information of the air purifier is further processed... The system uses row coding to extract the operating parameters of air purifiers, including their operating mode, fan speed, air volume, and purification efficiency. Fan speed is coded as "1200", air volume as "2000", and PM2.5 removal rate as "0800". The operating mode is coded accordingly. The system can be concatenated in the order of "air purifier location information coding result - air purifier operating parameter information coding result", combining the two types of coding results for the same air purifier and using separators to differentiate between different air purifiers, thus generating vector information for indoor air purifiers.

[0051] Step S204: Based on multiple preset indoor environment feature extraction vectors, feature extraction calculations are performed on the indoor air environment vector information, indoor wireless communication environment vector information, and indoor air purification device vector information to generate indoor air environment feature information, indoor wireless communication environment feature information, and indoor air purification device feature information.

[0052] In this embodiment, the specific composition of multiple preset indoor environment feature extraction vectors can be defined first, including preset indoor environment feature extraction vectors of the air environment class that specifically correspond to indoor air environment vector information, preset indoor environment feature extraction vectors of the wireless communication class that correspond to indoor wireless communication environment vector information, and preset indoor environment feature extraction vectors of the device class that correspond to indoor air purification device vector information. The indoor environmental feature extraction vectors for the air environment category include three core feature dimensions: pollution density of functional zones, pollution source concentration gradient, and personnel-pollution overlap. The weights for each dimension are set as follows: pollution density of functional zones 40%, pollution source concentration gradient 35%, and personnel-pollution overlap 25%. The indoor environmental feature extraction vectors for the wireless communication category include four core feature dimensions: communication quality of air purification equipment, signal propagation path loss, indoor antenna coverage adaptability, and communication parameter redundancy. The weights for each dimension are set as follows: communication quality of air purification equipment 35%, signal propagation path loss 30%, indoor antenna coverage adaptability 20%, and communication parameter redundancy 15%. The indoor environmental feature extraction vectors for the equipment category include core feature dimensions: equipment-pollution source matching degree and operating parameter-pollution demand matching degree. The weights for each dimension are set as follows: equipment-pollution source matching degree 40% and operating parameter-pollution demand matching degree 45%. Furthermore, based on the preset indoor environmental feature extraction vectors for air environment categories, the indoor air environment vector information is first decoded to restore the original data of indoor spatial structure information, indoor pollution source location information, indoor personnel location information, and indoor air quality characterization information. Then, corresponding features are extracted according to the weight of each feature dimension: In the functional zoning pollution density dimension, the pollution density of the living room functional zoning is calculated by combining the functional zoning of the indoor space in the indoor spatial structure information with the specific spatial coordinates of various indoor pollution sources in the indoor pollution source location information; in the pollution source concentration gradient dimension, the formaldehyde concentration gradient of the pollution source is calculated by combining the specific spatial coordinates of various indoor pollution sources in the indoor pollution source location information with the gaseous pollutant concentration in the indoor air quality characterization information; in the personnel-pollution overlap dimension, the personnel are identified as being in a high-concentration pollution area by combining the real-time spatial coordinates of each person in the indoor personnel location information with the particulate pollutant concentration in the indoor air quality characterization information, and the personnel-pollution overlap is marked. The three types of features—functional zoning pollution density, pollution source concentration gradient, and personnel-pollution overlap—are integrated to generate indoor air environment feature information containing "functional zoning-pollution density-concentration gradient-personnel overlap".Then, feature extraction and calculation can be performed on the indoor wireless communication environment vector information. Specifically, the indoor wireless communication environment vector information can be decoded first to restore the original data of indoor distributed antenna location parameters, indoor distributed antenna communication parameters, and wireless signal quality characterization information. Then, features are extracted according to the weight of each dimension: in the communication quality dimension of air purification equipment, the received signal strength indication, signal-to-noise ratio, bit error rate, and signal delay in the wireless signal quality characterization information are verified to determine whether the communication quality of air purification equipment meets the standards; in the signal propagation path loss dimension, the wall location distribution in the indoor spatial structure information and the signal propagation path loss in the indoor distributed antenna communication parameters are combined. In terms of broadcast characteristics, regarding the coverage adaptability dimension of indoor distributed antennas (DDAs), the proportion of air purifiers located in the main coverage area of ​​the DDAs is calculated by combining the installation angle and coverage range parameters of the DDAs in the DDA location parameter information with the distribution location of the air purifiers in the air purifier location information, thus determining the coverage adaptability level. In terms of communication parameter redundancy, the actual operating parameters in the DDA communication parameter information are compared with the communication requirement parameters of the air purifiers to calculate the proportion of parameter redundancy. The features are then integrated according to weights to generate indoor wireless communication environment feature information that includes "communication quality, path loss, coverage adaptability, and parameter redundancy". To extract features from the vector information of indoor air purification equipment, the vector information of the indoor air purification equipment can be decoded first to restore the original data of the equipment's location information and operating parameters. Then, features are extracted according to the weights of each dimension: in the equipment-pollution source matching degree dimension, the matching degree score is calculated by combining the purification type parameters of the air purification equipment with the type attributes of the indoor pollution source; in the operating parameter-pollution demand matching degree dimension, the current operating parameters of the air purification equipment are compared with the demand parameters determined based on the indoor air quality characterization information to calculate the parameter matching percentage. Finally, the features are integrated according to the weights to generate indoor air purification equipment feature information containing "pollution source matching degree - parameter matching degree".

[0053] The indoor air purification method provided in this application encodes and extracts features from various information of the indoor air environment, wireless communication environment, and air purification equipment, thereby achieving a multi-dimensional and accurate characterization of complex indoor environments. This overcomes the limitations of existing technologies that focus on single-dimensional parameters, comprehensively reflects the operation of indoor air purification, and effectively improves the intelligence level and operational efficiency of indoor air purification.

[0054] Figure 3 The flowchart illustrating the implementation of the indoor air purification method provided in Embodiment 3 of this application is shown. The difference between this method and Embodiment 2 described above is that: Multiple preset indoor environment feature extraction vectors include preset indoor environment feature location extraction vectors, preset indoor environment feature label extraction vectors, and preset indoor environment feature content extraction vectors; Step S204 specifically includes: Step S301: Based on the indoor air environment vector information, the preset indoor environment feature location extraction vector, the preset indoor environment feature label extraction vector, and the preset indoor environment feature content extraction vector, generate the indoor air environment feature location vector, the indoor air environment feature label vector, and the indoor air environment feature content vector.

[0055] In this embodiment, the preset indoor environmental feature location extraction vector, the preset indoor environmental feature label extraction vector, and the preset indoor environmental feature content extraction vector can all be manually set. The construction process of the preset indoor environmental feature location extraction vector can involve first extracting the core location-related dimensions from the indoor air environment vector information, including the indoor space planar dimension coordinate range, wall and door / window location distribution coordinates, indoor fixed obstacle location coordinates, and indoor space functional zoning coordinates from the indoor space structure information; the specific spatial coordinates of various indoor pollution sources and the boundary coordinates of the pollution source's influence range from the indoor pollution source location information; and the location information of each person in the indoor environment. Real-time spatial coordinates are used, and based on the location data format and distribution patterns of these dimensions, vector dimensions and weights are set to ensure that each location-related dimension has a corresponding vector element, forming a preset indoor environment feature location extraction vector. The construction process of the preset indoor environment feature label extraction vector can be to first define the classification label system of each data in the indoor air environment vector information, and then set recognition rules and weights for each label to form an indoor environment feature label extraction vector. The construction of the preset indoor environment feature content extraction vector can be to focus on the specific content attributes of each data in the indoor air environment vector information, and then set weights according to the degree of influence of the content on air purification decisions to form an indoor environment feature content extraction vector.

[0056] This can be achieved by: matching indoor air environment vector information with preset indoor environment feature location extraction vectors according to their corresponding dimensions; extracting all location-related encoded data from the indoor air environment vector information; weighting and integrating the data according to the preset weights of the indoor environment feature location extraction vectors to form an indoor air environment feature location vector containing weighted expressions of all location features; matching indoor air environment vector information with preset indoor environment feature label extraction vectors to identify the label type corresponding to each encoded segment in the indoor air environment vector information; weighting the encoded data corresponding to each label according to the preset weights of the indoor environment feature label extraction vectors to generate an indoor air environment feature label vector with label weight attributes; and matching indoor air environment vector information with preset indoor environment feature content extraction vectors to extract the specific numerical content of each encoded segment in the indoor air environment vector information; weighting the content content extraction vector according to the preset weights of the indoor environment feature content extraction vectors to form an indoor air environment feature content vector containing weighted expressions of content features.

[0057] Step S302: Perform interactive mapping calculation based on the indoor air environment feature location vector and the indoor air environment feature label vector to obtain the indoor air environment feature interactive mapping vector.

[0058] In this embodiment, each position element in the indoor air environment feature position vector can be used as the row dimension of a matrix, and each label element in the indoor air environment feature label vector can be used as the column dimension of a matrix. The matrix is ​​filled by judging the belonging relationship between the position element and the label element: if a position element belongs to the category corresponding to a label element, the corresponding position in the matrix is ​​marked as 1, otherwise it is marked as 0. Then, the original weights of the preset indoor environment feature position extraction vector and the preset indoor environment feature label extraction vector are combined to assign weight coefficients to the 1 value positions in the association matrix. Then, an indoor air environment feature interaction mapping vector is generated, and the association weight coefficients are arranged in the order of position element-label element to form continuous vector data, thereby generating an indoor air environment feature interaction mapping vector that reflects the association strength between position and label.

[0059] Step S303: Based on the indoor wireless communication environment vector information, the preset indoor environment feature location extraction vector, the preset indoor environment feature tag extraction vector, and the preset indoor environment feature content extraction vector, generate the indoor wireless communication environment feature location vector, the indoor wireless communication environment feature tag vector, and the indoor wireless communication environment feature content vector.

[0060] In this embodiment, the preset indoor environment feature location extraction vector, the preset indoor environment feature label extraction vector, and the preset indoor environment feature content extraction vector can be manually set. The construction process of the preset indoor environment feature location extraction vector can be as follows: First, focus on the core location-related dimensions in the indoor wireless communication environment vector information, including the installation coordinates of the indoor distributed antenna and the relative positional relationship between the indoor distributed antenna and surrounding objects in the indoor distributed antenna location parameter information; second, for the air purification device, set weights based on the degree of influence of these location dimensions on wireless communication to form the indoor environment feature location extraction vector; the preset indoor environment feature label extraction vector... The process of constructing the quantity can be as follows: first, define a classification label system for indoor wireless communication environment vector information; then, set weights according to the priority of the label's impact on communication effect to form an indoor environment feature label extraction vector. The process of constructing the preset indoor environment feature content extraction vector can be as follows: first, extract the core data related to the content attributes from the indoor wireless communication environment vector information, including the operating frequency range, transmit power, receive sensitivity, and gain of the indoor distributed antenna in the indoor distributed antenna communication parameter information, and the received signal strength indication, signal-to-noise ratio, bit error rate, and signal delay in the wireless signal quality characterization information. Then, set weights according to the degree of impact of the content on communication quality to form an indoor environment feature content extraction vector.

[0061] This can be achieved by: performing dimensional matching between indoor wireless communication environment vector information and preset indoor environment feature location extraction vectors to extract all location-related encoded data; weighting and integrating these data according to the preset weights of the indoor environment feature location extraction vectors to generate an indoor wireless communication environment feature location vector; performing label matching between indoor wireless communication environment vector information and preset indoor environment feature label extraction vectors to identify the label type corresponding to each encoded segment; weighting these data according to the preset weights of the indoor environment feature label extraction vectors to generate an indoor wireless communication environment feature label vector; and performing content matching between indoor wireless communication environment vector information and preset indoor environment feature content extraction vectors to extract the specific numerical content of each encoded segment; weighting these data according to the preset weights of the indoor environment feature content extraction vectors to generate an indoor wireless communication environment feature content vector.

[0062] Step S304: Perform interactive mapping calculation based on the indoor wireless communication environment feature location vector and the indoor wireless communication environment feature label vector to obtain the indoor wireless communication environment feature interactive mapping vector.

[0063] In this embodiment, a location-tag association matrix can be constructed first, using the location elements in the indoor wireless communication environment feature location vector as the matrix row dimension and the tag elements in the indoor wireless communication environment feature tag vector as the matrix column dimension. The matrix is ​​filled according to the belonging relationship between the location elements and the tag elements: if the location element belongs to the information category corresponding to a certain tag element, the corresponding location in the matrix is ​​marked as 1, otherwise it is marked as 0. Then, the association weight is calculated for the location marked as 1 in the matrix by combining the original weights of the preset indoor environment feature location extraction vector and the preset indoor environment feature tag extraction vector. Finally, all association weight coefficients are arranged in a fixed order of tag element-location element to generate an indoor wireless communication environment feature interaction mapping vector that reflects the strength of the association between location and tag.

[0064] Step S305: Based on the indoor air purification device vector information, the preset indoor environment feature location extraction vector, the preset indoor environment feature label extraction vector, and the preset indoor environment feature content extraction vector, generate the indoor air purification device feature location vector, the indoor air purification device feature label vector, and the indoor air purification device feature content vector.

[0065] In this embodiment, the indoor air purification device vector information can be matched dimensionally with a preset indoor environmental feature location extraction vector to extract all location-related encoded data. This data is then weighted and integrated according to the preset weights of the indoor environmental feature location extraction vectors to generate an indoor air purification device feature location vector. Next, the indoor air purification device vector information can be matched with a preset indoor environmental feature label extraction vector to identify the label type corresponding to each encoded segment. This data is then weighted according to the preset weights of the indoor environmental feature label extraction vectors to generate an indoor air purification device feature label vector. Finally, the indoor air purification device vector information can be matched with a preset indoor environmental feature content extraction vector to extract the specific numerical content of each encoded segment. This data is then weighted according to the preset weights of the indoor environmental feature content extraction vectors to generate an indoor air purification device feature content vector.

[0066] Step S306: Perform interactive mapping calculation based on the feature location vector and feature label vector of the indoor air purification device to obtain the feature interactive mapping vector of the indoor air purification device.

[0067] In this embodiment, the position elements in the feature position vector of the indoor air purification device can be used as the row dimension of the matrix, and the label elements in the feature label vector of the indoor air purification device can be used as the column dimension of the matrix. The matrix is ​​filled according to the belonging relationship between the position elements and the label elements: if the position element belongs to the information category corresponding to a certain label element, the corresponding position in the matrix is ​​marked as 1, otherwise it is marked as 0. Then, the original weights of the preset indoor environment feature position extraction vector and the preset indoor environment feature label extraction vector can be combined to calculate the association weight of the position marked as 1 in the matrix. Then, all association weight coefficients are arranged in a fixed order of label element-position element to generate an indoor air purification device feature interaction mapping vector that can reflect the association strength between the device position and the functional label.

[0068] Step S307: Perform multiplication calculation based on the indoor air environment feature interaction mapping vector and the indoor air environment feature content vector to generate indoor air environment feature information.

[0069] In this embodiment, each element in the indoor air environment feature interaction mapping vector can be multiplied with the corresponding element in the indoor air environment feature content vector, and the result of the multiplication can be used as the indoor air environment feature information.

[0070] Step S308: Perform multiplication calculation based on the indoor wireless communication environment feature content vector and the indoor wireless communication environment feature interaction mapping vector to generate indoor wireless communication environment feature information.

[0071] In this embodiment, each associated weight coefficient element in the indoor wireless communication environment feature interaction mapping vector can be multiplied one by one with the content feature elements of the same dimension in the indoor wireless communication environment feature content vector, and the multiplication result can be used as the indoor wireless communication environment feature information.

[0072] Step S309: Perform multiplication calculation based on the indoor air purification device feature content vector and the indoor air purification device feature interaction mapping vector to generate indoor air purification device feature information.

[0073] In this embodiment, each associated weight coefficient in the feature interaction mapping vector of the indoor air purification device can be multiplied one by one with the content feature value of the corresponding dimension in the feature content vector of the indoor air purification device, and the result of the multiplication can be used as the feature information of the indoor air purification device.

[0074] The indoor air purification method provided in this application accurately captures the inherent connections of multi-dimensional information in a complex indoor environment, providing comprehensive and accurate feature basis for the calculation of subsequent target indoor beamforming parameter information, thereby optimizing the beamforming control effect of the indoor distributed antenna and ensuring that the indoor air purification command signal is transmitted to the air purification equipment more stably and accurately, so as to improve the efficiency and intelligence level of indoor air purification.

[0075] Figure 4 The flowchart illustrating the implementation of the indoor air purification method provided in Embodiment 4 of this application is shown. The difference between this method and Embodiment 1 is that step S103 specifically includes: Step S401: The indoor air environment feature information, indoor wireless communication environment feature information, and indoor air purification equipment feature information are spliced ​​together to generate multiple indoor environment feature vectors.

[0076] In this embodiment, the pollution density of functional zones in the indoor air environment characteristic information can be converted into structured data containing functional zone name, pollution density value, and statistical period. The communication quality of air purification equipment in the indoor wireless communication environment characteristic information can be converted into a numerical sequence format containing received signal strength indication, signal-to-noise ratio, bit error rate, and signal delay. The matching degree of equipment-pollution source in the indoor air purification equipment characteristic information can be converted into a structured data format containing matching degree score, calculation basis, and evaluation time. Then, the indoor air environment characteristic information, indoor wireless communication environment characteristic information, and indoor air purification equipment characteristic information are spliced ​​in the order of indoor air environment characteristic information, indoor wireless communication environment characteristic information, and indoor air purification equipment characteristic information. After splicing, an indoor environment feature vector corresponding to each air purification equipment is generated.

[0077] Step S402: Based on the multiple indoor environment feature vectors and the preset indoor feature fusion weights, calculate multiple indoor environment feature fusion variable information.

[0078] In this embodiment, multiple indoor environmental feature vectors can be multiplied with indoor feature fusion weights, and the result of the multiplication can be used as multiple indoor environmental feature fusion variable information.

[0079] Step S403: Based on the multiple indoor environmental feature fusion variable information and the preset indoor feature fusion bias, multiple indoor environmental feature fusion correction variable information is calculated.

[0080] In this embodiment, multiple indoor environmental feature fusion variable information and indoor feature fusion bias can be added together, and the sum can be used as multiple indoor environmental feature fusion correction variable information.

[0081] Step S404: Normalize the multiple indoor environmental feature fusion correction variable information to generate multiple indoor environmental feature fusion normalized variable information.

[0082] In this embodiment, the Sigmoid function or the hyperbolic tangent function can be used to normalize the fusion correction variable information of multiple indoor environmental features. That is, the fusion correction variable information of multiple indoor environmental features is used as the independent variable of the Sigmoid function or the hyperbolic tangent function, and the function value calculated by the Sigmoid function or the hyperbolic tangent function is used as the fusion normalized variable information of multiple indoor environmental features.

[0083] Step S405: Count the number of times the fusion and normalization variable information of the multiple indoor environmental features is generated to obtain the number of times the fusion and normalization variable information of indoor environmental features is generated.

[0084] In this embodiment, each time the complete process of indoor environmental feature vector to indoor environmental feature fusion variable information, then to indoor environmental feature fusion correction variable information, and finally to indoor environmental feature fusion normalized variable information is completed, and a new set of indoor environmental feature fusion normalized variable information is generated, the counting module increments the count by 1. After each generation operation is completed, the count value is updated in real time as the indoor environmental feature fusion normalized variable generation count information.

[0085] Step S406: Determine whether the number of times the indoor environmental feature fusion normalized variable is generated is greater than the preset threshold for the number of times the indoor environmental feature fusion normalized variable is generated; if yes, proceed to step S407; if no, proceed to step S408.

[0086] In this embodiment, the preset threshold for the number of times the indoor environmental feature fusion normalized variable is generated can be set manually. When the number of times the indoor environmental feature fusion normalized variable is generated is greater than the threshold, it indicates that the currently generated multiple indoor environmental feature fusion normalized variable information has sufficient stability and representativeness, and no further iteration is needed; when the number of times the indoor environmental feature fusion normalized variable is generated is less than or equal to the threshold, it indicates that the previous data may still have fluctuations, and further iteration and optimization are needed.

[0087] Step S407: Based on the normalized variable information fused from the multiple indoor environmental features and the preset mapping relationship library between indoor environmental features and indoor beamforming parameters, a matching calculation is performed to obtain multiple initial indoor beamforming parameter information.

[0088] In this embodiment, the method for constructing the pre-defined mapping relationship library between indoor environmental features and indoor beamforming parameters can be as follows: First, the key dimensions of indoor environmental features and the core dimensions of indoor beamforming parameters can be identified. The key dimensions of indoor environmental features include normalized values ​​of pollution density in functional zones, normalized values ​​of communication quality of air purification equipment, and normalized values ​​of equipment-pollution source matching degree. The core dimensions of indoor beamforming parameters include horizontal azimuth angle, vertical elevation angle, beamwidth, and antenna element weighting coefficients. Then, a large amount of sample data under different indoor scenarios is collected. Each set of samples contains complete normalized data of indoor environmental features and the corresponding optimal... Indoor beamforming parameter data is collected; then, the sample data is classified and associated, and intervals are divided according to the numerical range of key dimensions of indoor environmental features. Each interval combination is associated with the corresponding optimal indoor beamforming parameter. Then, machine learning algorithms can be used to train the sample data to optimize the accuracy of the mapping relationship. Finally, all the association relationships can be organized in the format of indoor environmental feature combination - indoor beamforming parameter range - parameter adaptation scenario - adaptation effect evaluation to form a mapping relationship library between indoor environmental features and indoor beamforming parameters. The mapping relationship in the library is updated regularly according to new sample data to ensure its timeliness.

[0089] The process can involve searching a database of mapping relationships between indoor environmental features and indoor beamforming parameters, identifying the feature combination interval that most closely matches the fused normalized variable information of each indoor environmental feature, and then extracting the range of indoor beamforming parameters associated with that feature combination interval. Finally, within this range of indoor beamforming parameters, the parameter values ​​are adjusted according to the specific details of the indoor scene to generate a complete set of indoor beamforming parameter data, which serves as the initial indoor beamforming parameter information for the corresponding indoor environmental feature fused normalized variable information.

[0090] Step S408: The multiple indoor environmental features are fused and normalized as multiple indoor environmental feature vectors, and then the process returns to step S402.

[0091] In this embodiment, a new round of iterative calculation of indoor environmental feature fusion variable information is initiated based on the new indoor environmental feature vector and the preset indoor feature fusion weights, until the indoor environmental feature fusion normalized variable generation time information meets the conditions.

[0092] The indoor air purification method provided in this application improves the accuracy and adaptability of the initial indoor beamforming parameter information, ensures that the beamforming control of the indoor distributed antenna can more accurately match the indoor air purification needs, and guarantees the stable transmission of indoor air purification command signals and the efficient operation of air purification equipment, thereby effectively improving the overall efficiency and intelligence level of indoor air purification.

[0093] Figure 5The flowchart illustrating the implementation of the indoor air purification method provided in Embodiment 5 of this application is shown. The difference between this method and Embodiment 1 is that step S103 specifically includes: Step S501: Generate multiple combinations of indoor air environment features based on the indoor air environment feature information, indoor wireless communication environment feature information, and indoor air purification equipment feature information.

[0094] In this embodiment, a multi-dimensional cross-combination approach can be adopted, combining indoor air environment feature dimensions, indoor wireless communication environment feature dimensions, and indoor air purification device feature dimensions. Specifically, a specific feature value is selected from each core dimension of the indoor air environment feature information, a specific feature value is selected from each core dimension of the indoor wireless communication environment feature information, and a specific feature value is selected from each core dimension of the indoor air purification device feature information, forming a set of combined indoor air environment feature information. All indoor air environment feature information, indoor wireless communication environment feature information, and indoor air purification device feature information are traversed to generate multiple combined indoor air environment feature information sets.

[0095] Step S502: Based on the preset number of indoor air environment feature combinations to be extracted, randomly extract the multiple indoor air environment feature combination information to obtain multiple main combination information of indoor air environment features.

[0096] In this embodiment, the preset number of indoor air environment feature combinations to be extracted can be set manually. A random number generation algorithm can be used to assign a unique random number to each indoor air environment feature combination. Then, based on the number of indoor air environment feature combinations to be extracted, a corresponding number of numbers are randomly selected from all the random numbers, and the indoor air environment feature combination information corresponding to the number is determined as multiple main combinations of indoor air environment features.

[0097] Step S503: Based on the multiple indoor air environment feature combination information and the multiple indoor air environment feature master combination information, multiple indoor air environment feature slave combination information is obtained.

[0098] In this embodiment, the portion of indoor air environment feature combination information that has been selected as the main combination information can be removed from all indoor air environment feature combination information, and the remaining unselected indoor air environment feature combination information is the secondary combination information of indoor air environment features.

[0099] Step S504: Calculate the logical distance between the master combination information of the multiple indoor air environment features and the slave combination information of the multiple indoor air environment features to obtain the combination distance information of the multiple indoor air environment features.

[0100] In this embodiment, the logical distance can be Euclidean distance. The Euclidean distance between the combined information of all indoor air environment features and the master combined information of all indoor air environment features can be calculated, and this Euclidean distance value can be used as the combined distance information of multiple indoor air environment features.

[0101] Step S505: Based on the distance information of the multiple indoor air environment feature combinations, classify the multiple indoor air environment feature combination information to obtain multiple indoor air environment feature combination category information.

[0102] In this embodiment, each indoor air environment feature main combination information can be used as the category center. All indoor air environment feature sub-combinations with logical distance values ​​less than a threshold are classified into the category corresponding to the indoor air environment feature main combination information, and the indoor air environment feature main combination information itself is also classified into the category. If the logical distance value between a certain indoor air environment feature sub-combination information and multiple indoor air environment feature main combination information is less than the threshold, the size of these logical distance values ​​is compared, and it is classified into the category corresponding to the indoor air environment feature main combination information with the smallest logical distance value. If the logical distance value between a certain indoor air environment feature sub-combination information and all indoor air environment feature main combination information is greater than the threshold, a new category is created for it. The new category is centered on the indoor air environment feature sub-combination information. If there are other similar combinations, they can be classified into this category. Finally, all categories are sorted out. Each category contains a central combination information and multiple similar combination information. All combination information in each category is recorded in the format of indoor air environment feature combination category number-central combination information-similar combination information list, thereby generating multiple indoor air environment feature combination category information.

[0103] Step S506: Calculate the mean and median of the multiple indoor air environment feature combination category information to obtain the mean information and median information of the multiple indoor air environment feature combination category information.

[0104] In this embodiment, the median and mean of each indoor air environment feature combination category information can be calculated as the mean information of multiple indoor air environment feature combination categories and the median information of multiple indoor air environment feature combination categories.

[0105] Step S507: Based on the mean information of multiple indoor air environment feature combination categories, the median information of multiple indoor air environment feature combination categories, and the multiple indoor air environment feature combination information, obtain multiple indoor air environment feature center combination information.

[0106] In this embodiment, for each category of indoor air environment feature combination information, the mean information of the corresponding indoor air environment feature combination category can be compared with the median information of the indoor air environment feature combination category. If the difference between the mean and median of a certain core dimension is small, it indicates that the data distribution of that dimension is relatively concentrated, and the mean is used as the target feature value of that dimension; if the difference is large, it indicates that the data distribution is skewed, and the median is used as the target feature value of that dimension. The indoor air environment feature combination information where the target feature value is located is taken as the indoor air environment feature center combination information.

[0107] Step S508: Determine whether the multiple indoor air environment feature center combination information is the same as the multiple indoor air environment feature main combination information; if yes, proceed to step S509; if no, proceed to step S512.

[0108] In this embodiment, all indoor air environment feature center combination information can be cross-compared with all indoor air environment feature main combination information, and the proportion of indoor air environment feature center combination information that is determined to be the same to the total number of indoor air environment feature center combination information can be counted. If this proportion reaches 100%, that is, all indoor air environment feature center combination information is the same as the corresponding indoor air environment feature main combination information, it indicates that the indoor air environment feature main combination information is accurately selected and can represent the core features of the indoor air environment feature combination category, and no further iterative adjustment is needed; if this proportion does not reach 100%, that is, at least one indoor air environment feature center combination information is different from all indoor air environment feature main combination information, it indicates that the selection of indoor air environment feature main combination information may be biased and cannot accurately represent the core features of the indoor air environment feature combination category, and the indoor air environment feature main combination information needs to be readjusted.

[0109] Step S509: Based on the multiple indoor air environment feature combination category information and the preset indoor feature fusion weights, obtain multiple indoor air environment feature combination category weighted information.

[0110] In this embodiment, the values ​​in the combined category information of multiple indoor air environment features can be multiplied one by one with the indoor feature fusion weights, and the multiplication result can be used as the weighted information of multiple combined category information of indoor air environment features.

[0111] Step S510: Based on the weighted information of the multiple indoor air environment feature combination categories and the preset indoor feature fusion bias, multiple indoor air environment feature combination category correction information is obtained.

[0112] In this embodiment, the weighted information of multiple indoor air environment feature combination categories can be added one by one with the indoor feature fusion bias, and the addition result can be used as the correction information of multiple indoor air environment feature combination categories.

[0113] Step S511: Match the multiple indoor air environment feature combination category correction information and the preset indoor beamforming parameter library to obtain multiple initial indoor beamforming parameter information.

[0114] In this embodiment, the preset indoor beamforming parameter library can be manually set. The setting method can be to first define the core dimensions of the indoor beamforming parameters, including horizontal azimuth, vertical elevation, beamwidth, and antenna element weighting coefficients, and then collect a large amount of sample data under different indoor scenarios. Each set of samples contains complete indoor feature correction data and corresponding optimal indoor beamforming parameter data. Then, the sample data is classified and associated, and intervals are divided according to the numerical range of each dimension of the indoor feature correction data. Each interval combination is associated with the corresponding optimal indoor beamforming parameter. Then, the sample data is trained by machine learning algorithm to optimize the accuracy of the association relationship and reduce the error of manual interval division. Then, all the association relationships are organized in the format of feature correction interval combination - beamforming parameter range - adapted scenario - effect evaluation to form the preset indoor beamforming parameter library. The indoor beamforming parameters in the library are updated regularly according to new sample data to ensure timeliness.

[0115] The feature correction interval combination that is closest to the correction information of each indoor air environment feature combination can be found in the indoor beamforming parameter library. The range of indoor beamforming parameters associated with this interval combination can be extracted. A complete set of indoor beamforming parameter data can be obtained by adjusting within the range of indoor beamforming parameters, which can be used as the initial indoor beamforming parameter information for the corresponding category, thereby obtaining multiple initial indoor beamforming parameter information.

[0116] Step S512: The combined information of the multiple indoor air environment feature centers is used as the main combined information of multiple indoor air environment features, and the process returns to step S503.

[0117] In this embodiment, based on the new multiple main combination information of indoor air environment features and all combination information of indoor air environment features, the indoor air environment features are re-screened from combination information, logical distance is calculated, classification processing is performed, the central combination information of indoor air environment features is determined and it is judged whether it is the same as the main combination information of indoor air environment features, until the final central combination information of indoor air environment features is completely consistent with the corresponding main combination information of indoor air environment features, ensuring that the main combination information of indoor air environment features can accurately represent the core features of the category of indoor air environment feature combination.

[0118] The indoor air purification method provided in this application embodiment achieves refined classification and core feature extraction of complex indoor environmental characteristics. By iteratively adjusting the main combination information and the central combination information of indoor air environmental features through multiple rounds, the accuracy and representativeness of the classification results are ensured. This effectively improves the adaptability and accuracy of the initial indoor beamforming parameter information, ensuring that the beamforming control of the subsequent indoor distributed antenna can accurately match the location of the air purification equipment and the distribution of pollution sources. It also ensures the stable transmission of indoor air purification command signals, thereby improving the response efficiency of the air purification equipment and the effect of indoor air purification.

[0119] Figure 6 The flowchart illustrating the implementation of the indoor air purification method provided in Embodiment Six of this application is shown. The difference between this method and Embodiment Five is that step S507 specifically includes: Step S601: Calculate the difference between the mean information of the multiple indoor air environment feature combination categories and the median information of the multiple indoor air environment feature combination categories to obtain the center discrimination difference information of the multiple indoor air environment feature combination categories.

[0120] In this embodiment, an absolute value calculation method can be used. That is, for each core dimension, the specific value of that dimension in the mean information of the indoor air environment feature combination category is subtracted from the specific value of that dimension in the median information of the indoor air environment feature combination category. The absolute value of the calculated result is then taken to eliminate the interference of positive and negative signs on the difference judgment, ensuring that the difference only reflects the difference in numerical magnitude. Then, the differences of all core dimensions under each indoor air environment feature combination category information are integrated in the order of the differences to form the indoor air environment feature combination category center discrimination difference information corresponding to that indoor air environment feature combination category information.

[0121] Step S602: Determine whether the difference information of the indoor air environment feature combination category center is less than the preset indoor air environment feature combination category center difference threshold; if yes, proceed to step S603; if no, proceed to step S604.

[0122] In this embodiment, the preset threshold for the difference between the center values ​​of the indoor air environment feature combination categories can be set manually. When the differences of all core dimensions in the difference information for the center value of the indoor air environment feature combination categories are less than the preset threshold for the difference between the center values ​​of the indoor air environment feature combination categories, it indicates that the data distribution of the core dimensions under the indoor air environment feature combination category information is relatively concentrated, and the mean information of the indoor air environment feature combination categories can effectively represent the central trend of the category data. If the difference of any core dimension is greater than or equal to the preset threshold for the difference between the center values ​​of the indoor air environment feature combination categories, it indicates that the data distribution of the core dimensions under the indoor air environment feature combination category information is skewed. In this case, the median information of the indoor air environment feature combination categories is more suitable as the central representative of the category data.

[0123] Step S603: The indoor air environment feature combination information corresponding to the average value information of the indoor air environment feature combination category is taken as the indoor air environment feature center combination information.

[0124] In this embodiment, the indoor air environment feature combination information corresponding to the average information of indoor air environment feature combination categories is used as the indoor air environment feature center combination information, which is used to accurately reflect the central trend of all indoor air environment feature combination information under the indoor air environment feature combination category information.

[0125] Step S604: The indoor air environment feature combination information corresponding to the median information of the indoor air environment feature combination category is taken as the indoor air environment feature center combination information.

[0126] In this embodiment, the indoor air environment feature combination information corresponding to the median information of the indoor air environment feature combination category is used as the indoor air environment feature center combination information to avoid the adverse effects of data distribution skewness, so as to reflect the core features of all indoor air environment feature combination information under the indoor air environment feature combination category information.

[0127] The indoor air purification method provided in this application improves the accuracy and representativeness of the combination information of indoor air environment features, effectively avoids the problem of core feature distortion caused by single-dimensional judgment or subjective selection, corrects the main combination information of indoor air environment features, ensures that the classification results of indoor air environment feature combination categories are more in line with the actual indoor environment features, thereby improving the adaptability of the initial indoor beamforming parameter information, ensuring that the beamforming control of the indoor distributed antenna can accurately match the indoor air purification needs, optimize the transmission effect of indoor air purification command signals and the operating efficiency of air purification equipment, and comprehensively improve the overall effect of indoor air purification.

[0128] Figure 7The flowchart illustrating the implementation of the indoor air purification method provided in Embodiment Seven of this application is shown. Its difference from Embodiment One described above lies in: The initial indoor beamforming parameter information includes the initial indoor beam horizontal azimuth angle parameter information, the initial indoor beam vertical elevation angle parameter information, the initial indoor beamwidth parameter information, and the initial indoor distributed antenna array parameter information; The initial indoor distributed antenna array parameter information includes the initial indoor distributed antenna array amplitude weight parameter information and the initial indoor distributed antenna array phase weight parameter information; Step S104 specifically includes: Step S701: Based on the indoor distributed antenna position parameter information, air purification equipment position information, multiple initial indoor beam horizontal azimuth angle parameter information, multiple initial indoor beam vertical elevation angle parameter information, and multiple initial indoor beam width parameter information, calculate the spatial coverage information of multiple indoor beams.

[0129] In this embodiment, for each initial indoor beam horizontal azimuth angle parameter and initial indoor beam vertical elevation angle parameter, the initial pointing direction of the indoor beam can be determined through spatial geometric calculations, combined with the installation coordinates of the indoor distributed antenna. Specifically, based on the horizontal azimuth and vertical elevation angles, a spatial ray is established pointing from the indoor distributed antenna location towards the area where the air purification device is located. This ray represents the main lobe center direction of the indoor beam. Then, the initial indoor beamwidth parameter is introduced. Based on the determined main lobe center direction, the coverage angle range of the beam in the horizontal and vertical directions is calculated. The horizontal coverage range is defined by the horizontal... Centered on the azimuth angle, the coverage range extends to both sides by half the angle corresponding to the initial indoor beamwidth parameter information. Centered on the vertical elevation angle, the coverage range extends upwards and downwards by half the angle corresponding to the initial indoor beamwidth parameter information. Then, by combining the installation position coordinates of the indoor distributed antenna with the horizontal and vertical coverage angle ranges of the beam, spatial three-dimensional geometric modeling is used to determine the three-dimensional boundary coordinates of the area that the indoor beam can cover in the indoor space. These boundary coordinates are integrated to form the indoor beam spatial coverage range information corresponding to each initial indoor beamforming parameter information, thus generating multiple indoor beam spatial coverage range information.

[0130] Step S702: Based on the indoor pollution source location information and the multiple indoor beam spatial coverage range information, calculate the coverage degree characterization information of multiple indoor beam spatial areas.

[0131] In this embodiment, for each indoor beam spatial coverage area, the corresponding boundary coordinates of the coverage area can be spatially superimposed with the boundary coordinates of the influence range of each indoor pollution source to determine whether the indoor beam spatial coverage area overlaps with the influence range of the indoor pollution source, and the specific spatial range of the overlapping area. This allows for the calculation of core indicators of coverage degree, including the ratio of the overlapping area to the total area of ​​the influence range of the indoor pollution source, and the ratio of the overlapping area volume to the total volume of the influence range of the indoor pollution source. If the indoor beam spatial coverage area completely includes the influence range of a certain indoor pollution source, the overlap ratio is 100%; if there is partial overlap, it is calculated as the ratio of the actual overlapping area or volume to the area or volume of the pollution source influence range; if there is no overlap, the overlap ratio is 0%. Simultaneously, the number of indoor pollution sources covered by each indoor beam spatial coverage area can be counted, i.e., the total number of indoor pollution sources included within the coverage area. Finally, the overlap ratio, the number of covered pollution sources, and other indicators are integrated to form the indoor beam spatial area coverage degree characterization information corresponding to each indoor beam spatial coverage area information. Multiple indoor beam spatial coverage area information results in multiple indoor beam spatial area coverage degree characterization information.

[0132] Step S703: Based on the indoor distributed antenna position parameter information, multiple initial indoor distributed antenna array amplitude weight parameter information, multiple initial indoor distributed antenna array phase weight parameter information, and preset indoor wireless signal wavelength information, multiple indoor wireless signal strength information are calculated.

[0133] In this embodiment, the preset indoor wireless signal wavelength information can be determined by the hardware parameters of the indoor distributed antenna. The installation coordinates of the indoor distributed antenna, the number of indoor distributed antennas, and the relative positional relationships between them can be extracted from the indoor distributed antenna location parameter information. Then, for each initial indoor distributed antenna array amplitude weight parameter information, the signal output amplitude ratio of each indoor distributed antenna element can be determined, which directly affects the energy of the transmitted signal of each antenna element. For each initial indoor distributed antenna array phase weight parameter information, the phase difference of the transmitted signal of each indoor distributed antenna element can be determined. The phase difference affects the superposition effect of the transmitted signals of different antenna elements at a certain point in space. Furthermore, preset indoor wireless signal wavelength information can be introduced, and combined with the spatial layout of the indoor distributed antenna array, the signal propagation distance from each indoor distributed antenna array to the location of each air purifier in the air purifier location information can be calculated. Then, based on the wireless signal wavelength and propagation distance, the phase offset of the transmitted signal of each antenna array when it reaches the location of the air purifier can be calculated. Then, based on the amplitude weight, initial phase weight and phase offset of each antenna array, the combined signal amplitude of all indoor distributed antenna array transmitted signals at the location of the air purifier can be calculated by the vector superposition principle. The magnitude of the combined signal amplitude directly corresponds to the wireless signal strength at that location. The wireless signal strength at the location of each air purifier is integrated to form the indoor wireless signal strength information corresponding to each initial indoor distributed antenna array parameter information. Multiple initial indoor distributed antenna array parameter information will generate multiple indoor wireless signal strength information.

[0134] Step S704: Based on the multiple indoor beam spatial area coverage characterization information, multiple indoor wireless signal strength information, and preset indoor air purification efficiency calculation coefficients, multiple indoor air purification efficiency characterization information are calculated.

[0135] In this embodiment, for the coverage information of each indoor beam spatial area, the average overlap volume ratio and the number of covered pollution sources can be extracted. The average overlap volume ratio is multiplied by the coverage efficiency coefficient to obtain the coverage contribution efficiency value. Simultaneously, the number of air purification devices within the coverage area is counted, and combined with the number of covered pollution sources, the coverage contribution efficiency value is corrected. If the number of air purification devices and the number of covered pollution sources are highly matched, the coverage contribution efficiency value is appropriately increased; otherwise, it is decreased. Furthermore, for the corresponding indoor wireless signal strength information, the wireless signal strength value at the location of each air purification device can be extracted. It is determined whether the signal strength meets the minimum threshold requirement for the air purification device to receive commands. If it does, the signal strength value is multiplied by the signal transmission efficiency coefficient to obtain the signal transmission contribution efficiency value. If it does not meet the threshold, the signal transmission contribution efficiency value is reduced proportionally according to the degree to which the signal strength is below the threshold. If the signal strength is too low to allow the device to receive commands, the signal transmission contribution efficiency value is 0. The coverage contribution efficiency value is added to the signal transmission contribution efficiency value to obtain a comprehensive indoor air purification efficiency characterization value. This is then integrated to form the indoor air purification efficiency characterization information corresponding to each initial indoor beamforming parameter information. Multiple initial indoor beamforming parameter information can then generate multiple indoor air purification efficiency characterization information.

[0136] Step S705: Determine whether the maximum value of the plurality of indoor air purification efficiency characterization information is greater than or equal to a preset indoor air purification efficiency threshold; if yes, proceed to step S706; if no, proceed to step S707.

[0137] In this embodiment, the air purification efficiency characterization values ​​in all indoor air purification efficiency characterization information can be sorted, and the air purification efficiency characterization value with the largest value can be selected, that is, the maximum value of multiple indoor air purification efficiency characterization information. This maximum value is compared with a preset indoor air purification efficiency threshold. If the maximum value is greater than or equal to the preset indoor air purification efficiency threshold, it means that there is initial indoor beamforming parameter information that can meet the indoor air purification requirements, and no further optimization is needed. This parameter information can be directly selected as the target indoor beamforming parameter information. If the maximum value is less than the preset indoor air purification efficiency threshold, it means that the purification efficiency corresponding to all the initial indoor beamforming parameter information has not met the expected requirements, and further optimization and adjustment are needed to improve the purification efficiency.

[0138] Step S706: The initial indoor beamforming parameter information corresponding to the maximum value of the plurality of indoor air purification efficiency characterization information is used as the target indoor beamforming parameter information.

[0139] In this embodiment, the specific identifier of the initial indoor beamforming parameter information corresponding to the maximum value of multiple indoor air purification efficiency characterization information can be determined first. By tracing the indoor air purification efficiency characterization information to which the maximum value belongs, the number or unique identifier of the associated initial indoor beamforming parameter information can be found to ensure that the correspondence of parameter information is accurate. Then, all core parameters contained in the initial indoor beamforming parameter information can be extracted, including the initial indoor beam horizontal azimuth angle parameter information, the initial indoor beam vertical elevation angle parameter information, the initial indoor beamwidth parameter information, the initial indoor distributed antenna array amplitude weight parameter information, and the initial indoor distributed antenna array phase weight. The parameters are checked one by one to ensure their completeness and accuracy, avoiding any missing or incorrect parameters. Then, the indoor beamforming parameters are verified to cover the indoor spatial range and wireless signal strength. This confirms whether the coverage of indoor pollution sources and the location of the air purification equipment meet the actual needs, and whether the wireless signal strength can stably support the transmission of air purification command signals. If there are minor deviations that do not affect the overall purification efficiency, no adjustment is needed. If the deviations may cause problems in subsequent operation, fine-tuning is performed before final determination. Finally, the initial indoor beamforming parameters are determined as the target indoor beamforming parameters.

[0140] Step S707: The initial indoor beamforming parameter information corresponding to the maximum value of the plurality of indoor air purification efficiency characterization information is used as the calibration indoor beamforming parameter information.

[0141] In this embodiment, the maximum value of multiple indoor air purification efficiency characterization information can be traced to the corresponding indoor air purification efficiency characterization information. Through the association record of this characterization information, the corresponding initial indoor beamforming parameter information can be found, and all core components of this parameter information can be identified, including the initial indoor beam horizontal azimuth angle parameter information, the initial indoor beam vertical elevation angle parameter information, the initial indoor beamwidth parameter information, the initial indoor distributed antenna array amplitude weight parameter information, and the initial indoor distributed antenna array phase weight parameter information. Then, the indoor beam spatial area coverage characterization information and indoor wireless signal strength information corresponding to the initial indoor beamforming parameter information can be analyzed to find the reasons why the corresponding air purification efficiency does not reach the preset indoor air purification efficiency threshold. For example, it may be that the overlap ratio between the indoor beam spatial coverage area and the indoor pollution source influence area is too low, or the wireless signal strength at some air purification device locations is insufficient. The optimization direction can be identified, and then the initial indoor beamforming parameter information can be marked as the calibration indoor beamforming parameter information.

[0142] Step S708: Based on the calibrated indoor beamforming parameter information, the preset indoor beam angle adjustment range, the preset indoor beamwidth adjustment range, and the preset indoor antenna element weighting coefficient vector, optimize the multiple initial indoor beam horizontal azimuth angle parameters, multiple initial indoor beam vertical elevation angle parameters, multiple initial indoor beamwidth parameters, and multiple initial indoor distributed antenna array parameters to obtain multiple intermediate indoor beam horizontal azimuth angle parameters, multiple intermediate indoor beam vertical elevation angle parameters, multiple intermediate indoor beamwidth parameters, and multiple intermediate indoor distributed antenna array parameters.

[0143] In this embodiment, the initial indoor beam horizontal azimuth and vertical elevation parameters can be optimized first. The corresponding initial indoor beam horizontal azimuth and vertical elevation parameters are extracted from the calibrated indoor beamforming parameters. Combined with the location information of the indoor pollution source and the air purification equipment, the angle adjustment direction is determined. If the current beam direction deviates from the pollution source or equipment, it is adjusted towards the target. Based on a preset indoor beam angle adjustment range, the initial indoor beam horizontal azimuth and vertical elevation parameters are gradually adjusted according to this range. For example, if the horizontal... If the azimuth needs to be adjusted in a certain direction, the horizontal angle value corresponding to the preset indoor beam angle adjustment range is adjusted each time. The vertical elevation angle is adjusted similarly. After adjustment, multiple intermediate indoor beam horizontal azimuth angle parameters and multiple intermediate indoor beam vertical elevation angle parameters are obtained. Then, the initial indoor beamwidth parameters can be optimized. Based on the indoor beam spatial coverage information corresponding to the calibrated indoor beamforming parameters, if the coverage area is too small, resulting in some pollution sources or equipment not being covered, the initial indoor beamwidth parameters are increased by the preset indoor beamwidth adjustment range. If the coverage area is too large, resulting in signal energy dispersion, the initial beamwidth parameters are decreased by the preset range. The indoor beamwidth parameters are adjusted to generate multiple intermediate indoor beamwidth parameters. Then, the initial indoor distributed antenna array (DDA) parameters can be optimized. From the preset indoor antenna element weighting coefficient vectors, a vector suitable for the current scenario is selected. The initial indoor DDA amplitude weight parameters and initial indoor DDA phase weight parameters from the calibrated indoor beamforming parameters are compared with the selected vector. Amplitude and phase weights with large deviations are corrected, referencing standard values ​​in the vectors during correction. This ensures that the adjusted parameters improve signal energy concentration or anti-interference capability. After correction, multiple intermediate indoor DDA amplitude weight parameters are obtained. The numerical information, the phase weight parameters of multiple intermediate indoor antenna arrays, the amplitude weight parameters of multiple intermediate indoor antenna arrays, and the phase weight parameters of multiple intermediate indoor antenna arrays together constitute multiple intermediate indoor antenna array parameters. Then, the optimized intermediate indoor beam horizontal azimuth angle parameters, intermediate indoor beam vertical elevation angle parameters, intermediate indoor beamwidth parameters, and intermediate indoor antenna array parameters are integrated to generate multiple intermediate indoor beam horizontal azimuth angle parameters, multiple intermediate indoor beam vertical elevation angle parameters, multiple intermediate indoor beamwidth parameters, and multiple intermediate indoor antenna array parameters.

[0144] Step S709: The multiple intermediate indoor beam horizontal azimuth angle parameters, multiple intermediate indoor beam vertical elevation angle parameters, multiple intermediate indoor beamwidth parameters, and multiple intermediate indoor distributed antenna array parameters are respectively used as multiple initial indoor beam horizontal azimuth angle parameters, multiple initial indoor beam vertical elevation angle parameters, multiple initial indoor beamwidth parameters, and multiple initial indoor distributed antenna array parameters, and then the process returns to step S701.

[0145] In this embodiment, multiple intermediate indoor beam horizontal azimuth angle parameters, multiple intermediate indoor beam vertical elevation angle parameters, multiple intermediate indoor beamwidth parameters, and multiple intermediate indoor distributed antenna array parameters can be used to replace the original multiple initial indoor beam horizontal azimuth angle parameters, multiple initial indoor beam vertical elevation angle parameters, multiple initial indoor beamwidth parameters, and multiple initial indoor distributed antenna array parameters, respectively. The indoor beam spatial coverage information, indoor beam spatial area coverage characterization information, indoor wireless signal strength information, and indoor air purification efficiency characterization information are recalculated. It is then determined whether the maximum value of the new indoor air purification efficiency characterization information reaches a preset indoor air purification efficiency threshold, until the maximum value of the obtained indoor air purification efficiency characterization information meets the threshold requirement, thereby determining the target indoor beamforming parameter information.

[0146] The indoor air purification method provided in this application calculates purification efficiency from two key aspects: spatial coverage and signal strength. It continuously optimizes the parameter corresponding to the maximum purification efficiency as a calibration benchmark, effectively improving the accuracy and adaptability of the target indoor beamforming parameter information. This ensures that the directional beam generated by the indoor distributed antenna can maximize the coverage of the indoor pollution source area and guarantee the wireless signal strength for the air purification device to receive commands, thereby improving the response speed and purification efficiency of the air purification device and achieving comprehensive optimization of the indoor air purification effect.

[0147] Corresponding to the method in the above embodiments, Figure 8 A structural block diagram of an indoor air purification device provided in an embodiment of this application is shown. For ease of explanation, only the parts related to the embodiment of this application are shown. Figure 8 The example indoor air purification device can be the subject of the indoor air purification method provided in the aforementioned embodiment 1.

[0148] Reference Figure 8 The indoor air purification device includes: The information acquisition module 810 is used to acquire indoor space structure information, indoor pollution source location information, indoor personnel location information, indoor air quality characterization information, indoor distributed antenna location parameter information, indoor distributed antenna communication parameter information, wireless signal quality characterization information, air purification equipment location information, and air purification equipment operation parameter information. The indoor feature information generation module 820 is used to generate indoor air environment feature information, indoor wireless communication environment feature information, and indoor air purification equipment feature information based on multiple preset indoor environmental feature extraction vectors, according to the indoor space structure information, indoor pollution source location information, indoor personnel location information, indoor air quality characterization information, indoor distributed antenna location parameter information, indoor distributed antenna communication parameter information, wireless signal quality characterization information, air purification equipment location information, and air purification equipment operation parameter information. The initial indoor beamforming parameter information generation module 830 is used to perform fusion and matching calculations based on the indoor air environment feature information, indoor wireless communication environment feature information, indoor air purification equipment feature information, preset indoor feature fusion weights and preset indoor feature fusion biases to obtain multiple initial indoor beamforming parameter information. The target indoor beamforming parameter information generation module 840 is used to optimize and calculate the multiple initial indoor beamforming parameter information based on the indoor pollution source location information, indoor distributed antenna location parameter information, air purification equipment location information, preset indoor beam angle adjustment range, preset indoor beam width adjustment range, preset indoor antenna array weighting coefficient vector, and preset indoor air purification efficiency calculation coefficient to obtain the target indoor beamforming parameter information. The indoor wireless directional beamforming module 850 is used to perform beamforming control processing on the indoor distributed antenna according to the target indoor beamforming parameter information to generate multiple indoor wireless directional beams. The indoor air purification module 860 is used to transmit indoor air purification command signals to the air purification equipment according to the plurality of indoor wireless directional beams, so as to perform indoor air purification treatment through the air purification equipment.

[0149] The process by which each module in the indoor air purification device provided in this application implements its respective function can be specifically referred to the foregoing. Figure 1 The description of Embodiment 1 shown will not be repeated here.

[0150] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0151] The indoor air purification method provided in this application can be applied to terminal devices such as mobile phones and tablets. This application does not impose any restrictions on the specific type of terminal device.

[0152] For example, the terminal device may be a station in a WLAN, a cellular phone, a computing device or other processing device connected to a wireless modem, an in-vehicle device, a vehicle networking terminal, a computer and / or other devices for communication over a wireless system, and a next-generation communication system.

[0153] Figure 9 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. For example... Figure 9 As shown, the terminal device 9 of this embodiment includes: at least one processor 90 ( Figure 9 (Only one is shown in the image) a memory 91, which stores a computer program 92 that can run on the processor 90. When the processor 90 executes the computer program 92, it implements the steps in the various indoor air purification method embodiments described above, for example... Figure 1 Steps S101 to S106 are shown. Alternatively, when the processor 90 executes the computer program 92, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 8 The functions of modules 810 to 860 are shown.

[0154] The terminal device 9 can be a desktop computer, a cloud server, or other computing device. Those skilled in the art will understand that... Figure 9 This is merely an example of terminal device 9 and does not constitute a limitation on terminal device 9.

[0155] The processor 90 can be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits (ASICs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0156] In some embodiments, the memory 91 may be an internal storage unit of the terminal device 9, such as a hard disk or memory of the terminal device 9. The memory 91 may also be an external storage device of the terminal device 9, such as a plug-in hard disk or smart memory card equipped on the terminal device 9. Furthermore, the memory 91 may include both internal and external storage units of the terminal device 9. The memory 91 is used to store operating systems, applications, bootloaders, data, and other programs, such as the program code of computer programs. The memory 91 can also be used to temporarily store data that has been sent or will be sent.

[0157] This application also provides a terminal device, which includes at least one memory, at least one processor, and a computer program stored in the at least one memory and executable on the at least one processor. When the processor executes the computer program, it causes the terminal device to implement the steps in any of the above method embodiments.

[0158] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0159] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0160] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0161] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for purifying indoor air, characterized in that, include: Acquire indoor space structure information, indoor pollution source location information, indoor personnel location information, indoor air quality characterization information, indoor distributed antenna location parameter information, indoor distributed antenna communication parameter information, wireless signal quality characterization information, air purification equipment location information, and air purification equipment operation parameter information; Based on multiple preset indoor environmental feature extraction vectors, indoor air environment feature information, indoor wireless communication environment feature information, and indoor air purification equipment feature information are generated according to the indoor space structure information, indoor pollution source location information, indoor personnel location information, indoor air quality characterization information, indoor distributed antenna location parameter information, indoor distributed antenna communication parameter information, wireless signal quality characterization information, air purification equipment location information, and air purification equipment operation parameter information. Based on the indoor air environment characteristic information, indoor wireless communication environment characteristic information, indoor air purification equipment characteristic information, preset indoor feature fusion weights and preset indoor feature fusion biases, multiple initial indoor beamforming parameter information are obtained through fusion and matching calculations. Based on the indoor pollution source location information, indoor distributed antenna location parameter information, air purification equipment location information, preset indoor beam angle adjustment range, preset indoor beam width adjustment range, preset indoor antenna element weighting coefficient vector, and preset indoor air purification efficiency calculation coefficient, the multiple initial indoor beamforming parameter information are optimized and calculated to obtain the target indoor beamforming parameter information. Based on the target indoor beamforming parameter information, beamforming control processing is performed on the indoor distributed antenna to generate multiple indoor wireless directional beams. Based on the multiple indoor wireless directional beams, indoor air purification command signals are transmitted to the air purification equipment for indoor air purification.

2. The indoor air purification method as described in claim 1, characterized in that, The step of generating indoor air environment feature information, indoor wireless communication environment feature information, and indoor air purification equipment feature information based on multiple preset indoor environment feature extraction vectors, according to the indoor space structure information, indoor pollution source location information, indoor occupant location information, indoor air quality characterization information, indoor distributed antenna location parameter information, indoor distributed antenna communication parameter information, wireless signal quality characterization information, air purification equipment location information, and air purification equipment operating parameter information, specifically includes: The indoor space structure information, indoor pollution source location information, indoor personnel location information, and indoor air quality characterization information are encoded and spliced ​​to generate indoor air environment vector information. The indoor distributed antenna location parameter information, indoor distributed antenna communication parameter information, and wireless signal quality characterization information are encoded and spliced ​​to generate indoor wireless communication environment vector information. The location information and operating parameter information of the air purification equipment are encoded and spliced ​​to generate vector information of the indoor air purification equipment. Based on multiple preset indoor environment feature extraction vectors, feature extraction calculations are performed on the indoor air environment vector information, indoor wireless communication environment vector information, and indoor air purification device vector information to generate indoor air environment feature information, indoor wireless communication environment feature information, and indoor air purification device feature information.

3. The indoor air purification method as described in claim 2, characterized in that, Multiple preset indoor environment feature extraction vectors include preset indoor environment feature location extraction vectors, preset indoor environment feature label extraction vectors, and preset indoor environment feature content extraction vectors; The step of extracting and calculating features from the indoor air environment vector information, indoor wireless communication environment vector information, and indoor air purification device vector information based on multiple preset indoor environment feature extraction vectors to generate indoor air environment feature information, indoor wireless communication environment feature information, and indoor air purification device feature information specifically includes: Based on the indoor air environment vector information, the preset indoor environment feature location extraction vector, the preset indoor environment feature label extraction vector, and the preset indoor environment feature content extraction vector, an indoor air environment feature location vector, an indoor air environment feature label vector, and an indoor air environment feature content vector are generated. Based on the indoor air environment feature location vector and the indoor air environment feature label vector, an interactive mapping calculation is performed to obtain the indoor air environment feature interactive mapping vector. Based on the indoor wireless communication environment vector information, the preset indoor environment feature location extraction vector, the preset indoor environment feature tag extraction vector, and the preset indoor environment feature content extraction vector, an indoor wireless communication environment feature location vector, an indoor wireless communication environment feature tag vector, and an indoor wireless communication environment feature content vector are generated. Based on the indoor wireless communication environment feature location vector and the indoor wireless communication environment feature label vector, an interactive mapping calculation is performed to obtain the indoor wireless communication environment feature interactive mapping vector. Based on the indoor air purification device vector information, the preset indoor environment feature location extraction vector, the preset indoor environment feature tag extraction vector, and the preset indoor environment feature content extraction vector, an indoor air purification device feature location vector, an indoor air purification device feature tag vector, and an indoor air purification device feature content vector are generated. Based on the feature location vector and feature label vector of the indoor air purification device, an interactive mapping calculation is performed to obtain the feature interactive mapping vector of the indoor air purification device. Indoor air environment feature information is generated by multiplying the indoor air environment feature interaction mapping vector and the indoor air environment feature content vector. The indoor wireless communication environment feature information is generated by multiplying the indoor wireless communication environment feature content vector and the indoor wireless communication environment feature interaction mapping vector. The feature information of the indoor air purification device is generated by multiplying the feature content vector and the feature interaction mapping vector of the indoor air purification device.

4. The indoor air purification method as described in claim 1, characterized in that, The step of fusing and matching the indoor air environment feature information, indoor wireless communication environment feature information, indoor air purification equipment feature information, preset indoor feature fusion weights, and preset indoor feature fusion bias to obtain multiple initial indoor beamforming parameter information specifically includes: The indoor air environment feature information, indoor wireless communication environment feature information, and indoor air purification equipment feature information are spliced ​​together to generate multiple indoor environment feature vectors. Based on the multiple indoor environment feature vectors and the preset indoor feature fusion weights, multiple indoor environment feature fusion variable information is calculated; Based on the multiple indoor environmental feature fusion variable information and the preset indoor feature fusion bias, multiple indoor environmental feature fusion correction variable information are calculated; The multiple indoor environmental feature fusion correction variable information is normalized to generate multiple indoor environmental feature fusion normalized variable information; The number of times the fusion and normalization variables of the multiple indoor environmental features are generated is counted to obtain the number of times the fusion and normalization variables of the indoor environmental features are generated. Determine whether the number of times the indoor environmental feature fusion normalized variable is generated is greater than a preset threshold for the number of times the indoor environmental feature fusion normalized variable is generated; If so, then the matching calculation is performed based on the normalized variable information fused from the multiple indoor environmental features and the preset mapping relationship library between indoor environmental features and indoor beamforming parameters to obtain multiple initial indoor beamforming parameter information. If not, the normalized variable information of the multiple indoor environmental features is used as multiple indoor environmental feature vectors, and the process is returned to the step of calculating multiple indoor environmental feature fusion variable information based on the multiple indoor environmental feature vectors and the preset indoor feature fusion weights.

5. The indoor air purification method as described in claim 1, characterized in that, The step of fusing and matching the indoor air environment feature information, indoor wireless communication environment feature information, indoor air purification equipment feature information, preset indoor feature fusion weights, and preset indoor feature fusion bias to obtain multiple initial indoor beamforming parameter information specifically includes: Based on the indoor air environment characteristic information, indoor wireless communication environment characteristic information, and indoor air purification equipment characteristic information, multiple indoor air environment characteristic combination information are generated. Based on the preset number of indoor air environment feature combinations to be extracted, the multiple indoor air environment feature combination information is randomly extracted to obtain multiple main combination information of indoor air environment features. Based on the multiple indoor air environment feature combination information and the multiple indoor air environment feature master combination information, multiple indoor air environment feature slave combination information is obtained; Calculate the logical distance between the main combination information of the multiple indoor air environment features and the secondary combination information of the multiple indoor air environment features to obtain the combination distance information of the multiple indoor air environment features. Based on the distance information of the multiple indoor air environment feature combinations, the multiple indoor air environment feature combination information is classified to obtain multiple indoor air environment feature combination category information. Calculate the mean and median of the multiple indoor air environment feature combination category information to obtain the mean information and median information of the multiple indoor air environment feature combination category information; Based on the mean information of multiple indoor air environment feature combination categories, the median information of multiple indoor air environment feature combination categories, and the information of multiple indoor air environment feature combinations, we obtain multiple indoor air environment feature center combination information. Determine whether the combined information of the multiple indoor air environment feature centers is the same as the combined information of the multiple indoor air environment feature mains; If so, then based on the multiple indoor air environment feature combination category information and the preset indoor feature fusion weight, multiple indoor air environment feature combination category weighted information is obtained; Based on the weighted information of the multiple indoor air environment feature combination categories and the preset indoor feature fusion bias, multiple indoor air environment feature combination category correction information is obtained. Based on the combined category correction information of the multiple indoor air environment features and the preset indoor beamforming parameter library, a matching process is performed to obtain multiple initial indoor beamforming parameter information. If not, then the combined information of the multiple indoor air environment feature centers is taken as the main combined information of multiple indoor air environment features, and the process returns to the step of obtaining the secondary combined information of multiple indoor air environment features based on the combined information of the multiple indoor air environment features and the main combined information of multiple indoor air environment features.

6. The indoor air purification method as described in claim 5, characterized in that, The step of obtaining multiple indoor air environment feature center combination information based on the mean information of multiple indoor air environment feature combination categories, the median information of multiple indoor air environment feature combination categories, and the multiple indoor air environment feature combination information specifically includes: Calculate the difference between the mean information of the multiple indoor air environment feature combination categories and the median information of the multiple indoor air environment feature combination categories to obtain the center discrimination difference information of the multiple indoor air environment feature combination categories. Determine whether the difference information between the category centers of the indoor air environment feature combination is less than the preset threshold value for the difference between the category centers of the indoor air environment feature combination; If so, the indoor air environment feature combination information corresponding to the average value information of the indoor air environment feature combination category shall be used as the indoor air environment feature center combination information. If not, then the indoor air environment feature combination information corresponding to the median information of the indoor air environment feature combination category will be used as the indoor air environment feature center combination information.

7. The indoor air purification method as described in claim 1, characterized in that, The initial indoor beamforming parameter information includes the initial indoor beam horizontal azimuth angle parameter information, the initial indoor beam vertical elevation angle parameter information, the initial indoor beamwidth parameter information, and the initial indoor distributed antenna array parameter information; The initial indoor distributed antenna array parameter information includes the initial indoor distributed antenna array amplitude weight parameter information and the initial indoor distributed antenna array phase weight parameter information; The step of optimizing the multiple initial indoor beamforming parameter information based on the indoor pollution source location information, indoor distributed antenna location parameter information, air purification equipment location information, preset indoor beam angle adjustment range, preset indoor beamwidth adjustment range, preset indoor antenna element weighting coefficient vector, and preset indoor air purification efficiency calculation coefficient to obtain the target indoor beamforming parameter information specifically includes: Based on the indoor distributed antenna location parameters, air purification equipment location information, multiple initial indoor beam horizontal azimuth angle parameters, multiple initial indoor beam vertical elevation angle parameters, and multiple initial indoor beam width parameters, the spatial coverage information of multiple indoor beams is calculated. Based on the indoor pollution source location information and the coverage information of multiple indoor beam spaces, the coverage degree characterization information of multiple indoor beam spaces is calculated. Based on the indoor antenna position parameter information, multiple initial indoor antenna array amplitude weight parameter information, multiple initial indoor antenna array phase weight parameter information, and preset indoor wireless signal wavelength information, multiple indoor wireless signal strength information are calculated. Based on the coverage information of multiple indoor beam spatial areas, multiple indoor wireless signal strength information, and preset indoor air purification efficiency calculation coefficients, multiple indoor air purification efficiency information is calculated. Determine whether the maximum value of the multiple indoor air purification efficiency characterization information is greater than or equal to a preset indoor air purification efficiency threshold. If so, the initial indoor beamforming parameter information corresponding to the maximum value of the plurality of indoor air purification efficiency characterization information shall be used as the target indoor beamforming parameter information. If not, the initial indoor beamforming parameter information corresponding to the maximum value of the plurality of indoor air purification efficiency characterization information shall be used as the calibration indoor beamforming parameter information. Based on the calibrated indoor beamforming parameters, the preset indoor beam angle adjustment range, the preset indoor beamwidth adjustment range, and the preset indoor antenna element weighting coefficient vector, the multiple initial indoor beam horizontal azimuth angle parameters, multiple initial indoor beam vertical elevation angle parameters, multiple initial indoor beamwidth parameters, and multiple initial indoor distributed antenna array parameters are optimized and calculated to obtain multiple intermediate indoor beam horizontal azimuth angle parameters, multiple intermediate indoor beam vertical elevation angle parameters, multiple intermediate indoor beamwidth parameters, and multiple intermediate indoor distributed antenna array parameters. The multiple intermediate indoor beam horizontal azimuth angle parameters, multiple intermediate indoor beam vertical elevation angle parameters, multiple intermediate indoor beamwidth parameters, and multiple intermediate indoor distributed antenna array parameters are respectively used as multiple initial indoor beam horizontal azimuth angle parameters, multiple initial indoor beam vertical elevation angle parameters, multiple initial indoor beamwidth parameters, and multiple initial indoor distributed antenna array parameters. The process is then returned to the step of calculating multiple indoor beam spatial coverage information based on the indoor distributed antenna position parameters, air purification equipment position information, multiple initial indoor beam horizontal azimuth angle parameters, multiple initial indoor beam vertical elevation angle parameters, and multiple initial indoor beamwidth parameters.

8. An indoor air purification device, characterized in that, include: The information acquisition module is used to acquire indoor space structure information, indoor pollution source location information, indoor personnel location information, indoor air quality characterization information, indoor distributed antenna location parameter information, indoor distributed antenna communication parameter information, wireless signal quality characterization information, air purification equipment location information, and air purification equipment operation parameter information. The indoor feature information generation module is used to generate indoor air environment feature information, indoor wireless communication environment feature information, and indoor air purification equipment feature information based on multiple preset indoor environmental feature extraction vectors, according to the indoor space structure information, indoor pollution source location information, indoor personnel location information, indoor air quality characterization information, indoor distributed antenna location parameter information, indoor distributed antenna communication parameter information, wireless signal quality characterization information, air purification equipment location information, and air purification equipment operation parameter information. The initial indoor beamforming parameter information generation module is used to perform fusion and matching calculations based on the indoor air environment feature information, indoor wireless communication environment feature information, indoor air purification equipment feature information, preset indoor feature fusion weights, and preset indoor feature fusion biases to obtain multiple initial indoor beamforming parameter information. The target indoor beamforming parameter information generation module is used to optimize and calculate the multiple initial indoor beamforming parameter information based on the indoor pollution source location information, indoor distributed antenna location parameter information, air purification equipment location information, preset indoor beam angle adjustment range, preset indoor beam width adjustment range, preset indoor antenna array weighting coefficient vector, and preset indoor air purification efficiency calculation coefficient to obtain the target indoor beamforming parameter information. The indoor wireless directional beamforming module is used to perform beamforming control processing on the indoor distributed antenna according to the target indoor beamforming parameter information to generate multiple indoor wireless directional beams. The indoor air purification module is used to transmit indoor air purification command signals to the air purification equipment according to the multiple indoor wireless directional beams, so as to perform indoor air purification treatment through the air purification equipment.

9. A terminal device, characterized in that, The terminal device includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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